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shareholding, corporate_land_value and consumption_shareholding depend on who is simulated #2896

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@juaristi22

Summary

shareholding, corporate_land_value and consumption_shareholding each divide a household's key by the key's weighted total over the households in the simulation. So a household's value depends on which other households are simulated. Only a run over the whole UK at UK weights gets it right.

The most visible case is a bug. Since #1296 (August 2025), a household situation with any corporate wealth gets shareholding 1, and since #1824 (2.92.0) so does one with any private pension wealth. The household then pays all UK business rates (£36.20bn in 2026-27) and all corporate SDLT in England and Northern Ireland (£4.57bn). The household below ends up with household_net_income of −£40.77bn.

policyengine-api and policyengine.py's household calculator build households this way. The app's household builder offers private_pension_wealth under Assets.

What each formula does:

  • shareholding is a household's share of corporate_sector_wealth.
    • business_rates and corporate_sdlt multiply it by UK receipts. Both levels are in household_tax, and so in household_net_income; gov_tax lists them too.
    • Under a reform, corporate_incident_tax_revenue_change multiplies it by the reform's revenue change.
  • corporate_land_value is the same share times the corporate land parameter (£2,058.6bn, its 2024 value held flat).
    • The land value tax LVT reads it directly at its corporate rate, and through land_value at its full rate.
    • All LVT rates are 0 at current law. hbai_household_net_income subtracts LVT, so under an LVT reform the share reaches HBAI incomes too.
  • consumption_shareholding is a household's share of consumption. No formula reads it. consumer_incident_tax_revenue_change and employer_ni_response_consumer_incidence compute their own copies of the share.

The same thing happens in any simulation of part of the UK: a filtered region, a set of axes, an extract, a subsample, or Microcosm UK's per-clone engine blocks.

Separately, three modelling choices are open: the key, the incidence, and where the levels sit. These are not bugs.

Each proposal below can land as its own PR:

  1. Household situations stop using the weighted total. This is the bug fix.
  2. A national total of each key that the data can carry, so any part of the UK gets its own share.
  3. A decision on where business rates and corporate SDLT sit.
  4. A review of the key.
  5. Remove consumption_shareholding, or have the formulas that copy it read it.

File and line references are to policyengine-uk 2.174.1. On main (2.176.1) the formulas are unchanged, and household_tax takes its terms from programs.yaml (#2809).

What happens

policyengine_uk.Simulation(situation=...) gives the household the default household_weight of 1. Here is a household with private pension wealth (the comments are the outputs on 2.174.1):

from policyengine_uk import Simulation

situation = {
    'people': {'adult': {'age': {2026: 45}, 'employment_income': {2026: 40_000}}},
    'benunits': {'bu': {'members': ['adult']}},
    'households': {'hh': {'members': ['adult'], 'region': {2026: 'LONDON'},
                          'private_pension_wealth': {2026: 50_000}}},
}
sim = Simulation(situation=situation)
print(sim.calculate('shareholding', 2026))          # [1.]
print(sim.calculate('business_rates', 2026))        # [3.620134e+10]
print(sim.calculate('household_net_income', 2026))  # [-4.0774574e+10]

situation['axes'] = [[{'name': 'employment_income', 'count': 5,
                       'min': 0, 'max': 80_000, 'period': 2026}]]
print(Simulation(situation=situation).calculate('shareholding', 2026))  # [0.2 0.2 0.2 0.2 0.2]
variable 2026
shareholding 1
business_rates £36.20bn
corporate_sdlt £4.57bn
corporate_land_value £2,058.6bn
household_net_income −£40.77bn
hbai_household_net_income £32,420
  • Axes. The axis copies share the totals. Five copies each get shareholding 0.2 and £7.24bn of business rates, so the result depends on the number of axis points.
  • Reforms. The household takes the whole change.
    • With gov.contrib.policyengine.budget.corporate_incident_tax_change at 1 (£1bn), its corporate_incident_tax_revenue_change is £1bn.
    • Under a 1% land value tax (gov.contrib.ubi_center.land_value_tax.rate), its LVT is £20.59bn and its hbai_household_net_income falls from £32,420 to 0.
  • Zero weight. Setting household_weight to 0 shows the fallback that proposal 1 builds on: the key over household financial net worth. It charges this household about £383 of business rates and corporate SDLT.
  • Who builds households this way.
    • policyengine-api (policyengine_api/country.py) and policyengine.py's household calculator (src/policyengine/tax_benefit_models/uk/household.py) both build through policyengine_uk.Simulation(situation=...).
    • policyengine-api runs policyengine-uk 2.151.0, which has the same code.
    • PolicyEngine/policyengine-app-v2's household builder (app/src/libs/flagship/householdVariableGroups.ts) hides corporate_wealth as a dataset-only input, but offers private_pension_wealth under Assets.
    • Not run end to end in the app.

Expected: a household's shareholding does not depend on which other households are simulated, or on its own weight.

Any simulation of part of the UK hands the UK totals to whichever of its households hold the key:

Cause, read in the code

  • The weighted branch. variables/household/wealth/shareholding.py:12-23 takes it when household.simulation.dataset is not None.
  • Other formulas.
    • variables/household/wealth/corporate_land_value.py:13-27 has no such check.
    • variables/household/consumption/consumption_share.py:12-19 and variables/contrib/policyengine/consumer_incident_tax_revenue_change.py:11-22 use the same gate as shareholding.
    • So do pre_budget_change_ons_household_income_decile and the NHS, education and other public spending budget changes, so situations take their data branch too.
  • Tests.
  • The fallback. It divides by household financial net worth at its 2021 value. parameters/household/wealth/national_balance_sheet.yaml declares the uprating on the parent node, and it does not reach financial_net_worth. Uprate benefits by the previous September's CPI #2557 noted this and kept it as an exception in its uprating test.

Open modelling choices

These are not bugs. Proposals 3 and 4 ask for a decision on each.

The weighted total keeps the allocations summing to the receipts on any full dataset. #774's closing comment credits #840 with that. What the formulas spread, and onto whom, are choices the code calls modelling conventions (parameters/gov/hmrc/business_rates/statistics.yaml:17-20; parameters/gov/hmrc/stamp_duty/statistics.yaml:21-24).

  • The key.
    • corporate_sector_wealth adds private_pension_wealth to corporate_wealth, because pension funds hold corporate equity (Add private_pension_wealth as an explicit capital disregard; corporate_wealth keeps the share-like holdings #1824). corporate_sector_wealth.py:6-16 calls the addition a compatibility proxy.
    • private_pension_wealth includes retained defined benefit rights and pensions in payment, valued at the SCAPE rate (variables/input/private_pension_wealth.py:6-31). Defined benefit rights are promised amounts that do not move with company returns, and most public service schemes are unfunded.
    • There is no overseas share. Account for foreign ownership of corporations in land value tax distribution #224 (2021) kept it out so that the accounts add up. Overseas investors held 58.8% of UK quoted shares at the end of 2024 (ONS, Ownership of UK quoted shares: 2024, 29 January 2026).
    • The rating lists include public bodies, charities and unincorporated businesses. Corporate SDLT payers include partnerships and collective investment schemes.
  • The incidence.
    • The ONS's Effects of taxes and benefits counts rates on commercial and industrial property, and stamp duties, among intermediate taxes. It assumes they fall on consumers, allocates them through household spending, and deducts them in post-tax income, not disposable income (technical report, financial year ending 2019, section 7).
    • HM Treasury's distributional analysis excludes business taxes (Impact on households: distributional analysis to accompany Budget 2026, paragraph 1.2).
    • Bond, Denny, Hall and McCluskey (Fiscal Studies, 1996) find that much of the burden of business rates shifts to property owners through lower rents in the long run, while occupiers bear more of it in the short run.
  • The placement.
    • The levels of business rates and corporate SDLT are in household_tax, and so in household_net_income.
    • VAT, and since Charge VAT on private school fees at current law #2319 private school VAT, enter household_tax only as changes. The employer NICs level is only in gov_tax (data/fiscal_partition.yaml:797-804, 811-817).
    • Fuel, alcohol, tobacco and gambling duties enter household_tax as levels.
    • hbai_household_net_income already leaves business rates and corporate SDLT out (variables/household/income/hbai_excluded_income.py).
  • Corporate land. Ownership of corporate equity is a reasonable first attribution for a tax on corporate land. Its caveats (overseas owners, housing associations, public corporations) are scoped as D7 in Project scope: land value tax results for every constituency uk-land-value-tax#20.
History

The denominator.

The placement.

Proposals

  1. Situations (the bug; can land on its own).

    • Take the weighted branch only when built_from_data(simulation) (utils/data_source.py), as attends_private_school does. Give corporate_land_value the same fallback.
    • Base the fallback on a parameter holding the key's national total, uprated with the key, in place of household financial net worth at its 2021 value. That also removes Uprate benefits by the previous September's CPI #2557's exception.
    • The other choice is 0 in situations, which is what attends_private_school returns. The household calculator would then show no corporate incidence.
    • A multi-household situation with weights set would no longer act as a population. tests/test_corporate_land_value.py:5-43 and check 5 of tests/test_sdlt_counted_once_properties.py would build small datasets instead, as tests/test_weighted_incidence.py does.
    • Test through policyengine_uk.Simulation(situation=...), with and without axes: a household's shareholding does not depend on its own weight or on the number of axis points.
    • Nothing changes on data.
  2. A national total the data can carry.

    • Add a household input that holds the national weighted total of each key, the same on every row: corporate_sector_wealth, and consumption if consumption_shareholding stays. The formulas divide by it when it is supplied, and keep today's weighted total otherwise.
    • Uprating:
      • both parts of corporate_sector_wealth uprate with OBR GDP per capita, and household_weight with ONS population;
      • uprating the input by the product of the two keeps it equal to the projected weighted total;
      • a variable takes one uprating index today, so this needs a combined index;
      • a builder that sets weights year by year writes the input year by year.
    • As a column it survives row filters, filter_dataset, household subsets, subsamples and weight replacement. Each part of the UK then gets its own share of the UK total, and the full dataset keeps the identity OBR March EFO update #840 was after.
    • Microcosm UK would write it at its release weights, and at its measurement weights inside the build. policyengine-uk-data would need to write it too, for Scoped UK runs give the region UK-wide business rates and region-relative ranks policyengine.py#567's regional runs.
    • This is option 2 of Scoped UK runs give the region UK-wide business rates and region-relative ranks policyengine.py#567, without carrying formula values. It is the same route as the national income rank that attends_private_school waits for (attends_private_school.py:72-78).
  3. Where business rates and corporate SDLT sit. The options:

    • keep the levels, and document the incidence;
    • count only the change from baseline, as for VAT and, since Charge VAT on private school fees at current law #2319, private school VAT. This was the rule for business rates and corporate SDLT from April 2022 to September 2024;
    • carry them in a separate income measure.

    The first two both keep the revenue identity in docs/book/usage/simulations.md for reforms; they differ only in the baseline level of household_net_income. The third takes reforms to these taxes out of household_net_income.

  4. The key. Decide it alongside D7 in Project scope: land value tax results for every constituency uk-land-value-tax#20, which covers overseas owners, housing associations, public corporations and corporate SDLT. Add the defined benefit part of private_pension_wealth and the ratepayers outside the corporate sector. That project leaves business rates out of scope, so business rates need their own decision.

  5. consumption_shareholding. No formula reads it in 2.174.1, nor in the commit that added it (Add employer NI incidence #961). Remove it, or have consumer_incident_tax_revenue_change and employer_ni_response_consumer_incidence read it in place of their own copies of the share.

Related

Duplicate check

On 6 November 2026 I searched open and closed issues and PRs for shareholding, business rates, corporate SDLT, corporate land value and household situations, in policyengine-uk, policyengine.py, policyengine-uk-data, policyengine-api and policyengine-app-v2, and pe-uk's changelog.

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Activity

  1. vahid-ahmadi commented on Nov 9, 2026

    @vahid-ahmadi
    Collaborator

    Review of the issue as of 9 November 2026, checked against policyengine-uk main e050200 (2.176.1). I reran the reproducer, the axes, zero-weight, LVT and corporate-incident cases in a fresh install, read each cited file and line, and spot-checked the history.

    The reproducer gives the same figures on main. shareholding is 1, business_rates £36.20bn, corporate_sdlt £4.57bn, household_net_income −£40.77bn and hbai_household_net_income £32,420. Five axis points give 0.2 each, and eleven give 1/11 each. At zero weight the household pays £339.78 of business rates and £42.92 of corporate SDLT, £383 in all. Under the 1% LVT, LVT is £20.59bn and HBAI income is 0. The corporate incident change is £1bn. It is live too: the same household sent to api.policyengine.org/uk/calculate today (2.151.0) returns shareholding 1, business_rates £31.73bn and household_net_income −£34.86bn.

    Should

    1. Proposal 1 should cover every formula with this dependence, not only the two keys. WHOLE_POPULATION_VARIABLES in utils/household_subset.py:39-61 already lists them. Three cases the proposals leave unfixed:

      • employer_ni_response_capital_incidence and employer_ni_response_consumer_incidence. Both are household_tax terms (programs.yaml:1169-1170). Both divide by the simulation's own weighted totals of the employer cost change and of corporate_sector_wealth or consumption. Their guard at line 17 of each, if not hasattr(person.simulation, "dataset"), is meant to return 0 "in single-household simulations". It never fires, because Simulation always has the attribute. Take the household in the issue, with gov.contrib.policyengine.employer_ni.employee_incidence at 0 and the employer rate at 25%. It gets £1,750 of capital incidence, which is half of its own £3,500 rise in employer cost. Given £20,000 of consumption, it also gets £1,750 of consumer incidence. Over five axis points, each copy gets £1,800 of capital incidence, which is half of the copies' mean rise. Proposal 5's second option would not fix the consumer side. Reading consumption_shareholding changes only the share, and amount_paid_by_employers (line 25) still sums the simulation's own cost change.
      • carbon_tax. Lines 16-21 multiply shareholding by the simulation's weighted total of carbon_consumption, with no gate. Take 10 tonnes at £100 a tonne, with consumer_incidence 0. The household pays £1,000, because its own emissions stand in for the UK total. At zero weight it pays £0, because the weighted emissions total is then 0. If only shareholding were fixed, the corporate part for a weight-1 household would become its own emissions times a very small national share. So this formula needs a national emissions total on the situation path too.
      • The spending changes and their decile. The issue names nhs_budget_change, education_budget_change, other_public_spending_budget_change and pre_budget_change_ons_household_income_decile, but no proposal changes them. Each already has a situation branch that the dataset gate skips: fixed decile bounds at pre_budget_change_ons_household_income_decile.py:21-31, and 2.8m households per decile at nhs_budget_change.py:37 (education line 38, other spending line 39).
        • With gov.contrib.policyengine.budget.nhs at 1 (£1bn), the household without pension wealth ranks in decile 10 against itself and gets £70m.
        • With the £50,000 of pension wealth, its pre-budget net income is −£40.77bn. That puts it in decile −1, so it gets 0.

      Suggested fix: in the same PR, gate each weighted branch in that set on built_from_data. Add one test that runs each of them through policyengine_uk.Simulation(situation=...) and checks the result depends on neither the household's weight nor the number of axis points.

    2. The two fallbacks in proposal 1 are not equivalent next to the consumer side. consumer_incident_tax_revenue_change.py:13 already falls back to a national total, consumption over a literal £846bn. Suppose situations returned 0 for corporate incidence. A reform split between capital and consumers would then show only the consumer half in the household calculator. So the 0 option means changing the consumer fallback as well. The national-total option should replace the £846bn literal with the same kind of uprated parameter. Whichever is chosen, both sides should move together.

    Nit

    1. Proposal 5: removing consumption_shareholding also means taking it out of WHOLE_POPULATION_VARIABLES (utils/household_subset.py:44), SHARES in tests/test_variable_units.py:20, and the cases in tests/test_weighted_incidence.py (lines 96, 113, 129 and 145).

    Checked and fine:

    • Formula and test references at main. shareholding.py:12-23, build_from_situation at simulation.py:1182-1202, household_weight.py:11, corporate_land_value.py:13-27, consumption_share.py:12-19 and consumer_incident_tax_revenue_change.py:11-22 match. So do attends_private_school.py:42 and 72-78, filter_dataset.py:47-57, the cited test lines and the "modelling convention" text in both statistics files.
    • fiscal_partition.yaml. Lines 797-804 and 811-817 match at 2.174.1. On main those guards have moved to 811-818 and 825-831.
    • The stale fallback denominator. The uprating sits on the household node and never reaches financial_net_worth. At zero weight, shareholding is 50,000 / £5,327bn, the 2021 value.
    • Corporate land. The £2,058.6bn 2024 value is repeated for 2025 and 2026.
    • Proposal 1's gate reaches both callers and axes. build_from_situation sets built_from_dataset=False (simulation.py:1188), and the axes case reads False too. policyengine.py's household calculator imports policyengine_uk.Simulation (household.py:173), and policyengine-api builds through country_package.Simulation(situation=...) (country.py:565-577).
    • Proposal 2's combined index is workable. Core takes one parameter path for uprating. The indices are generated from yoy_growth in create_economic_assumption_indices.py, so a product of the per-capita GDP and population indices can be generated in the same pass. Both parts of the key uprate with indices.obr.per_capita.gdp and household_weight with indices.ons.population. There is no nominal GDP index to use instead.
    • History. Add OBR labour supply response elasticities #1296 sets self.dataset = UKSingleYearDataset.from_simulation(...). 8223275 is in Begin refactoring and adding statistics #597. OBR March EFO update #840 merged on 12 April 2024. Improve corporate land allocation #1583 replaced the parameter total with the weighted one.
    • Sources. ONS puts overseas ownership at 58.8% at the end of 2024 (release of 29 January 2026). HM Treasury's paragraph 1.2 excludes business taxes.
    • Duplicates. I found nothing open in policyengine-uk, policyengine.py or policyengine-api that tracks the situation case, and no PR since 7 November touches these formulas.

    Verdict: the diagnosis and the reproduction hold on main. Widen proposal 1 to the whole-population set, and choose its fallback together with the consumer-incidence one.

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  2. juaristi22 commented on Nov 9, 2026

    @juaristi22
    CollaboratorAuthor

    Findings from the land value tax work, checked against policyengine-uk main e050200 (2.176.1), PolicyEngine/microcosm main 3db65154b, PolicyEngine/policyengine-uk-data main ed19e336b and PolicyEngine/uk-land-value-tax main 371e2b1. They add one more caller, one case for proposal 3, and the consumers outside this repository that proposals 2, 4 and 5 would move.

    One more caller of the situation path (proposal 1)

    PolicyEngine/uk-land-value-tax's pipeline builds an example household through Simulation(situation=...) (src/uk_land_value_tax/pipeline.py:400-442). The household has a £400,000 home and £50,000 of corporate_wealth. In dashboard/public/data/lvt_results.json:2986-2994, its corporate_land_value is £2,058.6bn and its LVT at 1% is £20.6bn. The dashboard does not show this block, but the file is in the dashboard's public folder. Proposal 1 fixes it once the pipeline runs again.

    Proposal 3 should cover the corporate part of LVT

    • hbai_household_net_income subtracts the whole of LVT (variables/household/income/hbai_household_net_income.py:100). LVT charges corporate_land_value at the full rate through land_value, and again at corporate_rate (variables/contrib/ubi_center/land_value_tax.py:13-16).
    • Business rates and corporate SDLT stay out of HBAI income (variables/household/income/hbai_excluded_income.py:14).
    • So under an LVT reform, the corporate land charge is shared out by the key and taken from HBAI income. The poverty measures read it.
    • PolicyEngine/uk-land-value-tax replaces council tax at a budget-neutral rate of 0.83% (lvt_results.json:1972). On £2,058.6bn of corporate land, that takes about £17bn from HBAI incomes, shared out by corporate_sector_wealth.
    • This is the same choice as D7 in Project scope: land value tax results for every constituency uk-land-value-tax#20, so the two can be decided together.

    Proposal 2: carry the total, not the shares

    Writing shareholding or corporate_land_value into the data as inputs would not do what proposal 2 does:

    • They would not move when a reform changes the receipts, aggregate_corporate_land_value or the key.
    • Household situations, and datasets without these columns, would still need the formulas. So each variable would have two versions.
    • An incidence choice, such as a UK-owned share (proposal 4), would need a rebuild of the data. As a parameter, a scenario can vary it.

    So the data should carry the parts of the key and its national total. The shares, the levels and the incidence should stay in the formulas. Until proposal 2 lands, PolicyEngine/uk-land-value-tax's seat results have to come from one UK-wide run grouped by constituency, not from runs filtered to each seat.

    Proposal 4: what the data can give

    • WAS round 8 records directly held UK shares apart from overseas shares. It also records pension wealth by type: workplace DB and DC schemes, AVCs, personal pensions, retained DB and DC rights, pensions in payment, and pensions from a former partner.

    • Microcosm's key already uses part of this (packages/microcosm-build/src/microcosm/build/uk_runtime/was_wealth.py:562-570):

      • corporate_wealth counts UK shares, employee shares, collective investments and stocks and shares ISAs. It leaves overseas shares out.
      • private_pension_wealth is total private pension wealth less current-employment DB rights. So it keeps retained DB rights and pensions in payment.
    • The pension part changes the key a lot. Microcosm's experiments/452-uk-pension-wealth-split.md:92-96 puts 54.4% of the key in the top tenth of households when the key includes pension wealth, and 86.7% when it does not.

    • A weight for each part, such as 0 for the DB-type parts, needs private_pension_wealth split into columns by type. That would follow the split of direct shares and trusts written for Itemise corporate_wealth's share-like components for the means tests #1974.

    • WAS cannot give the UK-owned share of UK companies, or how much of a fund, an ISA or a pension scheme is in UK companies. Published aggregates can:

      • ONS's share ownership survey, for quoted shares;
      • ONS's sector accounts, for all equity;
      • ONS's statistics on what insurers, pension funds and trusts invest in.

      They would come in through Chronicle as facts.

    • aggregate_corporate_land_value is described as the land of non-financial and financial corporations (parameters/household/wealth/land/value.yaml:25). Microcosm binds its target to ONS's non-financial corporations' land (E43B). Workstream 2 of Epic: UK land value data for land value tax results by constituency microcosm#1521 checks which one the value is. Its 2025 and 2026 values repeat the 2024 value.

    Consumers outside policyengine-uk (proposals 2, 4 and 5)

    • Microcosm's engine tests pin the three formulas and their keys.
      • UK_WEIGHT_SHARE_FORMULA_INPUTS (packages/microcosm-build/src/microcosm/build/uk_runtime/full_measure.py:50) names shareholding, corporate_land_value and consumption_shareholding, with the columns that corporate_sector_wealth and consumption add.
      • packages/microcosm-build/tests/engine/uk/test_uk_weight_share_formula_inputs.py:32-48 checks them against the installed engine.
      • Removing consumption_shareholding (proposal 5), or changing what corporate_sector_wealth adds (proposal 4), fails that test at Microcosm's next engine bump. So each needs a Microcosm change in the same bump. This adds to nit 3 of the review above.
    • Two calibrations target corporate_land_value: Microcosm's ons.land.corporate_land_value (packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json:16319) and the ons/corporate_land_value row of uk-data's loss matrix (policyengine_uk_data/targets/build_loss_matrix.py:408). Both also target land_value, whose corporate half is the same quantity.
      • corporate_land_value divides by the weighted total it is computed with. So its weighted total always comes back to aggregate_corporate_land_value, and a target on it says nothing about households.
      • With values frozen at the measurement weights, the target can only push the weighted total of corporate_sector_wealth up or down, by the ratio of the target to the parameter. Computed again at the solved weights, the total is the parameter again. Microcosm's check of frozen against recomputed values lists this row among the bound rows that differ by more than 1% (experiments/929-council-tax-taxbase-receipts.md:188-190).
      • Proposal 2 keeps this property. So these rows work better as diagnostics than as weight targets. Workstream 2 of Epic: UK land value data for land value tax results by constituency microcosm#1521 already recommends keeping land out of the weight solve.
    • Per-block runs. Before Make the dense role run on a current spine: identity branch, scratch aliases, per-block national problem, compiled-row axis, holdout shape, engine blocks that represent the pool microcosm#1115 scaled the block weights, 15 engine blocks gave 15 times the single-block corporate land total (experiments/762-uk-rowwise-candidate-receipts.md:315-321). This is the issue's partial-UK case inside a build. As the issue says, proposal 2 would make that scaling unnecessary.

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