diff --git a/changelog.d/1095-cgt-and-area-income-after-loss-relief.changed.md b/changelog.d/1095-cgt-and-area-income-after-loss-relief.changed.md new file mode 100644 index 0000000000..5256e9d191 --- /dev/null +++ b/changelog.d/1095-cgt-and-area-income-after-loss-relief.changed.md @@ -0,0 +1 @@ +The CGT income proxy and the area self-employment rows read the profit after losses brought forward (microcosm#1095). The CGT stages band people and draw their gains on the sum of the stored income columns, which stands in for HMRC's total income. The area target matrix reads the engine's `self_employment_income` directly. Since the SPI channel moved the tape's losses brought forward into `loss_relief`, both read the year's profit before that relief, which put some SPI rows in a higher income band and changed where the CGT stages placed gains. Both now read the profit after the relief, so their income bands are as before. diff --git a/changelog.d/1095-childcare-fitter-allocated-hours.changed.md b/changelog.d/1095-childcare-fitter-allocated-hours.changed.md new file mode 100644 index 0000000000..5c19714750 --- /dev/null +++ b/changelog.d/1095-childcare-fitter-allocated-hours.changed.md @@ -0,0 +1 @@ +`tools/fit_uk_childcare_takeup.py` (tool version 4) follows policyengine-uk's childcare rules since pe-uk#2177 (microcosm#1095). Universal and targeted hours now stay beside the working-parent hours. A family that does not claim them on their own still receives them, allocated first from a participating child's working-parent hours (DfE guidance A1.11). So the expectation basis gains a third engine run, with the extended flag on and the targeted and universal flags off, and a targeted row's expected count adds that allocation at the extended rate times one minus its own rate. The universal row counts universal-only children, so every run leaves out a child who receives extended hours. The fit runs on a spine built before the extended-hours anchor moved (#1126), so it sets the hours the take-up stage draws under the current contract in place of the stored ones. diff --git a/changelog.d/1095-childcare-take-up-refit.changed.md b/changelog.d/1095-childcare-take-up-refit.changed.md new file mode 100644 index 0000000000..8f78cbd2a6 --- /dev/null +++ b/changelog.d/1095-childcare-take-up-refit.changed.md @@ -0,0 +1 @@ +The four UK childcare take-up rates are refitted with the version-4 fitter on the policyengine-uk 2.132.6 spine, feed 735820c, at the 2024 base year (microcosm#1095): Tax-Free Childcare 0.9797 (was 0.88), extended hours 0.6579 (0.6054), universal hours 0.4353 (0.4539) and targeted hours 0.7769 (0.597). The fit converges with no rate at its bound. Two changes to the expectation it fits let the targeted and Tax-Free Childcare rates come off the bound they reached before: the engine now allocates targeted hours from working-parent hours, and the extended-hours anchor is DfE's (#1126). The three DfE rows match their targets. The two HMRC Tax-Free Childcare rows still cannot both be met, the spend below and the children above; that residual sits in the frame's spend per child with a used account, not in the rate. Each rate's fitting receipt names the input spine, feed, engine and contract digests. diff --git a/changelog.d/1095-esa-support-group.added.md b/changelog.d/1095-esa-support-group.added.md new file mode 100644 index 0000000000..587fea19ba --- /dev/null +++ b/changelog.d/1095-esa-support-group.added.md @@ -0,0 +1 @@ +The UK root stage sets `esa_includes_support_component` from the FRS ESA group, which the benefits tab records on each ESA row (BENEFIT 16) as VAR3, ESAGROUP: 1 the work-related activity group, 2 the support group, 3 the assessment phase, as the FRS 2024-25 variable listing formats the questionnaire's ESAGroup (UK Data Service SN 9563, sheet BENEFITS_table; microcosm#1095). policyengine-uk reads the input since 2.129.0 for the benefit cap's ESA exemption and Universal Credit's test of limited capability for work-related activity, and without it assumes the support group for anyone with an ESA award of their own. A stored column replaces that default for everyone, so the column is false only where a person's ESA rows record a group outside the support group and none records it, and true everywhere else; a benefits tab without VAR3 refuses. On SPI-redrawn rows, whose ESA receipts the stage-2 fill takes from other FRS donors, the column returns to true. It is a net-new export column, so it joins the reviewed extra columns of the export-surface gate. The release reports the column only as support group against not support group: the assessment-phase answers are too few to report on their own. diff --git a/changelog.d/1095-frs-trading-losses.added.md b/changelog.d/1095-frs-trading-losses.added.md new file mode 100644 index 0000000000..19def975b7 --- /dev/null +++ b/changelog.d/1095-frs-trading-losses.added.md @@ -0,0 +1 @@ +The UK root stage splits the FRS self-employment income (SEINCAM2, which carries a loss as a negative amount) by its sign, as uk-data#528 does: profits go to `self_employment_income` and losses to `trading_loss` as a positive amount (microcosm#1095). policyengine-uk reads `trading_loss` from 2.131.0 (pe-uk#2085): income tax relieves the loss against general income, Class 4 against trading profits, tax credits against the claimants' income, and HBAI household income counts it as negative income. Before this the spine dropped every loss. The engine uprates `trading_loss` into each projected year as it does `self_employment_income`, so a loss persists like a profit. The concept mapping gains a `signed_part` transform, which splits the sum of the farm and non-farm self-employment concepts into the two inputs so that they never both hold an amount. The pinned incumbent (uk-data 1.56.16) does not carry `trading_loss`, so it joins the reviewed extra columns of the export surface. A hand-computed case shows that the salary-sacrifice rate classes follow the loss-relieved income and still add up to the engine's `earned_income_tax`. diff --git a/changelog.d/1095-hbai-income-before-reset.fixed.md b/changelog.d/1095-hbai-income-before-reset.fixed.md new file mode 100644 index 0000000000..363ecac6b0 --- /dev/null +++ b/changelog.d/1095-hbai-income-before-reset.fixed.md @@ -0,0 +1 @@ +The UK stages that band or match households on HBAI income read the engine's figure before negative incomes are reset to zero (`hbai_household_net_income_before_reset_to_zero`; policyengine-uk 2.132.4, pe-uk#2402), under the predictor name they had: the LCFS consumption predictors and NEED energy band, the NTS income band, the ETB VAT rate's disposable income and the ETB services predictors (microcosm#1095; Vahid's review of microcosm#1271). The surveys these stages match to do not reset their incomes, and the gross income the stages build, HBAI net income plus the HBAI deductions, holds only before the reset: after it, a household with negative income would be given its deductions as gross income. On the build's data the figure equals the HBAI net income policyengine-uk 2.130.1 returned. An engine test pins the reset and the gross income of a household with negative income. diff --git a/changelog.d/1095-hmrc-total-income-after-loss-relief.changed.md b/changelog.d/1095-hmrc-total-income-after-loss-relief.changed.md new file mode 100644 index 0000000000..392a95b389 --- /dev/null +++ b/changelog.d/1095-hmrc-total-income-after-loss-relief.changed.md @@ -0,0 +1 @@ +Every HMRC row on total income reads it after losses brought forward, as HMRC counts it (microcosm#1095). The SPI and Income Tax Liabilities rows by total-income band now band on `total_income_after_loss_relief`, and the regional rows read and filter on it. The engine's `total_income` adds the year's profit. Since the SPI channel moved the tape's losses brought forward into `loss_relief`, that profit is the profit before the relief, so without this change a row with such losses would move up a band. diff --git a/changelog.d/1095-obr-esa-sda.changed.md b/changelog.d/1095-obr-esa-sda.changed.md new file mode 100644 index 0000000000..653722327b --- /dev/null +++ b/changelog.d/1095-obr-esa-sda.changed.md @@ -0,0 +1 @@ +`obr.esa` binds every benefit on OBR's "Incapacity benefits" line that policyengine-uk carries: `esa_income + esa_contrib + incapacity_benefit + sda` (uk-data#582, microcosm#1095). OBR table 4.9 footnote 2 says the line includes Incapacity Benefit, ESA, Severe Disablement Allowance and the incapacity part of Income Support, and excludes the health element of Universal Credit. The engine pays Income Support as one benefit, so its incapacity part stays a residual of the row with no declared difference (María's ruling of 2026-10-16). Severe Disablement Allowance is a small part of the line: DWP reports GBP 53.8m for England and Wales in 2024-25, against OBR's GBP 12.41bn. The Scottish Government pays the Scottish part, GBP 5.0m in 2024-25, outside DWP's line; at well under 0.1 percent of the row it stays inside the row's Great Britain scope, with no per-term mask. diff --git a/changelog.d/1095-owner-manager-inputs.added.md b/changelog.d/1095-owner-manager-inputs.added.md new file mode 100644 index 0000000000..feabdc3cc8 --- /dev/null +++ b/changelog.d/1095-owner-manager-inputs.added.md @@ -0,0 +1 @@ +The UK root stage sets six of policyengine-uk's eleven owned-company inputs from the FRS job tab, the inputs through which Universal Credit treats a company's owner as self-employed (UC Regs 2013 reg 77, pe-uk#1965; microcosm#1095). A director (DIRECTOR 1, or NATURE 1 "Director") who owns at least half of the company (DIRPERC 100%, more than 50% or 50%; María's ruling of 2026-10-16) stands as its sole owner or partner and works in it (`stands_as_sole_owner_or_partner_of_company`, `is_engaged_in_owned_company_trade`). The company is their main employment when the job is their first (`owned_company_is_main_employment`). It carries on a property business when the main job's SIC division is 68, real estate, and a trade otherwise (`owned_company_carries_on_property_business`, `owned_company_carries_on_trade`). A company held in another job carries on neither, since the FRS codes the industry of the main job only, so the engine's company-owner rules stay off for it. The income share (`owned_company_income_share`) is the dividends the job records over 12 months, after tax: the engine reads the company's profit, which the survey does not ask, and the dividends paid out of it are the part it sees. The stocks and the intermediaries inputs keep the engine's defaults. On SPI-redrawn rows the stage-2 FRS fill draws the six inputs last, the flags as 0 or 1, so they follow the drawn incomes, and the root stage's rules then hold on those rows too. The six columns are new to the export surface and join its reviewed extra columns; a job tab without the director answers refuses. diff --git a/changelog.d/1095-parity-fixture-scottish-rows.fixed.md b/changelog.d/1095-parity-fixture-scottish-rows.fixed.md new file mode 100644 index 0000000000..7b705f0d2f --- /dev/null +++ b/changelog.d/1095-parity-fixture-scottish-rows.fixed.md @@ -0,0 +1 @@ +The incumbent local parity fixture (`uk/local_registry_parity_fixture_2025.json`) puts its Scottish constituency rows in their seats (microcosm#1095, uk-data#589). Five unchanged Scottish constituencies keep their 2010 codes (S14000006, S14000008, S14000010, S14000040, S14000058) in the incumbent's boundary-change source. The incumbent's mapping sorts its 2024 rows by those codes, so the mapped values sat out of the PCON24 roster's order for every Scottish row between them. `tools/extract_uk_local_registry_fixture.py` now maps the five codes to S14000107 to S14000111 as uk-data#589 does. It asserts that the grain is the mapping's 2010 order and that the mapped rows are the 2024 roster, once each, and takes the rows by code into the roster's order. The fix is signed in the fixture's provenance text (María, 2026-10-16). Re-extracted at uk-data 12a1e028: 798 Scottish constituency values move and no row is added or lost; the local compile parity regenerates with it. diff --git a/changelog.d/1095-policyengine-uk-2-132.changed.md b/changelog.d/1095-policyengine-uk-2-132.changed.md new file mode 100644 index 0000000000..193710d8f8 --- /dev/null +++ b/changelog.d/1095-policyengine-uk-2-132.changed.md @@ -0,0 +1 @@ +The lock's UK side moves from policyengine-uk 2.130.1 to 2.132.6, and the UK extras of microcosm-build, microcosm-data and microcosm-frame require >=2.132.6 (microcosm#1095). policyengine-core stays at 3.33.3 on the UK side and 3.32.5 on the US side. Nothing the UK build writes is renamed, removed, re-typed or turned into a formula. 2.131.0 adds trade losses (pe-uk#2085): `trading_loss` is relieved by each programme's own rule and joins HBAI's subtractions, and `loss_relief` (losses brought forward) becomes an input. 2.131.2 takes ground rent and service charges off income after housing costs (pe-uk#2411), 2.132.4 resets negative income before housing costs to zero (pe-uk#2402), and the other releases are housekeeping. One part of the build follows: the survey-concept gross income (`uk_runtime.household_income`) leaves `trading_loss` subtracted, because the surveys count self-employment income net of losses, and the engine lockstep test now declares that. Re-derived with no change to data: the uprating pins (the version stamp only), the concept coverage (299 inputs to 300: `loss_relief` joins, uncovered), the texts that cite the locked engine, the H2 fixture's dependency, the coverage manifest's source digests and `APPROVED_UV_LOCK_SHA256`. diff --git a/changelog.d/1095-sparse-band-support-floor.added.md b/changelog.d/1095-sparse-band-support-floor.added.md new file mode 100644 index 0000000000..b8496e3976 --- /dev/null +++ b/changelog.d/1095-sparse-band-support-floor.added.md @@ -0,0 +1 @@ +The UK national and full-build solves apply a signed sparse-band support floor to the UC payment-distribution bands (microcosm#1095). `uk/calibration_measure_exclusions.json` moves to schema 3 and declares the floor once, in a `support_floor` block that María signed on 2026-10-16 (expires 2027-01-08): a band row of the four `dwp.uc.payment_distribution_*` targets whose compiled matrix row reaches fewer than 10 distinct source households (`household_source_id`) with a positive prior weight is dropped from the solve, not pooled, because the solve could fit it only by stretching those few weights and a fit that rests on fewer than 10 source households is a cell the release must not report. The floor runs on the compiled problem, before the loss weights, families and target metadata are drawn, in the national problem node and in the full build's measures node. Its receipt reports each family's dropped published total, and the run refuses outside the approval window, when the drops pass the block's ceilings on rows or on published share, and when a per-row exclusion names a band the floor would drop (the national node compiles those rows only to measure them). The drops join the problem's measure exclusions as `kind: support_floor`; the national gates, the written diagnostics, the full-build target-surface and reference-coverage gates and the release certifier read them back and check them against the declared floor, since none of them can recompute support. The register written as the scoring surface stays the approved one. On the head spine the floor drops 19 band rows, 3.1% of the bands' published total, against ceilings of 24 rows and 5%, which leave a margin of five rows and about two points: ten bands the solve bound before and the nine that per-row exclusions held out, whose entries retire. The other twelve per-row band entries are well supported and stay, each on its own renewal. diff --git a/changelog.d/1095-spi-losses-brought-forward.changed.md b/changelog.d/1095-spi-losses-brought-forward.changed.md new file mode 100644 index 0000000000..1d1fbd9ef3 --- /dev/null +++ b/changelog.d/1095-spi-losses-brought-forward.changed.md @@ -0,0 +1 @@ +The SPI channel moves the tape's losses brought forward (LOSSBF) out of `self_employment_income` into `loss_relief` and `ni_class_4_losses_brought_forward`, the inputs policyengine-uk relieves them against: income tax under ITA 2007 s.83 and Class 4 under SSCBA 1992 Sch 2 para 3 (uk-data#528, microcosm#1095). The stage-1 chain still draws the profit net of those losses, as the tape holds it. Last of all, so that no earlier draw moves, it draws the year's profit before the losses and the losses themselves. After the FRS-only fill and the HMRC aggregates, a drawn row's profit becomes the year's profit and both inputs take its losses. Where the drawn net profit is positive, the losses are its gap to the year's profit. Where it is zero, they are at least that profit. So on every row the profit after the relief is the drawn net profit. Nothing is counted twice: the losses move, they are not copied, and each programme reads one version. Income tax and Class 4 give the same results as before. Universal Credit, HBAI and market income now read the year's profit, as the law intends. The HMRC aggregates and the four HMRC self-employment rows read the profit after the relief. The rows bind `self_employment_income_after_loss_relief` and band on `total_income_after_loss_relief`, two measures that the UK measure resolver computes from the engine, so at fixed weights the rows keep their values and their band membership. FRS rows carry no losses brought forward. The pinned incumbent does not carry the two inputs, so they join the reviewed extra columns of the export surface. The engine carries both losses into each projected year at their nominal value, while the profit grows with its index. diff --git a/changelog.d/1095-spi-trading-losses.added.md b/changelog.d/1095-spi-trading-losses.added.md new file mode 100644 index 0000000000..02b09af3ff --- /dev/null +++ b/changelog.d/1095-spi-trading-losses.added.md @@ -0,0 +1 @@ +The SPI income stage draws `trading_loss` for the rows it redraws, as its FRS-only stage 2 draws the other inputs it reads no tape value for: the loss comes from the FRS rows, conditioned on the drawn incomes (uk-data#528, microcosm#1095). A redrawn row whose drawn `self_employment_income` is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows. diff --git a/changelog.d/1095-stored-private-school-attendance.added.md b/changelog.d/1095-stored-private-school-attendance.added.md new file mode 100644 index 0000000000..ae5260d60d --- /dev/null +++ b/changelog.d/1095-stored-private-school-attendance.added.md @@ -0,0 +1 @@ +A new last spine stage, `private_school_attendance`, stores policyengine-uk's `attends_private_school` at the spine's weights (uk-data#568, microcosm#1095). The engine imputes attendance with a formula that ranks households by net income at their weights, so every simulation re-imputed it: a reform to income tax, National Insurance or benefits switched pupils in or out and moved the fee VAT, a population under one million people of weight imputed none (pe-uk#1905), and the calibration and the release read different flags once the weights changed. The stage materializes the engine's attendance once, at the calibration year and the spine's design weights, from the stored `attends_private_school_random_draw`, and stores it, as the root stage stores `months_since_last_birthday`; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters. The column joins the export surface's reviewed extra columns. The pupil series the attendance is held against stays ISC's member-school census: DfE's count of all independent-school pupils, the series María chose on 2026-10-16, is not yet a Chronicle fact. diff --git a/changelog.d/1095-uc-five-or-more-children-diagnostic.changed.md b/changelog.d/1095-uc-five-or-more-children-diagnostic.changed.md new file mode 100644 index 0000000000..724138e721 --- /dev/null +++ b/changelog.d/1095-uc-five-or-more-children-diagnostic.changed.md @@ -0,0 +1 @@ +DWP's count of Universal Credit households with five or more children leaves the UK targets and becomes a diagnostic beside `dwp.uc.two_child_limit.households_5_children`, pinned to its April 2025 fact, the month of the two-child-limit publication it sits beside (María's ruling of 2026-10-16, microcosm#1095). The count includes families outside the two-child limit, whose third and later children were all born before 6 April 2017 or who meet an exception. DWP's two-child-limit counts of households with five and with six or more children leave those families out. Fewer than 10 FRS source households carry that difference, so the frame cannot fit the row beside the limit rows. The row was held out from 2026-10-14 (microcosm#1271); its exclusion entry retires with it. The registry-parity mapping moves the incumbent row to the excluded rows, the frozen fixture's fitted row becomes fixture-only on the compile-parity surface, and the contract holds 414 targets and 1,291 active references. diff --git a/changelog.d/1126-uk-extended-hours-usage-dfe.changed.md b/changelog.d/1126-uk-extended-hours-usage-dfe.changed.md new file mode 100644 index 0000000000..9f61632a2a --- /dev/null +++ b/changelog.d/1126-uk-extended-hours-usage-dfe.changed.md @@ -0,0 +1 @@ +Source the UK `maximum_extended_childcare_hours_usage` draw from DfE: the mean moves from the frozen 15.019 to 31.141 (sd 4.972, clip [0, 30]), so a working-parent 3- or 4-year-old averages the 28.5 weekly hours implied by DfE's January 2025 additional-hours PTEs, lifting 2026-27 free early-years spending from £5.81bn to about £9.30bn against DfE's £9.9bn (#1126). diff --git a/changelog.d/1240-need-zero-kwh-cells.fixed.md b/changelog.d/1240-need-zero-kwh-cells.fixed.md new file mode 100644 index 0000000000..332d65b649 --- /dev/null +++ b/changelog.d/1240-need-zero-kwh-cells.fixed.md @@ -0,0 +1 @@ +The UK LCFS stage starts a mobile-home NEED cell at its NEED mean when every household in it drew no kWh, so the spine builds again after #1270 (microcosm#1240). Mobile homes have no NEED property-type row, and the energy rake takes them to the all-dwellings mean, the incumbent's translation. A household whose drawn spend is at or below the standing charge has no kWh, and the rake, which multiplies, cannot move a cell in which no household has any: the `energy_rake` gate stopped main's spine build at such a cell once #1270's copies changed the frame and with it the draws. The `price_domestic_energy` step now declares `zero_kwh_cells_at_need_mean` (the accommodation margin's `all_dwellings` cells). Before the first sweep, the rake starts each declared cell in which no household drew kWh at the cell's NEED mean (electricity over every household, gas over the gas-connected ones) and leaves a cell with any positive household to the rake. The households of a started cell take its NEED mean in place of the spend they drew, at or below the standing charge, so their energy spend rises above the draw, as the incumbent's translation for mobile homes has it; the rake is not biased, since the accommodation margin holds the cell's mean wherever it starts. The `energy_rake` receipt lists the cells it started. The gate fails on a started cell the stage does not declare and on any other cell with a zero mean, and its details count the started cells without naming them. diff --git a/changelog.d/1284-council-tax-on-household-basis.changed.md b/changelog.d/1284-council-tax-on-household-basis.changed.md new file mode 100644 index 0000000000..bc9b2f8e42 --- /dev/null +++ b/changelog.d/1284-council-tax-on-household-basis.changed.md @@ -0,0 +1 @@ +The UK national register puts the council-tax dwelling stock on the household basis of its area (microcosm#1284). The dwelling stock (MHCLG and StatsWales taxbase lines, Scotland's CTAXBASE chargeable dwellings) and each nation's official household estimate bound the same weighted household count of an area at two published levels, so the solve traded one family against the other. A new national-only declaration in `uk/cross_grain_declarations.json`, `household_basis_partitions`, now rescales each English region's, Wales's and Scotland's dwelling total onto its household control, and its bands by the same factor, the area's published households per chargeable dwelling: 0.980 to 1.023 across the eleven areas on the 735820c feed. The bands keep their published mix and the level follows the household estimate. María signed the assumption on 2026-10-16: households per chargeable dwelling are the same in every band of an area. They are not exactly, since multi-household dwellings concentrate in lower bands. A tripwire refuses a factor further than 0.035 from one, the measured 0.023 with a margin, and the national closure check requires every rescaled total to equal its control. Northern Ireland has no council-tax rows. The local band cells follow the national rows by signature. diff --git a/changelog.d/868-student-loan-borrower-cohorts.changed.md b/changelog.d/868-student-loan-borrower-cohorts.changed.md new file mode 100644 index 0000000000..15ac5e9a1b --- /dev/null +++ b/changelog.d/868-student-loan-borrower-cohorts.changed.md @@ -0,0 +1 @@ +The UK `student_loans` stage reads the FRS borrowers below the repayment threshold and dates each borrower's course from the age they left full-time education (María's ruling of 2026-10-16, microcosm#868). The `frs_education` stage stores two adult-table answers for it: `reports_outstanding_student_loan` is SLOS ("Do you have an outstanding student loan?"), which adults who make no repayment answer, and `age_left_full_time_education` is EDUCLEFT, with 0 for no answer. A reported borrower now repays, as before, or, in England or Wales, reports an outstanding loan and is not in full-time education. A course starts three years before its borrower left full-time education, or at 18 without an answer, and its start year sets the plan. Before 2026 a repayer never takes Plan 5, and a repayer whose dates point to a course from 2023/24 takes Plan 2: nobody repays a Plan 5 loan before April 2026, so SLC counts no Plan 5 borrower above the threshold in academic year 2024/25. The Plan 2 top-up walk takes nobody in full-time education, since a student is not yet liable to repay; the Plan 5 walk keeps the age rule. The four SLC borrower rows bind the stock the stage tops up to, SLC academic year 2024/25, whose reference date falls inside calendar 2025: Chronicle stores it under the opening year 2024, so each row pins that period and a signed in-year-snapshot hold declares it. Before, the rows bound academic year 2025/26 while the stage topped up to 2024/25. Both new columns are new to the export surface and join its reviewed extra columns. A tab without SLOS or EDUCLEFT refuses the build. diff --git a/docs/concept-coverage/policyengine-uk.md b/docs/concept-coverage/policyengine-uk.md index 5353ae7982..f7f197615d 100644 --- a/docs/concept-coverage/policyengine-uk.md +++ b/docs/concept-coverage/policyengine-uk.md @@ -1,9 +1,9 @@ # Concept coverage: policyengine-uk -Engine version `2.130.1`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. +Engine version `2.132.6`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. -- Engine inputs: 299 -- Fed by a concept: 40 +- Engine inputs: 300 +- Fed by a concept: 41 - Structural: 11 - Uncovered: 248 - Mapped but missing from the engine: 0 @@ -17,8 +17,8 @@ Engine version `2.130.1`; concept schema `5a55669d0447`. Generated by `tools/ref | `fact:person.parent_1_person_id` | `is_parent` (person) | | `fact:person.parent_2_person_id` | `is_parent` (person) | | `fact:person.employment_income` | `employment_income_before_lsr` (person) | -| `fact:person.nonfarm_self_employment_income` | `self_employment_income` (person) | -| `fact:person.farm_self_employment_income` | `self_employment_income` (person) | +| `fact:person.nonfarm_self_employment_income` | `self_employment_income` (person)
`trading_loss` (person) | +| `fact:person.farm_self_employment_income` | `self_employment_income` (person)
`trading_loss` (person) | | `fact:person.interest_income` | `individual_savings_account_interest_income` (person)
`savings_interest_income` (person) | | `fact:person.dividend_income` | `dividend_income` (person) | | `fact:person.rental_income` | `property_income` (person) | @@ -60,4 +60,4 @@ Engine version `2.130.1`; concept schema `5a55669d0447`. Generated by `tools/ref ### person (156) -`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_engaged_in_owned_company_trade`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `owned_company_capital`, `owned_company_carries_on_property_business`, `owned_company_carries_on_trade`, `owned_company_holding_value`, `owned_company_income_share`, `owned_company_intermediary_earnings_chapter`, `owned_company_intermediary_earnings_from_main_employment`, `owned_company_is_main_employment`, `owned_company_trade_assets`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `ssmg_reported`, `stands_as_sole_owner_or_partner_of_company`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `trading_loss`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` +`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_engaged_in_owned_company_trade`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `loss_relief`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `owned_company_capital`, `owned_company_carries_on_property_business`, `owned_company_carries_on_trade`, `owned_company_holding_value`, `owned_company_income_share`, `owned_company_intermediary_earnings_chapter`, `owned_company_intermediary_earnings_from_main_employment`, `owned_company_is_main_employment`, `owned_company_trade_assets`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `ssmg_reported`, `stands_as_sole_owner_or_partner_of_company`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` diff --git a/experiments/1095-uk-data-ports-5-receipts.md b/experiments/1095-uk-data-ports-5-receipts.md new file mode 100644 index 0000000000..13affbd52d --- /dev/null +++ b/experiments/1095-uk-data-ports-5-receipts.md @@ -0,0 +1,208 @@ +# microcosm#1095 receipts: the fifth ports PR + +Branch `uk-data-ports-1095-5` on main `5d4eb37b3` (#1286), Chronicle feed 735820c throughout. The +engine lock moves from policyengine-uk 2.130.1 to 2.132.6, frozen for this PR on María's ruling of +15 October; later releases go to the next bump. + +Every figure here is a weighted aggregate or a publisher's figure. Survey support is counted in +distinct source households, FRS and SPI channels together, and a cell under 10 has no weighted +figure. Effective sample sizes and weight ratios are not given. A spine holds copies of survey +households, so its row counts overstate support and are not given. The support floor's per-row +support counts stay on the machine that measured them; this file gives each band's category only. + +## Commits, in order + +Which items port merged incumbent code and which go first. Every uk-data item below is open on +16 October (uk-data#528, #546 and #589 are open PRs; #568 and #582 are open issues), so each port +goes first and follows its upstream's rule: + +- `b5d82c2af` (#1240): mobile-home NEED cells start at their mean when they drew no kWh, so main's + spine builds again after #1270. Microcosm only. +- V1 `985aae5b9`: a test pins how the spine stores birth months (Vahid's second should on #1271). +- B1 `cd9bb6ec0`: the lock at policyengine-uk 2.132.6. +- B2 `55d3a6091`: HBAI income before the reset to zero (pe-uk#2402, merged). +- B3 `08ebfa175`: the ESA support group from the FRS ESA group. Microcosm only. +- L1 `374b7de2a`, L2 `2d38875ef`, L3 `1f2961929`, and L3's follow-ups `ca050e0cc`, `01b6b2927`, + `81203a14b`: self-employment losses. **Goes first**: uk-data#528 is an open PR; pe-uk#2085 is + merged. +- U2a `88e355305`: Vahid's #1127 commit, with his authorship (#1126). U2b `870cb0c9d`: the fitter + follows pe-uk#2177 (merged). `94ed6eaac`: the H2 fixture under the moved anchor. **Goes first** + beside uk-data#546, an open PR with a different anchor. +- U1 `502840383`: the five-or-more-children count becomes a diagnostic. Microcosm only. +- E1 `8cc8532bb`: `obr.esa` binds the whole incapacity line. **Goes first**: uk-data#582 is open. +- D1a `e4b10872b` (#868): borrowers below the threshold, courses dated from leaving education. + Microcosm only. +- O1 `8589c6603`: six owner-manager inputs (pe-uk#1965, merged). Microcosm only. +- U4 `caae98e2d`: private-school attendance stored at the spine's weights. **Goes first**: + uk-data#568 is open. +- U2c `45c1bdeb0`: the four childcare take-up rates refitted. +- C1 `933ee4eef` (#1284): the council-tax stock on the household basis of its area. Microcosm only. +- P1 `dbfa76d1a`: the parity fixture's Scottish rows in their seats. **Goes first** with + uk-data#589's mapping (an open PR). +- U3 `7f862f94e`: the sparse-band support floor. Microcosm only. +- `f57c50091` (#868): before 2026 a repayer never takes Plan 5, which nobody repays before April + 2026. Found on the head's first calibration (below). Microcosm only. +- Vahid's review of #1299: `968a9f945` pins each ESA group code and the SPI stage's ESA and + trading-loss resets; `d918388e2` cites the FRS variable listing for the ESA group codes; + `84e1ffece` corrects the childcare fitter's docstring on the universal row; `09a2c8f1b` says in + the #1240 fragment that a started NEED cell replaces the spend its households drew; + `18be8d205` notes when the Plan 5 repayment year needs revisiting. No build output moves. +- This commit: the receipts. + +O2, the directors' dividends, was on the branch and is taken out (below); it stays open in #1095. + +## María's rulings + +Of 2026-10-15, at the plan: the engine bump to 2.132.6, frozen; LOSSBF to `loss_relief` and +`ni_class_4_losses_brought_forward`, with the HMRC self-employment rows measured after the relief; +#1287 out; the #866 renewal in its own small PR; three heavy builds. + +Of 2026-10-16, "go with your recommendations on all of them": D1 (academic year 2024/25; the SLOS +cohort; start year from EDUCLEFT; plan 2 for England and Wales starters 2012/13 to 2022/23), O1 +(DIRPERC at least 50%), O2 (the larger of the two dividends; the questionnaire does not exclude +own-company shares), the SPI rows' owner-manager inputs drawn in the stage-2 fill, U2a (#1127's +mean), U2c (a bound hit keeps the bound), U4 (DfE's series), U1 (diagnostic), E1 (the Income +Support part stays a residual), C1 (the households-per-dwelling assumption, signed), U3 (floor 10, +drop not pool, expiry 2027-01-08) and P1 (the remap, signed). + +Of 2026-10-16, after the head's calibration: O2 leaves this PR and stays open in #1095 with what +the head found; the support floor's ceilings keep a little headroom over the head's drops; and +the Plan 5 repayer rule (`f57c50091`) is confirmed. + +## Arms + +Each arm builds its spine from the commit named, locally, on feed 735820c, and runs the national +role's calibration on it (`tools/build_uk_rowwise_candidate.py --release-role national`). The +review date is 2026-10-16 on the head arms, the date of María's signatures, and 2026-10-15 on the +control. + +- **control** (`b5d82c2af`: main `5d4eb37b3` plus the NEED fix, policyengine-uk 2.130.1): loss + 0.29148 to 0.00607; 98.6% of 1,226 rows within 10%; all 7 calibrated-seam gates pass. +- **head with O2, first run** (calibrated on a spine built with O2, before the Plan 5 fix): + blocked at the battery. `slc.borrowers.plan_5_above_threshold` binds academic year 2024/25, + where SLC publishes zero, and a few young repayers still held Plan 5, so the row's miss held + the loss near 0.30. `f57c50091` fixes the stage. The weight-ratio gate failed too (next arm). +- **head with O2** (the Plan 5 fix in): loss 0.26546 to 0.00583; 98.4% of 1,217 rows within 10%; + 6 of the 7 calibrated-seam gates pass. `uk_weight_ratio` fails: the largest family-folded + weight passes the reviewed maximum ratio to the median. The record-level figures stay local. +- **head** (built at `da8892ed5`, a measurement commit that is the head with O2 except that the + directors' dividends are left out of `dividend_income`; taking O2 out of the branch changes + nothing else the build reads, so this is the head's spine and calibration): loss 0.26817 to + 0.00584; 98.4% of 1,216 rows within 10%; all 7 calibrated-seam gates pass. + +On the head's written file: + +- **E1 and E2**: the weights sit at the dataset's year with the engine's projection factor, the + identifiers run 1 to n inside 32 bits with the build identifiers beside them, every other column + equals the spine's (the reviewed export drops aside), and policyengine-uk reads back the + identifiers and the solved 2025 weights. +- **M**: the stored birth months are the spine's, row for row. +- **Written against the solve**: of the 1,216 solved rows only the two ONS land rows, declared + weight-share formulas, read differently from the file (by at most 0.22 points). The + private-school pupils row, 3.4 points off on #1271's head, now matches the solve (U4). The + file's loss over the written register is higher than the solve's because that register, the + scoring surface, keeps the bands the support floor drops. +- **Input mass**: the certifier's input-mass gate passes on the calibrated weights. + +## O2. The directors' dividends, taken out + +DIVIDGRO, the gross dividends a director received from their own company, was taken into +`dividend_income` as the larger of the accounts' dividends and the director's. On the head's +spine it added about £5.8bn of dividends on FRS-channel people at the spine's weights, more than +doubling that channel's dividends. The directors move up the income bands, the bands they leave +lose support, and the national solve concentrates weight on fewer households, so the weight-ratio +gate fails (the "head with O2" arm). With only the directors' dividends left out, all seven +gates pass. How FRS directors' dividends sit beside the SPI channel's high-income copies, which +carry the tape's dividends already, is left to its own PR; the item stays open in #1095 with this +finding. O1, the owner-manager inputs, stays: its income share reads job DIVIDEND and does not +touch `dividend_income`. + +## U2c. The childcare take-up refit + +The four rates, fitted on the bump arm's spine (policyengine-uk 2.132.6, feed 735820c, 2024 base +year) by the version-4 fitter: Tax-Free Childcare 0.9797, extended hours 0.6579, universal hours +0.4353 and targeted hours 0.7769. L-BFGS-B converged inside the bounds of 0 and 1 for every rate. + +- **The Tax-Free Childcare rate is near its bound, not at it.** María's U2c ruling, that a rate at + its bound keeps the bound and the receipt records the residual, applies to a rate the + optimizer stops at 0 or 1; 0.98 is an interior optimum, so the rule does not apply. The rate + sits near 1 because HMRC's two rows pull against each other: even at a rate of 1 the frame's + Tax-Free Childcare spend is 0.88 of HMRC's while its children with used accounts are 1.13 of + HMRC's. The fit settles where the two misses balance, at spend 0.86 and children 1.11, so the + residual is the frame's spend per child, about 0.78 of HMRC's, not the rate. +- **The universal rate is low because the universal-only pool is large.** With every scheme at + a rate of 1 the frame already gives 1.77 times DfE's universal-only children. At the fitted + extended-hours rate about a third of the children eligible for extended hours take none, and + they join the universal-only pool, so the standalone rate that meets DfE's count is 0.44. + +## U3. The support floor on the head spine + +The floor's receipt as the head's national problem binds it: 19 bands dropped, 3.1% of the +scope's published total, against the ceilings of 24 rows and 5%, a margin of five rows and about +two points. The twelve remaining per-row band entries were compiled beside the register, measured +above the floor and left the problem again. By family, in DWP's published households (the monthly +average the rows bind): + +- COUPLE_NO_CHILDREN: 10 bands, 42,229 households. +- SINGLE: 8 bands, 71,069 households. +- COUPLE_WITH_CHILDREN: 1 band, 12,836 households. +- LONE_PARENT: none. + +The register census agrees: 1,291 compiled rows, 1,216 solved, 75 excluded (56 per-row entries +and the 19 bands). The 21 per-row band entries, sorted on the spine with O2 and confirmed on the +head's (support counts stay local; the category is the only thing given): + +- (a) below the floor, so the floor drops the band and the entry retires (9): COUPLE_NO_CHILDREN + 22,800–24,000, 24,000–25,200, 26,400–27,600, 27,600–28,800 and 28,800–30,000; SINGLE + 21,600–22,800, 26,400–27,600, 27,600–28,800 and 28,800–30,000. +- (b) at or above the floor but out of reach with every weight at 10 times its prior: none. +- (c) neither, so the entry stays and is renewed per row, or not, before 26 November (12): + LONE_PARENT 12,000–13,200, 13,200–14,400, 14,400–15,600, 15,600–16,800, 16,800–18,000, + 18,000–19,200, 19,200–20,400 and 21,600–22,800; SINGLE 8,400–9,600, 9,600–10,800, + 13,200–14,400 and 14,400–15,600. + +The floor also drops ten bands the solve bound before: COUPLE_NO_CHILDREN 10,800–12,000, +19,200–20,400, 20,400–21,600, 21,600–22,800 and 25,200–26,400; COUPLE_WITH_CHILDREN 28,800–30,000; +SINGLE 20,400–21,600, 22,800–24,000, 24,000–25,200 and 25,200–26,400. Which bands sit under the +floor can move from one spine to the next, so the ceilings keep a margin over the head's drops. + +## C1. The council-tax stock on the household basis + +The factors, each area's published households over its chargeable dwellings on feed 735820c, run +from 0.980 to 1.023 across the eleven areas; the largest shift from one is 0.0227, and the +tripwire refuses 0.035. On the head's calibration, against the control: + +- the 97 council-tax stock rows miss by 0.128% on average (control 0.644%), and none by more than + 2% (control: 10 rows); +- the 11 household controls miss by 0.061% on average (control 0.072%). + +Both acceptance conditions of the plan hold: each family within 0.2% on average, and no stock row +past 2%. + +## D1b. The plan 2 stock + +The head spine's Plan 2 England stock, at design weights, is about 41% of SLC's academic year +2024/25 stock of borrowers liable to repay; the top-up walk's whole pool, measured before the +head, would bring it to about 47%. The plan's acceptance, within 25% of SLC, cannot be met by the +cohort rule, so the two #868 exclusions stay until María rules on them (they expire on 3 November). + +## Tax credits (Vahid's fourth nit on #1271) + +Child tax credit and working tax credit are zero from 2025 on the control's file (policyengine-uk +2.130.1) and on the head's (2.132.6), so the legacy closure takes effect on both engines. +Universal Credit at 2025 is £77.7bn on both at their calibrated weights. + +## Tests + +- CI runs on every push; the final head's run is in the PR. +- The touched surfaces at the head: 1,479 tests pass, 1 skipped (engine-free), and the SPI engine + tests pass, 88 with 1 skipped across the SPI income and state-pension-age files. +- CI's engine-uk job list at the head's code (`f57c50091`): 213 tests, all pass. The review + commits add two tests and change no build code. At their head, the FRS spine, SPI income + (engine-free and engine), student-loan, take-up tool and contract, energy pricing and graph + tests pass: 1,073 with 2 skipped. Mutations Vahid listed now fail them: reading only code 3 + as outside the support group, and deleting either SPI-stage reset. +- The feed-gated tests (`CHRONICLE_UK_FACTS` on feed 735820c) pass at the PR's first push: 103, + 2 skipped. The engine-free lane there ran 18,895 tests; its three failures were a contract hash + the next push repinned and two tests that read this machine's environment (a live release + pointer and a spawned-worker US scorer), which pass in CI. diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index 09c2ab4ef7..62f946acef 100644 --- a/packages/microcosm-build/pyproject.toml +++ b/packages/microcosm-build/pyproject.toml @@ -46,7 +46,7 @@ us = [ # The UK extra adds the rules engine for local metric generation from a # Microcosm UK H5. Target tables remain explicit inputs, and the base package # still does not import policyengine-uk at import time. -uk = ["policyengine-uk>=2.130.1", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] +uk = ["policyengine-uk>=2.132.6", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] [project.scripts] microcosm-build-uk = "microcosm.build.uk_runtime.full_build_cli:main" diff --git a/packages/microcosm-build/src/microcosm/build/source_manifest.py b/packages/microcosm-build/src/microcosm/build/source_manifest.py index 0e8bffffb4..11d4425db9 100644 --- a/packages/microcosm-build/src/microcosm/build/source_manifest.py +++ b/packages/microcosm-build/src/microcosm/build/source_manifest.py @@ -181,6 +181,7 @@ "split_liable_gainers_by_residential_probability", "stack_income_band_donor_households", "stack_zero_weight_donors", + "store_rules_engine_input", "strict_read_private_table", "support_clip", "sub_aea_remainder", diff --git a/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json b/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json index 1a30fd4b43..e5fab6700b 100644 --- a/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json +++ b/packages/microcosm-build/src/microcosm/build/spec_engine/schema/sources.schema.json @@ -9472,8 +9472,11 @@ "reported_country_gate", "plan_1_before", "plan_5_from", + "plan_5_repayments_from", "enum_domain", - "plan_4_imputation" + "plan_4_imputation", + "reported_outstanding_test", + "reported_outstanding_region_exclusions" ], "properties": { "kind": { @@ -9501,6 +9504,9 @@ "plan_5_from": { "type": "integer" }, + "plan_5_repayments_from": { + "type": "integer" + }, "enum_domain": { "type": "array", "items": { @@ -9509,6 +9515,15 @@ }, "plan_4_imputation": { "type": "boolean" + }, + "reported_outstanding_test": { + "type": "string" + }, + "reported_outstanding_region_exclusions": { + "type": "array", + "items": { + "type": "string" + } } } }, @@ -9590,6 +9605,44 @@ }, "realization_bound": { "type": "string" + }, + "cohort_start_formula": { + "type": "string" + }, + "current_education": { + "type": "string" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "output", + "draw", + "year_rule", + "weights" + ], + "properties": { + "kind": { + "const": "store_rules_engine_input" + }, + "output": { + "type": "string" + }, + "draw": { + "type": "string" + }, + "year_rule": { + "type": "string", + "enum": [ + "survey_year", + "calibration_year" + ] + }, + "weights": { + "type": "string" } } }, @@ -10022,6 +10075,45 @@ "exclusiveMinimum": 0 } } + }, + "zero_kwh_cells_at_need_mean": { + "type": "object", + "additionalProperties": false, + "minProperties": 1, + "properties": { + "income": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "minLength": 1 + } + }, + "tenure": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "minLength": 1 + } + }, + "accommodation": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "minLength": 1 + } + }, + "region": { + "type": "array", + "minItems": 1, + "items": { + "type": "string", + "minLength": 1 + } + } + } } }, "if": { diff --git a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json index 05120c2cd7..b8f4c7456d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json @@ -1,5 +1,23 @@ { - "schema_version": 2, + "schema_version": 3, + "support_floor": { + "contract_target_ids": [ + "dwp.uc.payment_distribution_single", + "dwp.uc.payment_distribution_lone_parent", + "dwp.uc.payment_distribution_couple_no_children", + "dwp.uc.payment_distribution_couple_with_children" + ], + "metric": "distinct_source_households_with_positive_prior_weight", + "floor": 10, + "max_dropped_rows": 24, + "max_dropped_published_share": 0.05, + "reason": "Sparse-band support floor: a UC payment-distribution band whose compiled matrix row reaches fewer than 10 distinct source households (household_source_id) with a positive prior weight is fitted only by stretching those few weights, and a fit that rests on fewer than 10 source households is a cell the release must not report (the UK Data Service working floor of 10, CD171 section 5.2.1). Such rows are dropped from the solve, not pooled; the floor's receipt reports each family's dropped published total, and the run refuses when the drops pass either ceiling or when a per-row exclusion names a row the floor drops.", + "tracking": "microcosm#1095", + "approved_by": "juaristi22", + "adjudication": "microcosm#1095 U3, ruled 2026-10-16 with the fifth ports PR: floor 10 distinct source households with a positive prior weight, rows dropped not pooled, dropped published totals reported; the per-row payment-band entries the floor covers retire", + "approved_on": "2026-10-16", + "expires_on": "2027-01-08" + }, "exclusions": [ { "name": "hmrc/employment_income_income_band_1_000_000_to_inf", @@ -118,60 +136,6 @@ "approved_on": "2026-08-26", "expires_on": "2026-11-26" }, - { - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_27_600_to_28_800", - "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, - { - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_28_800_to_30_000", - "reason": "Single-row support: one FRS household (a disabled single council tenant, design weight 760 against the published 1,654) is the only row whose UC award falls in this band on every main-based spine, so the cell is a scaling of one row rather than a composition the spine carries. On the #1063 stack its award moved GBP 678 lower, out of the band and into the 27,600-28,800 band that is already excluded for the same class (uk-data#452), when the stages drawn after the SPI income band donors read their rows in a new order; the cell then sits at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever, as the sibling entries say.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#1063 (the stack arm of 2026-10-02 on the review of #1089; same class as the 2026-08-26 microcosm#757 dispositions)", - "approved_on": "2026-10-02", - "expires_on": "2026-11-26" - }, - { - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_22_800_to_24_000", - "reason": "Two-household support: two FRS households (design weights 1,025 and 1,999 against the published 2,761, with their incidence clones) are the only rows whose UC award falls in this band on every main-based spine. On the #1063 stack both awards moved out of the band when the stages drawn after the WAS wealth chain read their rows in a new order, and the cell sits at -100% with final estimate 0.0; the same class as its 2026-08-26 siblings (uk-data#452): the payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#1063 (the stack arm of 2026-10-02 on the review of #1089; same class as the 2026-08-26 microcosm#757 dispositions)", - "approved_on": "2026-10-02", - "expires_on": "2026-11-26" - }, - { - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_26_400_to_27_600", - "reason": "Engine-moved support: fewer than 10 FRS households support this band, and under policyengine-uk 2.122.2 none of them stays in it (the same spine under 2.100.0 keeps them, and the band sits at -100% with final estimate 0.0 on arms B0, B2 and head). 2.122.2 reads the published LHA rates, which lower the supporting households' housing element and move their awards to the band below. The same class as its 2026-08-26 siblings (uk-data#452): the payment distribution needs support for high awards beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#1095 (the B0, B2 and head arms of 2026-10-06 on the second ports PR; same class as the 2026-08-26 microcosm#757 dispositions)", - "approved_on": "2026-10-07", - "expires_on": "2026-11-26" - }, - { - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_24_000_to_25_200", - "reason": "Engine-moved support: fewer than 10 FRS households support this band, and under policyengine-uk 2.122.2 none of them stays in it (the same spine under 2.100.0 keeps them, and the band sits at -100% with final estimate 0.0 on arms B0, B2 and head). 2.122.2 corrects the supporting awards three ways: the 2019 mixed-age couple saving keeps couples who report Pension Credit on it and off UC, the published LHA rates move awards to neighbouring bands, and Carer Support Payment counts as unearned income as Carer's Allowance does. The same class as its 2026-08-26 siblings (uk-data#452): the payment distribution needs support for high awards beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#1095 (the B0, B2 and head arms of 2026-10-06 on the second ports PR; same class as the 2026-08-26 microcosm#757 dispositions)", - "approved_on": "2026-10-07", - "expires_on": "2026-11-26" - }, - { - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_26_400_to_27_600", - "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, { "name": "dwp/uc_payment_dist/LONE_PARENT_annual_payment_13_200_to_14_400", "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", @@ -262,15 +226,6 @@ "approved_on": "2026-08-26", "expires_on": "2026-11-26" }, - { - "name": "dwp/uc_payment_dist/SINGLE_annual_payment_21_600_to_22_800", - "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", - "tracking": "uk-data#452", - "approved_by": "juaristi22", - "adjudication": "microcosm#757 (the uk_target_fit dispositions, issue comment 5427936411)", - "approved_on": "2026-08-26", - "expires_on": "2026-11-26" - }, { "name": "dwp/uc_payment_dist/SINGLE_annual_payment_8_400_to_9_600", "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.", @@ -370,24 +325,6 @@ "approved_on": "2026-09-16", "expires_on": "2026-12-08" }, - { - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_27_600_to_28_800", - "reason": "Zero-support channel under the corrected UC composition proxy: since #879 the payment-distribution rows are measured on the relationship-based uc_calibration_family_type, and no benefit unit in the spine is a couple without children with an annual UC award in this band (final estimate 0.0, -100% on #879's corrected comparison; no pool carrier on the K=15 rowwise clone of spine-p either, where the #355 size selection refuses the row by name). The dense seam carries the row as a capped miss; a size selection cannot carry it. The band catalogue and the unbounded top band are tracked in microcosm#736; the sibling 26,400-27,600 band already sits in this register.", - "tracking": "microcosm#736", - "approved_by": "juaristi22", - "adjudication": "microcosm#355 (dataset-size experiment; ruling by María 2026-09-08 after the 45,800 smoke's by-name refusal)", - "approved_on": "2026-09-08", - "expires_on": "2026-12-08" - }, - { - "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_28_800_to_30_000", - "reason": "Zero-support channel under the corrected UC composition proxy: since #879 the payment-distribution rows are measured on the relationship-based uc_calibration_family_type, and no benefit unit in the spine is a couple without children with an annual UC award in this band (final estimate 0.0, -100% on #879's corrected comparison; no pool carrier on the K=15 rowwise clone of spine-p either, where the #355 size selection refuses the row by name). The dense seam carries the row as a capped miss; a size selection cannot carry it. The band catalogue and the unbounded top band are tracked in microcosm#736; the sibling 26,400-27,600 band already sits in this register.", - "tracking": "microcosm#736", - "approved_by": "juaristi22", - "adjudication": "microcosm#355 (dataset-size experiment; ruling by María 2026-09-08 after the 45,800 smoke's by-name refusal)", - "approved_on": "2026-09-08", - "expires_on": "2026-12-08" - }, { "name": "mhclg.council_tax_stock.band_h@E12000001", "reason": "Spine support absent for band H in the North East region-tier cell: the spine carries no council-tax band-H household there (spine-m held 170 band-H households from 49 raw FRS households, none in the North East, North West or Yorkshire and the Humber; microcosm#762 A14), so the cell measures 0 against the MHCLG taxbase stock (-100% on the #905 spine-q comparison against the VOA row, docs/evidence/uk-region-tier-905/calibration-comparison.json; microcosm#929 re-based the family on the taxbase return and the cell still has no support) while the other 78 composed MHCLG region cells fit within 3.7% on the #929 measurement (experiments/929-council-tax-taxbase-receipts.md Part D). The same class is signed for the local band-H family under council_tax_band_h_spine_support_absent; the cell binds when a spine vintage carries broader band-H support.", @@ -585,15 +522,6 @@ "adjudication": "María's approval of 2026-10-08 of the VAT, road fuel and domestic energy plan (microcosm#1113, microcosm#1123 item 3), decision D2, with the binding confirmed on 2026-10-09: the stage sets the household road-fuel level and incidence, and the household-scope ONS 07.2.2 row is bound in place of the OBR cars receipts, which include business fuel. The reason's figures were restated on 2026-10-11 after her review of PolicyEngine/microcosm#1167, which found bases mixed. Receipts: experiments/1113-consumption-tax-levels-receipts.md.", "approved_on": "2026-10-08", "expires_on": "2027-01-08" - }, - { - "name": "dwp.uc.households_children_5_or_more", - "reason": "Two DWP counts the frame cannot both meet once dependants in approved training qualify as young people (microcosm#1271, uk-data#561). DWP's count of Universal Credit households with five or more children includes families outside the two-child limit, whose third and later children were all born before 6 April 2017 or who meet an exception; DWP's own two-child-limit counts of households with five and with six or more children leave them out, about 21,000 households fewer in DWP's published figures. Fewer than 10 FRS source households have five or more children on Universal Credit and sit outside the limit, so the frame has no real support for that difference and the calibration can reach this row only through the two-child-limit rows, which carry six times its loss weight and are kept. Before the fix the row passed the fit gate through an artefact: a dependant aged 16 to 19 in training counted as a child in this row but failed the engine's qualifying-young-person test, so the two-child-limit rows counted one child fewer in that family. Held out, the row is neither fitted nor gated, and the two-child-limit rows are. Rebind it when the frame can carry large families outside the limit.", - "tracking": "microcosm#1095", - "approved_by": "juaristi22", - "adjudication": "María's ruling of 2026-10-14 on microcosm#1271 (item 4 of Vahid's review, issue comment 6168355102): hold the row out with an expiry rather than keep a fit that rested on fewer than 10 source households", - "approved_on": "2026-10-14", - "expires_on": "2026-11-26" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json b/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json index 78eb259e04..0596557275 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json +++ b/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json @@ -557,6 +557,53 @@ "reason": "The ten LFS household-composition rows partition all UK households; the nations' official estimates sum to a different total than the LFS UK figure (about 29.09m against 29.003m), and the nations win, so the national reconciliation scales the composition by one factor onto the nations' sum, keeping its shares." } ], + "household_basis_partitions": [ + { + "partition_id": "council_tax_stock_on_household_controls", + "control_target_ids": [ + "ons.households.english_regions", + "welshgov.households.wales", + "nrs.households.scotland" + ], + "parent_target_ids": [ + "mhclg.council_tax_stock.total", + "welshgov.council_tax_stock.total", + "scotgov.council_tax_stock.total" + ], + "member_target_ids": [ + "mhclg.council_tax_stock.band_a", + "mhclg.council_tax_stock.band_b", + "mhclg.council_tax_stock.band_c", + "mhclg.council_tax_stock.band_d", + "mhclg.council_tax_stock.band_e", + "mhclg.council_tax_stock.band_f", + "mhclg.council_tax_stock.band_g", + "mhclg.council_tax_stock.band_h", + "welshgov.council_tax_stock.band_a", + "welshgov.council_tax_stock.band_b", + "welshgov.council_tax_stock.band_c", + "welshgov.council_tax_stock.band_d", + "welshgov.council_tax_stock.band_e", + "welshgov.council_tax_stock.band_f", + "welshgov.council_tax_stock.band_g", + "welshgov.council_tax_stock.band_h", + "welshgov.council_tax_stock.band_i", + "scotgov.council_tax_stock.band_a", + "scotgov.council_tax_stock.band_b", + "scotgov.council_tax_stock.band_c", + "scotgov.council_tax_stock.band_d", + "scotgov.council_tax_stock.band_e", + "scotgov.council_tax_stock.band_f", + "scotgov.council_tax_stock.band_g", + "scotgov.council_tax_stock.band_h" + ], + "max_factor_shift": 0.035, + "reason": "The council-tax dwelling stock (MHCLG and StatsWales taxbase lines, Scotland's CTAXBASE chargeable dwellings) and each nation's official household estimate bind the same weighted household count of an area at two published levels, so the solve traded one against the other (microcosm#1284). Each English region's, Wales's and Scotland's dwelling total takes its household control and its bands move by the same factor, the area's published households per chargeable dwelling: 0.980 to 1.023 across the eleven areas on the 735820c feed. The signed assumption is that households per chargeable dwelling are the same in every band of an area. They are not exactly: multi-household dwellings concentrate in lower bands, and second homes, where still counted, in particular areas. The bands therefore bind their area's band mix and the level follows the household estimate. The tripwire refuses a factor further than 0.035 from one, the measured 0.023 with a margin. Northern Ireland has no council-tax rows. National register only: the local band cells follow the national rows by signature.", + "tracking": "microcosm#1284", + "approved_by": "juaristi22", + "approved_on": "2026-10-16" + } + ], "local_routes": { "ons.rent.private_rent": { "route": "no_higher_control", diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 8440336fd4..07196c2078 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -991,7 +991,9 @@ "person.a_and_e_visits", "person.aa_category", "person.admitted_patient_visits", + "person.age_left_full_time_education", "person.age_started_or_accepted_current_education_or_training", + "person.attends_private_school", "person.attends_private_school_random_draw", "person.capital_gains_asset_type", "person.capital_gains_badr", @@ -1005,6 +1007,7 @@ "person.dla_sc_category", "person.employment_sector", "person.esa_health_condition_proxy", + "person.esa_includes_support_component", "person.esa_support_group_proxy", "person.gift_aid", "person.has_lifetime_isa", @@ -1012,6 +1015,7 @@ "person.is_before_universal_credit_qualifying_young_person_terminal_date", "person.is_blind", "person.is_claimant_or_partner", + "person.is_engaged_in_owned_company_trade", "person.is_hbai_dependent_child", "person.is_in_non_advanced_education", "person.is_parent", @@ -1021,10 +1025,16 @@ "person.local_bus_single_fare_share", "person.local_bus_trips", "person.local_bus_use_band", + "person.loss_relief", + "person.ni_class_4_losses_brought_forward", "person.ons_family_index", "person.ons_family_role", "person.other_local_bus_trips", "person.outpatient_visits", + "person.owned_company_carries_on_property_business", + "person.owned_company_carries_on_trade", + "person.owned_company_income_share", + "person.owned_company_is_main_employment", "person.pension_contributions_via_salary_sacrifice", "person.pip_dl_category", "person.pip_m_category", @@ -1034,13 +1044,16 @@ "person.rent_paid_as_boarder", "person.rent_paid_as_lodger", "person.relationship_to_head", + "person.reports_outstanding_student_loan", "person.salary_sacrifice_asked", "person.salary_sacrifice_reported", "person.sic_industry_division", + "person.stands_as_sole_owner_or_partner_of_company", "person.statutory_paternity_pay", "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", + "person.trading_loss", "person.uc_is_in_startup_period", "person.uc_is_in_gainful_self_employment", "person.is_looked_after_by_local_authority", diff --git a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json index 444d41a8c2..63f0d9b476 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json +++ b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json @@ -2,7 +2,7 @@ "schema_version": 1, "engine": { "package": "policyengine-uk", - "version": "2.130.1" + "version": "2.132.6" }, "basis": "policyengine-uk parameter ratio between 1 January of the fact's opening year and 1 January of the calibration year (the SPI donor rebasing convention)", "written_by": "tools/pin_uk_uprating_engine_values.py", diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index ff29f0dd84..10368c9ffc 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 1292, + "compiled_count": 1291, "counts_by_kind": { - "calibration_drift": 566, - "fixture_only": 45, + "calibration_drift": 561, + "fixture_only": 46, "ledger_only": 700 }, - "difference_count": 1311, + "difference_count": 1307, "differences": [ { "fixture_value": 1608000.0, @@ -21,6 +21,13 @@ "period": 2025, "reason": "microcosm#1095 (María's ruling of 2026-10-10) keeps DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) out of the fit: the two rows resolve and fit, but they push the heaviest support family's weight over the uk_weight_ratio fence. The exact figures are retained as diagnostic provenance on obr.pip, so the frozen fixture's fitted rows are fixture-only on the current surface." }, + { + "fixture_value": 75910.0, + "kind": "fixture_only", + "name": "dwp.uc.households_children_5_or_more", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-16) keeps DWP's count of Universal Credit households with five or more children out of the fit: it includes families outside the two-child limit, which the bound two-child-limit rows leave out and too few FRS source households carry. The April 2025 figure is retained as diagnostic provenance on dwp.uc.two_child_limit.households_5_children, so the frozen fixture's fitted row is fixture-only on the current surface." + }, { "fixture_value": 320866000.0, "kind": "fixture_only", @@ -5286,14 +5293,6 @@ "period": 2025, "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." }, - { - "fixture_value": 75910.0, - "kind": "calibration_drift", - "ledger_value": 76320.08333333333, - "name": "dwp.uc.households_children_5_or_more", - "period": 2025, - "reason": "Inherited PR #891 contract with #892 source-window averaging: current paid-UC counts use the explicit January-December 2025 source windows on the ec7169 national feed. This compares that declaration with the frozen 2025 incumbent baseline; it does not redatum the observations to the comparison year or create a new year ruling." - }, { "fixture_value": 231368.0, "kind": "calibration_drift", @@ -9582,38 +9581,6 @@ "period": 2025, "reason": "Ledger-compiled value differs from the fixture value at this comparison period." }, - { - "fixture_value": 3985000.0, - "kind": "calibration_drift", - "ledger_value": 4460000.0, - "name": "slc.borrowers.plan_2_above_threshold", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, - { - "fixture_value": 8940000.0, - "kind": "calibration_drift", - "ledger_value": 9710000.0, - "name": "slc.borrowers.plan_2_liable", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, - { - "fixture_value": 0.0, - "kind": "calibration_drift", - "ledger_value": 35000.0, - "name": "slc.borrowers.plan_5_above_threshold", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, - { - "fixture_value": 10000.0, - "kind": "calibration_drift", - "ledger_value": 230000.0, - "name": "slc.borrowers.plan_5_liable", - "period": 2025, - "reason": "Ledger-compiled value differs from the fixture value at this comparison period." - }, { "fixture_value": 1852699178.55, "kind": "calibration_drift", diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_local_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_local_incumbent_2025_signed_differences.json index be47a8ac13..365ff052bc 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_local_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_local_incumbent_2025_signed_differences.json @@ -6966,399 +6966,399 @@ "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 69058682.80002052, + "fixture_value": 74012743.07395093, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000021", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 73852194.82433282, + "fixture_value": 19976789.345339715, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000027", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 67032716.7929348, + "fixture_value": 73791033.58638306, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000045", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 74012743.07395093, + "fixture_value": 69983746.5240106, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000048", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 19976789.345339715, + "fixture_value": 34364970.57302029, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000051", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 73194711.51637292, + "fixture_value": 89696472.22450225, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000060", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 73791033.58638306, + "fixture_value": 102803172.61404051, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000061", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 69983746.5240106, + "fixture_value": 76274730.2982848, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000062", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 34364970.57302029, + "fixture_value": 79825583.58024338, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000063", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 74532613.59652387, + "fixture_value": 62121882.22392539, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000064", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 89696472.22450225, + "fixture_value": 82209266.37778774, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000065", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 102803172.61404051, + "fixture_value": 84260583.71800368, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000066", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 76274730.2982848, + "fixture_value": 76169751.57502009, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000067", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 79825583.58024338, + "fixture_value": 59305330.76479159, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000068", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 62121882.22392539, + "fixture_value": 109632807.66818608, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000069", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 82209266.37778774, + "fixture_value": 73583314.73199618, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000070", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 84260583.71800368, + "fixture_value": 69763397.87398714, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000071", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 76169751.57502009, + "fixture_value": 72681239.75664912, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000072", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 59305330.76479159, + "fixture_value": 75128018.24796474, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000073", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 109632807.66818608, + "fixture_value": 68695232.1435041, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000074", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 73583314.73199618, + "fixture_value": 85763529.39876205, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000075", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 69763397.87398714, + "fixture_value": 66470675.59679551, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000076", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 72681239.75664912, + "fixture_value": 67438353.41332708, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000077", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 75128018.24796474, + "fixture_value": 91329905.40642643, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000078", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 68695232.1435041, + "fixture_value": 81849103.13781111, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000079", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 85763529.39876205, + "fixture_value": 80699959.9282826, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000080", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 66470675.59679551, + "fixture_value": 81818446.06728882, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000081", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 67438353.41332708, + "fixture_value": 77621179.66143923, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000082", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 91329905.40642643, + "fixture_value": 64159988.736744136, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000083", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 81849103.13781111, + "fixture_value": 78432003.46438093, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000084", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 80699959.9282826, + "fixture_value": 99284538.4363992, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000085", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 81818446.06728882, + "fixture_value": 96743055.07115552, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000086", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 77621179.66143923, + "fixture_value": 70106073.54158622, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000087", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 64159988.736744136, + "fixture_value": 78916507.31301983, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000088", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 78432003.46438093, + "fixture_value": 90735142.94798402, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000089", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 99284538.4363992, + "fixture_value": 73308563.16081639, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000090", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 96743055.07115552, + "fixture_value": 64770729.56860093, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000091", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 70106073.54158622, + "fixture_value": 68817050.03919052, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000092", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 78916507.31301983, + "fixture_value": 80819807.1907156, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000093", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 90735142.94798402, + "fixture_value": 86714711.60155293, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000094", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 73308563.16081639, + "fixture_value": 71854668.79289374, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000095", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 64770729.56860093, + "fixture_value": 63959242.26348356, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000096", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 68817050.03919052, + "fixture_value": 79703031.74970154, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000097", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 80819807.1907156, + "fixture_value": 80087204.63676274, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000098", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 86714711.60155293, + "fixture_value": 76342711.01426594, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000099", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 71854668.79289374, + "fixture_value": 79765339.76086284, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000100", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 63959242.26348356, + "fixture_value": 69032475.81511588, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000101", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 79703031.74970154, + "fixture_value": 74663587.96536602, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000102", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 80087204.63676274, + "fixture_value": 76611667.55815, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000103", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 76342711.01426594, + "fixture_value": 61042309.922564715, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000104", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 79765339.76086284, + "fixture_value": 84514219.37178129, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000105", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 69032475.81511588, + "fixture_value": 72852759.03499582, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000106", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 74663587.96536602, + "fixture_value": 69058682.80002052, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000107", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 76611667.55815, + "fixture_value": 73852194.82433282, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000108", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 61042309.922564715, + "fixture_value": 67032716.7929348, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000109", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 84514219.37178129, + "fixture_value": 73194711.51637292, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000110", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 72852759.03499582, + "fixture_value": 74532613.59652387, "kind": "fixture_only", "name": "housing/scotland_private_rent_amount@S14000111", "period": 2025, @@ -11516,399 +11516,399 @@ "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5760.6508842192625, + "fixture_value": 6173.902491987898, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000021", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6160.510078773174, + "fixture_value": 1666.3988442892655, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000027", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5591.651384128695, + "fixture_value": 6155.408207072327, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000045", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6173.902491987898, + "fixture_value": 5837.816693694578, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000048", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 1666.3988442892655, + "fixture_value": 2866.614161913604, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000051", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6105.664957989065, + "fixture_value": 7482.188206915434, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000060", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6155.408207072327, + "fixture_value": 8575.50655772777, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000061", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5837.816693694578, + "fixture_value": 6362.5901149720385, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000062", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 2866.614161913604, + "fixture_value": 6658.790755776056, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000063", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6217.268401445101, + "fixture_value": 5182.005524184634, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000064", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7482.188206915434, + "fixture_value": 6857.629827976956, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000065", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 8575.50655772777, + "fixture_value": 7028.744053887526, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000066", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6362.5901149720385, + "fixture_value": 6353.83313104939, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000067", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6658.790755776056, + "fixture_value": 4947.057955020988, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000068", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5182.005524184634, + "fixture_value": 9145.212518200375, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000069", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6857.629827976956, + "fixture_value": 6138.080975308323, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000070", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7028.744053887526, + "fixture_value": 5819.435925424353, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000071", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6353.83313104939, + "fixture_value": 6062.832812533292, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000072", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 4947.057955020988, + "fixture_value": 6266.935122452849, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000073", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 9145.212518200375, + "fixture_value": 5730.333011636978, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000074", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6138.080975308323, + "fixture_value": 7154.1148981282995, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000075", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5819.435925424353, + "fixture_value": 5544.7677341337585, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000076", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6062.832812533292, + "fixture_value": 5625.488272716639, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000077", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6266.935122452849, + "fixture_value": 7618.443894429966, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000078", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5730.333011636978, + "fixture_value": 6827.586180998591, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000079", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7154.1148981282995, + "fixture_value": 6731.728389079297, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000080", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5544.7677341337585, + "fixture_value": 6825.028867808543, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000081", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5625.488272716639, + "fixture_value": 6474.906544998267, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000082", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7618.443894429966, + "fixture_value": 5352.017745807819, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000083", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6827.586180998591, + "fixture_value": 6542.542831529941, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000084", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6731.728389079297, + "fixture_value": 8281.993529896497, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000085", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6825.028867808543, + "fixture_value": 8069.991247176803, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000086", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6474.906544998267, + "fixture_value": 5848.020815948133, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000087", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5352.017745807819, + "fixture_value": 6582.958567986305, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000088", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6542.542831529941, + "fixture_value": 7568.830743075077, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000089", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 8281.993529896497, + "fixture_value": 6115.1620921601925, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000090", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 8069.991247176803, + "fixture_value": 5402.963761144556, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000091", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5848.020815948133, + "fixture_value": 5740.494664597141, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000092", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6582.958567986305, + "fixture_value": 6741.72565821785, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000093", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7568.830743075077, + "fixture_value": 7233.4594262223, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000094", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6115.1620921601925, + "fixture_value": 5993.882949023501, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000095", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5402.963761144556, + "fixture_value": 5335.272127417715, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000096", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5740.494664597141, + "fixture_value": 6648.567880355485, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000097", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6741.72565821785, + "fixture_value": 6680.614334064292, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000098", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7233.4594262223, + "fixture_value": 6368.260845367529, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000099", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5993.882949023501, + "fixture_value": 6653.765412150721, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000100", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5335.272127417715, + "fixture_value": 5758.464782709032, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000101", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6648.567880355485, + "fixture_value": 6228.193857638141, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000102", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6680.614334064292, + "fixture_value": 6390.696326172005, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000103", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6368.260845367529, + "fixture_value": 5091.951111325051, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000104", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6653.765412150721, + "fixture_value": 7049.9015158309385, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000105", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5758.464782709032, + "fixture_value": 6077.140393309629, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000106", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6228.193857638141, + "fixture_value": 5760.6508842192625, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000107", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6390.696326172005, + "fixture_value": 6160.510078773174, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000108", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 5091.951111325051, + "fixture_value": 5591.651384128695, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000109", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 7049.9015158309385, + "fixture_value": 6105.664957989065, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000110", "period": 2025, "reason": "Ruled signed exclusion: the incumbent y-values for devolved private rent are hardcoded constants in devolved_housing.py with no source or year, then allocated by population share. Adopting them would violate the facts-only doctrine; a Chronicle facts request covers sourced Scottish Government and StatsWales private-rent statistics." }, { - "fixture_value": 6077.140393309629, + "fixture_value": 6217.268401445101, "kind": "fixture_only", "name": "housing/scotland_private_renter_households@S14000111", "period": 2025, @@ -107326,7 +107326,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1167904934.2878237, + "fixture_value": 1802439852.0494895, "kind": "calibration_drift", "ledger_value": 1945921568.627451, "name": "hmrc.employment_income.amount@S14000021", @@ -107334,7 +107334,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1142335783.426102, + "fixture_value": 331040214.055142, "kind": "calibration_drift", "ledger_value": 374215686.2745098, "name": "hmrc.employment_income.amount@S14000027", @@ -107342,7 +107342,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1185815692.137822, + "fixture_value": 1686081687.2584658, "kind": "calibration_drift", "ledger_value": 1664466013.0718954, "name": "hmrc.employment_income.amount@S14000045", @@ -107350,7 +107350,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1802439852.0494895, + "fixture_value": 1159875973.872307, "kind": "calibration_drift", "ledger_value": 1363505882.352941, "name": "hmrc.employment_income.amount@S14000048", @@ -107358,7 +107358,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 331040214.055142, + "fixture_value": 653063287.9513192, "kind": "calibration_drift", "ledger_value": 739019281.0457516, "name": "hmrc.employment_income.amount@S14000051", @@ -107366,7 +107366,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1378510742.110218, + "fixture_value": 1783750902.6515257, "kind": "calibration_drift", "ledger_value": 2068392156.862745, "name": "hmrc.employment_income.amount@S14000060", @@ -107374,7 +107374,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1686081687.2584658, + "fixture_value": 2236411338.631553, "kind": "calibration_drift", "ledger_value": 2489101307.1895423, "name": "hmrc.employment_income.amount@S14000061", @@ -107382,7 +107382,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1159875973.872307, + "fixture_value": 1330779190.0523124, "kind": "calibration_drift", "ledger_value": 1592117647.0588236, "name": "hmrc.employment_income.amount@S14000062", @@ -107390,7 +107390,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 653063287.9513192, + "fixture_value": 1291214943.5740054, "kind": "calibration_drift", "ledger_value": 1381196078.4313724, "name": "hmrc.employment_income.amount@S14000063", @@ -107398,7 +107398,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2105193420.9484272, + "fixture_value": 1540211534.4293618, "kind": "calibration_drift", "ledger_value": 1546984967.3202615, "name": "hmrc.employment_income.amount@S14000064", @@ -107406,7 +107406,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1783750902.6515257, + "fixture_value": 1389441245.2974343, "kind": "calibration_drift", "ledger_value": 1473729411.764706, "name": "hmrc.employment_income.amount@S14000065", @@ -107414,7 +107414,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2236411338.631553, + "fixture_value": 1340295360.981718, "kind": "calibration_drift", "ledger_value": 1533150326.7973857, "name": "hmrc.employment_income.amount@S14000066", @@ -107422,7 +107422,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1330779190.0523124, + "fixture_value": 1236962554.8243895, "kind": "calibration_drift", "ledger_value": 1397071895.4248366, "name": "hmrc.employment_income.amount@S14000067", @@ -107430,7 +107430,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1291214943.5740054, + "fixture_value": 1636762871.4876459, "kind": "calibration_drift", "ledger_value": 1664466013.0718954, "name": "hmrc.employment_income.amount@S14000068", @@ -107438,7 +107438,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1540211534.4293618, + "fixture_value": 1292903223.3163412, "kind": "calibration_drift", "ledger_value": 1396164705.8823528, "name": "hmrc.employment_income.amount@S14000069", @@ -107446,7 +107446,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1389441245.2974343, + "fixture_value": 1496094249.7831178, "kind": "calibration_drift", "ledger_value": 1413968300.6535947, "name": "hmrc.employment_income.amount@S14000070", @@ -107454,7 +107454,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1340295360.981718, + "fixture_value": 1263653375.3045564, "kind": "calibration_drift", "ledger_value": 1460801960.7843137, "name": "hmrc.employment_income.amount@S14000071", @@ -107462,7 +107462,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1236962554.8243895, + "fixture_value": 1421244774.7479062, "kind": "calibration_drift", "ledger_value": 1521130065.359477, "name": "hmrc.employment_income.amount@S14000072", @@ -107470,7 +107470,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1636762871.4876459, + "fixture_value": 1171163456.9918442, "kind": "calibration_drift", "ledger_value": 1236952941.1764705, "name": "hmrc.employment_income.amount@S14000073", @@ -107478,7 +107478,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1292903223.3163412, + "fixture_value": 1154888136.6172523, "kind": "calibration_drift", "ledger_value": 1283559803.9215686, "name": "hmrc.employment_income.amount@S14000074", @@ -107486,7 +107486,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1496094249.7831178, + "fixture_value": 1237724774.0611553, "kind": "calibration_drift", "ledger_value": 1433359477.124183, "name": "hmrc.employment_income.amount@S14000075", @@ -107494,7 +107494,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1263653375.3045564, + "fixture_value": 1439991580.0735261, "kind": "calibration_drift", "ledger_value": 1518862091.503268, "name": "hmrc.employment_income.amount@S14000076", @@ -107502,7 +107502,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1421244774.7479062, + "fixture_value": 1530764536.0819974, "kind": "calibration_drift", "ledger_value": 1790792156.862745, "name": "hmrc.employment_income.amount@S14000077", @@ -107510,7 +107510,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1171163456.9918442, + "fixture_value": 1784794667.5055428, "kind": "calibration_drift", "ledger_value": 1990147058.8235292, "name": "hmrc.employment_income.amount@S14000078", @@ -107518,7 +107518,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1154888136.6172523, + "fixture_value": 2626854742.7394032, "kind": "calibration_drift", "ledger_value": 2679837908.4967318, "name": "hmrc.employment_income.amount@S14000079", @@ -107526,7 +107526,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1237724774.0611553, + "fixture_value": 1883995561.5009475, "kind": "calibration_drift", "ledger_value": 1986518300.6535947, "name": "hmrc.employment_income.amount@S14000080", @@ -107534,7 +107534,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1439991580.0735261, + "fixture_value": 1898787377.0356874, "kind": "calibration_drift", "ledger_value": 2025300653.5947711, "name": "hmrc.employment_income.amount@S14000081", @@ -107542,7 +107542,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1530764536.0819974, + "fixture_value": 2218561106.790841, "kind": "calibration_drift", "ledger_value": 2382393137.254902, "name": "hmrc.employment_income.amount@S14000082", @@ -107550,7 +107550,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1784794667.5055428, + "fixture_value": 1620488786.337734, "kind": "calibration_drift", "ledger_value": 1719350980.3921568, "name": "hmrc.employment_income.amount@S14000083", @@ -107558,7 +107558,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2626854742.7394032, + "fixture_value": 1435600805.7582588, "kind": "calibration_drift", "ledger_value": 1483254901.9607842, "name": "hmrc.employment_income.amount@S14000084", @@ -107566,7 +107566,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1883995561.5009475, + "fixture_value": 1382195136.0398123, "kind": "calibration_drift", "ledger_value": 1560819607.8431373, "name": "hmrc.employment_income.amount@S14000085", @@ -107574,7 +107574,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1898787377.0356874, + "fixture_value": 1398128714.1317, "kind": "calibration_drift", "ledger_value": 1369856209.1503267, "name": "hmrc.employment_income.amount@S14000086", @@ -107582,7 +107582,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2218561106.790841, + "fixture_value": 1437517669.6398144, "kind": "calibration_drift", "ledger_value": 1573406862.745098, "name": "hmrc.employment_income.amount@S14000087", @@ -107590,7 +107590,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1620488786.337734, + "fixture_value": 1292009682.2406945, "kind": "calibration_drift", "ledger_value": 1306352941.1764705, "name": "hmrc.employment_income.amount@S14000088", @@ -107598,7 +107598,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1435600805.7582588, + "fixture_value": 1428166774.2184894, "kind": "calibration_drift", "ledger_value": 1598694771.24183, "name": "hmrc.employment_income.amount@S14000089", @@ -107606,7 +107606,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1382195136.0398123, + "fixture_value": 1108369575.1944292, "kind": "calibration_drift", "ledger_value": 1195222222.222222, "name": "hmrc.employment_income.amount@S14000090", @@ -107614,7 +107614,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1398128714.1317, + "fixture_value": 1712937942.2360287, "kind": "calibration_drift", "ledger_value": 1782287254.9019608, "name": "hmrc.employment_income.amount@S14000091", @@ -107622,7 +107622,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1437517669.6398144, + "fixture_value": 1524059736.5227013, "kind": "calibration_drift", "ledger_value": 1650517973.856209, "name": "hmrc.employment_income.amount@S14000092", @@ -107630,7 +107630,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1292009682.2406945, + "fixture_value": 1314787824.594594, "kind": "calibration_drift", "ledger_value": 1357155555.5555556, "name": "hmrc.employment_income.amount@S14000093", @@ -107638,7 +107638,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1428166774.2184894, + "fixture_value": 1477880583.5627358, "kind": "calibration_drift", "ledger_value": 1737041176.4705882, "name": "hmrc.employment_income.amount@S14000094", @@ -107646,7 +107646,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1108369575.1944292, + "fixture_value": 1740747790.6660192, "kind": "calibration_drift", "ledger_value": 1961457189.5424836, "name": "hmrc.employment_income.amount@S14000095", @@ -107654,7 +107654,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1712937942.2360287, + "fixture_value": 1621800594.9471612, "kind": "calibration_drift", "ledger_value": 1878335947.7124183, "name": "hmrc.employment_income.amount@S14000096", @@ -107662,7 +107662,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1524059736.5227013, + "fixture_value": 1770200487.9194925, "kind": "calibration_drift", "ledger_value": 1923241830.0653594, "name": "hmrc.employment_income.amount@S14000097", @@ -107670,7 +107670,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1314787824.594594, + "fixture_value": 1435394710.1449986, "kind": "calibration_drift", "ledger_value": 1515686928.1045752, "name": "hmrc.employment_income.amount@S14000098", @@ -107678,7 +107678,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1477880583.5627358, + "fixture_value": 1490842074.4466906, "kind": "calibration_drift", "ledger_value": 1585427124.1830065, "name": "hmrc.employment_income.amount@S14000099", @@ -107686,7 +107686,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1740747790.6660192, + "fixture_value": 1174203344.9276266, "kind": "calibration_drift", "ledger_value": 1193861437.9084966, "name": "hmrc.employment_income.amount@S14000100", @@ -107694,7 +107694,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1621800594.9471612, + "fixture_value": 1440129876.3178196, "kind": "calibration_drift", "ledger_value": 1714588235.2941177, "name": "hmrc.employment_income.amount@S14000101", @@ -107702,7 +107702,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1770200487.9194925, + "fixture_value": 1319741417.9971533, "kind": "calibration_drift", "ledger_value": 1367588235.2941177, "name": "hmrc.employment_income.amount@S14000102", @@ -107710,7 +107710,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1435394710.1449986, + "fixture_value": 1590813748.6419847, "kind": "calibration_drift", "ledger_value": 1773555555.5555556, "name": "hmrc.employment_income.amount@S14000103", @@ -107718,7 +107718,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1490842074.4466906, + "fixture_value": 1418121927.1263385, "kind": "calibration_drift", "ledger_value": 1673764705.8823528, "name": "hmrc.employment_income.amount@S14000104", @@ -107726,7 +107726,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1174203344.9276266, + "fixture_value": 1773247787.2033508, "kind": "calibration_drift", "ledger_value": 1962250980.3921568, "name": "hmrc.employment_income.amount@S14000105", @@ -107734,7 +107734,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1440129876.3178196, + "fixture_value": 1351385208.1525652, "kind": "calibration_drift", "ledger_value": 1519542483.6601307, "name": "hmrc.employment_income.amount@S14000106", @@ -107742,7 +107742,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1319741417.9971533, + "fixture_value": 1167904934.2878237, "kind": "calibration_drift", "ledger_value": 1216200980.3921568, "name": "hmrc.employment_income.amount@S14000107", @@ -107750,7 +107750,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1590813748.6419847, + "fixture_value": 1142335783.426102, "kind": "calibration_drift", "ledger_value": 1279704248.366013, "name": "hmrc.employment_income.amount@S14000108", @@ -107758,7 +107758,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1418121927.1263385, + "fixture_value": 1185815692.137822, "kind": "calibration_drift", "ledger_value": 1354660784.3137255, "name": "hmrc.employment_income.amount@S14000109", @@ -107766,7 +107766,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1773247787.2033508, + "fixture_value": 1378510742.110218, "kind": "calibration_drift", "ledger_value": 1472708823.5294118, "name": "hmrc.employment_income.amount@S14000110", @@ -107774,7 +107774,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1351385208.1525652, + "fixture_value": 2105193420.9484272, "kind": "calibration_drift", "ledger_value": 2439886274.509804, "name": "hmrc.employment_income.amount@S14000111", @@ -115382,7 +115382,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 38291.96505861717, + "fixture_value": 46938.53781378879, "kind": "calibration_drift", "ledger_value": 41975.47683923706, "name": "hmrc.employment_income.count@S14000021", @@ -115390,7 +115390,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 39527.1897379274, + "fixture_value": 12352.246793102313, "kind": "calibration_drift", "ledger_value": 10762.942779291554, "name": "hmrc.employment_income.count@S14000027", @@ -115398,7 +115398,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 37056.74037930694, + "fixture_value": 51879.436531029714, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000045", @@ -115406,7 +115406,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46938.53781378879, + "fixture_value": 37056.74037930694, "kind": "calibration_drift", "ledger_value": 38746.594005449595, "name": "hmrc.employment_income.count@S14000048", @@ -115414,7 +115414,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 12352.246793102313, + "fixture_value": 20998.819548273932, "kind": "calibration_drift", "ledger_value": 20449.591280653953, "name": "hmrc.employment_income.count@S14000051", @@ -115422,7 +115422,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44468.08845516833, + "fixture_value": 50965.37026834014, "kind": "calibration_drift", "ledger_value": 51662.12534059946, "name": "hmrc.employment_income.count@S14000060", @@ -115430,7 +115430,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 51879.436531029714, + "fixture_value": 54275.772408891564, "kind": "calibration_drift", "ledger_value": 53814.71389645777, "name": "hmrc.employment_income.count@S14000061", @@ -115438,7 +115438,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 37056.74037930694, + "fixture_value": 43677.54466040978, "kind": "calibration_drift", "ledger_value": 41975.47683923706, "name": "hmrc.employment_income.count@S14000062", @@ -115446,7 +115446,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 20998.819548273932, + "fixture_value": 42788.18289130642, "kind": "calibration_drift", "ledger_value": 37670.29972752044, "name": "hmrc.employment_income.count@S14000063", @@ -115454,7 +115454,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 48173.76249309902, + "fixture_value": 46320.92547413368, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000064", @@ -115462,7 +115462,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 50965.37026834014, + "fixture_value": 46345.62996771988, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000065", @@ -115470,7 +115470,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 54275.772408891564, + "fixture_value": 44035.759817409744, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000066", @@ -115478,7 +115478,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43677.54466040978, + "fixture_value": 38793.392898733204, "kind": "calibration_drift", "ledger_value": 37670.29972752044, "name": "hmrc.employment_income.count@S14000067", @@ -115486,7 +115486,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42788.18289130642, + "fixture_value": 50533.04163058156, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000068", @@ -115494,7 +115494,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46320.92547413368, + "fixture_value": 43031.62555177605, "kind": "calibration_drift", "ledger_value": 38746.594005449595, "name": "hmrc.employment_income.count@S14000069", @@ -115502,7 +115502,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46345.62996771988, + "fixture_value": 48272.58046744383, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000070", @@ -115510,7 +115510,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44035.759817409744, + "fixture_value": 41725.88966709962, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000071", @@ -115518,7 +115518,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 38793.392898733204, + "fixture_value": 44052.853330648686, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000072", @@ -115526,7 +115526,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 50533.04163058156, + "fixture_value": 42961.11434640984, "kind": "calibration_drift", "ledger_value": 38746.594005449595, "name": "hmrc.employment_income.count@S14000073", @@ -115534,7 +115534,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43031.62555177605, + "fixture_value": 35907.981427548424, "kind": "calibration_drift", "ledger_value": 35517.71117166213, "name": "hmrc.employment_income.count@S14000074", @@ -115542,7 +115542,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 48272.58046744383, + "fixture_value": 42837.59187847882, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000075", @@ -115550,7 +115550,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 41725.88966709962, + "fixture_value": 42825.23963168572, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000076", @@ -115558,7 +115558,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44052.853330648686, + "fixture_value": 45419.2114582372, "kind": "calibration_drift", "ledger_value": 45204.359673024526, "name": "hmrc.employment_income.count@S14000077", @@ -115566,7 +115566,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42961.11434640984, + "fixture_value": 54041.079719822614, "kind": "calibration_drift", "ledger_value": 53814.71389645777, "name": "hmrc.employment_income.count@S14000078", @@ -115574,7 +115574,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 35907.981427548424, + "fixture_value": 61995.92665458051, "kind": "calibration_drift", "ledger_value": 60272.4795640327, "name": "hmrc.employment_income.count@S14000079", @@ -115582,7 +115582,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42837.59187847882, + "fixture_value": 44529.84968913384, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000080", @@ -115590,7 +115590,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42825.23963168572, + "fixture_value": 51879.436531029714, "kind": "calibration_drift", "ledger_value": 50585.8310626703, "name": "hmrc.employment_income.count@S14000081", @@ -115598,7 +115598,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 45419.2114582372, + "fixture_value": 52879.968521271, "kind": "calibration_drift", "ledger_value": 50585.8310626703, "name": "hmrc.employment_income.count@S14000082", @@ -115606,7 +115606,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 54041.079719822614, + "fixture_value": 50298.34894151262, "kind": "calibration_drift", "ledger_value": 45204.359673024526, "name": "hmrc.employment_income.count@S14000083", @@ -115614,7 +115614,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 61995.92665458051, + "fixture_value": 47075.822060825245, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000084", @@ -115622,7 +115622,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44529.84968913384, + "fixture_value": 42140.72948019074, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000085", @@ -115630,7 +115630,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 51879.436531029714, + "fixture_value": 49162.0287109222, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000086", @@ -115638,7 +115638,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 52879.968521271, + "fixture_value": 43084.26991592127, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000087", @@ -115646,7 +115646,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 50298.34894151262, + "fixture_value": 44343.832190398105, "kind": "calibration_drift", "ledger_value": 38746.594005449595, "name": "hmrc.employment_income.count@S14000088", @@ -115654,7 +115654,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 47075.822060825245, + "fixture_value": 43232.863775858095, "kind": "calibration_drift", "ledger_value": 40899.182561307905, "name": "hmrc.employment_income.count@S14000089", @@ -115662,7 +115662,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42140.72948019074, + "fixture_value": 38304.31730541027, "kind": "calibration_drift", "ledger_value": 36594.005449591285, "name": "hmrc.employment_income.count@S14000090", @@ -115670,7 +115670,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 49162.0287109222, + "fixture_value": 46864.42433303017, "kind": "calibration_drift", "ledger_value": 41975.47683923706, "name": "hmrc.employment_income.count@S14000091", @@ -115678,7 +115678,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43084.26991592127, + "fixture_value": 45826.835602409585, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000092", @@ -115686,7 +115686,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44343.832190398105, + "fixture_value": 42902.65519822071, "kind": "calibration_drift", "ledger_value": 36594.005449591285, "name": "hmrc.employment_income.count@S14000093", @@ -115694,7 +115694,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43232.863775858095, + "fixture_value": 49969.71172680361, "kind": "calibration_drift", "ledger_value": 49509.53678474115, "name": "hmrc.employment_income.count@S14000094", @@ -115702,7 +115702,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 38304.31730541027, + "fixture_value": 52274.708428408994, "kind": "calibration_drift", "ledger_value": 52738.41961852861, "name": "hmrc.employment_income.count@S14000095", @@ -115710,7 +115710,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46864.42433303017, + "fixture_value": 43479.90871172014, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000096", @@ -115718,7 +115718,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 45826.835602409585, + "fixture_value": 46197.40300620265, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000097", @@ -115726,7 +115726,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 42902.65519822071, + "fixture_value": 46695.23598301556, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000098", @@ -115734,7 +115734,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 49969.71172680361, + "fixture_value": 47074.41252851292, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000099", @@ -115742,7 +115742,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 52274.708428408994, + "fixture_value": 36772.63870306559, "kind": "calibration_drift", "ledger_value": 34441.416893732974, "name": "hmrc.employment_income.count@S14000100", @@ -115750,7 +115750,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43479.90871172014, + "fixture_value": 45494.42563425461, "kind": "calibration_drift", "ledger_value": 45204.359673024526, "name": "hmrc.employment_income.count@S14000101", @@ -115758,7 +115758,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46197.40300620265, + "fixture_value": 43290.22222878844, "kind": "calibration_drift", "ledger_value": 38746.594005449595, "name": "hmrc.employment_income.count@S14000102", @@ -115766,7 +115766,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 46695.23598301556, + "fixture_value": 50372.46242227124, "kind": "calibration_drift", "ledger_value": 49509.53678474115, "name": "hmrc.employment_income.count@S14000103", @@ -115774,7 +115774,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 47074.41252851292, + "fixture_value": 44183.98677892697, "kind": "calibration_drift", "ledger_value": 44128.06539509537, "name": "hmrc.employment_income.count@S14000104", @@ -115782,7 +115782,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 36772.63870306559, + "fixture_value": 47790.84284251285, "kind": "calibration_drift", "ledger_value": 45204.359673024526, "name": "hmrc.employment_income.count@S14000105", @@ -115790,7 +115790,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 45494.42563425461, + "fixture_value": 45950.358070340604, "kind": "calibration_drift", "ledger_value": 43051.771117166216, "name": "hmrc.employment_income.count@S14000106", @@ -115798,7 +115798,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 43290.22222878844, + "fixture_value": 38291.96505861717, "kind": "calibration_drift", "ledger_value": 35517.71117166213, "name": "hmrc.employment_income.count@S14000107", @@ -115806,7 +115806,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 50372.46242227124, + "fixture_value": 39527.1897379274, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000108", @@ -115814,7 +115814,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 44183.98677892697, + "fixture_value": 37056.74037930694, "kind": "calibration_drift", "ledger_value": 35517.71117166213, "name": "hmrc.employment_income.count@S14000109", @@ -115822,7 +115822,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 47790.84284251285, + "fixture_value": 44468.08845516833, "kind": "calibration_drift", "ledger_value": 39822.88828337875, "name": "hmrc.employment_income.count@S14000110", @@ -115830,7 +115830,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 45950.358070340604, + "fixture_value": 48173.76249309902, "kind": "calibration_drift", "ledger_value": 47356.94822888284, "name": "hmrc.employment_income.count@S14000111", @@ -123430,7 +123430,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 81332039.54473099, + "fixture_value": 183584131.25582325, "kind": "calibration_drift", "ledger_value": 219993464.05228758, "name": "hmrc.self_employment_income.amount@S14000021", @@ -123438,7 +123438,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 165118983.17020845, + "fixture_value": 18358413.125582322, "kind": "calibration_drift", "ledger_value": 21092156.8627451, "name": "hmrc.self_employment_income.amount@S14000027", @@ -123446,7 +123446,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 74287532.18258893, + "fixture_value": 109296599.0732343, "kind": "calibration_drift", "ledger_value": 137779411.76470587, "name": "hmrc.self_employment_income.amount@S14000045", @@ -123454,7 +123454,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 183584131.25582325, + "fixture_value": 61159132.09859693, "kind": "calibration_drift", "ledger_value": 53410784.31372549, "name": "hmrc.self_employment_income.amount@S14000048", @@ -123462,7 +123462,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 18358413.125582322, + "fixture_value": 65962205.30005742, "kind": "calibration_drift", "ledger_value": 61915686.2745098, "name": "hmrc.self_employment_income.amount@S14000051", @@ -123470,7 +123470,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 90511246.10752216, + "fixture_value": 86045455.37976423, "kind": "calibration_drift", "ledger_value": 78245098.03921568, "name": "hmrc.self_employment_income.amount@S14000060", @@ -123478,7 +123478,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 109296599.0732343, + "fixture_value": 125033601.58041953, "kind": "calibration_drift", "ledger_value": 94234313.7254902, "name": "hmrc.self_employment_income.amount@S14000061", @@ -123486,7 +123486,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 61159132.09859693, + "fixture_value": 125853326.07346877, "kind": "calibration_drift", "ledger_value": 104326797.3856209, "name": "hmrc.self_employment_income.amount@S14000062", @@ -123494,7 +123494,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 65962205.30005742, + "fixture_value": 55246015.312798895, "kind": "calibration_drift", "ledger_value": 75523529.41176471, "name": "hmrc.self_employment_income.amount@S14000063", @@ -123502,7 +123502,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 149962618.8455998, + "fixture_value": 111831554.37400512, "kind": "calibration_drift", "ledger_value": 76884313.7254902, "name": "hmrc.self_employment_income.amount@S14000064", @@ -123510,7 +123510,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 86045455.37976423, + "fixture_value": 134116746.67918149, "kind": "calibration_drift", "ledger_value": 188468627.4509804, "name": "hmrc.self_employment_income.amount@S14000065", @@ -123518,7 +123518,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 125033601.58041953, + "fixture_value": 121741895.4130186, "kind": "calibration_drift", "ledger_value": 110223529.4117647, "name": "hmrc.self_employment_income.amount@S14000066", @@ -123526,7 +123526,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 125853326.07346877, + "fixture_value": 158327046.6540838, "kind": "calibration_drift", "ledger_value": 179623529.4117647, "name": "hmrc.self_employment_income.amount@S14000067", @@ -123534,7 +123534,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 55246015.312798895, + "fixture_value": 69231497.12585153, "kind": "calibration_drift", "ledger_value": 85389215.68627451, "name": "hmrc.self_employment_income.amount@S14000068", @@ -123542,7 +123542,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 111831554.37400512, + "fixture_value": 136062704.01300237, "kind": "calibration_drift", "ledger_value": 141521568.62745097, "name": "hmrc.self_employment_income.amount@S14000069", @@ -123550,7 +123550,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 134116746.67918149, + "fixture_value": 58866465.15709979, "kind": "calibration_drift", "ledger_value": 63956862.74509804, "name": "hmrc.self_employment_income.amount@S14000070", @@ -123558,7 +123558,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 121741895.4130186, + "fixture_value": 68648724.24407433, "kind": "calibration_drift", "ledger_value": 91172549.01960784, "name": "hmrc.self_employment_income.amount@S14000071", @@ -123566,7 +123566,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 158327046.6540838, + "fixture_value": 71305874.90211867, "kind": "calibration_drift", "ledger_value": 70760784.31372549, "name": "hmrc.self_employment_income.amount@S14000072", @@ -123574,7 +123574,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 69231497.12585153, + "fixture_value": 144190392.20704463, "kind": "calibration_drift", "ledger_value": 145603921.56862745, "name": "hmrc.self_employment_income.amount@S14000073", @@ -123582,7 +123582,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 136062704.01300237, + "fixture_value": 150056545.61042833, "kind": "calibration_drift", "ledger_value": 150820261.4379085, "name": "hmrc.self_employment_income.amount@S14000074", @@ -123590,7 +123590,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 58866465.15709979, + "fixture_value": 88350930.51646526, "kind": "calibration_drift", "ledger_value": 63616666.666666664, "name": "hmrc.self_employment_income.amount@S14000075", @@ -123598,7 +123598,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 68648724.24407433, + "fixture_value": 96667718.60219419, "kind": "calibration_drift", "ledger_value": 98430065.35947712, "name": "hmrc.self_employment_income.amount@S14000076", @@ -123606,7 +123606,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 71305874.90211867, + "fixture_value": 92342818.0216791, "kind": "calibration_drift", "ledger_value": 77224509.80392157, "name": "hmrc.self_employment_income.amount@S14000077", @@ -123614,7 +123614,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 144190392.20704463, + "fixture_value": 157751068.87916806, "kind": "calibration_drift", "ledger_value": 121903594.77124183, "name": "hmrc.self_employment_income.amount@S14000078", @@ -123622,7 +123622,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 150056545.61042833, + "fixture_value": 435824458.2029231, "kind": "calibration_drift", "ledger_value": 365937581.6993464, "name": "hmrc.self_employment_income.amount@S14000079", @@ -123630,7 +123630,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 88350930.51646526, + "fixture_value": 646787174.0566714, "kind": "calibration_drift", "ledger_value": 578333333.3333334, "name": "hmrc.self_employment_income.amount@S14000080", @@ -123638,7 +123638,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 96667718.60219419, + "fixture_value": 147294244.8447884, "kind": "calibration_drift", "ledger_value": 155356209.1503268, "name": "hmrc.self_employment_income.amount@S14000081", @@ -123646,7 +123646,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 92342818.0216791, + "fixture_value": 257098902.3277772, "kind": "calibration_drift", "ledger_value": 268187908.49673203, "name": "hmrc.self_employment_income.amount@S14000082", @@ -123654,7 +123654,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 157751068.87916806, + "fixture_value": 81560452.35920045, "kind": "calibration_drift", "ledger_value": 97976470.58823529, "name": "hmrc.self_employment_income.amount@S14000083", @@ -123662,7 +123662,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 435824458.2029231, + "fixture_value": 86307624.36626194, "kind": "calibration_drift", "ledger_value": 87090196.07843137, "name": "hmrc.self_employment_income.amount@S14000084", @@ -123670,7 +123670,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 646787174.0566714, + "fixture_value": 112725581.1941819, "kind": "calibration_drift", "ledger_value": 96955882.35294117, "name": "hmrc.self_employment_income.amount@S14000085", @@ -123678,7 +123678,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 147294244.8447884, + "fixture_value": 61577076.48409936, "kind": "calibration_drift", "ledger_value": 65317647.058823526, "name": "hmrc.self_employment_income.amount@S14000086", @@ -123686,7 +123686,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 257098902.3277772, + "fixture_value": 107172467.6904102, "kind": "calibration_drift", "ledger_value": 117481045.75163399, "name": "hmrc.self_employment_income.amount@S14000087", @@ -123694,7 +123694,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 81560452.35920045, + "fixture_value": 84699316.17739846, "kind": "calibration_drift", "ledger_value": 122924183.00653595, "name": "hmrc.self_employment_income.amount@S14000088", @@ -123702,7 +123702,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 86307624.36626194, + "fixture_value": 127492775.05956729, "kind": "calibration_drift", "ledger_value": 130181699.34640522, "name": "hmrc.self_employment_income.amount@S14000089", @@ -123710,7 +123710,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 112725581.1941819, + "fixture_value": 74249107.59697726, "kind": "calibration_drift", "ledger_value": 69059803.92156863, "name": "hmrc.self_employment_income.amount@S14000090", @@ -123718,7 +123718,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 61577076.48409936, + "fixture_value": 100147278.29925223, "kind": "calibration_drift", "ledger_value": 144583333.33333334, "name": "hmrc.self_employment_income.amount@S14000091", @@ -123726,7 +123726,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 107172467.6904102, + "fixture_value": 87650749.17865235, "kind": "calibration_drift", "ledger_value": 83915032.67973857, "name": "hmrc.self_employment_income.amount@S14000092", @@ -123734,7 +123734,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 84699316.17739846, + "fixture_value": 66565627.69786467, "kind": "calibration_drift", "ledger_value": 72121568.62745097, "name": "hmrc.self_employment_income.amount@S14000093", @@ -123742,7 +123742,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 127492775.05956729, + "fixture_value": 140977838.78166163, "kind": "calibration_drift", "ledger_value": 154789215.6862745, "name": "hmrc.self_employment_income.amount@S14000094", @@ -123750,7 +123750,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 74249107.59697726, + "fixture_value": 73057945.44301505, "kind": "calibration_drift", "ledger_value": 88450980.39215685, "name": "hmrc.self_employment_income.amount@S14000095", @@ -123758,7 +123758,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 100147278.29925223, + "fixture_value": 268254840.35036945, "kind": "calibration_drift", "ledger_value": 185747058.8235294, "name": "hmrc.self_employment_income.amount@S14000096", @@ -123766,7 +123766,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 87650749.17865235, + "fixture_value": 175290824.86130145, "kind": "calibration_drift", "ledger_value": 209220588.2352941, "name": "hmrc.self_employment_income.amount@S14000097", @@ -123774,7 +123774,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 66565627.69786467, + "fixture_value": 99908235.1140159, "kind": "calibration_drift", "ledger_value": 121903594.77124183, "name": "hmrc.self_employment_income.amount@S14000098", @@ -123782,7 +123782,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 140977838.78166163, + "fixture_value": 69215486.88184667, "kind": "calibration_drift", "ledger_value": 82327450.98039216, "name": "hmrc.self_employment_income.amount@S14000099", @@ -123790,7 +123790,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 73057945.44301505, + "fixture_value": 160562467.72642294, "kind": "calibration_drift", "ledger_value": 150820261.4379085, "name": "hmrc.self_employment_income.amount@S14000100", @@ -123798,7 +123798,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 268254840.35036945, + "fixture_value": 70581461.87826994, "kind": "calibration_drift", "ledger_value": 69400000.0, "name": "hmrc.self_employment_income.amount@S14000101", @@ -123806,7 +123806,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 175290824.86130145, + "fixture_value": 73937304.13205077, "kind": "calibration_drift", "ledger_value": 80626470.58823529, "name": "hmrc.self_employment_income.amount@S14000102", @@ -123814,7 +123814,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 99908235.1140159, + "fixture_value": 155395428.3112518, "kind": "calibration_drift", "ledger_value": 157057189.54248366, "name": "hmrc.self_employment_income.amount@S14000103", @@ -123822,7 +123822,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 69215486.88184667, + "fixture_value": 60343677.00394897, "kind": "calibration_drift", "ledger_value": 107955555.55555555, "name": "hmrc.self_employment_income.amount@S14000104", @@ -123830,7 +123830,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 160562467.72642294, + "fixture_value": 169217605.63545477, "kind": "calibration_drift", "ledger_value": 178602941.17647058, "name": "hmrc.self_employment_income.amount@S14000105", @@ -123838,7 +123838,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 70581461.87826994, + "fixture_value": 72801781.53893715, "kind": "calibration_drift", "ledger_value": 74843137.25490196, "name": "hmrc.self_employment_income.amount@S14000106", @@ -123846,7 +123846,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 73937304.13205077, + "fixture_value": 81332039.54473099, "kind": "calibration_drift", "ledger_value": 91852941.1764706, "name": "hmrc.self_employment_income.amount@S14000107", @@ -123854,7 +123854,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 155395428.3112518, + "fixture_value": 165118983.17020845, "kind": "calibration_drift", "ledger_value": 160345751.63398692, "name": "hmrc.self_employment_income.amount@S14000108", @@ -123862,7 +123862,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 60343677.00394897, + "fixture_value": 74287532.18258893, "kind": "calibration_drift", "ledger_value": 86409803.92156863, "name": "hmrc.self_employment_income.amount@S14000109", @@ -123870,7 +123870,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 169217605.63545477, + "fixture_value": 90511246.10752216, "kind": "calibration_drift", "ledger_value": 91626143.79084967, "name": "hmrc.self_employment_income.amount@S14000110", @@ -123878,7 +123878,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 72801781.53893715, + "fixture_value": 149962618.8455998, "kind": "calibration_drift", "ledger_value": 163861111.1111111, "name": "hmrc.self_employment_income.amount@S14000111", @@ -131486,7 +131486,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3202.0488009736614, + "fixture_value": 5336.748001622768, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000021", @@ -131494,7 +131494,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 7471.447202271876, + "fixture_value": 1067.3496003245536, "kind": "calibration_drift", "ledger_value": 1076.2942779291554, "name": "hmrc.self_employment_income.count@S14000027", @@ -131502,7 +131502,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3202.0488009736614, + "fixture_value": 4269.398401298215, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000045", @@ -131510,7 +131510,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5336.748001622768, + "fixture_value": 3202.0488009736614, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000048", @@ -131518,7 +131518,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 1067.3496003245536, + "fixture_value": 3202.0488009736614, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000051", @@ -131526,7 +131526,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4269.398401298215, + "fixture_value": 3842.4585611683933, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000060", @@ -131534,7 +131534,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4269.398401298215, + "fixture_value": 3842.4585611683938, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000061", @@ -131542,7 +131542,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3202.0488009736614, + "fixture_value": 4440.174337350143, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000062", @@ -131550,7 +131550,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3202.0488009736614, + "fixture_value": 2284.1281446945445, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000063", @@ -131558,7 +131558,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5336.748001622768, + "fixture_value": 3970.5405132073397, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000064", @@ -131566,7 +131566,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3842.4585611683933, + "fixture_value": 5048.563609535139, "kind": "calibration_drift", "ledger_value": 6457.765667574932, "name": "hmrc.self_employment_income.count@S14000065", @@ -131574,7 +131574,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3842.4585611683938, + "fixture_value": 4909.808161492947, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000066", @@ -131582,7 +131582,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4440.174337350143, + "fixture_value": 6974.759764497084, "kind": "calibration_drift", "ledger_value": 6457.765667574932, "name": "hmrc.self_employment_income.count@S14000067", @@ -131590,7 +131590,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2284.1281446945445, + "fixture_value": 2679.04749681463, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000068", @@ -131598,7 +131598,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3970.5405132073397, + "fixture_value": 6343.893064035226, "kind": "calibration_drift", "ledger_value": 6457.765667574932, "name": "hmrc.self_employment_income.count@S14000069", @@ -131606,7 +131606,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5048.563609535139, + "fixture_value": 3095.313840941206, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000070", @@ -131614,7 +131614,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4909.808161492947, + "fixture_value": 3202.0488009736614, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000071", @@ -131622,7 +131622,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 6974.759764497084, + "fixture_value": 3298.433704275697, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000072", @@ -131630,7 +131630,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2679.04749681463, + "fixture_value": 6553.5265459927605, "kind": "calibration_drift", "ledger_value": 6457.765667574932, "name": "hmrc.self_employment_income.count@S14000073", @@ -131638,7 +131638,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 6343.893064035226, + "fixture_value": 5400.788977642242, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000074", @@ -131646,7 +131646,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3095.313840941206, + "fixture_value": 4023.9079932235677, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000075", @@ -131654,7 +131654,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3202.0488009736614, + "fixture_value": 3917.173033191112, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000076", @@ -131662,7 +131662,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3298.433704275697, + "fixture_value": 3927.8465291943576, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000077", @@ -131670,7 +131670,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 6553.5265459927605, + "fixture_value": 5838.402313775308, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000078", @@ -131678,7 +131678,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5400.788977642242, + "fixture_value": 6126.586705862939, "kind": "calibration_drift", "ledger_value": 7534.059945504087, "name": "hmrc.self_employment_income.count@S14000079", @@ -131686,7 +131686,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4023.9079932235677, + "fixture_value": 5603.585401703906, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000080", @@ -131694,7 +131694,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3917.173033191112, + "fixture_value": 4269.398401298215, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000081", @@ -131702,7 +131702,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3927.8465291943576, + "fixture_value": 4546.909297382599, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000082", @@ -131710,7 +131710,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5838.402313775308, + "fixture_value": 3564.9476650840093, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000083", @@ -131718,7 +131718,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 6126.586705862939, + "fixture_value": 3863.5429589137784, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000084", @@ -131726,7 +131726,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5603.585401703906, + "fixture_value": 3949.299199379099, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000085", @@ -131734,7 +131734,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4269.398401298215, + "fixture_value": 3106.5605610022317, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000086", @@ -131742,7 +131742,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4546.909297382599, + "fixture_value": 4265.593986881217, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000087", @@ -131750,7 +131750,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3564.9476650840093, + "fixture_value": 3717.441276298696, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000088", @@ -131758,7 +131758,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3863.5429589137784, + "fixture_value": 3874.47904917813, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000089", @@ -131766,7 +131766,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3949.299199379099, + "fixture_value": 3298.110265002871, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000090", @@ -131774,7 +131774,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3106.5605610022317, + "fixture_value": 3885.152545181375, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000091", @@ -131782,7 +131782,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4265.593986881217, + "fixture_value": 3757.0705931424286, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000092", @@ -131790,7 +131790,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3717.441276298696, + "fixture_value": 2673.657909723882, "kind": "calibration_drift", "ledger_value": 2152.588555858311, "name": "hmrc.self_employment_income.count@S14000093", @@ -131798,7 +131798,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3874.47904917813, + "fixture_value": 6465.04935930159, "kind": "calibration_drift", "ledger_value": 7534.059945504087, "name": "hmrc.self_employment_income.count@S14000094", @@ -131806,7 +131806,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3298.110265002871, + "fixture_value": 3927.8465291943576, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000095", @@ -131814,7 +131814,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3885.152545181375, + "fixture_value": 5635.6058897136445, "kind": "calibration_drift", "ledger_value": 6457.765667574932, "name": "hmrc.self_employment_income.count@S14000096", @@ -131822,7 +131822,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3757.0705931424286, + "fixture_value": 4589.603281395581, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000097", @@ -131830,7 +131830,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2673.657909723882, + "fixture_value": 4765.338619630836, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000098", @@ -131838,7 +131838,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 6465.04935930159, + "fixture_value": 2689.7209928178754, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000099", @@ -131846,7 +131846,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3927.8465291943576, + "fixture_value": 5753.014345749344, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000100", @@ -131854,7 +131854,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5635.6058897136445, + "fixture_value": 3082.8438159077114, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000101", @@ -131862,7 +131862,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4589.603281395581, + "fixture_value": 3326.6433731303596, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000102", @@ -131870,7 +131870,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 4765.338619630836, + "fixture_value": 5390.115481638996, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000103", @@ -131878,7 +131878,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2689.7209928178754, + "fixture_value": 2860.496928869804, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000104", @@ -131886,7 +131886,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5753.014345749344, + "fixture_value": 5817.0553217688175, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000105", @@ -131894,7 +131894,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3082.8438159077114, + "fixture_value": 3330.130753012608, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000106", @@ -131902,7 +131902,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3326.6433731303596, + "fixture_value": 3202.0488009736614, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000107", @@ -131910,7 +131910,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5390.115481638996, + "fixture_value": 7471.447202271876, "kind": "calibration_drift", "ledger_value": 7534.059945504087, "name": "hmrc.self_employment_income.count@S14000108", @@ -131918,7 +131918,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 2860.496928869804, + "fixture_value": 3202.0488009736614, "kind": "calibration_drift", "ledger_value": 3228.882833787466, "name": "hmrc.self_employment_income.count@S14000109", @@ -131926,7 +131926,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 5817.0553217688175, + "fixture_value": 4269.398401298215, "kind": "calibration_drift", "ledger_value": 4305.177111716622, "name": "hmrc.self_employment_income.count@S14000110", @@ -131934,7 +131934,7 @@ "reason": "Scaling and operation class: both sides read the same SPI tables for tax year 2023-24; ours resolves counts at identity and amounts as the declared count_x_mean operation, while the incumbent multiplies every column by a national income-projection ratio for the target year." }, { - "fixture_value": 3330.130753012608, + "fixture_value": 5336.748001622768, "kind": "calibration_drift", "ledger_value": 5381.471389645777, "name": "hmrc.self_employment_income.count@S14000111", @@ -155982,7 +155982,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10624.959775941114, + "fixture_value": 11387.16213781534, "kind": "calibration_drift", "ledger_value": 11143.0, "name": "ons.age.0_10@S14000021", @@ -155990,7 +155990,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11362.461135347194, + "fixture_value": 3073.5104499650324, "kind": "calibration_drift", "ledger_value": 2138.0, "name": "ons.age.0_10@S14000027", @@ -155998,7 +155998,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10313.256649557366, + "fixture_value": 11353.05122964504, "kind": "calibration_drift", "ledger_value": 11543.0, "name": "ons.age.0_10@S14000045", @@ -156006,7 +156006,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11387.16213781534, + "fixture_value": 10767.284599686149, "kind": "calibration_drift", "ledger_value": 7677.0, "name": "ons.age.0_10@S14000048", @@ -156014,7 +156014,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3073.5104499650324, + "fixture_value": 5287.190766396028, "kind": "calibration_drift", "ledger_value": 4235.0, "name": "ons.age.0_10@S14000051", @@ -156022,7 +156022,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11261.30464904907, + "fixture_value": 13800.167781782144, "kind": "calibration_drift", "ledger_value": 11668.0, "name": "ons.age.0_10@S14000060", @@ -156030,7 +156030,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11353.05122964504, + "fixture_value": 15816.68704898882, "kind": "calibration_drift", "ledger_value": 10983.0, "name": "ons.age.0_10@S14000061", @@ -156038,7 +156038,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10767.284599686149, + "fixture_value": 11735.178090303687, "kind": "calibration_drift", "ledger_value": 9348.0, "name": "ons.age.0_10@S14000062", @@ -156046,7 +156046,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5287.190766396028, + "fixture_value": 12281.491338129883, "kind": "calibration_drift", "ledger_value": 9772.0, "name": "ons.age.0_10@S14000063", @@ -156054,7 +156054,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11467.14633628362, + "fixture_value": 9557.704738538141, "kind": "calibration_drift", "ledger_value": 9216.0, "name": "ons.age.0_10@S14000064", @@ -156062,7 +156062,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13800.167781782144, + "fixture_value": 12648.230650489088, "kind": "calibration_drift", "ledger_value": 8555.0, "name": "ons.age.0_10@S14000065", @@ -156070,7 +156070,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15816.68704898882, + "fixture_value": 12963.834182786377, "kind": "calibration_drift", "ledger_value": 9451.0, "name": "ons.age.0_10@S14000066", @@ -156078,7 +156078,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11735.178090303687, + "fixture_value": 11719.026685921317, "kind": "calibration_drift", "ledger_value": 7297.0, "name": "ons.age.0_10@S14000067", @@ -156086,7 +156086,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12281.491338129883, + "fixture_value": 9124.366818571965, "kind": "calibration_drift", "ledger_value": 10110.0, "name": "ons.age.0_10@S14000068", @@ -156094,7 +156094,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9557.704738538141, + "fixture_value": 16867.454234120138, "kind": "calibration_drift", "ledger_value": 8589.0, "name": "ons.age.0_10@S14000069", @@ -156102,7 +156102,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12648.230650489088, + "fixture_value": 11321.092837404112, "kind": "calibration_drift", "ledger_value": 9788.0, "name": "ons.age.0_10@S14000070", @@ -156110,7 +156110,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12963.834182786377, + "fixture_value": 10733.383061917724, "kind": "calibration_drift", "ledger_value": 9136.0, "name": "ons.age.0_10@S14000071", @@ -156118,7 +156118,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11719.026685921317, + "fixture_value": 11182.304926321292, "kind": "calibration_drift", "ledger_value": 9525.0, "name": "ons.age.0_10@S14000072", @@ -156126,7 +156126,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9124.366818571965, + "fixture_value": 11558.75176829406, "kind": "calibration_drift", "ledger_value": 8205.0, "name": "ons.age.0_10@S14000073", @@ -156134,7 +156134,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16867.454234120138, + "fixture_value": 10569.041411306083, "kind": "calibration_drift", "ledger_value": 7540.0, "name": "ons.age.0_10@S14000074", @@ -156142,7 +156142,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11321.092837404112, + "fixture_value": 13195.068500558182, "kind": "calibration_drift", "ledger_value": 9545.0, "name": "ons.age.0_10@S14000075", @@ -156150,7 +156150,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10733.383061917724, + "fixture_value": 10226.784321107452, "kind": "calibration_drift", "ledger_value": 9570.0, "name": "ons.age.0_10@S14000076", @@ -156158,7 +156158,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11182.304926321292, + "fixture_value": 10375.665496650461, "kind": "calibration_drift", "ledger_value": 9756.0, "name": "ons.age.0_10@S14000077", @@ -156166,7 +156166,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11558.75176829406, + "fixture_value": 14051.478133369486, "kind": "calibration_drift", "ledger_value": 8689.0, "name": "ons.age.0_10@S14000078", @@ -156174,7 +156174,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10569.041411306083, + "fixture_value": 12592.818068285545, "kind": "calibration_drift", "ledger_value": 9042.0, "name": "ons.age.0_10@S14000079", @@ -156182,7 +156182,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13195.068500558182, + "fixture_value": 12416.01770252426, "kind": "calibration_drift", "ledger_value": 8951.0, "name": "ons.age.0_10@S14000080", @@ -156190,7 +156190,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10226.784321107452, + "fixture_value": 12588.101353052345, "kind": "calibration_drift", "ledger_value": 9164.0, "name": "ons.age.0_10@S14000081", @@ -156198,7 +156198,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10375.665496650461, + "fixture_value": 11942.334811860112, "kind": "calibration_drift", "ledger_value": 10033.0, "name": "ons.age.0_10@S14000082", @@ -156206,7 +156206,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14051.478133369486, + "fixture_value": 9871.275731203767, "kind": "calibration_drift", "ledger_value": 9358.0, "name": "ons.age.0_10@S14000083", @@ -156214,7 +156214,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12592.818068285545, + "fixture_value": 12067.083358202628, "kind": "calibration_drift", "ledger_value": 10874.0, "name": "ons.age.0_10@S14000084", @@ -156222,7 +156222,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12416.01770252426, + "fixture_value": 15275.330841660763, "kind": "calibration_drift", "ledger_value": 8467.0, "name": "ons.age.0_10@S14000085", @@ -156230,7 +156230,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12588.101353052345, + "fixture_value": 14884.313268893942, "kind": "calibration_drift", "ledger_value": 10760.0, "name": "ons.age.0_10@S14000086", @@ -156238,7 +156238,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11942.334811860112, + "fixture_value": 10786.105109845823, "kind": "calibration_drift", "ledger_value": 8838.0, "name": "ons.age.0_10@S14000087", @@ -156246,7 +156246,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9871.275731203767, + "fixture_value": 12141.626249760286, "kind": "calibration_drift", "ledger_value": 10458.0, "name": "ons.age.0_10@S14000088", @@ -156254,7 +156254,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12067.083358202628, + "fixture_value": 13959.971505368921, "kind": "calibration_drift", "ledger_value": 9010.0, "name": "ons.age.0_10@S14000089", @@ -156262,7 +156262,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15275.330841660763, + "fixture_value": 11278.821188513624, "kind": "calibration_drift", "ledger_value": 9218.0, "name": "ons.age.0_10@S14000090", @@ -156270,7 +156270,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14884.313268893942, + "fixture_value": 9965.240697069019, "kind": "calibration_drift", "ledger_value": 9833.0, "name": "ons.age.0_10@S14000091", @@ -156278,7 +156278,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10786.105109845823, + "fixture_value": 10587.783590988343, "kind": "calibration_drift", "ledger_value": 9731.0, "name": "ons.age.0_10@S14000092", @@ -156286,7 +156286,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12141.626249760286, + "fixture_value": 12434.456692249774, "kind": "calibration_drift", "ledger_value": 8192.0, "name": "ons.age.0_10@S14000093", @@ -156294,7 +156294,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13959.971505368921, + "fixture_value": 13341.411758704446, "kind": "calibration_drift", "ledger_value": 9627.0, "name": "ons.age.0_10@S14000094", @@ -156302,7 +156302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11278.821188513624, + "fixture_value": 11055.133615114944, "kind": "calibration_drift", "ledger_value": 11427.0, "name": "ons.age.0_10@S14000095", @@ -156310,7 +156310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9965.240697069019, + "fixture_value": 9840.390068212882, "kind": "calibration_drift", "ledger_value": 10766.0, "name": "ons.age.0_10@S14000096", @@ -156318,7 +156318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10587.783590988343, + "fixture_value": 12262.6362395792, "kind": "calibration_drift", "ledger_value": 9802.0, "name": "ons.age.0_10@S14000097", @@ -156326,7 +156326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12434.456692249774, + "fixture_value": 12321.74280383054, "kind": "calibration_drift", "ledger_value": 9212.0, "name": "ons.age.0_10@S14000098", @@ -156334,7 +156334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13341.411758704446, + "fixture_value": 11745.637200491627, "kind": "calibration_drift", "ledger_value": 9985.0, "name": "ons.age.0_10@S14000099", @@ -156342,7 +156342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11055.133615114944, + "fixture_value": 12272.222581013264, "kind": "calibration_drift", "ledger_value": 7143.0, "name": "ons.age.0_10@S14000100", @@ -156350,7 +156350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9840.390068212882, + "fixture_value": 10620.92772451513, "kind": "calibration_drift", "ledger_value": 9772.0, "name": "ons.age.0_10@S14000101", @@ -156358,7 +156358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12262.6362395792, + "fixture_value": 11487.297276675245, "kind": "calibration_drift", "ledger_value": 8851.0, "name": "ons.age.0_10@S14000102", @@ -156366,7 +156366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12321.74280383054, + "fixture_value": 11787.017260816836, "kind": "calibration_drift", "ledger_value": 9504.0, "name": "ons.age.0_10@S14000103", @@ -156374,7 +156374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11745.637200491627, + "fixture_value": 9391.608140513052, "kind": "calibration_drift", "ledger_value": 10358.0, "name": "ons.age.0_10@S14000104", @@ -156382,7 +156382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12272.222581013264, + "fixture_value": 13002.857061733197, "kind": "calibration_drift", "ledger_value": 9102.0, "name": "ons.age.0_10@S14000105", @@ -156390,7 +156390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10620.92772451513, + "fixture_value": 11208.693866268344, "kind": "calibration_drift", "ledger_value": 8945.0, "name": "ons.age.0_10@S14000106", @@ -156398,7 +156398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11487.297276675245, + "fixture_value": 10624.959775941114, "kind": "calibration_drift", "ledger_value": 8036.0, "name": "ons.age.0_10@S14000107", @@ -156406,7 +156406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11787.017260816836, + "fixture_value": 11362.461135347194, "kind": "calibration_drift", "ledger_value": 8321.0, "name": "ons.age.0_10@S14000108", @@ -156414,7 +156414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9391.608140513052, + "fixture_value": 10313.256649557366, "kind": "calibration_drift", "ledger_value": 8098.0, "name": "ons.age.0_10@S14000109", @@ -156422,7 +156422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13002.857061733197, + "fixture_value": 11261.30464904907, "kind": "calibration_drift", "ledger_value": 9599.0, "name": "ons.age.0_10@S14000110", @@ -156430,7 +156430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11208.693866268344, + "fixture_value": 11467.14633628362, "kind": "calibration_drift", "ledger_value": 10353.0, "name": "ons.age.0_10@S14000111", @@ -164046,7 +164046,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10307.638180975822, + "fixture_value": 11047.076854868474, "kind": "calibration_drift", "ledger_value": 13774.0, "name": "ons.age.10_20@S14000021", @@ -164054,7 +164054,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11023.11356451084, + "fixture_value": 2981.7179859282437, "kind": "calibration_drift", "ledger_value": 2665.0, "name": "ons.age.10_20@S14000027", @@ -164062,7 +164062,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10005.244278843795, + "fixture_value": 11013.984691993648, "kind": "calibration_drift", "ledger_value": 11256.0, "name": "ons.age.10_20@S14000045", @@ -164070,7 +164070,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11047.076854868474, + "fixture_value": 10445.712377798372, "kind": "calibration_drift", "ledger_value": 9881.0, "name": "ons.age.10_20@S14000048", @@ -164078,7 +164078,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 2981.7179859282437, + "fixture_value": 5129.285245597955, "kind": "calibration_drift", "ledger_value": 5173.0, "name": "ons.age.10_20@S14000051", @@ -164086,7 +164086,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10924.978184951016, + "fixture_value": 13388.016456633628, "kind": "calibration_drift", "ledger_value": 11993.0, "name": "ons.age.10_20@S14000060", @@ -164094,7 +164094,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11013.984691993648, + "fixture_value": 15344.311014886847, "kind": "calibration_drift", "ledger_value": 11435.0, "name": "ons.age.10_20@S14000061", @@ -164102,7 +164102,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10445.712377798372, + "fixture_value": 11384.699076044351, "kind": "calibration_drift", "ledger_value": 10928.0, "name": "ons.age.10_20@S14000062", @@ -164110,7 +164110,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5129.285245597955, + "fixture_value": 11914.696309993167, "kind": "calibration_drift", "ledger_value": 11008.0, "name": "ons.age.10_20@S14000063", @@ -164118,7 +164118,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11124.672271264619, + "fixture_value": 9272.25743560267, "kind": "calibration_drift", "ledger_value": 10945.0, "name": "ons.age.10_20@S14000064", @@ -164126,7 +164126,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13388.016456633628, + "fixture_value": 12270.482705260129, "kind": "calibration_drift", "ledger_value": 10363.0, "name": "ons.age.10_20@S14000065", @@ -164134,7 +164134,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15344.311014886847, + "fixture_value": 12576.660525050534, "kind": "calibration_drift", "ledger_value": 11637.0, "name": "ons.age.10_20@S14000066", @@ -164142,7 +164142,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11384.699076044351, + "fixture_value": 11369.03004425516, "kind": "calibration_drift", "ledger_value": 9345.0, "name": "ons.age.10_20@S14000067", @@ -164150,7 +164150,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11914.696309993167, + "fixture_value": 8851.861445095275, "kind": "calibration_drift", "ledger_value": 11669.0, "name": "ons.age.10_20@S14000068", @@ -164158,7 +164158,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9272.25743560267, + "fixture_value": 16363.696328824824, "kind": "calibration_drift", "ledger_value": 10281.0, "name": "ons.age.10_20@S14000069", @@ -164166,7 +164166,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12270.482705260129, + "fixture_value": 10982.98075870713, "kind": "calibration_drift", "ledger_value": 10807.0, "name": "ons.age.10_20@S14000070", @@ -164174,7 +164174,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12576.660525050534, + "fixture_value": 10412.823332337053, "kind": "calibration_drift", "ledger_value": 10786.0, "name": "ons.age.10_20@S14000071", @@ -164182,7 +164182,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11369.03004425516, + "fixture_value": 10848.337842262921, "kind": "calibration_drift", "ledger_value": 10665.0, "name": "ons.age.10_20@S14000072", @@ -164190,7 +164190,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8851.861445095275, + "fixture_value": 11213.541845219497, "kind": "calibration_drift", "ledger_value": 10088.0, "name": "ons.age.10_20@S14000073", @@ -164198,7 +164198,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16363.696328824824, + "fixture_value": 10253.3898560424, "kind": "calibration_drift", "ledger_value": 9593.0, "name": "ons.age.10_20@S14000074", @@ -164206,7 +164206,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10982.98075870713, + "fixture_value": 12800.988873851782, "kind": "calibration_drift", "ledger_value": 11408.0, "name": "ons.age.10_20@S14000075", @@ -164214,7 +164214,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10412.823332337053, + "fixture_value": 9921.354504847048, "kind": "calibration_drift", "ledger_value": 11376.0, "name": "ons.age.10_20@S14000076", @@ -164222,7 +164222,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10848.337842262921, + "fixture_value": 10065.789243595951, "kind": "calibration_drift", "ledger_value": 10994.0, "name": "ons.age.10_20@S14000077", @@ -164230,7 +164230,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11213.541845219497, + "fixture_value": 13631.821254950315, "kind": "calibration_drift", "ledger_value": 10637.0, "name": "ons.age.10_20@S14000078", @@ -164238,7 +164238,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10253.3898560424, + "fixture_value": 12216.725057224503, "kind": "calibration_drift", "ledger_value": 9074.0, "name": "ons.age.10_20@S14000079", @@ -164246,7 +164246,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12800.988873851782, + "fixture_value": 12045.204953717091, "kind": "calibration_drift", "ledger_value": 10746.0, "name": "ons.age.10_20@S14000080", @@ -164254,7 +164254,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9921.354504847048, + "fixture_value": 12212.149209875264, "kind": "calibration_drift", "ledger_value": 11677.0, "name": "ons.age.10_20@S14000081", @@ -164262,7 +164262,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10065.789243595951, + "fixture_value": 11585.66892229223, "kind": "calibration_drift", "ledger_value": 10912.0, "name": "ons.age.10_20@S14000082", @@ -164270,7 +164270,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13631.821254950315, + "fixture_value": 9576.463418929357, "kind": "calibration_drift", "ledger_value": 11013.0, "name": "ons.age.10_20@S14000083", @@ -164278,7 +164278,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12216.725057224503, + "fixture_value": 11706.6917690999, "kind": "calibration_drift", "ledger_value": 9794.0, "name": "ons.age.10_20@S14000084", @@ -164286,7 +164286,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12045.204953717091, + "fixture_value": 14819.12277607597, "kind": "calibration_drift", "ledger_value": 11137.0, "name": "ons.age.10_20@S14000085", @@ -164294,7 +164294,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12212.149209875264, + "fixture_value": 14439.783207034943, "kind": "calibration_drift", "ledger_value": 12576.0, "name": "ons.age.10_20@S14000086", @@ -164302,7 +164302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11585.66892229223, + "fixture_value": 10463.97080071933, "kind": "calibration_drift", "ledger_value": 7692.0, "name": "ons.age.10_20@S14000087", @@ -164310,7 +164310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9576.463418929357, + "fixture_value": 11779.008386888881, "kind": "calibration_drift", "ledger_value": 10453.0, "name": "ons.age.10_20@S14000088", @@ -164318,7 +164318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11706.6917690999, + "fixture_value": 13543.047534156867, "kind": "calibration_drift", "ledger_value": 9652.0, "name": "ons.age.10_20@S14000089", @@ -164326,7 +164326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14819.12277607597, + "fixture_value": 10941.97158114177, "kind": "calibration_drift", "ledger_value": 10847.0, "name": "ons.age.10_20@S14000090", @@ -164334,7 +164334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14439.783207034943, + "fixture_value": 9667.622057667919, "kind": "calibration_drift", "ledger_value": 11042.0, "name": "ons.age.10_20@S14000091", @@ -164342,7 +164342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10463.97080071933, + "fixture_value": 10271.572287878522, "kind": "calibration_drift", "ledger_value": 10831.0, "name": "ons.age.10_20@S14000092", @@ -164350,7 +164350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11779.008386888881, + "fixture_value": 12063.093250569162, "kind": "calibration_drift", "ledger_value": 9644.0, "name": "ons.age.10_20@S14000093", @@ -164358,7 +164358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13543.047534156867, + "fixture_value": 12942.961491819946, "kind": "calibration_drift", "ledger_value": 11431.0, "name": "ons.age.10_20@S14000094", @@ -164366,7 +164366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10941.97158114177, + "fixture_value": 10724.964588099312, "kind": "calibration_drift", "ledger_value": 13208.0, "name": "ons.age.10_20@S14000095", @@ -164374,7 +164374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9667.622057667919, + "fixture_value": 9546.500177109803, "kind": "calibration_drift", "ledger_value": 12319.0, "name": "ons.age.10_20@S14000096", @@ -164382,7 +164382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10271.572287878522, + "fixture_value": 11896.404331686843, "kind": "calibration_drift", "ledger_value": 11232.0, "name": "ons.age.10_20@S14000097", @@ -164390,7 +164390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12063.093250569162, + "fixture_value": 11953.745638503173, "kind": "calibration_drift", "ledger_value": 11316.0, "name": "ons.age.10_20@S14000098", @@ -164398,7 +164398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12942.961491819946, + "fixture_value": 11394.845817847212, "kind": "calibration_drift", "ledger_value": 11293.0, "name": "ons.age.10_20@S14000099", @@ -164406,7 +164406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10724.964588099312, + "fixture_value": 11905.704370563732, "kind": "calibration_drift", "ledger_value": 10700.0, "name": "ons.age.10_20@S14000100", @@ -164414,7 +164414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9546.500177109803, + "fixture_value": 10303.72654948708, "kind": "calibration_drift", "ledger_value": 10340.0, "name": "ons.age.10_20@S14000101", @@ -164422,7 +164422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11896.404331686843, + "fixture_value": 11144.221390220682, "kind": "calibration_drift", "ledger_value": 9955.0, "name": "ons.age.10_20@S14000102", @@ -164430,7 +164430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11953.745638503173, + "fixture_value": 11434.990034741568, "kind": "calibration_drift", "ledger_value": 11576.0, "name": "ons.age.10_20@S14000103", @@ -164438,7 +164438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11394.845817847212, + "fixture_value": 9111.121424583562, "kind": "calibration_drift", "ledger_value": 10234.0, "name": "ons.age.10_20@S14000104", @@ -164446,7 +164446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11905.704370563732, + "fixture_value": 12614.517959379331, "kind": "calibration_drift", "ledger_value": 12269.0, "name": "ons.age.10_20@S14000105", @@ -164454,7 +164454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10303.72654948708, + "fixture_value": 10873.938658707388, "kind": "calibration_drift", "ledger_value": 10423.0, "name": "ons.age.10_20@S14000106", @@ -164462,7 +164462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11144.221390220682, + "fixture_value": 10307.638180975822, "kind": "calibration_drift", "ledger_value": 9589.0, "name": "ons.age.10_20@S14000107", @@ -164470,7 +164470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11434.990034741568, + "fixture_value": 11023.11356451084, "kind": "calibration_drift", "ledger_value": 10079.0, "name": "ons.age.10_20@S14000108", @@ -164478,7 +164478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9111.121424583562, + "fixture_value": 10005.244278843795, "kind": "calibration_drift", "ledger_value": 9778.0, "name": "ons.age.10_20@S14000109", @@ -164486,7 +164486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12614.517959379331, + "fixture_value": 10924.978184951016, "kind": "calibration_drift", "ledger_value": 10691.0, "name": "ons.age.10_20@S14000110", @@ -164494,7 +164494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10873.938658707388, + "fixture_value": 11124.672271264619, "kind": "calibration_drift", "ledger_value": 12353.0, "name": "ons.age.10_20@S14000111", @@ -172110,7 +172110,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11424.934921954009, + "fixture_value": 12244.525072449545, "kind": "calibration_drift", "ledger_value": 8488.0, "name": "ons.age.20_30@S14000021", @@ -172118,7 +172118,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12217.964280535341, + "fixture_value": 3304.9213938963617, "kind": "calibration_drift", "ledger_value": 1958.0, "name": "ons.age.20_30@S14000027", @@ -172126,7 +172126,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11089.763023989011, + "fixture_value": 12207.845883615642, "kind": "calibration_drift", "ledger_value": 10029.0, "name": "ons.age.20_30@S14000045", @@ -172134,7 +172134,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12244.525072449545, + "fixture_value": 11577.975675364443, "kind": "calibration_drift", "ledger_value": 8718.0, "name": "ons.age.20_30@S14000048", @@ -172142,7 +172142,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3304.9213938963617, + "fixture_value": 5685.274269255317, "kind": "calibration_drift", "ledger_value": 3772.0, "name": "ons.age.20_30@S14000051", @@ -172150,7 +172150,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12109.191513648586, + "fixture_value": 14839.210890560007, "kind": "calibration_drift", "ledger_value": 16014.0, "name": "ons.age.20_30@S14000060", @@ -172158,7 +172158,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12207.845883615642, + "fixture_value": 17007.558054458987, "kind": "calibration_drift", "ledger_value": 21567.0, "name": "ons.age.20_30@S14000061", @@ -172166,7 +172166,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11577.975675364443, + "fixture_value": 12618.743864127659, "kind": "calibration_drift", "ledger_value": 9282.0, "name": "ons.age.20_30@S14000062", @@ -172174,7 +172174,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5685.274269255317, + "fixture_value": 13206.190163693795, "kind": "calibration_drift", "ledger_value": 11064.0, "name": "ons.age.20_30@S14000063", @@ -172182,7 +172182,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12330.531446266981, + "fixture_value": 10277.324050516472, "kind": "calibration_drift", "ledger_value": 9791.0, "name": "ons.age.20_30@S14000064", @@ -172190,7 +172190,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14839.210890560007, + "fixture_value": 13600.542035642873, "kind": "calibration_drift", "ledger_value": 8485.0, "name": "ons.age.20_30@S14000065", @@ -172198,7 +172198,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17007.558054458987, + "fixture_value": 13939.908009131086, "kind": "calibration_drift", "ledger_value": 9844.0, "name": "ons.age.20_30@S14000066", @@ -172206,7 +172206,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12618.743864127659, + "fixture_value": 12601.376387181106, "kind": "calibration_drift", "ledger_value": 8102.0, "name": "ons.age.20_30@S14000067", @@ -172214,7 +172214,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13206.190163693795, + "fixture_value": 9811.359224368229, "kind": "calibration_drift", "ledger_value": 9492.0, "name": "ons.age.20_30@S14000068", @@ -172222,7 +172222,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10277.324050516472, + "fixture_value": 18137.439669204847, "kind": "calibration_drift", "ledger_value": 7845.0, "name": "ons.age.20_30@S14000069", @@ -172230,7 +172230,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13600.542035642873, + "fixture_value": 12173.481278077119, "kind": "calibration_drift", "ledger_value": 11514.0, "name": "ons.age.20_30@S14000070", @@ -172238,7 +172238,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13939.908009131086, + "fixture_value": 11541.521620862, "kind": "calibration_drift", "ledger_value": 10047.0, "name": "ons.age.20_30@S14000071", @@ -172246,7 +172246,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12601.376387181106, + "fixture_value": 12024.243738781614, "kind": "calibration_drift", "ledger_value": 10280.0, "name": "ons.age.20_30@S14000072", @@ -172254,7 +172254,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9811.359224368229, + "fixture_value": 12429.03404028024, "kind": "calibration_drift", "ledger_value": 8388.0, "name": "ons.age.20_30@S14000073", @@ -172262,7 +172262,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 18137.439669204847, + "fixture_value": 11364.806348258702, "kind": "calibration_drift", "ledger_value": 7005.0, "name": "ons.age.20_30@S14000074", @@ -172270,7 +172270,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12173.481278077119, + "fixture_value": 14188.552435837286, "kind": "calibration_drift", "ledger_value": 21268.0, "name": "ons.age.20_30@S14000075", @@ -172278,7 +172278,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11541.521620862, + "fixture_value": 10996.780015495448, "kind": "calibration_drift", "ledger_value": 9744.0, "name": "ons.age.20_30@S14000076", @@ -172286,7 +172286,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12024.243738781614, + "fixture_value": 11156.87076195968, "kind": "calibration_drift", "ledger_value": 10444.0, "name": "ons.age.20_30@S14000077", @@ -172294,7 +172294,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12429.03404028024, + "fixture_value": 15109.442917094382, "kind": "calibration_drift", "ledger_value": 28636.0, "name": "ons.age.20_30@S14000078", @@ -172302,7 +172302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11364.806348258702, + "fixture_value": 13540.957325782003, "kind": "calibration_drift", "ledger_value": 23221.0, "name": "ons.age.20_30@S14000079", @@ -172310,7 +172310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14188.552435837286, + "fixture_value": 13350.845295657029, "kind": "calibration_drift", "ledger_value": 19748.0, "name": "ons.age.20_30@S14000080", @@ -172318,7 +172318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10996.780015495448, + "fixture_value": 13535.885479326007, "kind": "calibration_drift", "ledger_value": 24789.0, "name": "ons.age.20_30@S14000081", @@ -172326,7 +172326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11156.87076195968, + "fixture_value": 12841.49784271558, "kind": "calibration_drift", "ledger_value": 10871.0, "name": "ons.age.20_30@S14000082", @@ -172334,7 +172334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15109.442917094382, + "fixture_value": 10614.504450270028, "kind": "calibration_drift", "ledger_value": 10744.0, "name": "ons.age.20_30@S14000083", @@ -172342,7 +172342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13540.957325782003, + "fixture_value": 12975.638964529415, "kind": "calibration_drift", "ledger_value": 20864.0, "name": "ons.age.20_30@S14000084", @@ -172350,7 +172350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13350.845295657029, + "fixture_value": 16425.44202119889, "kind": "calibration_drift", "ledger_value": 36102.0, "name": "ons.age.20_30@S14000085", @@ -172358,7 +172358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13535.885479326007, + "fixture_value": 16004.983928518197, "kind": "calibration_drift", "ledger_value": 23501.0, "name": "ons.age.20_30@S14000086", @@ -172366,7 +172366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12841.49784271558, + "fixture_value": 11598.213220569945, "kind": "calibration_drift", "ledger_value": 15249.0, "name": "ons.age.20_30@S14000087", @@ -172374,7 +172374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10614.504450270028, + "fixture_value": 13055.79434421085, "kind": "calibration_drift", "ledger_value": 17479.0, "name": "ons.age.20_30@S14000088", @@ -172382,7 +172382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12975.638964529415, + "fixture_value": 15011.046566248777, "kind": "calibration_drift", "ledger_value": 20612.0, "name": "ons.age.20_30@S14000089", @@ -172390,7 +172390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16425.44202119889, + "fixture_value": 12128.026909514605, "kind": "calibration_drift", "ledger_value": 10337.0, "name": "ons.age.20_30@S14000090", @@ -172398,7 +172398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16004.983928518197, + "fixture_value": 10715.544232310902, "kind": "calibration_drift", "ledger_value": 7287.0, "name": "ons.age.20_30@S14000091", @@ -172406,7 +172406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11598.213220569945, + "fixture_value": 11384.959665323511, "kind": "calibration_drift", "ledger_value": 10697.0, "name": "ons.age.20_30@S14000092", @@ -172414,7 +172414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13055.79434421085, + "fixture_value": 13370.672594967622, "kind": "calibration_drift", "ledger_value": 9226.0, "name": "ons.age.20_30@S14000093", @@ -172422,7 +172422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15011.046566248777, + "fixture_value": 14345.914179867012, "kind": "calibration_drift", "ledger_value": 10859.0, "name": "ons.age.20_30@S14000094", @@ -172430,7 +172430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12128.026909514605, + "fixture_value": 11887.497437138005, "kind": "calibration_drift", "ledger_value": 11232.0, "name": "ons.age.20_30@S14000095", @@ -172438,7 +172438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10715.544232310902, + "fixture_value": 10581.293341980348, "kind": "calibration_drift", "ledger_value": 9218.0, "name": "ons.age.20_30@S14000096", @@ -172446,7 +172446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11384.959665323511, + "fixture_value": 13185.915425865951, "kind": "calibration_drift", "ledger_value": 7644.0, "name": "ons.age.20_30@S14000097", @@ -172454,7 +172454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13370.672594967622, + "fixture_value": 13249.47224530549, "kind": "calibration_drift", "ledger_value": 9626.0, "name": "ons.age.20_30@S14000098", @@ -172462,7 +172462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14345.914179867012, + "fixture_value": 12629.990462303904, "kind": "calibration_drift", "ledger_value": 11314.0, "name": "ons.age.20_30@S14000099", @@ -172470,7 +172470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11887.497437138005, + "fixture_value": 13196.223542727894, "kind": "calibration_drift", "ledger_value": 13580.0, "name": "ons.age.20_30@S14000100", @@ -172478,7 +172478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10581.293341980348, + "fixture_value": 11420.599289056065, "kind": "calibration_drift", "ledger_value": 11369.0, "name": "ons.age.20_30@S14000101", @@ -172486,7 +172486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13185.915425865951, + "fixture_value": 12352.199592541921, "kind": "calibration_drift", "ledger_value": 11978.0, "name": "ons.age.20_30@S14000102", @@ -172494,7 +172494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13249.47224530549, + "fixture_value": 12674.486112758275, "kind": "calibration_drift", "ledger_value": 9990.0, "name": "ons.age.20_30@S14000103", @@ -172502,7 +172502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12629.990462303904, + "fixture_value": 10098.721696887651, "kind": "calibration_drift", "ledger_value": 10218.0, "name": "ons.age.20_30@S14000104", @@ -172510,7 +172510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13196.223542727894, + "fixture_value": 13981.869001157076, "kind": "calibration_drift", "ledger_value": 14274.0, "name": "ons.age.20_30@S14000105", @@ -172518,7 +172518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11420.599289056065, + "fixture_value": 12052.619556470558, "kind": "calibration_drift", "ledger_value": 10036.0, "name": "ons.age.20_30@S14000106", @@ -172526,7 +172526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12352.199592541921, + "fixture_value": 11424.934921954009, "kind": "calibration_drift", "ledger_value": 8530.0, "name": "ons.age.20_30@S14000107", @@ -172534,7 +172534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12674.486112758275, + "fixture_value": 12217.964280535341, "kind": "calibration_drift", "ledger_value": 8499.0, "name": "ons.age.20_30@S14000108", @@ -172542,7 +172542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10098.721696887651, + "fixture_value": 11089.763023989011, "kind": "calibration_drift", "ledger_value": 8332.0, "name": "ons.age.20_30@S14000109", @@ -172550,7 +172550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13981.869001157076, + "fixture_value": 12109.191513648586, "kind": "calibration_drift", "ledger_value": 9844.0, "name": "ons.age.20_30@S14000110", @@ -172558,7 +172558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12052.619556470558, + "fixture_value": 12330.531446266981, "kind": "calibration_drift", "ledger_value": 7036.0, "name": "ons.age.20_30@S14000111", @@ -180174,7 +180174,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11852.65285435425, + "fixture_value": 12702.92619096684, "kind": "calibration_drift", "ledger_value": 10434.0, "name": "ons.age.30_40@S14000021", @@ -180182,7 +180182,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12675.37103653958, + "fixture_value": 3428.648500877632, "kind": "calibration_drift", "ledger_value": 2562.0, "name": "ons.age.30_40@S14000027", @@ -180190,7 +180190,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11504.933048486444, + "fixture_value": 12664.87383485301, "kind": "calibration_drift", "ledger_value": 14488.0, "name": "ons.age.30_40@S14000045", @@ -180198,7 +180198,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12702.92619096684, + "fixture_value": 12011.423029863698, "kind": "calibration_drift", "ledger_value": 9557.0, "name": "ons.age.30_40@S14000048", @@ -180206,7 +180206,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3428.648500877632, + "fixture_value": 5898.115197644454, "kind": "calibration_drift", "ledger_value": 5328.0, "name": "ons.age.30_40@S14000051", @@ -180214,7 +180214,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12562.5261184089, + "fixture_value": 15394.749862459543, "kind": "calibration_drift", "ledger_value": 17471.0, "name": "ons.age.30_40@S14000060", @@ -180222,7 +180222,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12664.87383485301, + "fixture_value": 17644.273940888394, "kind": "calibration_drift", "ledger_value": 18366.0, "name": "ons.age.30_40@S14000061", @@ -180230,7 +180230,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12011.423029863698, + "fixture_value": 13091.154698143142, "kind": "calibration_drift", "ledger_value": 11744.0, "name": "ons.age.30_40@S14000062", @@ -180238,7 +180238,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5898.115197644454, + "fixture_value": 13700.593360761057, "kind": "calibration_drift", "ledger_value": 12413.0, "name": "ons.age.30_40@S14000063", @@ -180246,7 +180246,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12792.152405302731, + "fixture_value": 10662.07860916584, "kind": "calibration_drift", "ledger_value": 12095.0, "name": "ons.age.30_40@S14000064", @@ -180254,7 +180254,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15394.749862459543, + "fixture_value": 14109.708674993219, "kind": "calibration_drift", "ledger_value": 10634.0, "name": "ons.age.30_40@S14000065", @@ -180262,7 +180262,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17644.273940888394, + "fixture_value": 14461.779570960112, "kind": "calibration_drift", "ledger_value": 11840.0, "name": "ons.age.30_40@S14000066", @@ -180270,7 +180270,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13091.154698143142, + "fixture_value": 13073.137030943306, "kind": "calibration_drift", "ledger_value": 9729.0, "name": "ons.age.30_40@S14000067", @@ -180278,7 +180278,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13700.593360761057, + "fixture_value": 10178.669349996942, "kind": "calibration_drift", "ledger_value": 12634.0, "name": "ons.age.30_40@S14000068", @@ -180286,7 +180286,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10662.07860916584, + "fixture_value": 18816.455195099814, "kind": "calibration_drift", "ledger_value": 10167.0, "name": "ons.age.30_40@S14000069", @@ -180294,7 +180294,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14109.708674993219, + "fixture_value": 12629.222713624975, "kind": "calibration_drift", "ledger_value": 12727.0, "name": "ons.age.30_40@S14000070", @@ -180302,7 +180302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14461.779570960112, + "fixture_value": 11973.604236487392, "kind": "calibration_drift", "ledger_value": 11971.0, "name": "ons.age.30_40@S14000071", @@ -180310,7 +180310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13073.137030943306, + "fixture_value": 12474.398134037338, "kind": "calibration_drift", "ledger_value": 11456.0, "name": "ons.age.30_40@S14000072", @@ -180318,7 +180318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10178.669349996942, + "fixture_value": 12894.342663721536, "kind": "calibration_drift", "ledger_value": 10252.0, "name": "ons.age.30_40@S14000073", @@ -180326,7 +180326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 18816.455195099814, + "fixture_value": 11790.273233331776, "kind": "calibration_drift", "ledger_value": 8518.0, "name": "ons.age.30_40@S14000074", @@ -180334,7 +180334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12629.222713624975, + "fixture_value": 14719.732556605168, "kind": "calibration_drift", "ledger_value": 15675.0, "name": "ons.age.30_40@S14000075", @@ -180342,7 +180342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11973.604236487392, + "fixture_value": 11408.46901358766, "kind": "calibration_drift", "ledger_value": 12805.0, "name": "ons.age.30_40@S14000076", @@ -180350,7 +180350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12474.398134037338, + "fixture_value": 11574.553114372231, "kind": "calibration_drift", "ledger_value": 12819.0, "name": "ons.age.30_40@S14000077", @@ -180358,7 +180358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12894.342663721536, + "fixture_value": 15675.098627902906, "kind": "calibration_drift", "ledger_value": 19091.0, "name": "ons.age.30_40@S14000078", @@ -180366,7 +180366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11790.273233331776, + "fixture_value": 14047.893278561396, "kind": "calibration_drift", "ledger_value": 24502.0, "name": "ons.age.30_40@S14000079", @@ -180374,7 +180374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14719.732556605168, + "fixture_value": 13850.663980372758, "kind": "calibration_drift", "ledger_value": 13456.0, "name": "ons.age.30_40@S14000080", @@ -180382,7 +180382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11408.46901358766, + "fixture_value": 14042.631556216007, "kind": "calibration_drift", "ledger_value": 17580.0, "name": "ons.age.30_40@S14000081", @@ -180390,7 +180390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11574.553114372231, + "fixture_value": 13322.247968972673, "kind": "calibration_drift", "ledger_value": 13036.0, "name": "ons.age.30_40@S14000082", @@ -180398,7 +180398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15675.098627902906, + "fixture_value": 11011.882109568427, "kind": "calibration_drift", "ledger_value": 12907.0, "name": "ons.age.30_40@S14000083", @@ -180406,7 +180406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14047.893278561396, + "fixture_value": 13461.410963004075, "kind": "calibration_drift", "ledger_value": 18674.0, "name": "ons.age.30_40@S14000084", @@ -180414,7 +180414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13850.663980372758, + "fixture_value": 17040.36509498964, "kind": "calibration_drift", "ledger_value": 18647.0, "name": "ons.age.30_40@S14000085", @@ -180422,7 +180422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14042.631556216007, + "fixture_value": 16604.16621539936, "kind": "calibration_drift", "ledger_value": 18823.0, "name": "ons.age.30_40@S14000086", @@ -180430,7 +180430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13322.247968972673, + "fixture_value": 12032.418212732024, "kind": "calibration_drift", "ledger_value": 18005.0, "name": "ons.age.30_40@S14000087", @@ -180438,7 +180438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11011.882109568427, + "fixture_value": 13544.567138182574, "kind": "calibration_drift", "ledger_value": 16687.0, "name": "ons.age.30_40@S14000088", @@ -180446,7 +180446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13461.410963004075, + "fixture_value": 15573.018590101807, "kind": "calibration_drift", "ledger_value": 16458.0, "name": "ons.age.30_40@S14000089", @@ -180454,7 +180454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17040.36509498964, + "fixture_value": 12582.066659348458, "kind": "calibration_drift", "ledger_value": 11343.0, "name": "ons.age.30_40@S14000090", @@ -180462,7 +180462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16604.16621539936, + "fixture_value": 11116.704541310148, "kind": "calibration_drift", "ledger_value": 11058.0, "name": "ons.age.30_40@S14000091", @@ -180470,7 +180470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12032.418212732024, + "fixture_value": 11811.181034791012, "kind": "calibration_drift", "ledger_value": 12583.0, "name": "ons.age.30_40@S14000092", @@ -180478,7 +180478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13544.567138182574, + "fixture_value": 13871.233558875676, "kind": "calibration_drift", "ledger_value": 10389.0, "name": "ons.age.30_40@S14000093", @@ -180486,7 +180486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15573.018590101807, + "fixture_value": 14882.985488659599, "kind": "calibration_drift", "ledger_value": 13296.0, "name": "ons.age.30_40@S14000094", @@ -180494,7 +180494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12582.066659348458, + "fixture_value": 12332.532429456034, "kind": "calibration_drift", "ledger_value": 14685.0, "name": "ons.age.30_40@S14000095", @@ -180502,7 +180502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11116.704541310148, + "fixture_value": 10977.427669332668, "kind": "calibration_drift", "ledger_value": 12469.0, "name": "ons.age.30_40@S14000096", @@ -180510,7 +180510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11811.181034791012, + "fixture_value": 13679.559592881586, "kind": "calibration_drift", "ledger_value": 9977.0, "name": "ons.age.30_40@S14000097", @@ -180518,7 +180518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13871.233558875676, + "fixture_value": 13745.49580367751, "kind": "calibration_drift", "ledger_value": 11963.0, "name": "ons.age.30_40@S14000098", @@ -180526,7 +180526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14882.985488659599, + "fixture_value": 13102.82233781777, "kind": "calibration_drift", "ledger_value": 12484.0, "name": "ons.age.30_40@S14000099", @@ -180534,7 +180534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12332.532429456034, + "fixture_value": 13690.253617099781, "kind": "calibration_drift", "ledger_value": 8694.0, "name": "ons.age.30_40@S14000100", @@ -180542,7 +180542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10977.427669332668, + "fixture_value": 11848.15490736424, "kind": "calibration_drift", "ledger_value": 14388.0, "name": "ons.age.30_40@S14000101", @@ -180550,7 +180550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13679.559592881586, + "fixture_value": 12814.631746983774, "kind": "calibration_drift", "ledger_value": 13516.0, "name": "ons.age.30_40@S14000102", @@ -180558,7 +180558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13745.49580367751, + "fixture_value": 13148.983782234483, "kind": "calibration_drift", "ledger_value": 12627.0, "name": "ons.age.30_40@S14000103", @@ -180566,7 +180566,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13102.82233781777, + "fixture_value": 10476.789877895679, "kind": "calibration_drift", "ledger_value": 13264.0, "name": "ons.age.30_40@S14000104", @@ -180574,7 +180574,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13690.253617099781, + "fixture_value": 14505.311466354337, "kind": "calibration_drift", "ledger_value": 11419.0, "name": "ons.age.30_40@S14000105", @@ -180582,7 +180582,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11848.15490736424, + "fixture_value": 12503.836263779254, "kind": "calibration_drift", "ledger_value": 11802.0, "name": "ons.age.30_40@S14000106", @@ -180590,7 +180590,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12814.631746983774, + "fixture_value": 11852.65285435425, "kind": "calibration_drift", "ledger_value": 9937.0, "name": "ons.age.30_40@S14000107", @@ -180598,7 +180598,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13148.983782234483, + "fixture_value": 12675.37103653958, "kind": "calibration_drift", "ledger_value": 9884.0, "name": "ons.age.30_40@S14000108", @@ -180606,7 +180606,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10476.789877895679, + "fixture_value": 11504.933048486444, "kind": "calibration_drift", "ledger_value": 9903.0, "name": "ons.age.30_40@S14000109", @@ -180614,7 +180614,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14505.311466354337, + "fixture_value": 12562.5261184089, "kind": "calibration_drift", "ledger_value": 12124.0, "name": "ons.age.30_40@S14000110", @@ -180622,7 +180622,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12503.836263779254, + "fixture_value": 12792.152405302731, "kind": "calibration_drift", "ledger_value": 10983.0, "name": "ons.age.30_40@S14000111", @@ -188238,7 +188238,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11158.88070346332, + "fixture_value": 11959.384931941593, "kind": "calibration_drift", "ledger_value": 13480.0, "name": "ons.age.40_50@S14000021", @@ -188246,7 +188246,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11933.442665277946, + "fixture_value": 3227.959180576737, "kind": "calibration_drift", "ledger_value": 3043.0, "name": "ons.age.40_50@S14000027", @@ -188254,7 +188254,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10831.51400508872, + "fixture_value": 11923.55989702513, "kind": "calibration_drift", "ledger_value": 12980.0, "name": "ons.age.40_50@S14000045", @@ -188262,7 +188262,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11959.384931941593, + "fixture_value": 11308.357573287194, "kind": "calibration_drift", "ledger_value": 10036.0, "name": "ons.age.40_50@S14000048", @@ -188270,7 +188270,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3227.959180576737, + "fixture_value": 5552.880412052212, "kind": "calibration_drift", "ledger_value": 5195.0, "name": "ons.age.40_50@S14000051", @@ -188278,7 +188278,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11827.202906560151, + "fixture_value": 14493.64790193239, "kind": "calibration_drift", "ledger_value": 15309.0, "name": "ons.age.40_50@S14000060", @@ -188286,7 +188286,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11923.55989702513, + "fixture_value": 16611.50043159069, "kind": "calibration_drift", "ledger_value": 15316.0, "name": "ons.age.40_50@S14000061", @@ -188294,7 +188294,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11308.357573287194, + "fixture_value": 12324.889233003822, "kind": "calibration_drift", "ledger_value": 11526.0, "name": "ons.age.40_50@S14000062", @@ -188302,7 +188302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5552.880412052212, + "fixture_value": 12898.655580149747, "kind": "calibration_drift", "ledger_value": 12074.0, "name": "ons.age.40_50@S14000063", @@ -188310,7 +188310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12043.388462090548, + "fixture_value": 10037.994423073127, "kind": "calibration_drift", "ledger_value": 11936.0, "name": "ons.age.40_50@S14000064", @@ -188318,7 +188318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14493.64790193239, + "fixture_value": 13283.824119343019, "kind": "calibration_drift", "ledger_value": 11188.0, "name": "ons.age.40_50@S14000065", @@ -188326,7 +188326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16611.50043159069, + "fixture_value": 13615.287225158416, "kind": "calibration_drift", "ledger_value": 11946.0, "name": "ons.age.40_50@S14000066", @@ -188334,7 +188334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12324.889233003822, + "fixture_value": 12307.926195166789, "kind": "calibration_drift", "ledger_value": 10257.0, "name": "ons.age.40_50@S14000067", @@ -188342,7 +188342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12898.655580149747, + "fixture_value": 9582.880591570532, "kind": "calibration_drift", "ledger_value": 12825.0, "name": "ons.age.40_50@S14000068", @@ -188350,7 +188350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10037.994423073127, + "fixture_value": 17715.070319219347, "kind": "calibration_drift", "ledger_value": 10928.0, "name": "ons.age.40_50@S14000069", @@ -188358,7 +188358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13283.824119343019, + "fixture_value": 11889.995545346492, "kind": "calibration_drift", "ledger_value": 12349.0, "name": "ons.age.40_50@S14000070", @@ -188366,7 +188366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13615.287225158416, + "fixture_value": 11272.752429964357, "kind": "calibration_drift", "ledger_value": 11912.0, "name": "ons.age.40_50@S14000071", @@ -188374,7 +188374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12307.926195166789, + "fixture_value": 11744.233323604916, "kind": "calibration_drift", "ledger_value": 11671.0, "name": "ons.age.40_50@S14000072", @@ -188382,7 +188382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9582.880591570532, + "fixture_value": 12139.597211031734, "kind": "calibration_drift", "ledger_value": 9970.0, "name": "ons.age.40_50@S14000073", @@ -188390,7 +188390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17715.070319219347, + "fixture_value": 11100.15235312095, "kind": "calibration_drift", "ledger_value": 9499.0, "name": "ons.age.40_50@S14000074", @@ -188398,7 +188398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11889.995545346492, + "fixture_value": 13858.141430819045, "kind": "calibration_drift", "ledger_value": 11709.0, "name": "ons.age.40_50@S14000075", @@ -188406,7 +188406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11272.752429964357, + "fixture_value": 10740.696306229447, "kind": "calibration_drift", "ledger_value": 12951.0, "name": "ons.age.40_50@S14000076", @@ -188414,7 +188414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11744.233323604916, + "fixture_value": 10897.058994833473, "kind": "calibration_drift", "ledger_value": 11968.0, "name": "ons.age.40_50@S14000077", @@ -188422,7 +188422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12139.597211031734, + "fixture_value": 14757.586993660405, "kind": "calibration_drift", "ledger_value": 13813.0, "name": "ons.age.40_50@S14000078", @@ -188430,7 +188430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11100.15235312095, + "fixture_value": 13225.626967794235, "kind": "calibration_drift", "ledger_value": 15665.0, "name": "ons.age.40_50@S14000079", @@ -188438,7 +188438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13858.141430819045, + "fixture_value": 13039.9421057841, "kind": "calibration_drift", "ledger_value": 12561.0, "name": "ons.age.40_50@S14000080", @@ -188446,7 +188446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10740.696306229447, + "fixture_value": 13220.673230207512, "kind": "calibration_drift", "ledger_value": 13830.0, "name": "ons.age.40_50@S14000081", @@ -188454,7 +188454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10897.058994833473, + "fixture_value": 12542.45590539755, "kind": "calibration_drift", "ledger_value": 13889.0, "name": "ons.age.40_50@S14000082", @@ -188462,7 +188462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14757.586993660405, + "fixture_value": 10367.322850945911, "kind": "calibration_drift", "ledger_value": 12456.0, "name": "ons.age.40_50@S14000083", @@ -188470,7 +188470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13225.626967794235, + "fixture_value": 12673.473262255573, "kind": "calibration_drift", "ledger_value": 12474.0, "name": "ons.age.40_50@S14000084", @@ -188478,7 +188478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13039.9421057841, + "fixture_value": 16042.9402240187, "kind": "calibration_drift", "ledger_value": 11608.0, "name": "ons.age.40_50@S14000085", @@ -188486,7 +188486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13220.673230207512, + "fixture_value": 15632.273403675255, "kind": "calibration_drift", "ledger_value": 13302.0, "name": "ons.age.40_50@S14000086", @@ -188494,7 +188494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12542.45590539755, + "fixture_value": 11328.123843661762, "kind": "calibration_drift", "ledger_value": 11829.0, "name": "ons.age.40_50@S14000087", @@ -188502,7 +188502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10367.322850945911, + "fixture_value": 12751.762051269781, "kind": "calibration_drift", "ledger_value": 12366.0, "name": "ons.age.40_50@S14000088", @@ -188510,7 +188510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12673.473262255573, + "fixture_value": 14661.482013785875, "kind": "calibration_drift", "ledger_value": 12391.0, "name": "ons.age.40_50@S14000089", @@ -188518,7 +188518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16042.9402240187, + "fixture_value": 11845.599679662771, "kind": "calibration_drift", "ledger_value": 10955.0, "name": "ons.age.40_50@S14000090", @@ -188526,7 +188526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15632.273403675255, + "fixture_value": 10466.00970402649, "kind": "calibration_drift", "ledger_value": 12341.0, "name": "ons.age.40_50@S14000091", @@ -188534,7 +188534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11328.123843661762, + "fixture_value": 11119.836356788499, "kind": "calibration_drift", "ledger_value": 12410.0, "name": "ons.age.40_50@S14000092", @@ -188542,7 +188542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12751.762051269781, + "fixture_value": 13059.307683723071, "kind": "calibration_drift", "ledger_value": 10669.0, "name": "ons.age.40_50@S14000093", @@ -188550,7 +188550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14661.482013785875, + "fixture_value": 14011.838667688406, "kind": "calibration_drift", "ledger_value": 13612.0, "name": "ons.age.40_50@S14000094", @@ -188558,7 +188558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11845.599679662771, + "fixture_value": 11610.671454140904, "kind": "calibration_drift", "ledger_value": 13906.0, "name": "ons.age.40_50@S14000095", @@ -188566,7 +188566,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10466.00970402649, + "fixture_value": 10334.885134848102, "kind": "calibration_drift", "ledger_value": 12624.0, "name": "ons.age.40_50@S14000096", @@ -188574,7 +188574,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11119.836356788499, + "fixture_value": 12878.852983263165, "kind": "calibration_drift", "ledger_value": 12489.0, "name": "ons.age.40_50@S14000097", @@ -188582,7 +188582,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13059.307683723071, + "fixture_value": 12940.929745263316, "kind": "calibration_drift", "ledger_value": 11718.0, "name": "ons.age.40_50@S14000098", @@ -188590,7 +188590,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14011.838667688406, + "fixture_value": 12335.87392991683, "kind": "calibration_drift", "ledger_value": 12618.0, "name": "ons.age.40_50@S14000099", @@ -188598,7 +188598,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11610.671454140904, + "fixture_value": 12888.921053420721, "kind": "calibration_drift", "ledger_value": 9207.0, "name": "ons.age.40_50@S14000100", @@ -188606,7 +188606,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10334.885134848102, + "fixture_value": 11154.646035116179, "kind": "calibration_drift", "ledger_value": 12263.0, "name": "ons.age.40_50@S14000101", @@ -188614,7 +188614,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12878.852983263165, + "fixture_value": 12064.552018907196, "kind": "calibration_drift", "ledger_value": 11200.0, "name": "ons.age.40_50@S14000102", @@ -188622,7 +188622,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12940.929745263316, + "fixture_value": 12379.333403308596, "kind": "calibration_drift", "ledger_value": 12349.0, "name": "ons.age.40_50@S14000103", @@ -188630,7 +188630,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12335.87392991683, + "fixture_value": 9863.551209950569, "kind": "calibration_drift", "ledger_value": 12674.0, "name": "ons.age.40_50@S14000104", @@ -188638,7 +188638,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12888.921053420721, + "fixture_value": 13656.271065102852, "kind": "calibration_drift", "ledger_value": 12440.0, "name": "ons.age.40_50@S14000105", @@ -188646,7 +188646,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11154.646035116179, + "fixture_value": 11771.948349258644, "kind": "calibration_drift", "ledger_value": 10930.0, "name": "ons.age.40_50@S14000106", @@ -188654,7 +188654,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12064.552018907196, + "fixture_value": 11158.88070346332, "kind": "calibration_drift", "ledger_value": 9939.0, "name": "ons.age.40_50@S14000107", @@ -188662,7 +188662,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12379.333403308596, + "fixture_value": 11933.442665277946, "kind": "calibration_drift", "ledger_value": 10403.0, "name": "ons.age.40_50@S14000108", @@ -188670,7 +188670,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9863.551209950569, + "fixture_value": 10831.51400508872, "kind": "calibration_drift", "ledger_value": 10405.0, "name": "ons.age.40_50@S14000109", @@ -188678,7 +188678,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13656.271065102852, + "fixture_value": 11827.202906560151, "kind": "calibration_drift", "ledger_value": 11582.0, "name": "ons.age.40_50@S14000110", @@ -188686,7 +188686,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11771.948349258644, + "fixture_value": 12043.388462090548, "kind": "calibration_drift", "ledger_value": 13447.0, "name": "ons.age.40_50@S14000111", @@ -196302,7 +196302,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12047.952812473344, + "fixture_value": 12912.236375241275, "kind": "calibration_drift", "ledger_value": 13446.0, "name": "ons.age.50_60@S14000021", @@ -196310,7 +196310,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12884.227185707128, + "fixture_value": 3485.1434406058725, "kind": "calibration_drift", "ledger_value": 4216.0, "name": "ons.age.50_60@S14000027", @@ -196318,7 +196318,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11694.503515971026, + "fixture_value": 12873.557018265548, "kind": "calibration_drift", "ledger_value": 13476.0, "name": "ons.age.50_60@S14000045", @@ -196326,7 +196326,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12912.236375241275, + "fixture_value": 12209.339095027231, "kind": "calibration_drift", "ledger_value": 13814.0, "name": "ons.age.50_60@S14000048", @@ -196334,7 +196334,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3485.1434406058725, + "fixture_value": 5995.300331237426, "kind": "calibration_drift", "ledger_value": 6723.0, "name": "ons.age.50_60@S14000051", @@ -196342,7 +196342,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12769.52288571015, + "fixture_value": 15648.414087704085, "kind": "calibration_drift", "ledger_value": 14143.0, "name": "ons.age.50_60@S14000060", @@ -196350,7 +196350,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12873.557018265548, + "fixture_value": 17935.004295015948, "kind": "calibration_drift", "ledger_value": 13541.0, "name": "ons.age.50_60@S14000061", @@ -196358,7 +196358,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12209.339095027231, + "fixture_value": 13306.861847900649, "kind": "calibration_drift", "ledger_value": 14527.0, "name": "ons.age.50_60@S14000062", @@ -196366,7 +196366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 5995.300331237426, + "fixture_value": 13926.342426598283, "kind": "calibration_drift", "ledger_value": 14197.0, "name": "ons.age.50_60@S14000063", @@ -196374,7 +196374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13002.932798494698, + "fixture_value": 10837.761094042431, "kind": "calibration_drift", "ledger_value": 14664.0, "name": "ons.age.50_60@S14000064", @@ -196382,7 +196382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15648.414087704085, + "fixture_value": 14342.198864924538, "kind": "calibration_drift", "ledger_value": 14865.0, "name": "ons.age.50_60@S14000065", @@ -196390,7 +196390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17935.004295015948, + "fixture_value": 14700.070945831392, "kind": "calibration_drift", "ledger_value": 14368.0, "name": "ons.age.50_60@S14000066", @@ -196398,7 +196398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13306.861847900649, + "fixture_value": 13288.547297826339, "kind": "calibration_drift", "ledger_value": 14825.0, "name": "ons.age.50_60@S14000067", @@ -196406,7 +196406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13926.342426598283, + "fixture_value": 10346.386545648396, "kind": "calibration_drift", "ledger_value": 14095.0, "name": "ons.age.50_60@S14000068", @@ -196414,7 +196414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10837.761094042431, + "fixture_value": 19126.499955265277, "kind": "calibration_drift", "ledger_value": 14639.0, "name": "ons.age.50_60@S14000069", @@ -196422,7 +196422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14342.198864924538, + "fixture_value": 12837.318462092087, "kind": "calibration_drift", "ledger_value": 14333.0, "name": "ons.age.50_60@S14000070", @@ -196430,7 +196430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14700.070945831392, + "fixture_value": 12170.897149277083, "kind": "calibration_drift", "ledger_value": 14021.0, "name": "ons.age.50_60@S14000071", @@ -196438,7 +196438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13288.547297826339, + "fixture_value": 12679.942788308002, "kind": "calibration_drift", "ledger_value": 13575.0, "name": "ons.age.50_60@S14000072", @@ -196446,7 +196446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10346.386545648396, + "fixture_value": 13106.806878538475, "kind": "calibration_drift", "ledger_value": 14253.0, "name": "ons.age.50_60@S14000073", @@ -196454,7 +196454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 19126.499955265277, + "fixture_value": 11984.545342451755, "kind": "calibration_drift", "ledger_value": 14206.0, "name": "ons.age.50_60@S14000074", @@ -196462,7 +196462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12837.318462092087, + "fixture_value": 14962.274305457042, "kind": "calibration_drift", "ledger_value": 11646.0, "name": "ons.age.50_60@S14000075", @@ -196470,7 +196470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12170.897149277083, + "fixture_value": 11596.45001226663, "kind": "calibration_drift", "ledger_value": 14182.0, "name": "ons.age.50_60@S14000076", @@ -196478,7 +196478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12679.942788308002, + "fixture_value": 11765.270733985439, "kind": "calibration_drift", "ledger_value": 14158.0, "name": "ons.age.50_60@S14000077", @@ -196486,7 +196486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13106.806878538475, + "fixture_value": 15933.382249566344, "kind": "calibration_drift", "ledger_value": 12790.0, "name": "ons.age.50_60@S14000078", @@ -196494,7 +196494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11984.545342451755, + "fixture_value": 14279.364916402936, "kind": "calibration_drift", "ledger_value": 12996.0, "name": "ons.age.50_60@S14000079", @@ -196502,7 +196502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14962.274305457042, + "fixture_value": 14078.885807884963, "kind": "calibration_drift", "ledger_value": 11742.0, "name": "ons.age.50_60@S14000080", @@ -196510,7 +196510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11596.45001226663, + "fixture_value": 14274.016494972844, "kind": "calibration_drift", "ledger_value": 12296.0, "name": "ons.age.50_60@S14000081", @@ -196518,7 +196518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11765.270733985439, + "fixture_value": 13541.762916585158, "kind": "calibration_drift", "ledger_value": 13525.0, "name": "ons.age.50_60@S14000082", @@ -196526,7 +196526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15933.382249566344, + "fixture_value": 11193.3284187819, "kind": "calibration_drift", "ledger_value": 14599.0, "name": "ons.age.50_60@S14000083", @@ -196534,7 +196534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14279.364916402936, + "fixture_value": 13683.218943850556, "kind": "calibration_drift", "ledger_value": 12043.0, "name": "ons.age.50_60@S14000084", @@ -196542,7 +196542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14078.885807884963, + "fixture_value": 17321.144649599053, "kind": "calibration_drift", "ledger_value": 11052.0, "name": "ons.age.50_60@S14000085", @@ -196550,7 +196550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14274.016494972844, + "fixture_value": 16877.758381331958, "kind": "calibration_drift", "ledger_value": 13760.0, "name": "ons.age.50_60@S14000086", @@ -196558,7 +196558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13541.762916585158, + "fixture_value": 12230.680222249557, "kind": "calibration_drift", "ledger_value": 11149.0, "name": "ons.age.50_60@S14000087", @@ -196566,7 +196566,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11193.3284187819, + "fixture_value": 13767.745309966838, "kind": "calibration_drift", "ledger_value": 12850.0, "name": "ons.age.50_60@S14000088", @@ -196574,7 +196574,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13683.218943850556, + "fixture_value": 15829.620206280708, "kind": "calibration_drift", "ledger_value": 12332.0, "name": "ons.age.50_60@S14000089", @@ -196582,7 +196582,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 17321.144649599053, + "fixture_value": 12789.38540240265, "kind": "calibration_drift", "ledger_value": 13408.0, "name": "ons.age.50_60@S14000090", @@ -196590,7 +196590,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 16877.758381331958, + "fixture_value": 11299.878043311654, "kind": "calibration_drift", "ledger_value": 14011.0, "name": "ons.age.50_60@S14000091", @@ -196598,7 +196598,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12230.680222249557, + "fixture_value": 12005.797648453525, "kind": "calibration_drift", "ledger_value": 14529.0, "name": "ons.age.50_60@S14000092", @@ -196606,7 +196606,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13767.745309966838, + "fixture_value": 14099.794317922406, "kind": "calibration_drift", "ledger_value": 13427.0, "name": "ons.age.50_60@S14000093", @@ -196614,7 +196614,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15829.620206280708, + "fixture_value": 15128.217208371572, "kind": "calibration_drift", "ledger_value": 15627.0, "name": "ons.age.50_60@S14000094", @@ -196622,7 +196622,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12789.38540240265, + "fixture_value": 12535.739517065147, "kind": "calibration_drift", "ledger_value": 15866.0, "name": "ons.age.50_60@S14000095", @@ -196630,7 +196630,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11299.878043311654, + "fixture_value": 11158.306261696778, "kind": "calibration_drift", "ledger_value": 14794.0, "name": "ons.age.50_60@S14000096", @@ -196638,7 +196638,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12005.797648453525, + "fixture_value": 13904.96207858722, "kind": "calibration_drift", "ledger_value": 13410.0, "name": "ons.age.50_60@S14000097", @@ -196646,7 +196646,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14099.794317922406, + "fixture_value": 13971.98474145132, "kind": "calibration_drift", "ledger_value": 15261.0, "name": "ons.age.50_60@S14000098", @@ -196654,7 +196654,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15128.217208371572, + "fixture_value": 13318.721739011962, "kind": "calibration_drift", "ledger_value": 14452.0, "name": "ons.age.50_60@S14000099", @@ -196662,7 +196662,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12535.739517065147, + "fixture_value": 13915.832311668328, "kind": "calibration_drift", "ledger_value": 12352.0, "name": "ons.age.50_60@S14000100", @@ -196670,7 +196670,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11158.306261696778, + "fixture_value": 12043.380751369848, "kind": "calibration_drift", "ledger_value": 12957.0, "name": "ons.age.50_60@S14000101", @@ -196678,7 +196678,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13904.96207858722, + "fixture_value": 13025.782539489956, "kind": "calibration_drift", "ledger_value": 14053.0, "name": "ons.age.50_60@S14000102", @@ -196686,7 +196686,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13971.98474145132, + "fixture_value": 13365.64380033631, "kind": "calibration_drift", "ledger_value": 15029.0, "name": "ons.age.50_60@S14000103", @@ -196694,7 +196694,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13318.721739011962, + "fixture_value": 10649.419300989253, "kind": "calibration_drift", "ledger_value": 13173.0, "name": "ons.age.50_60@S14000104", @@ -196702,7 +196702,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13915.832311668328, + "fixture_value": 14744.32013021162, "kind": "calibration_drift", "ledger_value": 15101.0, "name": "ons.age.50_60@S14000105", @@ -196710,7 +196710,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12043.380751369848, + "fixture_value": 12709.865979544282, "kind": "calibration_drift", "ledger_value": 13232.0, "name": "ons.age.50_60@S14000106", @@ -196718,7 +196718,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13025.782539489956, + "fixture_value": 12047.952812473344, "kind": "calibration_drift", "ledger_value": 13486.0, "name": "ons.age.50_60@S14000107", @@ -196726,7 +196726,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13365.64380033631, + "fixture_value": 12884.227185707128, "kind": "calibration_drift", "ledger_value": 14695.0, "name": "ons.age.50_60@S14000108", @@ -196734,7 +196734,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10649.419300989253, + "fixture_value": 11694.503515971026, "kind": "calibration_drift", "ledger_value": 13331.0, "name": "ons.age.50_60@S14000109", @@ -196742,7 +196742,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14744.32013021162, + "fixture_value": 12769.52288571015, "kind": "calibration_drift", "ledger_value": 14540.0, "name": "ons.age.50_60@S14000110", @@ -196750,7 +196750,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12709.865979544282, + "fixture_value": 13002.932798494698, "kind": "calibration_drift", "ledger_value": 15193.0, "name": "ons.age.50_60@S14000111", @@ -204366,7 +204366,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9485.343295479906, + "fixture_value": 10165.79303039311, "kind": "calibration_drift", "ledger_value": 12822.0, "name": "ons.age.60_70@S14000021", @@ -204374,7 +204374,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10143.74141861352, + "fixture_value": 2743.8505514324142, "kind": "calibration_drift", "ledger_value": 4198.0, "name": "ons.age.60_70@S14000027", @@ -204382,7 +204382,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9207.07295635645, + "fixture_value": 10135.340804602243, "kind": "calibration_drift", "ledger_value": 12202.0, "name": "ons.age.60_70@S14000045", @@ -204390,7 +204390,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10165.79303039311, + "fixture_value": 9612.402582400427, "kind": "calibration_drift", "ledger_value": 14347.0, "name": "ons.age.60_70@S14000048", @@ -204398,7 +204398,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 2743.8505514324142, + "fixture_value": 4720.094997584653, "kind": "calibration_drift", "ledger_value": 6464.0, "name": "ons.age.60_70@S14000051", @@ -204406,7 +204406,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10053.434817992318, + "fixture_value": 12319.983482839165, "kind": "calibration_drift", "ledger_value": 12960.0, "name": "ons.age.60_70@S14000060", @@ -204414,7 +204414,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10135.340804602243, + "fixture_value": 14120.21406392018, "kind": "calibration_drift", "ledger_value": 12402.0, "name": "ons.age.60_70@S14000061", @@ -204422,7 +204422,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9612.402582400427, + "fixture_value": 10476.481338986076, "kind": "calibration_drift", "ledger_value": 13696.0, "name": "ons.age.60_70@S14000062", @@ -204430,7 +204430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 4720.094997584653, + "fixture_value": 10964.197886792115, "kind": "calibration_drift", "ledger_value": 11866.0, "name": "ons.age.60_70@S14000063", @@ -204438,7 +204438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10237.198249488943, + "fixture_value": 8532.560355395713, "kind": "calibration_drift", "ledger_value": 12840.0, "name": "ons.age.60_70@S14000064", @@ -204446,7 +204446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12319.983482839165, + "fixture_value": 11291.601317114011, "kind": "calibration_drift", "ledger_value": 15577.0, "name": "ons.age.60_70@S14000065", @@ -204454,7 +204454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 14120.21406392018, + "fixture_value": 11573.353710745143, "kind": "calibration_drift", "ledger_value": 13968.0, "name": "ons.age.60_70@S14000066", @@ -204462,7 +204462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10476.481338986076, + "fixture_value": 10462.062308843693, "kind": "calibration_drift", "ledger_value": 15561.0, "name": "ons.age.60_70@S14000067", @@ -204470,7 +204470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10964.197886792115, + "fixture_value": 8145.701579409027, "kind": "calibration_drift", "ledger_value": 11893.0, "name": "ons.age.60_70@S14000068", @@ -204478,7 +204478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8532.560355395713, + "fixture_value": 15058.277612844331, "kind": "calibration_drift", "ledger_value": 15231.0, "name": "ons.age.60_70@S14000069", @@ -204486,7 +204486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11291.601317114011, + "fixture_value": 10106.81021926645, "kind": "calibration_drift", "ledger_value": 12462.0, "name": "ons.age.60_70@S14000070", @@ -204494,7 +204494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11573.353710745143, + "fixture_value": 9582.13727027131, "kind": "calibration_drift", "ledger_value": 12807.0, "name": "ons.age.60_70@S14000071", @@ -204502,7 +204502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10462.062308843693, + "fixture_value": 9982.908481316914, "kind": "calibration_drift", "ledger_value": 12157.0, "name": "ons.age.60_70@S14000072", @@ -204510,7 +204510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8145.701579409027, + "fixture_value": 10318.978226888694, "kind": "calibration_drift", "ledger_value": 15732.0, "name": "ons.age.60_70@S14000073", @@ -204518,7 +204518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 15058.277612844331, + "fixture_value": 9435.422646717909, "kind": "calibration_drift", "ledger_value": 14354.0, "name": "ons.age.60_70@S14000074", @@ -204526,7 +204526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10106.81021926645, + "fixture_value": 11779.786199151189, "kind": "calibration_drift", "ledger_value": 11833.0, "name": "ons.age.60_70@S14000075", @@ -204534,7 +204534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9582.13727027131, + "fixture_value": 9129.875513899846, "kind": "calibration_drift", "ledger_value": 12451.0, "name": "ons.age.60_70@S14000076", @@ -204542,7 +204542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9982.908481316914, + "fixture_value": 9262.787928632722, "kind": "calibration_drift", "ledger_value": 14115.0, "name": "ons.age.60_70@S14000077", @@ -204550,7 +204550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10318.978226888694, + "fixture_value": 12544.338681238265, "kind": "calibration_drift", "ledger_value": 11136.0, "name": "ons.age.60_70@S14000078", @@ -204558,7 +204558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9435.422646717909, + "fixture_value": 11242.132201355122, "kind": "calibration_drift", "ledger_value": 10517.0, "name": "ons.age.60_70@S14000079", @@ -204566,7 +204566,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11779.786199151189, + "fixture_value": 11084.295164850795, "kind": "calibration_drift", "ledger_value": 10702.0, "name": "ons.age.60_70@S14000080", @@ -204574,7 +204574,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9129.875513899846, + "fixture_value": 11237.921393581973, "kind": "calibration_drift", "ledger_value": 10479.0, "name": "ons.age.60_70@S14000081", @@ -204582,7 +204582,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9262.787928632722, + "fixture_value": 10661.418756290768, "kind": "calibration_drift", "ledger_value": 12621.0, "name": "ons.age.60_70@S14000082", @@ -204590,7 +204590,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12544.338681238265, + "fixture_value": 8812.498216400407, "kind": "calibration_drift", "ledger_value": 12638.0, "name": "ons.age.60_70@S14000083", @@ -204598,7 +204598,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11242.132201355122, + "fixture_value": 10772.786969688648, "kind": "calibration_drift", "ledger_value": 11289.0, "name": "ons.age.60_70@S14000084", @@ -204606,7 +204606,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11084.295164850795, + "fixture_value": 13636.922872242163, "kind": "calibration_drift", "ledger_value": 9587.0, "name": "ons.age.60_70@S14000085", @@ -204614,7 +204614,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11237.921393581973, + "fixture_value": 13287.845229552446, "kind": "calibration_drift", "ledger_value": 12174.0, "name": "ons.age.60_70@S14000086", @@ -204622,7 +204622,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10661.418756290768, + "fixture_value": 9629.204434230945, "kind": "calibration_drift", "ledger_value": 10480.0, "name": "ons.age.60_70@S14000087", @@ -204630,7 +204630,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8812.498216400407, + "fixture_value": 10839.334507897984, "kind": "calibration_drift", "ledger_value": 11646.0, "name": "ons.age.60_70@S14000088", @@ -204638,7 +204638,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10772.786969688648, + "fixture_value": 12462.64691028563, "kind": "calibration_drift", "ledger_value": 11482.0, "name": "ons.age.60_70@S14000089", @@ -204646,7 +204646,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13636.922872242163, + "fixture_value": 10069.072560974306, "kind": "calibration_drift", "ledger_value": 12897.0, "name": "ons.age.60_70@S14000090", @@ -204654,7 +204654,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 13287.845229552446, + "fixture_value": 8896.384647763487, "kind": "calibration_drift", "ledger_value": 12623.0, "name": "ons.age.60_70@S14000091", @@ -204662,7 +204662,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9629.204434230945, + "fixture_value": 9452.154569674865, "kind": "calibration_drift", "ledger_value": 12939.0, "name": "ons.age.60_70@S14000092", @@ -204670,7 +204670,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10839.334507897984, + "fixture_value": 11100.756417529075, "kind": "calibration_drift", "ledger_value": 13905.0, "name": "ons.age.60_70@S14000093", @@ -204678,7 +204678,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12462.64691028563, + "fixture_value": 11910.432909517052, "kind": "calibration_drift", "ledger_value": 14524.0, "name": "ons.age.60_70@S14000094", @@ -204686,7 +204686,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10069.072560974306, + "fixture_value": 9869.3773650053, "kind": "calibration_drift", "ledger_value": 12943.0, "name": "ons.age.60_70@S14000095", @@ -204694,7 +204694,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8896.384647763487, + "fixture_value": 8784.925301061903, "kind": "calibration_drift", "ledger_value": 13680.0, "name": "ons.age.60_70@S14000096", @@ -204702,7 +204702,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9452.154569674865, + "fixture_value": 10947.365156467029, "kind": "calibration_drift", "ledger_value": 13796.0, "name": "ons.age.60_70@S14000097", @@ -204710,7 +204710,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11100.756417529075, + "fixture_value": 11000.132043567137, "kind": "calibration_drift", "ledger_value": 14772.0, "name": "ons.age.60_70@S14000098", @@ -204718,7 +204718,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11910.432909517052, + "fixture_value": 10485.818621459608, "kind": "calibration_drift", "ledger_value": 12603.0, "name": "ons.age.60_70@S14000099", @@ -204726,7 +204726,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9869.3773650053, + "fixture_value": 10955.923281991012, "kind": "calibration_drift", "ledger_value": 13346.0, "name": "ons.age.60_70@S14000100", @@ -204734,7 +204734,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8784.925301061903, + "fixture_value": 9481.743715550472, "kind": "calibration_drift", "ledger_value": 12384.0, "name": "ons.age.60_70@S14000101", @@ -204742,7 +204742,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10947.365156467029, + "fixture_value": 10255.1878316965, "kind": "calibration_drift", "ledger_value": 12836.0, "name": "ons.age.60_70@S14000102", @@ -204750,7 +204750,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11000.132043567137, + "fixture_value": 10522.76032157418, "kind": "calibration_drift", "ledger_value": 14252.0, "name": "ons.age.60_70@S14000103", @@ -204758,7 +204758,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10485.818621459608, + "fixture_value": 8384.27901732921, "kind": "calibration_drift", "ledger_value": 12771.0, "name": "ons.age.60_70@S14000104", @@ -204766,7 +204766,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10955.923281991012, + "fixture_value": 11608.191057049895, "kind": "calibration_drift", "ledger_value": 13463.0, "name": "ons.age.60_70@S14000105", @@ -204774,7 +204774,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9481.743715550472, + "fixture_value": 10006.466985055287, "kind": "calibration_drift", "ledger_value": 13226.0, "name": "ons.age.60_70@S14000106", @@ -204782,7 +204782,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10255.1878316965, + "fixture_value": 9485.343295479906, "kind": "calibration_drift", "ledger_value": 13776.0, "name": "ons.age.60_70@S14000107", @@ -204790,7 +204790,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10522.76032157418, + "fixture_value": 10143.74141861352, "kind": "calibration_drift", "ledger_value": 15468.0, "name": "ons.age.60_70@S14000108", @@ -204798,7 +204798,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8384.27901732921, + "fixture_value": 9207.07295635645, "kind": "calibration_drift", "ledger_value": 13780.0, "name": "ons.age.60_70@S14000109", @@ -204806,7 +204806,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11608.191057049895, + "fixture_value": 10053.434817992318, "kind": "calibration_drift", "ledger_value": 13527.0, "name": "ons.age.60_70@S14000110", @@ -204814,7 +204814,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10006.466985055287, + "fixture_value": 10237.198249488943, "kind": "calibration_drift", "ledger_value": 13948.0, "name": "ons.age.60_70@S14000111", @@ -212430,7 +212430,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7663.2234324779065, + "fixture_value": 8212.959819530455, "kind": "calibration_drift", "ledger_value": 9518.0, "name": "ons.age.70_80@S14000021", @@ -212438,7 +212438,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8195.14428846857, + "fixture_value": 2216.761079272088, "kind": "calibration_drift", "ledger_value": 3221.0, "name": "ons.age.70_80@S14000027", @@ -212446,7 +212446,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7438.4083976493175, + "fixture_value": 8188.35741949261, "kind": "calibration_drift", "ledger_value": 9108.0, "name": "ons.age.70_80@S14000045", @@ -212454,7 +212454,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8212.959819530455, + "fixture_value": 7765.874825739261, "kind": "calibration_drift", "ledger_value": 11082.0, "name": "ons.age.70_80@S14000048", @@ -212462,7 +212462,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 2216.761079272088, + "fixture_value": 3813.3720058660674, "kind": "calibration_drift", "ledger_value": 4958.0, "name": "ons.age.70_80@S14000051", @@ -212470,7 +212470,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8122.185446977027, + "fixture_value": 9953.333598207653, "kind": "calibration_drift", "ledger_value": 9607.0, "name": "ons.age.70_80@S14000060", @@ -212478,7 +212478,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8188.35741949261, + "fixture_value": 11407.742652582885, "kind": "calibration_drift", "ledger_value": 8209.0, "name": "ons.age.70_80@S14000061", @@ -212486,7 +212486,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7765.874825739261, + "fixture_value": 8463.965381737265, "kind": "calibration_drift", "ledger_value": 10463.0, "name": "ons.age.70_80@S14000062", @@ -212494,7 +212494,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 3813.3720058660674, + "fixture_value": 8857.992330591665, "kind": "calibration_drift", "ledger_value": 7878.0, "name": "ons.age.70_80@S14000063", @@ -212502,7 +212502,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8270.648205826094, + "fixture_value": 6893.468630245526, "kind": "calibration_drift", "ledger_value": 9102.0, "name": "ons.age.70_80@S14000064", @@ -212510,7 +212510,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9953.333598207653, + "fixture_value": 9122.502065343386, "kind": "calibration_drift", "ledger_value": 12355.0, "name": "ons.age.70_80@S14000065", @@ -212518,7 +212518,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11407.742652582885, + "fixture_value": 9350.13025736251, "kind": "calibration_drift", "ledger_value": 11496.0, "name": "ons.age.70_80@S14000066", @@ -212526,7 +212526,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8463.965381737265, + "fixture_value": 8452.316225115452, "kind": "calibration_drift", "ledger_value": 12116.0, "name": "ons.age.70_80@S14000067", @@ -212534,7 +212534,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8857.992330591665, + "fixture_value": 6580.924830316467, "kind": "calibration_drift", "ledger_value": 8430.0, "name": "ons.age.70_80@S14000068", @@ -212542,7 +212542,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 6893.468630245526, + "fixture_value": 12165.605636065466, "kind": "calibration_drift", "ledger_value": 11879.0, "name": "ons.age.70_80@S14000069", @@ -212550,7 +212550,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9122.502065343386, + "fixture_value": 8165.307515733013, "kind": "calibration_drift", "ledger_value": 7821.0, "name": "ons.age.70_80@S14000070", @@ -212558,7 +212558,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9350.13025736251, + "fixture_value": 7741.423433536132, "kind": "calibration_drift", "ledger_value": 10178.0, "name": "ons.age.70_80@S14000071", @@ -212566,7 +212566,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8452.316225115452, + "fixture_value": 8065.20711114016, "kind": "calibration_drift", "ledger_value": 8689.0, "name": "ons.age.70_80@S14000072", @@ -212574,7 +212574,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 6580.924830316467, + "fixture_value": 8336.718375307039, "kind": "calibration_drift", "ledger_value": 12440.0, "name": "ons.age.70_80@S14000073", @@ -212582,7 +212582,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 12165.605636065466, + "fixture_value": 7622.8924635882795, "kind": "calibration_drift", "ledger_value": 10819.0, "name": "ons.age.70_80@S14000074", @@ -212590,7 +212590,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8165.307515733013, + "fixture_value": 9516.907382143201, "kind": "calibration_drift", "ledger_value": 7848.0, "name": "ons.age.70_80@S14000075", @@ -212598,7 +212598,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7741.423433536132, + "fixture_value": 7376.0404651947565, "kind": "calibration_drift", "ledger_value": 9018.0, "name": "ons.age.70_80@S14000076", @@ -212606,7 +212606,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8065.20711114016, + "fixture_value": 7483.4206094151095, "kind": "calibration_drift", "ledger_value": 8780.0, "name": "ons.age.70_80@S14000077", @@ -212614,7 +212614,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8336.718375307039, + "fixture_value": 10134.590507948547, "kind": "calibration_drift", "ledger_value": 7442.0, "name": "ons.age.70_80@S14000078", @@ -212622,7 +212622,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7622.8924635882795, + "fixture_value": 9082.53589066122, "kind": "calibration_drift", "ledger_value": 6574.0, "name": "ons.age.70_80@S14000079", @@ -212630,7 +212630,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9516.907382143201, + "fixture_value": 8955.0191061892, "kind": "calibration_drift", "ledger_value": 8420.0, "name": "ons.age.70_80@S14000080", @@ -212638,7 +212638,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7376.0404651947565, + "fixture_value": 9079.133972587022, "kind": "calibration_drift", "ledger_value": 7936.0, "name": "ons.age.70_80@S14000081", @@ -212646,7 +212646,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7483.4206094151095, + "fixture_value": 8613.376605525724, "kind": "calibration_drift", "ledger_value": 9369.0, "name": "ons.age.70_80@S14000082", @@ -212654,7 +212654,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10134.590507948547, + "fixture_value": 7119.630858565842, "kind": "calibration_drift", "ledger_value": 9050.0, "name": "ons.age.70_80@S14000083", @@ -212662,7 +212662,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9082.53589066122, + "fixture_value": 8703.351156362536, "kind": "calibration_drift", "ledger_value": 6207.0, "name": "ons.age.70_80@S14000084", @@ -212670,7 +212670,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8955.0191061892, + "fixture_value": 11017.290955748456, "kind": "calibration_drift", "ledger_value": 5749.0, "name": "ons.age.70_80@S14000085", @@ -212678,7 +212678,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9079.133972587022, + "fixture_value": 10735.270591499886, "kind": "calibration_drift", "ledger_value": 6505.0, "name": "ons.age.70_80@S14000086", @@ -212686,7 +212686,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8613.376605525724, + "fixture_value": 7779.449067666597, "kind": "calibration_drift", "ledger_value": 6617.0, "name": "ons.age.70_80@S14000087", @@ -212694,7 +212694,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7119.630858565842, + "fixture_value": 8757.115014801118, "kind": "calibration_drift", "ledger_value": 6270.0, "name": "ons.age.70_80@S14000088", @@ -212702,7 +212702,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8703.351156362536, + "fixture_value": 10068.59160059185, "kind": "calibration_drift", "ledger_value": 6761.0, "name": "ons.age.70_80@S14000089", @@ -212710,7 +212710,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 11017.290955748456, + "fixture_value": 8134.81920357577, "kind": "calibration_drift", "ledger_value": 9399.0, "name": "ons.age.70_80@S14000090", @@ -212718,7 +212718,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10735.270591499886, + "fixture_value": 7187.402835442507, "kind": "calibration_drift", "ledger_value": 9611.0, "name": "ons.age.70_80@S14000091", @@ -212726,7 +212726,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7779.449067666597, + "fixture_value": 7636.410209871142, "kind": "calibration_drift", "ledger_value": 9414.0, "name": "ons.age.70_80@S14000092", @@ -212734,7 +212734,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8757.115014801118, + "fixture_value": 8968.318177537754, "kind": "calibration_drift", "ledger_value": 9476.0, "name": "ons.age.70_80@S14000093", @@ -212742,7 +212742,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 10068.59160059185, + "fixture_value": 9622.457060321844, "kind": "calibration_drift", "ledger_value": 10646.0, "name": "ons.age.70_80@S14000094", @@ -212750,7 +212750,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8134.81920357577, + "fixture_value": 7973.485147713798, "kind": "calibration_drift", "ledger_value": 8970.0, "name": "ons.age.70_80@S14000095", @@ -212758,7 +212758,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7187.402835442507, + "fixture_value": 7097.354657869506, "kind": "calibration_drift", "ledger_value": 9884.0, "name": "ons.age.70_80@S14000096", @@ -212766,7 +212766,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7636.410209871142, + "fixture_value": 8844.393141881093, "kind": "calibration_drift", "ledger_value": 10835.0, "name": "ons.age.70_80@S14000097", @@ -212774,7 +212774,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8968.318177537754, + "fixture_value": 8887.023591100276, "kind": "calibration_drift", "ledger_value": 11156.0, "name": "ons.age.70_80@S14000098", @@ -212782,7 +212782,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9622.457060321844, + "fixture_value": 8471.508986604042, "kind": "calibration_drift", "ledger_value": 8470.0, "name": "ons.age.70_80@S14000099", @@ -212790,7 +212790,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7973.485147713798, + "fixture_value": 8851.30726465035, "kind": "calibration_drift", "ledger_value": 10843.0, "name": "ons.age.70_80@S14000100", @@ -212798,7 +212798,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7097.354657869506, + "fixture_value": 7660.31532631843, "kind": "calibration_drift", "ledger_value": 8637.0, "name": "ons.age.70_80@S14000101", @@ -212806,7 +212806,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8844.393141881093, + "fixture_value": 8285.182016951216, "kind": "calibration_drift", "ledger_value": 8265.0, "name": "ons.age.70_80@S14000102", @@ -212814,7 +212814,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8887.023591100276, + "fixture_value": 8501.354242925814, "kind": "calibration_drift", "ledger_value": 11255.0, "name": "ons.age.70_80@S14000103", @@ -212822,7 +212822,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8471.508986604042, + "fixture_value": 6773.671909233656, "kind": "calibration_drift", "ledger_value": 8942.0, "name": "ons.age.70_80@S14000104", @@ -212830,7 +212830,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8851.30726465035, + "fixture_value": 9378.27540300581, "kind": "calibration_drift", "ledger_value": 10243.0, "name": "ons.age.70_80@S14000105", @@ -212838,7 +212838,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 7660.31532631843, + "fixture_value": 8084.240062532447, "kind": "calibration_drift", "ledger_value": 8645.0, "name": "ons.age.70_80@S14000106", @@ -212846,7 +212846,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8285.182016951216, + "fixture_value": 7663.2234324779065, "kind": "calibration_drift", "ledger_value": 10961.0, "name": "ons.age.70_80@S14000107", @@ -212854,7 +212854,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8501.354242925814, + "fixture_value": 8195.14428846857, "kind": "calibration_drift", "ledger_value": 12368.0, "name": "ons.age.70_80@S14000108", @@ -212862,7 +212862,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 6773.671909233656, + "fixture_value": 7438.4083976493175, "kind": "calibration_drift", "ledger_value": 10439.0, "name": "ons.age.70_80@S14000109", @@ -212870,7 +212870,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 9378.27540300581, + "fixture_value": 8122.185446977027, "kind": "calibration_drift", "ledger_value": 9794.0, "name": "ons.age.70_80@S14000110", @@ -212878,7 +212878,7 @@ "reason": "Vintage and scaling class: ours holds the mid-2024 per-area publisher estimates at identity; the incumbent scales its age columns by uk_total_population / targets_total_pop * 0.9 (a bare literal) and, at constituency grain, boundary-maps 2010-vintage rows to PCON24." }, { - "fixture_value": 8084.240062532447, + "fixture_value": 8270.648205826094, "kind": "calibration_drift", "ledger_value": 10122.0, "name": "ons.age.70_80@S14000111", diff --git a/packages/microcosm-build/src/microcosm/build/uk/local_registry_parity_fixture_2025.json b/packages/microcosm-build/src/microcosm/build/uk/local_registry_parity_fixture_2025.json index c6b117a859..4b32a356ee 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/local_registry_parity_fixture_2025.json +++ b/packages/microcosm-build/src/microcosm/build/uk/local_registry_parity_fixture_2025.json @@ -6,7 +6,7 @@ "source_repository": "policyengine-uk-data", "pinned_ref": "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3", "pinned_version": "1.56.16", - "method": "y-side replay of datasets/local_areas/{constituencies,local_authorities}/loss.py: published statistics with the builders' declared national-consistency scalings; constituency rows boundary-mapped 2010->PCON24 with the incumbent's own mapping_matrix. EXCEPTION, fix-and-signed: the UC family corrects policyengine-uk-data#468 at extraction - totals join to their areas BY NAME (never by row position), attach post-mapping on native PCON24/LAD codes, and the child splits recompute from the corrected totals with the incumbent's own country-proportion buckets keyed by true country prefix.", + "method": "y-side replay of datasets/local_areas/{constituencies,local_authorities}/loss.py: published statistics with the builders' declared national-consistency scalings; constituency rows boundary-mapped 2010->PCON24 with the incumbent's own mapping_matrix. EXCEPTION, fix-and-signed: the UC family corrects policyengine-uk-data#468 at extraction - totals join to their areas BY NAME (never by row position), attach post-mapping on native PCON24/LAD codes, and the child splits recompute from the corrected totals with the incumbent's own country-proportion buckets keyed by true country prefix. EXCEPTION 2, fix-and-signed (Mar\u00eda, 2026-10-16; uk-data#589): five unchanged Scottish constituencies keep their 2010 codes (S14000006, S14000008, S14000010, S14000040, S14000058) in the incumbent's boundary-change source, so its mapped rows, sorted by those codes, sat out of the PCON24 roster's order for every Scottish row between them; the codes map to S14000107 to S14000111 as uk-data#589 maps them, and the mapped rows are taken by code into the roster's order.", "time_period": 2025 }, "surface": { @@ -6231,8 +6231,8 @@ "area_type": "constituency", "geography_id": "S14000021", "period": 2025, - "raw_value": 76200000.0, - "value": 81332039.54473099, + "raw_value": 172000000.0, + "value": 183584131.25582325, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6242,8 +6242,8 @@ "area_type": "constituency", "geography_id": "S14000027", "period": 2025, - "raw_value": 154700000.0, - "value": 165118983.17020845, + "raw_value": 17200000.0, + "value": 18358413.125582322, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6253,8 +6253,8 @@ "area_type": "constituency", "geography_id": "S14000045", "period": 2025, - "raw_value": 69600000.0, - "value": 74287532.18258893, + "raw_value": 102400000.0, + "value": 109296599.0732343, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6264,8 +6264,8 @@ "area_type": "constituency", "geography_id": "S14000048", "period": 2025, - "raw_value": 172000000.0, - "value": 183584131.25582325, + "raw_value": 57300000.0, + "value": 61159132.09859693, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6275,8 +6275,8 @@ "area_type": "constituency", "geography_id": "S14000051", "period": 2025, - "raw_value": 17200000.0, - "value": 18358413.125582322, + "raw_value": 61800000.0, + "value": 65962205.30005742, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6286,8 +6286,8 @@ "area_type": "constituency", "geography_id": "S14000060", "period": 2025, - "raw_value": 84800000.0, - "value": 90511246.10752216, + "raw_value": 80616000.0, + "value": 86045455.37976423, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6297,8 +6297,8 @@ "area_type": "constituency", "geography_id": "S14000061", "period": 2025, - "raw_value": 102400000.0, - "value": 109296599.0732343, + "raw_value": 117144000.0, + "value": 125033601.58041953, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6308,8 +6308,8 @@ "area_type": "constituency", "geography_id": "S14000062", "period": 2025, - "raw_value": 57300000.0, - "value": 61159132.09859693, + "raw_value": 117912000.0, + "value": 125853326.07346877, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6319,8 +6319,8 @@ "area_type": "constituency", "geography_id": "S14000063", "period": 2025, - "raw_value": 61800000.0, - "value": 65962205.30005742, + "raw_value": 51760000.0, + "value": 55246015.312798895, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6330,8 +6330,8 @@ "area_type": "constituency", "geography_id": "S14000064", "period": 2025, - "raw_value": 140500000.0, - "value": 149962618.8455998, + "raw_value": 104775000.0, + "value": 111831554.37400512, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6341,8 +6341,8 @@ "area_type": "constituency", "geography_id": "S14000065", "period": 2025, - "raw_value": 80616000.0, - "value": 86045455.37976423, + "raw_value": 125654000.0, + "value": 134116746.67918149, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6352,8 +6352,8 @@ "area_type": "constituency", "geography_id": "S14000066", "period": 2025, - "raw_value": 117144000.0, - "value": 125033601.58041953, + "raw_value": 114060000.0, + "value": 121741895.4130186, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6363,8 +6363,8 @@ "area_type": "constituency", "geography_id": "S14000067", "period": 2025, - "raw_value": 117912000.0, - "value": 125853326.07346877, + "raw_value": 148336633.66336635, + "value": 158327046.6540838, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6374,8 +6374,8 @@ "area_type": "constituency", "geography_id": "S14000068", "period": 2025, - "raw_value": 51760000.0, - "value": 55246015.312798895, + "raw_value": 64863000.0, + "value": 69231497.12585153, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6385,8 +6385,8 @@ "area_type": "constituency", "geography_id": "S14000069", "period": 2025, - "raw_value": 104775000.0, - "value": 111831554.37400512, + "raw_value": 127477167.71677168, + "value": 136062704.01300237, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6396,8 +6396,8 @@ "area_type": "constituency", "geography_id": "S14000070", "period": 2025, - "raw_value": 125654000.0, - "value": 134116746.67918149, + "raw_value": 55152000.0, + "value": 58866465.15709979, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6407,8 +6407,8 @@ "area_type": "constituency", "geography_id": "S14000071", "period": 2025, - "raw_value": 114060000.0, - "value": 121741895.4130186, + "raw_value": 64317000.0, + "value": 68648724.24407433, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6418,8 +6418,8 @@ "area_type": "constituency", "geography_id": "S14000072", "period": 2025, - "raw_value": 148336633.66336635, - "value": 158327046.6540838, + "raw_value": 66806484.84848485, + "value": 71305874.90211867, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6429,8 +6429,8 @@ "area_type": "constituency", "geography_id": "S14000073", "period": 2025, - "raw_value": 64863000.0, - "value": 69231497.12585153, + "raw_value": 135092000.0, + "value": 144190392.20704463, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6440,8 +6440,8 @@ "area_type": "constituency", "geography_id": "S14000074", "period": 2025, - "raw_value": 127477167.71677168, - "value": 136062704.01300237, + "raw_value": 140588000.0, + "value": 150056545.61042833, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6451,8 +6451,8 @@ "area_type": "constituency", "geography_id": "S14000075", "period": 2025, - "raw_value": 55152000.0, - "value": 58866465.15709979, + "raw_value": 82776000.0, + "value": 88350930.51646526, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6462,8 +6462,8 @@ "area_type": "constituency", "geography_id": "S14000076", "period": 2025, - "raw_value": 64317000.0, - "value": 68648724.24407433, + "raw_value": 90568000.0, + "value": 96667718.60219419, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6473,8 +6473,8 @@ "area_type": "constituency", "geography_id": "S14000077", "period": 2025, - "raw_value": 66806484.84848485, - "value": 71305874.90211867, + "raw_value": 86516000.0, + "value": 92342818.0216791, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6484,8 +6484,8 @@ "area_type": "constituency", "geography_id": "S14000078", "period": 2025, - "raw_value": 135092000.0, - "value": 144190392.20704463, + "raw_value": 147797000.0, + "value": 157751068.87916806, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6495,8 +6495,8 @@ "area_type": "constituency", "geography_id": "S14000079", "period": 2025, - "raw_value": 140588000.0, - "value": 150056545.61042833, + "raw_value": 408324000.0, + "value": 435824458.2029231, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6506,8 +6506,8 @@ "area_type": "constituency", "geography_id": "S14000080", "period": 2025, - "raw_value": 82776000.0, - "value": 88350930.51646526, + 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"adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6627,8 +6627,8 @@ "area_type": "constituency", "geography_id": "S14000091", "period": 2025, - "raw_value": 57691572.15721573, - "value": 61577076.48409936, + "raw_value": 93828000.0, + "value": 100147278.29925223, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6638,8 +6638,8 @@ "area_type": "constituency", "geography_id": "S14000092", "period": 2025, - "raw_value": 100409900.99009901, - "value": 107172467.6904102, + "raw_value": 82120000.0, + "value": 87650749.17865235, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6649,8 +6649,8 @@ "area_type": "constituency", "geography_id": "S14000093", "period": 2025, - "raw_value": 79354801.98019801, - "value": 84699316.17739846, + "raw_value": 62365346.53465346, + "value": 66565627.69786467, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -6660,8 +6660,8 @@ 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@@ "area_type": "constituency", "geography_id": "S14000061", "period": 2025, - "raw_value": 4000.0, - "value": 4269.398401298215, + "raw_value": 3600.0, + "value": 3842.4585611683938, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -13458,8 +13458,8 @@ "area_type": "constituency", "geography_id": "S14000062", "period": 2025, - "raw_value": 3000.0, - "value": 3202.0488009736614, + "raw_value": 4160.0, + "value": 4440.174337350143, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -13469,8 +13469,8 @@ "area_type": "constituency", "geography_id": "S14000063", "period": 2025, - "raw_value": 3000.0, - "value": 3202.0488009736614, + "raw_value": 2140.0, + "value": 2284.1281446945445, "adjustment_factor": 1.0673496003245537, "boundary_mapped_from_2010": true }, @@ -13480,8 +13480,8 @@ "area_type": "constituency", "geography_id": "S14000064", "period": 2025, - "raw_value": 5000.0, - "value": 5336.748001622768, + "raw_value": 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+113789,8 @@ "area_type": "constituency", "geography_id": "S14000083", "period": 2025, - "raw_value": 91329905.40642643, - "value": 91329905.40642643, + "raw_value": 64159988.736744136, + "value": 64159988.736744136, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113800,8 +113800,8 @@ "area_type": "constituency", "geography_id": "S14000084", "period": 2025, - "raw_value": 81849103.13781111, - "value": 81849103.13781111, + "raw_value": 78432003.46438093, + "value": 78432003.46438093, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113811,8 +113811,8 @@ "area_type": "constituency", "geography_id": "S14000085", "period": 2025, - "raw_value": 80699959.9282826, - "value": 80699959.9282826, + "raw_value": 99284538.4363992, + "value": 99284538.4363992, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113822,8 +113822,8 @@ "area_type": "constituency", "geography_id": "S14000086", "period": 2025, - "raw_value": 81818446.06728882, - "value": 81818446.06728882, + "raw_value": 96743055.07115552, + "value": 96743055.07115552, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113833,8 +113833,8 @@ "area_type": "constituency", "geography_id": "S14000087", "period": 2025, - "raw_value": 77621179.66143923, - "value": 77621179.66143923, + "raw_value": 70106073.54158622, + "value": 70106073.54158622, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113844,8 +113844,8 @@ "area_type": "constituency", "geography_id": "S14000088", "period": 2025, - "raw_value": 64159988.736744136, - "value": 64159988.736744136, + "raw_value": 78916507.31301983, + "value": 78916507.31301983, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113855,8 +113855,8 @@ "area_type": "constituency", "geography_id": "S14000089", "period": 2025, - "raw_value": 78432003.46438093, - "value": 78432003.46438093, + "raw_value": 90735142.94798402, + "value": 90735142.94798402, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113866,8 +113866,8 @@ "area_type": "constituency", "geography_id": "S14000090", "period": 2025, - "raw_value": 99284538.4363992, - "value": 99284538.4363992, + "raw_value": 73308563.16081639, + "value": 73308563.16081639, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113877,8 +113877,8 @@ "area_type": "constituency", "geography_id": "S14000091", "period": 2025, - "raw_value": 96743055.07115552, - "value": 96743055.07115552, + "raw_value": 64770729.56860093, + "value": 64770729.56860093, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113888,8 +113888,8 @@ "area_type": "constituency", "geography_id": "S14000092", "period": 2025, - "raw_value": 70106073.54158622, - "value": 70106073.54158622, + "raw_value": 68817050.03919052, + "value": 68817050.03919052, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113899,8 +113899,8 @@ "area_type": "constituency", "geography_id": "S14000093", "period": 2025, - "raw_value": 78916507.31301983, - "value": 78916507.31301983, + "raw_value": 80819807.1907156, + "value": 80819807.1907156, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113910,8 +113910,8 @@ "area_type": "constituency", "geography_id": "S14000094", "period": 2025, - "raw_value": 90735142.94798402, - "value": 90735142.94798402, + "raw_value": 86714711.60155293, + "value": 86714711.60155293, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113921,8 +113921,8 @@ "area_type": "constituency", "geography_id": "S14000095", "period": 2025, - "raw_value": 73308563.16081639, - "value": 73308563.16081639, + "raw_value": 71854668.79289374, + "value": 71854668.79289374, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113932,8 +113932,8 @@ "area_type": "constituency", "geography_id": "S14000096", "period": 2025, - "raw_value": 64770729.56860093, - "value": 64770729.56860093, + "raw_value": 63959242.26348356, + "value": 63959242.26348356, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113943,8 +113943,8 @@ "area_type": "constituency", "geography_id": "S14000097", "period": 2025, - "raw_value": 68817050.03919052, - "value": 68817050.03919052, + "raw_value": 79703031.74970154, + "value": 79703031.74970154, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113954,8 +113954,8 @@ "area_type": "constituency", "geography_id": "S14000098", "period": 2025, - "raw_value": 80819807.1907156, - "value": 80819807.1907156, + "raw_value": 80087204.63676274, + "value": 80087204.63676274, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113965,8 +113965,8 @@ "area_type": "constituency", "geography_id": "S14000099", "period": 2025, - "raw_value": 86714711.60155293, - "value": 86714711.60155293, + "raw_value": 76342711.01426594, + "value": 76342711.01426594, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113976,8 +113976,8 @@ "area_type": "constituency", "geography_id": "S14000100", "period": 2025, - "raw_value": 71854668.79289374, - "value": 71854668.79289374, + "raw_value": 79765339.76086284, + "value": 79765339.76086284, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113987,8 +113987,8 @@ "area_type": "constituency", "geography_id": "S14000101", "period": 2025, - "raw_value": 63959242.26348356, - "value": 63959242.26348356, + "raw_value": 69032475.81511588, + "value": 69032475.81511588, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -113998,8 +113998,8 @@ "area_type": "constituency", "geography_id": "S14000102", "period": 2025, - "raw_value": 79703031.74970154, - "value": 79703031.74970154, + "raw_value": 74663587.96536602, + "value": 74663587.96536602, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114009,8 +114009,8 @@ "area_type": "constituency", "geography_id": "S14000103", "period": 2025, - "raw_value": 80087204.63676274, - "value": 80087204.63676274, + "raw_value": 76611667.55815, + "value": 76611667.55815, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114020,8 +114020,8 @@ "area_type": "constituency", "geography_id": "S14000104", "period": 2025, - "raw_value": 76342711.01426594, - "value": 76342711.01426594, + "raw_value": 61042309.922564715, + "value": 61042309.922564715, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114031,8 +114031,8 @@ "area_type": "constituency", "geography_id": "S14000105", "period": 2025, - "raw_value": 79765339.76086284, - "value": 79765339.76086284, + "raw_value": 84514219.37178129, + "value": 84514219.37178129, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114042,8 +114042,8 @@ "area_type": "constituency", "geography_id": "S14000106", "period": 2025, - "raw_value": 69032475.81511588, - "value": 69032475.81511588, + "raw_value": 72852759.03499582, + "value": 72852759.03499582, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114053,8 +114053,8 @@ "area_type": "constituency", "geography_id": "S14000107", "period": 2025, - "raw_value": 74663587.96536602, - "value": 74663587.96536602, + "raw_value": 69058682.80002052, + "value": 69058682.80002052, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114064,8 +114064,8 @@ "area_type": "constituency", "geography_id": "S14000108", "period": 2025, - "raw_value": 76611667.55815, - "value": 76611667.55815, + "raw_value": 73852194.82433282, + "value": 73852194.82433282, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114075,8 +114075,8 @@ "area_type": "constituency", "geography_id": "S14000109", "period": 2025, - "raw_value": 61042309.922564715, - "value": 61042309.922564715, + "raw_value": 67032716.7929348, + "value": 67032716.7929348, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114086,8 +114086,8 @@ "area_type": "constituency", "geography_id": "S14000110", "period": 2025, - "raw_value": 84514219.37178129, - "value": 84514219.37178129, + "raw_value": 73194711.51637292, + "value": 73194711.51637292, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, @@ -114097,8 +114097,8 @@ "area_type": "constituency", "geography_id": "S14000111", "period": 2025, - "raw_value": 72852759.03499582, - "value": 72852759.03499582, + "raw_value": 74532613.59652387, + "value": 74532613.59652387, "adjustment_factor": 1.0, "boundary_mapped_from_2010": true }, diff --git a/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json b/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json index 9afa8120b1..cbd3f8b576 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json +++ b/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json @@ -470,7 +470,7 @@ "required_mass_change_reason": "Capital-gains incidence anchor moves the mass of non-liable clone households back to their paired originals until the sub-exempt and loss-making clone mass match the Advani-Summers reporter composition at the redrawn liable mass; every pair's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, the HMRC Table 3 redrawn gains on the spine and Advani-Summers capital-gains incidence" @@ -493,7 +493,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, and Advani-Summers capital-gains incidence" @@ -516,7 +516,7 @@ "required_mass_change_reason": "Capital-gains residential split divides every household carrying liable gainers into arms weighted by the solved residential probabilities, one arm per subset of residential gainers; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative) on the amounts the Table 3 redraw placed" @@ -538,7 +538,7 @@ "required_mass_change_reason": "Capital-gains support split divides the wealthiest households of each HMRC Table 3 income band into light copies at equal weight; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2026 release, Table 3 for 2024-25, via the vendored conditioning facts", "survey": "Family Resources Survey 2024-25, SPI synthetic support and HMRC Capital Gains Tax statistics Table 3 (2024-25 individuals by size of gain and taxable income)" @@ -567,7 +567,7 @@ "required_mass_change_reason": "ETB public-services imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab, DfT rail fare index, and public NHS activity/cost table.", "survey": "Effects of Taxes and Benefits 1977-2024 and NHS age-gender public table" @@ -587,7 +587,7 @@ "required_mass_change_reason": "ETB VAT expenditure-rate imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab and cited VAT anchor resource.", "survey": "Effects of Taxes and Benefits 1977-2024" @@ -609,7 +609,7 @@ "required_mass_change_reason": "High-value home support divides every owner household in the top value stratum of its region into light copies at equal weight; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Microcosm structural operator (microcosm#1240 fix 1); no publisher input", "survey": "Family Resources Survey 2024-25 and SPI synthetic support, after the WAS wealth draw and the regional property uprating" @@ -630,7 +630,7 @@ "required_mass_change_reason": "CGT asset-type assignment on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative), Table 4.1 (individuals claiming Business Asset Disposal Relief or Investors' Relief by band of qualifying gain, 2024-25, administrative) and Table 7 (disposals, proceeds and gains by asset type, 2023-24, sample-based), vendored from the pinned Chronicle feed" @@ -654,7 +654,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "223feb96f44361b6764e977d88e292d2edda167717f84d3969cccbadfc80fa80", @@ -743,7 +743,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -785,7 +785,7 @@ "required_mass_change_reason": "LCFS consumption imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "UK Data Service SN 9603 Living Costs and Food Survey 2024-25 (2nd edition) household/person tabs and the vendored Chronicle facts for NEED 2024 mean kWh, DESNZ Energy Trends and QEP average prices paid for calendar 2024, road-fuel outturn (DESNZ, HMRC, ONS Consumer Trends) and road fuel by vehicle type (DESNZ), ONS Consumer Trends household spending by COICOP division, the DfT VEH1103 licensed-car stock, NTS bus use, and the DfT and devolved bus finance tables and fares indices.", "survey": "Living Costs and Food Survey 2024-25" @@ -811,7 +811,7 @@ "required_mass_change_reason": "NTS bus-travel imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Department for Transport National Travel Survey, UK Data Service SN 5340 (End User Licence, 19th edition, November 2025), DOI 10.5255/UKDA-SN-5340-19; local licensed tab files (household, individual, trip); England residents only from 2013.", "survey": "National Travel Survey 2002-2024" @@ -819,6 +819,26 @@ "stage": "nts_bus_travel", "status": "required_at_build" }, + "private_school_attendance": { + "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", + "base_candidate_tier": "frs", + "effective_mass_requirements": {}, + "mass_change_semantics": "mass_conserving", + "output_weight_kind": "importance", + "outputs": [ + "attends_private_school" + ], + "required_mass_change_reason": "Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved.", + "rewrites": [], + "source_manifest": "source_stages.json", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", + "source_vintages": { + "source": "policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine", + "survey": "policyengine-uk private-school attendance imputation, stored at the spine's weights" + }, + "stage": "private_school_attendance", + "status": "required_at_build" + }, "property_components": { "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", "base_candidate_tier": "frs", @@ -834,7 +854,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "https://www.gov.uk/government/statistics/property-rental-income-statistics", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Property Rental Income Statistics 2026 (tax year 2024-25)" @@ -858,7 +878,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "MHCLG dwellings and the UK House Price Index regional average price averaged over the twelve months of calendar 2024 from the July 2026 full file, the frame's valuation basis, so the engine's annual uprating from 2024 projects the value once (microcosm#1240 fix 5).", "survey": "Public regional property reference" @@ -885,7 +905,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "HMRC, Salary sacrifice reform for pension contributions effective from 6 April 2029", "survey": "Family Resources Survey 2024-25 salary-sacrifice respondents and HMRC salary-sacrifice reform analysis" @@ -918,7 +938,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Reserved SPI support households for the HMRC Table 2.5 total-income bands from GBP 200,000, stacked at the published band taxpayer count over the donors per band (PolicyEngine/chronicle#280 lane).", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Income Tax liabilities statistics July 2026 Table 2.5" @@ -940,7 +960,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Explore Education Statistics Table 6a, Higher education total", "survey": "Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England" @@ -962,7 +982,7 @@ "required_mass_change_reason": "WAS Lifetime ISA imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Office for National Statistics Wealth and Assets Survey, UK Data Service SN 7215, DOI 10.5255/UKDA-SN-7215-20; local licensed round-8 person and household tabs (interviews April 2020 to March 2022, Great Britain).", "survey": "Wealth and Assets Survey round 8" @@ -997,7 +1017,7 @@ "required_mass_change_reason": "WAS wealth imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "44636ecb905a6cc473a47342cb983d10ebac6626fb1565c1bc9fdcdbef539e39", + "source_manifest_sha256": "99bc123bd454c3fd542bcc215402dfaabd5b1a0e0e1696442f652d9bacdb5eb7", "source_vintages": { "source": "Office for National Statistics Wealth and Assets Survey, UK Data Service SN 7215, DOI 10.5255/UKDA-SN-7215-20; local licensed 2006-22 household tab.", "survey": "Wealth and Assets Survey round 8" diff --git a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json index 97f924a389..b21032074c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -229,7 +229,7 @@ }, { "kind": "map_columns", - "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); esa_includes_support_component is false where a person's ESA rows (BENEFIT 16) record VAR3 (ESAGROUP) 1 or 3, the work-related activity group or the assessment phase, and none records 2, the support group, and true everywhere else, policyengine-uk's own default for an award of one's own, and a benefits tab without VAR3 refuses (microcosm#1095); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" }, { "kind": "map_coded_amounts", @@ -272,10 +272,19 @@ "rent_paid_as_lodger", "employment_income", "self_employment_income", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", "private_pension_income", "tax_free_savings_income", "savings_interest_income", "dividend_income", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", "property_income", "reports_rent_from_other_property", "maintenance_income", @@ -326,6 +335,7 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "esa_includes_support_component", "bsp_reported", "benunit_id", "frs_benunit_capital", @@ -359,10 +369,14 @@ "hours_worked", "employment_income", "self_employment_income", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", "private_pension_income", "tax_free_savings_income", "savings_interest_income", "dividend_income", + "owned_company_income_share", "maintenance_income", "miscellaneous_income", "student_loan_repayments", @@ -381,7 +395,7 @@ "external_child_payments", "care_hours" ], - "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)." + "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). SEINCAM2, the self-employment income the respondent gives, splits by its sign (uk-data#528), so profits go to self_employment_income and losses to trading_loss as a positive amount, the two inputs policyengine-uk reads from 2.131.0 (pe-uk#2085). The engine uprates trading_loss into each projected year as it does self_employment_income, so a loss persists like a profit; the engine documents that a one-off loss would be set to zero in later years, which a one-year survey cannot tell apart. Six of policyengine-uk's eleven owned-company inputs (pe-uk#1965) come from the FRS job tab (microcosm#1095). A director (DIRECTOR 1 or NATURE 1) who owns at least half of the company (DIRPERC 1 to 3, the ruling of 2026-10-16) stands as its sole owner or partner and works in it. The company is their main employment when the job is their first (JOBTYPE 1); it carries on a property business when the main job's SIC division is 68 and a trade otherwise, and its income share is the dividends the job records (DIVIDEND, after tax), the part of the company's profit the survey sees. A company held in another job carries on neither, since ADULT.SIC codes the main job only, which leaves the engine's company-owner rules off for it. The stocks and the intermediaries inputs keep the engine's defaults." }, { "stage": "age_tail", @@ -855,14 +869,17 @@ "is_before_universal_credit_qualifying_young_person_terminal_date", "adult_ema", "child_ema", - "receives_benefits_in_own_right" + "receives_benefits_in_own_right", + "reports_outstanding_student_loan", + "age_left_full_time_education" ], "nonnegative_outputs": [ "adult_ema", "child_ema", - "age_started_or_accepted_current_education_or_training" + "age_started_or_accepted_current_education_or_training", + "age_left_full_time_education" ], - "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build." + "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build. reports_outstanding_student_loan is SLOS = 1 (Do you have an outstanding student loan?) on an adult-table record, answered by adults not making repayments, and age_left_full_time_education is EDUCLEFT (Age completed full-time education) where it lies between 10 and 90, else 0; the student_loans stage reads both (microcosm#868). A tab without SLOS or EDUCLEFT refuses the build." }, { "stage": "frs_legacy_proxies", @@ -1083,7 +1100,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095)." + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.132.6's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095)." }, { "stage": "frs_person_draws", @@ -1724,7 +1741,9 @@ "hmrc_spi_miscellaneous_employment_income": "employment_income_before_lsr", "hmrc_spi_other_income": "miscellaneous_income", "hmrc_spi_state_pension_income": null, - "property_finance_costs": "property_finance_costs" + "property_finance_costs": "property_finance_costs", + "spi_profit_before_losses_draw": "self_employment_income", + "spi_losses_brought_forward_draw": "self_employment_income" }, "predictors": [ "age", @@ -1791,7 +1810,9 @@ "hmrc_spi_miscellaneous_employment_income": null, "hmrc_spi_other_income": "miscellaneous_income", "hmrc_spi_state_pension_income": "srp_regular_code5", - "property_finance_costs": null + "property_finance_costs": null, + "spi_profit_before_losses_draw": null, + "spi_losses_brought_forward_draw": null }, "weight": "household_weight", "weight_mapping": "household_to_person", @@ -1873,6 +1894,13 @@ ], "property_finance_costs": [ "LLIR_RESTRICT_AMT_TOT" + ], + "spi_profit_before_losses_draw": [ + "PROFITS", + "CAPALL" + ], + "spi_losses_brought_forward_draw": [ + "LOSSBF" ] }, "derived_policyengine_outputs": { @@ -1905,7 +1933,9 @@ "hmrc_spi_miscellaneous_employment_income", "hmrc_spi_other_income", "hmrc_spi_state_pension_income", - "property_finance_costs" + "property_finance_costs", + "spi_profit_before_losses_draw", + "spi_losses_brought_forward_draw" ], "joint_draw": true, "savings_interest_source_semantics": "INCBBS is taxable bank/building-society interest before reconstruction to the PolicyEngine gross input", @@ -1950,8 +1980,8 @@ "require_all_predictors": true, "require_all_outputs": true, "initialize_frs_channel_columns": { - "gift_aid": 0, - "charitable_investment_gifts": 0 + "gift_aid": 0.0, + "charitable_investment_gifts": 0.0 }, "n_estimators": 100, "seed": 42 @@ -2031,7 +2061,14 @@ "jsa_contrib_reported", "jsa_income_reported", "esa_contrib_reported", - "esa_income_reported" + "esa_income_reported", + "trading_loss", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share" ], "reviewed_absent_outputs": { "incapacity_benefit_reported": "Absent/all-default on the pinned enhanced-FRS export and certified Microcosm UK base; not a populated loader layer.", @@ -2408,11 +2445,20 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", "hmrc_spi_pay", "hmrc_spi_unemployment_benefit_income", "hmrc_spi_incapacity_benefit_income" ], - "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path." + "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095)." }, { "stage": "spi_housing_shell", @@ -3377,7 +3423,7 @@ { "kind": "materialize_rules_engine_predictors", "predictors": [ - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -3864,7 +3910,7 @@ "employment_income", "self_employment_income", "private_pension_income", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -3995,6 +4041,11 @@ "household_kwh_ceiling": { "electricity": 50000, "gas": 73200 + }, + "zero_kwh_cells_at_need_mean": { + "accommodation": [ + "all_dwellings" + ] } }, { @@ -4331,7 +4382,7 @@ "has_fuel_consumption", "uses_energy_prepayment_meter" ], - "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain's predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm's locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain's predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor's gas payers) and the gas such a donor pays (a regime-gated QRF of the payers' gas spend, at quantiles keyed on the household id), and the region's zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; María's ruling of 2026-10-12 on microcosm#1207's extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households' own NEED cells are re-raked to those cells' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Both rakes band households on gross income, NEED's concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine's household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine's CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor's distribution among households with positive road fuel, on the chain's predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor's own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary's 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain's draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain's own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain's domestic-energy target is the electricity and gas the stage derives from the diary's fuel, light and power (p537) by its labelled parts, from the LCF User Guide's derived-variable labels and P537 definition (microcosm#1164): each fuel's last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel's account payers, which pins the labels and the rebate's sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary's second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household's own electricity share where it pays for both fuels, and otherwise by the donor's mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary's other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor's realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary's range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5's year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area's fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland's calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England's index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (María's rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers' imputed rent and 12.6.1 FISIM), on ONS's domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel's frame litres with HMRC's clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total." + "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain's predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm's locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain's predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor's gas payers) and the gas such a donor pays (a regime-gated QRF of the payers' gas spend, at quantiles keyed on the household id), and the region's zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; María's ruling of 2026-10-12 on microcosm#1207's extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households' own NEED cells are re-raked to those cells' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Mobile homes have no NEED property-type row, so the rake takes them to the all-dwellings mean, the incumbent's translation; where every mobile home of a NEED geography draws a spend at or below the standing charge, that cell has no kWh for the rake to scale, and the declared zero_kwh_cells_at_need_mean starts it at its NEED mean before the first sweep (microcosm#1240). The receipt lists the cells it started, and the energy_rake gate fails on a started cell the stage does not declare and on any other cell with a zero mean. Both rakes band households on gross income, NEED's concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine's household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine's CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor's distribution among households with positive road fuel, on the chain's predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor's own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary's 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain's draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain's own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain's domestic-energy target is the electricity and gas the stage derives from the diary's fuel, light and power (p537) by its labelled parts, from the LCF User Guide's derived-variable labels and P537 definition (microcosm#1164): each fuel's last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel's account payers, which pins the labels and the rebate's sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary's second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household's own electricity share where it pays for both fuels, and otherwise by the donor's mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary's other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor's realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary's range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5's year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area's fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland's calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England's index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (María's rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers' imputed rent and 12.6.1 FISIM), on ONS's domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel's frame litres with HMRC's clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total." }, { "stage": "etb_vat", @@ -4392,7 +4443,7 @@ "kind": "materialize_rules_engine_predictors", "predictors": [ "is_SP_age", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -4545,7 +4596,7 @@ "is_SP_age", "dla", "pip", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "current_education" ], "derived_predictors": { @@ -5099,7 +5150,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both." + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.132.6 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both." }, { "stage": "pension_credit_take_up", @@ -5763,7 +5814,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.132.6 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount" ], @@ -5844,7 +5895,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.132.6 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount", "gov.hmrc.cgt.badr.lifetime_limit", @@ -6076,11 +6127,17 @@ { "kind": "assign_student_loan_plan_cohorts", "year_rule": "calibration_year", - "start_year_formula": "year - age + 18", + "start_year_formula": "year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18", "reported_repayment_test": "student_loan_repayments > 0", "reported_country_gate": false, + "reported_outstanding_test": "reports_outstanding_student_loan and current_education == NOT_IN_EDUCATION", + "reported_outstanding_region_exclusions": [ + "SCOTLAND", + "NORTHERN_IRELAND" + ], "plan_1_before": 2012, "plan_5_from": 2023, + "plan_5_repayments_from": 2026, "enum_domain": [ "NONE", "PLAN_1", @@ -6098,6 +6155,7 @@ "year_rule": "calibration_year", "age_min": 18, "age_max": 25, + "cohort_start_formula": "year - age + 18", "cohort_start_min": 2023, "eligible_region_exclusions": [ "SCOTLAND", @@ -6119,6 +6177,8 @@ "year_rule": "calibration_year", "age_min": 21, "age_max": 55, + "cohort_start_formula": "year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18", + "current_education": "NOT_IN_EDUCATION", "cohort_start_min": 2012, "cohort_start_max_exclusive": 2023, "eligible_region_exclusions": [ @@ -6140,7 +6200,42 @@ "rewrites": [ "student_loan_plan" ], - "notes": "Reported PAYE repayers are classified without a country gate. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year; PLAN_4 is never imputed." + "notes": "Reported borrowers are PAYE repayers, classified without a country gate, and, in England and Wales, adults who report an outstanding loan (FRS SLOS) and are not in full-time education, the borrowers below the repayment threshold (microcosm#868). A course starts three years before its borrower left full-time education (FRS EDUCLEFT), or at 18 without an answer, and its start year sets the plan, except that before 2026 a repayer never takes PLAN_5: nobody repays a Plan 5 loan before April 2026, so SLC counts no Plan 5 borrower above the threshold in academic year 2024/25. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year. The PLAN_2 walk takes nobody in full-time education, since a student is not yet liable to repay; the PLAN_5 walk keeps the age rule, since its starters have mostly not left education. The stocks are SLC academic year 2024/25, whose reference date falls in calendar 2025, the year the borrower targets bind (ruling of 2026-10-16). PLAN_4 is never imputed, and a Scottish or Northern Irish answer of an outstanding loan assigns no plan." + }, + { + "stage": "private_school_attendance", + "survey": "policyengine-uk private-school attendance imputation, stored at the spine's weights", + "source": "policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine", + "grain": "person", + "artifacts": [ + { + "role": "frs_release", + "kind": "public_aggregate_reference", + "resource": "frs_release.json", + "format": "json", + "runtime_sha256_required": true + } + ], + "operations": [ + { + "kind": "store_rules_engine_input", + "output": "attends_private_school", + "draw": "attends_private_school_random_draw", + "year_rule": "calibration_year", + "weights": "spine_design_weights" + }, + { + "kind": "record_mass_conservation_receipt", + "entity": "household", + "reason": "Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved.", + "declared_factor": 1.0, + "gate_coupling": "The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason." + } + ], + "outputs": [ + "attends_private_school" + ], + "notes": "The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine." } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml index 64f342a236..042bcb1750 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -368,7 +368,7 @@ stages: person_table: adult union child household_sort: set_index('household_id').sort_index() before positional household reads - kind: map_columns - scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit''s UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer''s property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it' + scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); esa_includes_support_component is false where a person''s ESA rows (BENEFIT 16) record VAR3 (ESAGROUP) 1 or 3, the work-related activity group or the assessment phase, and none records 2, the support group, and true everywhere else, policyengine-uk''s own default for an award of one''s own, and a benefits tab without VAR3 refuses (microcosm#1095); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit''s UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer''s property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it' - kind: map_coded_amounts scope: region, tenure, accommodation, council tax band, benefit-code amount mappings (Scotland's Adult and Child Disability Payment codes 117/118 and 121/122 into the PIP and DLA columns they replace; benefit recovery codes 69 and 70 unmapped); adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance - kind: annualize_periodic_amounts @@ -403,10 +403,19 @@ stages: - rent_paid_as_lodger - employment_income - self_employment_income + - trading_loss + - loss_relief + - ni_class_4_losses_brought_forward - private_pension_income - tax_free_savings_income - savings_interest_income - dividend_income + - stands_as_sole_owner_or_partner_of_company + - is_engaged_in_owned_company_trade + - owned_company_is_main_employment + - owned_company_carries_on_trade + - owned_company_carries_on_property_business + - owned_company_income_share - property_income - reports_rent_from_other_property - maintenance_income @@ -457,6 +466,7 @@ stages: - jsa_income_reported - esa_contrib_reported - esa_income_reported + - esa_includes_support_component - bsp_reported - benunit_id - frs_benunit_capital @@ -489,10 +499,14 @@ stages: - hours_worked - employment_income - self_employment_income + - trading_loss + - loss_relief + - ni_class_4_losses_brought_forward - private_pension_income - tax_free_savings_income - savings_interest_income - dividend_income + - owned_company_income_share - maintenance_income - miscellaneous_income - student_loan_repayments @@ -510,7 +524,7 @@ stages: - housing_service_charges - external_child_payments - care_hours - notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). + notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). SEINCAM2, the self-employment income the respondent gives, splits by its sign (uk-data#528), so profits go to self_employment_income and losses to trading_loss as a positive amount, the two inputs policyengine-uk reads from 2.131.0 (pe-uk#2085). The engine uprates trading_loss into each projected year as it does self_employment_income, so a loss persists like a profit; the engine documents that a one-off loss would be set to zero in later years, which a one-year survey cannot tell apart. Six of policyengine-uk's eleven owned-company inputs (pe-uk#1965) come from the FRS job tab (microcosm#1095). A director (DIRECTOR 1 or NATURE 1) who owns at least half of the company (DIRPERC 1 to 3, the ruling of 2026-10-16) stands as its sole owner or partner and works in it. The company is their main employment when the job is their first (JOBTYPE 1); it carries on a property business when the main job's SIC division is 68 and a trade otherwise, and its income share is the dividends the job records (DIVIDEND, after tax), the part of the company's profit the survey sees. A company held in another job carries on neither, since ADULT.SIC codes the main job only, which leaves the engine's company-owner rules off for it. The stocks and the intermediaries inputs keep the engine's defaults. - stage: age_tail survey: Family Resources Survey 2024-25 and ONS mid-year population estimates source: Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population). @@ -826,11 +840,14 @@ stages: - adult_ema - child_ema - receives_benefits_in_own_right + - reports_outstanding_student_loan + - age_left_full_time_education nonnegative_outputs: - adult_ema - child_ema - age_started_or_accepted_current_education_or_training - notes: 'Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build.' + - age_left_full_time_education + notes: 'Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build. reports_outstanding_student_loan is SLOS = 1 (Do you have an outstanding student loan?) on an adult-table record, answered by adults not making repayments, and age_left_full_time_education is EDUCLEFT (Age completed full-time education) where it lies between 10 and 90, else 0; the student_loans stage reads both (microcosm#868). A tab without SLOS or EDUCLEFT refuses the build.' - stage: frs_legacy_proxies survey: Family Resources Survey 2024-25 source: Department for Work and Pensions Family Resources Survey 2024-25, UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1; local licensed 2024_25 tabs. plus policyengine-uk DWP parameters. @@ -987,7 +1004,7 @@ stages: - maximum_extended_childcare_hours_usage nonnegative_outputs: - maximum_extended_childcare_hours_usage - notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). + notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.132.6's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). - stage: frs_person_draws survey: Family Resources Survey 2024-25 source: Sourced UK take-up contract rates and identity-keyed deterministic draws. @@ -1496,6 +1513,8 @@ stages: hmrc_spi_other_income: miscellaneous_income hmrc_spi_state_pension_income: null property_finance_costs: property_finance_costs + spi_profit_before_losses_draw: self_employment_income + spi_losses_brought_forward_draw: self_employment_income predictors: - age - gender @@ -1554,6 +1573,8 @@ stages: hmrc_spi_other_income: miscellaneous_income hmrc_spi_state_pension_income: srp_regular_code5 property_finance_costs: null + spi_profit_before_losses_draw: null + spi_losses_brought_forward_draw: null weight: household_weight weight_mapping: household_to_person fit_weight_kind: importance @@ -1614,6 +1635,11 @@ stages: - SRP property_finance_costs: - LLIR_RESTRICT_AMT_TOT + spi_profit_before_losses_draw: + - PROFITS + - CAPALL + spi_losses_brought_forward_draw: + - LOSSBF derived_policyengine_outputs: employment_income: source_columns: @@ -1642,6 +1668,8 @@ stages: - hmrc_spi_other_income - hmrc_spi_state_pension_income - property_finance_costs + - spi_profit_before_losses_draw + - spi_losses_brought_forward_draw joint_draw: true savings_interest_source_semantics: INCBBS is taxable bank/building-society interest before reconstruction to the PolicyEngine gross input employment_income_source_semantics: PolicyEngine input = PAY + EPB + TAXTERM, matching the pinned enhanced-FRS pipeline; it is not the Table 3.6 measure @@ -1751,6 +1779,13 @@ stages: - jsa_income_reported - esa_contrib_reported - esa_income_reported + - trading_loss + - stands_as_sole_owner_or_partner_of_company + - is_engaged_in_owned_company_trade + - owned_company_is_main_employment + - owned_company_carries_on_trade + - owned_company_carries_on_property_business + - owned_company_income_share reviewed_absent_outputs: incapacity_benefit_reported: Absent/all-default on the pinned enhanced-FRS export and certified Microcosm UK base; not a populated loader layer. maternity_allowance_reported: Absent from the pinned enhanced-FRS export and certified Microcosm UK base; no training source can be materialized for this stage. @@ -2061,10 +2096,19 @@ stages: - jsa_income_reported - esa_contrib_reported - esa_income_reported + - trading_loss + - loss_relief + - ni_class_4_losses_brought_forward + - stands_as_sole_owner_or_partner_of_company + - is_engaged_in_owned_company_trade + - owned_company_is_main_employment + - owned_company_carries_on_trade + - owned_company_carries_on_property_business + - owned_company_income_share - hmrc_spi_pay - hmrc_spi_unemployment_benefit_income - hmrc_spi_incapacity_benefit_income - notes: Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. + notes: Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). - stage: spi_housing_shell survey: Family Resources Survey 2024-25 source: Family Resources Survey 2024-25 base households' housing (tenure, dwelling type, bedrooms, council tax, housing costs and the housing-linked benefit reports), imputed onto the SPI support households from their own region, composition and post-SPI incomes. @@ -2822,7 +2866,7 @@ stages: codebook_status: verified 2026-09-22 against the deposited SN 5340 19th-edition lookup tables and the data extract user guide. Frequency is OrdBus2Freq_B01ID (asked since 2019; OrdBusFreq_B01ID ended in 2018), ten codes folded onto the seven NTS0313 bands (codes 1 to 3 are the three-or-more-times-a-week band, 9 and 10 the less-than-once-a-year-or-never band); code -9 (question not applicable, proxy responses) is declared a non-user because the published NTS0313 shares reproduce only with those persons in the base (2024 at-least-yearly share 0.526 without them, 0.505 with them, the published 0.505); code -8 (no answer) is dropped and receipted. Age_B01ID has 21 bands, each mapped to its lower age and then banded on the declared edges, so donor and recipient share one banding (every NTS band lies inside one declared band). Region codes 10 and 11 (Wales, Scotland) occur only before 2013 and never in the declared years. HHIncome2002_B02ID -8 (no answer) is dropped. The trip weight is W5, which carries the diary household weight W2 (the user guide's trips per person per year is 52.14 x sum(JJXSC x W5) over sum(W2) on the diary sample), so each person's counted trips are divided by W2 before the W2-weighted mean; W5xHH is the within-household factor only. W2 is zero for interview-only households, which leave the donor (the fit and the diary means use the diary sample, as the user guide prescribes for trip measures). Stage and ticket tables, verified 2026-09-23. A stage is a boarding (NumBoardings counts the vehicles), StageMode_B04ID carries the trip-mode categories, StageFareCost is the boarding cost paid at the point of use and IndTicketID links a stage to a held ticket; SpecialTicket_B01ID codes 7 to 11 are the concessionary passes (older people, scholars, disabled persons, subsidised tokens, other concessionary), 1 to 6 non-concessionary season tickets and passes. A boarding with no ticket record and a positive cost paid a single, return or day fare at the point of use; with no record and zero cost it travelled free (children, in the main); with no cost recorded its class is unknown. - kind: materialize_rules_engine_predictors predictors: - - hbai_household_net_income + - hbai_household_net_income_before_reset_to_zero - council_tax - domestic_rates - income_tax @@ -3185,7 +3229,7 @@ stages: - employment_income - self_employment_income - private_pension_income - - hbai_household_net_income + - hbai_household_net_income_before_reset_to_zero - council_tax - domestic_rates - income_tax @@ -3296,6 +3340,9 @@ stages: household_kwh_ceiling: electricity: 50000 gas: 73200 + zero_kwh_cells_at_need_mean: + accommodation: + - all_dwellings - kind: price_bus_journeys column: bus_fare_spending trips_columns: @@ -3552,7 +3599,7 @@ stages: - gas_consumption - has_fuel_consumption - uses_energy_prepayment_meter - notes: 'Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent''s WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain''s predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm''s locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María''s rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain''s raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household''s QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain''s predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor''s gas payers) and the gas such a donor pays (a regime-gated QRF of the payers'' gas spend, at quantiles keyed on the household id), and the region''s zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; María''s ruling of 2026-10-12 on microcosm#1207''s extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households'' own NEED cells are re-raked to those cells'' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Both rakes band households on gross income, NEED''s concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine''s household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk''s seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED''s Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person''s annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household''s non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area''s price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England''s trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame''s eligible trip share beside the publisher''s concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine''s CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey''s age-18 split) predict on both sides; the recipient counts its members by age, not through the engine''s is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor''s distribution among households with positive road fuel, on the chain''s predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor''s own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary''s 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain''s draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain''s own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain''s domestic-energy target is the electricity and gas the stage derives from the diary''s fuel, light and power (p537) by its labelled parts, from the LCF User Guide''s derived-variable labels and P537 definition (microcosm#1164): each fuel''s last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel''s account payers, which pins the labels and the rebate''s sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary''s second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household''s own electricity share where it pays for both fuels, and otherwise by the donor''s mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary''s other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor''s realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary''s range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5''s year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area''s fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland''s calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England''s index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (María''s rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers'' imputed rent and 12.6.1 FISIM), on ONS''s domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel''s frame litres with HMRC''s clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total.' + notes: 'Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent''s WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain''s predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm''s locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María''s rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain''s raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household''s QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain''s predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor''s gas payers) and the gas such a donor pays (a regime-gated QRF of the payers'' gas spend, at quantiles keyed on the household id), and the region''s zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; María''s ruling of 2026-10-12 on microcosm#1207''s extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households'' own NEED cells are re-raked to those cells'' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Mobile homes have no NEED property-type row, so the rake takes them to the all-dwellings mean, the incumbent''s translation; where every mobile home of a NEED geography draws a spend at or below the standing charge, that cell has no kWh for the rake to scale, and the declared zero_kwh_cells_at_need_mean starts it at its NEED mean before the first sweep (microcosm#1240). The receipt lists the cells it started, and the energy_rake gate fails on a started cell the stage does not declare and on any other cell with a zero mean. Both rakes band households on gross income, NEED''s concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine''s household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk''s seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED''s Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person''s annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household''s non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area''s price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England''s trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame''s eligible trip share beside the publisher''s concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine''s CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey''s age-18 split) predict on both sides; the recipient counts its members by age, not through the engine''s is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor''s distribution among households with positive road fuel, on the chain''s predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor''s own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary''s 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain''s draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain''s own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain''s domestic-energy target is the electricity and gas the stage derives from the diary''s fuel, light and power (p537) by its labelled parts, from the LCF User Guide''s derived-variable labels and P537 definition (microcosm#1164): each fuel''s last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel''s account payers, which pins the labels and the rebate''s sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary''s second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household''s own electricity share where it pays for both fuels, and otherwise by the donor''s mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary''s other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor''s realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary''s range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5''s year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area''s fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland''s calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England''s index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (María''s rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers'' imputed rent and 12.6.1 FISIM), on ONS''s domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel''s frame litres with HMRC''s clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total.' - stage: etb_vat survey: Effects of Taxes and Benefits 1977-2024 source: UK Data Service SN 8856 Effects of Taxes and Benefits household tab and cited VAT anchor resource. @@ -3597,7 +3644,7 @@ stages: - kind: materialize_rules_engine_predictors predictors: - is_SP_age - - hbai_household_net_income + - hbai_household_net_income_before_reset_to_zero - council_tax - domestic_rates - income_tax @@ -3708,7 +3755,7 @@ stages: - is_SP_age - dla - pip - - hbai_household_net_income + - hbai_household_net_income_before_reset_to_zero - current_education derived_predictors: is_adult: count of household members with is_uc_claimant (FRS claimants and partners) @@ -4132,7 +4179,7 @@ stages: rewrites: - frs_benunit_capital - would_claim_uc - notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. + notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.132.6 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. - stage: pension_credit_take_up survey: Family Resources Survey 2024-25 source: 'DWP Income-related benefits: estimates of take-up, financial year ending 2024 (Pension Credit caseload take-up by component), applied to PolicyEngine-UK pre-take-up Pension Credit entitlement after the SPI income chain.' @@ -4654,7 +4701,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.130.1 via microcosm-build[uk] + dependency: policyengine-uk>=2.132.6 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount instant_rule: raw dated parameter files evaluated at 1 June of the build period's tax year @@ -4719,7 +4766,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.130.1 via microcosm-build[uk] + dependency: policyengine-uk>=2.132.6 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount - gov.hmrc.cgt.badr.lifetime_limit @@ -4904,11 +4951,16 @@ stages: operations: - kind: assign_student_loan_plan_cohorts year_rule: calibration_year - start_year_formula: year - age + 18 + start_year_formula: year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18 reported_repayment_test: student_loan_repayments > 0 reported_country_gate: false + reported_outstanding_test: reports_outstanding_student_loan and current_education == NOT_IN_EDUCATION + reported_outstanding_region_exclusions: + - SCOTLAND + - NORTHERN_IRELAND plan_1_before: 2012 plan_5_from: 2023 + plan_5_repayments_from: 2026 enum_domain: - NONE - PLAN_1 @@ -4923,6 +4975,7 @@ stages: year_rule: calibration_year age_min: 18 age_max: 25 + cohort_start_formula: year - age + 18 cohort_start_min: 2023 eligible_region_exclusions: - SCOTLAND @@ -4941,6 +4994,8 @@ stages: year_rule: calibration_year age_min: 21 age_max: 55 + cohort_start_formula: year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18 + current_education: NOT_IN_EDUCATION cohort_start_min: 2012 cohort_start_max_exclusive: 2023 eligible_region_exclusions: @@ -4958,4 +5013,28 @@ stages: - student_loan_plan rewrites: - student_loan_plan - notes: Reported PAYE repayers are classified without a country gate. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year; PLAN_4 is never imputed. + notes: 'Reported borrowers are PAYE repayers, classified without a country gate, and, in England and Wales, adults who report an outstanding loan (FRS SLOS) and are not in full-time education, the borrowers below the repayment threshold (microcosm#868). A course starts three years before its borrower left full-time education (FRS EDUCLEFT), or at 18 without an answer, and its start year sets the plan, except that before 2026 a repayer never takes PLAN_5: nobody repays a Plan 5 loan before April 2026, so SLC counts no Plan 5 borrower above the threshold in academic year 2024/25. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year. The PLAN_2 walk takes nobody in full-time education, since a student is not yet liable to repay; the PLAN_5 walk keeps the age rule, since its starters have mostly not left education. The stocks are SLC academic year 2024/25, whose reference date falls in calendar 2025, the year the borrower targets bind (ruling of 2026-10-16). PLAN_4 is never imputed, and a Scottish or Northern Irish answer of an outstanding loan assigns no plan.' +- stage: private_school_attendance + survey: policyengine-uk private-school attendance imputation, stored at the spine's weights + source: policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine + grain: person + artifacts: + - role: frs_release + kind: public_aggregate_reference + resource: frs_release.json + format: json + runtime_sha256_required: true + operations: + - kind: store_rules_engine_input + output: attends_private_school + draw: attends_private_school_random_draw + year_rule: calibration_year + weights: spine_design_weights + - kind: record_mass_conservation_receipt + entity: household + reason: 'Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved.' + declared_factor: 1.0 + gate_coupling: The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason. + outputs: + - attends_private_school + notes: 'The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine''s attendance once, at the calibration year and the spine''s design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.' diff --git a/packages/microcosm-build/src/microcosm/build/uk/take_up_contract.json b/packages/microcosm-build/src/microcosm/build/uk/take_up_contract.json index b5fce6143e..f641937abd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/take_up_contract.json +++ b/packages/microcosm-build/src/microcosm/build/uk/take_up_contract.json @@ -144,7 +144,7 @@ "entity": "benunit", "values": { "2015-01-01": 0.586, - "2024-04-06": 0.88 + "2024-04-06": 0.9797 }, "source": { "source": "FRS empirical fit and HMRC Tax-Free Childcare statistics, June 2025 release", @@ -154,9 +154,54 @@ "status": "fitted_offline" }, "fitting_receipt": { - "source": "incumbent UK pipeline @ ebf733c", - "seed": 42, - "status": "frozen parity value" + "source": "tools/fit_uk_childcare_takeup.py (microcosm#1095 U2c, licensed lane)", + "tool_version": 4, + "receipt_sha256": "32b06ee06f235089847b5c469e7c49ad9ee39c51e46aa5908522fec61c130d96", + "seed": 0, + "engine_version": "2.132.6", + "model_period": 2024, + "vintage_overrides": { + "dfe.funded_childcare.working_parent_children_2_to_4": 2025 + }, + "input_h5_sha256": "c883a0506a7e626804e33a43e3e7b4dbf7ea7e55a67e5624edbc445187a7ab07", + "input_spine": "the bump arm of the fifth #1095 ports PR (policyengine-uk 2.132.6, spine before the extended-hours anchor moved)", + "ledger_facts_sha256": "3033e437ed61d7d5e970f4fef34a2a5523fe068770a7798e5768af656f2a48a4", + "stochastic_contract_sha256_at_fit": "3d9fd8cd58de359ecb07b9af478f4bf8b30045dc1ad108d1952f8f9bedb44d96", + "optimizer": { + "eps": 1e-06, + "loss": 0.030876275034388008, + "maxiter": 50, + "method": "L-BFGS-B", + "success": true + }, + "achieved": { + "dfe.funded_childcare.early_learning_2_year_olds": { + "target": 115852.0, + "value": 115851.869, + "ratio": 1.0 + }, + "dfe.funded_childcare.universal_only_children": { + "target": 416537.0, + "value": 416536.609, + "ratio": 1.0 + }, + "dfe.funded_childcare.working_parent_children_2_to_4": { + "target": 621482.0, + "value": 621481.151, + "ratio": 1.0 + }, + "hmrc.tfc.children_with_used_accounts": { + "target": 1085020.0, + "value": 1202038.13, + "ratio": 1.1078 + }, + "hmrc.tfc.government_top_up": { + "target": 632200000.0, + "value": 544497415.126, + "ratio": 0.8613 + } + }, + "status": "fitted at the 2024 base year with the expectation basis of tool version 4: targeted and universal hours allocated from working-parent hours (pe-uk#2177) and the DfE extended-hours anchor (#1126) in the simulation. No rate reached its bound. The two HMRC Tax-Free Childcare rows cannot both be met (spend below, children above): a residual of the frame's spend per child with a used account, not of the rate." } }, { @@ -165,7 +210,7 @@ "entity": "benunit", "values": { "2015-01-01": 0.812, - "2024-04-06": 0.6054 + "2024-04-06": 0.6579 }, "source": { "source": "FRS empirical fit", @@ -174,21 +219,22 @@ "status": "fitted_offline" }, "fitting_receipt": { - "source": "tools/fit_uk_childcare_takeup.py (microcosm#834 L5, licensed lane)", - "tool_version": 3, - "receipt_sha256": "c56ca568ef446f07f773ea03253bc2f9d878aeea6bc781ee38ed268d1fedde96", + "source": "tools/fit_uk_childcare_takeup.py (microcosm#1095 U2c, licensed lane)", + "tool_version": 4, + "receipt_sha256": "32b06ee06f235089847b5c469e7c49ad9ee39c51e46aa5908522fec61c130d96", "seed": 0, - "engine_version": "2.94.0", + "engine_version": "2.132.6", "model_period": 2024, "vintage_overrides": { "dfe.funded_childcare.working_parent_children_2_to_4": 2025 }, - "input_h5_sha256": "c2804c18c0fc6a1c00e2ccc68f5eda5cd8b739906970dcb95ca82471d9d436b8", - "ledger_facts_sha256": "6ae49d7d7ab297df25a0b9bfe2d6776827c672d284fbb360957fe8337089549f", - "stochastic_contract_sha256_at_fit": "ba3daedade4c9e0115307eab2575e98c6921fb09357664ae3ea15644a86939e0", + "input_h5_sha256": "c883a0506a7e626804e33a43e3e7b4dbf7ea7e55a67e5624edbc445187a7ab07", + "input_spine": "the bump arm of the fifth #1095 ports PR (policyengine-uk 2.132.6, spine before the extended-hours anchor moved)", + "ledger_facts_sha256": "3033e437ed61d7d5e970f4fef34a2a5523fe068770a7798e5768af656f2a48a4", + "stochastic_contract_sha256_at_fit": "3d9fd8cd58de359ecb07b9af478f4bf8b30045dc1ad108d1952f8f9bedb44d96", "optimizer": { "eps": 1e-06, - "loss": 0.07969210950295769, + "loss": 0.030876275034388008, "maxiter": 50, "method": "L-BFGS-B", "success": true @@ -196,31 +242,31 @@ "achieved": { "dfe.funded_childcare.early_learning_2_year_olds": { "target": 115852.0, - "value": 98692.247, - "ratio": 0.8519 + "value": 115851.869, + "ratio": 1.0 }, "dfe.funded_childcare.universal_only_children": { "target": 416537.0, - "value": 416536.695, + "value": 416536.609, "ratio": 1.0 }, "dfe.funded_childcare.working_parent_children_2_to_4": { "target": 621482.0, - "value": 612914.443, - "ratio": 0.9862 + "value": 621481.151, + "ratio": 1.0 }, "hmrc.tfc.children_with_used_accounts": { "target": 1085020.0, - "value": 1235197.414, - "ratio": 1.1384 + "value": 1202038.13, + "ratio": 1.1078 }, "hmrc.tfc.government_top_up": { "target": 632200000.0, - "value": 508305122.499, - "ratio": 0.804 + "value": 544497415.126, + "ratio": 0.8613 } }, - "status": "fitted on the L2 column twin at the 2024 base year (A6); hours distribution frozen; the same fit drove tax_free_childcare and targeted_childcare to the 1.0 bound, which are held at their incumbent values pending adjudication (plan A22/A23)" + "status": "fitted at the 2024 base year with the expectation basis of tool version 4: targeted and universal hours allocated from working-parent hours (pe-uk#2177) and the DfE extended-hours anchor (#1126) in the simulation. No rate reached its bound. The two HMRC Tax-Free Childcare rows cannot both be met (spend below, children above): a residual of the frame's spend per child with a used account, not of the rate." } }, { @@ -229,7 +275,7 @@ "entity": "benunit", "values": { "2015-01-01": 0.563, - "2024-04-06": 0.4539 + "2024-04-06": 0.4353 }, "source": { "source": "FRS empirical fit", @@ -238,21 +284,22 @@ "status": "fitted_offline" }, "fitting_receipt": { - "source": "tools/fit_uk_childcare_takeup.py (microcosm#834 L5, licensed lane)", - "tool_version": 3, - "receipt_sha256": "c56ca568ef446f07f773ea03253bc2f9d878aeea6bc781ee38ed268d1fedde96", + "source": "tools/fit_uk_childcare_takeup.py (microcosm#1095 U2c, licensed lane)", + "tool_version": 4, + "receipt_sha256": "32b06ee06f235089847b5c469e7c49ad9ee39c51e46aa5908522fec61c130d96", "seed": 0, - "engine_version": "2.94.0", + "engine_version": "2.132.6", "model_period": 2024, "vintage_overrides": { "dfe.funded_childcare.working_parent_children_2_to_4": 2025 }, - "input_h5_sha256": "c2804c18c0fc6a1c00e2ccc68f5eda5cd8b739906970dcb95ca82471d9d436b8", - "ledger_facts_sha256": "6ae49d7d7ab297df25a0b9bfe2d6776827c672d284fbb360957fe8337089549f", - "stochastic_contract_sha256_at_fit": "ba3daedade4c9e0115307eab2575e98c6921fb09357664ae3ea15644a86939e0", + "input_h5_sha256": "c883a0506a7e626804e33a43e3e7b4dbf7ea7e55a67e5624edbc445187a7ab07", + "input_spine": "the bump arm of the fifth #1095 ports PR (policyengine-uk 2.132.6, spine before the extended-hours anchor moved)", + "ledger_facts_sha256": "3033e437ed61d7d5e970f4fef34a2a5523fe068770a7798e5768af656f2a48a4", + "stochastic_contract_sha256_at_fit": "3d9fd8cd58de359ecb07b9af478f4bf8b30045dc1ad108d1952f8f9bedb44d96", "optimizer": { "eps": 1e-06, - "loss": 0.07969210950295769, + "loss": 0.030876275034388008, "maxiter": 50, "method": "L-BFGS-B", "success": true @@ -260,31 +307,31 @@ "achieved": { "dfe.funded_childcare.early_learning_2_year_olds": { "target": 115852.0, - "value": 98692.247, - "ratio": 0.8519 + "value": 115851.869, + "ratio": 1.0 }, "dfe.funded_childcare.universal_only_children": { "target": 416537.0, - "value": 416536.695, + "value": 416536.609, "ratio": 1.0 }, "dfe.funded_childcare.working_parent_children_2_to_4": { "target": 621482.0, - "value": 612914.443, - "ratio": 0.9862 + "value": 621481.151, + "ratio": 1.0 }, "hmrc.tfc.children_with_used_accounts": { "target": 1085020.0, - "value": 1235197.414, - "ratio": 1.1384 + "value": 1202038.13, + "ratio": 1.1078 }, "hmrc.tfc.government_top_up": { "target": 632200000.0, - "value": 508305122.499, - "ratio": 0.804 + "value": 544497415.126, + "ratio": 0.8613 } }, - "status": "fitted on the L2 column twin at the 2024 base year (A6); hours distribution frozen; the same fit drove tax_free_childcare and targeted_childcare to the 1.0 bound, which are held at their incumbent values pending adjudication (plan A22/A23)" + "status": "fitted at the 2024 base year with the expectation basis of tool version 4: targeted and universal hours allocated from working-parent hours (pe-uk#2177) and the DfE extended-hours anchor (#1126) in the simulation. No rate reached its bound. The two HMRC Tax-Free Childcare rows cannot both be met (spend below, children above): a residual of the frame's spend per child with a used account, not of the rate." } }, { @@ -292,7 +339,8 @@ "output": "would_claim_targeted_childcare", "entity": "benunit", "values": { - "2015-01-01": 0.597 + "2015-01-01": 0.597, + "2024-04-06": 0.7769 }, "source": { "source": "FRS empirical fit", @@ -301,9 +349,54 @@ "status": "fitted_offline" }, "fitting_receipt": { - "source": "incumbent UK pipeline @ ebf733c", - "seed": 42, - "status": "frozen parity value" + "source": "tools/fit_uk_childcare_takeup.py (microcosm#1095 U2c, licensed lane)", + "tool_version": 4, + "receipt_sha256": "32b06ee06f235089847b5c469e7c49ad9ee39c51e46aa5908522fec61c130d96", + "seed": 0, + "engine_version": "2.132.6", + "model_period": 2024, + "vintage_overrides": { + "dfe.funded_childcare.working_parent_children_2_to_4": 2025 + }, + "input_h5_sha256": "c883a0506a7e626804e33a43e3e7b4dbf7ea7e55a67e5624edbc445187a7ab07", + "input_spine": "the bump arm of the fifth #1095 ports PR (policyengine-uk 2.132.6, spine before the extended-hours anchor moved)", + "ledger_facts_sha256": "3033e437ed61d7d5e970f4fef34a2a5523fe068770a7798e5768af656f2a48a4", + "stochastic_contract_sha256_at_fit": "3d9fd8cd58de359ecb07b9af478f4bf8b30045dc1ad108d1952f8f9bedb44d96", + "optimizer": { + "eps": 1e-06, + "loss": 0.030876275034388008, + "maxiter": 50, + "method": "L-BFGS-B", + "success": true + }, + "achieved": { + "dfe.funded_childcare.early_learning_2_year_olds": { + "target": 115852.0, + "value": 115851.869, + "ratio": 1.0 + }, + "dfe.funded_childcare.universal_only_children": { + "target": 416537.0, + "value": 416536.609, + "ratio": 1.0 + }, + "dfe.funded_childcare.working_parent_children_2_to_4": { + "target": 621482.0, + "value": 621481.151, + "ratio": 1.0 + }, + "hmrc.tfc.children_with_used_accounts": { + "target": 1085020.0, + "value": 1202038.13, + "ratio": 1.1078 + }, + "hmrc.tfc.government_top_up": { + "target": 632200000.0, + "value": 544497415.126, + "ratio": 0.8613 + } + }, + "status": "fitted at the 2024 base year with the expectation basis of tool version 4: targeted and universal hours allocated from working-parent hours (pe-uk#2177) and the DfE extended-hours anchor (#1126) in the simulation. No rate reached its bound. The two HMRC Tax-Free Childcare rows cannot both be met (spend below, children above): a residual of the frame's spend per child with a used account, not of the rate." } }, { @@ -535,19 +628,19 @@ "output": "maximum_extended_childcare_hours_usage", "entity": "benunit", "distribution": "normal", - "mean": 15.019, + "mean": 31.141, "sd": 4.972, "lower": 0, "upper": 30, "source": { - "source": "FRS empirical fit, frozen by microcosm#834 adjudication", - "agency": "PolicyEngine", - "citation": "Incumbent offline childcare usage fit, clipped to [0, 30] hours. Frozen because it is underidentified without an independent extended-spending observation; replacing it needs evidence on hours actually used.", - "status": "frozen_by_adjudication", - "freeze": { - "decision": "Hold 15.019 / 4.972 / 0 / 30 fixed while fitting only four take-up rates", - "followup": "microcosm#834" - } + "source": "DfE 2026 to 2027 early years national funding formulae technical note (3- and 4-year-old additional-hours PTEs, January 2025 censuses) and DfE Education provision: children under 5 years of age, January 2025 (3- and 4-year-olds registered for the working parent entitlement)", + "agency": "DfE", + "status": "sourced_administrative_derived", + "url": "https://www.gov.uk/government/publications/early-years-funding-2026-to-2027/2026-to-2027-early-years-national-funding-formulae-technical-note", + "registrations_url": "https://explore-education-statistics.service.gov.uk/find-statistics/education-provision-children-under-5/2025", + "derivation": "15 + 15 * 341,988.09 additional-hours PTEs / 379,000 registered 3-4-year-olds = 28.535 mean weekly funded hours for a working-parent 3- or 4-year-old (one PTE is 15 hours a week for 38 weeks); mean solves E[clip(N(mean, 4.972), 0, 30)] = 28.535 with the sd and bounds held", + "citation": "Replaces the frozen 15.019 / 4.972 incumbent fit (microcosm#834), which #834 held pending evidence on hours actually used. The DfE additional-hours PTEs are that evidence: observed January 2025 census volumes, not a forecast. The variable caps a family's total weekly extended hours, so its mean is the total (universal plus additional) for a 3- or 4-year-old. Only the 3-4 cohort anchors the mean; DfE's under-2 and 2-year-old PTEs are partly forecast. The sd is not identified by any source and keeps the incumbent value. Re-mapping the 2024-25 national release's own draws through this distribution gives 2026-27 free-entitlement spending of 9.30bn against DfE's illustrative 9.9bn (5.81bn before); see microcosm#1126.", + "followup": "microcosm#1126" } } ] diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index 1b9df4431c..e03284d59b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,11 +1,11 @@ { "source_fact_feed": "chronicle-uk-735820c-consumer-facts", "target_period": 2025, - "candidate_count": 1312, - "contract_target_count": 357, - "active_reference_count": 1292, + "candidate_count": 1311, + "contract_target_count": 356, + "active_reference_count": 1291, "status_counts": { - "active": 1292, + "active": 1291, "no_fact_at_or_before_period": 5, "signed_excluded": 15 }, @@ -238,10 +238,6 @@ "geography_level": "country", "geography_id": "K03000001" }, - "dwp.uc.households_children_5_or_more": { - "geography_level": "country", - "geography_id": "K03000001" - }, "dwp.uc.households_single_no_children": { "geography_level": "country", "geography_id": "K03000001" @@ -6790,6 +6786,26 @@ "from": "2025-09", "to": "2025" }, + { + "name": "slc.borrowers.plan_2_above_threshold", + "from": "2024", + "to": "2025" + }, + { + "name": "slc.borrowers.plan_2_liable", + "from": "2024", + "to": "2025" + }, + { + "name": "slc.borrowers.plan_5_above_threshold", + "from": "2024", + "to": "2025" + }, + { + "name": "slc.borrowers.plan_5_liable", + "from": "2024", + "to": "2025" + }, { "name": "slc.support.maintenance_loan_recipients", "from": "2024", @@ -13705,22 +13721,6 @@ } ] }, - "dwp.uc.households_children_5_or_more": { - "status": "active", - "candidates": [ - { - "name": "dwp.uc.households_children_5_or_more", - "status": "active", - "matched_fact_count_overall": 12, - "matched_fact_count_at_or_before_period": 12, - "matched_fact_count_in_source_window": 12, - "resolved_period": 2025, - "resolved_value": 76320.08333333333, - "resolved_fact_period": "2025-12", - "resolved_fact_key": "ledger_aggregate_fact_v2_33d99593d166153ca2d0adec" - } - ] - }, "dwp.uc.households_single_no_children": { "status": "active", "candidates": [ @@ -18869,12 +18869,12 @@ { "name": "slc.borrowers.plan_2_above_threshold", "status": "active", - "matched_fact_count_overall": 6, - "matched_fact_count_at_or_before_period": 2, + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 4460000.0, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_02288c19caeae900fdaf1e0d" + "resolved_value": 3985000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_822c939c41011339709850be" } ] }, @@ -18884,12 +18884,12 @@ { "name": "slc.borrowers.plan_2_liable", "status": "active", - "matched_fact_count_overall": 6, - "matched_fact_count_at_or_before_period": 2, + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 9710000.0, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_37387933e73bcc8c1e04cf78" + "resolved_value": 8940000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_f1bc8691109e66f0dbaac8d1" } ] }, @@ -18899,12 +18899,12 @@ { "name": "slc.borrowers.plan_5_above_threshold", "status": "active", - "matched_fact_count_overall": 6, - "matched_fact_count_at_or_before_period": 2, + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 35000.0, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_dd16b085f81ef7708a00511e" + "resolved_value": 0.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_ce7713585cb33c638ad22a08" } ] }, @@ -18914,12 +18914,12 @@ { "name": "slc.borrowers.plan_5_liable", "status": "active", - "matched_fact_count_overall": 6, - "matched_fact_count_at_or_before_period": 2, + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 230000.0, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_73ff136d820950ea404ddb6b" + "resolved_value": 10000.0, + "resolved_fact_period": "2024", + "resolved_fact_key": "ledger_aggregate_fact_v2_875af7565e64b25a3c6a7219" } ] }, diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 56581bb4b3..3dac25899c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -346,7 +346,6 @@ "dwp.uc.households_children_2": "dwp.universal_credit", "dwp.uc.households_children_3": "dwp.universal_credit", "dwp.uc.households_children_4": "dwp.universal_credit", - "dwp.uc.households_children_5_or_more": "dwp.universal_credit", "dwp.uc.households_couple_no_children": "dwp.universal_credit", "dwp.uc.households_couple_with_children": "dwp.universal_credit", "dwp.uc.households_housing_element": "dwp.universal_credit", @@ -693,7 +692,6 @@ "dwp.uc.households_children_2": "Households children 2", "dwp.uc.households_children_3": "Households children 3", "dwp.uc.households_children_4": "Households children 4", - "dwp.uc.households_children_5_or_more": "Households children 5 or more", "dwp.uc.households_couple_no_children": "Households couple no children", "dwp.uc.households_couple_with_children": "Households couple with children", "dwp.uc.households_housing_element": "Households with housing entitlement", @@ -5819,53 +5817,6 @@ "period_match_policy": "source_window", "value_operation": "monthly_window_average" }, - { - "name": "dwp.uc.households_children_5_or_more", - "ledger_selector": { - "source_name": "dwp", - "source_concept": "dwp.uc_households", - "groupby_dimension": "dwp.uc_number_of_children", - "dimensions": [ - "number_of_children", - "payment_indicator", - "child_entitlement" - ], - "dimension_values": { - "number_of_children": "5 or more", - "payment_indicator": "Yes", - "child_entitlement": "all" - }, - "source_measure_id": "total_benefit_units", - "period_type": "month", - "period_value": [ - "2025-01", - "2025-02", - "2025-03", - "2025-04", - "2025-05", - "2025-06", - "2025-07", - "2025-08", - "2025-09", - "2025-10", - "2025-11", - "2025-12" - ], - "geography_level": "country", - "geography_id": "K03000001" - }, - "entity": "benunit", - "measure": "dwp/uc/claimants_with_5+_children", - "family": "dwp_universal_credit", - "period": 2025, - "metadata": { - "contract_target_id": "dwp.uc.households_children_5_or_more", - "measure_kind": "prepared_column", - "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" - }, - "period_match_policy": "source_window", - "value_operation": "monthly_window_average" - }, { "name": "dwp.uc.households_single_no_children", "ledger_selector": { @@ -23363,6 +23314,8 @@ "repayment_plan": "plan_2", "borrower_status": "above_repayment_threshold" }, + "period_type": "academic_year", + "period_value": 2024, "geography_level": "country", "geography_id": "E92000001" }, @@ -23374,7 +23327,9 @@ "contract_target_id": "slc.borrowers.plan_2_above_threshold", "measure_kind": "prepared_column" }, - "assertion_policy": "allow_source_projection" + "assertion_policy": "allow_source_projection", + "uprating_from_period": "2024", + "uprating_to_period": 2025 }, { "name": "slc.borrowers.plan_2_liable", @@ -23386,6 +23341,8 @@ "repayment_plan": "plan_2", "borrower_status": "liable_to_repay" }, + "period_type": "academic_year", + "period_value": 2024, "geography_level": "country", "geography_id": "E92000001" }, @@ -23397,7 +23354,9 @@ "contract_target_id": "slc.borrowers.plan_2_liable", "measure_kind": "prepared_column" }, - "assertion_policy": "allow_source_projection" + "assertion_policy": "allow_source_projection", + "uprating_from_period": "2024", + "uprating_to_period": 2025 }, { "name": "slc.borrowers.plan_5_above_threshold", @@ -23409,6 +23368,8 @@ "repayment_plan": "plan_5", "borrower_status": "above_repayment_threshold" }, + "period_type": "academic_year", + "period_value": 2024, "geography_level": "country", "geography_id": "E92000001" }, @@ -23420,7 +23381,9 @@ "contract_target_id": "slc.borrowers.plan_5_above_threshold", "measure_kind": "prepared_column" }, - "assertion_policy": "allow_source_projection" + "assertion_policy": "allow_source_projection", + "uprating_from_period": "2024", + "uprating_to_period": 2025 }, { "name": "slc.borrowers.plan_5_liable", @@ -23432,6 +23395,8 @@ "repayment_plan": "plan_5", "borrower_status": "liable_to_repay" }, + "period_type": "academic_year", + "period_value": 2024, "geography_level": "country", "geography_id": "E92000001" }, @@ -23443,7 +23408,9 @@ "contract_target_id": "slc.borrowers.plan_5_liable", "measure_kind": "prepared_column" }, - "assertion_policy": "allow_source_projection" + "assertion_policy": "allow_source_projection", + "uprating_from_period": "2024", + "uprating_to_period": 2025 }, { "name": "slc.support.maintenance_loan_recipients", diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json index cc60daca90..b12328a3b4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -25,8 +25,8 @@ "pinned_ref": "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3", "pinned_version": "1.56.16", "registry_rows": 652, - "mapped_rows": 620, - "excluded_rows": 32, + "mapped_rows": 619, + "excluded_rows": 33, "mapped": { "dwp/benefit_capped_households": "dwp.benefit_cap.capped_households", "dwp/esa_claimants": "dwp.esa_claimants", @@ -42,7 +42,6 @@ "dwp/uc/claimants_with_2_children": "dwp.uc.households_children_2", "dwp/uc/claimants_with_3_children": "dwp.uc.households_children_3", "dwp/uc/claimants_with_4_children": "dwp.uc.households_children_4", - "dwp/uc/claimants_with_5+_children": "dwp.uc.households_children_5_or_more", "dwp/uc/households": "dwp.uc.households", "dwp/uc/two_child_limit/3_children_households": "dwp.uc.two_child_limit.households_3_children", "dwp/uc/two_child_limit/3_children_households_total_children": "dwp.uc.two_child_limit.children_in_3_children_households", @@ -681,7 +680,8 @@ "dwp/pip_dl_standard_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted.", "dwp/pip_dl_enhanced_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted.", "ons/female_15_19": "Split, not dropped (María's ruling of 2026-10-13 (microcosm#1095, uk-data#563)): a fit to the five-year band leaves the ages inside it free to drift, so the contract binds ages 15, 16-17 and 18-19 instead (ons.population.female_15, female_16_17 and female_18_19). The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single contract row has this row's definition.", - "ons/male_15_19": "Split, not dropped (María's ruling of 2026-10-13 (microcosm#1095, uk-data#563)): a fit to the five-year band leaves the ages inside it free to drift, so the contract binds ages 15, 16-17 and 18-19 instead (ons.population.male_15, male_16_17 and male_18_19). The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single contract row has this row's definition." + "ons/male_15_19": "Split, not dropped (María's ruling of 2026-10-13 (microcosm#1095, uk-data#563)): a fit to the five-year band leaves the ages inside it free to drift, so the contract binds ages 15, 16-17 and 18-19 instead (ons.population.male_15, male_16_17 and male_18_19). The three rows partition this row's population and read the same ONS mid-year estimate by single year of age, so they sum to it. No single contract row has this row's definition.", + "dwp/uc/claimants_with_5+_children": "Diagnostic only (María's ruling of 2026-10-16, microcosm#1095): DWP's count of Universal Credit households with five or more children includes families outside the two-child limit, whose third and later children were all born before 6 April 2017 or who meet an exception, and the bound two-child-limit rows leave them out. Fewer than 10 FRS source households carry that difference, so the row cannot be fitted beside the limit rows. The April 2025 figure is retained only as diagnostic provenance on dwp.uc.two_child_limit.households_5_children; no source value is adjusted." }, "unmapped_declarations": { "hmrc.salary_sacrifice.it_relief_basic_rate": "New declaration backed by chronicle#139 salary-sacrifice facts; the incumbent source CSV is still 410-Gone at 12a1e028 and its rows are silently suppressed by the bare-except drop in build_loss_matrix.py:376-394.", @@ -945,7 +945,6 @@ "dwp.uc.households_children_2", "dwp.uc.households_children_3", "dwp.uc.households_children_4", - "dwp.uc.households_children_5_or_more", "dwp.uc.households_single_no_children", "dwp.uc.households_single_with_children", "dwp.uc.households_couple_no_children", @@ -2576,7 +2575,7 @@ "bindings": { "policyengine": { "metric_name": "obr/esa", - "value_expression": "esa_income + esa_contrib", + "value_expression": "esa_income + esa_contrib + incapacity_benefit + sda", "map_to": "household", "household_conditions": [ { @@ -2600,7 +2599,7 @@ "map_to": "household" } ], - "notes": "EFO incapacity-benefits line; uk-data sums benunit-level esa_income and person-level esa_contrib to household (compute/income.py:98-102). Bound at the calendar-2025 window of the OBR fiscal years (three twelfths of FY2024-25, nine twelfths of FY2025-26; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year (PolicyEngine/chronicle#280 lane). Scoped to Great Britain (microcosm#1095, uk-data#490): OBR table 4.9 reports this line inside 'DWP social security', whose figures cover Great Britain and residents overseas (DWP benefit expenditure and caseload tables, Spring 2026, Notes 3), and lists Northern Ireland social security on rows of its own, so the model column sums Great Britain households only. The Chronicle fact still carries the UK code K02000001." + "notes": "EFO incapacity-benefits line. OBR table 4.9 footnote 2: the line includes incapacity benefit, employment and support allowance, severe disablement allowance and the incapacity part of income support, and excludes the health element of universal credit. The model column sums benunit-level esa_income and person-level esa_contrib, incapacity_benefit and sda to household; uk-data sums the first two (compute/income.py:115-119), and adding the other two follows uk-data#582 (microcosm#1095). Incapacity Benefit has been closed to new claims since ESA began in October 2008, so its term carries only what the FRS still reports. Income Support has no incapacity part in the engine, which pays it as one benefit; that part stays a residual of the row, with no declared difference (María's ruling of 2026-10-16). Severe Disablement Allowance is a small part of the line: DWP reports GBP 53.8m for England and Wales in 2024-25 and GBP 46.8m in 2025-26, against OBR's GBP 12.41bn and GBP 7.21bn. The Scottish Government pays SDA in Scotland (GBP 5.0m in 2024-25, GBP 4.4m in 2025-26 in the Scottish Budget), outside DWP's line; at well under 0.1 percent of the row it stays inside the Great Britain scope below rather than a per-term mask. Bound at the calendar-2025 window of the OBR fiscal years (three twelfths of FY2024-25, nine twelfths of FY2025-26; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year (PolicyEngine/chronicle#280 lane). Scoped to Great Britain (microcosm#1095, uk-data#490): OBR table 4.9 reports this line inside 'DWP social security', whose figures cover Great Britain and residents overseas (DWP benefit expenditure and caseload tables, Spring 2026, Notes 3), and lists Northern Ireland social security on rows of its own, so the model column sums Great Britain households only. The Chronicle fact still carries the UK code K02000001." }, "axiom": { "metric_name": "obr/esa", @@ -3575,7 +3574,8 @@ "value_variable": "employment_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20).", "filters": [ { @@ -3629,7 +3629,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/employment_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "employment_income", @@ -3684,11 +3685,12 @@ "bindings": { "policyengine": { "metric_name": "hmrc/self_employment_income_income_band", - "value_variable": "self_employment_income", + "value_variable": "self_employment_income_after_loss_relief", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", - "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20).", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", + "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20). HMRC counts the profit after losses brought forward, which the spine keeps apart in loss_relief, so the value and the band read it after that relief (microcosm#1095).", "filters": [ { "variable": "income_tax", @@ -3741,10 +3743,11 @@ "bindings": { "policyengine": { "metric_name": "hmrc/self_employment_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { - "variable": "self_employment_income", + "variable": "self_employment_income_after_loss_relief", "operator": "!=", "value": 0 }, @@ -3799,7 +3802,8 @@ "value_variable": "dividend_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20).", "filters": [ { @@ -3853,7 +3857,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/dividend_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "dividend_income", @@ -3911,7 +3916,8 @@ "value_variable": "property_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20). SPI Table 3.7 net income from property is landlords' profit after allowable expenses and before residential finance costs, the concept property_income holds, so the facts bind unscaled. The incumbent's x1.9 adjustment set HMRC Property Rental Income Statistics receipts, which are before expenses, against this net amount and is retired (microcosm#1106, ruled 2026-10-05); the receipts are constructed on property_rental_income by the property_components stage, and PRIS stays a diagnostic (ruled 2026-10-08). Uprated to 2025 by total_income_growth_by_total_income_band, the index the paired count rows' taxpayer_count_growth_by_total_income_band belongs with (microcosm#1063): the flat obr.per_capita.gdp, which policyengine-uk gives property_income itself, would have implied per-band mean profit moves of about -13% (GBP 100,000 to 150,000) to +11% (GBP 15,000 to 20,000) between 2023 and 2025 against those counts, where the band index keeps the pairing within about 2.5%, the GBP 1,000,000 and over band at +2.51% (ruled 2026-10-11 on the microcosm#1145 review). The dividend amount rows keep the flat index.", "filters": [ { @@ -3965,7 +3971,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/property_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "property_income", @@ -4023,7 +4030,8 @@ "value_variable": "private_pension_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20).", "filters": [ { @@ -4077,7 +4085,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/private_pension_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "private_pension_income", @@ -4135,7 +4144,8 @@ "value_variable": "state_pension", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Banded by person-level total income; band edges come from the fact dimension (compute/income.py:8-20).", "filters": [ { @@ -4189,7 +4199,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/state_pension_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "state_pension", @@ -5299,125 +5310,6 @@ "category_id": "dwp.universal_credit", "label": "Households children 4" }, - { - "target_id": "dwp.uc.households_children_5_or_more", - "family": "dwp_universal_credit", - "geography_levels": [ - "country" - ], - "measurement": { - "entity": "benunit", - "concept": "uk.benefit_unit.count", - "filters": [ - { - "concept": "uk.benefits.universal_credit.amount", - "operator": ">", - "value": 0 - }, - { - "concept": "uk.benefit_unit.children", - "operator": ">=", - "value": 5 - } - ], - "source_months": [ - "2025-01", - "2025-02", - "2025-03", - "2025-04", - "2025-05", - "2025-06", - "2025-07", - "2025-08", - "2025-09", - "2025-10", - "2025-11", - "2025-12" - ] - }, - "bindings": { - "policyengine": { - "metric_name": "dwp/uc/claimants_with_5+_children", - "value_variable": "benunit_count", - "from_entity": "benunit", - "filters": [ - { - "variable": "universal_credit", - "operator": ">", - "value": 0 - }, - { - "variable": "uc_calibration_child_count", - "operator": ">=", - "value": 5 - } - ], - "notes": "Mean monthly paid GB benefit units over the exact twelve months of calendar 2025. Use the publisher Child Entitlement Total within each child-count/payment category, not the sum of perturbed Yes/No detail cells; no child-entitlement filter is added to the model. Benefit-unit count among positive UC awards. Child counts use retained FRS child members under 20 and UC child/qualifying-young-person status, excluding claimant/parent roles. DWP reported children can exceed those eligible for a child element. Benefit-unit grain retains the microcosm#809 correction for multibenunit households. Simulated entitlement is unchanged. Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/252", - "household_conditions": [ - { - "variable": "region", - "entity": "household", - "reduce": "any", - "operator": "in", - "value": [ - "NORTH_EAST", - "NORTH_WEST", - "YORKSHIRE", - "EAST_MIDLANDS", - "WEST_MIDLANDS", - "EAST_OF_ENGLAND", - "LONDON", - "SOUTH_EAST", - "SOUTH_WEST", - "WALES", - "SCOTLAND" - ], - "map_to": "benunit" - } - ] - }, - "axiom": { - "metric_name": "dwp/uc/claimants_with_5+_children", - "status": "pending", - "value_rule": "uk.benefit_unit.count" - } - }, - "ledger_selector": { - "source_name": "dwp", - "source_concept": "dwp.uc_households", - "groupby_dimension": "dwp.uc_number_of_children", - "dimensions": [ - "number_of_children", - "payment_indicator", - "child_entitlement" - ], - "dimension_values": { - "number_of_children": "5 or more", - "payment_indicator": "Yes", - "child_entitlement": "all" - }, - "source_measure_id": "total_benefit_units", - "period_type": "month", - "period_value": [ - "2025-01", - "2025-02", - "2025-03", - "2025-04", - "2025-05", - "2025-06", - "2025-07", - "2025-08", - "2025-09", - "2025-10", - "2025-11", - "2025-12" - ] - }, - "value_operation": "monthly_window_average", - "period_match_policy": "source_window", - "category_id": "dwp.universal_credit", - "label": "Households children 5 or more" - }, { "target_id": "dwp.uc.households_single_no_children", "family": "dwp_universal_credit", @@ -14474,7 +14366,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "England borrowers on this plan earning above the repayment threshold (proxied by positive repayments, matching what the FRS captures via PAYE; compute/other.py:77-96)." + "notes": "England borrowers on this plan earning above the repayment threshold (proxied by positive repayments, matching what the FRS captures via PAYE; compute/other.py:77-96). Bound at SLC academic year 2024/25, whose reference date falls inside calendar 2025, the stock the student_loans stage tops up to; Chronicle stores the academic year under its opening year, 2024 (María's ruling of 2026-10-16, microcosm#868)." }, "axiom": { "metric_name": "slc/plan_2_borrowers_above_threshold", @@ -14490,7 +14382,9 @@ "row_group": "Higher education total", "repayment_plan": "plan_2", "borrower_status": "above_repayment_threshold" - } + }, + "period_type": "academic_year", + "period_value": 2024 }, "category_id": "slc.borrowers", "label": "Plan 2 above threshold" @@ -14522,7 +14416,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "England borrowers on this plan liable to repay, including below-threshold holders (compute/other.py:99-113)." + "notes": "England borrowers on this plan liable to repay, including below-threshold holders (compute/other.py:99-113). Bound at SLC academic year 2024/25, whose reference date falls inside calendar 2025, the stock the student_loans stage tops up to; Chronicle stores the academic year under its opening year, 2024 (María's ruling of 2026-10-16, microcosm#868)." }, "axiom": { "metric_name": "slc/plan_2_borrowers_liable", @@ -14538,7 +14432,9 @@ "row_group": "Higher education total", "repayment_plan": "plan_2", "borrower_status": "liable_to_repay" - } + }, + "period_type": "academic_year", + "period_value": 2024 }, "category_id": "slc.borrowers", "label": "Plan 2 liable" @@ -14575,7 +14471,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "England borrowers on this plan earning above the repayment threshold (proxied by positive repayments, matching what the FRS captures via PAYE; compute/other.py:77-96)." + "notes": "England borrowers on this plan earning above the repayment threshold (proxied by positive repayments, matching what the FRS captures via PAYE; compute/other.py:77-96). Bound at SLC academic year 2024/25, whose reference date falls inside calendar 2025, the stock the student_loans stage tops up to; Chronicle stores the academic year under its opening year, 2024 (María's ruling of 2026-10-16, microcosm#868)." }, "axiom": { "metric_name": "slc/plan_5_borrowers_above_threshold", @@ -14591,7 +14487,9 @@ "row_group": "Higher education total", "repayment_plan": "plan_5", "borrower_status": "above_repayment_threshold" - } + }, + "period_type": "academic_year", + "period_value": 2024 }, "category_id": "slc.borrowers", "label": "Plan 5 above threshold" @@ -14623,7 +14521,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "England borrowers on this plan liable to repay, including below-threshold holders (compute/other.py:99-113)." + "notes": "England borrowers on this plan liable to repay, including below-threshold holders (compute/other.py:99-113). Bound at SLC academic year 2024/25, whose reference date falls inside calendar 2025, the stock the student_loans stage tops up to; Chronicle stores the academic year under its opening year, 2024 (María's ruling of 2026-10-16, microcosm#868)." }, "axiom": { "metric_name": "slc/plan_5_borrowers_liable", @@ -14639,7 +14537,9 @@ "row_group": "Higher education total", "repayment_plan": "plan_5", "borrower_status": "liable_to_repay" - } + }, + "period_type": "academic_year", + "period_value": 2024 }, "category_id": "slc.borrowers", "label": "Plan 5 liable" @@ -19794,7 +19694,8 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Bound at the 2025 calibration period as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections (3/12 and 9/12; value_operation calendar_year_window), from the Income Tax liabilities statistics of 15 July 2026, HMRC's own projection of the 2023-24 SPI base with ONS population projections and OBR March 2026 assumptions. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and facts published by tax year use the months to the end of it. Banded by person-level total income on the publisher's eleven bands (30-50k, 50-100k and 200-500k are single bands here; 1m+ splits at 2m); the all-ranges row carries no band and stays out of the fan-out.", "filters": [ { @@ -19838,10 +19739,11 @@ "bindings": { "policyengine": { "metric_name": "hmrc/itl_total_income_income_band", - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Bound at the 2025 calibration period as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections (3/12 and 9/12; value_operation calendar_year_window), from the Income Tax liabilities statistics of 15 July 2026, HMRC's own projection of the 2023-24 SPI base with ONS population projections and OBR March 2026 assumptions. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and facts published by tax year use the months to the end of it. Banded by person-level total income on the publisher's eleven bands (30-50k, 50-100k and 200-500k are single bands here; 1m+ splits at 2m); the all-ranges row carries no band and stays out of the fan-out." }, "axiom": { @@ -19881,7 +19783,8 @@ "value_variable": "income_tax", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Bound at the 2025 calibration period as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections (3/12 and 9/12; value_operation calendar_year_window), from the Income Tax liabilities statistics of 15 July 2026, HMRC's own projection of the 2023-24 SPI base with ONS population projections and OBR March 2026 assumptions. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and facts published by tax year use the months to the end of it. Banded by person-level total income on the publisher's eleven bands (30-50k, 50-100k and 200-500k are single bands here; 1m+ splits at 2m); the all-ranges row carries no band and stays out of the fan-out." }, "axiom": { @@ -20056,7 +19959,8 @@ "value_variable": "savings_interest_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "notes": "Taxable interest from banks and building societies of taxpayers by total-income band (SPI Table 3.7; ISA interest excluded), the taxpayer-concept anchor microcosm#866 asked for in place of the ONS D.41 row. Banded by person-level total income; band edges come from the fact dimension.", "filters": [ { @@ -20110,7 +20014,8 @@ "bindings": { "policyengine": { "metric_name": "hmrc/savings_interest_income_count_income_band", - "groupby_variable": "total_income", + "groupby_variable": "total_income_after_loss_relief", + "band_filter_dimension": "total_income", "filters": [ { "variable": "savings_interest_income", @@ -20182,12 +20087,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 12570 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 15000 } @@ -20258,12 +20163,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 15000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 20000 } @@ -20334,12 +20239,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 20000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 30000 } @@ -20410,12 +20315,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 30000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 40000 } @@ -20486,12 +20391,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 40000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 50000 } @@ -20562,12 +20467,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 50000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 70000 } @@ -20638,12 +20543,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 70000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 100000 } @@ -20714,12 +20619,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 100000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 150000 } @@ -20790,12 +20695,12 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 150000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 200000 } @@ -20861,7 +20766,7 @@ "value": 0 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 200000 } @@ -20922,17 +20827,17 @@ "metric_name": "hmrc/spi_region/total_income_12570_15000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 12570 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 15000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 12,570 to 15,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -20988,17 +20893,17 @@ "metric_name": "hmrc/spi_region/total_income_15000_20000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 15000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 20000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 15,000 to 20,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21054,17 +20959,17 @@ "metric_name": "hmrc/spi_region/total_income_20000_30000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 20000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 30000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 20,000 to 30,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21120,17 +21025,17 @@ "metric_name": "hmrc/spi_region/total_income_30000_40000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 30000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 40000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 30,000 to 40,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21186,17 +21091,17 @@ "metric_name": "hmrc/spi_region/total_income_40000_50000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 40000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 50000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 40,000 to 50,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21252,17 +21157,17 @@ "metric_name": "hmrc/spi_region/total_income_50000_70000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 50000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 70000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 50,000 to 70,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21318,17 +21223,17 @@ "metric_name": "hmrc/spi_region/total_income_70000_100000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 70000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 100000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 70,000 to 100,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21384,17 +21289,17 @@ "metric_name": "hmrc/spi_region/total_income_100000_150000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 100000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 150000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 100,000 to 150,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21450,17 +21355,17 @@ "metric_name": "hmrc/spi_region/total_income_150000_200000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 150000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 200000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 150,000 to 200,000, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21511,12 +21416,12 @@ "metric_name": "hmrc/spi_region/total_income_200000_plus", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 200000 } ], - "value_variable": "total_income", + "value_variable": "total_income_after_loss_relief", "from_entity": "person", "map_to": "household", "notes": "SPI 2023-24 Table 3.11 total income of taxpayers with total income 200,000 and over, one row per region-tier area (the nine English regions at Chronicle's region level, Wales, Scotland and Northern Ireland at country level; microcosm#905), scoped on the spine by the household-region predicate the generator stamps and projected to the person entity, sliced on the engine's total_income by the row's own band edges (the publisher's regional bands stop at 200,000 and over). Uprated from the 2023-24 fact to the 2025 calibration period by HMRC's projected growth in total income in the Table 2.5 band(s) the regional band spans, as the calendar-2025 window of HMRC's 2024-25 and 2025-26 projections over the 2023-24 outturn (Income Tax liabilities statistics, July 2026), from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the regions bind uprated, not as diagnostics." @@ -21572,12 +21477,12 @@ "metric_name": "hmrc/spi_region/income_tax_12570_15000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 12570 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 15000 } @@ -21638,12 +21543,12 @@ "metric_name": "hmrc/spi_region/income_tax_15000_20000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 15000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 20000 } @@ -21704,12 +21609,12 @@ "metric_name": "hmrc/spi_region/income_tax_20000_30000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 20000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 30000 } @@ -21770,12 +21675,12 @@ "metric_name": "hmrc/spi_region/income_tax_30000_40000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 30000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 40000 } @@ -21836,12 +21741,12 @@ "metric_name": "hmrc/spi_region/income_tax_40000_50000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 40000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 50000 } @@ -21902,12 +21807,12 @@ "metric_name": "hmrc/spi_region/income_tax_50000_70000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 50000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 70000 } @@ -21968,12 +21873,12 @@ "metric_name": "hmrc/spi_region/income_tax_70000_100000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 70000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 100000 } @@ -22034,12 +21939,12 @@ "metric_name": "hmrc/spi_region/income_tax_100000_150000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 100000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 150000 } @@ -22100,12 +22005,12 @@ "metric_name": "hmrc/spi_region/income_tax_150000_200000", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 150000 }, { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": "<", "value": 200000 } @@ -22161,7 +22066,7 @@ "metric_name": "hmrc/spi_region/income_tax_200000_plus", "filters": [ { - "variable": "total_income", + "variable": "total_income_after_loss_relief", "operator": ">=", "value": 200000 } @@ -28309,7 +28214,7 @@ "bindings": { "policyengine": { "metric_name": "hmrc/self_employment_income/amount", - "value_variable": "self_employment_income", + "value_variable": "self_employment_income_after_loss_relief", "from_entity": "person", "map_to": "household", "filters": [ @@ -28374,7 +28279,7 @@ "map_to": "household", "filters": [ { - "variable": "self_employment_income", + "variable": "self_employment_income_after_loss_relief", "operator": "!=", "value": 0 }, @@ -32129,6 +32034,46 @@ "ledger_fact_key": "ledger.aggregate_fact.v2:8d850c8ffb6eae8f4c71e611" }, "notes": "DWP FOI2025/24990: PIP daily living enhanced-rate claimants in England and Wales at the end of January 2025 (1.608m), retained as diagnostic provenance beside the bound OBR line obr.pip (María's ruling of 2026-10-10, microcosm#1095). DWP has reported PIP for England and Wales only since executive competence moved to the Scottish Government on 1 April 2020, so Chronicle stamps the fact K04000001 (uk-data#490); under the Great Britain default pin the row matched no fact. Fitted with its sibling it calibrates to within 0.1%, but the two rows push the heaviest support family's weight from 1,061 to 1,241 times the positive median, over the uk_weight_ratio fence of 1,151 (experiments/1095-uk-data-ports-3-receipts.md). Never insert it into the fitting registry." + }, + "dwp.uc.households_children_5_or_more": { + "attach_to_target": "dwp.uc.two_child_limit.households_5_children", + "metadata_prefix": "dwp_uc_children_5_or_more_diagnostic", + "value_unit": "count", + "required_period_type": "month", + "required_assertion": "observation", + "reference": { + "name": "dwp.uc.households_children_5_or_more", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_households", + "entity_name": "benefit_unit", + "geography_level": "country", + "geography_id": "K03000001", + "groupby_dimension": "dwp.uc_number_of_children", + "dimensions": [ + "number_of_children", + "payment_indicator", + "child_entitlement" + ], + "dimension_values": { + "number_of_children": "5 or more", + "payment_indicator": "Yes", + "child_entitlement": "all" + }, + "source_measure_id": "total_benefit_units" + }, + "entity": "benunit", + "measure": "dwp/uc/claimants_with_5+_children", + "family": "dwp_universal_credit", + "period": "2025-04", + "metadata": { + "diagnostic_role": "count_with_families_outside_the_limit" + }, + "assertion_policy": "observed_only", + "period_match_policy": "exact", + "ledger_fact_key": "ledger.aggregate_fact.v2:a195486577cc8d0200ff837b" + }, + "notes": "DWP Stat-Xplore: Universal Credit households (paid benefit units) with five or more children in Great Britain in April 2025 (76,885), the month of the two-child-limit publication it sits beside. Retained as diagnostic provenance beside dwp.uc.two_child_limit.households_5_children (María's ruling of 2026-10-16, microcosm#1095). The count includes families outside the two-child limit, whose third and later children were all born before 6 April 2017 or who meet an exception; DWP's two-child-limit counts of households with five and with six or more children leave them out, 21,605 households fewer in April 2025. Fewer than 10 FRS source households carry that difference, so the calibration could reach the row only through the two-child-limit rows; it was held out from 2026-10-14 (microcosm#1271) and now leaves the fit. Rebind it as a target when the frame can carry large families outside the limit. Never insert it into the fitting registry." } }, "hierarchy": { diff --git a/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json b/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json index 715dcb2866..cb3d0d076a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json @@ -357,6 +357,22 @@ "approved_on": "2026-10-15", "status": "approved", "expires_on": "2026-12-15" + }, + { + "scope": "national", + "kind": "in_year_snapshot", + "target_ids": [ + "slc.borrowers.plan_2_above_threshold", + "slc.borrowers.plan_2_liable", + "slc.borrowers.plan_5_above_threshold", + "slc.borrowers.plan_5_liable" + ], + "reason": "SLC Student loan forecasts for England, Table 6a: borrowers liable to repay and earning above the repayment threshold in academic year 2024/25, the stock whose reference date falls inside the calibration year 2025, which Chronicle stores under the academic year's opening year 2024. A stock at a date inside the calibration year, not a lagged period; the student_loans stage tops up to the same stock.", + "tracking": "microcosm#868", + "approved_by": "juaristi22", + "approved_on": "2026-10-16", + "status": "approved", + "expires_on": "2026-12-11" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py index 6265f71c47..8832719670 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py @@ -123,6 +123,10 @@ load_hmrc_cgt_conditioning_facts, load_hmrc_cgt_joint_distribution, ) +from microcosm.build.uk_runtime.hmrc_income import ( + LOSS_RELIEF_COLUMN, + self_employment_income_after_loss_relief, +) from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, uk_national_frame, @@ -387,7 +391,17 @@ def uk_cgt_component_sum_income(person: pd.DataFrame) -> np.ndarray: ) total = np.zeros(len(person)) for column in UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS: - total += pd.to_numeric(person[column], errors="raise").to_numpy(dtype=float) + values = pd.to_numeric(person[column], errors="raise").to_numpy(dtype=float) + if column == "self_employment_income" and LOSS_RELIEF_COLUMN in person: + # Total income holds the profit after losses brought forward, + # which policyengine-uk keeps apart in loss_relief (microcosm#1095). + values = self_employment_income_after_loss_relief( + values, + pd.to_numeric(person[LOSS_RELIEF_COLUMN], errors="raise").to_numpy( + dtype=float + ), + ) + total += values return total diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/energy_pricing.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/energy_pricing.py index 3294d20385..cb1ad37b85 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/energy_pricing.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/energy_pricing.py @@ -1027,6 +1027,106 @@ def declared_household_kwh_ceilings( return ceilings +#: The declared NEED cells whose households start from the cell's NEED mean +#: when every one of them drew no kWh (microcosm#1240). Mobile homes have no +#: NEED property-type row and the incumbent held them at the all-dwellings +#: mean; where every mobile home of a NEED geography drew a spend at or below +#: the standing charge, the multiplying rake has nothing to scale and leaves +#: the cell at zero. Any other cell with a zero current mean still fails the +#: ``energy_rake`` gate. +ZERO_KWH_CELLS_AT_NEED_MEAN_KEY = "zero_kwh_cells_at_need_mean" + + +def declared_zero_kwh_cells_at_need_mean( + parameters: Mapping[str, Any], +) -> dict[str, tuple[str, ...]] | None: + """The declared ``zero_kwh_cells_at_need_mean`` categories per margin, if any.""" + + declared = parameters.get(ZERO_KWH_CELLS_AT_NEED_MEAN_KEY) + if declared is None: + return None + if not isinstance(declared, Mapping) or not declared: + raise ValueError( + f"{ZERO_KWH_CELLS_AT_NEED_MEAN_KEY} must map NEED margins to categories." + ) + cells: dict[str, tuple[str, ...]] = {} + for margin, categories in declared.items(): + if margin not in NEED_MARGIN_DIMENSIONS: + raise ValueError( + f"{ZERO_KWH_CELLS_AT_NEED_MEAN_KEY} names {margin!r}, not a NEED " + f"margin ({sorted(NEED_MARGIN_DIMENSIONS)})." + ) + if ( + isinstance(categories, str) + or not isinstance(categories, Sequence) + or not categories + or not all(isinstance(c, str) and c for c in categories) + ): + raise ValueError( + f"{ZERO_KWH_CELLS_AT_NEED_MEAN_KEY}.{margin} must list NEED categories." + ) + cells[str(margin)] = tuple(str(c) for c in categories) + return cells + + +def _hold_zero_kwh_cells_at_need_mean( + frame: pd.DataFrame, + cells: Mapping[str, Sequence[str]], + *, + margins: NeedMargins, + connected: np.ndarray, + weights: np.ndarray, +) -> list[dict[str, str]]: + """Start each declared cell whose every household drew no kWh at its NEED mean. + + Works on the rake's own copy before the first sweep. A cell is its + ``_need_`` key in each NEED geography, over every row for + electricity and over the gas-connected rows for gas, as the rake sweeps + them. A cell with a positive row is left to the rake, and so is one with + no row or no weight. Records the cells it set, never a household. + """ + + held: list[dict[str, str]] = [] + for margin, categories in cells.items(): + column = f"_need_{margin}" + if column not in frame: + raise ValueError( + f"{ZERO_KWH_CELLS_AT_NEED_MEAN_KEY} names the {margin!r} margin, " + "which this rake does not sweep." + ) + keys = frame[column].to_numpy().astype(str) + for category in categories: + published = { + geography: cell + for (geography, name), cell in margins.targets[margin].items() + if name == category + } + if not published: + raise ValueError( + f"{ZERO_KWH_CELLS_AT_NEED_MEAN_KEY}: NEED publishes no {margin} " + f"cell {category!r} for {margins.period_value}." + ) + for geography, cell in sorted(published.items()): + key = f"{geography}:{category}" + for fuel, population in ( + (ELECTRICITY_KWH, np.ones(len(frame), dtype=bool)), + (GAS_KWH, connected), + ): + rows = (keys == key) & population + if not rows.any() or float(weights[rows].sum()) <= 0.0: + continue + values = ( + pd.to_numeric(frame.loc[rows, fuel], errors="coerce") + .fillna(0.0) + .to_numpy(dtype=float) + ) + if not (values == 0.0).all(): + continue + frame.loc[rows, fuel] = float(cell[fuel]) + held.append({"margin": margin, "cell": key, "column": fuel}) + return held + + def cap_household_kwh( values: np.ndarray, *, @@ -1162,6 +1262,7 @@ def rake_energy_kwh( gas_connected: Sequence[bool] | None = None, level: Mapping[str, float] | None = None, ceilings: Mapping[str, float] | None = None, + zero_kwh_cells_at_need_mean: Mapping[str, Sequence[str]] | None = None, ) -> tuple[pd.DataFrame, dict[str, Any]]: """Rake ``electricity_kwh`` and ``gas_kwh`` to the NEED shape, then to the level. @@ -1188,6 +1289,11 @@ def rake_energy_kwh( NEED cells, re-raking them to the levelled targets with the capped ones held, then restores the published total (``cap_household_kwh``; microcosm#1164). The fit is read after the cap. + + ``zero_kwh_cells_at_need_mean`` (NEED categories per margin) starts each + named cell whose every household drew no kWh at the cell's NEED mean + before the first sweep, since a cell mean of zero cannot be scaled; the + receipt lists the cells it set (microcosm#1240). """ frame = table.copy() @@ -1255,6 +1361,22 @@ def rake_energy_kwh( ) if len(connected) != len(frame): raise ValueError("gas_connected must align with the table.") + weight_values = ( + np.ones(len(frame), dtype=float) + if weights is None + else np.asarray(weights, dtype=float) + ) + held_at_need_mean = ( + [] + if zero_kwh_cells_at_need_mean is None + else _hold_zero_kwh_cells_at_need_mean( + frame, + zero_kwh_cells_at_need_mean, + margins=margins, + connected=connected, + weights=weight_values, + ) + ) electricity_specs = tuple( MarginSpec( spec.column, @@ -1294,11 +1416,6 @@ def rake_energy_kwh( ] raked.loc[connected, GAS_KWH] = gas_raked[GAS_KWH].to_numpy(dtype=float) raked.loc[~connected, GAS_KWH] = 0.0 - weight_values = ( - np.ones(len(frame), dtype=float) - if weights is None - else np.asarray(weights, dtype=float) - ) level_factor = {ELECTRICITY_KWH: 1.0, GAS_KWH: 1.0} level_receipt: dict[str, Any] | None = None if level is not None: @@ -1366,6 +1483,7 @@ def rake_energy_kwh( "gas_connected_rows": int(connected.sum()), "rows": int(len(frame)), "zero_current_cells": zero_cells, + "cells_held_at_need_mean": held_at_need_mean, "sweep_residuals": sweep_residuals, "level_factor": dict(level_factor), "level": level_receipt, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py index e76473d553..ccafd36e2b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/etb_services.py @@ -24,6 +24,10 @@ ) from microcosm.build.uk_runtime.etb_vat import UK_ETB_FAMILY_ROLE_COUNTS from microcosm.build.uk_runtime.frs_spine import read_pinned_tab +from microcosm.build.uk_runtime.household_income import ( + UK_HBAI_NET_INCOME_PREDICTOR, + UK_HBAI_NET_INCOME_VARIABLE, +) from microcosm.build.uk_runtime.national_frame import ( uk_household_mass_conservation_receipt, uk_household_weight_kind, @@ -60,9 +64,15 @@ "is_SP_age", "dla", "pip", - "hbai_household_net_income", + UK_HBAI_NET_INCOME_VARIABLE, "current_education", ) +#: Predictors read from an engine variable of another name: the HBAI net income +#: is read before the reset to zero (policyengine-uk 2.132.4), since the +#: donor's ``disinc`` is not reset. +_ETB_SERVICES_PREDICTOR_VARIABLES = { + UK_HBAI_NET_INCOME_PREDICTOR: UK_HBAI_NET_INCOME_VARIABLE, +} UK_ETB_SERVICES_EDUCATION_COUNTS = { "count_primary_education": ("PRIMARY",), "count_secondary_education": ("LOWER_SECONDARY",), @@ -413,8 +423,9 @@ def person_sum(values: np.ndarray) -> np.ndarray: labels = UK_ETB_SERVICES_EDUCATION_COUNTS[predictor] result[predictor] = person_sum(np.isin(education, labels)) continue - values = np.asarray(materialized[predictor]) - entity = str(engine.variable_metadata(predictor).entity) + variable = _ETB_SERVICES_PREDICTOR_VARIABLES.get(predictor, predictor) + values = np.asarray(materialized[variable]) + entity = str(engine.variable_metadata(variable).entity) if entity == "household": result[predictor] = values elif entity == "person": diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py index 4630cc2032..26baba1530 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_education.py @@ -44,6 +44,18 @@ #: dependant. FRS_TRAINEE_COLUMN = "trainee" FRS_TRAINEE_YES = 1 +#: FRS SLOS, "Do you have an outstanding student loan?" (1 yes, 2 no), adult +#: table of FRS 2024-25 (SN 9563). Adults who answer SLREPAY ("Whether making +#: student loan repayments") no answer it, so it carries the borrowers below the +#: repayment threshold whom the repayment amount misses (microcosm#868). +FRS_STUDENT_LOAN_OUTSTANDING_COLUMN = "slos" +FRS_STUDENT_LOAN_OUTSTANDING_YES = 1 +#: FRS EDUCLEFT, "Age completed full-time education", adult table. The +#: student-loan stage dates a borrower's course from it; an age outside +#: [MIN, MAX] is no answer and is stored as 0 (microcosm#868). +FRS_AGE_LEFT_FULL_TIME_EDUCATION_COLUMN = "educleft" +AGE_LEFT_FULL_TIME_EDUCATION_MIN = 10 +AGE_LEFT_FULL_TIME_EDUCATION_MAX = 90 #: The tab a raw person row came from; set before the adult and child tabs are #: concatenated, as ``frs_relationships`` does. FRS_TABLE_COLUMN = "_frs_table" @@ -122,6 +134,8 @@ "adult_ema", "child_ema", "receives_benefits_in_own_right", + "reports_outstanding_student_loan", + "age_left_full_time_education", ) @@ -243,6 +257,41 @@ def derive_frs_education( values["receives_benefits_in_own_right"] = ( person[benefit_columns].fillna(0).sum(axis=1) > 0 if benefit_columns else False ) + student_loan_columns = [ + column + for column in ( + FRS_STUDENT_LOAN_OUTSTANDING_COLUMN, + FRS_AGE_LEFT_FULL_TIME_EDUCATION_COLUMN, + ) + if column not in raw.columns + ] + if student_loan_columns: + # Fail rather than read nobody as a borrower: the student-loan stage + # would then assign plans from repayments alone. + raise KeyError( + f"FRS person rows lack {student_loan_columns}, so student-loan " + "borrowers and course dates cannot be derived." + ) + # Adult-table answers only; a child record has neither question. + adult = raw[FRS_TABLE_COLUMN].eq("adult") + values["reports_outstanding_student_loan"] = ( + _num(raw, FRS_STUDENT_LOAN_OUTSTANDING_COLUMN).eq( + FRS_STUDENT_LOAN_OUTSTANDING_YES + ) + & adult + ).to_numpy(dtype=bool) + left = _num(raw, FRS_AGE_LEFT_FULL_TIME_EDUCATION_COLUMN) + values["age_left_full_time_education"] = ( + left.where( + adult + & left.between( + AGE_LEFT_FULL_TIME_EDUCATION_MIN, AGE_LEFT_FULL_TIME_EDUCATION_MAX + ), + 0, + ) + .to_numpy() + .astype("int64") + ) return values diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py index 2c59cdef48..022fc09c37 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_spine.py @@ -184,6 +184,16 @@ # in ROYYR1 is a profit (1) or a loss (2). ROYYR1 itself is always positive. FRS_RENTPROF_LOSS = 2 +#: The six owned-company inputs the root stage sets from the FRS job tab +#: (microcosm#1095); see frs_owner_manager_inputs. +OWNER_MANAGER_COLUMNS = ( + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", +) OUTPUT_COLUMNS = ( "person_id", "person_benunit_id", @@ -207,10 +217,14 @@ "rent_paid_as_lodger", "employment_income", "self_employment_income", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", "private_pension_income", "tax_free_savings_income", "savings_interest_income", "dividend_income", + *OWNER_MANAGER_COLUMNS, "property_income", "reports_rent_from_other_property", "maintenance_income", @@ -261,6 +275,7 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "esa_includes_support_component", "bsp_reported", "benunit_id", "frs_benunit_capital", @@ -537,9 +552,23 @@ def _assemble_frame( pe_person["is_blind"] = _number(person, "spcreg1") == FRS_REGISTERED_YES pe_person["employment_income"] = _positive(person, "inearns") * WEEKS_IN_YEAR - pe_person["self_employment_income"] = _positive(person, "seincam2") * WEEKS_IN_YEAR + # SEINCAM2 is the self-employment income the respondent gives, a loss as a + # negative amount. policyengine-uk takes profits in self_employment_income, + # never below zero, and the year's losses in trading_loss as a positive + # amount (pe-uk#2085, from 2.131.0), so the amount splits by its sign + # (uk-data#528). + self_employment = _number(person, "seincam2") * WEEKS_IN_YEAR + pe_person["self_employment_income"] = np.maximum(self_employment, 0) + pe_person["trading_loss"] = np.maximum(-self_employment, 0) + # The FRS records no losses of earlier years; the SPI channel moves the + # tape's into both inputs (microcosm#1095). + pe_person["loss_relief"] = 0.0 + pe_person["ni_class_4_losses_brought_forward"] = 0.0 _add_private_pension(pe_person, person, frs["pension"]) _add_accounts(pe_person, person, frs["accounts"]) + owner_managers = frs_owner_manager_inputs(person, frs["job"]) + for column in OWNER_MANAGER_COLUMNS: + pe_person[column] = owner_managers[column].to_numpy() _add_person_income(pe_person, person, household, frs["oddjob"], evidence=evidence) _add_benefits(pe_person, person, frs["benefits"]) # The engine pays Carer's Allowance on hours or receipt; a dataset keeps it @@ -666,6 +695,72 @@ def _add_accounts( pe_person["dividend_income"] = np.maximum(0, dividends) +#: FRS job-table answers that make a person like a sole owner or partner of a +#: company, which Universal Credit treats as self-employed (UC Regs 2013 reg +#: 77, policyengine-uk's owned-company inputs from pe-uk#1965): a director +#: (DIRECTOR 1, or NATURE 1 "Director") who owns at least half of the company +#: (DIRPERC 1 "100%", 2 "More than 50% but not 100%", 3 "50%"; María's +#: ruling of 2026-10-16, microcosm#1095). +OWNER_MANAGER_DIRPERC_CODES = (1, 2, 3) +#: JOBTYPE 1, the first (main) job. +FRS_MAIN_JOB_JOBTYPE = 1 +#: SIC 2007 division 68, real estate activities. ADULT.SIC codes the main job +#: only, so it says whether an owned company held as the main job carries on +#: a property business or a trade; another job's company carries on neither +#: on this release, which leaves the engine's company-owner rules off for it. +PROPERTY_BUSINESS_SIC_DIVISION = 68 +OWNER_MANAGER_JOB_COLUMNS = ("director", "nature", "dirperc", "jobtype", "dividend") + + +def frs_owner_manager_inputs(person: pd.DataFrame, job: pd.DataFrame) -> pd.DataFrame: + """Six of policyengine-uk's eleven owned-company inputs, from the job tab. + + A director owning at least half of a company stands as its sole owner or + partner and works in it. The company is their main employment when the job + is their first. Its income share is the dividends the job records (DIVIDEND, + the last 12 months, after tax): the engine reads the company's profit, + which the FRS does not ask, and the dividends paid out of it are its part + the survey sees. The stocks and the intermediaries inputs keep the engine's + defaults. + """ + + missing = [column for column in OWNER_MANAGER_JOB_COLUMNS if column not in job] + if missing: + raise KeyError( + f"The FRS job tab has no {missing} column(s), so owner-managers " + "cannot be set." + ) + if "sic" not in person: + raise KeyError("The FRS adult tab has no SIC column.") + job_person = job["person_id"] + director = (_number(job, "director") == 1) | (_number(job, "nature") == 1) + owner = director & _number(job, "dirperc").isin(OWNER_MANAGER_DIRPERC_CODES) + main = owner & (_number(job, "jobtype") == FRS_MAIN_JOB_JOBTYPE) + persons = person["person_id"] + stands = _sum_to_entity(owner.astype(float), job_person, persons) > 0 + main_employment = _sum_to_entity(main.astype(float), job_person, persons) > 0 + property_division = ( + _number(person, "sic").to_numpy() == PROPERTY_BUSINESS_SIC_DIVISION + ) + return pd.DataFrame( + { + "stands_as_sole_owner_or_partner_of_company": stands, + "is_engaged_in_owned_company_trade": stands, + "owned_company_is_main_employment": main_employment, + "owned_company_carries_on_trade": main_employment & ~property_division, + "owned_company_carries_on_property_business": ( + main_employment & property_division + ), + "owned_company_income_share": _sum_to_entity( + np.maximum(_number(job, "dividend"), 0) * owner, + job_person, + persons, + ), + }, + index=person.index, + ) + + def _add_person_income( pe_person: pd.DataFrame, person: pd.DataFrame, @@ -876,6 +971,9 @@ def _add_benefits( ) * WEEKS_IN_YEAR ) + pe_person["esa_includes_support_component"] = _esa_includes_support_component( + benefits, person_id, person["person_id"] + ) pe_person["bsp_reported"] = ( _sum_to_entity(amount * benefit.isin((6, 9)), person_id, person["person_id"]) * WEEKS_IN_YEAR @@ -1121,6 +1219,41 @@ def frs_care_hours(person: pd.DataFrame) -> pd.Series: return codes.astype(int).map(FRS_CARE_HOURS_BY_BAND).astype("float64") +def _esa_includes_support_component( + benefits: pd.DataFrame, person_id: pd.Series, person_ids: pd.Series +) -> np.ndarray: + """Whether each person's ESA includes the support component. + + An ESA row (BENEFIT 16) records the group in VAR3 (ESAGROUP): 1 the + work-related activity group, 2 the support group, 3 the assessment phase. + The FRS 2024-25 variable listing gives the codes (UK Data Service SN 9563, + ``9563_frs2425_variable_listing_eul.xlsx``, sheet BENEFITS_table): on an + ESA row VAR3 holds the questionnaire's ESAGroup, formatted 1 + Work-related activity group, 2 Support group, 3 Assessment phase. + A stored column replaces policyengine-uk's default for everyone, which + assumes the support group for anyone with an ESA award of their own + (2.129.0), so the column is false only where the survey records a group + outside it and never the support group, and true everywhere else: the + engine reads it only where there is an award of one's own (microcosm#1095). + A tab without VAR3 refuses, rather than set nobody's group. + """ + + if "var3" not in benefits.columns: + raise KeyError( + "The FRS benefits tab has no VAR3 column, so the ESA group " + "(esa_includes_support_component) cannot be set." + ) + esa_row = _number(benefits, "benefit") == 16 + group = _number(benefits, "var3") + outside = _sum_to_entity( + (esa_row & group.isin((1, 3))).astype(float), person_id, person_ids + ) + support = _sum_to_entity( + (esa_row & (group == 2)).astype(float), person_id, person_ids + ) + return ~((np.asarray(outside) > 0) & ~(np.asarray(support) > 0)) + + def _number(frame: pd.DataFrame, column: str) -> pd.Series: if column not in frame.columns: return pd.Series(np.zeros(len(frame), dtype="float64"), index=frame.index) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py index 16d95baaf3..bc0bd09fe6 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py @@ -49,7 +49,7 @@ } UK_TAKE_UP_DECLARED_SEEDS = {output: 0 for output in FRS_TAKE_UP_OUTPUT_COLUMNS} # Universal Credit is claimable only by a benefit unit whose claimant or -# partner meets its age conditions: policyengine-uk 2.130.1's ``is_uc_eligible`` +# partner meets its age conditions: policyengine-uk 2.132.6's ``is_uc_eligible`` # (capital aside) needs a single or joint claimant, taken from # ``is_uc_claimant``, who meets UC's minimum age and has not reached Pension # Credit qualifying age. The draw's population is therefore the units with a diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py index 0323258d2e..a09571b2c5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py @@ -138,6 +138,8 @@ register_uk_national_kernels, replay_uk_dense_gate_battery, replay_uk_national_gate_battery, + solved_national_measure_exclusions, + solved_national_registry, uk_national_graph, ) from .graph_population import GEOGRAPHY_ASSIGNMENTS, PROPERTY_LOCALISATION_MODES @@ -2710,7 +2712,17 @@ def _execute_national_build( append_phase(state, "national_calibration_solved") frame = manifest.population(national.population) result, problem = national_result_from_manifest( - manifest, store, frame=frame, registry=national_registry + manifest, store, frame=frame, registry_document=registry_document + ) + # The support floor drops rows on the frame (microcosm#1095 U3): the + # diagnostics and the build record describe the rows the problem solved + # and every exclusion it bound; the written register stays the approved + # one, the scoring surface a frozen-register check re-derives. + inputs["measure_exclusions"] = solved_national_measure_exclusions( + registry_document, problem.bindings + ) + solved_registry = solved_national_registry( + registry_document, inputs["measure_exclusions"] ) evidence = json.loads( graph_payload(manifest, store, NATIONAL_GATES_NODE, "calibration_evidence") @@ -2722,7 +2734,7 @@ def _execute_national_build( telemetry, "calibration", "completed", - target_count=len(national_registry.specs), + target_count=len(solved_registry.specs), entity_row_counts={ entity: int(len(frame.table(entity))) for entity in frame.entities }, @@ -2759,7 +2771,7 @@ def _execute_national_build( paths["calibration_diagnostics"], frame, target_geography_levels=evidence["target_geography_levels"], - target_registry=national_registry, + target_registry=solved_registry, build=build_block, ) if isinstance(diagnostics_outcome, DiagnosticsWriteFailure): diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py index 72bee79bc5..8d1befd5e4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_gates.py @@ -26,6 +26,7 @@ from microcosm.build.uk_runtime.graph_evidence import uk_spine_gate_artifacts from microcosm.build.uk_runtime.national_frame import uk_release_export_frame from microcosm.build.uk_runtime.parity_reference import load_efrs_parity_reference +from microcosm.build.uk_runtime.support_floor import uk_support_floor_names from microcosm.build.uk_runtime.terminal_gates import uk_export_candidate_columns from microcosm.calibrate import TargetRegistry from microcosm.calibrate.artifacts import OrderedProblem, OrderedSolution @@ -239,8 +240,14 @@ def build_full_gate_context( raise ValueError( "Full-build gates require the complete approved national reference registry." ) + # The support floor's drops (microcosm#1095 U3) left the problem after + # selection; the problem binds them, and the reference side and the + # activated count lose them too. + floor_drops = uk_support_floor_names(bindings.get("measure_exclusions") or {}) selected = { - (str(row["name"]), row["period"]) for row in selection_receipt["included"] + (str(row["name"]), row["period"]) + for row in selection_receipt["included"] + if str(row["name"]) not in floor_drops } national_reference = { spec.to_target().row_name @@ -270,7 +277,7 @@ def build_full_gate_context( "stage_evidence": dict(stage_evidence), "build_stage_names": tuple(stage_evidence), "national_calibration": { - "activated_reference_count": len(selection_receipt["included"]), + "activated_reference_count": len(selected), "resolved_reference_count": len(problem.targets), "matrix_target_count": len(problem.names), }, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_targets.py index b5964d9678..ab584d0f18 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_targets.py @@ -42,6 +42,10 @@ assert_uk_subset_relations, reconcile_uk_national_registry, ) +from microcosm.build.uk_runtime.support_floor import ( + load_uk_support_floor_declaration, + uk_support_floor_from_payload, +) from microcosm.build.uk_runtime.uprating_holds import ( assert_uk_uprating_holds_declared, ) @@ -96,6 +100,24 @@ def _unsupported_names(unsupported: object) -> tuple[str, ...]: return tuple(names) +def _support_floor_input( + measure_exclusions: str | Path | None, evaluated_on: date +) -> dict[str, Any] | None: + """The register's signed support floor and the date it is evaluated on. + + The problem nodes apply it to the compiled matrix (microcosm#1095 U3); a + floor outside its window refuses here, before any engine runs. + """ + + declaration = load_uk_support_floor_declaration( + None if measure_exclusions is None else Path(measure_exclusions) + ) + if declaration is None: + return None + uk_support_floor_from_payload(declaration).assert_in_force(evaluated_on) + return {"declaration": declaration, "evaluated_on": evaluated_on.isoformat()} + + def load_uk_full_target_inputs( facts_path: str | Path, *, @@ -278,6 +300,7 @@ def load_uk_full_target_inputs( "uprating_holds": uprating_holds, "local_registry": local_registries[year], "measure_exclusions": exclusion_receipt, + "support_floor": _support_floor_input(measure_exclusions, evaluated_on), "reviewed_unbound_higher_targets": reviewed_unbound, "national_source_pin": pin.to_dict(), "local_source_pin": local_pin, @@ -411,6 +434,7 @@ def load_uk_national_target_inputs( "national_reconciliation": national_reconciliation, "uprating_holds": uprating_holds, "measure_exclusions": exclusion_receipt, + "support_floor": _support_floor_input(measure_exclusions, evaluated_on), "chronicle_feed_pin": pin.to_dict(), "chronicle_provenance": artifact.provenance(), "ledger_provenance": _ledger_provenance(artifact), diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py index 0beab8836f..9b4852200b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -479,6 +479,9 @@ } ), "salary_sacrifice": None, + # One engine materialization of private-school attendance over the whole + # frame at its weights: an open surface (microcosm#1095). + "private_school_attendance": None, "student_loans": frozenset( { ("person", "age"), @@ -486,6 +489,8 @@ ("person", "student_loan_repayments"), ("person", "current_education"), ("person", "highest_education"), + ("person", "reports_outstanding_student_loan"), + ("person", "age_left_full_time_education"), ("household", "region"), } ), @@ -525,6 +530,7 @@ def owned(self, *, rewrite: bool = False) -> Owned: _ROOT_PERSON_STRING = {"gender", "marital_status"} _ROOT_PERSON_BOOL = { "is_household_head", + "esa_includes_support_component", "is_benunit_head", "is_parent", "is_blind", @@ -534,6 +540,11 @@ def owned(self, *, rewrite: bool = False) -> Owned: "uc_is_in_startup_period", "uc_is_in_gainful_self_employment", "reports_rent_from_other_property", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", "would_claim_carers_allowance", } _ROOT_PERSON_INT = {"age"} @@ -669,6 +680,8 @@ def _cells( _Cell("person", "adult_ema", "float64"), _Cell("person", "child_ema", "float64"), _Cell("person", "receives_benefits_in_own_right", "bool"), + _Cell("person", "reports_outstanding_student_loan", "bool"), + _Cell("person", "age_left_full_time_education", "int64"), ), "frs_legacy_proxies": _cells( "person", @@ -983,6 +996,7 @@ def _cells( ), ), "student_loans": (_Cell("person", "student_loan_plan", "string"),), + "private_school_attendance": (_Cell("person", "attends_private_school", "bool"),), "age_tail": (_Cell("person", "age", "int64"),), } @@ -1038,6 +1052,10 @@ def _cells( "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", + "owned_company_income_share", "hmrc_spi_pay", "hmrc_spi_unemployment_benefit_income", "hmrc_spi_incapacity_benefit_income", @@ -1062,9 +1080,19 @@ def _cells( # uk-data#525: a redrawn row's gainful self-employment follows its drawn # incomes. "uc_is_in_gainful_self_employment", + # A redrawn row's ESA comes from another FRS donor, so its group returns + # to the engine's default for an award of one's own (microcosm#1095). + "esa_includes_support_component", # A redrawn row drops its FRS twin's landlord carrier: its property income # is an SPI donor's (microcosm#1095). "reports_rent_from_other_property", + # The stage-2 fill draws a redrawn row's owner-manager flags beside its + # incomes (microcosm#1095). + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", # The rows stage 1 drew, read by property_components (microcosm#840). "person_is_spi_income_recipient", ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py index 048eef1d51..3edaa9f1a3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py @@ -116,6 +116,7 @@ "hmrc_cgt_asset_type_spine": "cgt_asset_type", "salary_sacrifice": "salary_sacrifice", "student_loans": "student_loans", + "private_school_attendance": "private_school_attendance", "age_tail": "age_tail", } @@ -157,6 +158,8 @@ "pension_credit_take_up": (uc_capital_coherence, uk_engine_adapter), # The Child Benefit redraw shares the weight mapping and the adapter. "child_benefit_take_up": (uc_capital_coherence, uk_engine_adapter), + # The stored attendance is one engine materialization (microcosm#1095). + "private_school_attendance": (uk_engine_adapter,), } _COMPUTE = Capabilities( @@ -482,6 +485,7 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: from .lcfs_consumption import UKLCFSConsumptionStageTransform from .nts_bus_travel import UKNTSBusTravelStageTransform from .pension_credit_take_up import UKPensionCreditTakeUpStageTransform + from .private_school_attendance import UKPrivateSchoolAttendanceStageTransform from .property_components import UKPropertyComponentsStageTransform from .regional_uprating import UKRegionalPropertyUpratingStageTransform from .salary_sacrifice import UKSalarySacrificeStageTransform @@ -723,6 +727,11 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: "student_loans": UKStudentLoansStageTransform( stage=stages["student_loans"], calibration_year=calibration_year ), + "private_school_attendance": UKPrivateSchoolAttendanceStageTransform( + stage=stages["private_school_attendance"], + engine=engine, + calibration_year=calibration_year, + ), "age_tail": UKAgeTailStageTransform(stage=stages["age_tail"]), } ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py index 18fb979a38..f01a4d9a9d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_national.py @@ -91,6 +91,7 @@ national_calibration, national_doctrine, ) +from . import support_floor as support_floor_rules from .battery_bindings import UK_GATE_REGISTRY from .calibration_run import ( UK_CALIBRATION_GATE_SCOPE_EXCLUSIONS, @@ -480,6 +481,7 @@ def implementation_hash(self) -> str: ledger_targets, national_calibration, national_doctrine, + support_floor_rules, *uk_target_compile_modules(), ) return hashlib.sha256( @@ -515,6 +517,7 @@ def run(self, context: KernelContext) -> KernelResult: "national_registry": registry_payload(inputs["national_registry"]), "band_edge_registry": registry_payload(inputs["band_edge_registry"]), "measure_exclusions": inputs["measure_exclusions"], + "support_floor": inputs["support_floor"], "register_completeness": inputs["register_completeness"], "ledger_provenance": inputs["ledger_provenance"], "chronicle_feed_pin": inputs["chronicle_feed_pin"], @@ -534,6 +537,48 @@ def _registry_artifact(context: KernelContext) -> dict[str, Any]: return payload +def solved_national_measure_exclusions( + registry_document: Mapping[str, Any], bindings: Mapping[str, Any] +) -> dict[str, dict[str, str]]: + """The run's measure exclusions as its problem binds them. + + The register's per-row entries, unchanged, and the rows the support + floor dropped on this frame (microcosm#1095 U3), which only the problem + node could measure. + """ + + declared = registry_document["measure_exclusions"] + bound = bindings.get("measure_exclusions", declared) + changed = sorted( + name for name, record in declared.items() if bound.get(name) != record + ) + added = support_floor_rules.uk_support_floor_names(bound) + unexplained = sorted(set(bound) - set(declared) - added) + if changed or unexplained: + raise ValueError( + "The national problem's measure exclusions differ from the register's " + f"beyond the support floor: changed {changed}, added {unexplained}." + ) + return {name: dict(record) for name, record in bound.items()} + + +def solved_national_registry( + registry_document: Mapping[str, Any], + measure_exclusions: Mapping[str, Mapping[str, str]], +) -> TargetRegistry: + """The register the national problem solved: the approved register less + the support floor's drops, checked against the declared floor.""" + + floor = registry_document.get("support_floor") + return support_floor_rules.narrow_uk_registry_by_support_floor( + registry_from_payload(registry_document["national_registry"]), + measure_exclusions, + None + if floor is None + else support_floor_rules.uk_support_floor_from_payload(floor["declaration"]), + ) + + def materialize_uk_national_rows( frame: Frame, registry: TargetRegistry, @@ -618,6 +663,7 @@ def run(self, context: KernelContext) -> KernelResult: ), period=int(registry_document["calibration_year"]), measure_exclusions=registry_document["measure_exclusions"], + support_floor=registry_document.get("support_floor"), doctrine={ "target_weight_rule": str(context.params["target_weight_rule"]), "target_loss_cap": float(context.params["target_loss_cap"]), @@ -640,6 +686,7 @@ def encode_uk_national_problem( period: int, doctrine: Mapping[str, object], measure_exclusions: Mapping[str, Mapping[str, str]] | None = None, + support_floor: Mapping[str, Any] | None = None, resolver_factory=UKMeasureResolver, scratch_dir: Path | None = None, ) -> bytes: @@ -652,14 +699,44 @@ def encode_uk_national_problem( pristine tables, and encode the problem with the national doctrine's ``family_equal`` loss weights, mass reason and bounds in its bindings, which the shared dense solve node reads. + + ``support_floor`` (the register's signed floor and its evaluation date, + microcosm#1095 U3) drops the in-scope rows the frame supports with too + few source households before the loss weights, families and metadata + are drawn; the per-row exclusions in its scope are compiled beside the + register only to measure their support, and leave the problem again. """ assert_calibration_input_finite(frame) if not len(national_registry.specs): raise ValueError("The national register selects no target.") + exclusions = { + name: dict(record) for name, record in (measure_exclusions or {}).items() + } + floor = ( + None + if support_floor is None + else support_floor_rules.uk_support_floor_from_payload( + support_floor["declaration"] + ) + ) + probes = ( + () + if floor is None + else tuple( + spec + for spec in band_edge_registry.specs + if spec.name in exclusions and floor.in_scope(spec) + ) + ) + compiled = ( + TargetRegistry([*national_registry.specs, *probes], country="uk") + if probes + else national_registry + ) prepared, restore, rows, evidence = materialize_uk_national_rows( frame, - national_registry, + compiled, period=period, band_edge_registry=band_edge_registry, resolver_factory=resolver_factory, @@ -674,18 +751,32 @@ def encode_uk_national_problem( clean = restore(prepared) for entity in frame.entities: pd.testing.assert_frame_equal(clean.table(entity), frame.table(entity)) - if len(problem.targets) != len(national_registry.specs): + if len(problem.targets) != len(compiled.specs): raise ValueError( "UK national calibration matrix did not contain every activated " - f"reference: declared={len(national_registry.specs)}, " + f"reference: declared={len(compiled.specs)}, " f"rows={len(problem.targets)}." ) - families = [spec.family for spec in national_registry.specs] + registry = national_registry + floor_receipt = None + if floor is not None: + applied = support_floor_rules.apply_uk_support_floor( + compiled, + problem, + source_ids=lambda: support_floor_rules.uk_household_source_ids(frame), + floor=floor, + evaluated_on=date.fromisoformat(str(support_floor["evaluated_on"])), + row_excluded={spec.name for spec in probes}, + ) + registry, problem = applied.registry, applied.problem + exclusions.update(applied.exclusions) + floor_receipt = applied.receipt + families = [spec.family for spec in registry.specs] rule = str(doctrine["target_weight_rule"]) target_loss_weights = uk_national_target_loss_weights(families, rule=rule) mass_reason = national_calibration_mass_reason(families) ids = frame.table("household")["household_id"].tolist() - levels = national_target_geography_levels(national_registry) + levels = national_target_geography_levels(registry) target_metadata = [ { **spec.metadata, @@ -694,7 +785,7 @@ def encode_uk_national_problem( "geography_level": levels[spec.to_target().row_name], "materialization": NATIONAL_MATERIALIZATION, } - for spec in national_registry.specs + for spec in registry.specs ] return encode_problem( problem, @@ -718,12 +809,13 @@ def encode_uk_national_problem( f"national/{family}" for family in sorted(set(families)) ], "measure_resolution": _json_safe(evidence), - "measure_exclusions": dict(measure_exclusions or {}), + "measure_exclusions": exclusions, + "support_floor": floor_receipt, "register_sha256": national_registry.version, "band_edge_register_sha256": band_edge_registry.version, "calibration_year": int(period), - "activated_reference_count": len(national_registry.specs), - "resolved_reference_count": len(national_registry.specs), + "activated_reference_count": len(registry.specs), + "resolved_reference_count": len(registry.specs), }, ) @@ -902,6 +994,7 @@ def implementation_hash(self) -> str: calibration_run, weighted_integrity, diagnostics, + support_floor_rules, ), "country_resources": load_country_spec("uk").fingerprint, } @@ -911,8 +1004,11 @@ def implementation_hash(self) -> str: def run(self, context: KernelContext) -> KernelResult: frame = context_frame(context) registry_document = _registry_artifact(context) - national = registry_from_payload(registry_document["national_registry"]) result, problem = decode_national_result(context, frame) + measure_exclusions = solved_national_measure_exclusions( + registry_document, problem.bindings + ) + national = solved_national_registry(registry_document, measure_exclusions) doctrine = json.loads(str(context.params["doctrine"])) scope = tuple( str(gate) for gate in json.loads(str(context.params["gate_scope"])) @@ -986,9 +1082,7 @@ def run(self, context: KernelContext) -> KernelResult: "calibration": _json_safe(manifest_block), "diagnostics": _json_safe(diagnostics_rows), "target_geography_levels": uk_target_geography_levels(national), - "register": calibration_run._register_census( - national, registry_document["measure_exclusions"] - ), + "register": calibration_run._register_census(national, measure_exclusions), } return KernelResult( artifacts={ @@ -1106,7 +1200,12 @@ def graph_payload(manifest, store, node: str, artifact: str) -> bytes: def national_result_from_manifest( - manifest, store, *, frame: Frame, registry: TargetRegistry | None = None + manifest, + store, + *, + frame: Frame, + registry: TargetRegistry | None = None, + registry_document: Mapping[str, Any] | None = None, ): """The dense result and its ordered problem, rebound to the calibrated frame. @@ -1114,12 +1213,21 @@ def national_result_from_manifest( (each spec's entity and column measure), as the seam's result carried them: the graph solves the compiled rows, whose descriptors would otherwise read as callables on the weight entity in the diagnostics. - The numbers are the compiled rows' either way. + The numbers are the compiled rows' either way. With ``registry_document`` + (the national targets node's artifact) the register is the one the + problem solved, less the support floor's drops. """ problem = decode_problem( graph_payload(manifest, store, NATIONAL_PROBLEM_NODE, "problem") ) + if registry_document is not None: + if registry is not None: + raise ValueError("Pass a register or a register artifact, not both.") + registry = solved_national_registry( + registry_document, + solved_national_measure_exclusions(registry_document, problem.bindings), + ) dense = manifest.nodes["uk.full.dense"].opaque_artifacts solution = decode_solution( store.load_bytes(dense["solution"]), diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_targets.py index 66ce5c3a6d..0cad3e480e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_targets.py @@ -53,6 +53,7 @@ local_doctrine, local_rowwise, national_doctrine, + support_floor, target_weights, ) from .full_measure import resolve_uk_full_national_problem @@ -229,6 +230,7 @@ def implementation_hash(self) -> str: ladder_targets, ledger_targets, local_doctrine, + support_floor, *uk_target_compile_modules(), ) return hashlib.sha256( @@ -345,6 +347,7 @@ def compile_uk_full_target_surface( "household_dispersion": dispersion, "ladder_provenance": ladder_provenance, "measure_exclusions": inputs["measure_exclusions"], + "support_floor": inputs.get("support_floor"), "reviewed_unbound_higher_targets": inputs["reviewed_unbound_higher_targets"], "source_validation": { "national_source_pin": inputs["national_source_pin"], @@ -459,6 +462,25 @@ def run(self, context: KernelContext) -> KernelResult: local_grains=grains, ) ) + floor_document = full.get("support_floor") + floor_evidence = None + if national_problem is not None and floor_document is not None: + # microcosm#1095 U3: the pool's clones share their spine + # household's source id, so support counts spine households. + applied = support_floor.apply_uk_support_floor( + national, + national_problem, + source_ids=lambda: support_floor.uk_household_source_ids(frame), + floor=support_floor.uk_support_floor_from_payload( + floor_document["declaration"] + ), + evaluated_on=date.fromisoformat(str(floor_document["evaluated_on"])), + ) + national_problem = applied.problem + floor_evidence = { + "exclusions": applied.exclusions, + "receipt": applied.receipt, + } arrays = { f"metrics_{grain}": metrics[grain].to_numpy(dtype=np.float64) for grain in grains @@ -477,6 +499,7 @@ def run(self, context: KernelContext) -> KernelResult: "grains": {grain: metrics[grain].columns.tolist() for grain in grains}, "has_national": national_problem is not None, "evidence": evidence, + "support_floor": floor_evidence, } return KernelResult(artifacts={"measures": _pack(metadata, arrays)}) @@ -535,6 +558,14 @@ def reconstruct_uk_full_problem_inputs(context: KernelContext) -> UKFullProblemI metadata, arrays = decode_measures( context.artifacts["measures"].payload, selected=selected ) + floor_document = full.get("support_floor") + national = support_floor.narrow_uk_registry_by_support_floor( + national, + (metadata.get("support_floor") or {}).get("exclusions", {}), + None + if floor_document is None + else support_floor.uk_support_floor_from_payload(floor_document["declaration"]), + ) ids = frame.table("household")["household_id"].tolist() if ids != metadata["household_ids"]: raise ValueError("Measured contributions belong to a different household axis.") @@ -679,7 +710,11 @@ def run(self, context: KernelContext) -> KernelResult: "bound_families": list(bound_families), "measure_resolution": metadata["evidence"], "cross_geography": cross, - "measure_exclusions": full["measure_exclusions"], + "measure_exclusions": { + **full["measure_exclusions"], + **(metadata.get("support_floor") or {}).get("exclusions", {}), + }, + "support_floor": (metadata.get("support_floor") or {}).get("receipt"), "calibration_year": full["calibration_year"], **weight_receipt, }, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py index e2ba9a7b92..210cb461c8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py @@ -16,8 +16,10 @@ HMRC_SPI_INCOME_COMPONENTS, HMRC_SPI_PUBLICATION_URL, HMRC_SPI_TARGET_RECORD_COUNT, + LOSS_RELIEF_COLUMN, HMRCIncomeBandTargetRecord, HMRCIncomeTargetSet, + self_employment_income_after_loss_relief, ) from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, @@ -153,6 +155,15 @@ def materialize_uk_hmrc_calibration_frame( ) if not np.isfinite(values).all(): raise ValueError(f"HMRC component {component!r} must be finite.") + if component == "self_employment_income" and LOSS_RELIEF_COLUMN in person: + # Table 3.6 counts the profit after losses brought forward, which + # policyengine-uk keeps apart in loss_relief (microcosm#1095). + values = self_employment_income_after_loss_relief( + values, + pd.to_numeric(person[LOSS_RELIEF_COLUMN], errors="coerce") + .fillna(0.0) + .to_numpy(dtype=float), + ) if component == "savings_interest_income": if "tax_free_savings_income" not in person: raise ValueError( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_income.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_income.py index f6e9f58037..f811032ee0 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_income.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_income.py @@ -18,6 +18,8 @@ from xml.etree import ElementTree from zipfile import ZIP_STORED, BadZipFile, ZipFile +import numpy as np + __all__ = [ "HMRCIncomeBandTargetRecord", "HMRCIncomeSourceProvenance", @@ -36,9 +38,11 @@ "HMRC_SPI_SOURCE_TAX_YEAR_START", "HMRC_SPI_SOURCE_VINTAGE", "HMRC_SPI_TARGET_RECORD_COUNT", + "LOSS_RELIEF_COLUMN", "VerifiedHMRCODSIdentity", "hmrc_spi_component_source_columns", "materialize_hmrc_spi_income_band_targets", + "self_employment_income_after_loss_relief", "verify_hmrc_spi_collated_ods", ] @@ -87,6 +91,29 @@ "dividend_income", "other_investment_income", ) +#: policyengine-uk's input for the losses of earlier years relieved against a +#: trade's profit (ITA 2007 s.83, pe-uk#2085); the SPI tape's profits are after +#: that relief (microcosm#1095). +LOSS_RELIEF_COLUMN = "loss_relief" + + +def self_employment_income_after_loss_relief( + self_employment_income: object, loss_relief: object +) -> np.ndarray: + """The profit HMRC counts: the year's profit after losses brought forward. + + policyengine-uk holds a trade's profit for the year in + ``self_employment_income`` and the losses of earlier years it relieves + against that profit in ``loss_relief``, never taking the profit below zero + (ITA 2007 s.83). The SPI tape's profits, the HMRC accounting aggregates and + the HMRC self-employment rows are after that relief (microcosm#1095). + """ + + profit = np.asarray(self_employment_income, dtype=float) + relief = np.asarray(loss_relief, dtype=float) + return np.maximum(profit - relief, 0.0) + + HMRC_SPI_TARGET_RECORD_COUNT = ( len(HMRC_SPI_INCOME_BAND_LOWER_BOUNDS) * len(HMRC_SPI_INCOME_COMPONENTS) * 2 ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/household_income.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/household_income.py index 3864df81c7..0991a15813 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/household_income.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/household_income.py @@ -27,8 +27,9 @@ from microcosm.frame import Frame #: The deductions policyengine-uk takes from HBAI income to reach -#: ``hbai_household_net_income`` (its ``HBAI_HOUSEHOLD_NET_INCOME_SUBTRACTS``; -#: an engine-lane test fails if the two lists part). +#: ``hbai_household_net_income`` (its ``HBAI_HOUSEHOLD_NET_INCOME_SUBTRACTS``), +#: less the ones the gross income does not add back; an engine-lane test fails +#: if the two lists together part from the engine's. UK_HBAI_GROSS_INCOME_DEDUCTIONS = ( "council_tax", "domestic_rates", @@ -41,9 +42,23 @@ "external_child_payments", "LVT", ) +#: The engine's HBAI subtractions the gross income leaves subtracted. +#: ``trading_loss`` (policyengine-uk 2.131.0) is the year's self-employment +#: loss: the surveys' gross incomes count self-employment income net of losses, +#: so adding it back would band a loss-making household on its income before +#: the loss. +UK_HBAI_NET_INCOME_SUBTRACTS_NOT_ADDED_BACK = ("trading_loss",) +#: The engine variable the build reads for a household's HBAI net income: the +#: figure before negative incomes are reset to zero (policyengine-uk 2.132.4, +#: pe-uk#2402). The surveys' incomes the stages match recipients to are not +#: reset, and net income plus the deductions is gross income only before the +#: reset. +UK_HBAI_NET_INCOME_VARIABLE = "hbai_household_net_income_before_reset_to_zero" +#: The predictor name the stages and their donors give that income. +UK_HBAI_NET_INCOME_PREDICTOR = "hbai_household_net_income" #: The engine variables a stage materializes to build the gross income. UK_HBAI_GROSS_INCOME_VARIABLES = ( - "hbai_household_net_income", + UK_HBAI_NET_INCOME_VARIABLE, *UK_HBAI_GROSS_INCOME_DEDUCTIONS, ) #: The direct taxes the ETB takes from gross income for disposable income. diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py index 2dfda80ab7..2290874408 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py @@ -44,6 +44,7 @@ EnergyPrices, NeedMargins, declared_household_kwh_ceilings, + declared_zero_kwh_cells_at_need_mean, impose_gas_connection, kwh_to_spend, need_margins_from_facts, @@ -57,6 +58,8 @@ from microcosm.build.uk_runtime.frs_spine import read_pinned_tab from microcosm.build.uk_runtime.household_income import ( UK_HBAI_GROSS_INCOME_DEDUCTIONS, + UK_HBAI_NET_INCOME_PREDICTOR, + UK_HBAI_NET_INCOME_VARIABLE, hbai_gross_income, household_level, ) @@ -356,7 +359,7 @@ "employment_income", "self_employment_income", "private_pension_income", - "hbai_household_net_income", + UK_HBAI_NET_INCOME_VARIABLE, *UK_HBAI_GROSS_INCOME_DEDUCTIONS, ) UK_LCFS_CONSUMPTION_PREDICTORS = ( @@ -1863,6 +1866,7 @@ class LCFSEnergyPricing: connect_rule: str | None = None connect_salt: str | None = None connect_n_estimators: int | None = None + zero_kwh_cells_at_need_mean: Mapping[str, tuple[str, ...]] | None = None def lcfs_energy_pricing(stage: SourceStageSpec) -> LCFSEnergyPricing | None: @@ -1912,6 +1916,7 @@ def lcfs_energy_pricing(stage: SourceStageSpec) -> LCFSEnergyPricing | None: "integer seed." ) ceilings = declared_household_kwh_ceilings(parameters) + held_cells = declared_zero_kwh_cells_at_need_mean(parameters) connect_rule = parameters.get("connect_rule") connect_salt = parameters.get("connect_salt") connect_trees = parameters.get("connect_n_estimators") @@ -1939,6 +1944,7 @@ def lcfs_energy_pricing(stage: SourceStageSpec) -> LCFSEnergyPricing | None: connect_rule=None if connect_rule is None else str(connect_rule), connect_salt=None if connect_rule is None else str(connect_salt), connect_n_estimators=None if connect_rule is None else int(connect_trees), + zero_kwh_cells_at_need_mean=held_cells, receipt={ **prices_receipt, "need_margins": margins.receipt, @@ -1946,6 +1952,9 @@ def lcfs_energy_pricing(stage: SourceStageSpec) -> LCFSEnergyPricing | None: "gas_connection": connection_receipt, "household_kwh_ceiling": ceilings, "connect_rule": connect_rule, + "zero_kwh_cells_at_need_mean": None + if held_cells is None + else {margin: list(names) for margin, names in held_cells.items()}, }, ) @@ -2158,6 +2167,7 @@ def rake_recipient_energy( gas_connected=connected, level=energy.level, ceilings=energy.ceilings, + zero_kwh_cells_at_need_mean=energy.zero_kwh_cells_at_need_mean, ) receipt["gas_connection"] = {"rule": energy.gas_connected, **connection_receipt} receipt["gas_connected_share"] = float( @@ -2828,10 +2838,17 @@ def recipient_predictors(frame: Frame, engine: object) -> pd.DataFrame: household = frame.table("household") person = frame.table("person") result = pd.DataFrame(index=household.index) - for predictor in UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS: - if predictor not in UK_HBAI_GROSS_INCOME_DEDUCTIONS: + for variable in UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS: + if variable not in UK_HBAI_GROSS_INCOME_DEDUCTIONS: + # The HBAI net income is read before the reset to zero and keeps + # its predictor name, the donor's p389p column. + predictor = ( + UK_HBAI_NET_INCOME_PREDICTOR + if variable == UK_HBAI_NET_INCOME_VARIABLE + else variable + ) result[predictor] = household_level( - frame, engine, predictor, materialized[predictor] + frame, engine, variable, materialized[variable] ) result["household_gross_income"] = hbai_gross_income(frame, engine, materialized) age = pd.to_numeric(person["age"], errors="raise").to_numpy(dtype=float) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index aacc64eeca..89cd8fcda0 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -779,6 +779,13 @@ def _assert_region_facts_resolved_at_region( "dwp.pip.daily_living_standard_claimants", "dwp.pip.daily_living_enhanced_claimants", ), + # microcosm#1095 (María, 2026-10-16): DWP's count of UC households + # with five or more children carries the families outside the + # two-child limit, which the frame cannot hold; it sits beside the + # bound limit row of the same month. + "dwp.uc.two_child_limit.households_5_children": ( + "dwp.uc.households_children_5_or_more", + ), } ) #: A diagnostic is one exactly dated publisher figure; a caseload snapshot is diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/local_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/local_targets.py index 6315d5c192..9077e1b9df 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/local_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/local_targets.py @@ -28,6 +28,10 @@ uk_high_value_home_metric_names, uk_high_value_home_thresholds, ) +from microcosm.build.uk_runtime.hmrc_income import ( + LOSS_RELIEF_COLUMN, + self_employment_income_after_loss_relief, +) from microcosm.build.uk_runtime.tenure_constants import ( UK_TENURE_OWNED_OUTRIGHT, UK_TENURE_OWNED_WITH_MORTGAGE, @@ -301,6 +305,12 @@ def compute_household_metrics( for income_variable in INCOME_VARIABLES: income_values = _values(_calculate(sim, income_variable, period)) + if income_variable == "self_employment_income": + # HMRC's area rows count the profit after losses brought forward, + # which policyengine-uk keeps apart in loss_relief (microcosm#1095). + income_values = self_employment_income_after_loss_relief( + income_values, _values(_calculate(sim, LOSS_RELIEF_COLUMN, period)) + ) in_spi_frame = _values(_calculate(sim, "income_tax", period)) > 0 matrix[f"hmrc/{income_variable}/amount"] = _map_result( sim, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py index c37d1b7141..e461c46e8c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py @@ -17,6 +17,10 @@ from microcosm.build.uk_runtime.atomic_area_support import ( without_uk_native_alias_columns, ) +from microcosm.build.uk_runtime.hmrc_income import ( + LOSS_RELIEF_COLUMN, + self_employment_income_after_loss_relief, +) from microcosm.build.uk_runtime.income_tax_rate_classes import ( UK_RATE_CLASS_ALLOCATION, UK_RATE_CLASS_ENGINE_FLOAT32_STEPS, @@ -183,6 +187,25 @@ def household_bus_boardings(frame: Any, variable: str) -> np.ndarray: ).to_numpy(dtype=float) +#: HMRC's statistics count the year's profit after losses brought forward, +#: which policyengine-uk keeps apart in loss_relief: the self-employment rows +#: read that profit, and every row on HMRC's total income (its bands, its +#: amounts, its regional filters) reads total income with that profit +#: (microcosm#1095). The engine's total_income adds the profit before the +#: relief, so the total-income measure swaps the one for the other. +UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF = "self_employment_income_after_loss_relief" +UK_TOTAL_INCOME_AFTER_LOSS_RELIEF = "total_income_after_loss_relief" +_LOSS_RELIEF_MEASURES = ( + UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF, + UK_TOTAL_INCOME_AFTER_LOSS_RELIEF, +) +_LOSS_RELIEF_MEASURE_COMPONENTS = ( + "self_employment_income", + LOSS_RELIEF_COLUMN, + "total_income", +) + + #: Every binding key that names a measured variable: a dated CGT measure in #: any of them must carry its own measurement period (microcosm#1014 bands #: and filters on dated measures, not only gates and values). @@ -287,6 +310,45 @@ def compute_uk_measure_input( f"stage_boardings_per_trip:{_BUS_BOARDING_MEASURES[variable]}", ) + if variable in _LOSS_RELIEF_MEASURES: + if entity != "person": + raise KeyError(f"{variable} is person-only: {entity}") + person = frame.table("person") + if variable == UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF and ( + "self_employment_income" in person + ): + # A binding on a stored column reads the frame's own value, as the + # rows read self_employment_income before the move; the profit + # after the relief is read the same way (microcosm#1095). + stored_relief = ( + person[LOSS_RELIEF_COLUMN].to_numpy(dtype=float) + if LOSS_RELIEF_COLUMN in person + else np.zeros(len(person)) + ) + return ( + self_employment_income_after_loss_relief( + person["self_employment_income"].to_numpy(dtype=float), + stored_relief, + ), + "frame_columns:self_employment_income_less_loss_relief", + ) + profit = compute_uk_measure_input( + frame, simulation, "person", "self_employment_income", year + )[0] + relief = compute_uk_measure_input( + frame, simulation, "person", LOSS_RELIEF_COLUMN, year + )[0] + after = self_employment_income_after_loss_relief(profit, relief) + if variable == UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF: + return after, "engine_components:self_employment_income_less_loss_relief" + total = compute_uk_measure_input( + frame, simulation, "person", "total_income", year + )[0] + return ( + np.asarray(total, dtype=float) - np.asarray(profit, dtype=float) + after, + "engine_components:total_income_with_profit_after_loss_relief", + ) + if variable in UC_TARGET_VARIABLES: if entity != "benunit": raise KeyError(f"UC statistical measurements are benunit-only: {entity}") @@ -773,6 +835,10 @@ def knows(self, entity: str, variable: str) -> bool: return entity == "person" and all( self.knows("person", name) for name in _CGT_TAXABLE_INCOME_COMPONENTS ) + if variable in _LOSS_RELIEF_MEASURES: + return entity == "person" and all( + self.knows("person", name) for name in _LOSS_RELIEF_MEASURE_COMPONENTS + ) definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None or entity not in _ENTITY_ID: return False @@ -793,6 +859,8 @@ def entity_for(self, variable: str) -> str | None: return "household" if _cgt_model_measure(variable, self.year) is not None: return "person" + if variable in _LOSS_RELIEF_MEASURES: + return "person" if ( variable in _UC_CALIBRATION_VARIABLES @@ -1155,12 +1223,19 @@ def load_uk_calibration_measure_exclusions( else: text = Path(path).read_text(encoding="utf-8") payload = json.loads(text) + # Schema 3 adds the signed sparse-band support floor (microcosm#1095 U3), + # which ``support_floor.load_uk_support_floor_declaration`` reads. + schema_version = payload.get("schema_version") allowed_top = {"schema_version", "exclusions"} + if schema_version == 3: + allowed_top.add("support_floor") unknown = sorted(set(payload) - allowed_top) if unknown: raise ValueError(f"unknown top-level exclusion key(s): {unknown}") - if payload.get("schema_version") != 2: - raise ValueError("UK calibration measure exclusions schema_version must be 2.") + if schema_version not in (2, 3): + raise ValueError( + "UK calibration measure exclusions schema_version must be 2 or 3." + ) exclusions = payload.get("exclusions") if not isinstance(exclusions, list): raise ValueError("UK calibration measure exclusions must contain a list.") diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_reconciliation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_reconciliation.py index 59eb0bee0c..66efaeb563 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_reconciliation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_reconciliation.py @@ -269,6 +269,145 @@ def _apply_sum_bridges( return receipts +@dataclass(frozen=True) +class UKHouseholdBasisPartition: + """Dwelling cells put on the household basis of their area (microcosm#1284). + + At each geography where a parent target (a region's or nation's + council-tax dwelling total) and a control target (its official household + estimate) both have a cell, the parent takes the control's value and the + member cells (its bands) move by the same factor, so they keep the band + mix and the level follows the household estimate. The factor is the + area's published households per chargeable dwelling; the declaration signs + the assumption that it is the same in every band. National register only: + the local band cells follow the national rows by signature. A factor + further than ``max_factor_shift`` from one refuses, a tripwire set from + the measured factors. + """ + + partition_id: str + control_target_ids: tuple[str, ...] + parent_target_ids: tuple[str, ...] + member_target_ids: tuple[str, ...] + max_factor_shift: float + + +def uk_household_basis_partitions( + declarations: Mapping[str, Any] | None = None, +) -> tuple[UKHouseholdBasisPartition, ...]: + declared = declarations or load_uk_cross_grain_declarations() + return tuple( + UKHouseholdBasisPartition( + partition_id=str(entry["partition_id"]), + control_target_ids=tuple(str(v) for v in entry["control_target_ids"]), + parent_target_ids=tuple(str(v) for v in entry["parent_target_ids"]), + member_target_ids=tuple(str(v) for v in entry["member_target_ids"]), + max_factor_shift=_partition_bound(entry), + ) + for entry in declared.get("household_basis_partitions", ()) + ) + + +def _partition_bound(entry: Mapping[str, Any]) -> float: + bound = entry.get("max_factor_shift") + if not isinstance(bound, (int, float)) or not 0.0 < float(bound) < 1.0: + raise ValueError( + f"UK household-basis partition {entry.get('partition_id')!r} must " + f"declare a max_factor_shift in (0, 1), got {bound!r}." + ) + return float(bound) + + +def _household_basis_sides( + specs: Sequence[TargetSpec], partition: UKHouseholdBasisPartition +) -> dict[str, dict[str, list[int]]]: + """Each geography's control, parent and member positions.""" + + from microcosm.build.uk_runtime.ledger_targets import _spec_geography + + roles = { + **{target_id: "member" for target_id in partition.member_target_ids}, + **{target_id: "parent" for target_id in partition.parent_target_ids}, + **{target_id: "control" for target_id in partition.control_target_ids}, + } + sides: dict[str, dict[str, list[int]]] = {} + for position, spec in enumerate(specs): + role = roles.get(_contract_target_id(spec)) + if role is None: + continue + geography_id = _spec_geography(spec)[1] + cells = sides.setdefault( + geography_id, {"control": [], "parent": [], "member": []} + ) + cells[role].append(position) + return sides + + +def _apply_household_basis_partitions( + specs: list[TargetSpec], + partitions: Sequence[UKHouseholdBasisPartition], +) -> list[dict[str, Any]]: + receipts: list[dict[str, Any]] = [] + for partition in partitions: + for geography_id, sides in sorted( + _household_basis_sides(specs, partition).items() + ): + if not sides["parent"]: + if sides["member"]: + raise ValueError( + f"UK household-basis partition {partition.partition_id!r} " + f"has member cells at {geography_id!r} without their total." + ) + continue + if len(sides["parent"]) != 1 or len(sides["control"]) != 1: + raise ValueError( + f"UK household-basis partition {partition.partition_id!r} at " + f"{geography_id!r} needs one total and one household control, " + f"found {len(sides['parent'])} and {len(sides['control'])}." + ) + (parent,) = sides["parent"] + (control_position,) = sides["control"] + control = float(specs[control_position].value) + old_total = float(specs[parent].value) + if old_total <= 0.0 or control <= 0.0: + raise ValueError( + f"UK household-basis partition {partition.partition_id!r} at " + f"{geography_id!r} cannot scale {old_total!r} onto {control!r}." + ) + factor = control / old_total + if abs(factor - 1.0) > partition.max_factor_shift: + raise ValueError( + f"UK household-basis partition {partition.partition_id!r} at " + f"{geography_id!r} has factor {factor!r}, further than " + f"{partition.max_factor_shift!r} from one: the households per " + "dwelling it was signed at no longer hold (microcosm#1284)." + ) + for position in (parent, *sides["member"]): + spec = specs[position] + specs[position] = replace( + spec, + value=float(spec.value) * factor, + metadata={ + **spec.metadata, + "cross_grain_value_before": repr(float(spec.value)), + "cross_grain_factor": repr(factor), + "cross_grain_control": partition.partition_id, + }, + ) + receipts.append( + { + "partition_id": partition.partition_id, + "geography_id": geography_id, + "control": control, + "old_total": old_total, + "declared_factor": factor, + "max_factor_shift": partition.max_factor_shift, + "members": len(sides["member"]), + } + ) + return receipts + + def _contract_target_id(spec: TargetSpec) -> str: return str(spec.metadata.get("contract_target_id", spec.name.split("@", 1)[0])) @@ -477,6 +616,11 @@ def reconcile_uk_national_registry( }, ) moved += 1 + # Last, on the reconciled household controls: the council-tax dwelling + # cells take their area's household basis (microcosm#1284). + partition_receipts = _apply_household_basis_partitions( + specs, uk_household_basis_partitions(declarations) + ) if all(new is old for new, old in zip(specs, registry.specs, strict=True)): out = registry else: @@ -485,6 +629,7 @@ def reconcile_uk_national_registry( return out, { "band_bridges": band_receipts, "sum_bridges": sum_receipts, + "household_basis_partitions": partition_receipts, "fanout_sum_controls": uk_fanout_sum_controls(out, declarations), "cross_grain": receipt, "fanout_targets_not_controls": sorted(fanout), @@ -737,6 +882,18 @@ def _assert_national_closure( f"UK national register does not close on sum bridge " f"{sum_bridge.bridge_id!r}: {lower!r} against {higher!r}." ) + for partition in uk_household_basis_partitions(declarations): + for geography_id, sides in _household_basis_sides(specs, partition).items(): + if not sides["parent"]: + continue + total = float(specs[sides["parent"][0]].value) + control = float(specs[sides["control"][0]].value) + if not math.isclose(total, control, rel_tol=_CLOSURE_RTOL, abs_tol=0.0): + raise ValueError( + f"UK national register does not close on household-basis " + f"partition {partition.partition_id!r} at {geography_id!r}: " + f"{total!r} against {control!r}." + ) for bridge in uk_band_bridges(declarations): if bridge.higher_target_id not in bound and bound.isdisjoint( bridge.lower_target_ids diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py new file mode 100644 index 0000000000..1de31166ac --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py @@ -0,0 +1,218 @@ +"""Private-school attendance stored at the spine's weights (uk-data#568, microcosm#1095). + +policyengine-uk imputes ``attends_private_school`` with a formula: it ranks +households by net income at their weights, reads an attendance rate for each +income percentile, scales it to the national pupil count and compares it with +the stored ``attends_private_school_random_draw``. Left to the formula, the +attendance is re-imputed in every simulation: + +* a reform to income tax, National Insurance or benefits re-ranks households, + so pupils switch in or out and the fee VAT moves with them (uk-data#568); +* a population under one million people of weight, such as a constituency or + a local authority filtered from the national file, imputes no attendance at + all (pe-uk#1905); +* the calibration's engine run and the release's read different flags once the + weights change. + +This stage, the last of the spine, materializes the engine's attendance once, +at the calibration year and the spine's design weights, and stores it, as the +root stage stores ``months_since_last_birthday``. The solve and the release +then read the same flags, and no stage after it copies or re-keys a person. +Stored attendance no longer responds to income changes or to reforms of the +attendance parameters; a reform to the fee VAT does not move pupils either way +in the pinned engine. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from dataclasses import dataclass, field + +import numpy as np + +from microcosm.build.source_manifest import SourceStageSpec +from microcosm.build.uk_runtime.frs_release import resolve_uk_year_rule +from microcosm.build.uk_runtime.national_frame import ( + uk_household_weight_kind, + uk_national_frame, + uk_time_period, + validate_uk_national_frame, +) +from microcosm.frame import Frame, MassChangeRecord +from microcosm.frame.rules import assert_rules_engine_country + +PRIVATE_SCHOOL_ATTENDANCE_STAGE_NAME = "private_school_attendance" +PRIVATE_SCHOOL_ATTENDANCE_OUTPUT = "attends_private_school" +PRIVATE_SCHOOL_ATTENDANCE_DRAW = "attends_private_school_random_draw" +PRIVATE_SCHOOL_ATTENDANCE_YEAR_RULE = "calibration_year" +PRIVATE_SCHOOL_ATTENDANCE_WEIGHTS = "spine_design_weights" +PRIVATE_SCHOOL_ATTENDANCE_MASS_CHANGE_REASON = ( + "Private-school attendance stored on the source spine: household weights " + "pass through unchanged and total household mass is conserved." +) +_MASS_GATE_COUPLING = ( + "The terminal family gate requires a valid mass-conserving MassChangeRecord " + "carrying exactly this stage-specific reason." +) + + +@dataclass(frozen=True) +class UKPrivateSchoolAttendanceResult: + """The frame with the stored flags, and the stage's receipt.""" + + frame: Frame + year: int + weighted_pupils: float + + def evidence(self) -> dict[str, object]: + return { + "stage": PRIVATE_SCHOOL_ATTENDANCE_STAGE_NAME, + "year_rule": PRIVATE_SCHOOL_ATTENDANCE_YEAR_RULE, + "year": self.year, + "weights": PRIVATE_SCHOOL_ATTENDANCE_WEIGHTS, + # A national weighted total over every pupil the engine imputes. + "weighted_pupils": self.weighted_pupils, + } + + +@dataclass(frozen=True) +class UKPrivateSchoolAttendanceStageTransform: + """Whole-stage callable for the stored private-school attendance.""" + + stage: SourceStageSpec + engine: object + calibration_year: int | None = None + last_result: UKPrivateSchoolAttendanceResult | None = field( + default=None, init=False + ) + + def __call__(self, frame: Frame) -> Frame: + _assert_stage_parameters(self.stage) + result = store_private_school_attendance( + frame, engine=self.engine, calibration_year=self.calibration_year + ) + object.__setattr__(self, "last_result", result) + return result.frame + + @staticmethod + def output_columns() -> tuple[str, ...]: + return (PRIVATE_SCHOOL_ATTENDANCE_OUTPUT,) + + def checkpoint_metadata(self) -> dict[str, object]: + if self.last_result is None: + raise RuntimeError("checkpoint metadata requires a completed stage run.") + return {"evidence": self.last_result.evidence()} + + +def store_private_school_attendance( + frame: Frame, *, engine: object, calibration_year: int | None = None +) -> UKPrivateSchoolAttendanceResult: + """Store the engine's attendance at the calibration year and these weights.""" + + validate_uk_national_frame(frame) + assert_rules_engine_country(engine, "uk") + person = frame.table("person").copy() + if PRIVATE_SCHOOL_ATTENDANCE_DRAW not in person.columns: + raise ValueError( + "Private-school attendance needs the stored " + f"{PRIVATE_SCHOOL_ATTENDANCE_DRAW!r}; the engine would draw its own." + ) + if PRIVATE_SCHOOL_ATTENDANCE_OUTPUT in person.columns: + raise ValueError( + f"{PRIVATE_SCHOOL_ATTENDANCE_OUTPUT!r} is already stored: this stage " + "is its only writer and runs last." + ) + period = uk_time_period(frame) + year = ( + calibration_year + if calibration_year is not None + else resolve_uk_year_rule(PRIVATE_SCHOOL_ATTENDANCE_YEAR_RULE) + ) + materialized = engine.materialize( + frame, [PRIVATE_SCHOOL_ATTENDANCE_OUTPUT], year, dataset_period=period + ) + if PRIVATE_SCHOOL_ATTENDANCE_OUTPUT not in materialized: + raise ValueError( + "Private-school attendance engine output missing: " + f"{[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT]}." + ) + values = np.asarray(materialized[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT]) + if values.shape != (len(person),): + raise ValueError( + f"{PRIVATE_SCHOOL_ATTENDANCE_OUTPUT} has {values.shape[0]} values for " + f"{len(person)} people." + ) + attends = values.astype(bool) + person[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT] = attends + household_weights = frame.weights_for("household") + weight_by_household = dict( + zip( + frame.table("household")["household_id"].to_numpy(), + np.asarray(household_weights.values, dtype=float), + strict=True, + ) + ) + person_weights = ( + person["person_household_id"].map(weight_by_household).to_numpy(dtype=float) + ) + mass = MassChangeRecord( + entity="household", + old_total=household_weights.total, + new_total=household_weights.total, + declared_factor=1.0, + reason=PRIVATE_SCHOOL_ATTENDANCE_MASS_CHANGE_REASON, + ) + result = uk_national_frame( + person=person, + benunit=frame.table("benunit").copy(), + household=frame.table("household").copy(), + time_period=period, + weight_kind=uk_household_weight_kind(frame), + household_weights=household_weights.values, + mass_log=(*frame.mass_log, mass), + ) + validate_uk_national_frame(result) + return UKPrivateSchoolAttendanceResult( + frame=result, + year=int(year), + weighted_pupils=float(person_weights[attends].sum()), + ) + + +def _assert_stage_parameters(stage: SourceStageSpec) -> None: + """Bind the stage's two operations closed-world.""" + + expected: tuple[tuple[str, Mapping[str, object]], ...] = ( + ( + "store_rules_engine_input", + { + "output": PRIVATE_SCHOOL_ATTENDANCE_OUTPUT, + "draw": PRIVATE_SCHOOL_ATTENDANCE_DRAW, + "year_rule": PRIVATE_SCHOOL_ATTENDANCE_YEAR_RULE, + "weights": PRIVATE_SCHOOL_ATTENDANCE_WEIGHTS, + }, + ), + ( + "record_mass_conservation_receipt", + { + "entity": "household", + "reason": PRIVATE_SCHOOL_ATTENDANCE_MASS_CHANGE_REASON, + "declared_factor": 1.0, + "gate_coupling": _MASS_GATE_COUPLING, + }, + ), + ) + operations = tuple(stage.operations) + if tuple(operation.kind for operation in operations) != tuple( + kind for kind, _ in expected + ): + raise ValueError( + "private_school_attendance operation order drifted: " + f"{[operation.kind for operation in operations]!r}." + ) + for operation, (kind, parameters) in zip(operations, expected, strict=True): + if dict(operation.parameters) != dict(parameters): + raise ValueError( + f"private_school_attendance {kind} parameters drifted: " + f"{dict(operation.parameters)!r} != {dict(parameters)!r}." + ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_income.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_income.py index 0f05c31234..5f0d50f330 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_income.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_income.py @@ -31,6 +31,10 @@ FRS_LANDLORD_CARRIER_COLUMN, uc_gainful_self_employment, ) +from microcosm.build.uk_runtime.hmrc_income import ( + LOSS_RELIEF_COLUMN, + self_employment_income_after_loss_relief, +) from microcosm.build.uk_runtime.spi_rank_transport import ( SPI_STAGE1_ANALOGUE_COLUMNS, SPI_TRANSPORT_FIT_NAME, @@ -46,6 +50,7 @@ from microcosm.build.uk_runtime.spi_support import ( BASE_FRS_SUPPORT_CHANNEL, FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS, + FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS, FRS_ONLY_SPI_FILL_PERSON_COLUMNS, HOUSEHOLD_IS_SPI_SYNTHETIC_COLUMN, SPI_HMRC_EMPLOYED_INCOME_COLUMN, @@ -63,6 +68,9 @@ SPI_HMRC_TOTAL_INVESTMENT_INCOME_COLUMN, SPI_HMRC_UNEMPLOYMENT_BENEFIT_INCOME_COLUMN, SPI_INCOME_QRF_OUTPUT_COLUMNS, + SPI_LOSS_DRAW_COLUMNS, + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN, + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN, SPI_PROPERTY_FINANCE_COSTS_COLUMN, SPI_SYNTHETIC_SUPPORT_CHANNEL, UKSPISupportResult, @@ -207,6 +215,9 @@ "hmrc_spi_other_income": "miscellaneous_income", "hmrc_spi_state_pension_income": None, SPI_PROPERTY_FINANCE_COSTS_COLUMN: SPI_PROPERTY_FINANCE_COSTS_COLUMN, + # The year's profit and its losses brought forward move with profits. + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN: "self_employment_income", + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN: "self_employment_income", } SPI_DONOR_SHA256 = "5ef829461060c91a2a47be59ad541d9b519fc3976d66ca80d4920f711bb96f66" SPI_DONOR_SIZE_BYTES = 141_323_762 @@ -339,6 +350,8 @@ SPI_HMRC_OTHER_INCOME_COLUMN: ("OTHERINC",), SPI_HMRC_STATE_PENSION_INCOME_COLUMN: ("SRP",), SPI_PROPERTY_FINANCE_COSTS_COLUMN: ("LLIR_RESTRICT_AMT_TOT",), + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN: ("PROFITS", "CAPALL"), + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN: ("LOSSBF",), } SPI_QRF_SOURCE_COLUMNS = { output: sources @@ -351,7 +364,8 @@ _DIRECT_SPI_OUTPUT_SOURCE_COLUMNS = { output: sources for output, sources in SPI_INCOME_SOURCE_COLUMNS.items() - if output not in {"employment_income", "self_employment_income"} + if output + not in {"employment_income", "self_employment_income", *SPI_LOSS_DRAW_COLUMNS} } #: SPI 2022-23 AGERANGE codes as whole-year bands ``[low, high)``: code 6 is #: "65 to 74" and code 7 "75 and over", drawn over 75 to 89 and the ONS @@ -1115,6 +1129,7 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): dtype=np.float64, copy=True ) stage2_outputs, reviewed_absent = _stage2_outputs(person) + person = _owner_manager_flags_as_numbers(person) if ( state_pension_age_guard is not None and state_pension_age_guard.base_channel_columns @@ -1200,6 +1215,7 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): raise ValueError("FRS-only stage-2 produced negative non-negative outputs.") for column in stage2_outputs: person.loc[spi_people, column] = stage2_draws[column].to_numpy() + person = _owner_manager_flags_from_draws(person, spi_people=spi_people) if ( state_pension_age_guard is not None and state_pension_age_guard.spi_channel_columns @@ -1219,8 +1235,11 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): ) person.loc[spi_people, "savings_interest_income"] = taxable_interest_draw + tax_free person = derive_hmrc_income_auxiliaries(person, row_mask=spi_people) + person = _move_losses_brought_forward_to_loss_relief(person, spi_people=spi_people) person = _refresh_disability_derived_inputs(person, spi_people=spi_people) person = _refresh_carer_take_up_input(person, spi_people=spi_people) + person = _refresh_esa_support_component(person, spi_people=spi_people) + person = _clear_trading_loss_beside_profit(person, spi_people=spi_people) person = _refresh_uc_start_up_period(person, spi_people=spi_people) person, gainful_self_employment = _refresh_uc_gainful_self_employment( person, spi_people=spi_people @@ -1396,9 +1415,13 @@ def _resample_band_donor_leaves( # Restricted finance costs are a deduction the published total never held, # so they take no part in a record's band: the pools are cut on the income # leaves alone, and a drawn record's finance costs travel with its leaves - # (microcosm#1106). + # (microcosm#1106). The loss draws travel the same way: the published + # total holds the profit net of losses brought forward, which + # self_employment_income already carries (microcosm#1095). income_columns = [ - column for column in columns if column != SPI_PROPERTY_FINANCE_COSTS_COLUMN + column + for column in columns + if column not in (SPI_PROPERTY_FINANCE_COSTS_COLUMN, *SPI_LOSS_DRAW_COLUMNS) ] income_factors = np.asarray( [float(uprating_factors[column]) for column in income_columns] @@ -1724,6 +1747,13 @@ def _prepare_spi_donor( numeric["PROFITS"] - numeric["CAPALL"] - numeric["LOSSBF"], 0.0, ), + # The year's profit before losses brought forward and the losses + # themselves, which the stage moves into loss_relief + # (microcosm#1095). + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN: np.maximum( + numeric["PROFITS"] - numeric["CAPALL"], 0.0 + ), + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN: np.maximum(numeric["LOSSBF"], 0.0), } ) for output, sources in _DIRECT_SPI_OUTPUT_SOURCE_COLUMNS.items(): @@ -1974,6 +2004,14 @@ def derive_hmrc_income_auxiliaries( "HMRC non-negative accounting identity leaves contain negative values." ) + if LOSS_RELIEF_COLUMN in person.columns: + # HMRC's earned income holds the profit after losses brought forward, + # which policyengine-uk keeps apart in loss_relief (microcosm#1095). + relief = pd.to_numeric(person.loc[mask, LOSS_RELIEF_COLUMN], errors="coerce") + numeric["self_employment_income"] = self_employment_income_after_loss_relief( + numeric["self_employment_income"].to_numpy(dtype=float), + relief.fillna(0.0).to_numpy(dtype=float), + ) taxable_interest = ( numeric["savings_interest_income"] - numeric["tax_free_savings_income"] ) @@ -2574,6 +2612,169 @@ def _refresh_carer_take_up_input( return person +def _owner_manager_flags_as_numbers(person: pd.DataFrame) -> pd.DataFrame: + """Hold the owner-manager flags as 0 or 1 for the FRS-only fill to draw.""" + + for column in FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS: + if column in person.columns: + person[column] = person[column].astype(bool).astype(np.float64) + return person + + +def _owner_manager_flags_from_draws( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Store the drawn owner-manager inputs as the root stage does. + + The fill's zero regime gate draws each flag as 0 or 1 on a redrawn row. The + root stage's rules then hold on every row: the person who stands as an + owner works in the company, its other inputs need an owner, and a company + carries on a trade or a property business, never both (microcosm#1095). + """ + + present = [c for c in FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS if c in person] + if not present: + return person + if len(present) != len(FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS): + raise ValueError( + "The owner-manager inputs are set together; the frame carries " + f"only {present}." + ) + flags = { + column: pd.to_numeric(person[column], errors="coerce").to_numpy() > 0.5 + for column in present + } + redrawn = spi_people.to_numpy(dtype=bool) + owner = flags["stands_as_sole_owner_or_partner_of_company"] + main = flags["owned_company_is_main_employment"] & owner + property_business = flags["owned_company_carries_on_property_business"] & main + trade = flags["owned_company_carries_on_trade"] & main & ~property_business + held = { + "stands_as_sole_owner_or_partner_of_company": owner, + "is_engaged_in_owned_company_trade": owner, + "owned_company_is_main_employment": main, + "owned_company_carries_on_trade": trade, + "owned_company_carries_on_property_business": property_business, + } + for column, values in held.items(): + # FRS rows keep their root-stage answers; only redrawn rows move. + person[column] = np.where(redrawn, values, flags[column]) + if "owned_company_income_share" in person: + share = pd.to_numeric( + person["owned_company_income_share"], errors="coerce" + ).to_numpy(dtype=float) + person["owned_company_income_share"] = np.where(redrawn & ~owner, 0.0, share) + return person + + +def _refresh_esa_support_component( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Return a redrawn row's ESA group to the engine's default. + + The root stage reads the ESA group from the FRS benefits tab + (``esa_includes_support_component``); the SPI fill re-imputes the ESA + receipts on the redrawn rows from other FRS donors, so the twin's answer no + longer describes them. The flag returns to true, policyengine-uk's own + default for an award of one's own (microcosm#1095). + """ + + if "esa_includes_support_component" not in person.columns: + return person + _assign_spi_values( + person, + spi_people, + "esa_includes_support_component", + np.ones(int(spi_people.sum()), dtype=bool), + default=True, + ) + return person + + +#: The inputs the tape's losses brought forward move into: income tax relieves +#: them against the profit (ITA 2007 s.83), Class 4 against its profits (SSCBA +#: 1992 Sch 2 para 3). +SPI_LOSS_RELIEF_COLUMNS = (LOSS_RELIEF_COLUMN, "ni_class_4_losses_brought_forward") + + +def _move_losses_brought_forward_to_loss_relief( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Move the tape's losses brought forward out of the profit, into loss_relief. + + The chain draws ``self_employment_income`` net of losses brought forward, + as the tape's profits are, and then the year's profit before them and the + losses (LOSSBF). policyengine-uk relieves earlier losses itself: income + tax against ``loss_relief`` and Class 4 against + ``ni_class_4_losses_brought_forward``, while Universal Credit and HBAI read + the year's profit. So a drawn row's profit becomes the year's profit and + both inputs take its losses, held so that the profit after the relief is + the profit the chain drew: where that is positive the losses were all + relieved, so they are the gap; where it is zero they are at least the + profit. Runs after the HMRC aggregates, which read the profit net of the + relief either way, and drops the two draws (uk-data#528, microcosm#1095). + """ + + if SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN not in person.columns: + return person + rows = spi_people.reindex(person.index).fillna(False).to_numpy(dtype=bool) + net = ( + pd.to_numeric(person.loc[rows, "self_employment_income"], errors="coerce") + .fillna(0.0) + .to_numpy(dtype=float) + ) + profit = np.maximum( + pd.to_numeric( + person.loc[rows, SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN], errors="coerce" + ) + .fillna(0.0) + .to_numpy(dtype=float), + net, + ) + losses = ( + pd.to_numeric( + person.loc[rows, SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN], errors="coerce" + ) + .fillna(0.0) + .to_numpy(dtype=float) + ) + relief = np.where(net > 0.0, profit - net, np.maximum(losses, profit)) + person.loc[rows, "self_employment_income"] = profit + for column in SPI_LOSS_RELIEF_COLUMNS: + if column not in person.columns: + person[column] = 0.0 + person.loc[rows, column] = relief + return person.drop(columns=list(SPI_LOSS_DRAW_COLUMNS)) + + +def _clear_trading_loss_beside_profit( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """A redrawn row with a positive profit keeps no drawn trading loss. + + policyengine-uk takes a trade's profit in ``self_employment_income`` and + the year's loss in ``trading_loss``, and the FRS rows never hold both + (uk-data#528). Stage 2 draws the loss from the FRS rows, and its forests + can mix a loss into a row the tape gives a profit, so the tape's profit + holds and the loss goes (microcosm#1095). + """ + + if "trading_loss" not in person.columns: + return person + profit = pd.to_numeric( + person.loc[spi_people, "self_employment_income"], errors="coerce" + ).fillna(0.0) + loss = pd.to_numeric(person.loc[spi_people, "trading_loss"], errors="coerce") + _assign_spi_values( + person, + spi_people, + "trading_loss", + np.where(profit.to_numpy() > 0.0, 0.0, loss.fillna(0.0).to_numpy()), + default=0.0, + ) + return person + + #: employment_status values of the self-employed (FRS EMPSTATI 3 and 4). SELF_EMPLOYED_STATUSES = ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED") diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py index 07125c7896..d11eb52133 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py @@ -69,6 +69,8 @@ SPI_HMRC_TAXABLE_TERMINATION_PAY_COLUMN, SPI_HMRC_UNEMPLOYMENT_BENEFIT_INCOME_COLUMN, SPI_INCOME_QRF_OUTPUT_COLUMNS, + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN, + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN, SPI_PROPERTY_FINANCE_COSTS_COLUMN, ) @@ -132,6 +134,10 @@ # Restricted finance costs reach the FRS rows only at property_components, # after this stage, so the twin has no analogue to locate (microcosm#1106). SPI_PROPERTY_FINANCE_COSTS_COLUMN: None, + # The FRS records no losses brought forward, and its twin's profit is the + # self_employment_income analogue already located (microcosm#1095). + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN: None, + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN: None, } diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py index 99640a6290..befaa16588 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py @@ -244,6 +244,18 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + # The stage-2 fill's trading loss (uk-data#528, microcosm#1095). + "trading_loss", + # The tape's losses brought forward, moved out of the profit (microcosm#1095). + "loss_relief", + "ni_class_4_losses_brought_forward", + # The stage-2 fill's owner-manager inputs (microcosm#1095). + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", FRS_HMRC_PAY_COLUMN, FRS_HMRC_UBISJA_COLUMN, FRS_HMRC_INCPBEN_COLUMN, @@ -280,7 +292,14 @@ for column in UK_SPI_INCOME_SPINE_REWRITE_COLUMNS if column not in FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS and column - not in (FRS_HMRC_PAY_COLUMN, FRS_HMRC_UBISJA_COLUMN, FRS_HMRC_INCPBEN_COLUMN) + not in ( + FRS_HMRC_PAY_COLUMN, + FRS_HMRC_UBISJA_COLUMN, + FRS_HMRC_INCPBEN_COLUMN, + # Written from the stage-1 loss draws, not drawn by stage 2. + "loss_relief", + "ni_class_4_losses_brought_forward", + ) ) #: Both forests are queried over every SPI-channel person aged 16 and over, #: and the dependants' rows are discarded (uk-data#504, microcosm#1095). diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py index 356ca012f0..eae1d0fcc4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py @@ -113,6 +113,18 @@ #: last, so it conditions on every income leaf and leaves every earlier draw #: where it was (microcosm#1106). SPI_PROPERTY_FINANCE_COSTS_COLUMN = "property_finance_costs" +#: The year's trading profit before losses brought forward (PROFITS less +#: CAPALL, floored at zero) and the losses brought forward themselves (LOSSBF), +#: drawn after every other leaf so every earlier draw stays where it was. The +#: stage moves the losses out of self_employment_income, which the chain +#: still draws net of them, into loss_relief and the Class 4 losses brought +#: forward, then drops both draws (uk-data#528, microcosm#1095). +SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN = "spi_profit_before_losses_draw" +SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN = "spi_losses_brought_forward_draw" +SPI_LOSS_DRAW_COLUMNS = ( + SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN, + SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN, +) SPI_INCOME_QRF_OUTPUT_COLUMNS = ( *( column @@ -121,6 +133,7 @@ ), *SPI_HMRC_QRF_AUXILIARY_COLUMNS, SPI_PROPERTY_FINANCE_COSTS_COLUMN, + *SPI_LOSS_DRAW_COLUMNS, ) # The enhanced-FRS FRS-only stage uses these six income predictors. OTHERINV @@ -177,6 +190,28 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + # uk-data#528: the year's trading loss, which the SPI tape does not + # carry; a redrawn row with a positive profit keeps none (microcosm#1095). + "trading_loss", + # The owner-manager inputs the root stage reads from the FRS job tab, which + # the tape does not carry: drawn last, in this order, so each flag follows + # the drawn incomes and the ones before it, then held to the root stage's + # rules (microcosm#1095; María's ruling of 2026-10-16). + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", +) +#: The owner-manager flags among the FRS-only fill's outputs: drawn as 0 or 1 +#: (the fill's zero regime gate) and stored as booleans again. +FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS = ( + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", ) _PERSON_ID_COLUMNS = ( @@ -1265,6 +1300,7 @@ def _require_entity(entity: str) -> str: "DEFAULT_SPI_PRIOR_MASS_SHARE", "DEFAULT_SPI_SUPPORT_HOUSEHOLDS", "FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS", + "FRS_ONLY_SPI_FILL_OWNER_MANAGER_FLAGS", "FRS_ONLY_SPI_FILL_PERSON_COLUMNS", "FRS_ONLY_SPI_FILL_PREDICTOR_COLUMNS", "HOUSEHOLD_IS_SPI_SYNTHETIC_COLUMN", @@ -1288,8 +1324,11 @@ def _require_entity(entity: str) -> str: "SPI_HMRC_UNEMPLOYMENT_BENEFIT_INCOME_COLUMN", "SPI_INCOME_IMPUTATION_COLUMNS", "SPI_INCOME_QRF_OUTPUT_COLUMNS", + "SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN", + "SPI_LOSS_DRAW_COLUMNS", "SPI_PENSION_AGE_STRATUM_COLUMN", "SPI_PRIOR_MASS_CHANGE_REASON", + "SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN", "SPI_PROPERTY_FINANCE_COSTS_COLUMN", "SPI_REPLACEMENT_STRATA_COLUMNS", "UKSPISupportResult", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py index 20013e7acb..3d2d18e842 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py @@ -150,6 +150,9 @@ from microcosm.build.uk_runtime.pension_credit_take_up import ( UKPensionCreditTakeUpStageTransform, ) +from microcosm.build.uk_runtime.private_school_attendance import ( + UKPrivateSchoolAttendanceStageTransform, +) from microcosm.build.uk_runtime.property_components import ( UKPropertyComponentsStageTransform, ) @@ -1744,6 +1747,14 @@ def prepare_uk_spine_execution( stage=stages_by_name["student_loans"], calibration_year=frs_release.calibration_year, ) + if "private_school_attendance" in stage_names: + implementations["private_school_attendance"] = ( + UKPrivateSchoolAttendanceStageTransform( + stage=stages_by_name["private_school_attendance"], + engine=engine, + calibration_year=frs_release.calibration_year, + ) + ) if "age_tail" in stage_names: implementations["age_tail"] = UKAgeTailStageTransform( stage=stages_by_name["age_tail"] diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py index aa5e3add93..cea1be7d7a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py @@ -280,7 +280,9 @@ def _energy_rake_gate( presupposes the rake runs more sweeps than the window is long; a shorter series fails closed. The rake must have run in kWh with gas over gas-connected rows and no zero-current cell; a missing margin, block, tolerance or sweep - series fails closed. + series fails closed. A cell the rake started at its NEED mean because + every household in it drew no kWh must be one the stage declares in + ``zero_kwh_cells_at_need_mean`` (microcosm#1240). This is where NEED, DESNZ and the connection share are checked; the calibrated frame is held to the bound ONS 04.5.1 and 04.5.2 spend rows @@ -295,6 +297,7 @@ def _energy_rake_gate( GAS_KWH, PRICE_DOMESTIC_ENERGY_KIND, declared_household_kwh_ceilings, + declared_zero_kwh_cells_at_need_mean, need_margins_from_facts, pricing_operation, published_energy_level, @@ -544,6 +547,28 @@ def _close(observed: object, expected: float, rtol: float) -> bool: f"{stage}: {len(zero_cells)} NEED cell(s) had a zero current mean and " "could not be raked." ) + # A cell started at its NEED mean because every household in it drew no + # kWh stands only where the stage declares it (microcosm#1240). + held_rule = declared_zero_kwh_cells_at_need_mean(declared) + held = receipt.get("cells_held_at_need_mean") + if held is None and held_rule is not None: + failures.append( + f"{stage}: the stage declares zero_kwh_cells_at_need_mean but the " + "energy_rake receipt carries no cells_held_at_need_mean." + ) + held_entries = list(held or ()) + for entry in held_entries: + entry = _mapping(entry, label=f"{stage}.cells_held_at_need_mean") + margin = str(entry.get("margin")) + cell = str(entry.get("cell")) + if cell.partition(":")[2] not in (held_rule or {}).get(margin, ()): + failures.append( + f"{stage}: {margin} cell {cell!r} was started at its NEED mean, " + "which the stage does not declare." + ) + # The count only: a held cell can be a small one, so the details (which + # receipts may quote) never say which cell drew no kWh. + details["cells_held_at_need_mean"] = len(held_entries) if declared.get("gas_connected") == GAS_CONNECTED_PUBLISHED_METER_SHARE: connection = receipt.get("gas_connection") if ( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/student_loans.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/student_loans.py index 596985e005..1ec1c2b4ea 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/student_loans.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/student_loans.py @@ -36,6 +36,34 @@ YEAR_RULE = "calibration_year" STUDENT_LOAN_ENUM_DOMAIN = ("NONE", "PLAN_1", "PLAN_2", "PLAN_5") EXCLUDED_ENGLAND_REGIONS = ("SCOTLAND", "WALES", "NORTHERN_IRELAND") +#: A borrower's course starts this many years before they left full-time +#: education (FRS EDUCLEFT), the length of a first degree in England and +#: Wales; without an answer the course starts at 18 (microcosm#868). +ASSUMED_COURSE_YEARS = 3 +DEFAULT_COURSE_START_AGE = 18 +#: Adults who report an outstanding loan but no repayment are borrowers below +#: the threshold. They are read in the two nations whose starters from 2012/13 +#: hold Plan 2 loans; a Scottish (Plan 4) or Northern Irish (Plan 1) answer +#: assigns no plan here (María's ruling of 2026-10-16, microcosm#868). +OUTSTANDING_LOAN_REGIONS_EXCLUDED = ("SCOTLAND", "NORTHERN_IRELAND") +NOT_IN_EDUCATION = "NOT_IN_EDUCATION" +START_YEAR_FORMULA = ( + "year - age + age_left_full_time_education - 3 where " + "age_left_full_time_education > 0, else year - age + 18" +) +REPORTED_OUTSTANDING_TEST = ( + "reports_outstanding_student_loan and current_education == NOT_IN_EDUCATION" +) +#: No Plan 5 loan is repaid before April 2026 (SLC), so before then a borrower +#: who repays started a course before Plan 5 existed: a repayer whose dates +#: point to a course from 2023/24 takes Plan 2, and SLC's published zero of +#: Plan 5 borrowers above the threshold in academic year 2024/25 holds +#: (microcosm#868). The year is fixed, not derived from the cohort: with +#: three-year courses a 2023/24 starter leaves in 2026 and first repays in +#: April 2027, so a 2026 build would let a repayer take Plan 5 a year before +#: the assumed cohort repays. Revisit it when the build year moves (Vahid's +#: review of microcosm#1299). +PLAN_5_REPAYMENTS_FROM = 2026 PLAN_SALTS = { "PLAN_5": "student_loan_plan_5", "PLAN_2": "student_loan_plan_2", @@ -181,11 +209,18 @@ def assign_student_loan_plans( ) -> UKStudentLoansResult: """Assign reported cohorts, then top up PLAN_5 before PLAN_2. - Each top-up realises the plan's shortfall against the SLC liable stock by - the identity-keyed greedy walk in :func:`_walk_top_up`, so the realised - mass sits within one skipped person's weight of the shortfall rather than - being a Bernoulli draw whose outcome for a shortfall of about one weight - is zero or two rows (microcosm#1049). + A reported borrower repays (``student_loan_repayments > 0``) or, in + England or Wales, reports an outstanding loan and is not in full-time + education; their course starts :data:`ASSUMED_COURSE_YEARS` before they + left full-time education (microcosm#868). Before + :data:`PLAN_5_REPAYMENTS_FROM` a repayer never takes Plan 5, which nobody + repays before April 2026. Each top-up realises the plan's + shortfall against the SLC liable stock by the identity-keyed greedy walk + in :func:`_walk_top_up`, so the realised mass sits within one skipped + person's weight of the shortfall rather than being a Bernoulli draw whose + outcome for a shortfall of about one weight is zero or two rows + (microcosm#1049). The Plan 2 walk takes nobody in full-time education: a + student is not yet liable to repay. """ validate_uk_national_frame(frame) @@ -197,6 +232,9 @@ def assign_student_loan_plans( "age", "student_loan_repayments", "highest_education", + "current_education", + "reports_outstanding_student_loan", + "age_left_full_time_education", } missing = sorted(required - set(person.columns)) if missing: @@ -218,6 +256,20 @@ def assign_student_loan_plans( ).to_numpy(dtype=float) if not np.isfinite(age).all() or not np.isfinite(repayments).all(): raise ValueError("Student-loan age and repayment inputs must be finite.") + age_left = pd.to_numeric( + person["age_left_full_time_education"], errors="coerce" + ).to_numpy(dtype=float) + if not np.isfinite(age_left).all(): + raise ValueError("Student-loan education-leaving ages must be finite.") + in_education = ( + np.asarray( + [_enum_name(value) for value in person["current_education"]], dtype=object + ) + != NOT_IN_EDUCATION + ) + reports_outstanding = person["reports_outstanding_student_loan"].to_numpy( + dtype=bool + ) weights_by_household = pd.Series( frame.weights_for("household").values, index=household["household_id"], @@ -226,23 +278,42 @@ def assign_student_loan_plans( person["person_household_id"].map(weights_by_household).to_numpy(dtype=float) ) person_lineage = _person_lineage(person, household) - start_year = year - age + 18 - has_repayments = repayments > 0.0 + start_year = course_start_year(year=year, age=age, age_left=age_left) + reported = has_reported_student_loan( + repayments=repayments, + reports_outstanding=reports_outstanding, + in_education=in_education, + region_names=region_names, + ) plan = np.full(len(person), "NONE", dtype=object) - plan[has_repayments & (start_year < PLAN_1_BEFORE)] = "PLAN_1" - plan[has_repayments & (start_year >= PLAN_5_FROM)] = "PLAN_5" - plan[has_repayments & (plan == "NONE")] = "PLAN_2" + plan[reported & (start_year < PLAN_1_BEFORE)] = "PLAN_1" + may_repay_plan_5 = year >= PLAN_5_REPAYMENTS_FROM + plan[ + reported & (start_year >= PLAN_5_FROM) & (may_repay_plan_5 | ~(repayments > 0)) + ] = "PLAN_5" + plan[reported & (plan == "NONE")] = "PLAN_2" reported_plan = plan.copy() receipts: dict[str, UKStudentLoanPlanReceipt] = {} for plan_name in PLAN_PRIORITY: stock = _stock(stocks, plan_name, year) current_england = float(person_weights[(plan == plan_name) & is_england].sum()) shortfall = max(0.0, stock - current_england) + if plan_name == "PLAN_2": + # Liable borrowers have left education; their course dates from + # the age they left it. + cohort_start, not_a_student = start_year, ~in_education + else: + # Plan 5 starters from 2023/24 have mostly not left education, so + # the age they left it cannot date them: the walk keeps the age + # rule and takes students. + cohort_start = year - age + DEFAULT_COURSE_START_AGE + not_a_student = np.ones(len(person), dtype=bool) eligible = ( (plan == "NONE") & is_england & (education == "TERTIARY") - & _plan_age_cohort_eligibility(plan_name, age=age, start_year=start_year) + & not_a_student + & _plan_age_cohort_eligibility(plan_name, age=age, start_year=cohort_start) ) eligible_mass = float(person_weights[eligible].sum()) eligible_index = np.flatnonzero(eligible) @@ -420,6 +491,47 @@ def _walk_top_up( return np.asarray(taken, dtype=int), len(skipped), lightest +def course_start_year( + *, year: int, age: np.ndarray, age_left: np.ndarray +) -> np.ndarray: + """The year a borrower's course started (microcosm#868). + + The year they left full-time education less :data:`ASSUMED_COURSE_YEARS`; + with no leaving age (0), the year they turned + :data:`DEFAULT_COURSE_START_AGE`, the rule before #868. + """ + + left = np.asarray(age_left, dtype=float) + age = np.asarray(age, dtype=float) + return np.where( + left > 0, + year - age + left - ASSUMED_COURSE_YEARS, + year - age + DEFAULT_COURSE_START_AGE, + ) + + +def has_reported_student_loan( + *, + repayments: np.ndarray, + reports_outstanding: np.ndarray, + in_education: np.ndarray, + region_names: np.ndarray, +) -> np.ndarray: + """Who the FRS reports as a borrower (microcosm#868). + + A repayer anywhere, as before; and in England or Wales an adult who + reports an outstanding loan (SLOS) and is not in full-time education, a + borrower below the repayment threshold. + """ + + below_threshold = ( + np.asarray(reports_outstanding, dtype=bool) + & ~np.asarray(in_education, dtype=bool) + & ~np.isin(region_names, OUTSTANDING_LOAN_REGIONS_EXCLUDED) + ) + return (np.asarray(repayments, dtype=float) > 0.0) | below_threshold + + def _plan_age_cohort_eligibility( plan: str, *, @@ -480,11 +592,16 @@ def _assert_student_loans_stage_parameters( "assign_student_loan_plan_cohorts", { "year_rule": YEAR_RULE, - "start_year_formula": "year - age + 18", + "start_year_formula": START_YEAR_FORMULA, "reported_repayment_test": "student_loan_repayments > 0", "reported_country_gate": False, + "reported_outstanding_test": REPORTED_OUTSTANDING_TEST, + "reported_outstanding_region_exclusions": list( + OUTSTANDING_LOAN_REGIONS_EXCLUDED + ), "plan_1_before": PLAN_1_BEFORE, "plan_5_from": PLAN_5_FROM, + "plan_5_repayments_from": PLAN_5_REPAYMENTS_FROM, "enum_domain": list(STUDENT_LOAN_ENUM_DOMAIN), "plan_4_imputation": False, }, @@ -499,6 +616,7 @@ def _assert_student_loans_stage_parameters( "year_rule": YEAR_RULE, "age_min": PLAN_5_MIN_AGE, "age_max": PLAN_5_MAX_AGE, + "cohort_start_formula": "year - age + 18", "cohort_start_min": PLAN_5_FROM, "eligible_region_exclusions": region_exclusions, "highest_education": "TERTIARY", @@ -518,6 +636,8 @@ def _assert_student_loans_stage_parameters( "year_rule": YEAR_RULE, "age_min": PLAN_2_MIN_AGE, "age_max": PLAN_2_MAX_AGE, + "cohort_start_formula": START_YEAR_FORMULA, + "current_education": NOT_IN_EDUCATION, "cohort_start_min": PLAN_1_BEFORE, "cohort_start_max_exclusive": PLAN_5_FROM, "eligible_region_exclusions": region_exclusions, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py new file mode 100644 index 0000000000..65628c9c6f --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/support_floor.py @@ -0,0 +1,439 @@ +"""The sparse-band support floor (microcosm#1095; María's ruling of 2026-10-16). + +A target row the spine supports with only a few source households cannot be +fitted honestly: the solve reaches it by stretching those few weights, and a +fit that rests on fewer than ten source households is the kind of cell the +release must never report. The UC payment-distribution bands at the edges of +each family are such rows, and before this floor each was held out by a +per-row exclusion with its own expiry (uk-data#452, microcosm#736). + +The floor is declared once, in the ``support_floor`` block of +``uk/calibration_measure_exclusions.json`` (schema 3), and signed there. On a +built frame, a row in its scope whose matrix row reaches fewer than ``floor`` +distinct source households (``household_source_id``) with a positive prior +weight is dropped, not pooled; each target's dropped published total is +reported. The run refuses outside the block's approval window, when the +dropped rows pass either declared ceiling, and when a per-row exclusion names +a row the floor would drop (the floor covers it, so the entry must retire). +The dropped set joins the calibration's measure exclusions as +``kind: support_floor``, so the gates, the written register and the +certifier read it back rather than recompute support, which they cannot. +""" + +from __future__ import annotations + +import json +import math +from collections.abc import Callable, Collection, Mapping, Sequence +from dataclasses import dataclass, replace +from datetime import date +from importlib import resources as importlib_resources +from pathlib import Path +from typing import Any + +import numpy as np +from scipy import sparse + +from microcosm.calibrate import TargetRegistry + +UK_SUPPORT_FLOOR_KEY = "support_floor" +UK_SUPPORT_FLOOR_KIND = "support_floor" +UK_SUPPORT_FLOOR_METRIC = "distinct_source_households_with_positive_prior_weight" +#: The spine household each row was built from; every clone shares it. +UK_SUPPORT_LINEAGE_COLUMN = "household_source_id" +_FIELDS = ( + "contract_target_ids", + "metric", + "floor", + "max_dropped_rows", + "max_dropped_published_share", + "reason", + "tracking", + "approved_by", + "adjudication", + "approved_on", + "expires_on", +) +_TEXT_FIELDS = ("reason", "tracking", "approved_by", "adjudication") +#: The record fields a dropped row carries, as a per-row exclusion carries its +#: own (``measure_simulation._UK_MEASURE_EXCLUSION_FIELDS`` less the name). +_RECORD_FIELDS = ( + "reason", + "tracking", + "approved_by", + "adjudication", + "approved_on", + "expires_on", +) + + +@dataclass(frozen=True) +class UKSupportFloor: + """The signed floor: its scope, threshold, ceilings and approval window.""" + + contract_target_ids: frozenset[str] + floor: int + max_dropped_rows: int + max_dropped_published_share: float + reason: str + tracking: str + approved_by: str + adjudication: str + approved_on: date + expires_on: date + + def in_scope(self, spec: Any) -> bool: + return spec.metadata.get("contract_target_id") in self.contract_target_ids + + def record(self, evaluated_on: date) -> dict[str, str]: + """The measure-exclusion record a dropped row carries.""" + + return { + "kind": UK_SUPPORT_FLOOR_KIND, + "reason": self.reason, + "tracking": self.tracking, + "approved_by": self.approved_by, + "adjudication": self.adjudication, + "approved_on": self.approved_on.isoformat(), + "expires_on": self.expires_on.isoformat(), + "evaluated_on": evaluated_on.isoformat(), + } + + def assert_in_force(self, evaluated_on: date) -> None: + if not self.approved_on <= evaluated_on <= self.expires_on: + raise ValueError( + "UK support floor is outside its reviewed window " + f"({self.approved_on.isoformat()}..{self.expires_on.isoformat()}, " + f"evaluated {evaluated_on.isoformat()}): renew the signature " + f"({self.tracking})." + ) + + +def uk_support_floor_from_payload(block: Mapping[str, Any]) -> UKSupportFloor: + """Validate the ``support_floor`` block closed-world.""" + + if not isinstance(block, Mapping): + raise ValueError("UK support floor must be an object.") + unknown = sorted(set(block) - set(_FIELDS)) + missing = sorted(set(_FIELDS) - set(block)) + if unknown or missing: + raise ValueError( + f"UK support floor fields are closed-world: unknown {unknown}, " + f"missing {missing}." + ) + if block["metric"] != UK_SUPPORT_FLOOR_METRIC: + raise ValueError( + f"UK support floor metric must be {UK_SUPPORT_FLOOR_METRIC!r}, got " + f"{block['metric']!r}." + ) + targets = block["contract_target_ids"] + if ( + not isinstance(targets, list) + or not targets + or len(set(targets)) != len(targets) + or not all(isinstance(target, str) and target for target in targets) + ): + raise ValueError("UK support floor scope must list distinct target ids.") + floor = block["floor"] + rows = block["max_dropped_rows"] + share = block["max_dropped_published_share"] + if not isinstance(floor, int) or isinstance(floor, bool) or floor < 3: + raise ValueError( + "UK support floor must be an integer of at least 3, CD171's floor." + ) + if not isinstance(rows, int) or isinstance(rows, bool) or rows < 0: + raise ValueError("UK support floor max_dropped_rows must be a count.") + if ( + not isinstance(share, (int, float)) + or isinstance(share, bool) + or not 0.0 <= float(share) < 1.0 + ): + raise ValueError( + "UK support floor max_dropped_published_share must be in [0, 1)." + ) + for field in _TEXT_FIELDS: + if not isinstance(block[field], str) or not block[field].strip(): + raise ValueError(f"UK support floor {field} is empty.") + dates = {} + for field in ("approved_on", "expires_on"): + try: + parsed = date.fromisoformat(str(block[field])) + except ValueError: + parsed = None + if parsed is None or parsed.isoformat() != block[field]: + raise ValueError( + f"UK support floor {field} must be canonical ISO (YYYY-MM-DD), " + f"got {block[field]!r}." + ) + dates[field] = parsed + if dates["expires_on"] <= dates["approved_on"]: + raise ValueError("UK support floor expires_on must be after approved_on.") + return UKSupportFloor( + contract_target_ids=frozenset(targets), + floor=floor, + max_dropped_rows=rows, + max_dropped_published_share=float(share), + reason=block["reason"], + tracking=block["tracking"], + approved_by=block["approved_by"], + adjudication=block["adjudication"], + approved_on=dates["approved_on"], + expires_on=dates["expires_on"], + ) + + +def load_uk_support_floor_declaration( + path: Path | None = None, +) -> dict[str, Any] | None: + """The register's validated ``support_floor`` block, or ``None``. + + ``path`` is the measure-exclusion register the run reads its per-row + exclusions from (the packaged one by default), so the floor and the + per-row entries always come from one signed file. + """ + + if path is None: + text = ( + importlib_resources.files("microcosm.build.uk") + .joinpath("calibration_measure_exclusions.json") + .read_text(encoding="utf-8") + ) + else: + text = Path(path).read_text(encoding="utf-8") + block = json.loads(text).get(UK_SUPPORT_FLOOR_KEY) + if block is None: + return None + uk_support_floor_from_payload(block) + return dict(block) + + +def uk_household_source_ids(frame: Any) -> np.ndarray: + """The source household of each household column, or refuse.""" + + household = frame.table("household") + if UK_SUPPORT_LINEAGE_COLUMN not in household: + raise ValueError( + f"The UK support floor counts source households, and the frame has " + f"no household.{UK_SUPPORT_LINEAGE_COLUMN} column." + ) + return household[UK_SUPPORT_LINEAGE_COLUMN].to_numpy() + + +def uk_row_support( + matrix: Any, prior_weights: np.ndarray, source_ids: np.ndarray +) -> np.ndarray: + """Distinct source households behind each row's positive-weight entries.""" + + csr = sparse.csr_matrix(matrix) + weights = np.asarray(prior_weights, dtype=np.float64) + sources = np.asarray(source_ids) + if csr.shape[1] != len(weights) or len(weights) != len(sources): + raise ValueError( + "Support needs one prior weight and one source id per matrix column." + ) + support = np.zeros(csr.shape[0], dtype=np.int64) + for row in range(csr.shape[0]): + start, end = csr.indptr[row], csr.indptr[row + 1] + columns = csr.indices[start:end] + live = columns[(csr.data[start:end] != 0.0) & (weights[columns] > 0.0)] + support[row] = len(np.unique(sources[live])) + return support + + +@dataclass(frozen=True) +class UKSupportFloorResult: + """The narrowed register and problem, the dropped rows and the receipt.""" + + registry: TargetRegistry + problem: Any + exclusions: dict[str, dict[str, str]] + receipt: dict[str, Any] + + +def _assert_within_ceilings(floor: UKSupportFloor, dropped: int, share: float) -> None: + if dropped > floor.max_dropped_rows: + raise ValueError( + f"UK support floor drops {dropped} rows, more than its signed " + f"ceiling of {floor.max_dropped_rows} ({floor.tracking})." + ) + if share > floor.max_dropped_published_share: + raise ValueError( + f"UK support floor drops {share:.4f} of its scope's published " + "total, more than its signed ceiling of " + f"{floor.max_dropped_published_share} ({floor.tracking})." + ) + + +def _dropped_by_target(specs: Sequence[Any]) -> dict[str, dict[str, float | int]]: + totals: dict[str, dict[str, float | int]] = {} + for spec in specs: + entry = totals.setdefault( + str(spec.metadata["contract_target_id"]), + {"rows": 0, "published_total": 0.0}, + ) + entry["rows"] = int(entry["rows"]) + 1 + entry["published_total"] = float(entry["published_total"]) + float(spec.value) + return {key: totals[key] for key in sorted(totals)} + + +def apply_uk_support_floor( + registry: TargetRegistry, + problem: Any, + *, + source_ids: Sequence[object] | np.ndarray | Callable[[], Any], + floor: UKSupportFloor, + evaluated_on: date, + row_excluded: Collection[str] = (), +) -> UKSupportFloorResult: + """Drop the in-scope rows below the floor from a compiled problem. + + ``problem`` is the compiled constraint problem over ``registry``, row for + row; its columns are the households ``source_ids`` names, in order (or a + callable that returns them, read only when a row in scope needs them). + ``row_excluded`` names the registry rows that carry a per-row exclusion + and were compiled only so their support could be measured: one the floor + would drop refuses the run, and all of them leave the problem. + """ + + floor.assert_in_force(evaluated_on) + specs = list(registry.specs) + names = tuple(problem.names) + if names != tuple(spec.to_target().row_name for spec in specs): + raise ValueError("UK support floor needs the problem's rows in register order.") + probes = { + position for position, spec in enumerate(specs) if spec.name in row_excluded + } + unknown = sorted(set(row_excluded) - {specs[position].name for position in probes}) + if unknown: + raise ValueError(f"UK support floor probes name no compiled row: {unknown}.") + in_scope = [ + position + for position, spec in enumerate(specs) + if position not in probes and floor.in_scope(spec) + ] + measured = sorted({*in_scope, *(p for p in probes if floor.in_scope(specs[p]))}) + support: dict[int, int] = {} + if measured: + lineage = source_ids() if callable(source_ids) else source_ids + counts = uk_row_support( + sparse.csr_matrix(problem.matrix)[measured], + np.asarray(problem.initial_weights.values, dtype=np.float64), + np.asarray(lineage), + ) + support = dict(zip(measured, counts.tolist(), strict=True)) + redundant = sorted( + specs[position].name + for position in probes + if position in support and support[position] < floor.floor + ) + if redundant: + raise ValueError( + "UK support floor covers rows that also carry a per-row measure " + f"exclusion; retire those entries ({floor.tracking}): {redundant}." + ) + dropped = [position for position in in_scope if support[position] < floor.floor] + scope_total = math.fsum(float(specs[position].value) for position in in_scope) + dropped_total = math.fsum(float(specs[position].value) for position in dropped) + share = dropped_total / scope_total if scope_total > 0.0 else 0.0 + _assert_within_ceilings(floor, len(dropped), share) + record = floor.record(evaluated_on) + exclusions = {specs[position].name: dict(record) for position in dropped} + leaving = probes | set(dropped) + kept_rows = np.asarray( + [position for position in range(len(specs)) if position not in leaving], + dtype=np.int64, + ) + narrowed = TargetRegistry( + [specs[position] for position in kept_rows], country=registry.country + ) + receipt = { + "metric": UK_SUPPORT_FLOOR_METRIC, + "floor": floor.floor, + "tracking": floor.tracking, + "approved_on": floor.approved_on.isoformat(), + "expires_on": floor.expires_on.isoformat(), + "evaluated_on": evaluated_on.isoformat(), + "in_scope_rows": len(in_scope), + "probed_row_exclusions": len(probes), + "dropped_rows": len(dropped), + "dropped_published_share": share, + "dropped_by_target": _dropped_by_target([specs[p] for p in dropped]), + "dropped": sorted(exclusions), + "solved_register_sha256": narrowed.version, + } + if not leaving: + return UKSupportFloorResult(registry, problem, {}, receipt) + narrowed_problem = replace( + problem, + matrix=sparse.csr_array(sparse.csr_matrix(problem.matrix)[kept_rows]), + target_vector=np.asarray(problem.target_vector)[kept_rows], + names=tuple(names[row] for row in kept_rows), + targets=tuple(problem.targets[row] for row in kept_rows), + ) + return UKSupportFloorResult(narrowed, narrowed_problem, exclusions, receipt) + + +def uk_support_floor_names( + measure_exclusions: Mapping[str, Mapping[str, str]], +) -> frozenset[str]: + """The rows a run's floor dropped, read back from its measure exclusions.""" + + return frozenset( + name + for name, record in measure_exclusions.items() + if record.get("kind") == UK_SUPPORT_FLOOR_KIND + ) + + +def narrow_uk_registry_by_support_floor( + registry: TargetRegistry, + measure_exclusions: Mapping[str, Mapping[str, str]], + floor: UKSupportFloor | None, +) -> TargetRegistry: + """The register less the rows a run's floor dropped, checked against it. + + For the readers that cannot recompute support (the gates, the written + register, the certifier's recompiled register): each recorded drop must + be a row of ``registry`` in the declared scope, under the declared + signature, and the drops must sit within the declared row ceiling. + """ + + names = uk_support_floor_names(measure_exclusions) + if not names: + return registry + if floor is None: + raise ValueError( + "The run's measure exclusions record support-floor drops, and the " + "register declares no support floor." + ) + by_name: dict[str, list[Any]] = {} + for spec in registry.specs: + by_name.setdefault(spec.name, []).append(spec) + failures = [] + for name in sorted(names): + specs = by_name.get(name, []) + record = measure_exclusions[name] + if not specs: + failures.append(f"{name}: not a row of the register") + elif not all(floor.in_scope(spec) for spec in specs): + failures.append(f"{name}: outside the floor's scope") + elif any( + record.get(field) != value + for field, value in floor.record(floor.approved_on).items() + if field in _RECORD_FIELDS + ): + failures.append(f"{name}: not under the declared signature") + if failures: + raise ValueError( + "The run's support-floor drops do not match the declared floor: " + + "; ".join(failures) + ) + if len(names) > floor.max_dropped_rows: + raise ValueError( + f"The run's support floor dropped {len(names)} rows, more than its " + f"signed ceiling of {floor.max_dropped_rows} ({floor.tracking})." + ) + return TargetRegistry( + [spec for spec in registry.specs if spec.name not in names], + country=registry.country, + ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py index fbea211fe8..c966a40b93 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py @@ -242,7 +242,9 @@ def __post_init__(self) -> None: "person.a_and_e_visits", "person.aa_category", "person.admitted_patient_visits", + "person.age_left_full_time_education", "person.age_started_or_accepted_current_education_or_training", + "person.attends_private_school", "person.attends_private_school_random_draw", "person.capital_gains_asset_type", "person.capital_gains_badr", @@ -256,6 +258,7 @@ def __post_init__(self) -> None: "person.dla_sc_category", "person.employment_sector", "person.esa_health_condition_proxy", + "person.esa_includes_support_component", "person.esa_support_group_proxy", "person.gift_aid", "person.has_lifetime_isa", @@ -263,6 +266,7 @@ def __post_init__(self) -> None: "person.is_before_universal_credit_qualifying_young_person_terminal_date", "person.is_blind", "person.is_claimant_or_partner", + "person.is_engaged_in_owned_company_trade", "person.is_hbai_dependent_child", "person.is_in_non_advanced_education", "person.is_parent", @@ -272,10 +276,16 @@ def __post_init__(self) -> None: "person.local_bus_single_fare_share", "person.local_bus_trips", "person.local_bus_use_band", + "person.loss_relief", + "person.ni_class_4_losses_brought_forward", "person.ons_family_index", "person.ons_family_role", "person.other_local_bus_trips", "person.outpatient_visits", + "person.owned_company_carries_on_property_business", + "person.owned_company_carries_on_trade", + "person.owned_company_income_share", + "person.owned_company_is_main_employment", "person.pension_contributions_via_salary_sacrifice", "person.pip_dl_category", "person.pip_m_category", @@ -285,13 +295,16 @@ def __post_init__(self) -> None: "person.rent_paid_as_boarder", "person.rent_paid_as_lodger", "person.relationship_to_head", + "person.reports_outstanding_student_loan", "person.salary_sacrifice_asked", "person.salary_sacrifice_reported", "person.sic_industry_division", + "person.stands_as_sole_owner_or_partner_of_company", "person.statutory_paternity_pay", "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", + "person.trading_loss", "person.uc_is_in_startup_period", "person.uc_is_in_gainful_self_employment", "person.is_looked_after_by_local_authority", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py index 3cfc869d8f..19fe70ab6d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py @@ -74,7 +74,7 @@ _UC_CAPITAL_LEVELS = ("exact_cell", *UC_CAPITAL_COARSENING) #: The UC capital limits the receipt measures the redraw against. _UC_CAPITAL_RECEIPT_LIMITS = (6_000.0, 16_000.0) -#: policyengine-uk 2.130.1 counts these household property values as Universal +#: policyengine-uk 2.132.6 counts these household property values as Universal #: Credit and Pension Credit capital (the ``sources`` lists under #: ``gov.dwp.universal_credit.means_test.capital`` and #: ``gov.dwp.pension_credit.income.capital``, beside ``savings`` and diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py index c0cfc29030..4d245d73b0 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py @@ -28,7 +28,7 @@ PRIMARY_QRF_WORKER_MODULE = "microcosm.build.us_runtime.puf_qrf_worker" PRIMARY_QRF_INTERPRETER_PLACEHOLDER = "{python_interpreter}" APPROVED_UV_LOCK_SHA256 = ( - "c99b8d9ff73e1e4edb80340ac4c5d9b9992f2331e2fe450b19e7794ff84c4bbc" + "70663b9ce72b956b9b5473cc55fe76b31bb3c10be3dc5ce94bb7f44ae8ef4e69" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py b/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py new file mode 100644 index 0000000000..05f789e221 --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_esa_support_group.py @@ -0,0 +1,62 @@ +"""policyengine-uk reads the ESA group the spine stores (microcosm#1095). + +The root stage writes ``esa_includes_support_component`` from the FRS ESA +group (ESAGROUP, VAR3). A stored value replaces the engine's default, which +assumes the support group for anyone with an ESA award of their own, so the +benefit cap's ESA exemption follows the survey's answer. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd + +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind +from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine + +_READ = ["esa_includes_support_component", "is_benefit_cap_exempt_health_disability"] + + +def _frame(in_support_group: bool): + """One adult on income-related ESA, renting privately.""" + + person = pd.DataFrame( + { + "person_id": [1], + "person_benunit_id": [1], + "person_household_id": [1], + "age": [45.0], + "esa_income_reported": [5_000.0], + "esa_includes_support_component": [in_support_group], + } + ) + benunit = pd.DataFrame({"benunit_id": [1]}) + household = pd.DataFrame( + { + "household_id": [1], + "region": ["WALES"], + "council_tax": [0.0], + "tenure_type": ["RENT_PRIVATELY"], + "rent": [6_000.0], + } + ) + return uk_national_frame( + person=person, + benunit=benunit, + household=household, + household_weights=np.ones(1), + weight_kind=WeightKind.DESIGN, + time_period="2025", + ) + + +def test_the_benefit_cap_exemption_follows_the_stored_esa_group() -> None: + engine = PolicyEngineUKEngine() + support = engine.materialize(_frame(True), _READ, 2025) + work_related = engine.materialize(_frame(False), _READ, 2025) + + assert bool(support["esa_includes_support_component"][0]) + assert not bool(work_related["esa_includes_support_component"][0]) + assert bool(support["is_benefit_cap_exempt_health_disability"][0]) + assert not bool(work_related["is_benefit_cap_exempt_health_disability"][0]) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_hbai_gross_income_engine.py b/packages/microcosm-build/tests/engine/uk/test_uk_hbai_gross_income_engine.py index 54ac100653..56e1e5c6ba 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_hbai_gross_income_engine.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_hbai_gross_income_engine.py @@ -3,15 +3,28 @@ The LCFS NEED band and the NTS income band read a household's HBAI net income with the deductions policyengine-uk takes to reach it added back (microcosm#1164). If the engine changes that list, the gross income must move -with it, so this test fails until ``UK_HBAI_GROSS_INCOME_DEDUCTIONS`` and the -two stages' declared ``materialize_rules_engine_predictors`` match again. +with it, so this test fails until ``UK_HBAI_GROSS_INCOME_DEDUCTIONS`` (with the +stages' declared ``materialize_rules_engine_predictors``) and +``UK_HBAI_NET_INCOME_SUBTRACTS_NOT_ADDED_BACK`` together match the engine's +list again. """ from __future__ import annotations +import numpy as np +import pandas as pd +import pytest + from microcosm.build.uk_runtime.household_income import ( UK_HBAI_GROSS_INCOME_DEDUCTIONS, + UK_HBAI_GROSS_INCOME_VARIABLES, + UK_HBAI_NET_INCOME_SUBTRACTS_NOT_ADDED_BACK, + UK_HBAI_NET_INCOME_VARIABLE, + hbai_gross_income, ) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind +from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine def test_gross_income_deductions_are_the_engines_hbai_subtracts() -> None: @@ -19,6 +32,64 @@ def test_gross_income_deductions_are_the_engines_hbai_subtracts() -> None: HBAI_HOUSEHOLD_NET_INCOME_SUBTRACTS, ) - assert sorted(UK_HBAI_GROSS_INCOME_DEDUCTIONS) == sorted( - HBAI_HOUSEHOLD_NET_INCOME_SUBTRACTS + assert not set(UK_HBAI_GROSS_INCOME_DEDUCTIONS) & set( + UK_HBAI_NET_INCOME_SUBTRACTS_NOT_ADDED_BACK + ) + assert sorted( + UK_HBAI_GROSS_INCOME_DEDUCTIONS + UK_HBAI_NET_INCOME_SUBTRACTS_NOT_ADDED_BACK + ) == sorted(HBAI_HOUSEHOLD_NET_INCOME_SUBTRACTS) + + +def test_gross_income_reads_hbai_income_before_the_reset_to_zero() -> None: + """policyengine-uk 2.132.4 resets negative HBAI income to zero (pe-uk#2402). + + The build reads the figure before the reset: the surveys' incomes are not + reset, and net income plus the deductions is gross income only before it. + A household with no income that pays maintenance has negative HBAI income + and no gross income. + """ + + person = pd.DataFrame( + { + "person_id": [11, 21], + "person_benunit_id": [1, 2], + "person_household_id": [1, 2], + "age": [40.0, 40.0], + "employment_income": [0.0, 30_000.0], + "maintenance_expenses": [5_000.0, 0.0], + } ) + benunit = pd.DataFrame({"benunit_id": [1, 2], "would_claim_uc": [False, False]}) + household = pd.DataFrame( + { + "household_id": [1, 2], + "region": ["WALES", "WALES"], + "council_tax": [0.0, 0.0], + "tenure_type": ["OWNED_OUTRIGHT", "OWNED_OUTRIGHT"], + "rent": [0.0, 0.0], + } + ) + frame = uk_national_frame( + person=person, + benunit=benunit, + household=household, + household_weights=np.ones(2), + weight_kind=WeightKind.DESIGN, + time_period="2025", + ) + engine = PolicyEngineUKEngine() + materialized = engine.materialize( + frame, [*UK_HBAI_GROSS_INCOME_VARIABLES, "hbai_household_net_income"], 2025 + ) + before = np.asarray(materialized[UK_HBAI_NET_INCOME_VARIABLE], dtype=float) + after = np.asarray(materialized["hbai_household_net_income"], dtype=float) + + # The engine resets the first household's income and leaves the second's. + assert before[0] == pytest.approx(-5_000.0) + assert after[0] == 0.0 + assert before[1] == pytest.approx(after[1]) + assert after[1] > 0.0 + + gross = hbai_gross_income(frame, engine, materialized) + assert gross[0] == pytest.approx(0.0, abs=1e-6) + assert gross[1] == pytest.approx(30_000.0) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py b/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py index c04009dd02..9c89a561d5 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py @@ -463,6 +463,7 @@ def test_shipped_manifest_is_current(self) -> None: "hmrc_cgt_asset_type_spine", "salary_sacrifice", "student_loans", + "private_school_attendance", "was_wealth", "was_lisa", "regional_property_uprating", diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py index e2aa2ca7c7..5d1653267a 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py @@ -245,6 +245,60 @@ def test_a_donors_relief_bands_follow_the_engines_extended_bands(tmp_path): np.testing.assert_allclose(sum(relief.values()), earned, atol=0.01) +def test_a_traders_relief_bands_follow_the_loss_relieved_income(tmp_path): + """A trading loss is relieved against general income (ITA 2007 s.64; + pe-uk#2085, from 2.131.0), so a GBP 5,000 loss lowers the pay the returned + sacrifice lands on (microcosm#1095, uk-data#528). uk-data#533's first two + examples with that loss: rest of the UK, GBP 4,000 on GBP 48,000 of pay is + now all basic rate (800); Scotland, GBP 4,000 on GBP 42,000 is now all at + the intermediate rate, basic class (840). The classes still add up to the + rise in the engine's earned_income_tax.""" + + ids = np.arange(2, dtype=np.int64) + frame = uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": [40, 40], + "employment_income": [48_000.0, 42_000.0], + "pension_contributions_via_salary_sacrifice": [4_000.0, 4_000.0], + "trading_loss": [5_000.0, 5_000.0], + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "household_weight": [1.0, 1.0], + "region": ["LONDON", "SCOTLAND"], + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2025", + weight_kind=WeightKind.DESIGN, + ) + resolver = UKMeasureResolver( + simulation_source=None, frame=frame, scratch_dir=tmp_path, year=2025 + ) + + relief = { + rate_class: resolver.counterfactual_delta(_band_binding(rate_class), 2025)[0] + for rate_class in UK_RATE_CLASSES + } + + np.testing.assert_allclose(relief["basic"], [800.0, 840.0], atol=0.01) + np.testing.assert_allclose(relief["higher"], [0.0, 0.0], atol=0.01) + np.testing.assert_allclose(relief["additional"], [0.0, 0.0], atol=0.01) + earned, _ = resolver.counterfactual_delta( + _relief_binding("earned_income_tax"), 2025 + ) + np.testing.assert_allclose(sum(relief.values()), earned, atol=0.01) + + def test_relief_bands_refuse_when_they_leave_the_engines_earned_income_tax( tmp_path, monkeypatch ): diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_self_employment_after_loss_relief.py b/packages/microcosm-build/tests/engine/uk/test_uk_self_employment_after_loss_relief.py new file mode 100644 index 0000000000..829db920a8 --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_self_employment_after_loss_relief.py @@ -0,0 +1,86 @@ +"""The HMRC self-employment rows read the profit after losses brought forward. + +The SPI channel keeps the tape's losses brought forward in ``loss_relief`` +(microcosm#1095, uk-data#528), so the year's profit sits in +``self_employment_income``. HMRC's rows count the profit after that relief and +band on total income with it; the engine's ``total_income`` adds the profit +before the relief, so the band measure swaps the one for the other. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd + +from microcosm.build.uk_runtime.measure_simulation import ( + UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF, + UK_TOTAL_INCOME_AFTER_LOSS_RELIEF, + UKMeasureResolver, +) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind + + +def _frame(): + ids = np.arange(3, dtype=np.int64) + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": [45, 45, 45], + "employment_income": [20_000.0, 0.0, 0.0], + # Person 0 keeps a profit after its losses, person 1's losses + # exceed its profit, person 2 has none. + "self_employment_income": [10_000.0, 2_000.0, 5_000.0], + "loss_relief": [3_000.0, 5_000.0, 0.0], + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "household_weight": [1.0, 1.0, 1.0], + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2025", + weight_kind=WeightKind.DESIGN, + ) + + +def test_the_hmrc_measures_read_the_profit_after_loss_relief(tmp_path) -> None: + resolver = UKMeasureResolver( + simulation_source=None, frame=_frame(), scratch_dir=tmp_path, year=2025 + ) + for name in ( + UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF, + UK_TOTAL_INCOME_AFTER_LOSS_RELIEF, + ): + assert resolver.knows("person", name) + assert not resolver.knows("household", name) + assert resolver.entity_for(name) == "person" + + after, _ = resolver.compute("person", UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF) + banded, _ = resolver.compute("person", UK_TOTAL_INCOME_AFTER_LOSS_RELIEF) + profit, _ = resolver.compute("person", "self_employment_income") + total, _ = resolver.compute("person", "total_income") + + np.testing.assert_allclose(after, [7_000.0, 0.0, 5_000.0]) + # The rows read self_employment_income from the frame's stored column, so + # the profit after the relief is read from the stored columns too, at any + # measurement year (microcosm#1095). + later = UKMeasureResolver( + simulation_source=None, frame=_frame(), scratch_dir=tmp_path, year=2026 + ) + stored, route = later.compute("person", UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF) + np.testing.assert_allclose(stored, [7_000.0, 0.0, 5_000.0]) + assert route.startswith("frame_columns:") + # The band reads total income with the profit after the relief in place of + # the profit before it. + np.testing.assert_allclose(banded, np.asarray(total) - np.asarray(profit) + after) + np.testing.assert_allclose(np.asarray(total) - banded, [3_000.0, 2_000.0, 0.0]) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py b/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py index 7dcf7f2958..8344260548 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py @@ -449,11 +449,17 @@ def test_spi_qrf_stages_use_typed_weights_and_restore_gross_savings( result.person.loc[spi_people, "employment_income"], expected_pe_employment, ) + # HMRC's earned income holds the profit after losses brought forward, + # which the stage keeps apart in loss_relief (microcosm#1095). + profit_after_relief = ( + result.person.loc[spi_people, "self_employment_income"] + - result.person.loc[spi_people, "loss_relief"] + ).clip(lower=0.0) expected_total_earned = ( expected_employed + result.person.loc[spi_people, SPI_HMRC_OTHER_INCOME_COLUMN] + result.person.loc[spi_people, SPI_HMRC_STATE_PENSION_INCOME_COLUMN] - + result.person.loc[spi_people, "self_employment_income"] + + profit_after_relief + result.person.loc[spi_people, "private_pension_income"] ) expected_total_investment = ( @@ -930,3 +936,43 @@ def predict_from_uniforms(self, predictors, *, quantiles, sign_uniforms): assert result.stage1_transport["identity_key"] == "person_source_id" assert result.stage1_transport["recipients"] == 3 assert result.fit_weight_records[1].fit_name == "uk_frs_reference_conditional" + + +def test_the_stage_resets_the_esa_group_and_clears_a_loss_beside_a_profit( + monkeypatch, tmp_path +) -> None: + """The stage's output, not only its helpers (Vahid's review of + microcosm#1299): a redrawn row's ESA group returns to the engine's default, + and a redrawn row with a profit keeps no drawn trading loss. The fake + forests draw a positive value for every target, so each redrawn row draws + both a profit and a loss.""" + + from microcosm.build.uk_runtime.spi_income import SPI_INCOME_RECIPIENT_COLUMN + + support = _dead_support() + support = replace( + support, + person=support.person.assign(esa_includes_support_component=False), + ) + donor_path = tmp_path / SPI_DONOR_FILENAME + _write_donor(donor_path) + monkeypatch.setattr(spi_income, "QRF", _FakeQRF) + _bypass_reviewed_donor_identity(monkeypatch) + + person = impute_uk_spi_income_support( + support, + donor_path, + seed=9, + n_estimators=3, + donor_sample_size=None, + state_pension_age=66, + allow_in_sample_reference=True, + ).person + + drawn = person[SPI_INCOME_RECIPIENT_COLUMN].to_numpy(dtype=bool) + assert drawn.any() and (~drawn).any() + assert person.loc[drawn, "esa_includes_support_component"].all() + assert not person.loc[~drawn, "esa_includes_support_component"].any() + profit = person.loc[drawn, "self_employment_income"].to_numpy() > 0.0 + assert profit.any() + assert (person.loc[drawn, "trading_loss"].to_numpy()[profit] == 0.0).all() diff --git a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py index a364c1974f..3546d5c905 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py @@ -1028,7 +1028,7 @@ def test_uk_source_manifest_contains_only_canonical_spine_stages(self) -> None: # microcosm#1063 also adds cgt_residential_split after the anchor, and # microcosm#1106 property_components after the regional uprating; # microcosm#1240 high_value_home_support between them. - assert len(spec.sources.stages) == 41 + assert len(spec.sources.stages) == 42 assert not {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.intersection( stage.stage for stage in spec.sources.stages ) @@ -1156,7 +1156,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: references = {reference.name: reference for reference in spec.target_references} assert ( - len(references) == 1292 + len(references) == 1291 ) # microcosm#1095: + 4, the two national 15_19 sex bands split into ages 15, 16-17 and 18-19; microcosm#1106: + 13 SPI property amounts; microcosm#1113: + 1 ONS 07.2.2 household road fuel; PolicyEngine/chronicle#280 lane: 705 + 33 HMRC liabilities rows (Table 2.5, three # measures by eleven bands) + 26 SPI savings-interest rows (two measures by # thirteen bands) + 360 SPI Table 3.11 region-tier rows (three measures by @@ -1184,7 +1184,9 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: # microcosm#1106: the 13 SPI Table 3.7 net property-income amounts by # total-income band, bound unscaled once the x1.9 exclusion retired; # microcosm#1207: the 9 DfT BUS01a bus journey rows (London and the - # eight English regions outside London) + # eight English regions outside London); microcosm#1095: less DWP's + # five-or-more-children UC count, a diagnostic beside the two-child + # limit rows assert references["obr.esa"].value_operation == "calendar_year_window" assert references["dwp.uc.households"].value_operation == ( "monthly_window_sum_average" diff --git a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py index 25c5696383..370c040d88 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py @@ -33,7 +33,7 @@ #: policyengine-core its own floor brings. UK_ENGINE = { "policyengine-core": "3.33.3", - "policyengine-uk": "2.130.1", + "policyengine-uk": "2.132.6", } ENGINE_PACKAGES = ("microcosm-build", "microcosm-data", "microcosm-frame") diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py index 451ba0ccec..456ac8f4b7 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py @@ -198,6 +198,20 @@ def test_obr_pip_binds_dla_and_pip_together() -> None: assert "value_variable" not in binding +def test_obr_esa_binds_every_benefit_on_the_incapacity_line() -> None: + """OBR table 4.9 footnote 2: the "Incapacity benefits" line holds ESA, + Incapacity Benefit, Severe Disablement Allowance and the incapacity part of + Income Support (microcosm#1095, uk-data#582). The engine has no variable for + the Income Support part, which the notes record as a residual.""" + + binding = _targets()["obr.esa"]["bindings"]["policyengine"] + terms = [term.strip() for term in binding["value_expression"].split("+")] + assert terms == ["esa_income", "esa_contrib", "incapacity_benefit", "sda"] + assert "value_variable" not in binding + assert "Income Support" in binding["notes"] + assert "Severe Disablement Allowance" in binding["notes"] + + def test_pip_caseload_rows_are_england_and_wales_diagnostics_on_obr_pip() -> None: """The two rows resolve at K04000001, the geography Chronicle stamps DWP's caseload with, and fit; fitting them breaks the weight-ratio fence, so @@ -259,3 +273,76 @@ def test_pip_caseload_diagnostics_carry_the_january_2025_caseload() -> None: assert metadata[f"{prefix}_status"] == "available" assert float(metadata[f"{prefix}_value_count"]) == value assert metadata[f"{prefix}_period"] == "2025-01" + + +FIVE_OR_MORE_CHILDREN_ROW = "dwp.uc.households_children_5_or_more" +TWO_CHILD_LIMIT_FIVE_CHILDREN_ROW = "dwp.uc.two_child_limit.households_5_children" + + +def test_five_or_more_children_count_is_a_diagnostic_beside_the_limit_row() -> None: + """DWP's count carries the families outside the two-child limit, which the + bound limit rows leave out and the frame cannot hold, so it sits beside the + limit row of the same month (María's ruling of 2026-10-16, microcosm#1095).""" + + from microcosm.build.uk_runtime.ledger_targets import ( + UK_REQUIRED_TARGET_DIAGNOSTICS, + ) + + contract = load_uk_population_contract() + targets = {target["target_id"]: target for target in contract["targets"]} + assert FIVE_OR_MORE_CHILDREN_ROW not in targets + assert TWO_CHILD_LIMIT_FIVE_CHILDREN_ROW in targets + assert UK_REQUIRED_TARGET_DIAGNOSTICS[TWO_CHILD_LIMIT_FIVE_CHILDREN_ROW] == ( + FIVE_OR_MORE_CHILDREN_ROW, + ) + parity = contract["registry_parity"] + assert FIVE_OR_MORE_CHILDREN_ROW not in parity["scope_target_ids"] + assert FIVE_OR_MORE_CHILDREN_ROW not in parity["mapped"].values() + declaration = contract["diagnostic_references"][FIVE_OR_MORE_CHILDREN_ROW] + assert declaration["attach_to_target"] == TWO_CHILD_LIMIT_FIVE_CHILDREN_ROW + assert declaration["required_period_type"] == "month" + assert declaration["required_assertion"] == "observation" + reference = declaration["reference"] + # The limit rows are DWP's April 2025 publication, so the count is the + # same month's, which the exact fact pin fixes. + assert reference["period"] == "2025-04" + assert reference["period_match_policy"] == "exact" + assert reference["ledger_selector"]["geography_id"] == GREAT_BRITAIN_CODE + assert reference["ledger_selector"]["dimension_values"] == { + "number_of_children": "5 or more", + "payment_indicator": "Yes", + "child_entitlement": "all", + } + assert reference["ledger_fact_key"] + assert reference["measure"] in parity["excluded"] + assert reference["measure"] not in parity["mapped"] + + +def test_five_or_more_children_diagnostic_carries_the_april_2025_count() -> None: + """Feed-gated: the diagnostic resolves DWP's exact April 2025 count.""" + + from microcosm.build.uk_runtime.ledger_targets import ( + _attached_diagnostic_metadata, + ) + + root = paths_for("microcosm-build").repository + configured = os.environ.get("CHRONICLE_UK_FACTS") + feed = ( + Path(configured) if configured else root / ".codex-work/consumer_facts_uk.jsonl" + ) + if feed.is_dir(): + feed = feed / "consumer_facts.jsonl" + if not feed.is_file(): + pytest.skip("pinned UK Chronicle consumer feed is not present") + with feed.open(encoding="utf-8") as handle: + facts = tuple( + json.loads(line) for line in handle if '"dwp.uc_households"' in line + ) + + metadata = _attached_diagnostic_metadata(facts, TWO_CHILD_LIMIT_FIVE_CHILDREN_ROW) + + prefix = "dwp_uc_children_5_or_more_diagnostic" + assert metadata[f"{prefix}_role"] == "diagnostic_only_not_in_fit" + assert metadata[f"{prefix}_status"] == "available" + assert float(metadata[f"{prefix}_value_count"]) == 76_885.0 + assert metadata[f"{prefix}_period"] == "2025-04" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_imputation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_imputation.py index dc4b7d3ae4..ced02e0066 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_imputation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_imputation.py @@ -358,6 +358,12 @@ def test_component_sum_income_is_the_pre_allowance_proxy(self) -> None: assert total == pytest.approx([1_000.0 * components, 2_000.0 * components]) with pytest.raises(ValueError, match="missing"): uk_cgt_component_sum_income(person.drop(columns=["dividend_income"])) + # Total income holds the profit after losses brought forward, which + # the spine keeps apart in loss_relief (microcosm#1095). + relieved = person.assign(loss_relief=[500.0, 5_000.0]) + assert uk_cgt_component_sum_income(relieved) == pytest.approx( + [1_000.0 * components - 500.0, 2_000.0 * components - 2_000.0] + ) def test_allocation_preserves_the_existing_ranking(self) -> None: # Everyone sits in one income band, whose cells hold 100 people each diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_energy_pricing.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_energy_pricing.py index 0158434524..449c7cd8a6 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_energy_pricing.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_energy_pricing.py @@ -20,6 +20,7 @@ SCOTLAND_GEOGRAPHY_ID, cap_household_kwh, declared_household_kwh_ceilings, + declared_zero_kwh_cells_at_need_mean, fiscal_year_quarters, impose_gas_connection, income_band_ids, @@ -1348,3 +1349,161 @@ def run(energy_rake): failures = run(lost).failures assert any("total after the cap" in f for f in failures) assert any("left below the electricity_kwh ceiling" in f for f in failures) + + +def test_the_stage_starts_only_the_mobile_home_cells_at_the_need_mean() -> None: + # microcosm#1240: mobile homes are the one FRS dwelling type NEED has no + # row for; the declaration names the all-dwellings mean they map to. + declared = declared_zero_kwh_cells_at_need_mean(_declared()) + assert declared == {"accommodation": ("all_dwellings",)} + for mapping in NEED_PROPERTY_TYPE_IDS_BY_GEOGRAPHY.values(): + assert mapping["MOBILE"] == "all_dwellings" + assert declared_zero_kwh_cells_at_need_mean({}) is None + for bad in ( + [], + {}, + {"dwelling": ["all_dwellings"]}, + {"accommodation": "all_dwellings"}, + {"accommodation": []}, + {"accommodation": [""]}, + ): + with pytest.raises(ValueError, match="zero_kwh_cells_at_need_mean"): + declared_zero_kwh_cells_at_need_mean({"zero_kwh_cells_at_need_mean": bad}) + + +def _mobile_home_table(n: int = 60): + """England and Wales rows in one income band, every third a mobile home + that drew no kWh (so the mobile-home cell is the only one with no kWh).""" + + rng = np.random.default_rng(17) + accommodation = np.array(["HOUSE_DETACHED", "FLAT", "MOBILE"], dtype=object)[ + np.arange(n) % 3 + ] + mobile = accommodation == "MOBILE" + table = pd.DataFrame( + { + ELECTRICITY_KWH: np.where(mobile, 0.0, rng.uniform(1000.0, 5000.0, n)), + GAS_KWH: np.where(mobile, 0.0, rng.uniform(5000.0, 20000.0, n)), + } + ) + arguments = { + "margins": need_margins_from_facts(period_value=NEED_PERIOD), + "frs_region": np.full(n, "LONDON", dtype=object), + "income": rng.uniform(21e3, 29e3, n), + "weights": rng.uniform(0.5, 2.0, n), + "iterations": 50, + "accommodation": accommodation, + "gas_connected": ~mobile, + } + return table, mobile, arguments + + +def test_a_declared_cell_that_drew_no_kwh_starts_at_its_need_mean() -> None: + table, mobile, arguments = _mobile_home_table() + cell = f"{ENGLAND_AND_WALES_GEOGRAPHY_ID}:all_dwellings" + # Undeclared, the cell has nothing to scale and stays at zero. + plain, plain_receipt = rake_energy_kwh(table, **arguments) + assert { + (zero["margin"], zero["category"], zero["column"]) + for zero in plain_receipt["zero_current_cells"] + } == {("_need_accommodation", cell, ELECTRICITY_KWH)} + assert plain_receipt["cells_held_at_need_mean"] == [] + assert (plain.loc[mobile, ELECTRICITY_KWH] == 0.0).all() + # Declared, its households start at the NEED mean and the rake fits it. + raked, receipt = rake_energy_kwh( + table, + **arguments, + zero_kwh_cells_at_need_mean={"accommodation": ("all_dwellings",)}, + ) + assert receipt["zero_current_cells"] == [] + assert receipt["cells_held_at_need_mean"] == [ + {"margin": "accommodation", "cell": cell, "column": ELECTRICITY_KWH} + ] + assert (raked.loc[mobile, ELECTRICITY_KWH] > 0.0).all() + fitted = receipt["fit"]["accommodation"]["cells"][cell][ELECTRICITY_KWH] + assert abs(fitted["relative_deviation"]) < 0.05 + # The mobile homes have no gas connection, so gas has no cell to start + # and its rake runs as it did undeclared. + assert (raked.loc[mobile, GAS_KWH] == 0.0).all() + assert ( + receipt["sweep_residuals"][GAS_KWH] == plain_receipt["sweep_residuals"][GAS_KWH] + ) + # A cell with one positive row is left to the rake. + drawn = table.copy() + drawn.loc[np.flatnonzero(mobile)[0], ELECTRICITY_KWH] = 500.0 + _, kept = rake_energy_kwh( + drawn, + **arguments, + zero_kwh_cells_at_need_mean={"accommodation": ("all_dwellings",)}, + ) + assert kept["cells_held_at_need_mean"] == [] + assert kept["zero_current_cells"] == [] + + +def test_a_held_cell_must_be_a_published_cell_of_a_swept_margin() -> None: + table, _, arguments = _mobile_home_table() + with pytest.raises(ValueError, match="NEED publishes no accommodation cell"): + rake_energy_kwh( + table, + **arguments, + zero_kwh_cells_at_need_mean={"accommodation": ("park_homes",)}, + ) + with pytest.raises(ValueError, match="does not sweep"): + rake_energy_kwh( + table, + **arguments, + zero_kwh_cells_at_need_mean={"tenure": ("owner_occupied",)}, + ) + + +def test_the_energy_rake_gate_takes_only_the_declared_held_cells() -> None: + from microcosm.build.uk_runtime.stage_health import uk_stage_health_gate + + gates = json.loads( + ( + _TEST_PATHS.repository + / "packages/microcosm-build/src/microcosm/build/uk/gates.json" + ).read_text("utf-8") + ) + by_id = {gate["id"]: gate for gate in gates["gates"]} + parameters = { + **by_id["uk_stage_lcfs_consumption_energy_rake"]["parameters"], + "maximum_relative_deviation": 1.0, + } + _, _, _, _, receipt = _synthetic_rake_receipt() + + def run(energy_rake): + return uk_stage_health_gate( + evidence={"stage": "lcfs_consumption", "energy_rake": energy_rake}, + stage="lcfs_consumption", + check="energy_rake", + parameters=parameters, + ) + + passed = run(receipt) + assert passed.passed, passed.failures + assert passed.details["cells_held_at_need_mean"] == 0 + held = copy.deepcopy(receipt) + held["cells_held_at_need_mean"] = [ + { + "margin": "accommodation", + "cell": f"{SCOTLAND_GEOGRAPHY_ID}:all_dwellings", + "column": ELECTRICITY_KWH, + } + ] + declared = run(held) + assert declared.passed, declared.failures + # The details count the cells and never name one. + assert declared.details["cells_held_at_need_mean"] == 1 + assert "all_dwellings" not in json.dumps(declared.details) + undeclared = copy.deepcopy(receipt) + undeclared["cells_held_at_need_mean"] = [ + { + "margin": "accommodation", + "cell": f"{ENGLAND_AND_WALES_GEOGRAPHY_ID}:detached", + "column": ELECTRICITY_KWH, + } + ] + assert any("does not declare" in f for f in run(undeclared).failures) + missing = {k: v for k, v in receipt.items() if k != "cells_held_at_need_mean"} + assert any("carries no cells_held_at_need_mean" in f for f in run(missing).failures) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_services.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_services.py index 6e1297bb50..7f72136d28 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_services.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_services.py @@ -259,7 +259,7 @@ def test_recipient_predictors_derive_education_counts_and_aggregate() -> None: "is_SP_age": "person", "dla": "person", "pip": "person", - "hbai_household_net_income": "household", + "hbai_household_net_income_before_reset_to_zero": "household", "current_education": "person", } values = { @@ -268,7 +268,7 @@ def test_recipient_predictors_derive_education_counts_and_aggregate() -> None: "is_SP_age": np.array([0.0, 1.0, 0.0, 0.0]), "dla": np.array([0.0, 100.0, 0.0, 0.0]), "pip": np.array([50.0, 0.0, 0.0, 0.0]), - "hbai_household_net_income": np.array([1e4, 2e4]), + "hbai_household_net_income_before_reset_to_zero": np.array([1e4, 2e4]), "current_education": np.array( ["NOT_IN_EDUCATION", "TERTIARY", "PRIMARY", "LOWER_SECONDARY"] ), diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_vat.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_vat.py index a808812b85..496fde8eed 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_vat.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_etb_vat.py @@ -239,14 +239,14 @@ def test_recipient_predictors_aggregate_person_entities_to_household() -> None: # plus the HBAI deductions, less income tax, NI, council tax and rates, so # a pension contribution stays in it and the direct taxes do not. household_level = { - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "LVT", } values = { "is_SP_age": np.array([0.0, 1.0, 0.0, 0.0]), - "hbai_household_net_income": np.array([1e4, 2e4]), + "hbai_household_net_income_before_reset_to_zero": np.array([1e4, 2e4]), "income_tax": np.array([1000.0, 500.0, 0.0, 2000.0]), "council_tax": np.array([1200.0, 900.0]), "employee_pension_contributions": np.array([300.0, 0.0, 0.0, 0.0]), diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py index 6c64b97d9b..04342aefaa 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_education.py @@ -78,6 +78,8 @@ def test_training_qyp_ema_and_benefits_in_own_right() -> None: # training (uk-data#561). "trainee": [2, 1, 1], "_frs_table": ["child", "child", "adult"], + "slos": [np.nan, np.nan, 2], + "educleft": [np.nan, np.nan, 18], # 2023-24-shaped vintage: no adema pair, eduma/edumaamt instead. "eduma": [1, 1, 0], "edumaamt": [2.0, -1.0, 5.0], @@ -126,6 +128,8 @@ def test_adult_ema_prefers_the_adema_pair_when_present() -> None: # absent). "trainee": [2, 2], "_frs_table": ["child", "adult"], + "slos": [np.nan, 2], + "educleft": [np.nan, 18], "adema": [1, 1], "ademaamt": [4.0, -1.0], "eduma": [1, 1], @@ -162,6 +166,8 @@ def test_approved_training_is_trainee_on_child_table_records_only() -> None: # 1 yes, 2 no; a blank and an out-of-range code are not a yes. "trainee": [1, 1, 1, np.nan, 1], "_frs_table": ["child", "child", "adult", "child", "adult"], + "slos": [np.nan, np.nan, 2, np.nan, 2], + "educleft": [np.nan, np.nan, 17, np.nan, 16], "chema": [0, 0, 0, 0, 0], "chemaamt": [0.0, 0.0, 0.0, 0.0, 0.0], "eduma": [0, 0, 0, 0, 0], @@ -204,6 +210,8 @@ def test_approved_training_refuses_rows_without_its_columns(column: str) -> None "educqual": [86], "trainee": [1], "_frs_table": ["child"], + "slos": [np.nan], + "educleft": [np.nan], } ) @@ -238,3 +246,57 @@ def test_the_stage_refuses_a_tab_without_trainee(tmp_path, table: str) -> None: with pytest.raises(KeyError, match=f"The FRS {table} tab has no TRAINEE column"): add_frs_education(None, tmp_path, stage=SimpleNamespace(artifacts=artifacts)) + + +def test_student_loan_answers_are_read_from_adult_records_only() -> None: + """SLOS = 1 reports an outstanding loan and EDUCLEFT the age full-time + education ended, on adult-table records; a child record, a no, a blank and + an age outside 10 to 90 read as no loan and 0 (microcosm#868).""" + + person = pd.DataFrame({"person_id": [1, 2, 3, 4, 5], "age": [30, 25, 15, 40, 50]}) + raw = pd.DataFrame( + { + "person_id": [1, 2, 3, 4, 5], + "fted": [2, 2, 1, 2, 2], + "typeed2": [0, 0, 5, 0, 0], + "educqual": [3, 3, 86, 86, 86], + "trainee": [2, 2, 2, 2, 2], + "_frs_table": ["adult", "adult", "child", "adult", "adult"], + "slos": [1, 2, 1, np.nan, 1], + "educleft": [21, 22, 15, 97, -1], + } + ) + + result = derive_frs_education(person, raw) + + assert result["reports_outstanding_student_loan"].tolist() == [ + True, + False, + False, + False, + True, + ] + assert result["age_left_full_time_education"].tolist() == [21, 22, 0, 0, 0] + assert result["age_left_full_time_education"].dtype == np.int64 + + +@pytest.mark.parametrize("column", ["slos", "educleft"]) +def test_student_loan_answers_refuse_rows_without_their_columns(column: str) -> None: + """A missing column refuses rather than read nobody as a borrower.""" + + person = pd.DataFrame({"person_id": [1], "age": [30]}) + raw = pd.DataFrame( + { + "person_id": [1], + "fted": [2], + "typeed2": [0], + "educqual": [3], + "trainee": [2], + "_frs_table": ["adult"], + "slos": [1], + "educleft": [21], + } + ) + + with pytest.raises(KeyError, match="student-loan borrowers and course dates"): + derive_frs_education(person, raw.drop(columns=[column])) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py index 0e72c1521e..dfcf134f6a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py @@ -28,6 +28,10 @@ frs_take_up, spine_build, ) +from microcosm.build.uk_runtime.birth_month import ( + UK_BIRTH_MONTH_COLUMN, + uk_months_since_last_birthday, +) from microcosm.build.uk_runtime.content_identity import uk_frame_content_identity from microcosm.build.uk_runtime.frs_council_tax import ( frs_council_tax_operation_parameters, @@ -38,6 +42,7 @@ ) from microcosm.build.uk_runtime.frs_spine import ( FRS_SPINE_TABLES, + OWNER_MANAGER_COLUMNS, REGION_MAP, SCOTTISH_WATER_CHARGES_REDUCTION_RECIPIENT_SHARE, UC_CAPITAL_UNAVAILABLE, @@ -61,6 +66,12 @@ uk_time_period, validate_uk_national_frame, ) +from microcosm.build.uk_runtime.spi_support import ( + SPI_SYNTHETIC_SUPPORT_CHANNEL, + create_uk_spi_support_tables, + support_channel_column, + support_source_id_column, +) from microcosm.frame import Frame, WeightKind, engine_tables # The driver lives in the package now; ``tools/build_uk_frs_spine.py`` is a @@ -290,6 +301,9 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "PAREAMT": 13.0, "REDAMT": 100.0, "SLREPAMT": 2.0, + # A repayer answers no to SLOS; EDUCLEFT dates the course (#868). + "SLOS": 2, + "EDUCLEFT": 21, "SSPADJ": 1.0, "SMPADJ": 0.5, "SPPADJ": 0.25, @@ -413,6 +427,11 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "SEEND": "", "SEJBLONG": "", "JOBBUS": 1, + # Not a director: the owner-manager inputs keep their defaults. + "DIRECTOR": 2, + "NATURE": 4, + "DIRPERC": -1, + "DIVIDEND": 0.0, } ], "benefits": [ @@ -442,6 +461,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "PERSON": 1, "BENEFIT": 16, "VAR2": 3, + # ESAGROUP: 1 work-related activity group, 3 assessment phase. + "VAR3": 1, "BENAMT": 4.0, "UCSTART": "", "UCHOUSEL": "", @@ -452,6 +473,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "PERSON": 1, "BENEFIT": 16, "VAR2": 4, + # ESAGROUP: 1 work-related activity group, 3 assessment phase. + "VAR3": 3, "BENAMT": 5.0, "UCSTART": "", "UCHOUSEL": "", @@ -1140,6 +1163,7 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert other["property_income"] == pytest.approx(8 * WEEKS_IN_YEAR) assert adult["employment_income"] == pytest.approx(10 * WEEKS_IN_YEAR) assert adult["self_employment_income"] == pytest.approx(3 * WEEKS_IN_YEAR) + assert adult["trading_loss"] == 0.0 assert adult["private_pension_income"] == pytest.approx(15 * WEEKS_IN_YEAR) assert adult["tax_free_savings_income"] == pytest.approx(1 * WEEKS_IN_YEAR) assert adult["savings_interest_income"] == pytest.approx(3.5 * WEEKS_IN_YEAR) @@ -1271,6 +1295,71 @@ def test_frs_spine_refuses_an_adult_tab_without_sppadj(tmp_path: Path) -> None: build_uk_frs_spine_frame(tmp_path, stage=stage) +@pytest.mark.parametrize( + ("job", "sic", "expected"), + [ + # A director owning more than half of the company, as their first job, + # in a trade: like a sole owner, main employment, trading. + ( + {"DIRECTOR": 1, "NATURE": 4, "DIRPERC": 2, "JOBTYPE": 1}, + 84, + (True, True, True, True, False, 900.0), + ), + # NATURE 1 names a director too; SIC division 68 is real estate. + ( + {"DIRECTOR": 2, "NATURE": 1, "DIRPERC": 3, "JOBTYPE": 1}, + 68, + (True, True, True, False, True, 900.0), + ), + # A second job's company: no SIC, so neither trade nor property. + ( + {"DIRECTOR": 1, "NATURE": 4, "DIRPERC": 1, "JOBTYPE": 2}, + 84, + (True, True, False, False, False, 900.0), + ), + # Less than half of the company: an investor, not an owner. + ( + {"DIRECTOR": 1, "NATURE": 4, "DIRPERC": 4, "JOBTYPE": 1}, + 84, + (False, False, False, False, False, 0.0), + ), + ], +) +def test_owner_managers_come_from_the_job_tab( + tmp_path: Path, job: dict, sic: int, expected: tuple +) -> None: + """A director owning at least half of a company stands as its sole owner + or partner (María's ruling of 2026-10-16, microcosm#1095).""" + + tables = _fixture_tables() + tables["job"][0].update({**job, "DIVIDEND": 900.0}) + adult_1 = next(row for row in tables["adult"] if row["SERNUM"] == 1) + adult_1["SIC"] = sic + frame = build_uk_frs_spine_frame(tmp_path, stage=_write_fixture(tmp_path, tables)) + person = frame.table("person") + adult = person.loc[person["person_id"] == 1001].iloc[0] + + assert tuple(adult[column] for column in OWNER_MANAGER_COLUMNS) == expected + # Nobody else in the fixture holds a job: the inputs keep their defaults. + others = person.loc[person["person_id"] != 1001, list(OWNER_MANAGER_COLUMNS)] + assert not others.iloc[:, :5].to_numpy().any() + assert (others["owned_company_income_share"] == 0).all() + + +def test_frs_spine_refuses_a_job_tab_without_the_director_answers( + tmp_path: Path, +) -> None: + tables = _fixture_tables() + tables["job"] = [ + {key: value for key, value in row.items() if key != "DIRPERC"} + for row in tables["job"] + ] + stage = _write_fixture(tmp_path, tables) + + with pytest.raises(KeyError, match="owner-managers cannot be set"): + build_uk_frs_spine_frame(tmp_path, stage=stage) + + def test_is_blind_reads_blind_registration_on_the_adult_and_child_tabs( tmp_path: Path, ) -> None: @@ -1300,6 +1389,120 @@ def test_is_blind_reads_blind_registration_on_the_adult_and_child_tabs( assert not person["is_blind"].any() +def test_esa_support_group_is_read_from_the_esa_rows(tmp_path: Path) -> None: + """``esa_includes_support_component`` reads ESAGROUP (VAR3) on ESA rows. + + The fixture's adult in household 1 reports ESA in the work-related activity + group and in the assessment phase, so the column is false for them and + true for everyone else, the engine's own default (microcosm#1095). + """ + + stage = _write_fixture(tmp_path / "groups") + person = build_uk_frs_spine_frame(tmp_path / "groups", stage=stage).table("person") + group = person.set_index("person_id")["esa_includes_support_component"] + assert group.dtype == bool + assert group.to_dict() == {1001: False, 1002: True, 2001: True} + + # One ESA row in the support group is enough. + tables = _fixture_tables() + esa_rows = [row for row in tables["benefits"] if row["BENEFIT"] == 16] + esa_rows[0]["VAR3"] = 2 + stage = _write_fixture(tmp_path / "support", tables) + person = build_uk_frs_spine_frame(tmp_path / "support", stage=stage).table("person") + assert person["esa_includes_support_component"].all() + + # A benefits tab without VAR3 refuses rather than set nobody's group. + tables = _fixture_tables() + tables["benefits"] = [ + {key: value for key, value in row.items() if key != "VAR3"} + for row in tables["benefits"] + ] + stage = _write_fixture(tmp_path / "unasked", tables) + with pytest.raises(KeyError, match="no VAR3 column"): + build_uk_frs_spine_frame(tmp_path / "unasked", stage=stage) + + +@pytest.mark.parametrize( + ("code", "expected"), + [(1, False), (2, True), (3, False), (0, True)], + ids=["work_related_activity", "support", "assessment", "no_group"], +) +def test_each_esa_group_code_sets_the_support_component_on_its_own( + tmp_path: Path, code: int, expected: bool +) -> None: + """One ESA row with one ESAGROUP code, so each code is pinned on its own + (Vahid's review of microcosm#1299): the work-related activity group (1) + and the assessment phase (3) lie outside the support group (2), and any + other code records no group, which leaves the engine's default.""" + + tables = _fixture_tables() + esa_rows = [row for row in tables["benefits"] if row["BENEFIT"] == 16] + kept = esa_rows[0] + tables["benefits"] = [ + row for row in tables["benefits"] if row["BENEFIT"] != 16 or row is kept + ] + kept["VAR3"] = code + stage = _write_fixture(tmp_path / f"code_{code}", tables) + person = build_uk_frs_spine_frame(tmp_path / f"code_{code}", stage=stage).table( + "person" + ) + group = person.set_index("person_id")["esa_includes_support_component"] + assert group.to_dict() == {1001: expected, 1002: True, 2001: True} + + +def test_birth_months_are_spread_at_the_frs_weights_and_kept_by_copies( + tmp_path: Path, +) -> None: + """The spine stores ``months_since_last_birthday`` spread the engine's way + at each household's GROSS4, and an SPI support copy keeps its source + person's months (Vahid's review of microcosm#1271).""" + + tables = _fixture_tables() + # Both adults are then men aged 40: one stratum holding households of + # weight 10 and 20. A person alone in a stratum sits mid-year whatever the + # weight, so without this the months could not tell the weights apart. + adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2) + adult_2["SEX"] = 1 + stage = _write_fixture(tmp_path, tables) + frame = build_uk_frs_spine_frame(tmp_path, stage=stage) + person = frame.table("person") + + gross4 = {row["SERNUM"]: float(row["GROSS4"]) for row in tables["househol"]} + weights = person["person_household_id"].map(gross4).to_numpy(dtype=float) + assert not np.isnan(weights).any() + draw = { + "age": person["age"].to_numpy(), + "is_male": person["gender"].eq("MALE").to_numpy(), + "person_ids": person["person_id"].to_numpy(), + } + expected = uk_months_since_last_birthday(**draw, weights=weights) + equal_weights = uk_months_since_last_birthday(**draw, weights=np.ones(len(person))) + + np.testing.assert_array_equal(person[UK_BIRTH_MONTH_COLUMN].to_numpy(), expected) + # The fixture can tell the FRS weights from equal weights, and no month is + # left at zero. + assert not np.array_equal(expected, equal_weights) + assert (expected > 0.0).all() + + tables_with_weights = engine_tables(frame) + support = create_uk_spi_support_tables( + person=tables_with_weights["person"], + benunit=tables_with_weights["benunit"], + household=tables_with_weights["household"], + spi_household_count=None, + ) + copies = support.person[ + support.person[support_channel_column("person")] + == SPI_SYNTHETIC_SUPPORT_CHANNEL + ] + assert len(copies) == len(person) + source_months = person.set_index("person_id")[UK_BIRTH_MONTH_COLUMN] + np.testing.assert_array_equal( + copies[UK_BIRTH_MONTH_COLUMN].to_numpy(), + source_months.loc[copies[support_source_id_column("person")]].to_numpy(), + ) + + def test_person_types_follow_the_adult_and_child_tables(tmp_path: Path) -> None: """Claimant or partner is the adult table, HBAI dependent child the child table (uk-data#524, uk-data#486), whatever the ages say.""" @@ -1516,6 +1719,7 @@ def test_scottish_disability_payments_land_in_the_pip_and_dla_columns() -> None: "person_id": [1001, 1001, 1002, 1002, 1003, 1003, 1001, 1002], "benefit": [117, 118, 121, 122, 96, 1, 69, 70], "var2": [0] * 8, + "var3": [0] * 8, "benamt": [110.40, 28.70, 72.65, 28.70, 73.90, 29.20, 9.0, 11.0], } ) @@ -1527,7 +1731,11 @@ def test_scottish_disability_payments_land_in_the_pip_and_dla_columns() -> None: assert weekly["pip_m_reported"].tolist() == pytest.approx([28.70, 0.0, 0.0]) assert weekly["dla_sc_reported"].tolist() == pytest.approx([0.0, 72.65, 29.20]) assert weekly["dla_m_reported"].tolist() == pytest.approx([0.0, 28.70, 0.0]) - reported = pe_person.drop(columns="winter_fuel_allowance_reported") + # With no ESA rows, nobody's ESA group is recorded outside the support group. + assert pe_person["esa_includes_support_component"].all() + reported = pe_person.drop( + columns=["winter_fuel_allowance_reported", "esa_includes_support_component"] + ) assert reported.to_numpy().sum() == pytest.approx( (110.40 + 28.70 + 72.65 + 28.70 + 73.90 + 29.20) * WEEKS_IN_YEAR ) @@ -3424,6 +3632,28 @@ def test_without_the_raw_date_columns_the_spine_refuses( build_uk_frs_spine_frame(tmp_path, stage=stage) +def test_a_self_employment_loss_goes_to_trading_loss(tmp_path: Path) -> None: + # uk-data#528, pe-uk#2085: SEINCAM2 carries a loss as a negative amount; + # the engine takes profits in self_employment_income, never below zero, + # and the year's loss in trading_loss as a positive amount. + tables = _fixture_tables() + adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2) + adult_2["SEINCAM2"] = -4.0 + stage = _write_fixture(tmp_path, tables) + person = build_uk_frs_spine_frame(tmp_path, stage=stage).table("person") + by_id = person.set_index("person_id") + + assert by_id.loc[2001, "self_employment_income"] == 0.0 + assert by_id.loc[2001, "trading_loss"] == pytest.approx(4 * WEEKS_IN_YEAR) + assert by_id.loc[1001, "self_employment_income"] == pytest.approx(3 * WEEKS_IN_YEAR) + assert by_id.loc[1001, "trading_loss"] == 0.0 + # Never both, and never negative. + assert not ( + (person["self_employment_income"] > 0) & (person["trading_loss"] > 0) + ).any() + assert (person["trading_loss"] >= 0).all() + + def _gainful_flags(tmp_path: Path, tables) -> dict[int, bool]: stage = _write_fixture(tmp_path, tables) person = build_uk_frs_spine_frame(tmp_path, stage=stage).table("person") diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py index 91a3aae194..a194290090 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py @@ -178,7 +178,7 @@ def test_uc_take_up_population_excludes_units_without_a_working_age_adult() -> N def test_uc_take_up_population_needs_a_claimant_or_partner_of_working_age() -> None: """A working-age dependant does not bring a unit into the UC population. - policyengine-uk 2.130.1's is_uc_eligible reads the age of the unit's UC + policyengine-uk 2.132.6's is_uc_eligible reads the age of the unit's UC claimant or partner (is_uc_claimant), not of any adult: a pensioner unit with an 18-year-old dependant cannot claim Universal Credit. """ diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py index 7f49430771..d73ac4d833 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_gates.py @@ -179,6 +179,51 @@ def test_gate_diagnostics_use_bound_matrix_and_row_identity(evidence): assert context.artifacts["rules_engine"] is context.artifacts["coverage_engine"] +def test_gate_context_subtracts_the_support_floor_drops(evidence): + """A national row the support floor dropped (microcosm#1095 U3) was + selected but never solved: the reference side and the activated count + lose it, as the problem's bound measure exclusions record.""" + + frame, ordered, solution, supporting = evidence + selection = _selection() + selection["included"].append( + {"name": "thin", "period": 2024, "geography_level": "country"} + ) + thin = TargetSpec( + name="thin", + entity="household", + value=0.1, + measure="income", + period=2024, + source="test", + family="income", + ) + ordered = replace( + ordered, + bindings={ + "target_selection": selection, + "measure_exclusions": {"thin": {"kind": "support_floor"}}, + }, + ) + context = runtime.build_full_gate_context( + frame, + ordered_problem=ordered, + solution=solution, + selection_receipt=selection, + stage_evidence={"frs_spine": {}}, + supporting_evidence={ + **supporting, + "reference_registry": TargetRegistry( + [*supporting["reference_registry"].specs, thin], country="uk" + ), + }, + ) + assert context.artifacts["parity_evidence"].reference_targets == {"national@2024"} + coverage = context.artifacts["national_calibration"] + assert coverage["activated_reference_count"] == 2 + assert coverage["resolved_reference_count"] == 2 + + def _completed_result(evidence): frame, ordered, solution, _ = evidence problem = ordered.problem diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_targets.py index 441eae6174..ee10c590d9 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_full_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_full_targets.py @@ -78,6 +78,9 @@ def compile_local(facts, *, target_period, crosswalk): monkeypatch.setattr( runtime, "load_uk_calibration_measure_exclusions", lambda path: () ) + # Nor a support floor (#1095 U3, tested in test_uk_support_floor): the + # packaged one is signed after these tests' evaluation date. + monkeypatch.setattr(runtime, "load_uk_support_floor_declaration", lambda path: None) # The national reconciliation (#1123) has its own tests; these registers # carry no geography, so it passes them through here. monkeypatch.setattr( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py index 77d9c5dd46..de26d16ae2 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py @@ -56,7 +56,7 @@ def test_uk_spine_graph_contains_every_manifest_stage() -> None: # microcosm#1095 the spi_benefit_coherence pass after uc_reporter_redraw, # microcosm#1106 property_components after the regional uprating, and # microcosm#1240 high_value_home_support between those two. - assert len(expected) == 41 + assert len(expected) == 42 assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(expected) assert set(expected) <= ids assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(ids) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py index d1acbddb86..340b03d596 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_identity_stability_receipts.py @@ -209,7 +209,7 @@ class _FakeEngine: "is_SP_age": "person", "dla": "person", "pip": "person", - "hbai_household_net_income": "household", + "hbai_household_net_income_before_reset_to_zero": "household", "current_education": "person", } @@ -226,7 +226,9 @@ def materialize(self, frame, variables, period): "is_SP_age": np.ones(person_rows), "dla": np.zeros(person_rows), "pip": np.zeros(person_rows), - "hbai_household_net_income": np.full(household_rows, 100.0), + "hbai_household_net_income_before_reset_to_zero": np.full( + household_rows, 100.0 + ), "current_education": np.full( person_rows, "NOT_IN_EDUCATION", dtype=object ), diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_ladder_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_ladder_targets.py index 429fd67d70..058969a765 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_ladder_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_ladder_targets.py @@ -193,6 +193,7 @@ class FakeUKSimulation: data = { "household_id": [101, 102, 103], "self_employment_income": [0.0, 100.0, 0.0, 50.0], + "loss_relief": [0.0, 0.0, 0.0, 0.0], "employment_income": [10.0, 20.0, 0.0, 30.0], "income_tax": [1.0, 0.0, 2.0, 3.0], "tax_band": ["HIGHER", "NONE", "BASIC", "NONE"], diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py index 64b10fa558..8b604e2030 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py @@ -602,7 +602,9 @@ def materialize(self, frame, variables, period): assert "household_gross_income" not in variables assert set(UK_HBAI_GROSS_INCOME_DEDUCTIONS) <= set(variables) values = { - "hbai_household_net_income": np.array([20_000.0, 50_000.0]), + "hbai_household_net_income_before_reset_to_zero": np.array( + [20_000.0, 50_000.0] + ), "income_tax": np.array([3_000.0, 20_000.0]), "council_tax": np.array([1_500.0, 2_500.0]), "national_insurance": np.array([500.0, 7_500.0]), @@ -644,6 +646,8 @@ def test_recipient_predictors_read_the_vehicle_count_numerically() -> None: from microcosm.build.uk_runtime.household_income import ( UK_HBAI_GROSS_INCOME_DEDUCTIONS, + UK_HBAI_NET_INCOME_PREDICTOR, + UK_HBAI_NET_INCOME_VARIABLE, ) from microcosm.build.uk_runtime.lcfs_consumption import ( UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS, @@ -685,11 +689,15 @@ def variable_metadata(self, name): assert predictors["num_vehicles"].tolist() == [0, 3, 5] assert predictors["num_vehicles"].dtype == np.int64 - # The HBAI deductions only build the NEED band's gross income. - assert ( - set(UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS) - - set(UK_HBAI_GROSS_INCOME_DEDUCTIONS) - ) | {"household_gross_income"} <= set(predictors.columns) + # The HBAI deductions only build the NEED band's gross income, and the HBAI + # net income, read before the reset to zero, keeps its predictor name. + engine_columns = { + UK_HBAI_NET_INCOME_PREDICTOR if name == UK_HBAI_NET_INCOME_VARIABLE else name + for name in UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS + if name not in UK_HBAI_GROSS_INCOME_DEDUCTIONS + } + assert engine_columns | {"household_gross_income"} <= set(predictors.columns) + assert UK_HBAI_NET_INCOME_VARIABLE not in predictors.columns without = uk_national_frame( person=frame.table("person"), benunit=frame.table("benunit"), @@ -1089,9 +1097,9 @@ def materialize(self, frame, variables, period): "employment_income": rng.uniform(0.0, 5e4, rows), "self_employment_income": rng.uniform(0.0, 5e3, rows), "private_pension_income": rng.uniform(0.0, 1e4, rows), - "hbai_household_net_income": frame.table("household")[ - "household_gross_income" - ].to_numpy() + "hbai_household_net_income_before_reset_to_zero": frame.table( + "household" + )["household_gross_income"].to_numpy() * 0.8, # The NEED band adds the HBAI deductions back (microcosm#1164). "income_tax": frame.table("household")[ diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_local_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_local_targets.py index 1856b1d1dd..5b1061bca3 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_local_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_local_targets.py @@ -29,6 +29,8 @@ def __init__(self): self.data = { "household_id": [101, 102, 103], "self_employment_income": [0.0, 100.0, 0.0, 50.0], + # Losses brought forward the profit is counted after (microcosm#1095). + "loss_relief": [0.0, 0.0, 0.0, 0.0], "employment_income": [10.0, 20.0, 0.0, 30.0], "income_tax": [1.0, 0.0, 2.0, 3.0], "tax_band": ["HIGHER", "HIGHER", "BASIC", "HIGHER"], @@ -242,6 +244,20 @@ def test_compute_constituency_household_metrics() -> None: assert metrics["uc_hh_1_child"].tolist() == [0.0, 0.0, 1.0] +def test_the_area_self_employment_rows_count_the_profit_after_loss_relief() -> None: + # HMRC counts the profit after losses brought forward (microcosm#1095). + sim = FakeUKSimulation() + sim.data["loss_relief"] = [0.0, 0.0, 0.0, 20.0] + metrics = compute_household_metrics(sim, "constituency") + assert metrics["hmrc/self_employment_income/amount"].tolist() == [0.0, 0.0, 30.0] + assert metrics["hmrc/self_employment_income/count"].tolist() == [0.0, 0.0, 1.0] + # Losses that absorb the whole profit leave no self-employment income. + sim.data["loss_relief"] = [0.0, 0.0, 0.0, 80.0] + metrics = compute_household_metrics(sim, "constituency") + assert metrics["hmrc/self_employment_income/amount"].tolist() == [0.0, 0.0, 0.0] + assert metrics["hmrc/self_employment_income/count"].tolist() == [0.0, 0.0, 0.0] + + def test_uc_child_bands_sum_uc_benefit_units_to_household() -> None: class MultiBenunitUKSimulation(FakeUKSimulation): def __init__(self): @@ -254,6 +270,7 @@ def __init__(self): self.data = { "household_id": [101], "self_employment_income": [0.0] * 9, + "loss_relief": [0.0] * 9, "employment_income": [0.0] * 9, "income_tax": [0.0] * 9, "tax_band": ["NONE"] * 9, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py index 6f87f48bec..48c382d113 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py @@ -597,7 +597,12 @@ def test_exclusion_applier_warns_within_week_of_expiry(): "slc.": 5, # microcosm#1095 (2026-10-07): two bands whose few supporting awards # policyengine-uk 2.122.2 moves out join the uk-data#452 class. - "dwp/uc_payment_dist/": 21, + # microcosm#1095 U3 (2026-10-16): the sparse-band support floor drops + # every band below ten source households, so the nine entries it covers + # on the head spine retire (seven uk-data#452, the two microcosm#736 + # zero-support bands); twelve well-supported bands remain held out per + # row, each on its own renewal. + "dwp/uc_payment_dist/": 12, "obr.universal_credit_": 2, # microcosm#890 E1 (2026-09-11): the all-road-users obr.fuel_duties row # is signed out of the reference surface (target_reference_signed_ @@ -620,9 +625,10 @@ def test_exclusion_applier_warns_within_week_of_expiry(): # structural bias: the v20 measurement (-2.8 % unbound, at weights that # never solved toward the row) reproduces the 2.79 % other/unknown-tenure # gap the entry records, so it is a diagnostic, not a fit target - # (docs/evidence/uk-a16-v20/). microcosm#1095 (2026-10-14): the five-or-more - # children row joins it, held out on María's ruling of microcosm#1271. - "dwp.uc.households_": 2, + # (docs/evidence/uk-a16-v20/). microcosm#1095: the five-or-more children + # row, held out on María's ruling of microcosm#1271 (2026-10-14), left the + # targets for a diagnostic on 2026-10-16, so its entry retired. + "dwp.uc.households_": 1, # microcosm#882 repairs (2026-09-16): the three Housing Benefit caseload # rows and the thirteen benefit-cap amount bands outside the 25 percent # bound are measured on every evaluation but held out of the objective; @@ -664,7 +670,8 @@ def test_exclusion_applier_warns_within_week_of_expiry(): def test_packaged_exclusions_load(): exclusions = load_uk_calibration_measure_exclusions() names = [entry["name"] for entry in exclusions] - assert len(names) == len(set(names)) == 66 + # 65 less the nine payment bands the support floor covers (microcosm#1095 U3). + assert len(names) == len(set(names)) == 56 band_h_region_cells = [ entry for entry in exclusions @@ -705,8 +712,9 @@ def test_packaged_exclusions_load(): # the obr.fuel_duties entry (2026-09-11): the all-road-users row is # signed out of the reference surface and the cars receipts bind instead. tranche = [e for e in exclusions if e["approved_on"] == "2026-08-26"] - # Three 2026-08-26 rows retired 2026-09-21 on the v20 measurement. - assert len(tranche) == 32 + # Three 2026-08-26 rows retired 2026-09-21 on the v20 measurement, and + # three payment bands retired 2026-10-16 under the support floor. + assert len(tranche) == 29 for entry in tranche: assert "5427936411" in entry["adjudication"], entry["name"] assert entry["expires_on"] == "2026-11-26", entry["name"] @@ -727,39 +735,13 @@ def test_packaged_exclusions_load(): "dwp.jsa_claimants": "microcosm#869", } assert [e for e in exclusions if e["approved_on"] == "2026-09-03"] == [] - today = [e for e in exclusions if e["approved_on"] == "2026-10-02"] - # The single-row UC payment cell joined the uk-data#452 class on the - # #1063 stack arm (one supporting household, lost to a draw reshuffle). - thin = [ - e - for e in today - if e["name"] - in ( - "dwp/uc_payment_dist/SINGLE_annual_payment_28_800_to_30_000", - "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_22_800_to_24_000", - ) - ] - assert len(thin) == 2 - for entry in thin: - assert entry["tracking"] == "uk-data#452" - assert entry["expires_on"] == "2026-11-26" - assert "support" in entry["reason"] - renewed = [e for e in today if e not in thin] + renewed = [e for e in exclusions if e["approved_on"] == "2026-10-02"] + # The two thin UC payment cells that joined the uk-data#452 class on the + # #1063 stack arm retired 2026-10-16: the support floor drops them. assert sorted(e["name"] for e in renewed) == sorted(_A16_UNREACHABLE_ROWS[:2]) - # The engine bump of microcosm#1095 (policyengine-uk 2.122.2) moves every - # supporting award out of two more payment bands: the published LHA rates, - # the 2019 mixed-age couple saving and Carer Support Payment as unearned - # income. They join the class with its expiry. - engine_moved = [e for e in exclusions if e["approved_on"] == "2026-10-07"] - assert sorted(e["name"] for e in engine_moved) == [ - "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_24_000_to_25_200", - "dwp/uc_payment_dist/SINGLE_annual_payment_26_400_to_27_600", - ] - for entry in engine_moved: - assert entry["tracking"] == "uk-data#452" - assert entry["expires_on"] == "2026-11-26" - assert "microcosm#1095" in entry["adjudication"] - assert "policyengine-uk 2.122.2" in entry["reason"] + # So did the two bands the engine bump of microcosm#1095 (policyengine-uk + # 2.122.2) emptied, the 2026-10-07 entries. + assert [e for e in exclusions if e["approved_on"] == "2026-10-07"] == [] # The 2026-10-08 consumption-tax tranche (microcosm#1113, #1123 item 3): # VAT's level is an engine factor (policyengine-uk#1996) and the two ONS # energy rows are a published-source residual against the stage's DESNZ @@ -792,30 +774,14 @@ def test_packaged_exclusions_load(): "ons.household_electricity_expenditure": "microcosm#1207", "ons.household_gas_expenditure": "microcosm#1207", } - # microcosm#1095 (2026-10-14): once dependants in approved training qualify - # as young people, fewer than 10 source households separate DWP's count of - # UC households with five or more children from its two-child-limit - # counts, so the row is held out with the sparse-band expiry and the - # two-child-limit rows stay bound (María's ruling on microcosm#1271). - # Two rulings share that date (the housing-element renewal below), so the - # tranche is told apart by its tracking issue. + # microcosm#1095: the five-or-more children row held out on 2026-10-14 + # (María's ruling on microcosm#1271) is no target since 2026-10-16, but a + # diagnostic beside the two-child-limit rows, so no exclusion names it and + # only the housing-element renewal carries that date. assert sorted( e["name"] for e in exclusions if e["approved_on"] == "2026-10-14" - ) == (sorted([*_UC_ELEMENT_REGISTER_ROWS, "dwp.uc.households_children_5_or_more"])) - large_families = [ - e - for e in exclusions - if e["approved_on"] == "2026-10-14" and e["tracking"] == "microcosm#1095" - ] - assert [e["name"] for e in large_families] == [ - "dwp.uc.households_children_5_or_more" - ] - (large_family,) = large_families - assert large_family["tracking"] == "microcosm#1095" - assert large_family["expires_on"] == "2026-11-26" - assert "microcosm#1271" in large_family["adjudication"] - assert "6168355102" in large_family["adjudication"] - assert "fewer than 10 frs source households" in large_family["reason"].lower() + ) == sorted(_UC_ELEMENT_REGISTER_ROWS) + assert "dwp.uc.households_children_5_or_more" not in names for entry in renewed: assert entry["expires_on"] == "2026-11-03", entry["name"] assert entry["tracking"] == a16_issues[entry["name"]], entry["name"] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py index 692f195819..16603aee0b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py @@ -53,7 +53,7 @@ from microcosm.calibrate.artifacts import decode_problem from microcosm.frame import EntitySchema, Frame, MassChange, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 1292 +ACTIVE_REFERENCE_COUNT = 1291 def _uc_reference(**overrides) -> LedgerTargetReference: @@ -364,6 +364,10 @@ def _materialization_binding_frame( "region": "LONDON", "esa_income": [10.0, 20.0, 0.0], "esa_contrib": [1.0, 2.0, 0.0], + # OBR's incapacity line also carries Incapacity Benefit and SDA + # (table 4.9 footnote 2; microcosm#1095, uk-data#582). + "incapacity_benefit": [0.0, 0.0, 0.5], + "sda": [0.0, 3.0, 4.0], } ) salary_sacrifice_metric = "hmrc/salary_sacrifice_it_relief_basic_rate" @@ -712,7 +716,7 @@ def test_packaged_binding_classes_materialize_through_the_national_route() -> No materialize_uk_ledger_targets(adapter, registry, period=2025) materialized = { ("benunit", "dwp/uc/households"): [1.0, 0.0, 1.0], - ("household", "obr/esa"): [11.0, 22.0, 0.0], + ("household", "obr/esa"): [11.0, 25.0, 4.5], ("person", "hmrc/cgt_taxpayers"): [0.0, 1.0, 1.0, 0.0, 0.0, 0.0], # The prepared affected-child counts stay distinct from the claim # indicator [1.0, 0.0, 1.0] and legacy person-native entitlement flags. diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py index 35b88f8ea7..f054dcb71f 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_graph.py @@ -48,6 +48,9 @@ uk_national_target_loss_weights, ) from microcosm.build.uk_runtime.national_frame import write_uk_national_frame +from microcosm.build.uk_runtime.support_floor import ( + load_uk_support_floor_declaration, +) from microcosm.build.uk_runtime.terminal_gates import ( uk_default_degenerate_reviewed_exclusions, uk_default_target_fit_reviewed_exclusions, @@ -194,6 +197,8 @@ def field(record, name): uk_default_degenerate_reviewed_exclusions(), uk_default_input_mass_reviewed_exclusions(), load_uk_calibration_measure_exclusions(), + # The sparse-band support floor's signature (microcosm#1095 U3). + [load_uk_support_floor_declaration() or {}], ) for record in records(register) ] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_reconciliation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_reconciliation.py index 2558d6d150..e10c99ec44 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_reconciliation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_reconciliation.py @@ -377,3 +377,177 @@ def test_a_partly_bound_sum_bridge_is_refused(): specs = [_spec("a", bridge.higher_target_ids[0], 1.0)] with pytest.raises(ValueError, match="partly bound"): _apply_sum_bridges(specs, (bridge,)) + + +def _area(target_id: str, geography_id: str, value: float) -> TargetSpec: + level = "region" if geography_id.startswith("E12") else "country" + return _spec( + f"{target_id}@{geography_id}@2025", + target_id, + value, + geography_level=level, + geography_id=geography_id, + ) + + +def _council_tax_partition(bound: float = 0.05): + from microcosm.build.uk_runtime.national_reconciliation import ( + UKHouseholdBasisPartition, + ) + + return UKHouseholdBasisPartition( + partition_id="council_tax_on_households", + control_target_ids=("ons.households.english_regions",), + parent_target_ids=("mhclg.council_tax_stock.total",), + member_target_ids=( + "mhclg.council_tax_stock.band_a", + "mhclg.council_tax_stock.band_b", + ), + max_factor_shift=bound, + ) + + +def test_council_tax_cells_take_their_areas_household_basis(): + """Each area's dwelling total takes its household control and the bands + keep their mix (microcosm#1284).""" + + from microcosm.build.uk_runtime.national_reconciliation import ( + _apply_household_basis_partitions, + ) + + specs = [ + _area("ons.households.english_regions", "E12000001", 1020.0), + _area("mhclg.council_tax_stock.total", "E12000001", 1000.0), + _area("mhclg.council_tax_stock.band_a", "E12000001", 600.0), + _area("mhclg.council_tax_stock.band_b", "E12000001", 400.0), + _area("ons.households.english_regions", "E12000002", 990.0), + _area("mhclg.council_tax_stock.total", "E12000002", 1000.0), + _area("mhclg.council_tax_stock.band_a", "E12000002", 500.0), + _area("mhclg.council_tax_stock.band_b", "E12000002", 500.0), + ] + receipts = _apply_household_basis_partitions(specs, (_council_tax_partition(),)) + + values = [spec.value for spec in specs] + assert values == pytest.approx( + [1020.0, 1020.0, 612.0, 408.0, 990.0, 990.0, 495.0, 495.0] + ) + assert [r["geography_id"] for r in receipts] == ["E12000001", "E12000002"] + assert [r["declared_factor"] for r in receipts] == pytest.approx([1.02, 0.99]) + assert specs[2].metadata["cross_grain_control"] == "council_tax_on_households" + assert float(specs[2].metadata["cross_grain_value_before"]) == 600.0 + assert specs[0].metadata.get("cross_grain_factor") is None + + +def test_a_household_basis_factor_beyond_its_bound_is_refused(): + from microcosm.build.uk_runtime.national_reconciliation import ( + _apply_household_basis_partitions, + ) + + specs = [ + _area("ons.households.english_regions", "E12000001", 1200.0), + _area("mhclg.council_tax_stock.total", "E12000001", 1000.0), + ] + with pytest.raises(ValueError, match="households per dwelling"): + _apply_household_basis_partitions(specs, (_council_tax_partition(),)) + + +def test_band_cells_without_their_total_are_refused(): + from microcosm.build.uk_runtime.national_reconciliation import ( + _apply_household_basis_partitions, + ) + + specs = [ + _area("ons.households.english_regions", "E12000001", 1020.0), + _area("mhclg.council_tax_stock.band_a", "E12000001", 600.0), + ] + with pytest.raises(ValueError, match="without their total"): + _apply_household_basis_partitions(specs, (_council_tax_partition(),)) + + +def test_the_committed_partition_puts_council_tax_on_the_household_controls(): + from microcosm.build.uk_runtime.cross_grain_declarations import ( + load_uk_cross_grain_declarations, + ) + from microcosm.build.uk_runtime.national_reconciliation import ( + uk_household_basis_partitions, + ) + + (partition,) = uk_household_basis_partitions() + assert partition.control_target_ids == ( + "ons.households.english_regions", + "welshgov.households.wales", + "nrs.households.scotland", + ) + assert partition.parent_target_ids == ( + "mhclg.council_tax_stock.total", + "welshgov.council_tax_stock.total", + "scotgov.council_tax_stock.total", + ) + expected_members = { + f"{publisher}.council_tax_stock.band_{band}" + for publisher, bands in ( + ("mhclg", "abcdefgh"), + ("welshgov", "abcdefghi"), + ("scotgov", "abcdefgh"), + ) + for band in bands + } + assert set(partition.member_target_ids) == expected_members + assert 0.0 < partition.max_factor_shift < 0.1 + (entry,) = load_uk_cross_grain_declarations()["household_basis_partitions"] + # The signed assumption: households per chargeable dwelling are the same + # in every band of an area. + assert "households per chargeable dwelling" in entry["reason"] + assert entry["approved_by"] == "juaristi22" + assert entry["tracking"] == "microcosm#1284" + + +def test_the_committed_partition_closes_the_register_on_the_household_basis(): + """The committed partition, applied and then checked as the national + reconciliation does: each area's total equals its control.""" + + from microcosm.build.uk_runtime.cross_grain_declarations import ( + load_uk_cross_grain_declarations, + ) + from microcosm.build.uk_runtime.national_reconciliation import ( + _apply_household_basis_partitions, + _assert_national_closure, + uk_household_basis_partitions, + ) + + bands = (250_000.0, 500_000.0, 800_000.0, 800_000.0, 500_000.0, 300_000.0) + specs = [ + _area("ons.households.english_regions", "E12000007", 3_500_000.0), + _area("mhclg.council_tax_stock.total", "E12000007", 3_450_000.0), + *[ + _area(f"mhclg.council_tax_stock.band_{band}", "E12000007", value) + for band, value in zip( + "abcdefgh", (*bands, 200_000.0, 100_000.0), strict=True + ) + ], + _area("nrs.households.scotland", "S92000003", 2_570_000.0), + _area("scotgov.council_tax_stock.total", "S92000003", 2_623_000.0), + _area("scotgov.council_tax_stock.band_b", "S92000003", 2_623_000.0), + ] + receipts = _apply_household_basis_partitions(specs, uk_household_basis_partitions()) + by_name = {spec.name: spec.value for spec in specs} + assert by_name["mhclg.council_tax_stock.total@E12000007@2025"] == pytest.approx( + 3_500_000.0 + ) + assert math.fsum( + value + for name, value in by_name.items() + if name.startswith("mhclg.council_tax_stock.band_") + ) == pytest.approx(3_500_000.0) + assert by_name["scotgov.council_tax_stock.band_b@S92000003@2025"] == ( + pytest.approx(2_570_000.0) + ) + assert [r["geography_id"] for r in receipts] == ["E12000007", "S92000003"] + declarations = { + "household_basis_partitions": load_uk_cross_grain_declarations()[ + "household_basis_partitions" + ], + "band_bridges": [], + "sum_bridges": [], + } + _assert_national_closure(TargetRegistry(specs, country="uk"), None, declarations) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py index 02208f9bf0..5fac5c4b46 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_nts_bus_travel.py @@ -405,7 +405,11 @@ def variable_metadata(self, name): def materialize(self, frame, variables, period): # The band reads HBAI net income plus the HBAI deductions (#1164). rows = len(frame.table("household")) - values = {"hbai_household_net_income": self.rng.uniform(5e3, 9e4, rows)} + values = { + "hbai_household_net_income_before_reset_to_zero": self.rng.uniform( + 5e3, 9e4, rows + ) + } return {name: values.get(name, np.zeros(rows)) for name in variables} @@ -459,7 +463,7 @@ class _GrossEngine(_Engine): def materialize(self, frame, variables, period): requested.extend(variables) values = { - "hbai_household_net_income": net, + "hbai_household_net_income_before_reset_to_zero": net, "income_tax": np.full(rows, 6_000.0), } return {name: values.get(name, np.zeros(rows)) for name in variables} diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py index f77e9db2e3..d6e781868e 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py @@ -227,8 +227,9 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: # microcosm#1240 adds the three Microcosm-derived high-value homes local # targets; microcosm#1207 adds the three DESNZ local-authority energy # targets and DfT BUS01a journeys for London and the eight other English - # regions. - assert len(resource["targets"]) == 415 + # regions; microcosm#1095 keeps DWP's five-or-more-children count as a + # diagnostic beside the two-child-limit rows. + assert len(resource["targets"]) == 414 providers = resource["hierarchy"]["providers"] categories = resource["hierarchy"]["categories"] @@ -242,10 +243,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: target_ids = [target["target_id"] for target in resource["targets"]] registry_scope = resource["registry_parity"]["scope_target_ids"] profile_scope = resource["profile_parity"]["scope_target_ids"] - assert len(registry_scope) == 357 + assert len(registry_scope) == 356 assert len(profile_scope) == 58 - assert target_ids[:357] == registry_scope - assert target_ids[357:] == profile_scope + assert target_ids[:356] == registry_scope + assert target_ids[356:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -258,11 +259,12 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: assert set(mapped_target_ids).isdisjoint(unmapped_declarations) assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope) assert all(reason for reason in unmapped_declarations.values()) - # microcosm#1095: obr/ni and the two PIP daily-living caseload rows leave - # the mapped rows for diagnostics, and the two incumbent 15_19 sex bands - # leave them for the six rows that partition them; microcosm#1207's nine - # BUS01a journey rows have no uk-data metric. - assert len(mapped_target_ids) == 187 + # microcosm#1095: obr/ni, the two PIP daily-living caseload rows and the + # five-or-more-children UC count leave the mapped rows for diagnostics, + # and the two incumbent 15_19 sex bands leave them for the six rows that + # partition them; microcosm#1207's nine BUS01a journey rows have no + # uk-data metric. + assert len(mapped_target_ids) == 186 assert len(unmapped_declarations) == 170 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 @@ -434,7 +436,7 @@ def test_uk_population_targets_have_unique_target_ids() -> None: resource = _load() target_ids = [target["target_id"] for target in resource["targets"]] - assert len(target_ids) == 415 + assert len(target_ids) == 414 assert len(target_ids) == len(set(target_ids)) @@ -643,7 +645,7 @@ def test_uk_population_targets_declare_chronicle_loader_guarantees() -> None: _target_by_id(resource, "obr.esa")["bindings"]["policyengine"][ "value_expression" ] - == "esa_income + esa_contrib" + == "esa_income + esa_contrib + incapacity_benefit + sda" ) @@ -780,7 +782,6 @@ def test_uk_uc_composition_and_disability_children_targets_are_rebound() -> None "dwp.uc.households_children_2", "dwp.uc.households_children_3", "dwp.uc.households_children_4", - "dwp.uc.households_children_5_or_more", "dwp.uc.households_single_no_children", "dwp.uc.households_single_with_children", "dwp.uc.households_couple_no_children", @@ -844,7 +845,9 @@ def test_paid_uc_targets_preserve_entitlement_margins_and_exact_calendar_months( "dwp.uc.households_couple_with_children", } ] - assert len(paid_targets) == 10 + # microcosm#1095: the five-or-more-children count is a diagnostic, not a + # target. + assert len(paid_targets) == 9 for target in paid_targets: selector = target["ledger_selector"] assert target["measurement"]["source_months"] == months @@ -915,7 +918,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 415 + assert len(targets) == 414 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets @@ -937,7 +940,7 @@ def test_uc_gb_bindings_match_the_committed_source_geography_and_finite_bands(): if row["metadata"]["contract_target_id"].startswith("dwp.uc.") and row["ledger_selector"]["geography_id"] == "K03000001" } - assert len(gb_ids) == 36 + assert len(gb_ids) == 35 for target_id in gb_ids: binding = _target_by_id(resource, target_id)["bindings"]["policyengine"] geographic = [ diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py new file mode 100644 index 0000000000..3acba5258c --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py @@ -0,0 +1,145 @@ +"""Private-school attendance stored at the spine's weights (uk-data#568, microcosm#1095).""" + +from __future__ import annotations + +from dataclasses import replace + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.country_spec import load_country_spec +from microcosm.build.source_manifest import SourceOperationSpec +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.private_school_attendance import ( + PRIVATE_SCHOOL_ATTENDANCE_DRAW, + PRIVATE_SCHOOL_ATTENDANCE_OUTPUT, + UKPrivateSchoolAttendanceStageTransform, + store_private_school_attendance, +) + + +class _FakeEngine: + """The engine's attendance at the frame's weights, recorded per call.""" + + country = "uk" + + def __init__(self, attends): + self.attends = np.asarray(attends, dtype=bool) + self.calls = [] + + def materialize(self, frame, variables, period, *, dataset_period=None): + self.calls.append( + ( + tuple(variables), + period, + dataset_period, + frame.weights_for("household").values.tolist(), + ) + ) + return {PRIVATE_SCHOOL_ATTENDANCE_OUTPUT: self.attends} + + +def _frame(**person_columns): + person = pd.DataFrame( + { + "person_id": [1, 2, 3, 4], + "person_benunit_id": [10, 10, 20, 30], + "person_household_id": [1, 1, 2, 3], + "age": [40, 9, 12, 70], + PRIVATE_SCHOOL_ATTENDANCE_DRAW: [0.5, 0.01, 0.2, 0.9], + **person_columns, + } + ) + return uk_national_frame( + person=person, + benunit=pd.DataFrame({"benunit_id": [10, 20, 30]}), + household=pd.DataFrame({"household_id": [1, 2, 3], "region": ["LONDON"] * 3}), + household_weights=np.array([100.0, 50.0, 30.0]), + time_period="2024", + ) + + +def _stage(): + return load_country_spec("uk").sources.stage_map()["private_school_attendance"] + + +def test_the_stage_stores_the_engines_attendance_at_the_spines_weights(): + engine = _FakeEngine([False, True, True, False]) + result = store_private_school_attendance( + _frame(), engine=engine, calibration_year=2025 + ) + + person = result.frame.table("person") + assert person[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT].tolist() == [ + False, + True, + True, + False, + ] + assert person[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT].dtype == bool + # One materialization at the calibration year, the frame's own period as + # the dataset year, at the spine's design weights. + ((variables, period, dataset_period, weights),) = engine.calls + assert variables == (PRIVATE_SCHOOL_ATTENDANCE_OUTPUT,) + assert (period, dataset_period) == (2025, "2024") + assert weights == [100.0, 50.0, 30.0] + # The weights pass through and the receipt counts weighted pupils. + assert result.frame.weights_for("household").values.tolist() == weights + assert result.weighted_pupils == 150.0 + assert result.evidence()["weights"] == "spine_design_weights" + + +def test_the_stage_needs_the_stored_draw_and_writes_attendance_once(): + engine = _FakeEngine([False] * 4) + frame = _frame() + no_draw = uk_national_frame( + person=frame.table("person").drop(columns=[PRIVATE_SCHOOL_ATTENDANCE_DRAW]), + benunit=frame.table("benunit"), + household=frame.table("household"), + household_weights=frame.weights_for("household").values, + time_period="2024", + ) + with pytest.raises(ValueError, match="random_draw"): + store_private_school_attendance(no_draw, engine=engine, calibration_year=2025) + stored = _frame(**{PRIVATE_SCHOOL_ATTENDANCE_OUTPUT: [False] * 4}) + with pytest.raises(ValueError, match="only writer"): + store_private_school_attendance(stored, engine=engine, calibration_year=2025) + + +def test_the_stage_is_the_last_of_the_spine(): + """No later stage copies or re-keys a person, so the stored flags reach + the release as the engine set them.""" + + stages = [stage.stage for stage in load_country_spec("uk").sources.stages] + assert stages[-1] == "private_school_attendance" + + +def test_the_stage_refuses_drifted_parameters(): + stage = _stage() + operation, receipt = stage.operations + drifted = replace( + stage, + operations=( + SourceOperationSpec( + operation.kind, {**operation.parameters, "year_rule": "survey_year"} + ), + receipt, + ), + ) + transform = UKPrivateSchoolAttendanceStageTransform( + stage=drifted, engine=_FakeEngine([False] * 4), calibration_year=2025 + ) + with pytest.raises(ValueError, match="drifted"): + transform(_frame()) + stored = UKPrivateSchoolAttendanceStageTransform( + stage=stage, + engine=_FakeEngine([True, False, False, False]), + calibration_year=2025, + )(_frame()) + assert stored.table("person")[PRIVATE_SCHOOL_ATTENDANCE_OUTPUT].tolist() == [ + True, + False, + False, + False, + ] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py index 1c8dacfdcc..ddac7f0bb0 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py @@ -93,6 +93,8 @@ "hmrc_cgt_asset_type_spine", "salary_sacrifice", "student_loans", + # microcosm#1095: the stored private-school attendance runs last. + "private_school_attendance", ] UK_SOURCE_STAGE_NAMES = [ "frs_spine", @@ -266,6 +268,7 @@ def test_country_stage_plan_assembles_spine_plan(self) -> None: "hmrc_cgt_asset_type_spine": _identity, "salary_sacrifice": _identity, "student_loans": _identity, + "private_school_attendance": _identity, "age_tail": _identity, "frs_relationships": _identity, "hmrc_spi_income_fallback": _identity, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py index e2190ada74..51f81804da 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py @@ -94,6 +94,126 @@ def test_spi_carer_take_up_refresh_follows_the_refilled_receipt() -> None: ) +def test_spi_esa_group_returns_to_the_engines_default_on_redrawn_rows() -> None: + """A redrawn row's ESA comes from another donor, so its group is reset.""" + + person = pd.DataFrame( + { + # Root-stage answers: rows 20 and 40 reported a group outside the + # support group; row 40 is a base row whose answer must survive. + "esa_includes_support_component": [True, False, True, False], + }, + index=[10, 20, 30, 40], + ) + spi_people = pd.Series([False, True, True, False], index=person.index) + + result = spi_income._refresh_esa_support_component(person, spi_people=spi_people) + + assert result["esa_includes_support_component"].tolist() == [ + True, + True, + True, + False, + ] + assert result["esa_includes_support_component"].dtype == bool + # A frame without the flag (an older synthetic manifest) passes through. + bare = pd.DataFrame({"esa_income_reported": [1.0]}, index=[1]) + assert "esa_includes_support_component" not in ( + spi_income._refresh_esa_support_component( + bare, spi_people=pd.Series([True], index=[1]) + ) + ) + + +def test_a_redrawn_row_with_a_profit_keeps_no_drawn_trading_loss() -> None: + """Profit and loss never both hold an amount (uk-data#528, microcosm#1095).""" + + person = pd.DataFrame( + { + # Row 20: the tape's profit beside a drawn loss, which goes. Row 30: + # no profit, so its drawn loss stays. Row 40 is a base row whose + # FRS values are left alone. + "self_employment_income": [0.0, 900.0, 0.0, 0.0], + "trading_loss": [0.0, 250.0, 400.0, 600.0], + }, + index=[10, 20, 30, 40], + ) + spi_people = pd.Series([False, True, True, False], index=person.index) + + result = spi_income._clear_trading_loss_beside_profit(person, spi_people=spi_people) + + assert result["trading_loss"].tolist() == [0.0, 0.0, 400.0, 600.0] + assert result["self_employment_income"].tolist() == [0.0, 900.0, 0.0, 0.0] + # A frame without the column (an older synthetic manifest) passes through. + bare = pd.DataFrame({"self_employment_income": [1.0]}, index=[1]) + assert "trading_loss" not in spi_income._clear_trading_loss_beside_profit( + bare, spi_people=pd.Series([True], index=[1]) + ) + + +def test_drawn_owner_manager_inputs_keep_the_root_stages_rules() -> None: + """The fill draws the flags as 0 or 1; the root stage's rules then hold on + the redrawn rows, and the FRS rows keep their answers (microcosm#1095).""" + + person = pd.DataFrame( + { + # Row 10 (FRS) keeps an inconsistent answer as the root gave it. + # Row 20 drew an owner in a property business and a trade: the + # property business wins. Row 30 drew main employment and income + # without an owner: both go. Row 40 drew an owner in another job. + "stands_as_sole_owner_or_partner_of_company": [0.0, 1.0, 0.0, 1.0], + "is_engaged_in_owned_company_trade": [1.0, 0.0, 1.0, 1.0], + "owned_company_is_main_employment": [1.0, 1.0, 1.0, 0.0], + "owned_company_carries_on_trade": [1.0, 1.0, 1.0, 1.0], + "owned_company_carries_on_property_business": [0.0, 1.0, 0.0, 0.0], + "owned_company_income_share": [5.0, 700.0, 300.0, 50.0], + }, + index=[10, 20, 30, 40], + ) + spi_people = pd.Series([False, True, True, True], index=person.index) + + result = spi_income._owner_manager_flags_from_draws(person, spi_people=spi_people) + + assert result["stands_as_sole_owner_or_partner_of_company"].tolist() == [ + False, + True, + False, + True, + ] + assert result["is_engaged_in_owned_company_trade"].tolist() == [ + True, + True, + False, + True, + ] + assert result["owned_company_is_main_employment"].tolist() == [ + True, + True, + False, + False, + ] + assert result["owned_company_carries_on_trade"].tolist() == [ + True, + False, + False, + False, + ] + assert result["owned_company_carries_on_property_business"].tolist() == [ + False, + True, + False, + False, + ] + assert result["owned_company_income_share"].tolist() == [5.0, 700.0, 0.0, 50.0] + assert result["stands_as_sole_owner_or_partner_of_company"].dtype == bool + # The inputs are set together: a frame with only some of them refuses. + with pytest.raises(ValueError, match="set together"): + spi_income._owner_manager_flags_from_draws( + person[["stands_as_sole_owner_or_partner_of_company"]], + spi_people=spi_people, + ) + + def test_spi_uc_gainful_self_employment_follows_the_rows_drawn_incomes() -> None: """A drawn row's gainful self-employment takes the root's whole rule.""" @@ -295,6 +415,105 @@ def test_spi_donor_keeps_narrow_pe_employment_and_broad_hmrc_measure( ) +def test_spi_donor_carries_the_profit_before_losses_and_the_losses(tmp_path) -> None: + """The chain's two last draws (uk-data#528, microcosm#1095).""" + + donor_path = tmp_path / SPI_DONOR_FILENAME + _write_donor(donor_path) + raw = pd.read_csv(donor_path, delimiter="\t") + + donor = spi_income._prepare_spi_donor(raw, seed=7) + profit = donor[spi_income.SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN] + losses = donor[spi_income.SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN] + np.testing.assert_array_equal( + profit, (raw["PROFITS"] - raw["CAPALL"]).clip(lower=0.0) + ) + np.testing.assert_array_equal(losses, raw["LOSSBF"].clip(lower=0.0)) + # The profit after the losses is the profit the chain draws. + np.testing.assert_array_equal( + (profit - losses).clip(lower=0.0), donor["self_employment_income"] + ) + assert list(spi_income.SPI_INCOME_QRF_OUTPUT_COLUMNS[-2:]) == list( + spi_income.SPI_LOSS_DRAW_COLUMNS + ) + + +def test_losses_brought_forward_move_out_of_the_profit_into_loss_relief() -> None: + """The drawn rows' profit becomes the year's profit, its losses brought + forward go to both loss inputs, and the profit after the relief is the + one the chain drew (uk-data#528, microcosm#1095).""" + + profit_draw = spi_income.SPI_PROFIT_BEFORE_LOSSES_DRAW_COLUMN + loss_draw = spi_income.SPI_LOSSES_BROUGHT_FORWARD_DRAW_COLUMN + person = pd.DataFrame( + { + # Row 1: a profit left after its losses; row 2: losses larger than + # the profit; row 3: no losses; row 4: a base row. + "self_employment_income": [6_000.0, 0.0, 4_000.0, 500.0], + profit_draw: [9_000.0, 5_000.0, 4_000.0, 0.0], + loss_draw: [2_500.0, 8_000.0, 0.0, 0.0], + "loss_relief": [0.0, 0.0, 0.0, 0.0], + "ni_class_4_losses_brought_forward": [0.0, 0.0, 0.0, 0.0], + }, + index=[1, 2, 3, 4], + ) + spi_people = pd.Series([True, True, True, False], index=person.index) + net = person["self_employment_income"].copy() + + moved = spi_income._move_losses_brought_forward_to_loss_relief( + person, spi_people=spi_people + ) + + assert moved["self_employment_income"].tolist() == [ + 9_000.0, + 5_000.0, + 4_000.0, + 500.0, + ] + # Row 1's whole loss was relieved, so the relief is the gap to the drawn + # net profit; row 2 keeps its losses, at least the profit. + assert moved["loss_relief"].tolist() == [3_000.0, 8_000.0, 0.0, 0.0] + assert moved["ni_class_4_losses_brought_forward"].tolist() == [ + 3_000.0, + 8_000.0, + 0.0, + 0.0, + ] + np.testing.assert_array_equal( + (moved["self_employment_income"] - moved["loss_relief"]).clip(lower=0.0), net + ) + assert not set(spi_income.SPI_LOSS_DRAW_COLUMNS) & set(moved.columns) + # A frame without the draws (an older synthetic manifest) passes through. + bare = pd.DataFrame({"self_employment_income": [1.0]}, index=[1]) + assert spi_income._move_losses_brought_forward_to_loss_relief( + bare, spi_people=pd.Series([True], index=[1]) + ).equals(bare) + + +def test_hmrc_earned_income_reads_the_profit_after_loss_relief(tmp_path) -> None: + """TEI and TI are the same before and after the move (microcosm#1095).""" + + donor_path = tmp_path / SPI_DONOR_FILENAME + _write_donor(donor_path) + raw = pd.read_csv(donor_path, delimiter="\t") + donor = spi_income._prepare_spi_donor(raw, seed=7).assign( + tax_free_savings_income=0.0, loss_relief=0.0 + ) + before = spi_income.derive_hmrc_income_auxiliaries(donor.copy()) + moved = spi_income._move_losses_brought_forward_to_loss_relief( + donor.copy(), spi_people=pd.Series(True, index=donor.index) + ) + assert (moved["loss_relief"] > 0).any() + after = spi_income.derive_hmrc_income_auxiliaries(moved) + + for column in ( + SPI_HMRC_TOTAL_EARNED_INCOME_COLUMN, + SPI_HMRC_TOTAL_INVESTMENT_INCOME_COLUMN, + HMRC_SPI_ASSESSABLE_INCOME_COLUMN, + ): + np.testing.assert_array_equal(after[column], before[column]) + + def test_spi_donor_rejects_leaf_reconciliation_drift(tmp_path) -> None: donor_path = tmp_path / SPI_DONOR_FILENAME _write_donor(donor_path) @@ -366,9 +585,13 @@ def test_spi_donor_carries_restricted_finance_costs_with_their_relief( 3.0 * 200.0 + 4.0 * 300.0 ) assert check["median_relief_ratio"] == pytest.approx(0.2) - # The leaf is drawn last, so it conditions on every income leaf and moves - # no earlier draw. - assert spi_income.SPI_INCOME_QRF_OUTPUT_COLUMNS[-1] == "property_finance_costs" + # The leaf is drawn after every income leaf, so it conditions on them all + # and moves no earlier draw; only the two loss draws, which the stage + # drops, come after it (microcosm#1095). + assert spi_income.SPI_INCOME_QRF_OUTPUT_COLUMNS[-3] == "property_finance_costs" + assert spi_income.SPI_INCOME_QRF_OUTPUT_COLUMNS[-2:] == ( + spi_income.SPI_LOSS_DRAW_COLUMNS + ) wrong = raw.copy() wrong["TAX_CRED"] = wrong["LLIR_RESTRICT_AMT_TOT"] * 0.4 diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py index 8cb81aa828..3fb923b8cc 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py @@ -37,6 +37,7 @@ ) from microcosm.build.uk_runtime.spi_support import ( HOUSEHOLD_IS_SPI_SYNTHETIC_COLUMN, + SPI_LOSS_DRAW_COLUMNS, SPI_SYNTHETIC_SUPPORT_CHANNEL, UKSPISupportResult, build_uk_spi_support_channel, @@ -517,7 +518,12 @@ def test_spi_income_zero_initializes_frs_charity_and_keeps_frs_dividends( for event in _FakeQRF.events[1:-1]: assert set(event[2]) <= {20.0, 10.0} and event[2] assert _FakeQRF.events[-1][2] == [20.0, 10.0] - assert set(SPI_INCOME_QRF_OUTPUT_COLUMNS) <= set(person.columns) + # Every stage-1 output stays, except the two loss draws the stage moves into + # loss_relief and drops (microcosm#1095). + assert set(SPI_INCOME_QRF_OUTPUT_COLUMNS) - set(SPI_LOSS_DRAW_COLUMNS) <= set( + person.columns + ) + assert not set(SPI_LOSS_DRAW_COLUMNS) & set(person.columns) def test_reviewed_absent_incapacity_signal_raises(tmp_path: Path) -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py index 694d25f0f7..f3e43cf40b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py @@ -138,6 +138,10 @@ def _apply_pending_roster_transformations( "cgt_residential_split", "hmrc_cgt_asset_type_spine", ] + # microcosm#1095 re-mint pending: the stored private-school attendance is + # the last stage of the spine. + assert "private_school_attendance" not in roster + roster.append("private_school_attendance") return tuple(roster) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py index 2fee79f4d6..9d53700e44 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py @@ -89,40 +89,87 @@ def runner(path: Path, params: np.ndarray, seed: int, period: int, contract): def test_childcare_expected_counts_are_bilinear_in_the_extended_rate() -> None: tool = _load_tool("fit_uk_childcare_takeup") - # Two rows: the first family is extended-eligible (loses targeted and - # universal when it claims extended), the second is not. + # Two rows. Since pe-uk#2177 a working-parent family keeps its targeted + # hours beside the extended ones, and without its own targeted claim it + # still receives them, allocated from the working-parent hours (DfE A1.11). + # The second family is not extended-eligible. A child who receives + # extended hours is never universal-only. with_ext = { "hmrc.tfc.government_top_up": np.array([100.0, 50.0]), "hmrc.tfc.children_with_used_accounts": np.array([1.0, 1.0]), "dfe.funded_childcare.working_parent_children_2_to_4": np.array([1.0, 0.0]), - "dfe.funded_childcare.early_learning_2_year_olds": np.array([0.0, 1.0]), + "dfe.funded_childcare.early_learning_2_year_olds": np.array([1.0, 1.0]), "dfe.funded_childcare.universal_only_children": np.array([0.0, 1.0]), } without_ext = { **with_ext, - "dfe.funded_childcare.early_learning_2_year_olds": np.array([1.0, 1.0]), "dfe.funded_childcare.universal_only_children": np.array([1.0, 1.0]), } + allocated = { + "dfe.funded_childcare.early_learning_2_year_olds": np.array([1.0, 0.0]), + "dfe.funded_childcare.universal_only_children": np.array([0.0, 0.0]), + } basis = tool.ChildcareExpectationBasis( - with_extended=with_ext, without_extended=without_ext, rows=2 + with_extended=with_ext, + without_extended=without_ext, + allocated_from_extended=allocated, + rows=2, ) # RATE_KEYS order: tfc, extended, targeted, universal - expected = tool.expected_childcare_counts(basis, np.array([0.5, 0.25, 1.0, 0.4])) + expected = tool.expected_childcare_counts(basis, np.array([0.5, 0.25, 0.6, 0.4])) assert expected["hmrc.tfc.government_top_up"] == pytest.approx(75.0) assert expected["hmrc.tfc.children_with_used_accounts"] == pytest.approx(1.0) assert expected["dfe.funded_childcare.working_parent_children_2_to_4"] == ( pytest.approx(0.25) ) - # targeted: family 1 is eligible only when it does not claim extended. + # targeted: claimed on their own at 0.6 by both families, and for the + # working-parent family also allocated when it does not claim them but + # takes up the extended hours (0.4 * 0.25). assert expected["dfe.funded_childcare.early_learning_2_year_olds"] == ( - pytest.approx(1.0 * (0.75 + 1.0)) + pytest.approx(0.6 * 2.0 + 0.4 * 0.25 * 1.0) ) + # universal-only: the working-parent family counts only when it does not + # take up the extended hours. assert expected["dfe.funded_childcare.universal_only_children"] == ( pytest.approx(0.4 * (0.75 + 1.0)) ) ceiling = tool.expected_childcare_counts(basis, np.ones(4)) - assert ceiling["dfe.funded_childcare.early_learning_2_year_olds"] == 1.0 + assert ceiling["dfe.funded_childcare.early_learning_2_year_olds"] == 2.0 + # Without its own claim the targeted row rests on the allocation alone. + allocated_only = tool.expected_childcare_counts( + basis, np.array([1.0, 1.0, 0.0, 1.0]) + ) + assert allocated_only["dfe.funded_childcare.early_learning_2_year_olds"] == 1.0 + + +def test_the_fit_draws_extended_hours_at_the_contract_anchor() -> None: + """The spine predates the anchor, so the fit sets the hours the take-up + stage draws under the current contract (microcosm#1095, #1126).""" + + tool = _load_tool("fit_uk_childcare_takeup") + from microcosm.build.stochastic_assignment import ( + clipped_normal_from_uniforms, + stable_identity_uniforms, + ) + from microcosm.build.uk_runtime.take_up_contract import ( + load_uk_take_up_contract, + ) + + contract = load_uk_take_up_contract() + ids = np.arange(1, 2001, dtype=np.int64) + hours = tool._anchor_hours(ids, contract) + entry = contract.continuous_entry(tool.HOURS_OUTPUT) + expected = clipped_normal_from_uniforms( + stable_identity_uniforms(ids, seed=0, salt=tool.HOURS_OUTPUT), + mean=float(entry["mean"]), + sd=float(entry["sd"]), + lower=float(entry["lower"]), + upper=float(entry["upper"]), + ) + np.testing.assert_array_equal(hours, expected) + assert float(entry["mean"]) == 31.141 + assert hours.min() >= 0.0 and hours.max() <= 30.0 def test_childcare_fitter_family_allow_list_excludes_entitlement_spending() -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_student_loans.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_student_loans.py index a261848c58..1b9f54f043 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_student_loans.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_student_loans.py @@ -5,7 +5,9 @@ def test_reported_cohort_boundaries_and_country_independence() -> None: - # At 2025: these ages imply start years 2011, 2012, 2022, and 2023. + # At 2025: these ages imply start years 2011, 2012, 2022, and 2023. The + # last repays, and nobody repays a Plan 5 loan before April 2026, so its + # course started before Plan 5 existed (microcosm#868). result = assign_student_loan_plans( _frame( ages=[32, 31, 21, 20], @@ -20,8 +22,114 @@ def test_reported_cohort_boundaries_and_country_independence() -> None: "PLAN_1", "PLAN_2", "PLAN_2", + "PLAN_2", + ] + + +def test_no_repayer_holds_plan_5_before_its_first_repayments() -> None: + """SLC counts no Plan 5 borrower above the threshold in academic year + 2024/25: Plan 5 repayments start in April 2026 (microcosm#868).""" + + frame = _frame( + ages=[20, 20], + repayments=[100.0, 0.0], + reports_outstanding=[False, True], + ) + # Both courses date from 2023 at 2025 (no leaving age: started at 18). + at_2025 = assign_student_loan_plans( + frame, stocks=_stocks(plan_2=0, plan_5=0), year=2025 + ) + assert at_2025.frame.table("person")["student_loan_plan"].tolist() == [ + "PLAN_2", + "PLAN_5", + ] + # From 2026 a repayer may hold Plan 5 (both courses then date from 2024). + at_2026 = assign_student_loan_plans( + frame, stocks=_stocks(plan_2=0, plan_5=0, year=2026), year=2026 + ) + assert at_2026.frame.table("person")["student_loan_plan"].tolist() == [ "PLAN_5", + "PLAN_5", + ] + + +def test_course_start_dates_from_the_age_full_time_education_ended() -> None: + """Three years before leaving full-time education; at 18 without an answer + (microcosm#868).""" + + starts = course_start_year( + year=2025, + age=np.array([30.0, 30.0, 40.0, 30.0]), + age_left=np.array([21, 25, 18, 0]), + ) + # Left at 21 in 2016: started 2013. Left at 25 in 2020: started 2017, a + # Plan 2 course the age rule (2013) would also place, unlike the 40-year-old + # who left at 18 in 2003 and started in 2000. No answer: the age rule. + assert starts.tolist() == [2013.0, 2017.0, 2000.0, 2013.0] + + +def test_an_outstanding_loan_reports_a_borrower_in_england_and_wales_only() -> None: + reported = has_reported_student_loan( + repayments=np.array([0.0, 0.0, 0.0, 0.0, 0.0, 100.0]), + reports_outstanding=np.array([True, True, True, True, False, False]), + in_education=np.array([False, False, False, True, False, False]), + region_names=np.array( + ["LONDON", "WALES", "SCOTLAND", "LONDON", "LONDON", "SCOTLAND"], + dtype=object, + ), + ) + # A student is not liable; Scotland (Plan 4) and Northern Ireland take no + # plan from the answer, and a repayer is reported anywhere, as before. + assert reported.tolist() == [True, True, False, False, False, True] + + +def test_reported_borrowers_below_the_threshold_take_their_course_plan() -> None: + result = assign_student_loan_plans( + _frame( + ages=[30, 30, 40, 30, 30], + repayments=[0.0] * 5, + regions=["LONDON", "WALES", "LONDON", "LONDON", "SCOTLAND"], + reports_outstanding=[True, True, True, True, True], + current_education=[ + "NOT_IN_EDUCATION", + "NOT_IN_EDUCATION", + "NOT_IN_EDUCATION", + "TERTIARY", + "NOT_IN_EDUCATION", + ], + age_left=[21, 25, 18, 21, 21], + ), + stocks=_stocks(plan_2=0, plan_5=0), + year=2025, + ) + assert result.frame.table("person")["student_loan_plan"].tolist() == [ + "PLAN_2", + "PLAN_2", + "PLAN_1", + "NONE", + "NONE", + ] + receipt = result.plans["PLAN_2"] + assert receipt.reported_count == 2.0 and receipt.reported_england_count == 1.0 + + +def test_plan_2_walk_takes_nobody_in_full_time_education() -> None: + result = assign_student_loan_plans( + _frame( + ages=[30, 30], + repayments=[0.0, 0.0], + current_education=["TERTIARY", "NOT_IN_EDUCATION"], + age_left=[21, 21], + ), + stocks=_stocks(plan_2=2, plan_5=0), + year=2025, + ) + assert result.frame.table("person")["student_loan_plan"].tolist() == [ + "NONE", + "PLAN_2", ] + assert result.plans["PLAN_2"].eligible_rows == 1 + assert result.plans["PLAN_2"].pool_exhausted def test_topups_apply_eligibility_gates_and_plan5_priority() -> None: @@ -195,6 +303,8 @@ def test_committed_stocks_pin_full_2025_to_2030_series() -> None: "start_year_formula", "reported_repayment_test", "reported_country_gate", + "reported_outstanding_test", + "reported_outstanding_region_exclusions", "plan_1_before", "plan_5_from", "enum_domain", @@ -210,6 +320,7 @@ def test_committed_stocks_pin_full_2025_to_2030_series() -> None: "year_rule", "age_min", "age_max", + "cohort_start_formula", "cohort_start_min", "eligible_region_exclusions", "highest_education", @@ -228,6 +339,8 @@ def test_committed_stocks_pin_full_2025_to_2030_series() -> None: "year_rule", "age_min", "age_max", + "cohort_start_formula", + "current_education", "cohort_start_min", "cohort_start_max_exclusive", "eligible_region_exclusions", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py new file mode 100644 index 0000000000..4256c29234 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py @@ -0,0 +1,452 @@ +"""The sparse-band support floor (microcosm#1095 U3).""" + +from __future__ import annotations + +import json +from datetime import date + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.measure_simulation import ( + load_uk_calibration_measure_exclusions, +) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.support_floor import ( + UK_SUPPORT_FLOOR_KIND, + apply_uk_support_floor, + load_uk_support_floor_declaration, + narrow_uk_registry_by_support_floor, + uk_household_source_ids, + uk_row_support, + uk_support_floor_from_payload, + uk_support_floor_names, +) +from microcosm.calibrate import TargetRegistry, TargetSpec, build_constraint_matrix + +SCOPE = "dwp.uc.payment_distribution_single" +EVALUATED = date(2026, 10, 20) + + +def _block(**overrides): + return { + "contract_target_ids": [SCOPE], + "metric": "distinct_source_households_with_positive_prior_weight", + "floor": 3, + "max_dropped_rows": 2, + "max_dropped_published_share": 0.5, + "reason": "Sparse bands are dropped, not pooled.", + "tracking": "microcosm#1095", + "approved_by": "juaristi22", + "adjudication": "microcosm#1095 U3", + "approved_on": "2026-10-16", + "expires_on": "2027-01-08", + **overrides, + } + + +def _frame(): + # Six households from four source households (two are clones), one with a + # zero prior weight. + household = pd.DataFrame( + { + "household_id": [1, 2, 3, 4, 5, 6], + "region": ["LONDON"] * 6, + "household_source_id": [10, 10, 20, 30, 40, 40], + "band_low": [1.0, 1.0, 1.0, 1.0, 0.0, 0.0], + "band_thin": [1.0, 1.0, 0.0, 0.0, 0.0, 1.0], + "band_zero_weight": [0.0, 0.0, 0.0, 0.0, 1.0, 1.0], + "other_thin": [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + } + ) + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": [1, 2, 3, 4, 5, 6], + "person_benunit_id": [1, 2, 3, 4, 5, 6], + "person_household_id": [1, 2, 3, 4, 5, 6], + "age": [30] * 6, + } + ), + benunit=pd.DataFrame({"benunit_id": [1, 2, 3, 4, 5, 6]}), + household=household, + household_weights=np.array([10.0, 10.0, 10.0, 10.0, 0.0, 0.0]), + time_period="2025", + ) + + +def _spec(name, measure, value, target=SCOPE): + return TargetSpec( + name=name, + entity="household", + value=value, + measure=measure, + period=2025, + source="test", + family="dwp_universal_credit", + metadata={"contract_target_id": target}, + ) + + +def _registry(*specs): + return TargetRegistry(list(specs), country="uk") + + +def _compiled(registry): + problem = build_constraint_matrix(_frame(), registry.to_target_set(), "household") + assert not problem.skipped + return problem + + +def _apply(registry, **kwargs): + floor = uk_support_floor_from_payload(_block(**kwargs.pop("block", {}))) + return apply_uk_support_floor( + registry, + _compiled(registry), + source_ids=uk_household_source_ids(_frame()), + floor=floor, + evaluated_on=kwargs.pop("evaluated_on", EVALUATED), + **kwargs, + ) + + +def test_support_counts_distinct_source_households_with_a_positive_prior(): + frame = _frame() + registry = _registry( + _spec("low", "band_low", 40.0), + _spec("thin", "band_thin", 30.0), + _spec("zero_weight", "band_zero_weight", 5.0), + ) + problem = _compiled(registry) + support = uk_row_support( + problem.matrix, + problem.initial_weights.values, + uk_household_source_ids(frame), + ) + # Clones of one source household count once; a zero prior counts never. + assert support.tolist() == [3, 1, 0] + + +def test_the_floor_drops_thin_rows_in_scope_and_reports_published_totals(): + registry = _registry( + _spec("low", "band_low", 40.0), + _spec("thin", "band_thin", 30.0), + _spec("other", "other_thin", 7.0, target="dwp.uc.households"), + ) + result = _apply(registry) + + assert [spec.name for spec in result.registry.specs] == ["low", "other"] + assert result.problem.names == ("low@2025", "other@2025") + assert result.problem.target_vector.tolist() == [40.0, 7.0] + assert result.problem.matrix.shape == (2, 6) + assert set(result.exclusions) == {"thin"} + assert result.exclusions["thin"]["kind"] == UK_SUPPORT_FLOOR_KIND + assert result.exclusions["thin"]["evaluated_on"] == "2026-10-20" + receipt = result.receipt + assert receipt["dropped"] == ["thin"] + assert receipt["dropped_by_target"] == {SCOPE: {"rows": 1, "published_total": 30.0}} + assert receipt["dropped_published_share"] == pytest.approx(30.0 / 70.0) + assert receipt["solved_register_sha256"] == result.registry.version + # The receipt carries no row's support: support counts stay local. + assert set(receipt) == { + "metric", + "floor", + "tracking", + "approved_on", + "expires_on", + "evaluated_on", + "in_scope_rows", + "probed_row_exclusions", + "dropped_rows", + "dropped_published_share", + "dropped_by_target", + "dropped", + "solved_register_sha256", + } + + +def test_the_floor_refuses_outside_its_window_and_past_its_ceilings(): + registry = _registry( + _spec("low", "band_low", 40.0), + _spec("thin", "band_thin", 30.0), + _spec("zero_weight", "band_zero_weight", 5.0), + ) + with pytest.raises(ValueError, match="outside its reviewed window"): + _apply(registry, evaluated_on=date(2026, 10, 15)) + with pytest.raises(ValueError, match="outside its reviewed window"): + _apply(registry, evaluated_on=date(2027, 1, 9)) + with pytest.raises(ValueError, match="drops 2 rows"): + _apply(registry, block={"max_dropped_rows": 1}) + with pytest.raises(ValueError, match="published total"): + _apply(registry, block={"max_dropped_published_share": 0.4}) + + +def test_a_per_row_exclusion_the_floor_would_cover_refuses(): + registry = _registry( + _spec("low", "band_low", 40.0), + _spec("thin", "band_thin", 30.0), + ) + with pytest.raises(ValueError, match=r"retire those entries .*\['thin'\]"): + _apply(registry, row_excluded={"thin"}) + # A per-row entry above the floor is measured and leaves the problem. + result = _apply(_registry(_spec("low", "band_low", 40.0)), row_excluded={"low"}) + assert result.registry.specs == () + assert result.exclusions == {} + assert result.receipt["probed_row_exclusions"] == 1 + with pytest.raises(ValueError, match="name no compiled row"): + _apply(registry, row_excluded={"absent"}) + + +def test_lineage_is_read_only_when_a_row_in_scope_needs_it(): + registry = _registry(_spec("other", "other_thin", 7.0, target="dwp.uc.households")) + result = apply_uk_support_floor( + registry, + _compiled(registry), + source_ids=lambda: pytest.fail("lineage read with nothing in scope"), + floor=uk_support_floor_from_payload(_block()), + evaluated_on=EVALUATED, + ) + assert result.registry is registry + assert result.receipt["dropped_rows"] == 0 + frame = _frame() + no_lineage = uk_national_frame( + person=frame.table("person"), + benunit=frame.table("benunit"), + household=frame.table("household").drop(columns=["household_source_id"]), + household_weights=frame.weights_for("household").values, + time_period="2025", + ) + with pytest.raises(ValueError, match="household_source_id"): + uk_household_source_ids(no_lineage) + + +@pytest.mark.parametrize( + ("override", "message"), + [ + ({"metric": "rows"}, "metric"), + ({"floor": 2}, "at least 3"), + ({"floor": True}, "at least 3"), + ({"max_dropped_rows": -1}, "count"), + ({"max_dropped_published_share": 1.0}, r"\[0, 1\)"), + ({"contract_target_ids": [SCOPE, SCOPE]}, "distinct"), + ({"approved_by": " "}, "approved_by is empty"), + ({"expires_on": "2027-1-8"}, "canonical ISO"), + ({"expires_on": "2026-10-16"}, "after approved_on"), + ({"extra": 1}, "closed-world"), + ], +) +def test_the_declaration_is_validated_closed_world(override, message): + with pytest.raises(ValueError, match=message): + uk_support_floor_from_payload(_block(**override)) + + +def test_the_packaged_floor_is_signed_and_scoped_to_the_payment_bands(): + declaration = load_uk_support_floor_declaration() + floor = uk_support_floor_from_payload(declaration) + assert floor.contract_target_ids == { + f"dwp.uc.payment_distribution_{family}" + for family in ( + "single", + "lone_parent", + "couple_no_children", + "couple_with_children", + ) + } + assert floor.floor == 10 + assert (floor.approved_by, floor.approved_on, floor.expires_on) == ( + "juaristi22", + date(2026, 10, 16), + date(2027, 1, 8), + ) + + +def test_only_schema_3_registers_carry_a_floor(tmp_path): + path = tmp_path / "exclusions.json" + path.write_text( + json.dumps({"schema_version": 2, "support_floor": _block(), "exclusions": []}) + ) + with pytest.raises(ValueError, match="support_floor"): + load_uk_calibration_measure_exclusions(path) + path.write_text( + json.dumps({"schema_version": 3, "support_floor": _block(), "exclusions": []}) + ) + assert load_uk_calibration_measure_exclusions(path) == () + assert load_uk_support_floor_declaration(path) == _block() + + +def test_readers_narrow_by_the_recorded_drops_under_the_declared_floor(): + floor = uk_support_floor_from_payload(_block()) + registry = _registry( + _spec("low", "band_low", 40.0), + _spec("thin", "band_thin", 30.0), + _spec("other", "other_thin", 7.0, target="dwp.uc.households"), + ) + record = floor.record(EVALUATED) + per_row = {"per_row": {"reason": "reviewed", "approved_on": "2026-09-01"}} + narrowed = narrow_uk_registry_by_support_floor( + registry, {**per_row, "thin": record}, floor + ) + assert [spec.name for spec in narrowed.specs] == ["low", "other"] + assert uk_support_floor_names({**per_row, "thin": record}) == {"thin"} + assert narrow_uk_registry_by_support_floor(registry, per_row, None) is registry + with pytest.raises(ValueError, match="declares no support floor"): + narrow_uk_registry_by_support_floor(registry, {"thin": record}, None) + with pytest.raises(ValueError, match="outside the floor's scope"): + narrow_uk_registry_by_support_floor(registry, {"other": record}, floor) + with pytest.raises(ValueError, match="not a row of the register"): + narrow_uk_registry_by_support_floor(registry, {"absent": record}, floor) + with pytest.raises(ValueError, match="declared signature"): + narrow_uk_registry_by_support_floor( + registry, {"thin": {**record, "approved_by": "someone"}}, floor + ) + tight = uk_support_floor_from_payload(_block(max_dropped_rows=0)) + with pytest.raises(ValueError, match="signed ceiling"): + narrow_uk_registry_by_support_floor(registry, {"thin": record}, tight) + + +def _encode(monkeypatch, registry, *, band_edge=None, exclusions=None, **block): + """The national problem node's route, the engine replaced by the frame's + own columns (the measures are household columns here).""" + + from microcosm.build.uk_runtime import graph_national + from microcosm.build.uk_runtime.local_rowwise import UKRowwiseNationalRows + from microcosm.build.uk_runtime.national_doctrine import UK_NATIONAL_SOLVE_DOCTRINE + + materialized = [] + + def materialize(frame, compiled, **kwargs): + materialized.append([spec.name for spec in compiled.specs]) + rows = UKRowwiseNationalRows( + targets=compiled.to_target_set(), + registry=compiled, + families=tuple(sorted({spec.family for spec in compiled.specs})), + ) + return frame, (lambda prepared: prepared), rows, {"mode": "frame_only"} + + monkeypatch.setattr(graph_national, "materialize_uk_national_rows", materialize) + doctrine = UK_NATIONAL_SOLVE_DOCTRINE + payload = graph_national.encode_uk_national_problem( + _frame(), + national_registry=registry, + band_edge_registry=registry if band_edge is None else band_edge, + period=2025, + doctrine={ + "target_weight_rule": doctrine.target_weight_rule, + "target_loss_cap": doctrine.target_loss_cap, + "max_weight_ratio": doctrine.max_weight_ratio, + "l0_lambda": doctrine.l0_lambda, + "mass_rule": doctrine.mass_rule, + "scale_rule": doctrine.scale_rule, + }, + measure_exclusions=exclusions, + support_floor={"declaration": _block(**block), "evaluated_on": "2026-10-20"}, + resolver_factory=None, + ) + from microcosm.calibrate.artifacts import decode_problem + + return decode_problem(payload), materialized + + +def _national_spec(name, measure, value, target=SCOPE): + spec = _spec(name, measure, value, target=target) + return TargetSpec( + **{ + **spec.to_dict(), + "metadata": {**spec.metadata, "geography_level": "country"}, + } + ) + + +def test_the_national_problem_drops_thin_rows_before_weights_and_metadata( + monkeypatch, +): + registry = _registry( + _national_spec("low", "band_low", 40.0), + _national_spec("thin", "band_thin", 30.0), + _national_spec("other", "other_thin", 7.0, target="dwp.uc.households"), + ) + problem, materialized = _encode(monkeypatch, registry) + + assert materialized == [["low", "thin", "other"]] + assert problem.problem.names == ("low@2025", "other@2025") + assert [row["contract_target_id"] for row in problem.target_metadata] == [ + SCOPE, + "dwp.uc.households", + ] + bindings = problem.bindings + assert len(bindings["target_loss_weights"]) == 2 + assert bindings["activated_reference_count"] == 2 + assert bindings["resolved_reference_count"] == 2 + assert bindings["measure_exclusions"]["thin"]["kind"] == UK_SUPPORT_FLOOR_KIND + assert bindings["support_floor"]["dropped"] == ["thin"] + # The register the targets node froze stays the approved one. + assert bindings["register_sha256"] == registry.version + + +def test_the_national_problem_measures_per_row_exclusions_in_scope(monkeypatch): + approved = _registry(_national_spec("low", "band_low", 40.0)) + band_edges = _registry( + _national_spec("low", "band_low", 40.0), + _national_spec("thin", "band_thin", 30.0), + _national_spec("high", "band_low", 9.0), + ) + record = { + "reason": "reviewed", + "tracking": "uk-data#452", + "approved_by": "juaristi22", + "adjudication": "microcosm#757", + "approved_on": "2026-08-26", + "expires_on": "2026-11-26", + "evaluated_on": "2026-10-20", + } + with pytest.raises(ValueError, match=r"retire those entries .*\['thin'\]"): + _encode( + monkeypatch, + approved, + band_edge=band_edges, + exclusions={"thin": record, "high": record}, + ) + problem, materialized = _encode( + monkeypatch, approved, band_edge=band_edges, exclusions={"high": record} + ) + # The entry above the floor was compiled to be measured and left again. + assert materialized == [["low", "high"]] + assert problem.problem.names == ("low@2025",) + assert problem.bindings["measure_exclusions"] == {"high": record} + assert problem.bindings["support_floor"]["probed_row_exclusions"] == 1 + + +def test_readers_take_the_solved_register_from_the_problem_bindings(): + from microcosm.build.uk_runtime.graph_national import ( + solved_national_measure_exclusions, + solved_national_registry, + ) + from microcosm.build.uk_runtime.graph_targets import registry_payload + + registry = _registry( + _national_spec("low", "band_low", 40.0), + _national_spec("thin", "band_thin", 30.0), + ) + per_row = {"per_row": {"reason": "reviewed"}} + document = { + "national_registry": registry_payload(registry), + "measure_exclusions": per_row, + "support_floor": {"declaration": _block(), "evaluated_on": "2026-10-20"}, + } + record = uk_support_floor_from_payload(_block()).record(EVALUATED) + bound = {**per_row, "thin": record} + exclusions = solved_national_measure_exclusions( + document, {"measure_exclusions": bound} + ) + assert exclusions == bound + solved = solved_national_registry(document, exclusions) + assert [spec.name for spec in solved.specs] == ["low"] + with pytest.raises(ValueError, match="beyond the support floor"): + solved_national_measure_exclusions( + document, {"measure_exclusions": {"low": {"reason": "x"}, **per_row}} + ) + with pytest.raises(ValueError, match="beyond the support floor"): + solved_national_measure_exclusions( + document, {"measure_exclusions": {"thin": record}} + ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py index eab98eb41f..60c859b981 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py @@ -1,6 +1,8 @@ """Tests split from packages/microcosm-build/tests/test_uk_take_up_contract.py.""" # ruff: noqa: F403, F405 +import numpy as np + from test_support.microcosm_build.uk_take_up_contract import * from test_support.paths import paths_for @@ -39,10 +41,11 @@ def test_uk_contract_loads_and_selects_build_year_rates() -> None: 0.37, 0.55, 0.5, - 0.88, - 0.6054, - 0.4539, - 0.597, + # microcosm#1095 U2c: the v4 refit on the policyengine-uk 2.132.6 spine. + 0.9797, + 0.6579, + 0.4353, + 0.7769, 0.441, 0.2682, 0.97, @@ -84,7 +87,7 @@ def test_year_selection_uses_latest_value_at_or_before_build_year() -> None: assert contract.rate("child_benefit", 2021) == 0.97 assert contract.rate("child_benefit", 2023) == 0.89 - assert contract.rate("tax_free_childcare", 2024) == 0.88 + assert contract.rate("tax_free_childcare", 2024) == 0.9797 assert contract.rate("tax_free_childcare", 2023) == 0.586 assert contract.rate("tv_licence_evasion_rate", 2024) == 0.1252 assert contract.rate("tax_free_childcare_spend_routed_share", 2017) == 0.359 @@ -193,3 +196,31 @@ def test_forbidden_source_dependency_strings_absent_from_uk_resources() -> None: text = files("microcosm.build.uk").joinpath(resource).read_text().lower() assert "policyengine_" + "uk_data" not in text assert "policyengine-" + "uk-data" not in text + + +def test_extended_hours_usage_reproduces_dfe_additional_hours_ptes() -> None: + """The clipped draw's mean is the DfE-implied total weekly hours (#1126). + + DfE funds 341,988.09 additional-hours PTEs (15 hours x 38 weeks each) for + the 379,000 3- and 4-year-olds registered for the working parent + entitlement in January 2025, on top of the universal 15 hours. + """ + + from microcosm.build.stochastic_assignment import clipped_normal_from_uniforms + + contract = load_uk_take_up_contract() + entry = contract.continuous_entry("maximum_extended_childcare_hours_usage") + assert entry["source"]["status"] == "sourced_administrative_derived" + assert (entry["lower"], entry["upper"]) == (0, 30) + + n = 200_000 + uniforms = (np.arange(n) + 0.5) / n + draws = clipped_normal_from_uniforms( + uniforms, + mean=float(entry["mean"]), + sd=float(entry["sd"]), + lower=float(entry["lower"]), + upper=float(entry["upper"]), + ) + dfe_mean_hours = 15 + 15 * 341_988.09 / 379_000 + assert draws.mean() == pytest.approx(dfe_mean_hours, abs=0.01) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py index a85a13e724..e7bdc55fc6 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py @@ -65,7 +65,7 @@ _TEST_PATHS = paths_for("microcosm-build") -ACTIVE_REFERENCE_COUNT = 1292 +ACTIVE_REFERENCE_COUNT = 1291 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} @@ -805,7 +805,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 1292, + "active": 1291, "no_fact_at_or_before_period": 5, "signed_excluded": 15, } @@ -1034,6 +1034,12 @@ def test_uk_fixture_b_signed_differences_carry_ruled_rationales() -> None: caseload = differences[f"dwp.pip.daily_living_{rate}_claimants"] assert caseload["kind"] == "fixture_only" assert "diagnostic provenance on obr.pip" in caseload["reason"] + five_or_more = differences["dwp.uc.households_children_5_or_more"] + assert five_or_more["kind"] == "fixture_only" + assert ( + "diagnostic provenance on dwp.uc.two_child_limit.households_5_children" + in five_or_more["reason"] + ) assert "single-age-90 share" in differences["ons.population.female_85_89"]["reason"] assert "single-age-90 share" in differences["ons.population.male_85_89"]["reason"] assert ( @@ -1123,7 +1129,9 @@ def test_paid_child_count_windows_remove_only_their_uprating_holds() -> None: """A complete 2025 mean needs no December-to-annual hold (#891 review).""" membership = _load_uk_resource("target_reference_membership.json") holds = {row["name"]: row for row in membership["uprating_holds"]} - for children in ("1", "2", "3", "4", "5_or_more"): + # The five-or-more count is a diagnostic since microcosm#1095. + assert "dwp.uc.households_children_5_or_more" not in membership["targets"] + for children in ("1", "2", "3", "4"): name = f"dwp.uc.households_children_{children}" assert name not in holds candidates = membership["targets"][name]["candidates"] @@ -1152,8 +1160,7 @@ def test_uk_generator_averages_paid_monthly_sums_and_preserves_other_uc_operatio "dwp.uc.households_couple_with_children", } monthly_average_ids = { - f"dwp.uc.households_children_{children}" - for children in ("1", "2", "3", "4", "5_or_more") + f"dwp.uc.households_children_{children}" for children in ("1", "2", "3", "4") } | { # #882 element rows: paid-claim crosses on declared month windows. "dwp.uc.households_lcwra_element", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py index 5b44083621..59333844ef 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py @@ -358,10 +358,13 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case # (SINGLE 28,800-30,000 and COUPLE_NO_CHILDREN 22,800-24,000, the # uk-data#452 class). microcosm#1095 excluded two more whose few # supporting awards policyengine-uk 2.122.2 moves out (SINGLE - # 26,400-27,600 and COUPLE_NO_CHILDREN 24,000-25,200). + # 26,400-27,600 and COUPLE_NO_CHILDREN 24,000-25,200). 88 active / 12 + # excluded since microcosm#1095 U3: the support floor drops every band + # below ten source households on the built frame, so the nine per-row + # entries it covers retired. assert len(specs) == 100 - assert len(active) == 79 - assert len({s.name for s in specs} & excluded) == 21 + assert len(active) == 88 + assert len({s.name for s in specs} & excluded) == 12 registry = TargetRegistry(active, country="uk") result = materialize_uk_ledger_targets( adapter, registry, period=2025, band_edge_registry=full_registry @@ -371,7 +374,7 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case adapter.to_frame(), registry.to_target_set(), weight_entity="household" ) assert not problem.skipped - assert len(problem.names) == 79 + assert len(problem.names) == 88 assert not {f"{name}@2025" for name in excluded}.intersection(problem.names) expected_family = np.asarray(expected_families)[bu.benunit_id.to_numpy()] uc = bu.universal_credit.to_numpy() diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_source_periods.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_source_periods.py index 92f780019a..93a6e30af1 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_source_periods.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_source_periods.py @@ -455,7 +455,8 @@ def test_shipped_uc_monthly_references_preserve_each_declared_window(): for reference in references if reference.family == "dwp_universal_credit" ] - assert len(monthly) == 118 + # microcosm#1095: less the five-or-more-children count, a diagnostic. + assert len(monthly) == 117 calendar_2025 = [f"2025-{month:02}" for month in range(1, 13)] new_paid = { "dwp.uc.households", @@ -470,10 +471,10 @@ def test_shipped_uc_monthly_references_preserve_each_declared_window(): }, *{ f"dwp.uc.households_children_{children}" - for children in ("1", "2", "3", "4", "5_or_more") + for children in ("1", "2", "3", "4") }, } - assert len(new_paid) == 10 + assert len(new_paid) == 9 # The #882 element rows bind the chronicle#260 Payment Indicator crosses on # the same calendar-2025 paid basis; the deductions statistics start in # March 2025, so that row declares the ten published months. diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py index 20093f0441..a8fb599693 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py @@ -461,9 +461,12 @@ def test_qrf_columns_check_every_declared_output_regardless_of_density() -> None assert "density_filter" in surface # Declared outputs the fixture does not carry are reported, not invented. assert "dividend_income" in surface["absent_columns"] - # The 48 declared outputs, less the ten spi_benefit_coherence replaces on - # every SPI-channel row (microcosm#1100); they hold no forest draw. - assert surface["declared_qrf_outputs"] == 38 + # The 57 declared outputs (since microcosm#1095, stage 2's trading_loss and + # six owner-manager inputs, and stage 1's two loss draws, which the stage + # moves into loss_relief and drops, so a frame never carries them), less + # the ten spi_benefit_coherence replaces on every SPI-channel row + # (microcosm#1100); they hold no forest draw. + assert surface["declared_qrf_outputs"] == 47 assert surface["superseded_by_spi_benefit_coherence"] == [ "income_support_reported", "working_tax_credit_reported", diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index d37b166643..466c74a044 100644 --- a/packages/microcosm-data/pyproject.toml +++ b/packages/microcosm-data/pyproject.toml @@ -37,7 +37,7 @@ us = ["policyengine-us>=2.2.1,<3", "policyengine-core==3.32.5"] # The UK artifact lives in a PRIVATE repo (UK Data Service licence): loads # require an authenticated HF token with access. The loader surfaces the # 401 with that explanation rather than retrying. -uk = ["policyengine-uk>=2.130.1"] +uk = ["policyengine-uk>=2.132.6"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 201a80495f..a41e110985 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -420,13 +420,13 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: # fingerprint derives from the manifest digest. Editing the spec moves all # three here in the same reviewed change. _UK_GATE_BATTERY_POLICY_SHA256 = ( - "4f9c71b596600d03494f4cc38cbb85f50e10d71325d2cb7f81a5e6a949ec5760" + "c13333661bc62cfa0edd94591173339e9e963810e38f505f6ee52c3807bcadbe" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "e3d97e488a821501801df23242acfbc3565dd7a39386aae387808caf116e188d" + "57f5e27b9a1d612ab0d91864ed5a143890c4970f1773f96c1e0b5c648ee4388f" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "5d7f7401162a0bd53abf41995047b35bf31d20a4ed6251c5332b762060f43864" + "3f29c355db3473bb6e003fe0b92125c5db542c6ef0edecf05e7109528cfb463b" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -892,10 +892,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "7ea070e3efe5a36dfa246b175bb2de81e37fb52dd8b5a337249ebc02160112d4" + "507b7f7482b128dba12fe67b8451143d2a9a3774339c0c016b7c7cc2ff5262ab" ), "policy_sha256": ( - "cc7a00cc21ebba070f6b0e48e5198ebe673d05305eeb7b247b16610be8927010" + "c3c8622ec081579ad280fadb3c6e58515457cb8a3f114c8fd6a4ca19490d2b20" ), }, } @@ -6557,7 +6557,7 @@ def _check_uk_measure_exclusions( }, "local": { "rows": 23545, - "sha256": "1d783e5933ca632250a9e38f8ddd00223f8be93e4878973b148fb72620cf1e23", + "sha256": "0663375b80dbed14d1a6d2d6015bcdd5cc45c5e8af4dbe62dd2f393b2811578a", }, } _UK_DENSE_SURFACE_FILE = "incumbent_surface_evaluation.json" diff --git a/packages/microcosm-data/tests/engine_free/shared/test_contract.py b/packages/microcosm-data/tests/engine_free/shared/test_contract.py index b244011063..02c98882fa 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -213,13 +213,13 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: UK_GATE_BATTERY_PRODUCER = "microcosm.build.gate_battery" UK_GATE_BATTERY_SIGNING_KEY_ENV = "MICROCOSM_UK_TERMINAL_GATE_SIGNING_KEY" UK_GATE_BATTERY_POLICY_SHA256 = ( - "4f9c71b596600d03494f4cc38cbb85f50e10d71325d2cb7f81a5e6a949ec5760" + "c13333661bc62cfa0edd94591173339e9e963810e38f505f6ee52c3807bcadbe" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "e3d97e488a821501801df23242acfbc3565dd7a39386aae387808caf116e188d" + "57f5e27b9a1d612ab0d91864ed5a143890c4970f1773f96c1e0b5c648ee4388f" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "5d7f7401162a0bd53abf41995047b35bf31d20a4ed6251c5332b762060f43864" + "3f29c355db3473bb6e003fe0b92125c5db542c6ef0edecf05e7109528cfb463b" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6a179222c6b6058ad9caacfd347f7bdb8cb98a62237b50d1fc1136df09337251" diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 10742b2594..6933b76e2f 100644 --- a/packages/microcosm-frame/pyproject.toml +++ b/packages/microcosm-frame/pyproject.toml @@ -22,7 +22,7 @@ policyengine = [ "policyengine-core==3.32.5", "microunit>=0.1.0", ] -uk = ["policyengine-uk>=2.130.1"] +uk = ["policyengine-uk>=2.132.6"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py index 4f15afce00..4f0fc157b8 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py @@ -19,7 +19,7 @@ Recode, RelationshipRole, Role, - Sum, + SignedPart, TakeUpThreshold, bind, ) @@ -32,7 +32,7 @@ # --------------------------------------------------------------------------- # # Which PolicyEngine-UK inputs each engine-neutral concept feeds -# (microcosm.frame.concepts), reviewed against policyengine-uk 2.130.1. Every +# (microcosm.frame.concepts), reviewed against policyengine-uk 2.132.6. Every # target is a pure input (in ``PolicyEngineUKEngine.variables``). The UK # loader also accepts some formula-owned variables as overrides # (``employment_income``, ``state_pension_reported``, ``is_household_head``); @@ -200,7 +200,7 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: POLICYENGINE_UK_CONCEPT_MAPPING = ConceptMapping( engine="policyengine-uk", - engine_version="2.130.1", + engine_version="2.132.6", entity_correspondence={"person": "person", "household": "household"}, input_declaration=InputDeclaration.ENGINE_TYPED, bindings=( @@ -258,12 +258,34 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: "fact:person.nonfarm_self_employment_income", "fact:person.farm_self_employment_income", ), - Sum(), - AlignmentRelation.EXACT, + SignedPart("positive"), + AlignmentRelation.APPROXIMATE, ( "Engine documentation: 'Income from self-employment profits, " - "including gig work ... net of self-employment expenses'; it " - "has no farm split, so the two concepts sum into it." + "including gig work ... net of self-employment expenses', " + "never negative from 2.131.0, the year's losses going to " + "trading_loss (pe-uk#2085). It has no farm split, so the two " + "concepts sum and the profit is the positive part of the sum. " + "The concepts net each person's trades, so a profit beside " + "another trade's loss arrives net of that loss." + ), + ), + bind( + "trading_loss", + "person", + ( + "fact:person.nonfarm_self_employment_income", + "fact:person.farm_self_employment_income", + ), + SignedPart("negative"), + AlignmentRelation.APPROXIMATE, + ( + "Engine documentation: 'Losses made in the tax year by the " + "person's trades, professions or vocations, as a positive " + "amount' (pe-uk#2085, from 2.131.0): the negative part of the " + "two concepts' sum, so it and self_employment_income never " + "both hold an amount. The concepts net each person's trades, " + "so a loss beside another trade's profit arrives net of it." ), ), # --- Capital income ----------------------------------------------- diff --git a/packages/microcosm-frame/src/microcosm/frame/concept_mapping.py b/packages/microcosm-frame/src/microcosm/frame/concept_mapping.py index 0c7294cb6e..7ae97ebae8 100644 --- a/packages/microcosm-frame/src/microcosm/frame/concept_mapping.py +++ b/packages/microcosm-frame/src/microcosm/frame/concept_mapping.py @@ -90,6 +90,7 @@ "Role", "ScaledSum", "Share", + "SignedPart", "StateBinding", "StateTest", "Sum", @@ -240,6 +241,30 @@ def __post_init__(self) -> None: object.__setattr__(self, "factor", factor) +_SIGNED_PART_SIGNS = ("positive", "negative") + + +@dataclass(frozen=True) +class SignedPart: + """One sign of the sum of annual-flow amount concepts, never negative. + + A rule can split a signed amount between two inputs that are never + negative: policyengine-uk takes a trade's profit in + ``self_employment_income`` and its loss in ``trading_loss``. ``sign`` + ``"positive"`` gives ``max(sum, 0)`` and ``"negative"`` gives + ``max(-sum, 0)``, so two bindings of the same concepts never both hold an + amount. Not invertible. + """ + + sign: str + + def __post_init__(self) -> None: + if self.sign not in _SIGNED_PART_SIGNS: + raise ValueError( + f"A signed part is 'positive' or 'negative', not {self.sign!r}." + ) + + @dataclass(frozen=True) class Product: """The engine input is the product of two concepts (hours x weeks).""" @@ -420,6 +445,7 @@ def concepts(self) -> tuple[str, ...]: | Sum | Product | ScaledSum + | SignedPart | AllocateToReferencePerson | Predicate | RelationshipRole @@ -444,6 +470,7 @@ def concepts(self) -> tuple[str, ...]: "positive": Positive, "scale": Scale, "scaled_sum": ScaledSum, + "signed_part": SignedPart, "unit_composition": UnitComposition, } _KIND_BY_TRANSFORM: dict[type, str] = { @@ -475,6 +502,8 @@ def transform_to_dict(transform: Transform) -> dict[str, object]: out["parameter"] = transform.parameter elif isinstance(transform, Scale | ScaledSum): out["factor"] = transform.factor + elif isinstance(transform, SignedPart): + out["sign"] = transform.sign elif isinstance(transform, UnitComposition): out["feature"] = transform.feature.value elif isinstance(transform, CoresidentChildCount) and transform.max_age is not None: @@ -492,6 +521,7 @@ def transform_to_dict(transform: Transform) -> dict[str, object]: "fraction": frozenset({"parameter"}), "scale": frozenset({"factor"}), "scaled_sum": frozenset({"factor"}), + "signed_part": frozenset({"sign"}), "unit_composition": frozenset({"feature"}), "coresident_child_count": frozenset({"max_age"}), "take_up_threshold": frozenset({"program"}), @@ -579,6 +609,8 @@ def optional_age(name: str) -> int | None: if isinstance(factor, bool) or not isinstance(factor, int | float): raise ValueError("A scale factor must be a number.") return _TRANSFORM_KINDS[kind](factor=factor) + if kind == "signed_part": + return SignedPart(sign=text("sign")) if kind == "unit_composition": return UnitComposition(feature=UnitFeature(text("feature"))) if kind == "coresident_child_count": @@ -855,7 +887,7 @@ def bind( #: binding allocated to the reference unit also reads to place its value. A #: product is outside the rule: it multiplies a usual rate (weekly hours) by #: weeks. -_FLOW_TRANSFORMS = (Scale, Sum, ScaledSum, Share, Fraction) +_FLOW_TRANSFORMS = (Scale, Sum, ScaledSum, SignedPart, Share, Fraction) def _check_transform_arity(binding: InputBinding) -> None: @@ -891,7 +923,8 @@ def _check_transform_arity(binding: InputBinding) -> None: f"concept's domain {list(domain)}." ) if isinstance( - transform, Share | Fraction | Sum | ScaledSum | AllocateToReferencePerson + transform, + Share | Fraction | Sum | ScaledSum | SignedPart | AllocateToReferencePerson, ): for item in items: if item.monetary is None: @@ -907,7 +940,10 @@ def _check_transform_arity(binding: InputBinding) -> None: ) if isinstance(transform, Sum | ScaledSum) and len(items) < 2: raise ValueError(f"Binding for {name!r}: a sum reads two or more amounts.") - if isinstance(transform, ScaledSum) and len({item.entity for item in items}) != 1: + if ( + isinstance(transform, ScaledSum | SignedPart) + and len({item.entity for item in items}) != 1 + ): raise ValueError(f"Binding for {name!r}: a sum adds amounts on one entity.") if isinstance(transform, _FLOW_TRANSFORMS): for item in items: @@ -2100,6 +2136,15 @@ def _apply( axis=0, ) return total * transform.factor + if isinstance(transform, SignedPart): + total = np.sum( + [ + context.values(concept_id).to_numpy(dtype=np.float64) + for concept_id in concepts + ], + axis=0, + ) + return np.maximum(total if transform.sign == "positive" else -total, 0.0) if isinstance(transform, UnitComposition): raise ValueError( f"{binding.engine_input!r} tests unit composition; it needs unit " diff --git a/packages/microcosm-frame/tests/engine_free/shared/test_concept_mapping.py b/packages/microcosm-frame/tests/engine_free/shared/test_concept_mapping.py index 44161173e2..4f4349a061 100644 --- a/packages/microcosm-frame/tests/engine_free/shared/test_concept_mapping.py +++ b/packages/microcosm-frame/tests/engine_free/shared/test_concept_mapping.py @@ -44,6 +44,7 @@ Scale, ScaledSum, Share, + SignedPart, Sum, TakeUpThreshold, coverage_report, @@ -1082,6 +1083,8 @@ def test_serialised_mappings_refuse_unknown_fields(self) -> None: Scale(factor=1 / 52), Positive(), Sum(), + SignedPart(sign="positive"), + SignedPart(sign="negative"), Product(), AllocateToReferencePerson(), Predicate(clauses=(("fact:person.sex", ("female",)),)), @@ -1111,6 +1114,8 @@ def test_every_transform_serialises_and_reads_back(self, transform) -> None: ({"kind": "scale", "factor": True}, "number"), ({"kind": "coresident_child_count", "max_age": "17"}, "integer"), ({"kind": "take_up_threshold"}, "lacks"), + ({"kind": "signed_part"}, "lacks"), + ({"kind": "signed_part", "sign": "up"}, "'positive' or 'negative'"), ], ) def test_malformed_transforms_are_refused(self, data, message) -> None: @@ -1169,7 +1174,7 @@ def test_a_fraction_is_encoded_but_never_decoded(self) -> None: #: The transforms that do arithmetic on a year's amount, and so read annual #: flows only. -_ARITHMETIC = (Scale, Sum, ScaledSum, Share, Fraction) +_ARITHMETIC = (Scale, Sum, ScaledSum, SignedPart, Share, Fraction) _NOT_FLOWS = tuple( basis for basis in TemporalBasis if basis is not TemporalBasis.ANNUAL_FLOW ) @@ -1205,6 +1210,7 @@ def _passes_type_checks(kind: type, concept_id: str) -> bool: Scale: Scale(factor=1 / 52), Sum: Sum(), ScaledSum: ScaledSum(factor=1 / 52), + SignedPart: SignedPart(sign="negative"), Share: Share(parameter="p"), Fraction: Fraction(parameter="f"), } @@ -1220,6 +1226,10 @@ def _arithmetic(kind: type): return st.builds(Share, parameter=_parameter_names, complement=st.booleans()) if kind is Fraction: return st.builds(Fraction, parameter=_parameter_names) + if kind is SignedPart: + return st.sampled_from(("positive", "negative")).map( + lambda sign: SignedPart(sign=sign) + ) return st.just(Sum()) diff --git a/packages/microcosm-frame/tests/engine_free/uk/test_concept_mapping.py b/packages/microcosm-frame/tests/engine_free/uk/test_concept_mapping.py index be55891813..6cae320591 100644 --- a/packages/microcosm-frame/tests/engine_free/uk/test_concept_mapping.py +++ b/packages/microcosm-frame/tests/engine_free/uk/test_concept_mapping.py @@ -104,13 +104,23 @@ def test_savings_interest_holds_all_interest_and_isa_interest_a_part() -> None: assert out.loc[0, "individual_savings_account_interest_income"] == 100.0 -def test_self_employment_sums_farm_and_non_farm_profit() -> None: - out = MAPPING.encode( - _person( - nonfarm_self_employment_income=[100.0], farm_self_employment_income=[50.0] - ) - ).tables["person"] - assert out.loc[0, "self_employment_income"] == 150.0 +def test_self_employment_splits_the_farm_and_non_farm_sum_by_sign() -> None: + # policyengine-uk 2.131.0 (pe-uk#2085): the profit goes to + # self_employment_income and the loss to trading_loss, never both. + tables = { + "person": pd.DataFrame( + { + "person_id": [1, 2, 3], + "person_household_id": [1, 2, 3], + "nonfarm_self_employment_income": [100.0, -500.0, 300.0], + "farm_self_employment_income": [50.0, 200.0, -300.0], + } + ), + "household": pd.DataFrame({"household_id": [1, 2, 3]}), + } + out = MAPPING.encode(tables).tables["person"] + assert out["self_employment_income"].tolist() == [150.0, 0.0, 0.0] + assert out["trading_loss"].tolist() == [0.0, 300.0, 0.0] def test_annual_hours_are_usual_weekly_hours_times_weeks() -> None: diff --git a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json index c33847a846..8bfeb479ab 100644 --- a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json +++ b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json @@ -78,6 +78,10 @@ { "entity": "person", "name": "self_employment_income" + }, + { + "entity": "person", + "name": "trading_loss" } ], "fact:person.has_disability": [ @@ -100,6 +104,10 @@ { "entity": "person", "name": "self_employment_income" + }, + { + "entity": "person", + "name": "trading_loss" } ], "fact:person.parent_1_person_id": [ @@ -229,7 +237,7 @@ } ] }, - "covered_count": 40, + "covered_count": 41, "covered_inputs": [ { "entity": "person", @@ -319,6 +327,10 @@ "entity": "household", "name": "tenure_type" }, + { + "entity": "person", + "name": "trading_loss" + }, { "entity": "person", "name": "would_claim_adult_dependants_grant" @@ -393,8 +405,8 @@ } ], "engine": "policyengine-uk", - "engine_version": "2.130.1", - "input_count": 299, + "engine_version": "2.132.6", + "input_count": 300, "schema_sha256": "5a55669d0447a686692911a6327ae298f19741dde29559f2654a75807878a141", "structural_inputs": [ { @@ -943,6 +955,10 @@ "entity": "household", "name": "local_authority" }, + { + "entity": "person", + "name": "loss_relief" + }, { "entity": "household", "name": "lpg_kg" @@ -1331,10 +1347,6 @@ "entity": "person", "name": "tax_reported" }, - { - "entity": "person", - "name": "trading_loss" - }, { "entity": "household", "name": "transport_consumption" diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json index 3d00d204a1..613c03e248 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json @@ -2,70 +2,70 @@ "resource": "synthetic-cgt-asset-type.json", "resource_sha256": "synthetic", "source_commit": "synthetic", - "table4_all_gains": 6495999.6711802, + "table4_all_gains": 6492650.423955843, "table4_all_tax": 0.0, - "table4_all_taxpayers": 15.567349850568583, + "table4_all_taxpayers": 15.56124747987893, "table4_bands": [ { - "gains": 5348.044150899212, + "gains": 5353.605308331787, "lower_bound": 0, "tax": null, - "taxpayers": 1.3602483724746541, + "taxpayers": 1.3551306505826561, "upper_bound": 10000 }, { - "gains": 25228.55251079793, + "gains": 24683.340670393227, "lower_bound": 10000, "tax": null, - "taxpayers": 1.410789836498686, + "taxpayers": 1.3786932920051185, "upper_bound": 25000 }, { - "gains": 83718.08732477027, + "gains": 84332.55231110852, "lower_bound": 25000, "tax": null, - "taxpayers": 2.305833916121141, + "taxpayers": 2.323766819273882, "upper_bound": 50000 }, { - "gains": 153738.35798944073, + "gains": 154213.31481636307, "lower_bound": 50000, "tax": null, - "taxpayers": 2.1021712351431656, + "taxpayers": 2.1078867573915008, "upper_bound": 100000 }, { - "gains": 137875.84972057378, + "gains": 138484.83817406252, "lower_bound": 100000, "tax": null, - "taxpayers": 0.8765746482342658, + "taxpayers": 0.8780827922829209, "upper_bound": 250000 }, { - "gains": 602364.9252136986, + "gains": 599393.2291901679, "lower_bound": 250000, "tax": null, - "taxpayers": 1.738850948156532, + "taxpayers": 1.7311343090482085, "upper_bound": 500000 }, { - "gains": 668654.768129127, + "gains": 707496.8496493298, "lower_bound": 500000, "tax": null, - "taxpayers": 0.9538098077992454, + "taxpayers": 1.0078601654585555, "upper_bound": 1000000 }, { - "gains": 4819071.0861408925, + "gains": 4778692.693836086, "lower_bound": 1000000, "tax": null, - "taxpayers": 4.819071086140893, + "taxpayers": 4.778692693836087, "upper_bound": null } ], - "table4_individuals_gains": 6495999.6711802, + "table4_individuals_gains": 6492650.423955843, "table4_individuals_tax": 0.0, - "table4_individuals_taxpayers": 15.567349850568583, + "table4_individuals_taxpayers": 15.56124747987893, "table4_trusts_gains": 0.0, "table4_trusts_tax": 0.0, "table7_total_disposals": 2743095.2380952383, @@ -114,12 +114,12 @@ "proceeds": 9000000000.0 } ], - "table8a_disposals_total": 29.355574003929338, - "table8a_gains_total": 35848533.61804, - "table8a_tax_total": 7169706.723608001, - "table8a_taxpayers_total": 26.686885458117576, - "table8b_all_gains": 35848533.61804, - "table8b_all_taxpayers": 26.686885458117576, - "table8b_individuals_gains": 35848533.61804, - "table8b_individuals_taxpayers": 26.686885458117576 + "table8a_disposals_total": 29.344066676343125, + "table8a_gains_total": 35682972.13817826, + "table8a_tax_total": 7136594.427635652, + "table8a_taxpayers_total": 26.67642425122102, + "table8b_all_gains": 35682972.13817826, + "table8b_all_taxpayers": 26.67642425122102, + "table8b_individuals_gains": 35682972.13817826, + "table8b_individuals_taxpayers": 26.67642425122102 } diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json index bb706f4bbd..52c42eb9e5 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/fixture.json @@ -253,7 +253,7 @@ "cgt_residential_split": { "artifacts": [ { - "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.132.6 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -607,7 +607,7 @@ "is_SP_age", "dla", "pip", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "current_education" ] }, @@ -974,7 +974,7 @@ "kind": "materialize_rules_engine_predictors", "predictors": [ "is_SP_age", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -1153,8 +1153,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" }, @@ -1174,9 +1174,10 @@ "nonnegative_outputs": [ "adult_ema", "child_ema", - "age_started_or_accepted_current_education_or_training" + "age_started_or_accepted_current_education_or_training", + "age_left_full_time_education" ], - "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build.", + "notes": "Ports the incumbent education cascade including its unreachable POST_SECONDARY branch order. EDUCQUAL_MAP carries the corrected highest-qualification codeframe (1 = Doctorate, descending) per the FRS 2024-25 data dictionary (UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1, adult table), corroborated against the raw aggregates and adopted as a signed difference (PR #703); an upstream defect report records the incumbent inversion. Codes 1-87 are empirically present at 2024-25; code 87 remains undocumented in the SN 9563 dictionary and falls to the default. EMA uses the shared weeks-in-year constant. is_in_approved_training is TRAINEE = 1 (whether on a government scheme for employment training; adult and child tables) on a child-table record: the FRS counts a 16- to 19-year-old as a dependent child only in full-time non-advanced education or unwaged government training, so such a record is a dependant in approved training (uk-data#560, uk-data#561). No adult-table record is flagged, since the FRS has already decided it is not a dependant and TRAINEE does not say the training is unwaged (UC Regs 2013 reg 5(3)); the adult-table TRAIN2 scheme codes, which microcosm#1063 read, name DWP employment schemes and are no longer read. A tab without TRAINEE refuses the build. reports_outstanding_student_loan is SLOS = 1 (Do you have an outstanding student loan?) on an adult-table record, answered by adults not making repayments, and age_left_full_time_education is EDUCLEFT (Age completed full-time education) where it lies between 10 and 90, else 0; the student_loans stage reads both (microcosm#868). A tab without SLOS or EDUCLEFT refuses the build.", "operations": [ { "delimiter": "\t", @@ -1209,7 +1210,9 @@ "is_before_universal_credit_qualifying_young_person_terminal_date", "adult_ema", "child_ema", - "receives_benefits_in_own_right" + "receives_benefits_in_own_right", + "reports_outstanding_student_loan", + "age_left_full_time_education" ], "rewrites": [], "source": "Department for Work and Pensions Family Resources Survey 2024-25, UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1; local licensed 2024_25 tabs.", @@ -1269,8 +1272,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" } @@ -1312,8 +1315,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" }, @@ -1323,8 +1326,8 @@ "locator": "benefits.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "d79e0f4a8fed6ca6aeda4de928a3ca27f66c12274ac9669806e93e36af9b1844", - "size_bytes": 18064, + "sha256": "1df8dccae70d752fe287961ee7867fdf2887c1705bbaabf54d6c7af765347b91", + "size_bytes": 20049, "table": "benefits", "vintage": "synthetic-h2" }, @@ -1334,8 +1337,8 @@ "locator": "job.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "f54570510234710ec5e89e67be70139ae2b1033bc1cbcb6bd1a9c7d5a2e7804a", - "size_bytes": 3861, + "sha256": "c364262e4d355a12ac929b9b6d2dac0f40a21aa9e1106895ed603c9e6e3554d5", + "size_bytes": 5405, "table": "job", "vintage": "synthetic-h2" }, @@ -1530,8 +1533,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" } @@ -1641,8 +1644,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" }, @@ -1872,8 +1875,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "4977df8ad7e7cbdf75fe55d721bb2f0079f986a7aa541a5a1ea7ca3e37735ac9", - "size_bytes": 30789, + "sha256": "f41b0fcdbd39ea3aef4150454b87f40bbdc5b569d830e8eae7bd37c0198cb9d3", + "size_bytes": 31478, "table": "adult", "vintage": "synthetic-h2" }, @@ -1883,8 +1886,8 @@ "locator": "benefits.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "d79e0f4a8fed6ca6aeda4de928a3ca27f66c12274ac9669806e93e36af9b1844", - "size_bytes": 18064, + "sha256": "1df8dccae70d752fe287961ee7867fdf2887c1705bbaabf54d6c7af765347b91", + "size_bytes": 20049, "table": "benefits", "vintage": "synthetic-h2" }, @@ -1949,8 +1952,8 @@ "locator": "job.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "f54570510234710ec5e89e67be70139ae2b1033bc1cbcb6bd1a9c7d5a2e7804a", - "size_bytes": 3861, + "sha256": "c364262e4d355a12ac929b9b6d2dac0f40a21aa9e1106895ed603c9e6e3554d5", + "size_bytes": 5405, "table": "job", "vintage": "synthetic-h2" }, @@ -2019,10 +2022,14 @@ "hours_worked", "employment_income", "self_employment_income", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", "private_pension_income", "tax_free_savings_income", "savings_interest_income", "dividend_income", + "owned_company_income_share", "maintenance_income", "miscellaneous_income", "student_loan_repayments", @@ -2041,7 +2048,7 @@ "external_child_payments", "care_hours" ], - "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).", + "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except two identity-keyed draws. One is the UC start-up claim recency (uk-data#527). The other is each person's months_since_last_birthday, which policyengine-uk reads to place a date of birth (the two-child limit, the State Pension scheme, the Pension Credit qualifying age). Each single year of age and sex is spread evenly over the year at the FRS weights, in an order set by a draw on the person's stable id, as the engine spreads it when the column is unset, and the column is stored so every later engine read, the calibration's and the release's included, takes the same months whatever weights or identifiers the file carries (microcosm#1095). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). SEINCAM2, the self-employment income the respondent gives, splits by its sign (uk-data#528), so profits go to self_employment_income and losses to trading_loss as a positive amount, the two inputs policyengine-uk reads from 2.131.0 (pe-uk#2085). The engine uprates trading_loss into each projected year as it does self_employment_income, so a loss persists like a profit; the engine documents that a one-off loss would be set to zero in later years, which a one-year survey cannot tell apart. Six of policyengine-uk's eleven owned-company inputs (pe-uk#1965) come from the FRS job tab (microcosm#1095). A director (DIRECTOR 1 or NATURE 1) who owns at least half of the company (DIRPERC 1 to 3, the ruling of 2026-10-16) stands as its sole owner or partner and works in it. The company is their main employment when the job is their first (JOBTYPE 1); it carries on a property business when the main job's SIC division is 68 and a trade otherwise, and its income share is the dividends the job records (DIVIDEND, after tax), the part of the company's profit the survey sees. A company held in another job carries on neither, since ADULT.SIC codes the main job only, which leaves the engine's company-owner rules off for it. The stocks and the intermediaries inputs keep the engine's defaults.", "operations": [ { "delimiter": "\t", @@ -2065,7 +2072,7 @@ }, { "kind": "map_columns", - "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); esa_includes_support_component is false where a person's ESA rows (BENEFIT 16) record VAR3 (ESAGROUP) 1 or 3, the work-related activity group or the assessment phase, and none records 2, the support group, and true everywhere else, policyengine-uk's own default for an award of one's own, and a benefits tab without VAR3 refuses (microcosm#1095); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" }, { "kind": "map_coded_amounts", @@ -2108,10 +2115,19 @@ "rent_paid_as_lodger", "employment_income", "self_employment_income", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", "private_pension_income", "tax_free_savings_income", "savings_interest_income", "dividend_income", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", "property_income", "reports_rent_from_other_property", "maintenance_income", @@ -2162,6 +2178,7 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "esa_includes_support_component", "bsp_reported", "benunit_id", "frs_benunit_capital", @@ -2198,7 +2215,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.130.1's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095).", + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.132.6's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095).", "operations": [ { "aggregates": { @@ -2356,7 +2373,7 @@ "hmrc_cgt_asset_type_spine": { "artifacts": [ { - "dependency": "policyengine-uk>=2.130.1 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.132.6 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters and uk_cgt_badr_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -2769,7 +2786,7 @@ "hmrc_spi_total_investment_income", "hmrc_spi_assessable_income" ], - "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path.", + "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095).", "operations": [ { "artifact_roles": [ @@ -2905,7 +2922,9 @@ "property_finance_costs": "property_finance_costs", "property_income": "property_income", "savings_interest_income": "savings_interest_income", - "self_employment_income": "self_employment_income" + "self_employment_income": "self_employment_income", + "spi_losses_brought_forward_draw": "self_employment_income", + "spi_profit_before_losses_draw": "self_employment_income" }, "initialize_frs_channel_columns": { "charitable_investment_gifts": 0, @@ -2933,7 +2952,9 @@ "hmrc_spi_miscellaneous_employment_income", "hmrc_spi_other_income", "hmrc_spi_state_pension_income", - "property_finance_costs" + "property_finance_costs", + "spi_profit_before_losses_draw", + "spi_losses_brought_forward_draw" ], "post_sample_fit_weight": "uniform", "predictors": [ @@ -2973,7 +2994,9 @@ "property_finance_costs": null, "property_income": "property_income", "savings_interest_income": "taxable_savings_interest", - "self_employment_income": "self_employment_income" + "self_employment_income": "self_employment_income", + "spi_losses_brought_forward_draw": null, + "spi_profit_before_losses_draw": null }, "fit_weight_kind": "importance", "fold_key": "predictor cell (gender, region, whole-year age, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell's stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold's recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest", @@ -3054,6 +3077,13 @@ "PROFITS", "CAPALL", "LOSSBF" + ], + "spi_losses_brought_forward_draw": [ + "LOSSBF" + ], + "spi_profit_before_losses_draw": [ + "PROFITS", + "CAPALL" ] }, "source_leaf_reconciliation": { @@ -3151,7 +3181,14 @@ "jsa_contrib_reported", "jsa_income_reported", "esa_contrib_reported", - "esa_income_reported" + "esa_income_reported", + "trading_loss", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share" ], "postprocess": { "gross_savings_interest_income": "stage1 INCBBS draw + stage2 tax_free_savings_income", @@ -3529,6 +3566,15 @@ "jsa_income_reported", "esa_contrib_reported", "esa_income_reported", + "trading_loss", + "loss_relief", + "ni_class_4_losses_brought_forward", + "stands_as_sole_owner_or_partner_of_company", + "is_engaged_in_owned_company_trade", + "owned_company_is_main_employment", + "owned_company_carries_on_trade", + "owned_company_carries_on_property_business", + "owned_company_income_share", "hmrc_spi_pay", "hmrc_spi_unemployment_benefit_income", "hmrc_spi_incapacity_benefit_income" @@ -3655,7 +3701,7 @@ "has_fuel_consumption", "uses_energy_prepayment_meter" ], - "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain's predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm's locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (Mar\u00eda's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain's predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor's gas payers) and the gas such a donor pays (a regime-gated QRF of the payers' gas spend, at quantiles keyed on the household id), and the region's zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; Mar\u00eda's ruling of 2026-10-12 on microcosm#1207's extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households' own NEED cells are re-raked to those cells' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Both rakes band households on gross income, NEED's concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine's household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine's CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor's distribution among households with positive road fuel, on the chain's predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor's own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary's 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain's draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain's own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain's domestic-energy target is the electricity and gas the stage derives from the diary's fuel, light and power (p537) by its labelled parts, from the LCF User Guide's derived-variable labels and P537 definition (microcosm#1164): each fuel's last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel's account payers, which pins the labels and the rebate's sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary's second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household's own electricity share where it pays for both fuels, and otherwise by the donor's mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary's other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor's realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary's range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5's year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area's fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland's calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England's index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (Mar\u00eda's rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers' imputed rent and 12.6.1 FISIM), on ONS's domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel's frame litres with HMRC's clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total.", + "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). The frame holds no fuel type, so the draw, keyed on the household identity, is what makes a vehicle-owning household a fuel buyer or a zero-emission one: it is random with respect to income and every other characteristic, and a household with several vehicles is all one or the other. With the redraw of microcosm#1113 every flagged household buys fuel, so this flag alone sets fuel incidence; who holds the zero-emission cars is a known residual. The household input uses_energy_prepayment_meter, which policyengine-uk reads for the July 2023 to March 2024 Energy Price Guarantee prepayment discount (PolicyEngine/policyengine-uk#2098, from 2.123.3), is drawn by the declared impute_prepayment_meter step (microcosm#1110): the donor prepays when it last put money on an electricity, gas or combined key or meter (b1751, b1701, b233, which the LCF asks only of prepayment-meter households) or its diary records a slot-meter payment (c45114, c45214), so electricity-only prepayment counts and a household with no meter payment, including one that did not answer, does not; a weighted regime-gated QRF on the chain's predictors fits that flag and each recipient draws it at uniforms keyed on its household id, which moves no other draw. The release exports it: policyengine-uk reads it from 2.123.3 and an older engine, Microcosm's locked one among them, ignores a column it does not know; the incumbent does not carry it until uk-data#539, so it is a reviewed extra column of the export surface. The frame flags about 3.0 million Great Britain households (10.5 %) at prior weights, against the around three million prepayment households the government counted when the discount began in July 2023 (a different reference period, households in both). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (Mar\u00eda's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the calendar-2024 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and, where the draw leaves a region below its share, by the declared connect_rule (microcosm#1164): two weighted fits on the chain's predictors give each zero-gas household the probability that a donor like it pays for gas (a gradient-boosted classifier of the donor's gas payers) and the gas such a donor pays (a regime-gated QRF of the payers' gas spend, at quantiles keyed on the household id), and the region's zero-gas households are connected in an identity-keyed order drawn in proportion to that probability, by the same weight-fitting walk, until the share is met. The labelled energy split leaves about 80 % of donors paying for gas against the about 85 % of homes DESNZ counts gas meters for (the derivation it replaced gave gas to 98 %), so without the connect leg nine of the eleven Great Britain regions fell short of their share, and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four quarters of calendar 2024; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. After the level, no household is left above the domestic meter definitions behind the DESNZ total (NEED Annex B: gas meters under 73,200 kWh a year, electricity meters under 50,000 kWh on profile classes 1 and 2; Mar\u00eda's ruling of 2026-10-12 on microcosm#1207's extreme energy values): the declared household_kwh_ceiling caps a household there, the households below it in the capped households' own NEED cells are re-raked to those cells' levelled means, and a proportional step restores the DESNZ total (microcosm#1164); the receipt counts the households capped and the share of the total they gave up, and the energy_rake gate checks both. Mobile homes have no NEED property-type row, so the rake takes them to the all-dwellings mean, the incumbent's translation; where every mobile home of a NEED geography draws a spend at or below the standing charge, that cell has no kWh for the rake to scale, and the declared zero_kwh_cells_at_need_mean starts it at its NEED mean before the first sweep (microcosm#1240). The receipt lists the cells it started, and the energy_rake gate fails on a started cell the stage does not declare and on any other cell with a zero mean. Both rakes band households on gross income, NEED's concept: the donor on LCFS p344p, cash income before tax, National Insurance, council tax and pension contributions, and the recipient on the same concept, its HBAI net income with the deductions policyengine-uk takes to reach it added back (council tax and domestic rates, income tax, National Insurance, student loan repayments, employee and personal pension contributions, maintenance and child payments, LVT), which the stage materializes for that band alone (microcosm#1164). Before it the recipient fell back to its HBAI net income, which put households in too low a band; the engine's household_gross_income would put them in too high a one, since it adds capital gains and the in-kind NHS, education and transport spending to cash income. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2024-25 diary (UKDS SN 9603, 2nd edition, whose household tab carries the annual weight as cal_weight_annual, the name weighta had; microcosm#1164) is moved to the base year by the declared uprate_donor_columns step (microcosm#890). Consumption columns and bus fares take the engine's CPI index from the April-to-March diary year, placed on the calendar-year index at three quarters of 2024 and one of 2025, back a quarter to calendar 2024 (microcosm#1113; the diary sits a quarter past it, so the factor is below one; the fare factor survives only where no fare cell levels the column, i.e. Wales). Income predictors take a factor of one: the diary year is the FRS 2024-25 year they are matched against. Petrol and diesel take the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json, which sets only their donor distribution because the level step follows; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095). Road fuel incidence and level are set by two declared steps (microcosm#1113). The two-week diary leaves about a third of car-owning donors with no fuel purchase, which the chain reproduces among the households the fuel flag marks as fuel buyers, so the redraw_zero_road_fuel step redraws a flagged household that drew zero from the donor's distribution among households with positive road fuel, on the chain's predictors: a weighted regime-gated QRF draws the total, positive by construction, then the petrol share of it, at quantiles keyed on the household id; positive draws are kept. Then the level_road_fuel step, after the fuel flag has zeroed the households without a fuel car, scales petrol and diesel so their prior-weighted total equals ONS Consumer Trends household spending on COICOP 07.2.2 for calendar 2024 (the year of the DESNZ pump prices the donor uprating uses) less the donor's own other-fuels share of 07.2.2 (c72213), keeping the drawn mix. The diary's 07.2.2 has three lines (c72211 petrol, c72212 diesel and c72213, the other motor fuels and lubricants) and none for vehicle electricity. The LCFS diary records about a quarter less road fuel than ONS, and binding the OBR cars receipts had closed that gap by moving weight onto fuel-buying households; petrol and diesel therefore carry no committed support bound, like the other levelled columns. The totals that contain levelled columns are written back around them by the declared recompose_from_remainder step (microcosm#1113): each keeps its own chain draw less the chain's draws of the parts the stage re-levels, kept before any step touches them and floored at zero where the parts outgrow the total, and adds the levelled parts back, so housing_water_and_electricity_consumption is its draw less the drawn electricity and gas plus the levelled electricity and gas, and transport_consumption its draw less the drawn petrol, diesel and bus fares plus the levelled petrol and diesel and the priced bus fares. The chain draws each part after, and conditional on, its total, so the remainder is the chain's own split; a remainder drawn as a chain target of its own over-drew the vehicle-purchase tail of transport, 10 to 13 percent above the donor in-sample. The chain's domestic-energy target is the electricity and gas the stage derives from the diary's fuel, light and power (p537) by its labelled parts, from the LCF User Guide's derived-variable labels and P537 definition (microcosm#1164): each fuel's last account payment less its rebate (b489 electricity, b490 gas, read as the tab carries them; the stage refuses a donor where b489 equals b175 less b178, or b490 equals b170 less b173, to the penny for under 95 percent of that fuel's account payers, which pins the labels and the rebate's sign), its prepayment key or meter payment (b1751, b1701), its component of a combined utilities bill (b227 electricity, b226 gas) and of a combined key or meter payment (b2332, b2331), and the diary's second-dwelling account and slot-meter lines (c45112 and c45114, c45212 and c45214); what a combined payment leaves unsplit (the combined bill b231 less its rebate b174, and the combined key or meter payment b233, each less its parts) goes by the household's own electricity share where it pays for both fuels, and otherwise by the donor's mean share of the households that do, since a combined payment means the household pays for both; a donor with no such household fails. The diary's other fuels (central-heating oil b017 and c45312, bottled gas b018 and c45222, coal and coke c45411, wood and peat c45412, hot water, steam and ice c45511) are neither, so they stay in the housing remainder, and the donor_energy_split receipt records the split against p537: what reading refunds as zero adds, what the unclipped parts differ from p537 by, the weighted share of donors whose unclipped parts differ from their p537, and the account identity shares. The derivation it replaces read b226 as electricity by direct debit and b489 and b490 as prepayment payments, where the labels make them the gas component of a combined bill and the electricity and gas account payments less rebates. No column carries the other fuels; the receipt records their published ONS 04.5.3 and 04.5.4 spend. Each total is then held to the donor's realised maximum of that total (maximum_total: donor_support, the support every drawn column is clipped to; microcosm#1164), never below its parts, the excess coming out of its remainder: a remainder near the top of the diary's range plus levelled parts larger than the drawn ones would otherwise leave it. The two totals keep their committed support bounds, which the terminal support gate applies to the emitted frame; the recomposed_totals gate checks the declaration and the receipt, whose rows held at the maximum and weighted mass taken enter its identities. Every household spend column is at calendar-2024 prices as households paid them, the H5's year that policyengine-uk projects from and the calibration uprates to the calendar-2025 window (microcosm#1113): the diary columns by the CPI uprating to 2024, road fuel at ONS 07.2.2 for 2024, energy at DESNZ volume and QEP prices for 2024, and bus fares at each area's fiscal-year yield re-priced to 2024 by its fares index (the DfT local bus fares index by quarter for London and England outside London, Scotland's calendar-year index; Northern Ireland, which publishes no fares index, from its own yields of the two fiscal years 2024 overlaps, weighted three months and nine (microcosm#1207), in place of England's index, which carries the January 2025 rise in the English fare cap Northern Ireland did not have); the consumption_basis gate holds every level-setting step to that year. policyengine-uk applies the fiscal 2024-25 rules to model year 2024 (it samples each parameter at 30 April), so every levelled column sits about a quarter before the midpoint of the policy year it is taxed in; the calibration binds calendar-year facts as published and fiscal-year facts at the calendar window (Mar\u00eda's rule of 2026-09-22). The stage receipt also reports, for each of the twelve COICOP divisions, the prior-weighted frame total against ONS Consumer Trends for the same calendar year less the classes a household diary does not record (02.3 narcotics, 04.2 owner-occupiers' imputed rent and 12.6.1 FISIM), on ONS's domestic concept, and the twelve together against the national concept as well; it is report-only (microcosm#1113). The report-only litres audit compares each fuel's frame litres with HMRC's clearances of that fuel scaled by the cars share of its road use, from the vendored DESNZ sub-national road transport fuel consumption by vehicle type and fuel (PolicyEngine/chronicle#322); the OBR cars share of fuel duty receipts, uniform across fuels, stays beside the total.", "operations": [ { "annualization_weeks": 52.17857142857143, @@ -3829,7 +3875,7 @@ "employment_income", "self_employment_income", "private_pension_income", - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -3960,7 +4006,12 @@ "period_value": 2024, "resource": "qep_energy_prices.json", "seed": 0, - "vat_treatment": "including_vat" + "vat_treatment": "including_vat", + "zero_kwh_cells_at_need_mean": { + "accommodation": [ + "all_dwellings" + ] + } }, { "areas": [ @@ -4493,7 +4544,7 @@ "consumed_only": true, "kind": "materialize_rules_engine_predictors", "predictors": [ - "hbai_household_net_income", + "hbai_household_net_income_before_reset_to_zero", "council_tax", "domestic_rates", "income_tax", @@ -4765,6 +4816,43 @@ "stage": "pension_credit_take_up", "survey": "Family Resources Survey 2024-25" }, + "private_school_attendance": { + "artifacts": [ + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "frs_release.json", + "role": "frs_release", + "runtime_sha256_required": true + } + ], + "grain": "person", + "nonnegative_outputs": [], + "notes": "The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.", + "operations": [ + { + "draw": "attends_private_school_random_draw", + "kind": "store_rules_engine_input", + "output": "attends_private_school", + "weights": "spine_design_weights", + "year_rule": "calibration_year" + }, + { + "declared_factor": 1, + "entity": "household", + "gate_coupling": "The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason.", + "kind": "record_mass_conservation_receipt", + "reason": "Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved." + } + ], + "outputs": [ + "attends_private_school" + ], + "rewrites": [], + "source": "policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine", + "stage": "private_school_attendance", + "survey": "policyengine-uk private-school attendance imputation, stored at the spine's weights" + }, "property_components": { "artifacts": [ { @@ -5375,7 +5463,7 @@ ], "grain": "person", "nonnegative_outputs": [], - "notes": "Reported PAYE repayers are classified without a country gate. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year; PLAN_4 is never imputed.", + "notes": "Reported borrowers are PAYE repayers, classified without a country gate, and, in England and Wales, adults who report an outstanding loan (FRS SLOS) and are not in full-time education, the borrowers below the repayment threshold (microcosm#868). A course starts three years before its borrower left full-time education (FRS EDUCLEFT), or at 18 without an answer, and its start year sets the plan, except that before 2026 a repayer never takes PLAN_5: nobody repays a Plan 5 loan before April 2026, so SLC counts no Plan 5 borrower above the threshold in academic year 2024/25. England tertiary cohorts are then topped up PLAN_5 first and PLAN_2 second to the pinned liable stocks at the FRS release calibration year. The PLAN_2 walk takes nobody in full-time education, since a student is not yet liable to repay; the PLAN_5 walk keeps the age rule, since its starters have mostly not left education. The stocks are SLC academic year 2024/25, whose reference date falls in calendar 2025, the year the borrower targets bind (ruling of 2026-10-16). PLAN_4 is never imputed, and a Scottish or Northern Irish answer of an outstanding loan assigns no plan.", "operations": [ { "enum_domain": [ @@ -5388,14 +5476,21 @@ "plan_1_before": 2012, "plan_4_imputation": false, "plan_5_from": 2023, + "plan_5_repayments_from": 2026, "reported_country_gate": false, + "reported_outstanding_region_exclusions": [ + "SCOTLAND", + "NORTHERN_IRELAND" + ], + "reported_outstanding_test": "reports_outstanding_student_loan and current_education == NOT_IN_EDUCATION", "reported_repayment_test": "student_loan_repayments > 0", - "start_year_formula": "year - age + 18", + "start_year_formula": "year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18", "year_rule": "calibration_year" }, { "age_max": 25, "age_min": 18, + "cohort_start_formula": "year - age + 18", "cohort_start_min": 2023, "eligible_region_exclusions": [ "SCOTLAND", @@ -5417,8 +5512,10 @@ { "age_max": 55, "age_min": 21, + "cohort_start_formula": "year - age + age_left_full_time_education - 3 where age_left_full_time_education > 0, else year - age + 18", "cohort_start_max_exclusive": 2023, "cohort_start_min": 2012, + "current_education": "NOT_IN_EDUCATION", "eligible_region_exclusions": [ "SCOTLAND", "WALES", @@ -5452,7 +5549,7 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.130.1 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both.", + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.132.6 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both.", "operations": [ { "aggregates": { diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab index bc36ebd0bd..d21a4b7386 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab @@ -1,136 +1,136 @@ -SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL EMPSTATI SAMESIT SRENTAMT CONVBL SDEMP01 SDEMP02 SDEMP03 SDEMP04 SDEMP05 SDEMP06 SDEMP07 SDEMP08 SDEMP09 SDEMP10 SDEMP11 SDEMP12 MJOBSECT SIC FTED TYPEED2 EDUCQUAL TRAINEE EMAAMT CHEMAAMT INEARNS SEINCAM2 MNTUS1 MNTUSAM1 MNTAMT1 MNTAMT2 CVPAY ROYYR1 ROYYR2 ROYYR3 ROYYR4 ALLPAY2 ALLPAY3 ALLPAY4 CHAMTERN CHAMTTST APAMT APDAMT PAREAMT REDAMT SLREPAMT SSPADJ SMPADJ SPPADJ TUBORR ACCSSAMT ACCSSPD SPCREG1 SPCREG2 GRTDIR1 GRTDIR2 HEARTVAL -1 1 1 0 80 2 21 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 11 2 0 2 2 0.0 0.0 101.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -2 1 1 0 22 1 22 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 12 2 0 3 2 0.0 0.0 102.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -3 1 1 0 23 2 23 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 13 2 0 4 2 0.0 0.0 103.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -4 1 1 0 24 1 24 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2 0 5 2 0.0 0.0 104.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -5 1 1 0 25 2 25 1 1 7 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 15 2 0 6 2 0.0 0.0 105.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -6 1 1 0 26 1 26 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 16 2 0 7 2 0.0 0.0 106.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -7 1 1 0 27 2 27 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 17 2 0 8 2 0.0 0.0 107.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -8 1 1 0 28 1 28 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 18 2 0 9 2 0.0 0.0 108.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -9 1 1 0 29 2 29 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 19 2 0 10 2 0.0 0.0 109.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -10 1 1 0 30 1 30 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 20 2 0 11 2 0.0 0.0 110.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -11 1 1 0 31 2 31 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 21 2 0 12 2 0.0 0.0 111.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -12 1 1 0 32 1 32 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 22 2 0 13 2 0.0 0.0 112.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -13 1 1 0 33 2 33 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 23 2 0 14 2 0.0 0.0 113.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -14 1 1 0 34 1 34 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 24 2 0 15 2 0.0 0.0 114.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -15 1 1 0 35 2 35 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 25 2 0 16 2 0.0 0.0 115.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -16 1 1 0 36 1 36 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 26 2 0 17 2 0.0 0.0 116.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -17 1 1 0 37 2 37 1 1 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 27 2 0 18 2 0.0 0.0 117.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -18 1 1 0 38 1 38 1 1 1 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 28 2 0 1 2 0.0 0.0 118.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -19 1 1 0 39 2 39 1 1 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 29 2 0 2 2 0.0 0.0 119.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -20 1 1 0 40 1 40 1 1 7 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 30 2 0 3 2 0.0 0.0 120.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -21 1 1 0 41 2 41 1 1 1 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 31 2 0 4 2 0.0 0.0 121.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -22 1 1 0 42 1 42 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 32 2 0 5 2 0.0 0.0 122.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -23 1 1 0 43 2 43 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 33 2 0 6 2 0.0 0.0 123.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -24 1 1 0 44 1 44 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 34 2 0 7 2 0.0 0.0 124.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -25 1 1 0 45 2 20 1 1 7 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 35 2 0 8 2 0.0 0.0 125.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -26 1 1 0 46 1 21 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 36 2 0 9 2 0.0 0.0 126.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -27 1 1 0 47 2 22 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 37 2 0 10 2 0.0 0.0 127.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -28 1 1 0 48 1 23 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 38 2 0 11 2 0.0 0.0 128.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -29 1 1 0 49 2 24 1 1 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 39 2 0 12 2 0.0 0.0 129.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -30 1 1 0 50 1 25 1 1 7 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 40 2 0 13 2 0.0 0.0 130.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -31 1 1 0 51 2 26 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 41 2 0 14 2 0.0 0.0 131.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -32 1 1 0 52 1 27 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 42 2 0 15 2 0.0 0.0 132.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -33 1 1 0 53 2 28 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 43 2 0 16 2 0.0 0.0 133.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -34 1 1 0 54 1 29 1 1 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 44 2 0 17 2 0.0 0.0 134.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -35 1 1 0 55 2 30 1 1 7 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 45 2 0 18 2 0.0 0.0 135.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 1 2.0 3.0 5.0 -36 1 1 0 56 1 31 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 46 2 0 1 2 0.0 0.0 136.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -37 1 1 0 57 2 32 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 47 2 0 2 2 0.0 0.0 137.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -38 1 1 0 58 1 33 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 48 2 0 3 2 0.0 0.0 138.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -39 1 1 0 59 2 34 1 1 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 49 2 0 4 2 0.0 0.0 139.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -40 1 1 0 60 1 35 1 1 7 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 50 2 0 5 2 0.0 0.0 140.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -41 1 1 0 61 2 36 1 1 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 51 2 0 6 2 0.0 0.0 141.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -42 1 1 0 62 1 37 1 1 1 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 52 2 0 7 2 0.0 0.0 142.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -43 1 1 0 63 2 38 1 1 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 53 2 0 8 2 0.0 0.0 143.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -44 1 1 0 64 1 39 1 1 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 54 2 0 9 2 0.0 0.0 144.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -45 1 1 0 65 2 40 1 1 7 1 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 55 2 0 10 2 0.0 0.0 145.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -46 1 1 0 66 1 41 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 56 2 0 11 2 0.0 0.0 146.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -47 1 1 0 67 2 42 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 57 2 0 12 2 0.0 0.0 147.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -48 1 1 0 68 1 43 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 58 2 0 13 2 0.0 0.0 148.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -49 1 1 0 69 2 44 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 59 2 0 14 2 0.0 0.0 149.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -50 1 1 0 70 1 20 1 1 7 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 60 2 0 15 2 0.0 0.0 150.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -51 1 1 0 71 2 21 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 61 2 0 16 2 0.0 0.0 151.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -52 1 1 0 72 1 22 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 62 2 0 17 2 0.0 0.0 152.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -53 1 1 0 73 2 23 1 1 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 63 2 0 18 2 0.0 0.0 153.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -54 1 1 0 74 1 24 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 64 2 0 1 2 0.0 0.0 154.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -55 1 1 0 20 2 25 1 1 7 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 65 2 0 2 2 0.0 0.0 155.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -56 1 1 0 21 1 26 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 66 2 0 3 2 0.0 0.0 156.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 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5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -129 1 1 0 39 2 24 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 59 2 0 4 2 0.0 0.0 229.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -130 1 1 0 40 1 25 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 60 2 0 5 2 0.0 0.0 230.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 -131 1 1 0 41 2 26 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 61 2 0 6 2 0.0 0.0 231.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -132 1 1 0 42 1 27 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 62 2 0 7 2 0.0 0.0 232.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -133 1 1 0 43 2 28 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 63 2 0 8 2 0.0 0.0 233.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 -134 1 1 0 44 1 29 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 64 2 0 9 2 0.0 0.0 234.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 -135 1 1 0 45 2 30 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 65 2 0 10 2 0.0 0.0 235.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +SERNUM BENUNIT PERSON AGE80 AGE SEX TOTHOURS HRPID UPERSON RELHRP R01 R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 R14 MARITAL EMPSTATI SAMESIT SRENTAMT CONVBL SDEMP01 SDEMP02 SDEMP03 SDEMP04 SDEMP05 SDEMP06 SDEMP07 SDEMP08 SDEMP09 SDEMP10 SDEMP11 SDEMP12 MJOBSECT SIC FTED TYPEED2 EDUCQUAL TRAINEE EMAAMT CHEMAAMT INEARNS SEINCAM2 MNTUS1 MNTUSAM1 MNTAMT1 MNTAMT2 CVPAY ROYYR1 ROYYR2 ROYYR3 ROYYR4 ALLPAY2 ALLPAY3 ALLPAY4 CHAMTERN CHAMTTST APAMT APDAMT PAREAMT REDAMT SLREPAMT SLOS EDUCLEFT SSPADJ SMPADJ SPPADJ TUBORR ACCSSAMT ACCSSPD SPCREG1 SPCREG2 GRTDIR1 GRTDIR2 HEARTVAL +1 1 1 0 80 2 21 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 11 2 0 2 2 0.0 0.0 101.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 17 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +2 1 1 0 22 1 22 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 12 2 0 3 2 0.0 0.0 102.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +3 1 1 0 23 2 23 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 13 2 0 4 2 0.0 0.0 103.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 19 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +4 1 1 0 24 1 24 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 14 2 0 5 2 0.0 0.0 104.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 20 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +5 1 1 0 25 2 25 1 1 7 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 15 2 0 6 2 0.0 0.0 105.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +6 1 1 0 26 1 26 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 16 2 0 7 2 0.0 0.0 106.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 22 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +7 1 1 0 27 2 27 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 17 2 0 8 2 0.0 0.0 107.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 23 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +8 1 1 0 28 1 28 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 18 2 0 9 2 0.0 0.0 108.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 16 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +9 1 1 0 29 2 29 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 19 2 0 10 2 0.0 0.0 109.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 17 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +10 1 1 0 30 1 30 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 20 2 0 11 2 0.0 0.0 110.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +11 1 1 0 31 2 31 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 21 2 0 12 2 0.0 0.0 111.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 19 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +12 1 1 0 32 1 32 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 22 2 0 13 2 0.0 0.0 112.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 20 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +13 1 1 0 33 2 33 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 23 2 0 14 2 0.0 0.0 113.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +14 1 1 0 34 1 34 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 24 2 0 15 2 0.0 0.0 114.0 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39 2 39 1 1 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 29 2 0 2 2 0.0 0.0 119.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 19 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +20 1 1 0 40 1 40 1 1 7 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 30 2 0 3 2 0.0 0.0 120.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 20 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +21 1 1 0 41 2 41 1 1 1 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 31 2 0 4 2 0.0 0.0 121.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +22 1 1 0 42 1 42 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 32 2 0 5 2 0.0 0.0 122.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 22 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +23 1 1 0 43 2 43 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 33 2 0 6 2 0.0 0.0 123.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 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1 1 1 1 1 1 1 1 1 1 1 2 47 2 0 10 2 0.0 0.0 217.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +118 1 1 0 28 1 38 1 1 2 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 48 2 0 11 2 0.0 0.0 218.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 22 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +119 1 1 0 29 2 39 1 1 3 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 49 2 0 12 2 0.0 0.0 219.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 23 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +120 1 1 0 30 1 40 1 1 7 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 50 2 0 13 2 0.0 0.0 220.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 16 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +121 1 1 0 31 2 41 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 51 2 0 14 2 0.0 0.0 221.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 17 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +122 1 1 0 32 1 42 1 1 3 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 52 2 0 15 2 0.0 0.0 222.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +123 1 1 0 33 2 43 1 1 1 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 53 2 0 16 2 0.0 0.0 223.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 19 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +124 1 1 0 34 1 44 1 1 2 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 54 2 0 17 2 0.0 0.0 224.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 20 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +125 1 1 0 35 2 20 1 1 7 3 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 55 2 0 18 2 0.0 0.0 225.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +126 1 1 0 36 1 21 1 1 1 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 56 2 0 1 2 0.0 0.0 226.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 22 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +127 1 1 0 37 2 22 1 1 2 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 57 2 0 2 2 0.0 0.0 227.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 23 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +128 1 1 0 38 1 23 1 1 3 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 58 2 0 3 2 0.0 0.0 228.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 16 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +129 1 1 0 39 2 24 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 59 2 0 4 2 0.0 0.0 229.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 17 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +130 1 1 0 40 1 25 1 1 7 2 3 1 2 3 3 3 3 3 3 3 3 3 3 3 3 1 60 2 0 5 2 0.0 0.0 230.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 +131 1 1 0 41 2 26 1 1 3 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 61 2 0 6 2 0.0 0.0 231.0 1.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 19 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +132 1 1 0 42 1 27 1 1 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 62 2 0 7 2 0.0 0.0 232.0 2.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 20 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +133 1 1 0 43 2 28 1 1 2 6 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 63 2 0 8 2 0.0 0.0 233.0 3.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 21 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 +134 1 1 0 44 1 29 1 1 3 7 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 64 2 0 9 2 0.0 0.0 234.0 4.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 22 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 +135 1 1 0 45 2 30 1 1 7 1 8 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 65 2 0 10 2 0.0 0.0 235.0 0.0 2 1.0 2.0 1.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 20.0 2.0 2 23 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab index 5cfcc21c08..bc44836427 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab @@ -1,991 +1,991 @@ -SERNUM BENUNIT PERSON BENEFIT VAR2 BENAMT UCSTART UCHOUSEL -1 1 1 14 1 2.0 -1 1 1 14 2 3.0 -1 1 1 16 3 4.0 -1 1 1 16 4 5.0 -1 1 1 5 0 1.0 -1 1 1 6 0 6.0 -1 1 1 3 0 7.0 -2 1 1 14 1 2.0 -2 1 1 14 2 3.0 -2 1 1 16 3 4.0 -2 1 1 16 4 5.0 -2 1 1 5 0 1.0 -2 1 1 6 0 6.0 -2 1 1 3 0 7.0 -3 1 1 14 1 2.0 -3 1 1 14 2 3.0 -3 1 1 16 3 4.0 -3 1 1 16 4 5.0 -3 1 1 5 0 1.0 -3 1 1 6 0 6.0 -3 1 1 3 0 7.0 -3 1 1 95 0 8.0 01/15/2022 50.0 -4 1 1 14 1 2.0 -4 1 1 14 2 3.0 -4 1 1 16 3 4.0 -4 1 1 16 4 5.0 -4 1 1 5 0 1.0 -4 1 1 6 0 6.0 -4 1 1 3 0 7.0 -5 1 1 14 1 2.0 -5 1 1 14 2 3.0 -5 1 1 16 3 4.0 -5 1 1 16 4 5.0 -5 1 1 5 0 1.0 -5 1 1 6 0 6.0 -5 1 1 3 0 7.0 -6 1 1 14 1 2.0 -6 1 1 14 2 3.0 -6 1 1 16 3 4.0 -6 1 1 16 4 5.0 -6 1 1 5 0 1.0 -6 1 1 6 0 6.0 -6 1 1 3 0 7.0 -6 1 1 95 0 8.0 -7 1 1 14 1 2.0 -7 1 1 14 2 3.0 -7 1 1 16 3 4.0 -7 1 1 16 4 5.0 -7 1 1 5 0 1.0 -7 1 1 6 0 6.0 -7 1 1 3 0 7.0 -8 1 1 14 1 2.0 -8 1 1 14 2 3.0 -8 1 1 16 3 4.0 -8 1 1 16 4 5.0 -8 1 1 5 0 1.0 -8 1 1 6 0 6.0 -8 1 1 3 0 7.0 -9 1 1 14 1 2.0 -9 1 1 14 2 3.0 -9 1 1 16 3 4.0 -9 1 1 16 4 5.0 -9 1 1 5 0 1.0 -9 1 1 6 0 6.0 -9 1 1 3 0 7.0 -9 1 1 95 0 8.0 07/01/2024 50.0 -10 1 1 14 1 2.0 -10 1 1 14 2 3.0 -10 1 1 16 3 4.0 -10 1 1 16 4 5.0 -10 1 1 5 0 1.0 -10 1 1 6 0 6.0 -10 1 1 3 0 7.0 -11 1 1 14 1 2.0 -11 1 1 14 2 3.0 -11 1 1 16 3 4.0 -11 1 1 16 4 5.0 -11 1 1 5 0 1.0 -11 1 1 6 0 6.0 -11 1 1 3 0 7.0 -12 1 1 14 1 2.0 -12 1 1 14 2 3.0 -12 1 1 16 3 4.0 -12 1 1 16 4 5.0 -12 1 1 5 0 1.0 -12 1 1 6 0 6.0 -12 1 1 3 0 7.0 -12 1 1 95 0 8.0 01/15/2022 -13 1 1 14 1 2.0 -13 1 1 14 2 3.0 -13 1 1 16 3 4.0 -13 1 1 16 4 5.0 -13 1 1 5 0 1.0 -13 1 1 6 0 6.0 -13 1 1 3 0 7.0 -14 1 1 14 1 2.0 -14 1 1 14 2 3.0 -14 1 1 16 3 4.0 -14 1 1 16 4 5.0 -14 1 1 5 0 1.0 -14 1 1 6 0 6.0 -14 1 1 3 0 7.0 -15 1 1 14 1 2.0 -15 1 1 14 2 3.0 -15 1 1 16 3 4.0 -15 1 1 16 4 5.0 -15 1 1 5 0 1.0 -15 1 1 6 0 6.0 -15 1 1 3 0 7.0 -15 1 1 95 0 8.0 50.0 -16 1 1 14 1 2.0 -16 1 1 14 2 3.0 -16 1 1 16 3 4.0 -16 1 1 16 4 5.0 -16 1 1 5 0 1.0 -16 1 1 6 0 6.0 -16 1 1 3 0 7.0 -17 1 1 14 1 2.0 -17 1 1 14 2 3.0 -17 1 1 16 3 4.0 -17 1 1 16 4 5.0 -17 1 1 5 0 1.0 -17 1 1 6 0 6.0 -17 1 1 3 0 7.0 -18 1 1 14 1 2.0 -18 1 1 14 2 3.0 -18 1 1 16 3 4.0 -18 1 1 16 4 5.0 -18 1 1 5 0 1.0 -18 1 1 6 0 6.0 -18 1 1 3 0 7.0 -18 1 1 95 0 8.0 07/01/2024 -19 1 1 14 1 2.0 -19 1 1 14 2 3.0 -19 1 1 16 3 4.0 -19 1 1 16 4 5.0 -19 1 1 5 0 1.0 -19 1 1 6 0 6.0 -19 1 1 3 0 7.0 -20 1 1 14 1 2.0 -20 1 1 14 2 3.0 -20 1 1 16 3 4.0 -20 1 1 16 4 5.0 -20 1 1 5 0 1.0 -20 1 1 6 0 6.0 -20 1 1 3 0 7.0 -21 1 1 14 1 2.0 -21 1 1 14 2 3.0 -21 1 1 16 3 4.0 -21 1 1 16 4 5.0 -21 1 1 5 0 1.0 -21 1 1 6 0 6.0 -21 1 1 3 0 7.0 -21 1 1 95 0 8.0 01/15/2022 50.0 -22 1 1 14 1 2.0 -22 1 1 14 2 3.0 -22 1 1 16 3 4.0 -22 1 1 16 4 5.0 -22 1 1 5 0 1.0 -22 1 1 6 0 6.0 -22 1 1 3 0 7.0 -23 1 1 14 1 2.0 -23 1 1 14 2 3.0 -23 1 1 16 3 4.0 -23 1 1 16 4 5.0 -23 1 1 5 0 1.0 -23 1 1 6 0 6.0 -23 1 1 3 0 7.0 -24 1 1 14 1 2.0 -24 1 1 14 2 3.0 -24 1 1 16 3 4.0 -24 1 1 16 4 5.0 -24 1 1 5 0 1.0 -24 1 1 6 0 6.0 -24 1 1 3 0 7.0 -24 1 1 95 0 8.0 -25 1 1 14 1 2.0 -25 1 1 14 2 3.0 -25 1 1 16 3 4.0 -25 1 1 16 4 5.0 -25 1 1 5 0 1.0 -25 1 1 6 0 6.0 -25 1 1 3 0 7.0 -26 1 1 14 1 2.0 -26 1 1 14 2 3.0 -26 1 1 16 3 4.0 -26 1 1 16 4 5.0 -26 1 1 5 0 1.0 -26 1 1 6 0 6.0 -26 1 1 3 0 7.0 -27 1 1 14 1 2.0 -27 1 1 14 2 3.0 -27 1 1 16 3 4.0 -27 1 1 16 4 5.0 -27 1 1 5 0 1.0 -27 1 1 6 0 6.0 -27 1 1 3 0 7.0 -27 1 1 95 0 8.0 07/01/2024 50.0 -28 1 1 14 1 2.0 -28 1 1 14 2 3.0 -28 1 1 16 3 4.0 -28 1 1 16 4 5.0 -28 1 1 5 0 1.0 -28 1 1 6 0 6.0 -28 1 1 3 0 7.0 -29 1 1 14 1 2.0 -29 1 1 14 2 3.0 -29 1 1 16 3 4.0 -29 1 1 16 4 5.0 -29 1 1 5 0 1.0 -29 1 1 6 0 6.0 -29 1 1 3 0 7.0 -30 1 1 14 1 2.0 -30 1 1 14 2 3.0 -30 1 1 16 3 4.0 -30 1 1 16 4 5.0 -30 1 1 5 0 1.0 -30 1 1 6 0 6.0 -30 1 1 3 0 7.0 -30 1 1 95 0 8.0 01/15/2022 -31 1 1 14 1 2.0 -31 1 1 14 2 3.0 -31 1 1 16 3 4.0 -31 1 1 16 4 5.0 -31 1 1 5 0 1.0 -31 1 1 6 0 6.0 -31 1 1 3 0 7.0 -32 1 1 14 1 2.0 -32 1 1 14 2 3.0 -32 1 1 16 3 4.0 -32 1 1 16 4 5.0 -32 1 1 5 0 1.0 -32 1 1 6 0 6.0 -32 1 1 3 0 7.0 -33 1 1 14 1 2.0 -33 1 1 14 2 3.0 -33 1 1 16 3 4.0 -33 1 1 16 4 5.0 -33 1 1 5 0 1.0 -33 1 1 6 0 6.0 -33 1 1 3 0 7.0 -33 1 1 95 0 8.0 50.0 -34 1 1 14 1 2.0 -34 1 1 14 2 3.0 -34 1 1 16 3 4.0 -34 1 1 16 4 5.0 -34 1 1 5 0 1.0 -34 1 1 6 0 6.0 -34 1 1 3 0 7.0 -35 1 1 14 1 2.0 -35 1 1 14 2 3.0 -35 1 1 16 3 4.0 -35 1 1 16 4 5.0 -35 1 1 5 0 1.0 -35 1 1 6 0 6.0 -35 1 1 3 0 7.0 -36 1 1 14 1 2.0 -36 1 1 14 2 3.0 -36 1 1 16 3 4.0 -36 1 1 16 4 5.0 -36 1 1 5 0 1.0 -36 1 1 6 0 6.0 -36 1 1 3 0 7.0 -36 1 1 95 0 8.0 07/01/2024 -37 1 1 14 1 2.0 -37 1 1 14 2 3.0 -37 1 1 16 3 4.0 -37 1 1 16 4 5.0 -37 1 1 5 0 1.0 -37 1 1 6 0 6.0 -37 1 1 3 0 7.0 -38 1 1 14 1 2.0 -38 1 1 14 2 3.0 -38 1 1 16 3 4.0 -38 1 1 16 4 5.0 -38 1 1 5 0 1.0 -38 1 1 6 0 6.0 -38 1 1 3 0 7.0 -39 1 1 14 1 2.0 -39 1 1 14 2 3.0 -39 1 1 16 3 4.0 -39 1 1 16 4 5.0 -39 1 1 5 0 1.0 -39 1 1 6 0 6.0 -39 1 1 3 0 7.0 -39 1 1 95 0 8.0 01/15/2022 50.0 -40 1 1 14 1 2.0 -40 1 1 14 2 3.0 -40 1 1 16 3 4.0 -40 1 1 16 4 5.0 -40 1 1 5 0 1.0 -40 1 1 6 0 6.0 -40 1 1 3 0 7.0 -41 1 1 14 1 2.0 -41 1 1 14 2 3.0 -41 1 1 16 3 4.0 -41 1 1 16 4 5.0 -41 1 1 5 0 1.0 -41 1 1 6 0 6.0 -41 1 1 3 0 7.0 -42 1 1 14 1 2.0 -42 1 1 14 2 3.0 -42 1 1 16 3 4.0 -42 1 1 16 4 5.0 -42 1 1 5 0 1.0 -42 1 1 6 0 6.0 -42 1 1 3 0 7.0 -42 1 1 95 0 8.0 -43 1 1 14 1 2.0 -43 1 1 14 2 3.0 -43 1 1 16 3 4.0 -43 1 1 16 4 5.0 -43 1 1 5 0 1.0 -43 1 1 6 0 6.0 -43 1 1 3 0 7.0 -44 1 1 14 1 2.0 -44 1 1 14 2 3.0 -44 1 1 16 3 4.0 -44 1 1 16 4 5.0 -44 1 1 5 0 1.0 -44 1 1 6 0 6.0 -44 1 1 3 0 7.0 -45 1 1 14 1 2.0 -45 1 1 14 2 3.0 -45 1 1 16 3 4.0 -45 1 1 16 4 5.0 -45 1 1 5 0 1.0 -45 1 1 6 0 6.0 -45 1 1 3 0 7.0 -45 1 1 95 0 8.0 07/01/2024 50.0 -46 1 1 14 1 2.0 -46 1 1 14 2 3.0 -46 1 1 16 3 4.0 -46 1 1 16 4 5.0 -46 1 1 5 0 1.0 -46 1 1 6 0 6.0 -46 1 1 3 0 7.0 -47 1 1 14 1 2.0 -47 1 1 14 2 3.0 -47 1 1 16 3 4.0 -47 1 1 16 4 5.0 -47 1 1 5 0 1.0 -47 1 1 6 0 6.0 -47 1 1 3 0 7.0 -48 1 1 14 1 2.0 -48 1 1 14 2 3.0 -48 1 1 16 3 4.0 -48 1 1 16 4 5.0 -48 1 1 5 0 1.0 -48 1 1 6 0 6.0 -48 1 1 3 0 7.0 -48 1 1 95 0 8.0 01/15/2022 -49 1 1 14 1 2.0 -49 1 1 14 2 3.0 -49 1 1 16 3 4.0 -49 1 1 16 4 5.0 -49 1 1 5 0 1.0 -49 1 1 6 0 6.0 -49 1 1 3 0 7.0 -50 1 1 14 1 2.0 -50 1 1 14 2 3.0 -50 1 1 16 3 4.0 -50 1 1 16 4 5.0 -50 1 1 5 0 1.0 -50 1 1 6 0 6.0 -50 1 1 3 0 7.0 -51 1 1 14 1 2.0 -51 1 1 14 2 3.0 -51 1 1 16 3 4.0 -51 1 1 16 4 5.0 -51 1 1 5 0 1.0 -51 1 1 6 0 6.0 -51 1 1 3 0 7.0 -51 1 1 95 0 8.0 50.0 -52 1 1 14 1 2.0 -52 1 1 14 2 3.0 -52 1 1 16 3 4.0 -52 1 1 16 4 5.0 -52 1 1 5 0 1.0 -52 1 1 6 0 6.0 -52 1 1 3 0 7.0 -53 1 1 14 1 2.0 -53 1 1 14 2 3.0 -53 1 1 16 3 4.0 -53 1 1 16 4 5.0 -53 1 1 5 0 1.0 -53 1 1 6 0 6.0 -53 1 1 3 0 7.0 -54 1 1 14 1 2.0 -54 1 1 14 2 3.0 -54 1 1 16 3 4.0 -54 1 1 16 4 5.0 -54 1 1 5 0 1.0 -54 1 1 6 0 6.0 -54 1 1 3 0 7.0 -54 1 1 95 0 8.0 07/01/2024 -55 1 1 14 1 2.0 -55 1 1 14 2 3.0 -55 1 1 16 3 4.0 -55 1 1 16 4 5.0 -55 1 1 5 0 1.0 -55 1 1 6 0 6.0 -55 1 1 3 0 7.0 -56 1 1 14 1 2.0 -56 1 1 14 2 3.0 -56 1 1 16 3 4.0 -56 1 1 16 4 5.0 -56 1 1 5 0 1.0 -56 1 1 6 0 6.0 -56 1 1 3 0 7.0 -57 1 1 14 1 2.0 -57 1 1 14 2 3.0 -57 1 1 16 3 4.0 -57 1 1 16 4 5.0 -57 1 1 5 0 1.0 -57 1 1 6 0 6.0 -57 1 1 3 0 7.0 -57 1 1 95 0 8.0 01/15/2022 50.0 -58 1 1 14 1 2.0 -58 1 1 14 2 3.0 -58 1 1 16 3 4.0 -58 1 1 16 4 5.0 -58 1 1 5 0 1.0 -58 1 1 6 0 6.0 -58 1 1 3 0 7.0 -59 1 1 14 1 2.0 -59 1 1 14 2 3.0 -59 1 1 16 3 4.0 -59 1 1 16 4 5.0 -59 1 1 5 0 1.0 -59 1 1 6 0 6.0 -59 1 1 3 0 7.0 -60 1 1 14 1 2.0 -60 1 1 14 2 3.0 -60 1 1 16 3 4.0 -60 1 1 16 4 5.0 -60 1 1 5 0 1.0 -60 1 1 6 0 6.0 -60 1 1 3 0 7.0 -60 1 1 95 0 8.0 -61 1 1 14 1 2.0 -61 1 1 14 2 3.0 -61 1 1 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14 2 0 3.0 +131 1 1 16 3 2 4.0 +131 1 1 16 4 2 5.0 +131 1 1 5 0 0 1.0 +131 1 1 6 0 0 6.0 +131 1 1 3 0 0 7.0 +132 1 1 14 1 0 2.0 +132 1 1 14 2 0 3.0 +132 1 1 16 3 1 4.0 +132 1 1 16 4 1 5.0 +132 1 1 5 0 0 1.0 +132 1 1 6 0 0 6.0 +132 1 1 3 0 0 7.0 +132 1 1 95 0 0 8.0 +133 1 1 14 1 0 2.0 +133 1 1 14 2 0 3.0 +133 1 1 16 3 2 4.0 +133 1 1 16 4 2 5.0 +133 1 1 5 0 0 1.0 +133 1 1 6 0 0 6.0 +133 1 1 3 0 0 7.0 +134 1 1 14 1 0 2.0 +134 1 1 14 2 0 3.0 +134 1 1 16 3 1 4.0 +134 1 1 16 4 1 5.0 +134 1 1 5 0 0 1.0 +134 1 1 6 0 0 6.0 +134 1 1 3 0 0 7.0 +135 1 1 14 1 0 2.0 +135 1 1 14 2 0 3.0 +135 1 1 16 3 2 4.0 +135 1 1 16 4 2 5.0 +135 1 1 5 0 0 1.0 +135 1 1 6 0 0 6.0 +135 1 1 3 0 0 7.0 +135 1 1 95 0 0 8.0 07/01/2024 50.0 diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab index 0c718cef24..c692bef147 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab @@ -1,136 +1,136 @@ -SERNUM BENUNIT PERSON DEDUC1 SPNAMT SALSAC JOBSECT ETYPE JOBTYPE SEEND SEJBLONG JOBBUS -1 1 1 2.0 3.0 1 2 1 1 2 -2 1 1 2.0 3.0 1 1 4 1 2.0 1 -3 1 1 2.0 3.0 1 2 4 1 0.0 2 -4 1 1 2.0 3.0 1 1 1 1 1 -5 1 1 2.0 3.0 1 2 1 1 2 -6 1 1 2.0 3.0 1 1 1 1 1 -7 1 1 2.0 3.0 1 2 1 1 2 -8 1 1 2.0 3.0 1 1 1 1 1 -9 1 1 2.0 3.0 1 2 1 1 2 -10 1 1 2.0 3.0 1 1 4 1 1.0 1 -11 1 1 2.0 3.0 1 2 4 1 2.0 2 -12 1 1 2.0 3.0 1 1 1 1 1 -13 1 1 2.0 3.0 1 2 1 1 2 -14 1 1 2.0 3.0 1 1 1 1 1 -15 1 1 2.0 3.0 1 2 1 1 2 -16 1 1 2.0 3.0 1 1 1 1 1 -17 1 1 2.0 3.0 1 2 1 1 2 -18 1 1 2.0 3.0 1 1 4 1 0.0 1 -19 1 1 2.0 3.0 1 2 4 1 1.0 2 -20 1 1 2.0 3.0 1 1 1 1 1 -21 1 1 2.0 3.0 1 2 1 1 2 -22 1 1 2.0 3.0 1 1 1 1 1 -23 1 1 2.0 3.0 1 2 1 1 2 -24 1 1 2.0 3.0 1 1 1 1 1 -25 1 1 2.0 3.0 1 2 1 1 2 -26 1 1 2.0 3.0 1 1 4 1 2.0 1 -27 1 1 2.0 3.0 1 2 4 1 0.0 2 -28 1 1 2.0 3.0 1 1 1 1 1 -29 1 1 2.0 3.0 1 2 1 1 2 -30 1 1 2.0 3.0 1 1 1 1 1 -31 1 1 2.0 3.0 1 2 1 1 2 -32 1 1 2.0 3.0 1 1 1 1 1 -33 1 1 2.0 3.0 1 2 1 1 2 -34 1 1 2.0 3.0 1 1 4 1 1.0 1 -35 1 1 2.0 3.0 1 2 4 1 2.0 2 -36 1 1 2.0 3.0 1 1 1 1 1 -37 1 1 2.0 3.0 1 2 1 1 2 -38 1 1 2.0 3.0 1 1 1 1 1 -39 1 1 2.0 3.0 1 2 1 1 2 -40 1 1 2.0 3.0 1 1 1 1 1 -41 1 1 2.0 3.0 1 2 1 1 2 -42 1 1 2.0 3.0 1 1 4 1 0.0 1 -43 1 1 2.0 3.0 1 2 4 1 1.0 2 -44 1 1 2.0 3.0 1 1 1 1 1 -45 1 1 2.0 3.0 1 2 1 1 2 -46 1 1 2.0 3.0 1 1 1 1 1 -47 1 1 2.0 3.0 1 2 1 1 2 -48 1 1 2.0 3.0 1 1 1 1 1 -49 1 1 2.0 3.0 1 2 1 1 2 -50 1 1 2.0 3.0 1 1 4 1 2.0 1 -51 1 1 2.0 3.0 1 2 4 1 0.0 2 -52 1 1 2.0 3.0 1 1 1 1 1 -53 1 1 2.0 3.0 1 2 1 1 2 -54 1 1 2.0 3.0 1 1 1 1 1 -55 1 1 2.0 3.0 1 2 1 1 2 -56 1 1 2.0 3.0 1 1 1 1 1 -57 1 1 2.0 3.0 1 2 1 1 2 -58 1 1 2.0 3.0 1 1 4 1 1.0 1 -59 1 1 2.0 3.0 1 2 4 1 2.0 2 -60 1 1 2.0 3.0 1 1 1 1 1 -61 1 1 2.0 3.0 1 2 1 1 2 -62 1 1 2.0 3.0 1 1 1 1 1 -63 1 1 2.0 3.0 1 2 1 1 2 -64 1 1 2.0 3.0 1 1 1 1 1 -65 1 1 2.0 3.0 1 2 1 1 2 -66 1 1 2.0 3.0 1 1 4 1 0.0 1 -67 1 1 2.0 3.0 1 2 4 1 1.0 2 -68 1 1 2.0 3.0 1 1 1 1 1 -69 1 1 2.0 3.0 1 2 1 1 2 -70 1 1 2.0 3.0 1 1 1 1 1 -71 1 1 2.0 3.0 1 2 1 1 2 -72 1 1 2.0 3.0 1 1 1 1 1 -73 1 1 2.0 3.0 1 2 1 1 2 -74 1 1 2.0 3.0 1 1 4 1 2.0 1 -75 1 1 2.0 3.0 1 2 4 1 0.0 2 -76 1 1 2.0 3.0 1 1 1 1 1 -77 1 1 2.0 3.0 1 2 1 1 2 -78 1 1 2.0 3.0 1 1 1 1 1 -79 1 1 2.0 3.0 1 2 1 1 2 -80 1 1 2.0 3.0 1 1 1 1 1 -81 1 1 2.0 3.0 1 2 1 1 2 -82 1 1 2.0 3.0 1 1 4 1 1.0 1 -83 1 1 2.0 3.0 1 2 4 1 2.0 2 -84 1 1 2.0 3.0 1 1 1 1 1 -85 1 1 2.0 3.0 1 2 1 1 2 -86 1 1 2.0 3.0 1 1 1 1 1 -87 1 1 2.0 3.0 1 2 1 1 2 -88 1 1 2.0 3.0 1 1 1 1 1 -89 1 1 2.0 3.0 1 2 1 1 2 -90 1 1 2.0 3.0 1 1 4 1 0.0 1 -91 1 1 2.0 3.0 1 2 4 1 1.0 2 -92 1 1 2.0 3.0 1 1 1 1 1 -93 1 1 2.0 3.0 1 2 1 1 2 -94 1 1 2.0 3.0 1 1 1 1 1 -95 1 1 2.0 3.0 1 2 1 1 2 -96 1 1 2.0 3.0 1 1 1 1 1 -97 1 1 2.0 3.0 1 2 1 1 2 -98 1 1 2.0 3.0 1 1 4 1 2.0 1 -99 1 1 2.0 3.0 1 2 4 1 0.0 2 -100 1 1 2.0 3.0 1 1 1 1 1 -101 1 1 2.0 3.0 1 2 1 1 2 -102 1 1 2.0 3.0 1 1 1 1 1 -103 1 1 2.0 3.0 1 2 1 1 2 -104 1 1 2.0 3.0 1 1 1 1 1 -105 1 1 2.0 3.0 1 2 1 1 2 -106 1 1 2.0 3.0 1 1 4 1 1.0 1 -107 1 1 2.0 3.0 1 2 4 1 2.0 2 -108 1 1 2.0 3.0 1 1 1 1 1 -109 1 1 2.0 3.0 1 2 1 1 2 -110 1 1 2.0 3.0 1 1 1 1 1 -111 1 1 2.0 3.0 1 2 1 1 2 -112 1 1 2.0 3.0 1 1 1 1 1 -113 1 1 2.0 3.0 1 2 1 1 2 -114 1 1 2.0 3.0 1 1 4 1 0.0 1 -115 1 1 2.0 3.0 1 2 4 1 1.0 2 -116 1 1 2.0 3.0 1 1 1 1 1 -117 1 1 2.0 3.0 1 2 1 1 2 -118 1 1 2.0 3.0 1 1 1 1 1 -119 1 1 2.0 3.0 1 2 1 1 2 -120 1 1 2.0 3.0 1 1 1 1 1 -121 1 1 2.0 3.0 1 2 1 1 2 -122 1 1 2.0 3.0 1 1 4 1 2.0 1 -123 1 1 2.0 3.0 1 2 4 1 0.0 2 -124 1 1 2.0 3.0 1 1 1 1 1 -125 1 1 2.0 3.0 1 2 1 1 2 -126 1 1 2.0 3.0 1 1 1 1 1 -127 1 1 2.0 3.0 1 2 1 1 2 -128 1 1 2.0 3.0 1 1 1 1 1 -129 1 1 2.0 3.0 1 2 1 1 2 -130 1 1 2.0 3.0 1 1 4 1 1.0 1 -131 1 1 2.0 3.0 1 2 4 1 2.0 2 -132 1 1 2.0 3.0 1 1 1 1 1 -133 1 1 2.0 3.0 1 2 1 1 2 -134 1 1 2.0 3.0 1 1 1 1 1 -135 1 1 2.0 3.0 1 2 1 1 2 +SERNUM BENUNIT PERSON DEDUC1 SPNAMT SALSAC JOBSECT ETYPE JOBTYPE SEEND SEJBLONG JOBBUS DIRECTOR NATURE DIRPERC DIVIDEND +1 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +2 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 +3 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +4 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +5 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +6 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +7 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +8 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +9 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +10 1 1 2.0 3.0 1 1 4 1 1.0 1 1 4 2 1000.0 +11 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +12 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +13 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +14 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +15 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +16 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +17 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +18 1 1 2.0 3.0 1 1 4 1 0.0 1 2 4 -1 0.0 +19 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 +20 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +21 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +22 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +23 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +24 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +25 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +26 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 +27 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +28 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +29 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +30 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +31 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +32 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +33 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +34 1 1 2.0 3.0 1 1 4 1 1.0 1 2 4 -1 0.0 +35 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +36 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +37 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +38 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +39 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +40 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +41 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +42 1 1 2.0 3.0 1 1 4 1 0.0 1 2 4 -1 0.0 +43 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 +44 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +45 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +46 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +47 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +48 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +49 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +50 1 1 2.0 3.0 1 1 4 1 2.0 1 1 4 2 1000.0 +51 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +52 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +53 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +54 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +55 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +56 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +57 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +58 1 1 2.0 3.0 1 1 4 1 1.0 1 2 4 -1 0.0 +59 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +60 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +61 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +62 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +63 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +64 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +65 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +66 1 1 2.0 3.0 1 1 4 1 0.0 1 2 4 -1 0.0 +67 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 +68 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +69 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +70 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +71 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +72 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +73 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +74 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 +75 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +76 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +77 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +78 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +79 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +80 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +81 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +82 1 1 2.0 3.0 1 1 4 1 1.0 1 2 4 -1 0.0 +83 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +84 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +85 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +86 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +87 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +88 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +89 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +90 1 1 2.0 3.0 1 1 4 1 0.0 1 1 4 2 1000.0 +91 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 +92 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +93 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +94 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +95 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +96 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +97 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +98 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 +99 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +100 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +101 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +102 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +103 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +104 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +105 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +106 1 1 2.0 3.0 1 1 4 1 1.0 1 2 4 -1 0.0 +107 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +108 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +109 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +110 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +111 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +112 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +113 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +114 1 1 2.0 3.0 1 1 4 1 0.0 1 2 4 -1 0.0 +115 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 +116 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +117 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +118 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +119 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +120 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 +121 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +122 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 +123 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 +124 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +125 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +126 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +127 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +128 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +129 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +130 1 1 2.0 3.0 1 1 4 1 1.0 1 1 4 2 1000.0 +131 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 +132 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +133 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 +134 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 +135 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv index c4160e56b4..0ef7010267 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ -person_id,person_benunit_id,person_household_id,age,gender,months_since_last_birthday,marital_status,hours_worked,care_hours,is_household_head,is_benunit_head,is_parent,is_blind,employment_income,self_employment_income,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,property_income,reports_rent_from_other_property,maintenance_income,miscellaneous_income,private_transfer_income,lump_sum_income,student_loan_repayments,statutory_sick_pay,statutory_maternity_pay,statutory_paternity_pay,student_loans,access_fund,education_grants,healthy_start_vouchers,free_school_breakfasts,free_school_fruit_veg,free_school_meals,child_benefit_reported,income_support_reported,housing_benefit_reported,attendance_allowance_reported,dla_sc_reported,dla_m_reported,iidb_reported,carers_allowance_reported,sda_reported,afcs_reported,ssmg_reported,pension_credit_reported,child_tax_credit_reported,working_tax_credit_reported,state_pension_reported,winter_fuel_allowance_reported,incapacity_benefit_reported,universal_credit_reported,pip_m_reported,pip_dl_reported,jsa_contrib_reported,jsa_income_reported,esa_contrib_reported,esa_income_reported,bsp_reported,would_claim_carers_allowance,council_tax_benefit_reported,maintenance_expenses,childcare_expenses,personal_pension_contributions,employee_pension_contributions,pension_contributions_via_salary_sacrifice,salary_sacrifice_reported,salary_sacrifice_asked,is_uc_claimant,rent_paid_as_boarder,rent_paid_as_lodger,is_claimant_or_partner,is_hbai_dependent_child,uc_is_in_startup_period,uc_is_in_gainful_self_employment,__stratum__ 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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json index 2e20aa268d..4e0c91f4cb 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json @@ -47,6 +47,7 @@ "employee_pension_contributions": "float64", "employment_income": "float64", "esa_contrib_reported": "float64", + "esa_includes_support_component": "bool", "esa_income_reported": "float64", "free_school_breakfasts": "float64", "free_school_fruit_veg": "float64", @@ -61,18 +62,25 @@ "is_benunit_head": "bool", "is_blind": "bool", "is_claimant_or_partner": "bool", + "is_engaged_in_owned_company_trade": "bool", "is_hbai_dependent_child": "bool", "is_household_head": "bool", "is_parent": "bool", "is_uc_claimant": "bool", "jsa_contrib_reported": "float64", "jsa_income_reported": "float64", + "loss_relief": "float64", "lump_sum_income": "float64", "maintenance_expenses": "float64", "maintenance_income": "float64", "marital_status": "string", "miscellaneous_income": "float64", "months_since_last_birthday": "float64", + "ni_class_4_losses_brought_forward": "float64", + "owned_company_carries_on_property_business": "bool", + "owned_company_carries_on_trade": "bool", + "owned_company_income_share": "float64", + "owned_company_is_main_employment": "bool", "pension_contributions_via_salary_sacrifice": "float64", "pension_credit_reported": "float64", "person_benunit_id": "int64", @@ -93,6 +101,7 @@ "sda_reported": "float64", "self_employment_income": "float64", "ssmg_reported": "float64", + "stands_as_sole_owner_or_partner_of_company": "bool", "state_pension_reported": "float64", "statutory_maternity_pay": "float64", "statutory_paternity_pay": "float64", @@ -100,6 +109,7 @@ "student_loan_repayments": "float64", "student_loans": "float64", "tax_free_savings_income": "float64", + "trading_loss": "float64", "uc_is_in_gainful_self_employment": "bool", "uc_is_in_startup_period": "bool", "universal_credit_reported": "float64", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json index 498a15ad79..dc3ff1fdb5 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json @@ -1 +1 @@ -{"country":"uk","nodes":[{"artifact_outputs":[{"name":"stage_evidence","type":{"name":"microcosm.stage-evidence","schema_version":1}}],"base":null,"citation":"","description":"Load the source-bound UK FRS root population.","id":"create_uk_frs","inputs":[],"kernel":"uk.create@1","mass":"conserve","outputs":[{"column":"age","dtype":"int64","entity":"person","ownership":"produced","rows":"all"},{"column":"gender","dtype":"string","entity":"person","ownership":"produced","rows":"all"},{"column":"months_since_last_birthday","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"marital_status","dtype":"string","entity":"person","ownership":"produced","rows":"all"},{"column":"hours_worked","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"care_hours","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"is_household_head","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_benunit_head","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_parent","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_blind","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_uc_claimant","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_claimant_or_partner","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"is_hbai_dependent_child","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"uc_is_in_startup_period","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"uc_is_in_gainful_self_employment","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"rent_paid_as_boarder","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"rent_paid_as_lodger","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"employment_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"self_employment_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"private_pension_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"tax_free_savings_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"savings_interest_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"dividend_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"property_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"reports_rent_from_other_property","dtype":"bool","entity":"person","ownership":"produced","rows":"all"},{"column":"maintenance_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"miscellaneous_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"private_transfer_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"lump_sum_income","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"student_loan_repayments","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"statutory_sick_pay","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"statutory_maternity_pay","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"statutory_paternity_pay","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"student_loans","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"access_fund","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"education_grants","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"healthy_start_vouchers","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"free_school_breakfasts","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"free_school_fruit_veg","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"free_school_meals","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"council_tax_benefit_reported","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"maintenance_expenses","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"childcare_expenses","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"personal_pension_contributions","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"employee_pension_contributions","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"pension_contributions_via_salary_sacrifice","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"salary_sacrifice_reported","dtype":"float64","entity":"person","ownership"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UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/uk_spi_income.py b/test_support/microcosm_build/uk_spi_income.py index e8e2ac0aca..78e215b7a9 100644 --- a/test_support/microcosm_build/uk_spi_income.py +++ b/test_support/microcosm_build/uk_spi_income.py @@ -205,6 +205,21 @@ def _dead_support( position + 4, dtype=float, ) + # The root stage writes the owner-manager flags as booleans that follow its + # rules, and no income share without an owner (microcosm#1095). + owners = np.array([True, False, True, False]) + for column, values in { + "stands_as_sole_owner_or_partner_of_company": owners, + "is_engaged_in_owned_company_trade": owners, + "owned_company_is_main_employment": owners, + "owned_company_carries_on_trade": np.array([True, False, False, False]), + "owned_company_carries_on_property_business": np.array( + [False, False, True, False] + ), + "owned_company_income_share": np.where(owners, 0.5, 0.0), + }.items(): + if column in person_columns: + person_columns[column] = values person_columns["tax_free_savings_income"] = np.zeros(4, dtype=float) person = pd.DataFrame(person_columns) benunit = pd.DataFrame({"benunit_id": np.arange(201, 205, dtype="int64")}) diff --git a/test_support/microcosm_build/uk_student_loans.py b/test_support/microcosm_build/uk_student_loans.py index 7035e621c7..a1fa64371b 100644 --- a/test_support/microcosm_build/uk_student_loans.py +++ b/test_support/microcosm_build/uk_student_loans.py @@ -20,6 +20,8 @@ _assert_student_loans_stage_parameters, _walk_top_up, assign_student_loan_plans, + course_start_year, + has_reported_student_loan, load_slc_liable_stocks, ) @@ -41,11 +43,18 @@ def _frame( education=None, weights=None, household_columns=None, + current_education=None, + reports_outstanding=None, + age_left=None, ): n = len(ages) ids = np.arange(1, n + 1, dtype="int64") regions = regions or ["LONDON"] * n education = education or ["TERTIARY"] * n + # No leaving age dates a course at 18, the rule before microcosm#868. + current_education = current_education or ["NOT_IN_EDUCATION"] * n + reports_outstanding = reports_outstanding or [False] * n + age_left = age_left or [0] * n return uk_national_frame( person=pd.DataFrame( { @@ -55,6 +64,9 @@ def _frame( "age": ages, "student_loan_repayments": repayments, "highest_education": education, + "current_education": current_education, + "reports_outstanding_student_loan": reports_outstanding, + "age_left_full_time_education": np.asarray(age_left, dtype="int64"), } ), benunit=pd.DataFrame({"benunit_id": ids}), diff --git a/test_support/microcosm_frame/concept_engine_frames.py b/test_support/microcosm_frame/concept_engine_frames.py index 3a3022f555..e4436716ab 100644 --- a/test_support/microcosm_frame/concept_engine_frames.py +++ b/test_support/microcosm_frame/concept_engine_frames.py @@ -34,6 +34,7 @@ Scale, ScaledSum, Share, + SignedPart, Sum, TakeUpThreshold, ) @@ -172,6 +173,7 @@ def expected_input_dtypes(binding: InputBinding) -> set[str]: | Scale | Sum | ScaledSum + | SignedPart | Product | AllocateToReferencePerson, ): diff --git a/test_support/microcosm_frame/concept_reference.py b/test_support/microcosm_frame/concept_reference.py index aaeec57900..5116821af6 100644 --- a/test_support/microcosm_frame/concept_reference.py +++ b/test_support/microcosm_frame/concept_reference.py @@ -33,6 +33,7 @@ Scale, ScaledSum, Share, + SignedPart, Sum, TakeUpThreshold, ) @@ -105,6 +106,9 @@ def is_parent_of(row: dict, child: dict, max_age: int | None) -> bool: elif isinstance(transform, ScaledSum): total = sum(float(value(row, concept_id)) for concept_id in concepts) out.append(total * transform.factor) + elif isinstance(transform, SignedPart): + total = sum(float(value(row, concept_id)) for concept_id in concepts) + out.append(max(total if transform.sign == "positive" else -total, 0.0)) elif isinstance(transform, Product): first, second = (float(value(row, concept_id)) for concept_id in concepts) out.append(first * second) diff --git a/tools/build_uk_ledger_compile_parity_signed_differences.py b/tools/build_uk_ledger_compile_parity_signed_differences.py index 5431cc4667..de4bf03d9c 100644 --- a/tools/build_uk_ledger_compile_parity_signed_differences.py +++ b/tools/build_uk_ledger_compile_parity_signed_differences.py @@ -168,6 +168,15 @@ class ParityReceiptSpec: "retained as diagnostic provenance on obr.pip, so the frozen fixture's " "fitted rows are fixture-only on the current surface." ) +_DWP_UC_FIVE_OR_MORE_DIAGNOSTIC_RATIONALE = ( + "microcosm#1095 (María's ruling of 2026-10-16) keeps DWP's count of Universal " + "Credit households with five or more children out of the fit: it includes " + "families outside the two-child limit, which the bound two-child-limit rows " + "leave out and too few FRS source households carry. The April 2025 figure is " + "retained as diagnostic provenance on " + "dwp.uc.two_child_limit.households_5_children, so the frozen fixture's fitted " + "row is fixture-only on the current surface." +) _STATE_PENSION_PREFIXES = ("dwp.state_pension.", "dfc_ni.state_pension.") _STATE_PENSION_LEDGER_ONLY_RATIONALE = ( "microcosm#1069 binds the resident State Pension from DWP Stat-Xplore (Great " @@ -647,7 +656,12 @@ def _add_signed_rationale_notes( if not isinstance(row, dict): continue name = str(row.get("name", "")) - if name in _UC_PAID_WINDOW_NAMES: + if ( + name == "dwp.uc.households_children_5_or_more" + and row.get("kind") == "fixture_only" + ): + row["reason"] = _DWP_UC_FIVE_OR_MORE_DIAGNOSTIC_RATIONALE + elif name in _UC_PAID_WINDOW_NAMES: inherited_change = ( "PR #891 contract with #892 source-window averaging" if name.startswith("dwp.uc.households_children_") diff --git a/tools/build_uk_release_input_coverage_manifest.py b/tools/build_uk_release_input_coverage_manifest.py index 12e764794b..19ecef5845 100644 --- a/tools/build_uk_release_input_coverage_manifest.py +++ b/tools/build_uk_release_input_coverage_manifest.py @@ -890,6 +890,10 @@ def build_manifest( stage_name="salary_sacrifice", candidate_source=candidate_source, ), + "private_school_attendance": _source_stage_family_coverage_contract( + stage_name="private_school_attendance", + candidate_source=candidate_source, + ), "student_loans": _source_stage_family_coverage_contract( stage_name="student_loans", candidate_source=candidate_source, diff --git a/tools/certify_uk_release_cut.py b/tools/certify_uk_release_cut.py index 7800be90c3..1a68c002c5 100644 --- a/tools/certify_uk_release_cut.py +++ b/tools/certify_uk_release_cut.py @@ -64,6 +64,11 @@ from microcosm.build.uk_runtime.release_input_coverage import ( PolicyEngineUKCoverageEngine, ) +from microcosm.build.uk_runtime.support_floor import ( + load_uk_support_floor_declaration, + narrow_uk_registry_by_support_floor, + uk_support_floor_from_payload, +) from microcosm.build.uk_runtime.weight_year import uk_solve_year_frame from microcosm.build.uk_runtime.weighted_integrity import ( exclusion_evaluation_date, @@ -283,6 +288,17 @@ def _run(args: argparse.Namespace, state: AttemptState) -> dict[str, object]: reference_registry, _receipt = apply_uk_calibration_measure_exclusions( reference_compiled, exclusions, now=evaluated_on ) + # The support floor drops rows on the candidate's spine (microcosm#1095 + # U3). The certifier cannot recompute support, so it reads the drops the + # run recorded and checks them against the declared floor. + floor_declaration = load_uk_support_floor_declaration() + reference_registry = narrow_uk_registry_by_support_floor( + reference_registry, + _nested(diagnostics, "build", "measure_exclusions") or {}, + None + if floor_declaration is None + else uk_support_floor_from_payload(floor_declaration), + ) append_phase(state, "registries_compiled") frame, _provenance = load_uk_national_frame(args.candidate_h5) diff --git a/tools/extract_uk_local_registry_fixture.py b/tools/extract_uk_local_registry_fixture.py index 277ab8e480..60b22cf191 100644 --- a/tools/extract_uk_local_registry_fixture.py +++ b/tools/extract_uk_local_registry_fixture.py @@ -27,6 +27,8 @@ import pandas as pd from policyengine_uk_data.datasets.local_areas.constituencies.boundary_changes.mapping_matrix import ( mapping_matrix, + new_codes, + old_codes, ) from policyengine_uk_data.datasets.local_areas.constituencies.devolved_housing import ( add_private_rent_targets, @@ -56,6 +58,18 @@ from policyengine_uk_data.utils.uc_data import uc_la_households, uc_pc_households TIME_PERIOD = 2025 +#: uk-data#589 fix-and-sign (María, 2026-10-16): five unchanged Scottish +#: constituencies keep their 2010 codes in the incumbent's boundary-change +#: source, so the mapping's sorted 2024 rows put them, and every Scottish row +#: between them, out of the roster's order. Their 2024 codes, as uk-data#589 +#: maps them; the mapped rows are then taken by code into the roster's order. +SCOTTISH_UNCHANGED_2010_TO_2024 = { + "S14000006": "S14000107", # Ayr, Carrick and Cumnock + "S14000008": "S14000108", # Berwickshire, Roxburgh and Selkirk + "S14000010": "S14000109", # Central Ayrshire + "S14000040": "S14000110", # Kilmarnock and Loudoun + "S14000058": "S14000111", # West Aberdeenshire and Kincardine +} def _normalized_area_name(name): @@ -213,8 +227,27 @@ def constituency_surface(): raise ValueError( f"pre-mapping frame has {len(raw)} rows on a {len(grain_codes)}-row grain." ) - raw_mapped = pd.DataFrame(mapping_matrix @ raw.values, columns=list(raw.columns)) - cal_mapped = pd.DataFrame(mapping_matrix @ cal.values, columns=list(cal.columns)) + # The matrix's columns are the 2010 codes in sorted order, so the grain's + # rows must come in that order; its rows are the source's 2024 codes. + if list(grain_codes) != list(old_codes): + raise ValueError("the age-target grain is not the mapping's 2010 code order.") + mapped_codes = [SCOTTISH_UNCHANGED_2010_TO_2024.get(c, c) for c in new_codes] + if len(set(mapped_codes)) != len(mapped_codes) or set(mapped_codes) != set(codes): + raise ValueError("the mapping's 2024 rows are not the 2024 roster, once each.") + raw_mapped = ( + pd.DataFrame( + mapping_matrix @ raw.values, columns=list(raw.columns), index=mapped_codes + ) + .loc[codes] + .reset_index(drop=True) + ) + cal_mapped = ( + pd.DataFrame( + mapping_matrix @ cal.values, columns=list(cal.columns), index=mapped_codes + ) + .loc[codes] + .reset_index(drop=True) + ) # uk-data#468 fix-and-sign: UC is PCON24-native, so under the correction it # is attached AFTER the boundary mapping, by name-join to the 2024 roster -- @@ -485,7 +518,15 @@ def main(): "totals join to their areas BY NAME (never by row position), attach " "post-mapping on native PCON24/LAD codes, and the child splits " "recompute from the corrected totals with the incumbent's own " - "country-proportion buckets keyed by true country prefix." + "country-proportion buckets keyed by true country prefix. " + "EXCEPTION 2, fix-and-signed (María, 2026-10-16; uk-data#589): five " + "unchanged Scottish constituencies keep their 2010 codes " + "(S14000006, S14000008, S14000010, S14000040, S14000058) in the " + "incumbent's boundary-change source, so its mapped rows, sorted by " + "those codes, sat out of the PCON24 roster's order for every Scottish " + "row between them; the codes map to S14000107 to S14000111 as " + "uk-data#589 maps them, and the mapped rows are taken by code into " + "the roster's order." ), "time_period": TIME_PERIOD, }, diff --git a/tools/fit_uk_childcare_takeup.py b/tools/fit_uk_childcare_takeup.py index 219e02a192..2ace35ef99 100644 --- a/tools/fit_uk_childcare_takeup.py +++ b/tools/fit_uk_childcare_takeup.py @@ -23,6 +23,7 @@ clipped_normal_from_uniforms, stable_identity_uniforms, ) +from microcosm.build.uk_runtime.frs_take_up import UK_TAKE_UP_DECLARED_SEEDS from microcosm.build.uk_runtime.ledger_targets import _candidate_facts_for_reference from microcosm.build.uk_runtime.take_up_contract import ( UKTakeUpContract, @@ -30,7 +31,7 @@ ) from microcosm.calibrate import TargetRegistry -TOOL_VERSION = 3 +TOOL_VERSION = 4 TARGET_IDS = ( "hmrc.tfc.government_top_up", "hmrc.tfc.children_with_used_accounts", @@ -271,32 +272,46 @@ def _ceilings( class ChildcareExpectationBasis: - """Per-row weighted contributions with the take-up flags forced on. + """Per-row weighted contributions with the take-up flags forced on or off. ``with_extended`` holds each target's weighted per-row values when every flag is True; ``without_extended`` the same with the extended flag False. - The engine's only cross-scheme interactions are its mutual exclusions - (targeted and universal require a family that is not extended-eligible), - so at rates ``r`` a target's expected value is - ``r_own * sum(with_ext * r_e + without_ext * (1 - r_e))`` — linear in the - own rate, bilinear with the extended rate — and the extended row is - ``r_e * sum(with_ext)``. The expectation is the quantity a take-up *rate* + Since policyengine-uk 2.131 (pe-uk#2177) universal and targeted hours stay + beside the working-parent hours, and a family that does not claim them on + their own still receives them, allocated first from a participating + child's working-parent hours (DfE guidance A1.11). + ``allocated_from_extended`` holds those rows: the targeted and universal + flags False and the extended flag True. So at rates ``r`` a targeted or + universal target's expected value is + ``r_own * sum(with_ext * r_e + without_ext * (1 - r_e)) + + (1 - r_own) * r_e * sum(allocated)``, linear in the own rate and + bilinear with the extended rate, and the extended row is + ``r_e * sum(with_ext)``. The universal row counts universal-only children, + so every side leaves out a child who receives extended hours. Its + allocated term is computed like the others' and is near zero, not zero: a + child whose family takes no universal hours on their own and who uses 15 + hours or fewer gets the base hours from the allocation and no extended + hours, so counts as universal-only, which is rare at the DfE usage mean of + 31.141 hours (Vahid's review of microcosm#1299). The expectation is the + quantity a take-up *rate* controls: the spine's persisted flags are one draw from it, and later stages clone and re-key rows, so no re-drawn realisation can sit on the - build's own — the expectation can. + build's own; the expectation can. """ - __slots__ = ("with_extended", "without_extended", "rows") + __slots__ = ("with_extended", "without_extended", "allocated_from_extended", "rows") def __init__( self, *, with_extended: Mapping[str, np.ndarray], without_extended: Mapping[str, np.ndarray], + allocated_from_extended: Mapping[str, np.ndarray], rows: int, ) -> None: self.with_extended = dict(with_extended) self.without_extended = dict(without_extended) + self.allocated_from_extended = dict(allocated_from_extended) self.rows = int(rows) @@ -319,6 +334,11 @@ def expected_childcare_counts( expected[target] = rates[own] * float( (with_ext * extended + without_ext * (1.0 - extended)).sum() ) + allocated = basis.allocated_from_extended.get(target) + if allocated is not None: + expected[target] += ( + (1.0 - rates[own]) * extended * float(np.asarray(allocated).sum()) + ) return expected @@ -329,16 +349,53 @@ def _weighted_rows( return np.asarray(values, dtype=float) * weights +#: The flags the allocation side turns off: their hours then come only from a +#: participating child's working-parent hours. +_ALLOCATED_FLAGS = ("would_claim_targeted_childcare", "would_claim_universal_childcare") +HOURS_OUTPUT = "maximum_extended_childcare_hours_usage" + + +def _anchor_hours(benunit_ids: np.ndarray, contract: UKTakeUpContract) -> np.ndarray: + """The extended-hours usage the take-up stage draws under the contract. + + The fit runs on a spine built before its anchor moved, so the stored column + holds the old anchor's draws; the stage's identity-keyed draw at the + contract's distribution replaces it (microcosm#1095, #1126). + """ + + hours = contract.continuous_entry(HOURS_OUTPUT) + return clipped_normal_from_uniforms( + stable_identity_uniforms( + np.asarray(benunit_ids), + seed=UK_TAKE_UP_DECLARED_SEEDS[HOURS_OUTPUT], + salt=HOURS_OUTPUT, + ), + mean=float(hours["mean"]), + sd=float(hours["sd"]), + lower=float(hours["lower"]), + upper=float(hours["upper"]), + ) + + def _basis_side( - input_h5: Path, target_period: int, contract: UKTakeUpContract, *, extended: bool + input_h5: Path, + target_period: int, + contract: UKTakeUpContract, + *, + extended: bool, + allocated: bool = False, ) -> dict[str, np.ndarray]: from policyengine_uk import Microsimulation sim = Microsimulation(dataset=str(input_h5)) - benunit_count = np.asarray(sim.calculate("benunit_id", target_period)).shape[0] + benunit_ids = np.asarray(sim.calculate("benunit_id", target_period)) + benunit_count = benunit_ids.shape[0] for variable in FLAG_OUTPUTS: on = extended if variable == "would_claim_extended_childcare" else True + if allocated and variable in _ALLOCATED_FLAGS: + on = False sim.set_input(variable, target_period, np.full(benunit_count, on, dtype=bool)) + sim.set_input(HOURS_OUTPUT, target_period, _anchor_hours(benunit_ids, contract)) person_count = np.asarray(sim.calculate("person_id", target_period)).shape[0] sim.set_input( "tax_free_childcare_spend_routed_share", @@ -374,8 +431,15 @@ def rows(variable: str, values: np.ndarray | None = None) -> np.ndarray: "dfe.funded_childcare.early_learning_2_year_olds": rows( "is_child_receiving_targeted_childcare" ), + # Universal-only children: a child who also receives working-parent + # hours is not one (the target binds DfE's universal count less the + # working-parent registrations). "dfe.funded_childcare.universal_only_children": rows( - "is_child_receiving_universal_childcare" + "is_child_receiving_universal_childcare", + np.asarray( + sim.calculate("is_child_receiving_universal_childcare", target_period) + ) + & ~extended_receiving, ), } @@ -383,13 +447,22 @@ def rows(variable: str, values: np.ndarray | None = None) -> np.ndarray: def compute_childcare_basis( input_h5: Path, target_period: int, contract: UKTakeUpContract ) -> ChildcareExpectationBasis: - """Two engine runs (extended flag on / off, every other flag on).""" + """Three engine runs: extended on and off with every other flag on, and + extended on with the targeted and universal flags off.""" with_extended = _basis_side(input_h5, target_period, contract, extended=True) without_extended = _basis_side(input_h5, target_period, contract, extended=False) + allocation = _basis_side( + input_h5, target_period, contract, extended=True, allocated=True + ) return ChildcareExpectationBasis( with_extended=with_extended, without_extended=without_extended, + allocated_from_extended={ + target: allocation[target] + for target, own in OWN_RATE_BY_TARGET.items() + if own in ("targeted_childcare", "universal_childcare") + }, rows=int(with_extended["hmrc.tfc.children_with_used_accounts"].shape[0]), ) diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index b4dfbc1dfa..af154a9818 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -118,6 +118,9 @@ from microcosm.build.uk_runtime.pension_credit_take_up import ( UKPensionCreditTakeUpStageTransform, ) +from microcosm.build.uk_runtime.private_school_attendance import ( + UKPrivateSchoolAttendanceStageTransform, +) from microcosm.build.uk_runtime.property_components import ( UKPropertyComponentsStageTransform, ) @@ -175,7 +178,7 @@ _SPI_DONOR_SAMPLE_SIZE = 64 #: The packaged FRS spine roster the fixture exercises (manifest minus the #: certified-pair exclusions); moves whenever a spine stage is added. -UK_FIXTURE_STAGE_COUNT = 41 +UK_FIXTURE_STAGE_COUNT = 42 _QRF_ESTIMATORS = 4 # These are the complete object-string surface observed in the unchanged @@ -448,6 +451,11 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "PAREAMT": 13.0, "REDAMT": 20.0, "SLREPAMT": 2.0, + # SLOS ("outstanding student loan?") follows a no to SLREPAY, so a + # repayer answers no; EDUCLEFT dates each adult's course + # (microcosm#868). + "SLOS": 2, + "EDUCLEFT": 16 + household_id % 8, "SSPADJ": 1.0, "SMPADJ": 0.5, "SPPADJ": 0.25, @@ -521,6 +529,9 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "PERSON": 1, "BENEFIT": benefit, "VAR2": variant, + # ESAGROUP on the ESA rows: the support group on odd households, + # the work-related activity group on even ones. + "VAR3": (2 if household_id % 2 else 1) if benefit == 16 else 0, "BENAMT": amount, "UCSTART": "", "UCHOUSEL": "", @@ -545,6 +556,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "PERSON": 1, "BENEFIT": 95, "VAR2": 0, + "VAR3": 0, "BENAMT": 8.0, "UCSTART": ("07/01/2024", "01/15/2022", "")[household_id % 9 // 3], "UCHOUSEL": 50.0 if household_id % 2 else "", @@ -567,6 +579,12 @@ def _frs_tables() -> dict[str, pd.DataFrame]: if household_id % 8 in (2, 3) else "", "JOBBUS": 2 if household_id % 2 else 1, + # Every tenth adult directs a company they own more than half + # of, as their first job (microcosm#1095). + "DIRECTOR": 1 if household_id % 10 == 0 else 2, + "NATURE": 4, + "DIRPERC": 2 if household_id % 10 == 0 else -1, + "DIVIDEND": 1000.0 if household_id % 10 == 0 else 0.0, } ) pensions.append( @@ -1940,6 +1958,11 @@ def capture_root(frame: Frame) -> Frame: "student_loans": UKStudentLoansStageTransform( stage=stages["student_loans"], calibration_year=2025 ), + "private_school_attendance": UKPrivateSchoolAttendanceStageTransform( + stage=stages["private_school_attendance"], + engine=engine, + calibration_year=2025, + ), "age_tail": UKAgeTailStageTransform(stage=stages["age_tail"]), } return implementations, root_capture diff --git a/uv.lock b/uv.lock index b3bc4b0c9c..96d3bd21b8 100644 --- a/uv.lock +++ b/uv.lock @@ -857,7 +857,7 @@ requires-dist = [ { name = "openpyxl", marker = "extra == 'uk'", specifier = ">=3.1" }, { name = "pandas", specifier = ">=2" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.130.1" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.132.6" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, { name = "pyarrow", specifier = ">=15" }, { name = "pyyaml", specifier = ">=6" }, @@ -936,7 +936,7 @@ requires-dist = [ { name = "microcosm-diagnostics", editable = "packages/microcosm-diagnostics" }, { name = "packaging", specifier = ">=24" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.130.1" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.132.6" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, ] provides-extras = ["us", "uk"] @@ -1051,7 +1051,7 @@ requires-dist = [ { name = "numpy", specifier = ">=2" }, { name = "pandas", specifier = ">=2.3" }, { name = "policyengine-core", marker = "extra == 'policyengine'", specifier = "==3.32.5" }, - 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