diff --git a/changelog.d/eitc-t25-irs-reconciliation.added.md b/changelog.d/eitc-t25-irs-reconciliation.added.md new file mode 100644 index 0000000000..4760f6d823 --- /dev/null +++ b/changelog.d/eitc-t25-irs-reconciliation.added.md @@ -0,0 +1 @@ +The SOI Table 2.5 EITC child-split targets are now checked in CI against the real IRS rows. A fixture holds the 274 pinned-feed rows for TY2023 Publication 1304 Table 2.5 and the TY2024 filing-season EITC by AGI, verbatim. Tests check that each split's AGI bins tile AGI and add to its total row, that the four splits add to the all-returns total, and that the totals reconcile within 10% with IRS-published EITC: the TY2023 source rows with TY2023 (more than 23 million returns, about $64 billion, 27/36/24/13% by children), and the compiled 2024 rows with TY2024 (about 24 million, $69.6 billion). Two Hypothesis properties on the real layout check that the compiled AGI bins and total rows both add to any filing-season EITC by AGI, and that each split's bins add to its total row under uniform growth. Before #1227 the compile halved the total rows (11.92 million returns and $34.52 billion), and these tests fail on that code. diff --git a/packages/microcosm-build/tests/engine_free/us/test_us_eitc_table_2_5_irs_reconciliation.py b/packages/microcosm-build/tests/engine_free/us/test_us_eitc_table_2_5_irs_reconciliation.py new file mode 100644 index 0000000000..8aec1edccb --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/us/test_us_eitc_table_2_5_irs_reconciliation.py @@ -0,0 +1,518 @@ +"""SOI Table 2.5 EITC child splits on the real TY2023 rows, in CI. + +``tests/fixtures/us_soi_eitc_table_2_5_feed_rows.jsonl`` holds, verbatim and +sorted by source record id, the 274 rows of the pinned Chronicle feed +(``consumer_facts_us_51aa40f.jsonl``, sha256 965a29ac...) that the EITC +decomposition uprating reads: + +* IRS SOI Publication 1304 Table 2.5, tax year 2023 (``23in25ic.xls``, sha256 + c6a58f63...): the all-returns total row, and for each of the four + qualifying-child splits its 28 AGI bins and its total row, as returns and + amount; +* IRS SOI Filing Season Statistics Table 1, 2025 week 47, tax year 2024 + (``25inweek47.xlsx``, sha256 5b8f0573...): EITC returns and amount in 16 AGI + bins and in total. + +The pinned-feed tests in ``test_us_fiscal_targets.py`` skip in CI, which has no +feed. These run everywhere, so the halving fixed in microcosm#1227 cannot come +back unnoticed: the compile summed each split's AGI bins and its total row +into the all-AGI source total, uprated the four total rows by 0.4877 (returns) +and 0.5209 (amount), and shipped them at 11.92 million returns and $34.52 +billion against the TY2024 filing season's 23.84 million and $69.04 billion. + +IRS-published EITC (EITC statistics pages, read 2026-10-14): + +* TY2023, returns processed by December 2024: "more than 23 million" workers + and families received "about $64 billion"; 27% had no qualifying child, 36% + one, 24% two and 13% three or more. +* TY2024, processed by December 2025: about 24 million and $69.6 billion. + +IRS labels those "returns processed in" the filing year, "as of December". +Publication 1304 Table 2.5 is the SOI sample estimate for "Tax Year 2023 +(Filing Year 2024)"; its footnote 1 defines EIC earned income "for prior-year +returns", which implies they are in it. Its TY2023 EITC (24,439,936 returns, +$66.27 billion) sits 6.3% and 3.5% above the TY2023 December figures; the two +products' bases differ, and this module does not attribute the gap. The +reconciliation tolerance is 10%: wide enough for that observed gap, far too +narrow for a halving. Child shares are compared within 1.5 percentage points +because IRS rounds them to whole percents. +""" + +from __future__ import annotations + +import functools +import json + +import pytest +from hypothesis import HealthCheck, given, settings +from hypothesis import strategies as st + +from microcosm.build.ledger_artifact import load_ledger_consumer_artifact +from test_support.microcosm_build.us_fiscal_targets import ( + _load_repo_tool, + compile_us_fiscal_target_registry, + packaged_reference_facts, +) +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-build") +_FIXTURE = _TEST_PATHS.tests / "fixtures" / "us_soi_eitc_table_2_5_feed_rows.jsonl" + +_SPLITS = ( + "no_qualifying_children", + "one_qualifying_child", + "two_qualifying_children", + "three_or_more_qualifying_children", +) +_TABLE_2_5 = "irs_soi.ty2023.table_2_5." +_CHILD_RECORD_SET = f"{_TABLE_2_5}eitc_by_agi_children." +_ALL_RETURNS_RECORD_SET = f"{_TABLE_2_5}eitc_all_returns" +_FILING_SEASON = "irs_soi.ty2024.filing_season_week47." +_FILING_SEASON_BINS = f"{_FILING_SEASON}eitc_by_agi." +_FILING_SEASON_TOTAL = f"{_FILING_SEASON}eitc_all_returns." +# kind -> (Table 2.5 child-split measure, all-returns and filing-season measure) +_KINDS = { + "returns": ("eitc_returns", "total_earned_income_credit_returns"), + "amount": ("eitc_total", "total_earned_income_credit_amount"), +} + +# IRS-published EITC; see the module docstring. +_IRS_PUBLISHED = { + 2023: {"returns": 23_000_000, "amount": 64_000_000_000}, + 2024: {"returns": 24_000_000, "amount": 69_600_000_000}, +} +_IRS_TY2023_CHILD_SHARES = { + "no_qualifying_children": 0.27, + "one_qualifying_child": 0.36, + "two_qualifying_children": 0.24, + "three_or_more_qualifying_children": 0.13, +} +_PUBLISHED_TOLERANCE = 0.10 +_SHARE_TOLERANCE = 0.015 +# SOI rounds each cell ("Detail may not add to totals because of rounding"): +# counts to the return, amounts to $1,000. The widest gap between a split's +# bins and its total row in the TY2023 workbook is 3 returns and $1,000. +_ROUNDING = {"returns": 5, "amount": 5_000} + +_SETTINGS = settings( + max_examples=100, + deadline=None, + suppress_health_check=[HealthCheck.too_slow], +) + + +@functools.cache +def _feed_rows() -> tuple[dict[str, object], ...]: + return tuple(load_ledger_consumer_artifact(_FIXTURE).facts) + + +def _record_id(fact: dict[str, object]) -> str: + return fact["lineage"]["source_record_id"] + + +def _agi_bounds(fact: dict[str, object]) -> tuple[float, float]: + lower, upper = -float("inf"), float("inf") + for constraint in fact.get("universe_constraints", {}).get("constraints", ()): + if not constraint["variable"].endswith("adjusted_gross_income"): + continue + if constraint["operator"] == ">=": + lower = float(constraint["value"]) + elif constraint["operator"] == "<": + upper = float(constraint["value"]) + else: # pragma: no cover - the fixture uses only >= and < + raise AssertionError(f"unexpected AGI operator in {_record_id(fact)}") + return lower, upper + + +def _inside(inner: tuple[float, float], outer: tuple[float, float]) -> bool: + return outer[0] <= inner[0] and inner[1] <= outer[1] + + +def _split_rows(kind: str) -> dict[str, dict[str, dict[str, object]]]: + """split -> AGI row id (28 bins and ``total``) -> fact.""" + + measure = _KINDS[kind][0] + rows: dict[str, dict[str, dict[str, object]]] = {} + for fact in _feed_rows(): + layout = fact["layout"] + record_set = layout["record_set_id"] + if record_set.startswith(_CHILD_RECORD_SET) and layout["measure_id"] == measure: + split = record_set.removeprefix(_CHILD_RECORD_SET) + rows.setdefault(split, {})[layout["groupby_value_id"]] = fact + return rows + + +def _filing_season_bins(kind: str) -> dict[str, dict[str, object]]: + measure = _KINDS[kind][1] + return { + fact["layout"]["groupby_value_id"]: fact + for fact in _feed_rows() + if _record_id(fact).startswith(_FILING_SEASON_BINS) + and fact["layout"]["measure_id"] == measure + } + + +def _single(prefix: str, measure: str) -> dict[str, object]: + (fact,) = ( + fact + for fact in _feed_rows() + if _record_id(fact).startswith(prefix) + and fact["layout"]["measure_id"] == measure + ) + return fact + + +def _compile(facts) -> dict[str, object]: + registry = compile_us_fiscal_target_registry( + [*packaged_reference_facts(), *facts], + target_period=2024, + allow_unaged_dollar_targets=True, + ) + return {spec.name: spec for spec in registry.specs} + + +@functools.cache +def _compiled_real() -> dict[str, object]: + return _compile(_feed_rows()) + + +def _compiled_split_rows(by_name, kind: str) -> dict[str, dict[str, object]]: + """split -> AGI row id -> compiled spec, for every Table 2.5 child row.""" + + measure = _KINDS[kind][0] + return { + split: { + row_id: by_name[f"{_CHILD_RECORD_SET}{split}.{row_id}.{measure}"] + for row_id in rows + } + for split, rows in _split_rows(kind).items() + } + + +def _uprating_groups(kind: str) -> dict[tuple[float, float], list[str]]: + """The AGI group each Table 2.5 bin is uprated in -> the bins in it. + + A bin's group is the narrowest filing-season bin that contains it, or, + when filing-season bins are finer (Table 2.5's $50,000-and-over bin), the + bin's own interval covered by the filing-season bins inside it. + """ + + season = [_agi_bounds(fact) for fact in _filing_season_bins(kind).values()] + groups: dict[tuple[float, float], list[str]] = {} + for row_id, fact in next(iter(_split_rows(kind).values())).items(): + if row_id == "total": + continue + bounds = _agi_bounds(fact) + containing = [group for group in season if _inside(bounds, group)] + group = ( + min(containing, key=lambda item: item[1] - item[0]) + if containing + else bounds + ) + groups.setdefault(group, []).append(row_id) + return groups + + +def test_fixture_holds_every_table_2_5_and_filing_season_row() -> None: + """Four child splits x (28 AGI bins + total) x 2 measures, the 8-measure + all-returns total row, and 16 filing-season AGI bins plus a total for each + measure: 274 rows.""" + + assert len(_feed_rows()) == 274 + for kind in _KINDS: + rows = _split_rows(kind) + assert set(rows) == set(_SPLITS) + for split_rows in rows.values(): + assert len(split_rows) == 29 + assert "total" in split_rows + assert len(_filing_season_bins(kind)) == 16 + assert ( + sum( + 1 + for fact in _feed_rows() + if fact["layout"]["record_set_id"] == _ALL_RETURNS_RECORD_SET + ) + == 8 + ) + + +@pytest.mark.parametrize("kind", sorted(_KINDS)) +def test_each_child_split_s_agi_bins_tile_agi_and_sum_to_its_total_row( + kind: str, +) -> None: + """Source check: in each split the 28 Table 2.5 AGI bins tile AGI and add + to the split's total row within SOI rounding, so no bin is missing, read + twice or taken from the total row. A sum can't see two bins' values + swapped; the verbatim-feed check and the extraction's row guards cover + that.""" + + for split, rows in _split_rows(kind).items(): + bins = {row_id: fact for row_id, fact in rows.items() if row_id != "total"} + edges = sorted(_agi_bounds(fact) for fact in bins.values()) + assert edges[0][0] == -float("inf") + assert edges[-1][1] == float("inf") + assert all( + left[1] == right[0] for left, right in zip(edges, edges[1:], strict=False) + ), split + assert _agi_bounds(rows["total"]) == (-float("inf"), float("inf")) + gap = sum(fact["value"] for fact in bins.values()) - rows["total"]["value"] + assert abs(gap) <= _ROUNDING[kind], (split, gap) + + +@pytest.mark.parametrize("kind", sorted(_KINDS)) +def test_filing_season_agi_bins_sum_to_the_filing_season_total(kind: str) -> None: + """Source check for the uprating index: the 16 TY2024 filing-season AGI + bins tile AGI and add to its all-returns total within SOI rounding.""" + + bins = _filing_season_bins(kind) + edges = sorted(_agi_bounds(fact) for fact in bins.values()) + assert edges[0][0] == -float("inf") + assert edges[-1][1] == float("inf") + assert all( + left[1] == right[0] for left, right in zip(edges, edges[1:], strict=False) + ) + total = _single(_FILING_SEASON_TOTAL, _KINDS[kind][1])["value"] + assert abs(sum(fact["value"] for fact in bins.values()) - total) <= _ROUNDING[kind] + + +@pytest.mark.parametrize("kind", sorted(_KINDS)) +def test_child_split_totals_add_to_the_all_returns_total(kind: str) -> None: + """The four splits' total rows add to Table 2.5's all-returns total + (24,439,936 returns; $66,270,000,000), so no split is a subset such as one + filing status or one component of the credit.""" + + split_total = sum(rows["total"]["value"] for rows in _split_rows(kind).values()) + all_returns = _single(_ALL_RETURNS_RECORD_SET, _KINDS[kind][1])["value"] + assert abs(split_total - all_returns) <= _ROUNDING[kind] + + +@pytest.mark.parametrize("kind", sorted(_KINDS)) +def test_ty2023_table_2_5_totals_reconcile_with_irs_published(kind: str) -> None: + """The TY2023 child-split total rows add to within 10% of the IRS-published + TY2023 EITC (more than 23 million returns, about $64 billion): 24.44 + million is 6.3% above it and $66.27 billion 3.5% above.""" + + total = sum(rows["total"]["value"] for rows in _split_rows(kind).values()) + assert total == pytest.approx(_IRS_PUBLISHED[2023][kind], rel=_PUBLISHED_TOLERANCE) + + +def test_ty2023_child_split_shares_match_irs_published() -> None: + """Childless 27.7%, one child 35.5%, two 23.7%, three or more 13.1%, + against IRS's published 27/36/24/13.""" + + rows = _split_rows("returns") + total = sum(split_rows["total"]["value"] for split_rows in rows.values()) + for split, share in _IRS_TY2023_CHILD_SHARES.items(): + assert rows[split]["total"]["value"] / total == pytest.approx( + share, abs=_SHARE_TOLERANCE + ), split + + +@pytest.mark.parametrize("kind", sorted(_KINDS)) +def test_compiled_total_rows_sum_to_the_filing_season_and_irs_published( + kind: str, +) -> None: + """The regression for microcosm#1227 on real data. Each total row takes + one factor, the filing-season EITC over the source total rows (23,837,149 + / 24,439,936 and $69,041,650,000 / $66,269,999,000), so the four total rows + add to the filing season's AGI bins, as their 112 compiled bins do, and to + within 10% of IRS's published TY2024 EITC (about 24 million returns and + $69.6 billion; the compiled values are 0.7% and 0.8% below).""" + + by_name = _compiled_real() + compiled = _compiled_split_rows(by_name, kind) + source = _split_rows(kind) + active = sum(fact["value"] for fact in _filing_season_bins(kind).values()) + source_total = sum(rows["total"]["value"] for rows in source.values()) + + totals = {split: rows["total"] for split, rows in compiled.items()} + bins = [ + spec + for rows in compiled.values() + for row_id, spec in rows.items() + if row_id != "total" + ] + assert len(bins) == 4 * 28 + assert sum(spec.value for spec in totals.values()) == pytest.approx( + active, rel=1e-12 + ) + assert sum(spec.value for spec in bins) == pytest.approx(active, rel=1e-12) + for split, spec in totals.items(): + assert float(spec.metadata["uprating_factor"]) == pytest.approx( + active / source_total, rel=1e-12 + ) + assert spec.metadata["uprating_agi_lower_bound"] == "-inf" + assert spec.metadata["uprating_agi_upper_bound"] == "inf" + assert spec.value == pytest.approx( + source[split]["total"]["value"] * active / source_total, rel=1e-12 + ) + compiled_total = sum(spec.value for spec in totals.values()) + assert compiled_total == pytest.approx( + _IRS_PUBLISHED[2024][kind], rel=_PUBLISHED_TOLERANCE + ) + + +def _with_filing_season( + values: dict[tuple[str, str], float], +) -> list[dict[str, object]]: + """The fixture rows with filing-season AGI bins replaced by ``values`` + (keyed by kind and AGI row id) and each filing-season total set to their + sum.""" + + measure_kind = {measures[1]: kind for kind, measures in _KINDS.items()} + totals = { + kind: sum( + value for (value_kind, _), value in values.items() if value_kind == kind + ) + for kind in _KINDS + } + rows = [] + for fact in _feed_rows(): + record_id = _record_id(fact) + kind = measure_kind.get(fact["layout"]["measure_id"]) + if record_id.startswith(_FILING_SEASON_BINS): + fact = {**fact, "value": values[(kind, fact["layout"]["groupby_value_id"])]} + elif record_id.startswith(_FILING_SEASON_TOTAL): + fact = {**fact, "value": totals[kind]} + rows.append(fact) + return rows + + +@st.composite +def _filing_seasons(draw): + """Any nonnegative TY2024 filing-season EITC by AGI, returns and amount, + with at least one return and one dollar in total.""" + + values = {} + for kind, ceiling in (("returns", 10**7), ("amount", 5 * 10**10)): + for row_id in _filing_season_bins(kind): + values[(kind, row_id)] = draw(st.integers(min_value=0, max_value=ceiling)) + if not any( + value for (value_kind, _), value in values.items() if value_kind == kind + ): + values[(kind, "10k_to_15k")] = 1 + return values + + +@given(_filing_seasons()) +@_SETTINGS +def test_compiled_agi_bins_and_total_rows_both_sum_to_any_filing_season(values) -> None: + """For any filing-season EITC by AGI, on the real Table 2.5 layout: + + * the four total rows add to the filing season's total, and keep the + TY2023 child split of the total rows; + * the 112 AGI bins add to the same total, and within each uprating AGI + group to the filing season's EITC in that group, keeping the group's + TY2023 child split. + + The real layout covers both grouping paths: Table 2.5 bins inside one + filing-season bin, and the $50,000-and-over bin, which spans seven.""" + + by_name = _compile(_with_filing_season(values)) + for kind in _KINDS: + compiled = _compiled_split_rows(by_name, kind) + source = _split_rows(kind) + season = { + row_id: (_agi_bounds(fact), values[(kind, row_id)]) + for row_id, fact in _filing_season_bins(kind).items() + } + active = sum(value for _, value in season.values()) + source_total = sum(rows["total"]["value"] for rows in source.values()) + + totals = sum(rows["total"].value for rows in compiled.values()) + assert totals == pytest.approx(active, rel=1e-9, abs=1e-6) + for split, rows in compiled.items(): + assert rows["total"].value == pytest.approx( + source[split]["total"]["value"] * active / source_total, + rel=1e-9, + abs=1e-6, + ) + + bins_total = 0.0 + for group, row_ids in _uprating_groups(kind).items(): + group_active = sum( + value for bounds, value in season.values() if _inside(bounds, group) + ) + group_source = sum( + source[split][row_id]["value"] + for split in _SPLITS + for row_id in row_ids + ) + group_compiled = 0.0 + for split in _SPLITS: + for row_id in row_ids: + spec = compiled[split][row_id] + assert spec.value == pytest.approx( + source[split][row_id]["value"] * group_active / group_source, + rel=1e-9, + abs=1e-6, + ) + group_compiled += spec.value + assert group_compiled == pytest.approx(group_active, rel=1e-9, abs=1e-6) + bins_total += group_compiled + assert bins_total == pytest.approx(active, rel=1e-9, abs=1e-6) + + +@given(growth=st.integers(min_value=25, max_value=400)) +@_SETTINGS +def test_uniform_growth_keeps_each_split_s_bins_summing_to_its_total_row( + growth: int, +) -> None: + """When every AGI group's EITC grows by one factor, each split's 28 + compiled AGI bins add to its compiled total row, up to the source's own + rounding gap between bins and total (at most 5 returns or $5,000 before + scaling).""" + + factor = growth / 100 + values: dict[tuple[str, str], float] = {} + for kind in _KINDS: + source = _split_rows(kind) + season = { + row_id: _agi_bounds(fact) + for row_id, fact in _filing_season_bins(kind).items() + } + for row_id in season: + values[(kind, row_id)] = 0.0 + for group, row_ids in _uprating_groups(kind).items(): + group_source = sum( + source[split][row_id]["value"] + for split in _SPLITS + for row_id in row_ids + ) + # Put the whole group on one filing-season bin inside it; the + # $50,000-and-over group sums its seven filing-season bins. + first = min( + (row_id for row_id, bounds in season.items() if _inside(bounds, group)), + key=lambda row_id: season[row_id], + ) + values[(kind, first)] = group_source * factor + + by_name = _compile(_with_filing_season(values)) + for kind in _KINDS: + for split, rows in _compiled_split_rows(by_name, kind).items(): + bins = sum(spec.value for row_id, spec in rows.items() if row_id != "total") + assert bins == pytest.approx( + rows["total"].value, abs=2 * _ROUNDING[kind] * factor + ), (kind, split) + + +def test_fixture_rows_are_verbatim_pinned_feed_rows() -> None: + """Where the pinned feed is present (not in CI), the fixture is exactly + the feed's rows for these record sets, byte for byte.""" + + feed_path = _load_repo_tool("build_us_target_parity_manifest").DEFAULT_FEED_PATH + if not feed_path.exists(): + pytest.skip(f"pinned feed not present at {feed_path}") + record_sets = {fact["layout"]["record_set_id"] for fact in _feed_rows()} + with feed_path.open(encoding="utf-8") as feed: + feed_rows = sorted( + line.rstrip("\n") + for line in feed + if json.loads(line)["layout"]["record_set_id"] in record_sets + ) + fixture_rows = sorted( + line.rstrip("\n") + for line in _FIXTURE.read_text(encoding="utf-8").splitlines() + if line.strip() + ) + assert fixture_rows == feed_rows diff --git a/packages/microcosm-build/tests/fixtures/us_soi_eitc_table_2_5_feed_rows.jsonl b/packages/microcosm-build/tests/fixtures/us_soi_eitc_table_2_5_feed_rows.jsonl new file mode 100644 index 0000000000..da5b1796df --- /dev/null +++ b/packages/microcosm-build/tests/fixtures/us_soi_eitc_table_2_5_feed_rows.jsonl @@ -0,0 +1,274 @@ +{"aggregate_fact_key": "ledger.aggregate_fact.v2:146f9bac781a3cb1cc7c634a", "aggregation": {"method": "sum"}, "assertion": "observation", "concept_alignment": {"authority": "ledger-us", "canonical_concept": "us:statutes/26/62#adjusted_gross_income", "concept_alignment_key": "ledger.concept_alignment.v2:2bd741b3ea7dffdd1d9a7c97", "evidence_notes": "IRS SOI Table 2.5 reports adjusted gross income less deficit for individual income tax returns with earned income credit; IRC section 62 defines adjusted gross income. This Ledger assertion treats the SOI total-row AGI column as adopting that legal concept for the tax-year source record.", "evidence_url": "https://uscode.house.gov/view.xhtml?req=(title:26%20section:62%20edition:prelim)", "legal_vintage": "tax_year_2023", "relation": "exact", "source_concept": "irs_soi.adjusted_gross_income"}, "dimension_labels": {"income_range": "Income range", "qualifying_children": "Qualifying children", "us:statutes/26/62#adjusted_gross_income": "Adjusted gross income"}, "dimension_set_key": "ledger.dimension_set.v2:926e7d749cf1dbf623c6c614", "dimension_value_labels": {"income_range": {"all": "All income ranges"}, "qualifying_children": {"all": "All qualifying-children counts"}, "us:statutes/26/62#adjusted_gross_income": {"total": "Total"}}, "dimensions": {"income_range": "all", "qualifying_children": "all"}, "entity": {"name": "tax_unit", "role": "filing_unit"}, "geography": {"id": "0100000US", "level": "country", "name": "United States", "vintage": "2020_census"}, "label": "United States 2023 tax year sum us:statutes/26/62#adjusted gross income for tax unit (income range=all, qualifying children=all) [irs_soi Publication 1304 Table 2.5 23in25ic.xls tax_year_2023]", "layout": {"groupby_dimension": "us:statutes/26/62#adjusted_gross_income", "groupby_dimension_label": "Adjusted gross income", "groupby_ordinal": 0, "groupby_value_id": "total", "groupby_value_label": "Total", "measure_id": "adjusted_gross_income", "measure_label": "Adjusted gross income", "measure_ordinal": 1, "record_set_id": "irs_soi.ty2023.table_2_5.eitc_all_returns", "record_set_spec_hash": "3e61c76e16b3e4f5952096f3", "record_set_spec_id": "irs_soi.table_2_5.eitc_all_returns.v1", "source_column_id": "adjusted_gross_income", "source_row_id": "total", "table_record_kind": "total"}, "legacy_fact_key": "ledger.fact.v1:c5c7a64415ea40af960ff53b", "lineage": {"source_cell_keys": ["ledger.source_cell.v1:1eb343b96146365c6490f46b", 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