diff --git a/changelog.d/1334-uc-take-up-by-award.added.md b/changelog.d/1334-uc-take-up-by-award.added.md new file mode 100644 index 000000000..bf46af66e --- /dev/null +++ b/changelog.d/1334-uc-take-up-by-award.added.md @@ -0,0 +1 @@ +The UK spine now draws Universal Credit take-up by award band and family type (microcosm#1334). `frs_take_up` and `spi_benefit_coherence` drew `would_claim_uc` at one contract rate spread uniformly over the age population whatever the award, which at design weights put the caseload above DWP's for single people without children, couples and the smallest awards and below it for lone parents, so the national solve met DWP's caseloads by down-weighting low-income families. A new stage, `uc_take_up`, runs after `uc_capital_coherence` and before `uc_legacy_closure_take_up`. One engine materialization at the calibration year gives each benefit unit the award it would be paid if it claimed (after the benefit cap and the deduction attributes `uc_deduction_attributes` assigns), whether the engine moves it off its legacy benefits, and DWP's family type. Within cells of family type by DWP's £100 monthly award band, pooled until the drawn units rest on 30 distinct source households, a reporter claims and the other entitled units of the age population claim at r = (T − F)/D, where T is DWP's calendar-2025 count (the Stat-Xplore facts the registry's payment-band rows average, vendored as `dwp_uc_award_band_facts.json`), F the other entitled units' expected claims (units moved off legacy benefits at their Move to UC claim probability) and D the drawn weight, all over Great Britain; units claim in the order of their identity uniform, so each pool realizes T to within one unit's weight, and Northern Ireland uses the same threshold. Units with no award keep the single-rate draw. A stage gate, `uk_stage_uc_take_up`, replaces `would_claim_uc` in the terminal take-up signal gate, and the roster moves from 47 to 48 stages. María's ruling of 2026-10-21 lifts the U8 freeze for entitled units; the take-up contract records it and keeps 0.55 for units with no award. A unit reporting a legacy benefit that the engine does not move at the legacy closure is not drawn onto UC, since policyengine-uk ends the kept benefit once the unit claims UC; the receipt reports, at the stage's weights over Great Britain, how many working-age units reporting Housing Benefit keep it before and after the redraw, beside DWP's 2025 caseload. diff --git a/changelog.d/1344-uk-withhold-thin-family-rests.fixed.md b/changelog.d/1344-uk-withhold-thin-family-rests.fixed.md new file mode 100644 index 000000000..4529487f5 --- /dev/null +++ b/changelog.d/1344-uk-withhold-thin-family-rests.fixed.md @@ -0,0 +1 @@ +A published UK total less some of its published parts can no longer give back a cell that fewer than ten source households carry (Vahid's second review of microcosm#1344, item 17, and four adversarial reviews of the fix). `uk_withheld_row_sets` only asked whether published rows give back a combination of withheld rows, so the Universal Credit family total less its bound payment bands gave back the unbound band above £2,500 a month, which no row carries. A residual check now runs before the span check and on each of its passes. A total's parts are the published rows inside it, and households whose parts hold the same shares form an atom, which every combination of the total and its parts treats alike. A test comes first: when every atom's share column lies within a tenth of the rounding share of the span of the atoms that rest on ten or more source households (as a unit column, with each row scaled, and summed over its stored entries), no combination of the total and its parts with weights between minus one and one can be thin, and the total is clear (a family total over bands whose every cell rests on ten households or more). Otherwise the check takes the total less any set of members whose shares stay within one (an exact branch-and-bound that tries every set, with a part of constant share also counted at share one; past a work cap, or for a total too large to search, greedy groups instead, and a warning names it), less any one part, and less any two parts, which gives back two overlapping parts' overlap. While one of these rests on one to nine source households, the part with the fewest carriers among those it subtracts is withheld. The note on a row withheld this way says it would give back "a row or a cell". Not checked: a total that is no row (a sum of published regions), a part that reaches outside its total, and, on a total the first test does not clear, combinations at other weights than these. diff --git a/changelog.d/uk-annual-income-measures.added.md b/changelog.d/uk-annual-income-measures.added.md new file mode 100644 index 000000000..4fe54f9e0 --- /dev/null +++ b/changelog.d/uk-annual-income-measures.added.md @@ -0,0 +1,5 @@ +The UK measure resolver now computes each HMRC income measure over a year of income as well as at the current rate: the 12 months before each FRS interview, applied to the engine's amounts at the calibration year, as a proxy for HMRC's tax year. Seven annual measures sit beside their current ones: `employment_income_annual`, `self_employment_income_after_loss_relief_annual`, `private_pension_income_annual`, `state_pension_annual`, `total_income_after_loss_relief_annual`, `income_tax_annual` and `tax_band_annual`. Each is its current measure computed on one more policyengine-uk simulation per resolver, whose inputs are each person's year from the `frs_annual_income_spells` descriptors, so the engine's own rules give the tax on that year's income. + +policyengine-uk projects a dataset's inputs to its later years as it loads it, so the year's inputs are set at the measurement year: pay, profit, private pensions and contributory JSA, ESA and Incapacity Benefit at the engine's current amount times the annual share, plus the other spells carried from the dataset's year by the engine's own factor for that input, which must be one factor for every row. The State Pension takes the share of the fiscal year paid to someone who attains State Pension age within it (`months_since_state_pension_age`, measured to 6 October), and the earnings-tested carer benefits keep their baseline values. Pension contributions and salary sacrifice also keep their current values: for a part-year entrant they can be large against the year's pay, which lowers `income_tax_annual` a little. The simulation refuses if it does not read back an input it was given. + +A frame without the descriptors refuses every annual measure, with one exception for the rule-1 scorer: the incumbent it scores against, built before the descriptors existed, reads its current values as its annual ones (a share of 1 and no other spells), and the score receipt names that route. diff --git a/changelog.d/uk-annual-income-national-rows.changed.md b/changelog.d/uk-annual-income-national-rows.changed.md new file mode 100644 index 000000000..8340d6998 --- /dev/null +++ b/changelog.d/uk-annual-income-national-rows.changed.md @@ -0,0 +1 @@ +The national HMRC income rows now measure each person's income over a year, as HMRC's statistics count income over the tax year. The year is a proxy for the tax year: the 12 months before each FRS interview (April 2024 to March 2025), applied to the engine's 2025 amounts and fitted to HMRC targets for calendar 2025 (the SPI 2023-24 rows uprated to it, and the Income Tax liabilities rows' calendar-2025 window). The 49 declarations of `hmrc_spi`, `hmrc_spi_region`, `hmrc_itl` and `hmrc_itl_marginal_rate` read the annual measures for their values, their total-income bands and filters, their taxpayer test and their rate band (`employment_income_annual`, `self_employment_income_after_loss_relief_annual`, `private_pension_income_annual`, `state_pension_annual`, `total_income_after_loss_relief_annual`, `income_tax_annual`, `tax_band_annual`), and `obr.income_tax` reads `income_tax_annual`. Dividends, property and savings income have no spells and stay as they are. Target ids, facts, values and metric names do not change: only the person-side measure each row adds up moves, so a recent leaver whose year of income makes them liable to income tax counts as a taxpayer, and a part-year earner sits in the band of what they received. Poverty, benefits and the engine's published tax keep reading current income. Reform costings on files built from this code are lower: in 2025 the engine's income tax on current income falls 1.0% (£324.7bn to £321.5bn against the build before this change, on policyengine-uk 2.147.2; now below OBR's receipts by construction, since the calibration fits OBR's total on the year's income), and static costings of threshold changes fall 1.7% to 1.9% (a £1,000 rise in the personal allowance from £8.98bn to £8.81bn, and 1p on the basic rate from £6.62bn to £6.51bn), closer to HMRC's 2025-26 ready reckoner (January 2025, which includes behaviour: £6.55bn for 1p, and £0.78bn for a £100 rise and £9.65bn for a 10% rise, which put £1,000 near £7.7bn to £7.8bn). The five local `hmrc` rows, the CGT taxable-income bands and the salary-sacrifice reliefs stay on current income for now; the local rows follow once the local line computes its metrics from their declarations. diff --git a/changelog.d/uk-annual-income-spells.added.md b/changelog.d/uk-annual-income-spells.added.md new file mode 100644 index 000000000..6f3b0ae74 --- /dev/null +++ b/changelog.d/uk-annual-income-spells.added.md @@ -0,0 +1,3 @@ +The UK spine now records each adult's year of income beside their current income. HMRC's income statistics count the income each taxpayer received over a tax year (they cover individuals liable to UK income tax), while the spine's income columns are current, annualised incomes (usual weekly pay times 365.25 / 7), so a person who started, left or changed a job during the year sits in another HMRC band than their current pay gives, or in none. The year the descriptors describe is a proxy for the tax year: the 12 months before each interview (April 2024 to March 2025). A new stage, `frs_annual_income_spells`, runs after `frs_hmrc_spine_leaves` and before the SPI support channel. It reads the FRS 2024-25 adult, job and household tabs (pinned by size and sha256, read selectively) and writes six person descriptors: the share of the year paid at today's employee pay and at today's self-employment profit; annual pay from other spells, chiefly a recent leaver's last job at its usual gross pay, scaled to the DV pay concept by the stage's measured ratio and uprated by average earnings, else a donor's rate; ceased self-employment; and the shares of the year a private pension (after retiring in it) and contributory JSA, ESA and Incapacity Benefit (after leaving work) were in payment. No current column changes, so poverty, benefits and the engine's tax are unchanged; the descriptors are inputs for the calibration's annual HMRC measures, which follow separately. + +On the SPI channel, `hmrc_spi_income_spine` holds a drawn row's descriptors to its drawn incomes. The tape's income is the tax year's, so each share is 1, and the twin's other-spell amounts stay only where the tape drew none of that income; an undrawn copy keeps its twin's descriptors. A new release-blocking stage gate, `uk_stage_frs_annual_income_spells`, checks the descriptors' invariants, the measured usual-to-DV pay ratio (0.95 to 1.10), annual over current employee earners (1.0 to 1.15) and, where ten or more households carry it, that at least 0.7 of the pay outside the current job is observed rather than drawn from a donor. The receipt follows UKDS disclosure control: a support band per spell source, and weighted figures only over ten or more households. The descriptors join the export surface's reviewed extra columns, the SPI income family requires the stage before it, and the roster moves from 48 to 49 stages. diff --git a/docs/uk-full-build-graph.md b/docs/uk-full-build-graph.md index e7a9a5969..182c1d336 100644 --- a/docs/uk-full-build-graph.md +++ b/docs/uk-full-build-graph.md @@ -66,11 +66,11 @@ For smaller outputs, add `--dataset-households 150000` to use the common informe ## Graph owners and shared contracts -The spine graph is the 47-stage source roster declared by the UK spec, built by `uk_runtime.spine_build` (the spine tool moved into the package; `tools/build_uk_frs_spine.py` is a six-line shim over it). The superseded `frs_hmrc_retained_leaves` and `hmrc_spi_income` stages and the `UK_SPINE_EXCLUSIONS` list that hid them are removed; the active HMRC path is `frs_hmrc_spine_leaves`, `spi_support_channel`, `spi_income_band_donors` and `hmrc_spi_income_spine`, and the FRS HMRC leaf columns come from `uk_runtime.frs_hmrc_source`. The spine's assembled and transferred gate batteries are graph nodes (`uk_runtime.graph_evidence`); stage evidence and fit-weight records are read back from the content store through the shared `stage_evidence` artifact rather than from in-memory collectors; the HMRC replay sidecar is rebuilt from the SPI stage's checkpoint metadata; the spine sidecar records the operation inventory and the graph manifest, and the graph manifest is saved as `spine.graph.json` under the checkpoint root. +The spine graph is the 48-stage source roster declared by the UK spec, built by `uk_runtime.spine_build` (the spine tool moved into the package; `tools/build_uk_frs_spine.py` is a six-line shim over it). The superseded `frs_hmrc_retained_leaves` and `hmrc_spi_income` stages and the `UK_SPINE_EXCLUSIONS` list that hid them are removed; the active HMRC path is `frs_hmrc_spine_leaves`, `spi_support_channel`, `spi_income_band_donors` and `hmrc_spi_income_spine`, and the FRS HMRC leaf columns come from `uk_runtime.frs_hmrc_source`. The spine's assembled and transferred gate batteries are graph nodes (`uk_runtime.graph_evidence`); stage evidence and fit-weight records are read back from the content store through the shared `stage_evidence` artifact rather than from in-memory collectors; the HMRC replay sidecar is rebuilt from the SPI stage's checkpoint metadata; the spine sidecar records the operation inventory and the graph manifest, and the graph manifest is saved as `spine.graph.json` under the checkpoint root. | Operation | Graph owner | | --- | --- | -| Raw FRS and donor preparation, enrichment, support channels | The 47 UK spine stage nodes and their declared composite operations | +| Raw FRS and donor preparation, enrichment, support channels | The 48 UK spine stage nodes and their declared composite operations | | Assembled and transferred spine gates | `spine.gates.assembled`, `spine.gates.transferred` | | Bound checkpoint admission | `uk.full.spine_checkpoint`, when resuming a saved spine | | Pool sample and mass normalization | `uk.full.sample`, `uk.full.normalize` | diff --git a/packages/microcosm-build/src/microcosm/build/source_manifest.py b/packages/microcosm-build/src/microcosm/build/source_manifest.py index ba1514f38..8a5d1e1c8 100644 --- a/packages/microcosm-build/src/microcosm/build/source_manifest.py +++ b/packages/microcosm-build/src/microcosm/build/source_manifest.py @@ -51,6 +51,7 @@ "assign_bus_pass_eligibility", "assign_clipped_normal", "assign_component_take_up_residual", + "assign_award_band_take_up_residual", "assign_family_claims_by_child_age", "assign_opt_out_by_charge_income", "assign_period_constant", @@ -81,6 +82,7 @@ "declare_income_reference_offset", "derive", "derive_adult_care_inputs", + "derive_annual_income_spells", "derive_childcare_inputs", "derive_child_support_inputs", "derive_disability_benefits", 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 0d74cd5ad..7751f4b78 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 @@ -254,6 +254,84 @@ }, "source_parameter_record": { "oneOf": [ + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "outputs", + "window_months", + "leaving_date_share", + "unknown_leaving_months", + "donor_recent_start_months", + "minimum_donors", + "donor_age_edges", + "seed", + "salt", + "earnings_index", + "earnings_index_from", + "earnings_index_to", + "maximum_uprating_months" + ], + "properties": { + "kind": { + "const": "derive_annual_income_spells" + }, + "outputs": { + "type": "array", + "items": { + "type": "string" + }, + "minItems": 1 + }, + "window_months": { + "type": "integer", + "minimum": 1 + }, + "leaving_date_share": { + "type": "number", + "minimum": 0, + "maximum": 1 + }, + "unknown_leaving_months": { + "type": "number", + "minimum": 0 + }, + "donor_recent_start_months": { + "type": "integer", + "minimum": 1 + }, + "minimum_donors": { + "type": "integer", + "minimum": 1 + }, + "donor_age_edges": { + "type": "array", + "items": { + "type": "integer" + } + }, + "seed": { + "type": "integer" + }, + "salt": { + "type": "string" + }, + "earnings_index": { + "type": "string" + }, + "earnings_index_from": { + "type": "string" + }, + "earnings_index_to": { + "type": "string" + }, + "maximum_uprating_months": { + "type": "integer", + "minimum": 0 + } + } + }, { "type": "object", "additionalProperties": false, @@ -6496,6 +6574,90 @@ } } }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "output", + "draw", + "seed", + "anchor", + "population", + "award", + "family_type", + "award_bands", + "targets", + "pooling", + "minimum_pool_sources", + "drawn_units", + "undrawn_claims", + "weight_mapping", + "residual_rate", + "solve_scope", + "not_drawn" + ], + "properties": { + "kind": { + "const": "assign_award_band_take_up_residual" + }, + "output": { + "type": "string" + }, + "draw": { + "type": "string" + }, + "seed": { + "type": "integer" + }, + "anchor": { + "type": "string" + }, + "population": { + "type": "string" + }, + "award": { + "type": "string" + }, + "family_type": { + "type": "string" + }, + "award_bands": { + "type": "string" + }, + "targets": { + "type": "string" + }, + "pooling": { + "type": "string" + }, + "minimum_pool_sources": { + "type": "integer", + "minimum": 1 + }, + "drawn_units": { + "type": "string" + }, + "undrawn_claims": { + "type": "string" + }, + "weight_mapping": { + "type": "string" + }, + "residual_rate": { + "type": "string" + }, + "solve_scope": { + "type": "string", + "enum": [ + "great_britain" + ] + }, + "not_drawn": { + "type": "string" + } + } + }, { "type": "object", "additionalProperties": false, diff --git a/packages/microcosm-build/src/microcosm/build/target_materialization.py b/packages/microcosm-build/src/microcosm/build/target_materialization.py index d0840607b..72f532ccc 100644 --- a/packages/microcosm-build/src/microcosm/build/target_materialization.py +++ b/packages/microcosm-build/src/microcosm/build/target_materialization.py @@ -982,7 +982,9 @@ def _refuse_ignored_measurement_period( read at one of those two and is refused. So is the frame's period on a value the frame does not hold at that period: the engine computes it at the calibration period, so the row would be measured there under the - declared period. + declared period. Only the value terms are checked: a filter, condition or + band on an engine-computed variable is still read at the calibration + period under a declared frame period. """ declared = binding.get("measurement_period") diff --git a/packages/microcosm-build/src/microcosm/build/uk/country_package.json b/packages/microcosm-build/src/microcosm/build/uk/country_package.json index d357eb55b..aa1120aab 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/country_package.json +++ b/packages/microcosm-build/src/microcosm/build/uk/country_package.json @@ -107,6 +107,11 @@ "kind": "legacy_json", "schema_id": "legacy_json" }, + { + "path": "dwp_uc_award_band_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, { "path": "obr_hvcts_costing_facts.json", "kind": "legacy_json", diff --git a/packages/microcosm-build/src/microcosm/build/uk/dwp_uc_award_band_facts.json b/packages/microcosm-build/src/microcosm/build/uk/dwp_uc_award_band_facts.json new file mode 100644 index 000000000..8a1c4221d --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/dwp_uc_award_band_facts.json @@ -0,0 +1,55992 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "dwp_uc_award_band_facts.json", + "purpose": "DWP Stat-Xplore, Households on Universal Credit in Great Britain by Monthly Award Amount (bands) and Family Type, each month April to December 2025: the facts the registry's dwp/uc_payment_dist rows average over calendar 2025 (microcosm#1334). Consumed by the uc_take_up stage, which takes each family type and award band's calendar-2025 mean count as the caseload its Universal Credit take-up draw aims at on the spine's design weights, including the unbound GBP 2,500.01 or over band. No value is derived here.", + "consumers": [ + "uk_runtime.uc_take_up" + ], + "planned_consumers": [], + "policy": "Rows are copied verbatim from the pinned Chronicle consumer feed named in source_fact_feed; every value is a publisher-stated fact. Consumers declare their own arithmetic on these rows and never edit this file by hand: regenerate it with tools/vendor_uk_ledger_facts.py.", + "generator": { + "tool": "tools/vendor_uk_ledger_facts.py", + "version": 1 + }, + "source_fact_feed": { + "source_repo": "PolicyEngine/chronicle", + "source_commit": "abf0921b896d29ee0745ed2aefde8d6337ed7164", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "fact_row_count": 1499301, + "facts_sha256": "2b64fecfe746164203dac1726aae6ffdb3e5d8e478bd3d64d118ac76cb00780b", + "manifest_sha256": "b90fb1b8f1a910458fac41425360209d101796e6de0b134f952ad93323ca6e21" + }, + "selections": [ + { + "label": "uc_households_by_award_band_and_family_type_2025_04", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_05", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_05" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_06", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_06" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_07", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_07" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_08", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_08" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_09", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_09" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_10", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_10" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_11", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_11" + }, + "row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_12", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_12" + }, + "row_count": 135 + } + ], + "row_count": 1215, + "rows": [ + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_290d9d3b91d7af2026d993e5", + "semantic_fact_key": "ledger_semantic_fact_v2_9f4cc4b1200770e78d06af17", + "source_record_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04.0_01_to_100_00__couple_no_children.benefit_units", + "concept": "dwp.uc_households", + "measure_id": "benefit_units", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "benefit_unit", + "role": "universal_credit_unit_of_assessment" + }, + "geography": { + "id": "K03000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "month", + "value": "2025-04" + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04", + "groupby_dimension": "dwp.uc_monthly_award_band", + "groupby_value_id": "0_01_to_100_00__couple_no_children", + "table_record_kind": "detail" + }, + "dimensions": { + "family_type": "Couple, no children", + "monthly_award_amount_bands": "\u00a30.01 to \u00a3100.00" + }, + "source": { + "source_name": "dwp", + "source_table": "Households on 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"a3323badad32271e4ff95e5dcc6fe37bc4682ba47626292262089d7e45376632", + "vintage": "stat_xplore_2026_09_03", + "raw_r2_key": "raw/uk/dwp/dwp-uc-payment-distribution-april-december-2025/2025/a3323badad32271e4ff95e5dcc6fe37bc4682ba47626292262089d7e45376632/uc_households_payment_distribution_april_december_2025.json" + }, + "value": 61 + } + ] +} diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 6516cf26a..07fada7bf 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -486,6 +486,28 @@ }, "notes": "Stage-time source-signal gate over the retained FRS/HMRC leaves receipt. hmrc_spi_incapacity_benefit_income is the E7 reviewed-absent adjudication (microcosm#683, 2026-08-18): the FRS 2024-25 source carries no incapacity-benefit signal, the stage's own fence raises at build time if a future vintage does, and the reviewed list here makes the battery agree with that adjudication instead of re-litigating it every build." }, + { + "id": "uk_stage_frs_annual_income_spells", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "population_fact_check": true, + "parameters": { + "stage": "frs_annual_income_spells", + "check": "frs_annual_income_spells", + "pay_concept_ratio_bounds": [ + 0.95, + 1.1 + ], + "annual_over_current_earners_bounds": [ + 1.0, + 1.15 + ], + "minimum_observed_pay_share": 0.7 + }, + "notes": "Stage-time gate over the annual income descriptors' receipt: every share lies in [0, 1], no other-spell amount is negative and every full-year earner keeps the whole year, exactly; the usual-to-DV pay ratio the stage measures lies in [0.95, 1.10]; annual employee earners are between one and 1.15 times current ones (the survey's spells add part-year earners and cannot remove a current one); and, where ten or more households carry it, at least 0.7 of the pay outside the current job is observed rather than drawn from a donor." + }, { "id": "uk_stage_spi_support_channel_mass", "gate": "stage_health", @@ -547,6 +569,20 @@ }, "notes": "Stage-time structural gate over the SPI benefit coherence receipt (microcosm#1095, uk-data#514): no SPI-channel row keeps a zeroed legacy report, every restored report equals the FRS twin's, receives_benefits_in_own_right agrees with each person's own reports, FRS rows are unchanged, every SPI benefit unit with a Universal Credit reporter claims, and no SPI unit outside the Universal Credit age population claims without reporting." }, + { + "id": "uk_stage_uc_take_up", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "population_fact_check": true, + "parameters": { + "stage": "uc_take_up", + "check": "uc_take_up", + "maximum_caseload_deviation": 0.05 + }, + "notes": "Stage-time gate over the Universal Credit take-up receipt (microcosm#1334): every reporter claims and no unit outside the draw changes; in each DWP family type the pools whose target is reachable realize DWP's calendar-2025 caseload within 0.05 of it at the stage's weights, Great Britain only, counting units the engine moves off their legacy benefits at their expected claim at the closure; and the family type's caseload is no further from DWP's than before the redraw, give or take 0.05 of DWP's count. The terminal take-up signal gate no longer measures would_claim_uc, whose rate is now set by award band and family type, not as one share of the age population." + }, { "id": "uk_stage_pension_credit_take_up", "gate": "stage_health", @@ -1013,6 +1049,12 @@ "person.dla_m_category", "person.dla_sc_category", "person.employment_sector", + "person.contributory_benefits_annual_share", + "person.employment_income_annual_share", + "person.employment_income_other_spells", + "person.private_pension_income_annual_share", + "person.self_employment_income_annual_share", + "person.self_employment_income_other_spells", "person.esa_health_condition_proxy", "person.esa_includes_support_component", "person.esa_support_group_proxy", @@ -1135,7 +1177,7 @@ "parameters": { "maximum_share_deviation": 0.05 }, - "notes": "Every contract-driven stochastic flag must be non-constant and have a weighted share within +/-0.05 of its contract target. would_claim_pc is measured by uk_stage_pension_credit_take_up instead (microcosm#1069 R9), and would_claim_child_benefit and child_benefit_opts_out by uk_stage_child_benefit_take_up (microcosm#1063). would_claim_uc is drawn for FRS benefit units by frs_take_up and redrawn for SPI-channel units by spi_benefit_coherence on the same rule (microcosm#1095), so the gate measures both channels at the contract rate. would_claim_universal_childcare is nested under would_claim_extended_childcare, so its target is the nested marginal r_e + (1 - r_e) * r_u of the contract's conditional universal rate, and the gate fails on any benefit unit that claims the extended hours without the universal ones. max_free_entitlement_hours_used carries the under-2 usage draw: its share of persons with hours is measured over the children of whole-year age 0 or 1 in units that claim the extended hours, against the weighted mean of their ages' contract rates (extended_childcare_age_0_usage, extended_childcare_age_1_usage)." + "notes": "Every contract-driven stochastic flag must be non-constant and have a weighted share within +/-0.05 of its contract target. would_claim_pc is measured by uk_stage_pension_credit_take_up instead (microcosm#1069 R9), would_claim_child_benefit and child_benefit_opts_out by uk_stage_child_benefit_take_up (microcosm#1063), and would_claim_uc by uk_stage_uc_take_up (microcosm#1334): frs_take_up and spi_benefit_coherence draw it at the contract rate, and uc_take_up redraws every entitled unit by award band and family type. would_claim_universal_childcare is nested under would_claim_extended_childcare, so its target is the nested marginal r_e + (1 - r_e) * r_u of the contract's conditional universal rate, and the gate fails on any benefit unit that claims the extended hours without the universal ones. max_free_entitlement_hours_used carries the under-2 usage draw: its share of persons with hours is measured over the children of DfE age 0 or 1 in units that claim the extended hours, against the weighted mean of their ages' contract rates (extended_childcare_age_0_usage, extended_childcare_age_1_usage)." }, { "id": "uk_brma_enum_domain", diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json index 87b768471..bf07ce462 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json @@ -1329,6 +1329,88 @@ } ] }, + { + "resource": "dwp_uc_award_band_facts.json", + "purpose": "DWP Stat-Xplore, Households on Universal Credit in Great Britain by Monthly Award Amount (bands) and Family Type, each month April to December 2025: the facts the registry's dwp/uc_payment_dist rows average over calendar 2025 (microcosm#1334). Consumed by the uc_take_up stage, which takes each family type and award band's calendar-2025 mean count as the caseload its Universal Credit take-up draw aims at on the spine's design weights, including the unbound GBP 2,500.01 or over band. No value is derived here.", + "consumers": [ + "uk_runtime.uc_take_up" + ], + "planned_consumers": [], + "selections": [ + { + "label": "uc_households_by_award_band_and_family_type_2025_04", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_05", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_05" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_06", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_06" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_07", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_07" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_08", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_08" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_09", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_09" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_10", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_10" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_11", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_11" + }, + "expected_row_count": 135 + }, + { + "label": "uc_households_by_award_band_and_family_type_2025_12", + "selector": { + "source_name": "dwp", + "record_set_id": "dwp.statx.uc_benefit_units.award_band_family_type.month2025_12" + }, + "expected_row_count": 135 + } + ] + }, { "resource": "obr_hvcts_costing_facts.json", "purpose": "OBR costing of the high value council tax surcharge (2 April 2026): Table 1.1 and 1.2 properties in scope by band for 2028-29 to 2030-31 (static and post-behaviour) and the paragraph 1.13 non-owner-occupied share. Consumed by the uk_high_value_homes_benchmarks diagnostic gate and the high-value home stock builder's owner-occupied control (microcosm#1240).", 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 e041762f9..27cd9de94 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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "054b1ee1a66cae6ed3793053eb0d3b9eea6b3f8976953479948ae11f7eedcd35", @@ -709,6 +709,7 @@ "required_mass_change_reason": "Allocate 0.5 of certified UK national household prior mass in households without, and 0.2 in households with, a member at or over State Pension age to the rebuilt 2022-23 SPI support channel; total national mass is conserved.", "required_predecessor_stages": [ "frs_hmrc_spine_leaves", + "frs_annual_income_spells", "spi_support_channel", "spi_income_band_donors" ], @@ -743,7 +744,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -785,7 +786,7 @@ "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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 +812,7 @@ "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -831,7 +832,7 @@ "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -854,7 +855,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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)" @@ -875,7 +876,7 @@ "required_mass_change_reason": "Annual property purchases imputed on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "source_vintages": { "source": "https://www.gov.uk/government/statistics/uk-stamp-tax-statistics", "survey": "HMRC Annual Stamp Tax Statistics 2024 to 2025 (corrected 11 March 2026), Tables 6b, 8a and 8b" @@ -898,7 +899,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "source_vintages": { "source": "uk/high_value_home_value_knots.json, built by tools/build_uk_high_value_home_stock.py from PolicyEngine/chronicle#343's registrations", "survey": "HM Land Registry Price Paid latest sales uprated with the UK House Price Index, owner-weighted by region (the dense value node's knots, region level)" @@ -922,7 +923,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -949,7 +950,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -982,7 +983,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -1004,7 +1005,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -1026,7 +1027,7 @@ "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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" @@ -1061,7 +1062,7 @@ "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": "bd99f73a45d24b0770529dbd8713116fe97dac68ba248d5f52a1bd27b5981624", + "source_manifest_sha256": "88d7050e7db60faecb3d1318ed33f5b426f7e6fdd5e41da645612b17313f338b", "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 93ed765d2..2fb5e3d1a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -1462,6 +1462,119 @@ ], "notes": "Retains adjudicated raw-FRS HMRC leaves on the raw FRS spine, where person_id equals the raw sernum*1000+person identity, then draws employer pension contributions: each member of an employer scheme (PENPROV.STEMPPEN 1 to 4) with earnings takes an ASHE 2024 employer contribution rate for their scheme type and sector (Table P10 band shares, full pay, all industries; occupational schemes split into defined benefit and defined contribution by the sector's Table P2 job counts), uniform within the band (top band 20% to 30%), times pay. It replaces the incumbent's three times the employee contribution (microcosm#1069 c9, R6)." }, + { + "stage": "frs_annual_income_spells", + "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 adult, job and household tabs; the OBR average earnings index pinned in hmrc_uprating_engine_pins.json.", + "grain": "person", + "artifacts": [ + { + "role": "frs_table", + "table": "adult", + "kind": "licensed_microdata", + "format": "tab", + "vintage": "2024_25", + "locator": "adult.tab", + "sha256": "4eaea0809a7ccca0fddeb98e358771e4a6e5ebb81b21c4fac4070fc9d227658d", + "size_bytes": 34885825, + "runtime_sha256_required": true, + "tax_year_start": 2024, + "ukds_study_number": 9563, + "doi": "10.5255/UKDA-SN-9563-1" + }, + { + "role": "frs_table", + "table": "job", + "kind": "licensed_microdata", + "format": "tab", + "vintage": "2024_25", + "locator": "job.tab", + "sha256": "eb7faf7ada3a3851cb2afb83e2983f8907ffeec897cfbe01e56cb0dfefa853e2", + "size_bytes": 10518760, + "runtime_sha256_required": true, + "tax_year_start": 2024, + "ukds_study_number": 9563, + "doi": "10.5255/UKDA-SN-9563-1" + }, + { + "role": "frs_table", + "table": "househol", + "kind": "licensed_microdata", + "format": "tab", + "vintage": "2024_25", + "locator": "househol.tab", + "sha256": "2b93b6aed49e1591d6f5360736b4aee3a11ef506b276435f4d2c011a8afbb6a5", + "size_bytes": 12108606, + "runtime_sha256_required": true, + "tax_year_start": 2024, + "ukds_study_number": 9563, + "doi": "10.5255/UKDA-SN-9563-1" + }, + { + "role": "earnings_index", + "kind": "public_aggregate_reference", + "resource": "hmrc_uprating_engine_pins.json", + "format": "json", + "runtime_sha256_required": true + } + ], + "operations": [ + { + "kind": "read_tables", + "format": "tab", + "delimiter": "\t", + "lowercase_columns": true, + "numeric_errors": "coerce", + "runtime_sha256_required": true + }, + { + "kind": "derive_annual_income_spells", + "outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "window_months": 12, + "leaving_date_share": 0.75, + "unknown_leaving_months": 6.0, + "donor_recent_start_months": 24, + "minimum_donors": 30, + "donor_age_edges": [ + 25, + 35, + 50, + 65 + ], + "seed": 0, + "salt": "employment_income_other_spells", + "earnings_index": "gov.economic_assumptions.indices.obr.average_earnings", + "earnings_index_from": "2023-01-01", + "earnings_index_to": "2024-01-01", + "maximum_uprating_months": 24 + } + ], + "outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "nonnegative_outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "rewrites": [], + "notes": "Describes each FRS adult's 12 months before the interview beside the current income columns, which it never changes, so the HMRC income-band measures can read annual income while poverty, benefits and the published engine tax keep current income. HMRC counts income received over a tax year: a person who started, left or changed a job, or had a gap, sits in another HMRC band than current pay puts them in, or in none. Outputs: the share of the year paid at today's employee pay and annual pay from other spells (chiefly a recent leaver's last job), the same pair for self-employment, and the shares of the year a private pension started on retiring and contributory JSA, ESA and Incapacity Benefit started on leaving work were in payment; an annual amount is the current amount times its share plus the other spells. The State Pension needs none: policyengine-uk's months_since_state_pension_age gives its share. Months come from CURRJOBM and JOB.WRKPREV for people in work (a person in work is never a leaver), the SAMESIT/SDEMP calendar only where it records a change, and LSTYR, LSTWRK1/2 or RETIRE1 for people out of work who worked in the last 12 months (NUMJOB2 and their last-job JOB rows); a leaving date 12 months or more before the interview gives none. A leaver's pay is the last job's usual gross pay (JOB.UGROSS, asked of people out of work who last worked in the previous 12 months, in respect of that job), scaled to the DV concept by the weighted job-level UGRSPAY/UGROSS ratio on current employees who report both and uprated by the pinned OBR average earnings index for the months since leaving; otherwise a donor's weekly pay, drawn by GROSS4 weight from current employees in their first 24 months in the job within age band x sex x full- or part-time cells (sex dropped below 30 donors), on an identity-keyed uniform (seed 0, salt employment_income_other_spells). Self-employment keeps the whole year where SEJBLONG is one or more, (SEJBMTHS + 1)/12 under a year and at most the calendar's self-employed months where self-employment is the activity today (the calendar records the main activity only, so a side business beside a job keeps its whole year); a ceased self-employed last job pays SEINCAMT (else PROFIT1, a loss as zero) for its SELWKS weeks; SEINCAM2 is already uprated to the interview by the FRS, so it is not uprated again. The receipt gives each source's support as a band of distinct households, ratios only where ten or more households carry them, and the exact zeros its stage gate reads." + }, { "stage": "spi_support_channel", "survey": "Family Resources Survey 2024-25", @@ -2489,9 +2602,15 @@ "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. 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)." + "hmrc_spi_incapacity_benefit_income", + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "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). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes." }, { "stage": "spi_housing_shell", @@ -5287,7 +5406,7 @@ "receives_benefits_in_own_right", "would_claim_uc" ], - "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer's Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer's Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps." + "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer's Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. That draw stands for a unit with no award; uc_take_up redraws every entitled unit of both channels by award band and family type once uc_capital_coherence has set the capital the engine assesses (microcosm#1334). Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer's Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps." }, { "stage": "uc_capital_coherence", @@ -5359,6 +5478,99 @@ ], "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 2.147.2 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 shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). 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. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base." }, + { + "stage": "uc_take_up", + "survey": "Family Resources Survey 2024-25", + "source": "DWP Stat-Xplore, Households on Universal Credit by family type and monthly award amount band, April to December 2025, applied to PolicyEngine-UK pre-take-up Universal Credit awards after the SPI income chain and UC capital coherence.", + "grain": "benunit", + "artifacts": [ + { + "role": "uc_award_band_facts", + "kind": "public_aggregate_reference", + "resource": "dwp_uc_award_band_facts.json", + "format": "json", + "runtime_sha256_required": true + } + ], + "operations": [ + { + "kind": "verify_vendored_fact_resource", + "artifact_role": "uc_award_band_facts", + "resource": "dwp_uc_award_band_facts.json", + "feed_pin": "chronicle_feed.json", + "record_sets": [ + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_05", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_06", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_07", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_08", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_09", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_10", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_11", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_12" + ], + "source_vintage": "April to December 2025", + "mapped_build_period": 2025, + "period_mapping": "calendar_year_average_of_monthly_counts", + "require_before_source_read": true, + "runtime_sha256_required": true, + "fail_on_mismatch": true + }, + { + "kind": "materialize_rules_engine_predictors", + "predictors": [ + "universal_credit", + "legacy_benefits_closed", + "is_child_or_qualifying_young_person_for_universal_credit" + ], + "consumed_only": true, + "year_rule": "calibration_year", + "dataset_year_rule": "survey_year" + }, + { + "kind": "aggregate_person_to_benunit", + "method": "any_positive", + "consumed_only": true, + "aggregates": { + "universal_credit_reported_anchor": "universal_credit_reported" + } + }, + { + "kind": "aggregate_person_to_benunit", + "method": "any_uc_claimant_aged_18_under_pension_credit_age", + "consumed_only": true, + "aggregates": { + "uc_age_eligible": "age" + }, + "role_column": "is_uc_claimant" + }, + { + "kind": "assign_award_band_take_up_residual", + "output": "would_claim_uc", + "draw": "would_claim_uc", + "seed": 0, + "anchor": "universal_credit_reported_anchor", + "population": "uc_age_eligible", + "award": "universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes assigns from its identity and region", + "family_type": "two FRS claimant or partner roles make a couple; children are UC children or qualifying young people and other non-claimants under 20 (the calibration measure's uc_calibration_family_type)", + "award_bands": "GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, 1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000", + "targets": "DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025", + "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it", + "minimum_pool_sources": 30, + "drawn_units": "entitled, non-reporting units of the population that the engine does not move off their legacy benefits (legacy_benefits_closed) and that report none, since a legacy benefit the engine leaves in place ends once the unit claims UC", + "undrawn_claims": "a reporter at 1, a moved unit at the claim probability uc_legacy_closure_take_up's rule gives it (dwp_move_to_uc_facts.json), any other at its stored flag", + "weight_mapping": "household_to_benunit", + "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", + "solve_scope": "great_britain", + "not_drawn": "keeps its stored flag" + } + ], + "outputs": [], + "rewrites": [ + "would_claim_uc" + ], + "notes": "Runs after uc_capital_coherence, the last writer of universal_credit_reported and of the capital the engine assesses, and before uc_legacy_closure_take_up, which reads would_claim_uc. frs_take_up draws would_claim_uc early for FRS benefit units and spi_benefit_coherence redraws the SPI-channel units on the same rule: a reporter claims and the rest of the take-up contract's one rate times the age population is drawn uniformly over its non-reporting units, whatever their award. At the spine's design weights that put the caseload above DWP's for single people without children, couples and the smallest awards, and below it for lone parents, so the national solve met DWP's caseloads by reweighting low-income families. This stage redraws the flag for both channels. One temporary engine materialization, at the release's calibration year on the frame stamped with its survey year (as pension_credit_take_up reads Pension Credit), gives each benefit unit the award it would be paid if it claimed: universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes will assign it (they read only the unit's identity and region), so after the benefit cap and deductions, the award the registry's dwp/uc_payment_dist rows band. It also gives whether the engine moves the unit off its legacy benefits that year (legacy_benefits_closed) and the UC child status the calibration's family type reads; all are consumed only. A unit with an award falls into a cell of DWP's family type by DWP's GBP 100 monthly award band, banded as the registry rows band an award, with one more band above GBP 2,500 a month. Within a family type, adjacent bands pool, lowest first, until the drawn units rest on at least 30 distinct source households. In each pool a reporter claims, and each drawn unit (entitled, non-reporting, in frs_take_up's age population and not moved off its legacy benefits) claims at r = (T - F) / D clipped to [0, 1]: T is DWP's count for the pool's bands, the mean of the April to December 2025 Stat-Xplore months (the facts the registry rows average, vendored as dwp_uc_award_band_facts.json), D the weight of the drawn units, and F the expected claims of the other entitled units: 1 for a reporter, the claim probability uc_legacy_closure_take_up's rule gives a moved unit (whose flag this stage leaves alone, so that probability does not move), and the stored flag for any other. T, F and D are Great Britain's at the stage's household weights, which DWP's counts cover. The drawn units claim in the order of their uniform until their weight is nearest T - F, so each pool realizes r to within one unit's weight rather than only in expectation, which a pool resting on a few dozen households can miss by several per cent, and Northern Ireland's drawn units claim below the same uniform. A pool whose other claims reach T, or whose T exceeds every entitled unit, cannot meet DWP's count; the receipt flags it. A unit with no award, or outside the age population, keeps its stored flag, the single-rate draw, which is how a unit claims once a reform or a later year entitles it. The uniform is the would_claim_uc identity stream frs_take_up and spi_benefit_coherence use. The receipt gives each pool's DWP count, its support in bands of distinct source households and its unreachable flag (only where ten or more sources carry the pool), and each family type's weighted caseload before and after; it gives no unit count except in bands and no rate. The uk_stage_uc_take_up gate reads it; the terminal take-up signal gate no longer measures would_claim_uc." + }, { "stage": "uc_legacy_closure_take_up", "survey": "DWP Stat-Xplore, Households invited to Move to Universal Credit by legacy benefit and move status, notices July 2022 to June 2026", @@ -5415,7 +5627,7 @@ "outputs": [ "would_claim_uc_at_legacy_closure" ], - "notes": "Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_capital_coherence, the last writer of would_claim_uc and universal_credit_reported, and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; María's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id." + "notes": "Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_take_up, the last writer of would_claim_uc (uc_capital_coherence is the last writer of universal_credit_reported), and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; María's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id." }, { "stage": "pension_credit_take_up", @@ -6621,4 +6833,4 @@ "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." } ] -} +} \ No newline at end of file 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 5187ba99c..de937a099 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -1277,6 +1277,99 @@ stages: - srp_regular_code5 - employer_pension_contributions notes: 'Retains adjudicated raw-FRS HMRC leaves on the raw FRS spine, where person_id equals the raw sernum*1000+person identity, then draws employer pension contributions: each member of an employer scheme (PENPROV.STEMPPEN 1 to 4) with earnings takes an ASHE 2024 employer contribution rate for their scheme type and sector (Table P10 band shares, full pay, all industries; occupational schemes split into defined benefit and defined contribution by the sector''s Table P2 job counts), uniform within the band (top band 20% to 30%), times pay. It replaces the incumbent''s three times the employee contribution (microcosm#1069 c9, R6).' +- stage: frs_annual_income_spells + 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 adult, job and household tabs; the OBR average earnings index pinned in hmrc_uprating_engine_pins.json. + grain: person + artifacts: + - role: frs_table + table: adult + kind: licensed_microdata + format: tab + vintage: '2024_25' + locator: adult.tab + sha256: 4eaea0809a7ccca0fddeb98e358771e4a6e5ebb81b21c4fac4070fc9d227658d + size_bytes: 34885825 + runtime_sha256_required: true + tax_year_start: 2024 + ukds_study_number: 9563 + doi: 10.5255/UKDA-SN-9563-1 + - role: frs_table + table: job + kind: licensed_microdata + format: tab + vintage: '2024_25' + locator: job.tab + sha256: eb7faf7ada3a3851cb2afb83e2983f8907ffeec897cfbe01e56cb0dfefa853e2 + size_bytes: 10518760 + runtime_sha256_required: true + tax_year_start: 2024 + ukds_study_number: 9563 + doi: 10.5255/UKDA-SN-9563-1 + - role: frs_table + table: househol + kind: licensed_microdata + format: tab + vintage: '2024_25' + locator: househol.tab + sha256: 2b93b6aed49e1591d6f5360736b4aee3a11ef506b276435f4d2c011a8afbb6a5 + size_bytes: 12108606 + runtime_sha256_required: true + tax_year_start: 2024 + ukds_study_number: 9563 + doi: 10.5255/UKDA-SN-9563-1 + - role: earnings_index + kind: public_aggregate_reference + resource: hmrc_uprating_engine_pins.json + format: json + runtime_sha256_required: true + operations: + - kind: read_tables + format: tab + delimiter: "\t" + lowercase_columns: true + numeric_errors: coerce + runtime_sha256_required: true + - kind: derive_annual_income_spells + outputs: + - employment_income_annual_share + - employment_income_other_spells + - self_employment_income_annual_share + - self_employment_income_other_spells + - private_pension_income_annual_share + - contributory_benefits_annual_share + window_months: 12 + leaving_date_share: 0.75 + unknown_leaving_months: 6.0 + donor_recent_start_months: 24 + minimum_donors: 30 + donor_age_edges: + - 25 + - 35 + - 50 + - 65 + seed: 0 + salt: employment_income_other_spells + earnings_index: gov.economic_assumptions.indices.obr.average_earnings + earnings_index_from: '2023-01-01' + earnings_index_to: '2024-01-01' + maximum_uprating_months: 24 + outputs: + - employment_income_annual_share + - employment_income_other_spells + - self_employment_income_annual_share + - self_employment_income_other_spells + - private_pension_income_annual_share + - contributory_benefits_annual_share + nonnegative_outputs: + - employment_income_annual_share + - employment_income_other_spells + - self_employment_income_annual_share + - self_employment_income_other_spells + - private_pension_income_annual_share + - contributory_benefits_annual_share + rewrites: [] + notes: 'Describes each FRS adult''s 12 months before the interview beside the current income columns, which it never changes, so the HMRC income-band measures can read annual income while poverty, benefits and the published engine tax keep current income. HMRC counts income received over a tax year: a person who started, left or changed a job, or had a gap, sits in another HMRC band than current pay puts them in, or in none. Outputs: the share of the year paid at today''s employee pay and annual pay from other spells (chiefly a recent leaver''s last job), the same pair for self-employment, and the shares of the year a private pension started on retiring and contributory JSA, ESA and Incapacity Benefit started on leaving work were in payment; an annual amount is the current amount times its share plus the other spells. The State Pension needs none: policyengine-uk''s months_since_state_pension_age gives its share. Months come from CURRJOBM and JOB.WRKPREV for people in work (a person in work is never a leaver), the SAMESIT/SDEMP calendar only where it records a change, and LSTYR, LSTWRK1/2 or RETIRE1 for people out of work who worked in the last 12 months (NUMJOB2 and their last-job JOB rows); a leaving date 12 months or more before the interview gives none. A leaver''s pay is the last job''s usual gross pay (JOB.UGROSS, asked of people out of work who last worked in the previous 12 months, in respect of that job), scaled to the DV concept by the weighted job-level UGRSPAY/UGROSS ratio on current employees who report both and uprated by the pinned OBR average earnings index for the months since leaving; otherwise a donor''s weekly pay, drawn by GROSS4 weight from current employees in their first 24 months in the job within age band x sex x full- or part-time cells (sex dropped below 30 donors), on an identity-keyed uniform (seed 0, salt employment_income_other_spells). Self-employment keeps the whole year where SEJBLONG is one or more, (SEJBMTHS + 1)/12 under a year and at most the calendar''s self-employed months where self-employment is the activity today (the calendar records the main activity only, so a side business beside a job keeps its whole year); a ceased self-employed last job pays SEINCAMT (else PROFIT1, a loss as zero) for its SELWKS weeks; SEINCAM2 is already uprated to the interview by the FRS, so it is not uprated again. The receipt gives each source''s support as a band of distinct households, ratios only where ten or more households carry them, and the exact zeros its stage gate reads.' - stage: spi_support_channel survey: Family Resources Survey 2024-25 source: Synthetic SPI support channel sampled uniformly without replacement from the raw FRS spine before cloning. @@ -2135,7 +2228,13 @@ stages: - 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. 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). + - employment_income_annual_share + - employment_income_other_spells + - self_employment_income_annual_share + - self_employment_income_other_spells + - private_pension_income_annual_share + - contributory_benefits_annual_share + 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). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes. - 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. @@ -4287,7 +4386,7 @@ stages: - is_severely_disabled_for_benefits - receives_benefits_in_own_right - would_claim_uc - notes: 'Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer''s Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult''s benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit''s entitlement or claim history. This stage zeroes the SPI rows'' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household''s new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up''s Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract''s universal_credit rate on draws keyed on the SPI unit''s own identity. Contributory ESA, PIP, DLA, AA, Carer''s Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer''s Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.' + notes: 'Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer''s Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult''s benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit''s entitlement or claim history. This stage zeroes the SPI rows'' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household''s new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up''s Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract''s universal_credit rate on draws keyed on the SPI unit''s own identity. That draw stands for a unit with no award; uc_take_up redraws every entitled unit of both channels by award band and family type once uc_capital_coherence has set the capital the engine assesses (microcosm#1334). Contributory ESA, PIP, DLA, AA, Carer''s Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer''s Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.' - stage: uc_capital_coherence survey: Family Resources Survey 2024-25 source: Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel, plus the household's Wealth and Assets Survey property imputation for the unit that holds it. @@ -4342,6 +4441,78 @@ stages: - 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 2.147.2 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 shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). 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. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base. +- stage: uc_take_up + survey: Family Resources Survey 2024-25 + source: 'DWP Stat-Xplore, Households on Universal Credit by family type and monthly award amount band, April to December 2025, applied to PolicyEngine-UK pre-take-up Universal Credit awards after the SPI income chain and UC capital coherence.' + grain: benunit + artifacts: + - role: uc_award_band_facts + kind: public_aggregate_reference + resource: dwp_uc_award_band_facts.json + format: json + runtime_sha256_required: true + operations: + - kind: verify_vendored_fact_resource + artifact_role: uc_award_band_facts + resource: dwp_uc_award_band_facts.json + feed_pin: chronicle_feed.json + record_sets: + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_04 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_05 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_06 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_07 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_08 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_09 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_10 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_11 + - dwp.statx.uc_benefit_units.award_band_family_type.month2025_12 + source_vintage: April to December 2025 + mapped_build_period: 2025 + period_mapping: calendar_year_average_of_monthly_counts + require_before_source_read: true + runtime_sha256_required: true + fail_on_mismatch: true + - kind: materialize_rules_engine_predictors + predictors: + - universal_credit + - legacy_benefits_closed + - is_child_or_qualifying_young_person_for_universal_credit + consumed_only: true + year_rule: calibration_year + dataset_year_rule: survey_year + - kind: aggregate_person_to_benunit + method: any_positive + consumed_only: true + aggregates: + universal_credit_reported_anchor: universal_credit_reported + - kind: aggregate_person_to_benunit + method: any_uc_claimant_aged_18_under_pension_credit_age + consumed_only: true + aggregates: + uc_age_eligible: age + role_column: is_uc_claimant + - kind: assign_award_band_take_up_residual + output: would_claim_uc + draw: would_claim_uc + seed: 0 + anchor: universal_credit_reported_anchor + population: uc_age_eligible + award: universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes assigns from its identity and region + family_type: two FRS claimant or partner roles make a couple; children are UC children or qualifying young people and other non-claimants under 20 (the calibration measure's uc_calibration_family_type) + award_bands: GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, 1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000 + targets: DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025 + pooling: adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it + minimum_pool_sources: 30 + drawn_units: entitled, non-reporting units of the population that the engine does not move off their legacy benefits (legacy_benefits_closed) and that report none, since a legacy benefit the engine leaves in place ends once the unit claims UC + undrawn_claims: a reporter at 1, a moved unit at the claim probability uc_legacy_closure_take_up's rule gives it (dwp_move_to_uc_facts.json), any other at its stored flag + weight_mapping: household_to_benunit + residual_rate: (T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too + solve_scope: great_britain + not_drawn: keeps its stored flag + outputs: [] + rewrites: + - would_claim_uc + notes: 'Runs after uc_capital_coherence, the last writer of universal_credit_reported and of the capital the engine assesses, and before uc_legacy_closure_take_up, which reads would_claim_uc. frs_take_up draws would_claim_uc early for FRS benefit units and spi_benefit_coherence redraws the SPI-channel units on the same rule: a reporter claims and the rest of the take-up contract''s one rate times the age population is drawn uniformly over its non-reporting units, whatever their award. At the spine''s design weights that put the caseload above DWP''s for single people without children, couples and the smallest awards, and below it for lone parents, so the national solve met DWP''s caseloads by reweighting low-income families. This stage redraws the flag for both channels. One temporary engine materialization, at the release''s calibration year on the frame stamped with its survey year (as pension_credit_take_up reads Pension Credit), gives each benefit unit the award it would be paid if it claimed: universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes will assign it (they read only the unit''s identity and region), so after the benefit cap and deductions, the award the registry''s dwp/uc_payment_dist rows band. It also gives whether the engine moves the unit off its legacy benefits that year (legacy_benefits_closed) and the UC child status the calibration''s family type reads; all are consumed only. A unit with an award falls into a cell of DWP''s family type by DWP''s GBP 100 monthly award band, banded as the registry rows band an award, with one more band above GBP 2,500 a month. Within a family type, adjacent bands pool, lowest first, until the drawn units rest on at least 30 distinct source households. In each pool a reporter claims, and each drawn unit (entitled, non-reporting, in frs_take_up''s age population and not moved off its legacy benefits) claims at r = (T - F) / D clipped to [0, 1]: T is DWP''s count for the pool''s bands, the mean of the April to December 2025 Stat-Xplore months (the facts the registry rows average, vendored as dwp_uc_award_band_facts.json), D the weight of the drawn units, and F the expected claims of the other entitled units: 1 for a reporter, the claim probability uc_legacy_closure_take_up''s rule gives a moved unit (whose flag this stage leaves alone, so that probability does not move), and the stored flag for any other. T, F and D are Great Britain''s at the stage''s household weights, which DWP''s counts cover. The drawn units claim in the order of their uniform until their weight is nearest T - F, so each pool realizes r to within one unit''s weight rather than only in expectation, which a pool resting on a few dozen households can miss by several per cent, and Northern Ireland''s drawn units claim below the same uniform. A pool whose other claims reach T, or whose T exceeds every entitled unit, cannot meet DWP''s count; the receipt flags it. A unit with no award, or outside the age population, keeps its stored flag, the single-rate draw, which is how a unit claims once a reform or a later year entitles it. The uniform is the would_claim_uc identity stream frs_take_up and spi_benefit_coherence use. The receipt gives each pool''s DWP count, its support in bands of distinct source households and its unreachable flag (only where ten or more sources carry the pool), and each family type''s weighted caseload before and after; it gives no unit count except in bands and no rate. The uk_stage_uc_take_up gate reads it; the terminal take-up signal gate no longer measures would_claim_uc.' - stage: uc_legacy_closure_take_up survey: DWP Stat-Xplore, Households invited to Move to Universal Credit by legacy benefit and move status, notices July 2022 to June 2026 source: https://stat-xplore.dwp.gov.uk @@ -4385,7 +4556,7 @@ stages: seed: 0 outputs: - would_claim_uc_at_legacy_closure - notes: Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_capital_coherence, the last writer of would_claim_uc and universal_credit_reported, and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; María's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id. + notes: Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_take_up, the last writer of would_claim_uc (uc_capital_coherence is the last writer of universal_credit_reported), and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; María's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id. - 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.' 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 a5a469d00..0bb240842 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 @@ -135,11 +135,14 @@ "source": { "source": "U8 adjudication and upstream issue uk-data#452; lever run receipts in data/ukds/acceptance/757-swap (2026-08-26)", "agency": "PolicyEngine", - "citation": "Frozen at 0.55 by adjudication for parity with the incumbent UK pipeline. The pre-registered U8 lever (a raise to 0.85 at build year 2024) was run and measured on 2026-08-26 per the #757 uk_target_fit dispositions and REVERTED on the receipts: the raise left every failing UC caseload cell unchanged (dwp.uc.households -44.9% at both rates) while destroying the legacy housing-benefit surface (obr.housing_benefit -0.0% at 0.55, -52.4% at 0.85 - the raise moves claimants off legacy benefits) and polluting the QRF predictor surface through engine-computed household_net_income. The binding constraint on UC caseload is capital-test support (uk-data#452 mechanism 2, microcosm#750), not take-up support. Re-signed 2026-09-16 (#882, microcosm#867): the rate is a share of the benefit units with an adult under State Pension age; units with every adult at or over State Pension age cannot claim Universal Credit (the engine's is_uc_eligible requires a working-age adult) and are no longer drawn, which restores the reported Housing Benefit the draw had zeroed for 407k pension-age units on spine-q; those units have no Universal Credit either way, while the working-age draw moves with the population definition (see below). The 0.55 value is unchanged. Measured on the control and repaired twins at design weights (experiments/882-uc-repairs-receipts.md R5): the old all-units draw realized a 0.582 share among units with a working-age adult, because the reported-UC anchors sit almost entirely there while the count target was taken over every unit; the population draw realizes 0.559 among them and paid UC moves -1.8 percent at design weights. María's ruling of 2026-09-16: keep the rate over the eligible population; the calibrated UC rows absorb the level. Re-signed 2026-10-09 (microcosm#1095, the third ports PR, approved in María's plan of 2026-10-09): the population follows policyengine-uk's is_uc_eligible (2.122.2 when signed; the same in the locked 2.123.0), whose age conditions read the benefit unit's UC claimant or partner (is_uc_claimant) rather than any adult, so a unit whose only member aged 18 to under Pension Credit qualifying age is a dependant is no longer drawn. The 0.55 value is unchanged.", + "citation": "Frozen at 0.55 by adjudication for parity with the incumbent UK pipeline. The pre-registered U8 lever (a raise to 0.85 at build year 2024) was run and measured on 2026-08-26 per the #757 uk_target_fit dispositions and REVERTED on the receipts: the raise left every failing UC caseload cell unchanged (dwp.uc.households -44.9% at both rates) while destroying the legacy housing-benefit surface (obr.housing_benefit -0.0% at 0.55, -52.4% at 0.85 - the raise moves claimants off legacy benefits) and polluting the QRF predictor surface through engine-computed household_net_income. The binding constraint on UC caseload is capital-test support (uk-data#452 mechanism 2, microcosm#750), not take-up support. Re-signed 2026-09-16 (#882, microcosm#867): the rate is a share of the benefit units with an adult under State Pension age; units with every adult at or over State Pension age cannot claim Universal Credit (the engine's is_uc_eligible requires a working-age adult) and are no longer drawn, which restores the reported Housing Benefit the draw had zeroed for 407k pension-age units on spine-q; those units have no Universal Credit either way, while the working-age draw moves with the population definition (see below). The 0.55 value is unchanged. Measured on the control and repaired twins at design weights (experiments/882-uc-repairs-receipts.md R5): the old all-units draw realized a 0.582 share among units with a working-age adult, because the reported-UC anchors sit almost entirely there while the count target was taken over every unit; the population draw realizes 0.559 among them and paid UC moves -1.8 percent at design weights. María's ruling of 2026-09-16: keep the rate over the eligible population; the calibrated UC rows absorb the level. Re-signed 2026-10-09 (microcosm#1095, the third ports PR, approved in María's plan of 2026-10-09): the population follows policyengine-uk's is_uc_eligible (2.122.2 when signed; the same in the locked 2.123.0), whose age conditions read the benefit unit's UC claimant or partner (is_uc_claimant) rather than any adult, so a unit whose only member aged 18 to under Pension Credit qualifying age is a dependant is no longer drawn. The 0.55 value is unchanged. Re-signed 2026-10-21 (PolicyEngine/microcosm#1334, María's ruling \"lift U8\"): for a benefit unit with a Universal Credit award at the calibration year, take-up is no longer this one rate. The uc_take_up spine stage redraws would_claim_uc by DWP family type and monthly award band against DWP Stat-Xplore's calendar-2025 Households on Universal Credit (dwp_uc_award_band_facts.json): reporters claim, and the other entitled units of this population claim at each pooled cell's residual to DWP's count at the spine's design weights over Great Britain. At design weights the one rate put the caseload above DWP's for single people without children, couples and the smallest awards and below it for lone parents, and the national solve met DWP's caseloads by down-weighting low-income families. 0.55 stays the rate frs_take_up and spi_benefit_coherence draw at, which a unit with no award at the calibration year keeps: the rate at which it claims once a reform or a later year entitles it.", "status": "frozen_by_adjudication", "freeze": { - "decision": "U8 (re-opened and re-frozen by the 2026-08-26 lever run; population re-signed 2026-09-16 to benefit units with an adult under State Pension age, and 2026-10-09 to benefit units with a UC claimant or partner aged 18 to under Pension Credit qualifying age, rate unchanged)", - "followup": "uk-data#452" + "decision": "U8 (re-opened and re-frozen by the 2026-08-26 lever run; population re-signed 2026-09-16 to benefit units with an adult under State Pension age, and 2026-10-09 to benefit units with a UC claimant or partner aged 18 to under Pension Credit qualifying age, rate unchanged); lifted 2026-10-21 for entitled units, which uc_take_up draws by award band and family type, the 0.55 rate kept for units with no award", + "followup": "uk-data#452", + "approved_by": "juaristi22", + "approved_on": "2026-10-21", + "adjudication": "PolicyEngine/microcosm#1334 fix 2, María's ruling of 2026-10-21: lift U8" } }, "population": "benefit units with a UC claimant or partner aged 18 or over and under Pension Credit qualifying age" 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 1d4311161..6a92d3814 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 @@ -1980,7 +1980,7 @@ "bindings": { "policyengine": { "metric_name": "obr/income_tax", - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", "notes": "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)." @@ -3667,15 +3667,15 @@ "bindings": { "policyengine": { "metric_name": "hmrc/employment_income_income_band", - "value_variable": "employment_income", + "value_variable": "employment_income_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3696,7 +3696,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national earnings index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national earnings index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.employment_income.count_by_total_income_band", @@ -3725,16 +3725,16 @@ "bindings": { "policyengine": { "metric_name": "hmrc/employment_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { - "variable": "employment_income", + "variable": "employment_income_annual", "operator": "!=", "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3757,7 +3757,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere). Counted as income != 0: HMRC includes an individual whose source made a loss, with nil income (SPI Table 3.10 notes), so the national and area counts share one rule." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere). Counted as income != 0: HMRC includes an individual whose source made a loss, with nil income (SPI Table 3.10 notes), so the national and area counts share one rule." }, { "target_id": "hmrc.spi.self_employment_income.amount_by_total_income_band", @@ -3781,15 +3781,15 @@ "bindings": { "policyengine": { "metric_name": "hmrc/self_employment_income_income_band", - "value_variable": "self_employment_income_after_loss_relief", + "value_variable": "self_employment_income_after_loss_relief_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3810,7 +3810,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national mixed-income index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national mixed-income index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.self_employment_income.count_by_total_income_band", @@ -3839,16 +3839,16 @@ "bindings": { "policyengine": { "metric_name": "hmrc/self_employment_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { - "variable": "self_employment_income_after_loss_relief", + "variable": "self_employment_income_after_loss_relief_annual", "operator": "!=", "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3871,7 +3871,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere). Counted as income != 0: HMRC includes an individual whose source made a loss, with nil income (SPI Table 3.10 notes), so the national and area counts share one rule." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere). Counted as income != 0: HMRC includes an individual whose source made a loss, with nil income (SPI Table 3.10 notes), so the national and area counts share one rule." }, { "target_id": "hmrc.spi.dividend_income.amount_by_total_income_band", @@ -3898,12 +3898,12 @@ "value_variable": "dividend_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3924,7 +3924,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.gdp", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.obr.per_capita.gdp between 1 January 2023 and 1 January 2025, the instants the SPI donor rebasing uses (the engine's OBR indices are calendar-year series, so period 2025 is calendar 2025 for incomes). Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and published facts move to it; the v20 national build measured the held 2023-24 levels as income tax 13 percent under and the state-pension bands 8 to 18 percent over. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.obr.per_capita.gdp between 1 January 2023 and 1 January 2025, the instants the SPI donor rebasing uses (the engine's OBR indices are calendar-year series, so period 2025 is calendar 2025 for incomes). Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and published facts move to it; the v20 national build measured the held 2023-24 levels as income tax 13 percent under and the state-pension bands 8 to 18 percent over. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.dividend_income.count_by_total_income_band", @@ -3953,7 +3953,7 @@ "bindings": { "policyengine": { "metric_name": "hmrc/dividend_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { @@ -3962,7 +3962,7 @@ "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -3985,7 +3985,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.property_income.amount_by_total_income_band", @@ -4012,12 +4012,12 @@ "value_variable": "property_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4038,7 +4038,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national index ignores (microcosm#1063, 2026-10-02). Property joins the employment, self-employment and private pension amount rows on María's ruling of 2026-10-11 (microcosm#1145 review): paired with the count rows' taxpayer_count_growth_by_total_income_band, the flat per-capita GDP index implied per-band mean profit moves of -13% to +11% between 2023 and 2025. The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national index ignores (microcosm#1063, 2026-10-02). Property joins the employment, self-employment and private pension amount rows on María's ruling of 2026-10-11 (microcosm#1145 review): paired with the count rows' taxpayer_count_growth_by_total_income_band, the flat per-capita GDP index implied per-band mean profit moves of -13% to +11% between 2023 and 2025. The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.property_income.count_by_total_income_band", @@ -4067,7 +4067,7 @@ "bindings": { "policyengine": { "metric_name": "hmrc/property_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { @@ -4076,7 +4076,7 @@ "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4099,7 +4099,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.private_pension_income.amount_by_total_income_band", @@ -4123,15 +4123,15 @@ "bindings": { "policyengine": { "metric_name": "hmrc/private_pension_income_income_band", - "value_variable": "private_pension_income", + "value_variable": "private_pension_income_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4152,7 +4152,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national private pension index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national private pension index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane). Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.private_pension_income.count_by_total_income_band", @@ -4181,16 +4181,16 @@ "bindings": { "policyengine": { "metric_name": "hmrc/private_pension_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { - "variable": "private_pension_income", + "variable": "private_pension_income_annual", "operator": ">", "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4213,7 +4213,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.state_pension.amount_by_total_income_band", @@ -4237,15 +4237,15 @@ "bindings": { "policyengine": { "metric_name": "hmrc/state_pension_income_band", - "value_variable": "state_pension", + "value_variable": "state_pension_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4266,7 +4266,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_times_new_state_pension_rate_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period as recipients times the amount per recipient: the band's recipients grow by HMRC's projected Income Tax payer growth for the Table 2.5 band containing it (the index its count row declares, the calendar-2025 window of the 2024-25 and 2025-26 projections over the 2023-24 outturn), and the amount per recipient by the new State Pension weekly rate the engine pays (203.85 at 2023, 230.25 at 2025 on the pinned engine). The rate alone held the band's recipients at the 2023-24 number while the count row grew them, so the two rows described different populations (microcosm#1069). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period as recipients times the amount per recipient: the band's recipients grow by HMRC's projected Income Tax payer growth for the Table 2.5 band containing it (the index its count row declares, the calendar-2025 window of the 2024-25 and 2025-26 projections over the 2023-24 outturn), and the amount per recipient by the new State Pension weekly rate the engine pays (203.85 at 2023, 230.25 at 2025 on the pinned engine). The rate alone held the band's recipients at the 2023-24 number while the count row grew them, so the two rows described different populations (microcosm#1069). Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi.state_pension.count_by_total_income_band", @@ -4295,16 +4295,16 @@ "bindings": { "policyengine": { "metric_name": "hmrc/state_pension_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { - "variable": "state_pension", + "variable": "state_pension_annual", "operator": ">", "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -4327,7 +4327,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.cgt.taxpayers_total", @@ -21241,12 +21241,12 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -21286,10 +21286,10 @@ "bindings": { "policyengine": { "metric_name": "hmrc/itl_total_income_income_band", - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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." }, @@ -21327,10 +21327,10 @@ "bindings": { "policyengine": { "metric_name": "hmrc/itl_income_tax_income_band", - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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." }, @@ -21384,17 +21384,17 @@ "map_to": "household", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "tax_band", + "variable": "tax_band_annual", "operator": "==", "value": "HIGHER" } ], - "notes": "Higher rate Income Tax payers, 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), 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 Income Tax liabilities statistics Table 2.2 (July 2026). HMRC classes a taxpayer by the highest rate paid on any income; the engine's tax_band does the same on adjusted net income less allowances. From 2024-25 Scotland's higher row is the Scottish higher rate alone, as the engine's HIGHER band is for a Scottish taxpayer. microcosm#1123." + "notes": "Higher rate Income Tax payers, 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), 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 Income Tax liabilities statistics Table 2.2 (July 2026). HMRC classes a taxpayer by the highest rate paid on any income; the engine's tax_band, read on each person's income over the 12 months before their interview (tax_band_annual, a proxy for the tax year), does the same on adjusted net income less allowances. From 2024-25 Scotland's higher row is the Scottish higher rate alone, as the engine's HIGHER band is for a Scottish taxpayer. microcosm#1123." }, "axiom": { "metric_name": "hmrc/itl_taxpayers_marginal_rate/higher", @@ -21448,17 +21448,17 @@ "map_to": "household", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "tax_band", + "variable": "tax_band_annual", "operator": "==", "value": "ADDITIONAL" } ], - "notes": "Additional rate Income Tax payers, 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), 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 Income Tax liabilities statistics Table 2.2 (July 2026). HMRC classes a taxpayer by the highest rate paid on any income; the engine's tax_band does the same on adjusted net income less allowances. From 2024-25 Scotland's additional row is the Scottish advanced and top rates together, as the engine's ADDITIONAL band is for a Scottish taxpayer (the advanced rate began in 2024-25, so the 2023-24 row is the top rate alone). microcosm#1123." + "notes": "Additional rate Income Tax payers, 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), 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 Income Tax liabilities statistics Table 2.2 (July 2026). HMRC classes a taxpayer by the highest rate paid on any income; the engine's tax_band, read on each person's income over the 12 months before their interview (tax_band_annual, a proxy for the tax year), does the same on adjusted net income less allowances. From 2024-25 Scotland's additional row is the Scottish advanced and top rates together, as the engine's ADDITIONAL band is for a Scottish taxpayer (the advanced rate began in 2024-25, so the 2023-24 row is the top rate alone). microcosm#1123." }, "axiom": { "metric_name": "hmrc/itl_taxpayers_marginal_rate/additional", @@ -21506,12 +21506,12 @@ "value_variable": "savings_interest_income", "from_entity": "person", "map_to": "household", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "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": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -21561,7 +21561,7 @@ "bindings": { "policyengine": { "metric_name": "hmrc/savings_interest_income_count_income_band", - "groupby_variable": "total_income_after_loss_relief", + "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", "filters": [ { @@ -21570,7 +21570,7 @@ "value": 0 }, { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 } @@ -21593,7 +21593,7 @@ "category_id": "hmrc.survey_of_personal_incomes", "label": "Count by total income band", "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_by_total_income_band", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's own projected growth in Income Tax payers for the Table 2.5 band containing this band (Income Tax liabilities statistics, July 2026): the calendar-2025 window of the 2024-25 and 2025-26 projections (3/12 and 9/12) over the 2023-24 outturn, from the vendored hmrc_itl_taxpayer_counts.json. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): calendar 2025, months to the end of the year for fiscal- and tax-year facts. Taxpayers only (income_tax_annual > 0, the tax on each person's income over the 12 months before their interview, a proxy for the tax year), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." }, { "target_id": "hmrc.spi_region.taxpayers_by_region_12570_15000", @@ -21629,17 +21629,17 @@ "metric_name": "hmrc/spi_region/taxpayers_12570_15000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 12570 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 15000 } @@ -21647,7 +21647,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_12570_15000", @@ -21705,17 +21705,17 @@ "metric_name": "hmrc/spi_region/taxpayers_15000_20000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 15000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 20000 } @@ -21723,7 +21723,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_15000_20000", @@ -21781,17 +21781,17 @@ "metric_name": "hmrc/spi_region/taxpayers_20000_30000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 20000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 30000 } @@ -21799,7 +21799,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_20000_30000", @@ -21857,17 +21857,17 @@ "metric_name": "hmrc/spi_region/taxpayers_30000_40000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 30000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 40000 } @@ -21875,7 +21875,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_30000_40000", @@ -21933,17 +21933,17 @@ "metric_name": "hmrc/spi_region/taxpayers_40000_50000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 40000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 50000 } @@ -21951,7 +21951,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_40000_50000", @@ -22009,17 +22009,17 @@ "metric_name": "hmrc/spi_region/taxpayers_50000_70000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 50000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 70000 } @@ -22027,7 +22027,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_50000_70000", @@ -22085,17 +22085,17 @@ "metric_name": "hmrc/spi_region/taxpayers_70000_100000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 70000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 100000 } @@ -22103,7 +22103,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_70000_100000", @@ -22161,17 +22161,17 @@ "metric_name": "hmrc/spi_region/taxpayers_100000_150000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 100000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 150000 } @@ -22179,7 +22179,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_100000_150000", @@ -22237,17 +22237,17 @@ "metric_name": "hmrc/spi_region/taxpayers_150000_200000", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 150000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 200000 } @@ -22255,7 +22255,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_150000_200000", @@ -22308,12 +22308,12 @@ "metric_name": "hmrc/spi_region/taxpayers_200000_plus", "filters": [ { - "variable": "income_tax", + "variable": "income_tax_annual", "operator": ">", "value": 0 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 200000 } @@ -22321,7 +22321,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax payers 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 Income Tax payers 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." + "notes": "SPI 2023-24 Table 3.11 income tax payers 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax payers 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." }, "axiom": { "metric_name": "hmrc/spi_region/taxpayers_200000_plus", @@ -22374,20 +22374,20 @@ "metric_name": "hmrc/spi_region/total_income_12570_15000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 12570 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 15000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_12570_15000", @@ -22440,20 +22440,20 @@ "metric_name": "hmrc/spi_region/total_income_15000_20000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 15000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 20000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_15000_20000", @@ -22506,20 +22506,20 @@ "metric_name": "hmrc/spi_region/total_income_20000_30000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 20000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 30000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_20000_30000", @@ -22572,20 +22572,20 @@ "metric_name": "hmrc/spi_region/total_income_30000_40000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 30000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 40000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_30000_40000", @@ -22638,20 +22638,20 @@ "metric_name": "hmrc/spi_region/total_income_40000_50000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 40000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 50000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_40000_50000", @@ -22704,20 +22704,20 @@ "metric_name": "hmrc/spi_region/total_income_50000_70000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 50000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 70000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_50000_70000", @@ -22770,20 +22770,20 @@ "metric_name": "hmrc/spi_region/total_income_70000_100000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 70000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 100000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_70000_100000", @@ -22836,20 +22836,20 @@ "metric_name": "hmrc/spi_region/total_income_100000_150000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 100000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 150000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_100000_150000", @@ -22902,20 +22902,20 @@ "metric_name": "hmrc/spi_region/total_income_150000_200000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 150000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 200000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_150000_200000", @@ -22963,15 +22963,15 @@ "metric_name": "hmrc/spi_region/total_income_200000_plus", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 200000 } ], - "value_variable": "total_income_after_loss_relief", + "value_variable": "total_income_after_loss_relief_annual", "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." + "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 year's total income (total_income_after_loss_relief_annual) 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." }, "axiom": { "metric_name": "hmrc/spi_region/total_income_200000_plus", @@ -23024,20 +23024,20 @@ "metric_name": "hmrc/spi_region/income_tax_12570_15000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 12570 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 15000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_12570_15000", @@ -23090,20 +23090,20 @@ "metric_name": "hmrc/spi_region/income_tax_15000_20000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 15000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 20000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_15000_20000", @@ -23156,20 +23156,20 @@ "metric_name": "hmrc/spi_region/income_tax_20000_30000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 20000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 30000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_20000_30000", @@ -23222,20 +23222,20 @@ "metric_name": "hmrc/spi_region/income_tax_30000_40000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 30000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 40000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_30000_40000", @@ -23288,20 +23288,20 @@ "metric_name": "hmrc/spi_region/income_tax_40000_50000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 40000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 50000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_40000_50000", @@ -23354,20 +23354,20 @@ "metric_name": "hmrc/spi_region/income_tax_50000_70000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 50000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 70000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_50000_70000", @@ -23420,20 +23420,20 @@ "metric_name": "hmrc/spi_region/income_tax_70000_100000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 70000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 100000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_70000_100000", @@ -23486,20 +23486,20 @@ "metric_name": "hmrc/spi_region/income_tax_100000_150000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 100000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 150000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_100000_150000", @@ -23552,20 +23552,20 @@ "metric_name": "hmrc/spi_region/income_tax_150000_200000", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 150000 }, { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": "<", "value": 200000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_150000_200000", @@ -23613,15 +23613,15 @@ "metric_name": "hmrc/spi_region/income_tax_200000_plus", "filters": [ { - "variable": "total_income_after_loss_relief", + "variable": "total_income_after_loss_relief_annual", "operator": ">=", "value": 200000 } ], - "value_variable": "income_tax", + "value_variable": "income_tax_annual", "from_entity": "person", "map_to": "household", - "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 Income Tax liabilities 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." + "notes": "SPI 2023-24 Table 3.11 income tax liabilities 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 year's total income (total_income_after_loss_relief_annual) 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 Income Tax liabilities 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." }, "axiom": { "metric_name": "hmrc/spi_region/income_tax_200000_plus", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py index bf30171de..273a60142 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py @@ -2145,6 +2145,12 @@ def _ledger_compile_parity_required_artifacts( # microcosm#1069 c7 pension_credit_take_up stage-health check. "maximum_take_up_deviation", "minimum_entitled_units", + # microcosm#1334 uc_take_up stage-health check. + "maximum_caseload_deviation", + # frs_annual_income_spells stage-health check. + "pay_concept_ratio_bounds", + "annual_over_current_earners_bounds", + "minimum_observed_pay_share", # microcosm#1095 spi_benefit_coherence stage-health check. "zeroed_columns", "restored_columns", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py index f40ed607e..302544fc4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py @@ -115,9 +115,11 @@ "uk_stage_etb_services_support", "uk_stage_etb_services_support_pricing", "uk_stage_frs_hmrc_spine_leaves_signal", + "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", "uk_stage_spi_benefit_coherence", + "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", "uk_stage_child_benefit_take_up", "uk_stage_cgt_incidence_clone_mass", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py index 7b67059d9..432e0a219 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/candidate_score.py @@ -107,6 +107,7 @@ def score_uk_national_candidate( calibration_year, measure_resolver_factory, band_edge_registry=band_edge_registry, + incumbent=True, ) candidate = score_targets( candidate_frame, @@ -279,6 +280,15 @@ def _verify_artifact(role: str, path: str | Path, expected: str) -> dict[str, ob } +#: Rule 1 scores each file's best estimate of the year's income against the +#: same annual targets; an incumbent built before the annual income +#: descriptors existed has none, so its annual measures are its current ones. +UK_INCUMBENT_ANNUAL_INCOME_FALLBACK = ( + "incumbent without annual income descriptors: its annual measures are its " + "current values (a share of 1 and no other spells)" +) + + def _scored_frame( h5_path: str | Path, registry: TargetRegistry, @@ -286,6 +296,7 @@ def _scored_frame( factory: Callable[[Path, Any], Any] | None, *, band_edge_registry: TargetRegistry | None = None, + incumbent: bool = False, ) -> tuple[Any, Any]: frame, _provenance = load_uk_national_frame(h5_path) # policyengine-uk projects a file's household weights from the file's own @@ -293,6 +304,9 @@ def _scored_frame( # users get there (microcosm#796 A19). The engine reads the file itself. frame = uk_engine_year_frame(frame, simulation_year=calibration_year) resolver = None if factory is None else factory(Path(h5_path), frame) + fallback = getattr(resolver, "use_current_income_for_annual_measures", None) + if incumbent and fallback is not None: + fallback(reason=UK_INCUMBENT_ANNUAL_INCOME_FALLBACK) return prepare_uk_target_frame( frame, registry, @@ -556,6 +570,7 @@ def prune_incumbent_unresolvable( calibration_year, measure_resolver_factory, band_edge_registry=edges, + incumbent=True, ) except MeasureResolutionError as error: failing = _failing_measure(error) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py index a8af91ae9..896c53ed8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/diagnostics.py @@ -17,6 +17,7 @@ import json import math import sys +import warnings from collections.abc import Collection, Mapping, Sequence from importlib import resources as importlib_resources from pathlib import Path @@ -26,6 +27,7 @@ import numpy as np import pandas as pd from pydantic import ValidationError +from scipy import linalg as scipy_linalg from scipy import sparse from microcosm.calibrate import ( @@ -90,7 +92,7 @@ ) _COMPLEMENT_NOTE = ( "model figures withheld: with the published rows they would give back a row " - "that fewer than 10 distinct source households carry" + "or a cell that fewer than 10 distinct source households carry" ) _COUNTRY_BY_AREA_PREFIX = { @@ -987,6 +989,15 @@ def _declared_target_partition( #: (the eigenvectors carry errors near 1e-8). Treating a small entry as zero #: can only shrink a combination's support, so it errs toward withholding. _RECOVERY_ROUNDING = 1e-6 +#: How much work the exact residual search may do for one total: the share +#: entries it adds up and the source households it counts, over the sets it +#: tries, and at most ``_RESIDUAL_SEARCH_NODES`` sets. Counts, not a clock, so +#: the same problem always gets the same answer. A total whose search stops +#: there, or whose members times atoms pass ``_RESIDUAL_DENSE_LIMIT``, is +#: checked over greedy groups instead, and a warning names it. +_RESIDUAL_SEARCH_WORK = 200_000_000 +_RESIDUAL_SEARCH_NODES = 200_000 +_RESIDUAL_DENSE_LIMIT = 5_000_000 def _row_level(geography_levels: Mapping[str, object] | None): @@ -1109,9 +1120,16 @@ def uk_withheld_row_sets( withheld rows that rests on fewer source households than its rows' floor, the published row with the fewest carriers among those that give it back is withheld too (the next-smallest band beside a thin one, not its total), - and the span is measured again. Constituency and authority rows stay - outside, as they stay outside the per-row floor: whether a local row can - give back a national one is part of the local-line ruling still owed. + and the span is measured again. The span sees only combinations of + withheld rows, so a published total less some of its published parts, + which gives back whatever they leave out, is checked first and on every + pass (:class:`_ResidualCheck`; Vahid's second review of #1344, item 17): + the UC family total less its bound bands gives back the unbound band + above GBP 2,500 a month, which is no row and may rest on a handful of + households. What neither check sees is set out in :class:`_ResidualCheck`. + Constituency and authority rows stay outside, as they stay outside the + per-row floor: whether a local row can give back a national one is part + of the local-line ruling still owed. """ names, matrix, sources, carriers, floors = _scoped_rows( @@ -1122,20 +1140,28 @@ def uk_withheld_row_sets( for name, count, floor in zip(names, carriers, floors, strict=True) if count < floor } - if not thin: - return thin, set() - norms = np.sqrt(np.asarray(matrix.multiply(matrix).sum(axis=1)).ravel()) - scale = np.divide(1.0, norms, out=np.zeros_like(norms), where=norms > 0) - unit = sparse.csr_matrix(sparse.diags(scale) @ matrix) - gram = (unit @ unit.T).toarray() withheld = set(thin) - for _ in range(len(names)): + residuals = _ResidualCheck(matrix, sources, carriers, floors, names) + norms = np.sqrt(np.asarray(matrix.multiply(matrix).sum(axis=1)).ravel()) + unit = matrix + gram: np.ndarray | None = None + for _ in range(len(names) + 1): + # A total less some of its published parts gives back whatever they + # leave out, which need not be a row at all (Vahid's second review of + # #1344, item 17). A row the span check withholds can change the + # greedy groups of a total too large to search exactly, so the + # residuals are measured again on each pass. + residuals.withhold(withheld) held = [i for i, name in enumerate(names) if name in withheld and norms[i]] published = [ i for i, name in enumerate(names) if name not in withheld and norms[i] ] if not held or not published: break + if gram is None: + scale = np.divide(1.0, norms, out=np.zeros_like(norms), where=norms > 0) + unit = sparse.csr_matrix(sparse.diags(scale) @ matrix) + gram = (unit @ unit.T).toarray() recovery = _small_recovery(unit, gram, sources, held, published, floors) if recovery is None: break @@ -1161,6 +1187,604 @@ def uk_withheld_row_sets( return thin, withheld - thin +class _ResidualCheck: + """Withholds rows until no published total less some of its parts is thin. + + A total's parts are the other rows inside it: each one's households are + among the total's. A part's share is its entry over the total's, household + by household. Households whose parts all hold the same shares form an + atom: every combination of the total and its parts takes one value across + an atom, so its rest (the households where it is not nothing) is a union + of atoms. + + That gives a test first. An atom that rests on at least the total's + floor of source households can lie in no thin rest, so a thin combination + must vanish on every such atom. When every other atom's share column lies + within ``_SPAN_DISTANCE`` of the span of those atoms' columns + (:func:`_span_covers`), a combination with weights between minus one and + one that vanishes on them is within that distance of the total's entry + everywhere, and the total is clear (a UC family total over its payment + bands, a regional council-tax stock total over its bands, whenever each + cell rests on ten households or more). + + Where the test does not clear a total (amounts, whose households mostly + hold shares of their own), three kinds of combination are searched, each + the total less some of its parts: + + - less any set of members whose shares, summed, stay within one on every + atom (:meth:`_search` tries every such set, not only the largest). A + part whose share is the same on all its households (a count of children + beside the count of their households, a monthly band under a yearly + total) also stands as a part of share one. A member's shares lie above + nothing and at most one: a UC payment band within its family total, a + band holding half the entry of a household whose two benefit units sit + in two bands, an income component within a total-income band. A part of + whole numbers inside a total that is not, which holds the whole of the + total on none of its households, is likely a count beside an amount, + which multiplies the sets without partitioning anything, so the search + runs over the other members first and then over all of them; + - less any part on its own; + - less any two parts, which gives back, among other things, two + overlapping parts' overlap (claimants of either of two benefits less + the claimants of each). + + While a combination rests on one to nine source households (fewer than + the total's floor), the part with the fewest carriers among those it + subtracts is withheld, which adds that part's households to the rest, and + the check runs again. A total whose search passes its work or node cap, + or whose members times atoms pass ``_RESIDUAL_DENSE_LIMIT`` (the UK + population total, whose hundreds of person and household counts each + hold part of its entry), is searched over greedy groups of its members + instead, and a warning names it. Withholding a part only removes + combinations, so a total found clean stays clean; a greedily searched + total is searched again whenever one of its members is withheld. + + Not checked here: a total that is no row (a sum of published regions less + national bands), a part that reaches a household outside its total, and, + on a total the exact test does not clear, combinations at other weights + than these (three or more overlapping parts at once, for instance). + """ + + def __init__( + self, + matrix: sparse.csr_matrix, + sources: np.ndarray, + carriers: np.ndarray, + floors: np.ndarray, + names: list[str], + ) -> None: + self.carriers = np.asarray(carriers) + self.floors = np.asarray(floors) + self.names = names + matrix = sparse.csr_matrix(matrix, dtype=float) + self.matrix = matrix + self.size = np.diff(matrix.indptr) + # A missing source id counts as one source household of its own kind. + codes = np.asarray( + pd.factorize(pd.Series(np.asarray(sources)), use_na_sentinel=False)[0] + ) + self.codes = codes + support = sparse.csr_matrix( + (np.ones_like(matrix.data), matrix.indices, matrix.indptr), + shape=matrix.shape, + ) + overlap = (support @ support.T).toarray() + source_count = int(codes.max()) + 1 if len(codes) else 0 + owner = sparse.csr_matrix( + (np.ones(len(codes)), (np.arange(len(codes)), codes)), + shape=(len(codes), source_count), + ) + row_sources = sparse.csr_matrix(support @ owner) + row_sources.data[:] = 1.0 + #: Distinct source households two rows share. + self.shared_sources = (row_sources @ row_sources.T).toarray() + #: Per total: its parts' rows, their shares over its households (a row + #: may stand twice, as itself and scaled to share one), which parts + #: are members, and which are counts beside an amount. + self.parts: dict[ + int, tuple[np.ndarray, sparse.csr_matrix, np.ndarray, np.ndarray] + ] = {} + for total in np.flatnonzero(self.size): + inside = np.flatnonzero(overlap[:, total] == self.size) + inside = inside[(inside != total) & (self.size[inside] > 0)] + if not len(inside): + continue + start, stop = matrix.indptr[total], matrix.indptr[total + 1] + columns = matrix.indices[start:stop] + position = np.full(matrix.shape[1], -1) + position[columns] = np.arange(len(columns)) + block = sparse.csr_matrix(matrix[inside]) + local = position[block.indices] + share = sparse.csr_matrix( + (block.data / matrix.data[start:stop][local], local, block.indptr), + shape=(len(inside), len(columns)), + ) + starts = share.indptr[:-1] + low = np.minimum.reduceat(share.data, starts) + high = np.maximum.reduceat(share.data, starts) + whole_share = ( + np.add.reduceat( + (np.abs(share.data - 1) <= _RECOVERY_ROUNDING).astype(int), starts + ) + > 0 + ) + constant = (low * high > 0) & ( + np.abs(high - low) <= _RECOVERY_ROUNDING * np.abs(high) + ) + scaled = constant & (np.abs(high - 1) > _RECOVERY_ROUNDING) + counts = _whole_entries(block) & ~whole_share + if _whole_entries(matrix[total]).all(): + counts[:] = False + rows, shares = [inside], [share] + if scaled.any(): + ones = sparse.csr_matrix(share[scaled]) + ones.data = np.ones_like(ones.data) + rows.append(inside[scaled]) + shares.append(ones) + extra = int(scaled.sum()) + low = np.concatenate([low, np.ones(extra)]) + high = np.concatenate([high, np.ones(extra)]) + counts = np.concatenate([counts, np.zeros(extra, dtype=bool)]) + member = (low > 0) & (high <= 1 + _RECOVERY_ROUNDING) + self.parts[int(total)] = ( + np.concatenate(rows), + sparse.csr_matrix(sparse.vstack(shares)), + member, + counts, + ) + self._clean: set[int] = set() + #: Totals searched greedily: the live members their groups were last + #: searched over, and whether their single and paired parts are clean. + self._greedy_members: dict[int, bytes] = {} + self._parts_clean: set[int] = set() + #: Totals the span test did not clear (not tried again). + self._uncertified: set[int] = set() + self.capped: set[str] = set() + + def withhold(self, withheld: set[str]) -> None: + """Add to ``withheld`` until no published total's rest is thin.""" + + for _ in range(len(self.names) + 1): + pick = self._first_thin_rest(withheld) + if pick is None: + return + withheld.add(self.names[pick]) + + def _first_thin_rest(self, withheld: set[str]) -> int | None: + for total, (rows, _, member, _) in self.parts.items(): + if total in self._clean or self.names[total] in withheld: + continue + live = np.array([self.names[row] not in withheld for row in rows]) + if ( + total in self._parts_clean + and self._greedy_members.get(total) == (live & member).tobytes() + ): + continue + pick = self._thin_rest(total, live) + if pick is not None: + return pick + if self.names[total] in self.capped: + self._greedy_members[total] = (live & member).tobytes() + self._parts_clean.add(total) + else: + self._clean.add(total) + return None + + def _thin_rest(self, total: int, live: np.ndarray) -> int | None: + """The row to withhold so this total's rests stop being thin, or ``None``.""" + + all_rows, all_share, all_member, all_counts = self.parts[total] + rows = all_rows[live] + if not len(rows): + return None + share = sparse.csr_matrix(all_share[np.flatnonzero(live)]) + member = all_member[live] + counts = all_counts[live] + floor = int(self.floors[total]) + start, stop = self.matrix.indptr[total], self.matrix.indptr[total + 1] + _, local_codes = np.unique( + self.codes[self.matrix.indices[start:stop]], return_inverse=True + ) + atom, first = _share_atoms(share) + atom_sources = sparse.csr_matrix( + (np.ones(len(atom)), (atom, local_codes.ravel())), + shape=(len(first), int(local_codes.max()) + 1), + ) + atom_sources.sum_duplicates() + atom_sources.data[:] = 1.0 + atom_count = np.diff(atom_sources.indptr) + small = atom_count < floor + if not small.any(): + return None + atom_share = sparse.csr_matrix(share[:, first]) + name = self.names[total] + if total not in self._uncertified: + if _span_covers(atom_share, ~small): + return None + self._uncertified.add(total) + spent = [0] + + def carried(rest: np.ndarray) -> int: + """The rest's distinct source households, or ``floor`` once it has that many.""" + + atoms = np.flatnonzero(rest) + if not len(atoms): + return 0 + if atom_count[atoms].max() >= floor: + return floor + # Callers only compare the count with ``floor``, so a first slice + # of the rest that already reaches it settles the answer. + for chunk in (atoms[: 4 * floor], atoms): + selected = atom_sources[chunk] + spent[0] += selected.nnz + len(chunk) + count = len(np.unique(selected.indices)) + if count >= floor or len(chunk) == len(atoms): + return min(count, floor) + return floor + + def fewest(indices) -> int: + return min( + (int(rows[i]) for i in indices), + key=lambda row: (self.carriers[row], self.names[row]), + ) + + if name not in self.capped: + # The members bar the counts beside an amount first, which the + # search can finish where the counts would multiply its sets; + # then every member, with a fresh budget. + phases = [member & ~counts, member] if (member & counts).any() else [member] + for chosen in phases: + spent[0] = 0 + found = self._search( + atom_share, chosen, carried, floor, rows, small, spent + ) + if found is _CAPPED: + self._cap(name) + break + if found is not None: + return fewest(found) + if name in self.capped: + found = self._greedy(atom_share, member, carried, floor) + if found is not None: + return fewest(found) + if total in self._parts_clean: + return None + # One part on its own, and two parts together: shares at any value. + # A part (or pair) that leaves at least ``floor`` of the total's + # source households untouched leaves a rest of at least ``floor``. + uncovered = self.carriers[total] - self.carriers[rows] + alone = ~member | (name in self.capped) + for i in np.flatnonzero(alone & (uncovered < floor)): + row = atom_share[i].toarray().ravel() + count = carried(_unsettled(row)) + if 0 < count < floor: + return fewest([i]) + bound = ( + self.carriers[total] + - self.carriers[rows][:, None] + - self.carriers[rows][None, :] + + self.shared_sources[np.ix_(rows, rows)] + ) + for i, j in np.argwhere(np.triu(bound < floor, k=1)): + if rows[i] == rows[j]: + continue + pair = (atom_share[i] + atom_share[j]).toarray().ravel() + count = carried(_unsettled(pair)) + if 0 < count < floor: + return fewest([i, j]) + return None + + def _cap(self, name: str) -> None: + self.capped.add(name) + warnings.warn( + f"UK diagnostics: the residual search for {name} passed its cap, " + "so its members are grouped greedily.", + stacklevel=3, + ) + + def _search( + self, + atom_share: sparse.csr_matrix, + member: np.ndarray, + carried: Any, + floor: int, + rows: np.ndarray, + small: np.ndarray, + spent: list[int], + ) -> object: + """A set of members whose total less them is thin: branch and bound. + + Members join largest first while their shares, summed, stay within one + on every atom. Every set is tested, since a smaller set can leave a + thin rest that a larger one covers. A branch stops when its rest is + nothing, when every atom its rest could keep rests on at least + ``floor`` source households (a thin rest is a union of smaller + atoms), or when even every member it could still take leaves an atom + of that size uncovered. Returns the set's member indices, ``None``, or + ``_CAPPED`` past the work or node cap or the dense limit. + """ + + order = sorted( + np.flatnonzero(member).tolist(), + key=lambda i: (-int(self.size[rows[i]]), self.names[rows[i]]), + ) + if not order: + return None + atoms = atom_share.shape[1] + limit = 1 + _RECOVERY_ROUNDING + below = 1 - _RECOVERY_ROUNDING + # The bound errs toward searching on: reach comes from sums that can + # drift by a few ulps, so an atom counts as stranded only clearly so. + stranded_below = below - 1e-9 + big = ~small + # The root's bound, from sparse sums, before any dense block is made. + everything = np.asarray(atom_share[order].sum(axis=0)).ravel() + stranded = everything < stranded_below + if (stranded & big).any() or carried(stranded) >= floor: + return None + if len(order) * atoms > _RESIDUAL_DENSE_LIMIT: + return _CAPPED + dense = np.asarray(atom_share[order].todense()) + nodes = 0 + # Depth-first, one frame per chosen set: (chosen, summed, candidate + # positions into ``order``, next candidate to try, reach). ``reach`` + # is the summed shares with every candidate from the next one on, so + # the atoms it leaves under one stay uncovered whatever else joins. + stack: list[tuple[list[int], np.ndarray, list[int], int, np.ndarray]] = [ + ([], np.zeros(atoms), list(range(len(order))), 0, everything) + ] + while stack: + chosen, summed, candidates, k, reach = stack.pop() + if k == 0: + nodes += 1 + spent[0] += (len(candidates) + 1) * atoms + if nodes > _RESIDUAL_SEARCH_NODES or spent[0] > _RESIDUAL_SEARCH_WORK: + return _CAPPED + uncovered = summed < below + if not uncovered.any(): + continue + if not (uncovered & small).any(): + continue # every rest below here keeps an atom of floor size + count = carried(uncovered) + if chosen and 0 < count < floor: + return [order[i] for i in chosen] + if k >= len(candidates): + continue + stranded = reach < stranded_below + if (stranded & big).any() or carried(stranded) >= floor: + continue + head = candidates[k] + stack.append((chosen, summed, candidates, k + 1, reach - dense[head])) + joined = summed + dense[head] + if (joined > limit).any(): + continue + tail = candidates[k + 1 :] + if tail: + spent[0] += len(tail) * atoms + over = (joined + dense[tail] > limit).any(axis=1) + tail = [c for c, bad in zip(tail, over, strict=True) if not bad] + reach_after = joined + dense[tail].sum(axis=0) if tail else joined.copy() + stack.append(([*chosen, head], joined, tail, 0, reach_after)) + return None + + def _greedy( + self, + atom_share: sparse.csr_matrix, + member: np.ndarray, + carried: Any, + floor: int, + ) -> list[int] | None: + """A greedy group of members whose total less them is thin, or ``None``. + + For a total the exact search cannot finish. Each member seeds two + groups, which take the others that clash with none already in them, + once largest first and once those clashing with fewest others first. + A group's rest is wherever its shares do not sum to one, so a group + that passes one somewhere is still measured. + """ + + members = np.flatnonzero(member) + if not len(members): + return None + share = sparse.csr_matrix(atom_share[members]) + support = share.copy() + support.data = np.ones_like(support.data) + clash = (support @ support.T).toarray() > 0 + high = share.copy() + high.data = (high.data > 0.5 + _RECOVERY_ROUNDING).astype(float) + high.eliminate_zeros() + sure = (high @ high.T).toarray() > 0 + np.fill_diagonal(clash, False) + csc = share.tocsc() + for a in range(len(members)): + doubt = np.flatnonzero(clash[a] & ~sure[a]) + doubt = doubt[doubt > a] + if not len(doubt): + continue + # Two members clash where both hold a share and the two pass one. + columns = share.indices[share.indptr[a] : share.indptr[a + 1]] + values = share.data[share.indptr[a] : share.indptr[a + 1]] + block = sparse.csr_matrix(csc[:, columns][doubt]) + hit = block.data + values[block.indices] > 1 + _RECOVERY_ROUNDING + row_of = np.repeat(np.arange(len(doubt)), np.diff(block.indptr)) + over = np.zeros(len(doubt), dtype=bool) + over[np.unique(row_of[hit])] = True + clash[a, doubt] = clash[doubt, a] = over + size = np.diff(share.indptr) + orders = ( + sorted(range(len(members)), key=lambda i: (-size[i], i)), + sorted( + range(len(members)), + key=lambda i: (int(clash[i].sum()), -size[i], i), + ), + ) + tried: set[frozenset[int]] = set() + for order in orders: + for seed in order: + chosen = [seed] + blocked = clash[seed].copy() + for other in order: + if other != seed and not blocked[other]: + chosen.append(other) + blocked |= clash[other] + key = frozenset(chosen) + if key in tried: + continue + tried.add(key) + group = sorted(chosen) + summed = np.asarray(share[group].sum(axis=0)).ravel() + count = carried(_unsettled(summed)) + if 0 < count < floor: + return members[group].tolist() + return None + + +#: How far a small atom's share column may lie from the span of the big +#: atoms' columns, both as a unit column and summed over its entries as +#: stored (where the total's share is one), for the span test to clear its +#: total. A tenth of the rounding share, fixed: it does not grow with atoms +#: or parts. +_SPAN_DISTANCE = 0.1 * _RECOVERY_ROUNDING +#: Big-atom directions weaker than this share of the strongest singular value +#: are left out of the span, as the search counts a big atom held within the +#: rounding share of one as covered. Leaving directions out only shrinks the +#: span. +_SPAN_WEAK = 10 * _RECOVERY_ROUNDING + + +def _span_covers(atom_share: sparse.csr_matrix, big: np.ndarray) -> bool: + """Whether the big atoms' share columns, with the total's, span every atom's. + + Each atom's column is the total's share (one) over the parts' shares. A + combination of the total and its parts that vanishes on every big atom + vanishes on every atom when the big columns span them all. Measured + directly, not through ranks: with the span taken from the big atoms' unit + columns by their singular vectors, every small atom's column must lie + within ``_SPAN_DISTANCE`` of it, as a unit column (on the shares as they + are, and with each row scaled to its largest share, so a part of tiny + shares beside one of large shares is not lost) and summed over its stored + entries (so a combination with weights between minus one and one, as + every searched one has, that vanishes on the big atoms is within that + distance of the total's entry on every small atom, whatever the atom's + largest share or the number of parts). Only all three together clear it. + """ + + if not big.any(): + return False + count = atom_share.shape[1] + rows = sparse.vstack( + [sparse.csr_matrix(np.ones((1, count))), sparse.csr_matrix(atom_share)] + ).tocsr() + peak = np.maximum.reduceat(np.abs(rows.data), rows.indptr[:-1]) + scaled = sparse.csr_matrix(sparse.diags(1.0 / peak) @ rows) + return _within_span(rows, big, stored=True) and _within_span(scaled, big) + + +def _within_span( + rows: sparse.csr_matrix, big: np.ndarray, *, stored: bool = False +) -> bool: + """Whether every small atom's column lies within ``_SPAN_DISTANCE`` of the + big atoms' span: as a unit column, and with ``stored`` also summed over its + entries as they are in ``rows``.""" + + if rows.shape[0] * int(big.sum()) > _RESIDUAL_DENSE_LIMIT: + return False # too large to factor here; the search decides instead + norms = np.sqrt(np.asarray(rows.multiply(rows).sum(axis=0)).ravel()) + unit = sparse.csr_matrix(rows @ sparse.diags(1.0 / norms)).tocsc() + block = unit[:, np.flatnonzero(big)].toarray() + # Singular values reveal a weak direction that pivoted QR's diagonal can + # miss on a graded block. + vectors, strength, _ = scipy_linalg.svd(block, full_matrices=False) + if not len(strength) or strength[0] <= 0: + return False + basis = vectors[:, strength > _SPAN_WEAK * strength[0]] + small = np.flatnonzero(~big) + for start in range(0, len(small), 4096): + chunk = small[start : start + 4096] + columns = unit[:, chunk].toarray() + rest = columns - basis @ (basis.T @ columns) + if (np.linalg.norm(rest, axis=0) > _SPAN_DISTANCE).any(): + return False + if stored and (np.abs(rest).sum(axis=0) * norms[chunk] > _SPAN_DISTANCE).any(): + return False + return True + + +def _whole_entries(rows: sparse.csr_matrix) -> np.ndarray: + """Whether each row's entries are all whole numbers (a count).""" + + rows = sparse.csr_matrix(rows) + if not rows.shape[0]: + return np.zeros(0, dtype=bool) + whole = np.abs(rows.data - np.round(rows.data)) <= _RECOVERY_ROUNDING + off = ( + np.add.reduceat((~whole).astype(int), rows.indptr[:-1]) if len(rows.data) else 0 + ) + filled = np.diff(rows.indptr) > 0 + return filled & (np.asarray(off) == 0) + + +def _unsettled(summed: np.ndarray) -> np.ndarray: + """Where a total less parts summing to ``summed`` is not nothing. + + One rule for every check, the search's own: a share within the rounding + share of one, either side, counts as covered. A convention, at the exact + tie: there a rest can lose a household. + """ + + return (summed < 1 - _RECOVERY_ROUNDING) | (summed > 1 + _RECOVERY_ROUNDING) + + +#: What :meth:`_ResidualCheck._search` returns past its caps. +_CAPPED = object() + + +def _share_atoms(share: sparse.csr_matrix) -> tuple[np.ndarray, np.ndarray]: + """Group a total's households by the shares its parts hold in them. + + Returns each household's atom and, per atom, one household standing for + it. Each household's column of shares is hashed exactly: every (part, + share) entry, with the share's lowest twelve mantissa bits dropped so + float noise does not split a column, goes through two 64-bit mixers, and + a column's keys are their wrapping sums with its entry count. Two + households share an atom only when their columns agree entry for entry + to within the dropped bits (about 1e-12 of each share), barring a + collision of both 64-bit sums; a share on the edge of the dropped bits + can split an atom, which only costs time. + """ + + count = share.shape[1] + if not count: + return np.zeros(0, dtype=np.int64), np.zeros(0, dtype=np.int64) + csc = sparse.csc_matrix(share) + csc.sort_indices() + filled = np.diff(csc.indptr) + column = np.repeat(np.arange(count), filled) + bits = np.ascontiguousarray(csc.data, dtype=np.float64).view(np.uint64) + bits = bits & ~np.uint64(0xFFF) + part = csc.indices.astype(np.uint64) + keys = [] + with np.errstate(over="ignore"): + for salt in (np.uint64(0x9E3779B97F4A7C15), np.uint64(0xD1B54A32D192ED03)): + entry = _mix64(_mix64(part * salt + np.uint64(1)) ^ bits) + total = np.zeros(count, dtype=np.uint64) + np.add.at(total, column, entry) + keys.append(total) + table = np.column_stack([filled.astype(np.uint64), *keys]) + _, first, atom = np.unique(table, axis=0, return_index=True, return_inverse=True) + return atom.ravel(), first + + +def _mix64(values: np.ndarray) -> np.ndarray: + """SplitMix64's finaliser, elementwise on unsigned 64-bit integers.""" + + with np.errstate(over="ignore"): + values = values + np.uint64(0x9E3779B97F4A7C15) + values = (values ^ (values >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9) + values = (values ^ (values >> np.uint64(27))) * np.uint64(0x94D049BB133111EB) + return values ^ (values >> np.uint64(31)) + + def _small_recovery( unit: sparse.csr_matrix, gram: np.ndarray, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_annual_income.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_annual_income.py new file mode 100644 index 000000000..d2bf9d01b --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_annual_income.py @@ -0,0 +1,1077 @@ +"""Annual income descriptors on the UK FRS spine. + +HMRC's income statistics (the Survey of Personal Incomes and the Income Tax +liabilities statistics) count the income each taxpayer received over a tax +year, banded by what they received. They cover individuals liable to UK +income tax, so a person who left a job counts only where the year's income +makes them liable. The spine's income columns are current, annualised +incomes, the concept HBAI and policyengine-uk use: the usual weekly pay of +the job held at interview times 365.25 / 7. A person who started a job during +the year, left one, changed jobs or had a gap sits in another HMRC band than +their current pay puts them in, or in none, and a recent leaver who paid tax +in the year is no taxpayer at all. + +This stage writes what the FRS records about the 12 months before the +interview, for every adult, as descriptors beside the current columns, which +it never changes: + +* ``employment_income_annual_share``: the share of the 12 months paid at the + current pay rate (1 for a full-year employee, 0 with no current pay); +* ``employment_income_other_spells``: annual employee pay from spells outside + the current job, chiefly the job a recent leaver left (FRS basis, GBP a year + at the interview's prices); +* ``self_employment_income_annual_share`` and + ``self_employment_income_other_spells``: the same for self-employment; +* ``private_pension_income_annual_share``: the share of the year a private + pension was in payment for someone who retired during it; +* ``contributory_benefits_annual_share``: the share of the year contributory + JSA and ESA and Incapacity Benefit were in payment for someone who left work + during it. + +An annual amount is the current amount times its share plus the other spells. +So the year is a proxy for HMRC's tax year, not the tax year itself: the 12 +months before each interview (interviews from April 2024 to March 2025), +applied as shares and spells to the engine's amounts at the calibration year +(2025), and fitted to HMRC targets for calendar 2025 (the SPI 2023-24 rows +uprated to it, and the Income Tax liabilities rows' calendar-2025 window). +Only the calibration's HMRC measures read the descriptors; poverty, benefits +and the published engine's tax keep reading current income. The State Pension +needs no descriptor: policyengine-uk's ``months_since_state_pension_age`` +gives the share of the year it was in payment. + +What the survey records (UKDS SN 9563, the 2024-25 question instructions and +variable listing; every amount is GBP a week in the EUL): + +* ``ADULT.CURRJOBM`` (DV): months in the current job; ``JOB.WRKPREV``: what the + person did just before it, asked when the job is under 12 months old. Code 1 + ("in paid employment") includes a break of under three months. +* ``ADULT.SAMESIT`` (all adults) and ``SDEMP01``-``SDEMP12``, the self-defined + activity one to twelve months before the interview, asked when SAMESIT is 1. + The calendar under-records change: many people who started or left a job + in the year answer SAMESIT 2, so it decides months only where it records a + change, and never outranks CURRJOBM, WRKPREV or LSTYR. +* ``ADULT.NUMJOB2`` and the JOB rows of people out of work who worked in the + last 12 months: their last job's type and its usual pay, ``JOB.UGROSS`` + ("people currently out of work who last worked in the previous 12 months + ... in respect of that employment", question instructions p. 220), for + employee last jobs. ``ADULT.LSTYR``: weeks of regular paid work in the last + 12 months; ``ADULT.LSTWRK1``/``LSTWRK2``: when they left; + ``ADULT.RETIRE``/``RETIRE1``: retired in the last 12 months, and the month. +* ``JOB.SEJBLONG``/``SEJBMTHS``: time in the current self-employed job; + ``JOB.SEINCAMT`` (else ``PROFIT1``) and ``JOB.SELWKS`` on a ceased + self-employed last job. ``ADULT.SEINCAM2`` is already a 12-month concept + (accounts profit or a 12-month average) the FRS uprates by average earnings + to the interview, so continuing self-employment keeps its whole year and its + amount is not uprated again. A ceased business's SEINCAMT (else PROFIT1) is + the amount when it ceased, so it is uprated by average earnings for the + months since, as a leaver's last pay is. + +The rules, in order of precedence (a person in work at interview is never a +leaver): + +* A current earner (``employment_income > 0``) is paid at today's rate for + every month of the window that the current job, or an earlier spell, covers: + 12 months when CURRJOBM is 12 or more; for a job changer (WRKPREV 1) the + months in the job plus, where SAMESIT is 1, the calendar's employee months + before it started, and otherwise the whole year (the respondent reports no + change of situation); for an entrant the months in the job plus the + calendar's employee months before it, where SAMESIT is 1. +* An employee in work without recorded pay keeps none for the current job, as + the current columns do, and is paid a donor's rate for the calendar's + employee months before the job started. Anyone else in work (chiefly people + who moved from employment to self-employment) is paid a donor's rate for the + calendar's employee months. +* A leaver whose last job was an employee job works LSTYR x 12 / 52 months + where LSTYR is positive (LSTYR counts *regular* work, so zero falls + through), else the calendar's employee months, else ``LEAVING_DATE_SHARE`` of + the months between the window's start and leaving (LSTWRK1/2, else RETIRE1); + a leaving date 12 months or more before the interview gives none. Their pay + is the last job's usual gross pay, scaled to the DV concept by the weighted + job-level UGRSPAY / UGROSS ratio on current employees who report both, and + uprated by average earnings for the months since they left; without it, a + donor's rate. +* A person out of work outside the last-job block takes the calendar's + employee months at a donor's rate. +* Donors are current employee earners in their first ``DONOR_RECENT_START_MONTHS`` + months in the job, closer to the pay of part-year workers than long-tenure + employees, drawn by GROSS4 weight within age band x sex x full- or part-time + cells, which drop sex below ``MINIMUM_DONORS``. The draw is an inverse-CDF + read of one identity-keyed uniform per person. +* Self-employment: the whole year where SEJBLONG is one or more, (SEJBMTHS + 1) + / 12 for a business under a year old, and at most the calendar's + self-employed months where SAMESIT is 1 and self-employment is the activity + today (the calendar records the main activity only, so a side business + beside a job keeps its whole year). A ceased self-employed last job + pays SEINCAMT (else PROFIT1, a loss as zero), uprated by average earnings + for the months since it ceased, for SELWKS x 12 / 52 months, else LSTYR, the + calendar or the leaving date. +* A private pension is in payment for (months since RETIRE1 + 0.5) / 12 of the + year after retiring in it; contributory benefits for the months not worked + by a leaver or a ceased self-employed person. + +The receipt is build output and follows UKDS disclosure control +(:mod:`receipt_counts`): every cell gives its support as a band of distinct +source households, and a weighted figure only where ten or more carry it. +""" + +from __future__ import annotations + +import json +from collections.abc import Mapping +from dataclasses import dataclass, field +from importlib import resources as importlib_resources +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd + +from microcosm.build.source_manifest import SourceStageSpec +from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.frs_spine import ( + WEEKS_IN_YEAR, + frs_interview_dates, + normalize_ids, + read_pinned_tab, +) +from microcosm.build.uk_runtime.national_frame import ( + uk_household_weight_kind, + uk_national_frame, + uk_time_period, + validate_uk_national_frame, +) +from microcosm.build.uk_runtime.receipt_counts import ( + distinct_carriers, + extreme_count, + small_cell_share, + support_band, +) +from microcosm.frame import Frame + +FRS_ANNUAL_INCOME_STAGE_NAME = "frs_annual_income_spells" +FRS_ANNUAL_INCOME_OPERATION_KIND = "derive_annual_income_spells" + +EMPLOYMENT_INCOME_ANNUAL_SHARE = "employment_income_annual_share" +EMPLOYMENT_INCOME_OTHER_SPELLS = "employment_income_other_spells" +SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE = "self_employment_income_annual_share" +SELF_EMPLOYMENT_INCOME_OTHER_SPELLS = "self_employment_income_other_spells" +PRIVATE_PENSION_INCOME_ANNUAL_SHARE = "private_pension_income_annual_share" +CONTRIBUTORY_BENEFITS_ANNUAL_SHARE = "contributory_benefits_annual_share" +#: The descriptors in output order. A share is 1 for a whole year; an other +#: spells amount is GBP a year on the FRS basis. +FRS_ANNUAL_INCOME_OUTPUT_COLUMNS = ( + EMPLOYMENT_INCOME_ANNUAL_SHARE, + EMPLOYMENT_INCOME_OTHER_SPELLS, + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE, + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, + PRIVATE_PENSION_INCOME_ANNUAL_SHARE, + CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, +) +ANNUAL_SHARE_COLUMNS = ( + EMPLOYMENT_INCOME_ANNUAL_SHARE, + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE, + PRIVATE_PENSION_INCOME_ANNUAL_SHARE, + CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, +) +OTHER_SPELLS_COLUMNS = ( + EMPLOYMENT_INCOME_OTHER_SPELLS, + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, +) +#: The current columns each descriptor qualifies; the stage never writes them. +CURRENT_INCOME_COLUMNS = ("employment_income", "self_employment_income") + +WINDOW_MONTHS = 12 +#: The survey's activity codes (SELFDEMP, SDEMP01-12; variable listing). +SELF_DEFINED_EMPLOYEE_CODES = (1, 2) +SELF_DEFINED_PART_TIME_EMPLOYEE_CODE = 2 +SELF_DEFINED_SELF_EMPLOYED_CODES = (3, 4) +#: ADULT.SAMESIT 1: the respondent's situation changed in the last 12 months. +SITUATION_CHANGED_CODE = 1 +#: ADULT.EMPSTATI full- and part-time employee; 2 is part-time. +ILO_EMPLOYEE_CODES = (1, 2) +ILO_PART_TIME_EMPLOYEE_CODE = 2 +#: ADULT.WORKING and JOBAWAY 1: did paid work last week, or was away from a job. +WORKING_CODE = 1 +#: JOB.WRKPREV 1: in paid employment just before the current job. +WRKPREV_PAID_EMPLOYMENT_CODE = 1 +#: JOB.ETYPE 1 is an employee; 2 to 7 every self-employed description. +ETYPE_EMPLOYEE_CODE = 1 +ETYPE_SELF_EMPLOYED_CODES = (2, 3, 4, 5, 6, 7) +#: JOB.FTPT 2: part time. +JOB_PART_TIME_CODE = 2 +#: JOB.PROFIT2 2: PROFIT1 is a loss. +PROFIT_LOSS_CODE = 2 +#: ADULT.RETIRE 1: retired in the last 12 months. +RETIRED_IN_WINDOW_CODE = 1 + +#: The share of the months between the window's start and the leaving date a +#: leaver with no weeks or calendar months is taken to have worked: leavers +#: with LSTYR work about three quarters of the months open to them. +LEAVING_DATE_SHARE = 0.75 +#: Months open to a leaver whose leaving date is unknown, before the share. +UNKNOWN_LEAVING_MONTHS = 6.0 +DONOR_RECENT_START_MONTHS = 24 +MINIMUM_DONORS = 30 +DONOR_AGE_EDGES = (25, 35, 50, 65) +DRAW_SEED = 0 +DRAW_SALT = EMPLOYMENT_INCOME_OTHER_SPELLS +#: Leavers' usual pay is from when they left; it is uprated by the pinned OBR +#: average earnings index over the survey window (2024 over 2023), as many +#: months as they have been gone, at most two years. +EARNINGS_INDEX_RESOURCE = "hmrc_uprating_engine_pins.json" +EARNINGS_INDEX_PARAMETER = "gov.economic_assumptions.indices.obr.average_earnings" +EARNINGS_INDEX_FROM = "2023-01-01" +EARNINGS_INDEX_TO = "2024-01-01" +MAXIMUM_UPRATING_MONTHS = 24 +#: The ratio check: the job-level DV-to-usual pay ratio must lie in this range. +PAY_CONCEPT_RATIO_BOUNDS = (0.95, 1.10) + +_ADULT_COLUMNS = ( + "sernum", + "benunit", + "person", + "samesit", + "selfdemp", + *(f"sdemp{month:02d}" for month in range(1, 13)), + "working", + "jobaway", + "numjob2", + "currjobm", + "prevjobm", + "lstwrk1", + "lstwrk2", + "lstyr", + "retire", + "retire1", + "empstati", +) +_JOB_COLUMNS = ( + "sernum", + "benunit", + "person", + "jobtype", + "etype", + "ftpt", + "wrkprev", + "ugross", + "ugrspay", + "sejblong", + "sejbmths", + "selwks", + "seincamt", + "profit1", + "profit2", +) +_HOUSEHOLD_COLUMNS = ("sernum", "intdate") +_FRS_TABLES = ("adult", "job", "househol") + +#: Spell sources, for the receipt. +SOURCE_FULL_YEAR = "current, 12 or more months in the job" +SOURCE_CHANGER_CALENDAR = "current, job changer, months from the calendar" +SOURCE_CHANGER_CONTINUOUS = "current, job changer, situation unchanged" +SOURCE_ENTRANT = "current, entrant" +SOURCE_UNPAID_EARLIER = "in work without recorded pay, earlier spell" +SOURCE_SWITCHER = "in work, employee months before the current activity" +SOURCE_LEAVER_LSTYR = "leaver, weeks of regular work" +SOURCE_LEAVER_CALENDAR = "leaver, months from the calendar" +SOURCE_LEAVER_LEAVING_DATE = "leaver, months from the leaving date" +SOURCE_CALENDAR_ONLY = "out of work, months from the calendar only" +SOURCE_CEASED_SELF_EMPLOYED = "ceased self-employed" +SPELL_SOURCES = ( + SOURCE_FULL_YEAR, + SOURCE_CHANGER_CALENDAR, + SOURCE_CHANGER_CONTINUOUS, + SOURCE_ENTRANT, + SOURCE_UNPAID_EARLIER, + SOURCE_SWITCHER, + SOURCE_LEAVER_LSTYR, + SOURCE_LEAVER_CALENDAR, + SOURCE_LEAVER_LEAVING_DATE, + SOURCE_CALENDAR_ONLY, + SOURCE_CEASED_SELF_EMPLOYED, +) + + +@dataclass(frozen=True) +class UKFRSAnnualIncomeResult: + """Output frame and aggregate receipt for the annual income descriptors.""" + + frame: Frame + receipt: dict[str, object] + + def evidence(self) -> dict[str, object]: + return {"stage": FRS_ANNUAL_INCOME_STAGE_NAME, **self.receipt} + + +@dataclass(frozen=True) +class UKFRSAnnualIncomeStageTransform: + """Whole-stage callable for the FRS annual income descriptors.""" + + raw_dir: str | Path + stage: SourceStageSpec + last_result: UKFRSAnnualIncomeResult | None = field(default=None, init=False) + + def __call__(self, frame: Frame) -> Frame: + _assert_stage_parameters(self.stage) + result = add_frs_annual_income_spells(frame, self.raw_dir, stage=self.stage) + object.__setattr__(self, "last_result", result) + return result.frame + + @staticmethod + def output_columns() -> tuple[str, ...]: + return FRS_ANNUAL_INCOME_OUTPUT_COLUMNS + + 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 frs_annual_income_operation_parameters() -> tuple[ + tuple[str, dict[str, object]], ... +]: + """The reviewed operations the manifest must carry verbatim.""" + + return ( + ( + "read_tables", + { + "format": "tab", + "delimiter": "\t", + "lowercase_columns": True, + "numeric_errors": "coerce", + "runtime_sha256_required": True, + }, + ), + ( + FRS_ANNUAL_INCOME_OPERATION_KIND, + { + "outputs": list(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS), + "window_months": WINDOW_MONTHS, + "leaving_date_share": LEAVING_DATE_SHARE, + "unknown_leaving_months": UNKNOWN_LEAVING_MONTHS, + "donor_recent_start_months": DONOR_RECENT_START_MONTHS, + "minimum_donors": MINIMUM_DONORS, + "donor_age_edges": list(DONOR_AGE_EDGES), + "seed": DRAW_SEED, + "salt": DRAW_SALT, + "earnings_index": EARNINGS_INDEX_PARAMETER, + "earnings_index_from": EARNINGS_INDEX_FROM, + "earnings_index_to": EARNINGS_INDEX_TO, + "maximum_uprating_months": MAXIMUM_UPRATING_MONTHS, + }, + ), + ) + + +def add_frs_annual_income_spells( + frame: Frame, raw_dir: str | Path, *, stage: SourceStageSpec +) -> UKFRSAnnualIncomeResult: + """Add the annual income descriptors from the pinned FRS tabs.""" + + artifacts = _artifact_by_table(stage) + tables = { + name: normalize_ids( + read_pinned_tab( + Path(raw_dir) / str(artifacts[name]["locator"]), + artifacts[name], + columns=columns, + ) + ) + for name, columns in ( + ("adult", _ADULT_COLUMNS), + ("job", _JOB_COLUMNS), + ("househol", _HOUSEHOLD_COLUMNS), + ) + } + person = frame.table("person") + household = frame.table("household") + household_weights = pd.Series( + frame.weights_for("household").values, + index=household["household_id"].to_numpy(), + ) + person_weights = ( + person["person_household_id"].map(household_weights).to_numpy(dtype=float) + ) + if not np.isfinite(person_weights).all(): + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} needs a household weight for every " + "person row." + ) + interview = frs_interview_dates(tables["househol"].set_index("household_id")) + descriptors, receipt = derive_frs_annual_income_spells( + person, + person_weights=person_weights, + adult=tables["adult"], + job=tables["job"], + interview=interview, + earnings_growth=load_earnings_growth(), + ) + output = person.copy() + for column in FRS_ANNUAL_INCOME_OUTPUT_COLUMNS: + output[column] = descriptors[column].to_numpy(dtype=np.float64) + result = uk_national_frame( + person=output, + benunit=frame.table("benunit"), + household=household, + time_period=uk_time_period(frame), + weight_kind=uk_household_weight_kind(frame), + household_weights=frame.weights_for("household").values, + mass_log=frame.mass_log, + ) + validate_uk_national_frame(result) + return UKFRSAnnualIncomeResult(frame=result, receipt=receipt) + + +def load_earnings_growth() -> float: + """The pinned OBR average earnings index's growth over the survey window.""" + + payload = json.loads( + importlib_resources.files("microcosm.build.uk") + .joinpath(EARNINGS_INDEX_RESOURCE) + .read_text() + ) + values = payload["values"][EARNINGS_INDEX_PARAMETER] + growth = float(values[EARNINGS_INDEX_TO]) / float(values[EARNINGS_INDEX_FROM]) - 1 + if not np.isfinite(growth) or not 0.0 <= growth < 0.25: + raise ValueError( + f"{EARNINGS_INDEX_PARAMETER} grows {growth!r} from {EARNINGS_INDEX_FROM} " + f"to {EARNINGS_INDEX_TO}; expected a finite rate in [0, 0.25)." + ) + return growth + + +def derive_frs_annual_income_spells( + person: pd.DataFrame, + *, + person_weights: np.ndarray, + adult: pd.DataFrame, + job: pd.DataFrame, + interview: pd.Series, + earnings_growth: float, +) -> tuple[pd.DataFrame, dict[str, object]]: + """Person-aligned descriptors and their aggregate receipt. + + ``person`` is the spine's person table at the raw FRS identity + (``person_id`` = sernum * 1000 + person). ``adult`` and ``job`` are the + pinned tabs after :func:`normalize_ids`; ``interview`` is the interview date + by ``household_id``. ``person_weights`` are the household weights each + person row carries. + """ + + person_ids = person["person_id"].to_numpy() + rows = len(person) + weights = np.asarray(person_weights, dtype=float) + if weights.shape != (rows,): + raise ValueError("person weights must align with the person table.") + raw = adult.drop_duplicates("person_id").set_index("person_id") + if len(raw) != len(adult): + raise ValueError("FRS adult.tab carries a person twice.") + is_adult = np.isin(person_ids, raw.index.to_numpy()) + a = raw.reindex(person_ids) + main = ( + job.loc[_numeric(job, "jobtype") == 1] + .drop_duplicates("person_id") + .set_index("person_id") + .reindex(person_ids) + ) + + current = _amount(person, "employment_income") + pay = (current > 0) & is_adult + se_current = _amount(person, "self_employment_income") + working = (_numeric(a, "working") == WORKING_CODE) | ( + _numeric(a, "jobaway") == WORKING_CODE + ) + ilo = _numeric(a, "empstati") + ilo_employee = np.isin(ilo, ILO_EMPLOYEE_CODES) + + # Twelve months ending at the interview: month 0 is now (SELFDEMP), months + # 1-11 are SDEMP01-SDEMP11. Only a reported change of situation carries a + # calendar: SAMESIT 2 copies today's status into every month. + changed = _numeric(a, "samesit") == SITUATION_CHANGED_CODE + now = _numeric(a, "selfdemp") + calendar = np.column_stack( + [now, *(_numeric(a, f"sdemp{month:02d}") for month in range(1, 12))] + ) + calendar = np.where(changed[:, None], calendar, now[:, None]) + calendar_employee = ( + np.isin(calendar, SELF_DEFINED_EMPLOYEE_CODES) & changed[:, None] + ) + calendar_self_employed = ( + np.isin(calendar, SELF_DEFINED_SELF_EMPLOYED_CODES) & changed[:, None] + ) + calendar_employee_months = calendar_employee.sum(axis=1).astype(float) + calendar_self_employed_months = calendar_self_employed.sum(axis=1).astype(float) + + months_in_job = _numeric(a, "currjobm") + in_job = np.where( + np.isfinite(months_in_job), + np.minimum(np.floor(np.maximum(months_in_job, 0.0)) + 1.0, WINDOW_MONTHS), + float(WINDOW_MONTHS), + ) + before_job = ( + calendar_employee & (np.arange(WINDOW_MONTHS)[None, :] >= in_job[:, None]) + ).sum(axis=1) + wrkprev = _numeric(main, "wrkprev") + + source = np.full(rows, "", dtype=object) + employment_share = np.zeros(rows) + other_months = np.zeros(rows) + + full_year = pay & (in_job >= WINDOW_MONTHS) + changer = pay & ~full_year & (wrkprev == WRKPREV_PAID_EMPLOYMENT_CODE) + entrant = pay & ~full_year & ~changer + employment_share[full_year] = 1.0 + employment_share[changer & changed] = ( + np.minimum(in_job + before_job, WINDOW_MONTHS) / WINDOW_MONTHS + )[changer & changed] + employment_share[changer & ~changed] = 1.0 + employment_share[entrant] = ( + np.minimum(in_job + before_job, WINDOW_MONTHS) / WINDOW_MONTHS + )[entrant] + source[full_year] = SOURCE_FULL_YEAR + source[changer & changed] = SOURCE_CHANGER_CALENDAR + source[changer & ~changed] = SOURCE_CHANGER_CONTINUOUS + source[entrant] = SOURCE_ENTRANT + + unpaid = is_adult & working & ilo_employee & ~pay + unpaid_months = np.where(in_job < WINDOW_MONTHS, before_job, 0).astype(float) + other_months = np.where(unpaid, unpaid_months, other_months) + source[unpaid & (unpaid_months > 0)] = SOURCE_UNPAID_EARLIER + switcher = is_adult & working & ~pay & ~unpaid & (calendar_employee_months > 0) + other_months = np.where(switcher, calendar_employee_months, other_months) + source[switcher] = SOURCE_SWITCHER + + # Out of work: the last-job block (NUMJOB2 and its JOB rows). + etype = _numeric(main, "etype") + last_job = is_adult & ~working & np.isfinite(_numeric(a, "numjob2")) + employee_leaver = last_job & (etype == ETYPE_EMPLOYEE_CODE) + ceased_self_employed = last_job & np.isin(etype, ETYPE_SELF_EMPLOYED_CODES) + interview_dates = pd.to_datetime( + pd.Series(person["person_household_id"].to_numpy()).map(interview) + ) + if interview_dates.isna().any(): + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} needs an interview date for every " + "person's household." + ) + interview_year = interview_dates.dt.year.to_numpy(dtype=float) + interview_month = interview_dates.dt.month.to_numpy(dtype=float) + left_year = _numeric(a, "lstwrk2") + left_month = _numeric(a, "lstwrk1") + months_since_left = np.where( + np.isfinite(left_month), + (interview_year - left_year) * 12.0 + (interview_month - left_month), + (interview_year - left_year) * 12.0, + ) + retired = _numeric(a, "retire") == RETIRED_IN_WINDOW_CODE + months_since_retiring = np.mod(interview_month - _numeric(a, "retire1"), 12.0) + months_since_left = np.where( + np.isfinite(months_since_left), + months_since_left, + np.where(retired, months_since_retiring, np.nan), + ) + leaving_date_months = np.where( + np.isfinite(months_since_left), + np.where( + months_since_left >= WINDOW_MONTHS, + 0.0, + LEAVING_DATE_SHARE + * (WINDOW_MONTHS - np.clip(months_since_left, 0.0, WINDOW_MONTHS)), + ), + LEAVING_DATE_SHARE * UNKNOWN_LEAVING_MONTHS, + ) + weeks_worked = _numeric(a, "lstyr") + regular_weeks = np.isfinite(weeks_worked) & (weeks_worked > 0) + leaver_months = np.where( + regular_weeks, + np.minimum(weeks_worked, 52.0) * WINDOW_MONTHS / 52.0, + np.where( + calendar_employee_months > 0, calendar_employee_months, leaving_date_months + ), + ) + other_months = np.where(employee_leaver, leaver_months, other_months) + source[employee_leaver & regular_weeks] = SOURCE_LEAVER_LSTYR + source[employee_leaver & ~regular_weeks & (calendar_employee_months > 0)] = ( + SOURCE_LEAVER_CALENDAR + ) + source[employee_leaver & ~regular_weeks & ~(calendar_employee_months > 0)] = ( + SOURCE_LEAVER_LEAVING_DATE + ) + calendar_only = ( + is_adult + & ~working + & ~employee_leaver + & ~ceased_self_employed + & (calendar_employee_months > 0) + ) + other_months = np.where(calendar_only, calendar_employee_months, other_months) + source[calendar_only] = SOURCE_CALENDAR_ONLY + + # Pay for those months: the last job's usual pay, else a donor's rate. + uprating = (1.0 + float(earnings_growth)) ** ( + np.clip( + np.nan_to_num(months_since_left, nan=UNKNOWN_LEAVING_MONTHS), + 0.0, + MAXIMUM_UPRATING_MONTHS, + ) + / WINDOW_MONTHS + ) + ratio, ratio_support = pay_concept_ratio(person_ids, weights, job, working) + usual = _numeric(main, "ugross") + observed = employee_leaver & np.isfinite(usual) & (usual > 0) + weekly_rate = np.zeros(rows) + weekly_rate[observed] = (usual * ratio * uprating)[observed] + needs_donor = (other_months > 0) & ~observed + age = _amount(person, "age") + male = (person["gender"].astype(str) == "MALE").to_numpy() + part_time = np.isin(calendar, (SELF_DEFINED_PART_TIME_EMPLOYEE_CODE,)).any( + axis=1 + ) & ~np.isin(calendar, (SELF_DEFINED_EMPLOYEE_CODES[0],)).any(axis=1) + ftpt = _numeric(main, "ftpt") + part_time = np.where( + employee_leaver & np.isfinite(ftpt), ftpt == JOB_PART_TIME_CODE, part_time + ) + donor = ( + pay + & ilo_employee + & np.isfinite(months_in_job) + & (months_in_job <= DONOR_RECENT_START_MONTHS) + & (weights > 0) + ) + donor_part_time = ilo == ILO_PART_TIME_EMPLOYEE_CODE + draws = stable_identity_uniforms(person_ids, seed=DRAW_SEED, salt=DRAW_SALT) + donor_rates, donor_cells = _donor_rates( + needs_donor, + donor=donor, + current_weekly=current / WEEKS_IN_YEAR, + weights=weights, + age=age, + male=male, + part_time=part_time, + donor_part_time=donor_part_time, + draws=draws, + ) + weekly_rate[needs_donor] = donor_rates[needs_donor] + employment_other = weekly_rate * WEEKS_IN_YEAR * other_months / WINDOW_MONTHS + + # Self-employment. + se_now = (se_current > 0) & is_adult + years_in_business = _numeric(main, "sejblong") + months_in_business = _numeric(main, "sejbmths") + se_share = np.ones(rows) + new_business = se_now & (years_in_business == 0) & np.isfinite(months_in_business) + se_share[new_business] = ( + np.minimum(months_in_business + 1.0, WINDOW_MONTHS) / WINDOW_MONTHS + )[new_business] + # The calendar records the main activity only, so it bounds a business + # only where self-employment is the activity today: a side business beside + # a job or a pension keeps its whole year. + se_switcher = ( + se_now + & changed + & np.isin(now, SELF_DEFINED_SELF_EMPLOYED_CODES) + & (calendar_self_employed_months < WINDOW_MONTHS) + ) + se_share[se_switcher] = np.minimum( + se_share, np.maximum(calendar_self_employed_months, 1.0) / WINDOW_MONTHS + )[se_switcher] + se_income = _numeric(main, "seincamt") + profit = np.where( + _numeric(main, "profit2") == PROFIT_LOSS_CODE, 0.0, _numeric(main, "profit1") + ) + se_rate = np.where( + np.isfinite(se_income) & (se_income > 0), + se_income, + np.where(np.isfinite(profit), np.maximum(profit, 0.0), 0.0), + ) + se_weeks = _numeric(main, "selwks") + se_months = np.where( + np.isfinite(se_weeks), + np.minimum(np.maximum(se_weeks, 0.0), 52.0) * WINDOW_MONTHS / 52.0, + np.where( + regular_weeks, + np.minimum(weeks_worked, 52.0) * WINDOW_MONTHS / 52.0, + np.where( + calendar_self_employed_months > 0, + calendar_self_employed_months, + leaving_date_months, + ), + ), + ) + se_other = np.where( + ceased_self_employed, + se_rate * uprating * WEEKS_IN_YEAR * se_months / WINDOW_MONTHS, + 0.0, + ) + source[ceased_self_employed & (se_other > 0) & (source == "")] = ( + SOURCE_CEASED_SELF_EMPLOYED + ) + + # Pensions started on retiring, and contributory benefits on leaving. + pension_share = np.ones(rows) + retired_in_window = is_adult & retired & np.isfinite(months_since_retiring) + pension_share[retired_in_window] = ( + np.minimum(months_since_retiring + 0.5, WINDOW_MONTHS) / WINDOW_MONTHS + )[retired_in_window] + months_worked = np.maximum( + np.where(employee_leaver | calendar_only, other_months, 0.0), + np.where(ceased_self_employed, se_months, 0.0), + ) + benefits_share = np.ones(rows) + left_work = (employee_leaver | calendar_only | ceased_self_employed) & ( + months_worked > 0 + ) + benefits_share[left_work] = ( + np.clip(WINDOW_MONTHS - months_worked, 0.0, WINDOW_MONTHS) / WINDOW_MONTHS + )[left_work] + + descriptors = pd.DataFrame( + { + EMPLOYMENT_INCOME_ANNUAL_SHARE: employment_share, + EMPLOYMENT_INCOME_OTHER_SPELLS: employment_other, + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE: np.where(is_adult, se_share, 1.0), + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS: se_other, + PRIVATE_PENSION_INCOME_ANNUAL_SHARE: pension_share, + CONTRIBUTORY_BENEFITS_ANNUAL_SHARE: benefits_share, + }, + index=person.index, + ) + # Anyone outside adult.tab, and any adult with no current pay and no + # spell, keeps the current columns as they are: a share of 1 on a zero + # current amount and nothing from other spells. + no_employee_pay = ~pay + descriptors.loc[no_employee_pay, EMPLOYMENT_INCOME_ANNUAL_SHARE] = 1.0 + _check_descriptors(descriptors) + receipt = _receipt( + person, + weights=weights, + descriptors=descriptors, + current=current, + se_current=se_current, + pay=pay, + source=source, + observed=observed, + needs_donor=needs_donor, + donor_cells=donor_cells, + ratio=ratio, + ratio_support=ratio_support, + earnings_growth=earnings_growth, + employment_share=employment_share, + ) + return descriptors, receipt + + +def pay_concept_ratio( + person_ids: np.ndarray, + weights: np.ndarray, + job: pd.DataFrame, + working: np.ndarray, +) -> tuple[float, int]: + """Weighted UGRSPAY / UGROSS on current employee main jobs reporting both. + + UGRSPAY is the DV pay the spine's ``employment_income`` sums; UGROSS is the + usual gross pay the survey asks of a current job whose last pay was not + usual and of a recent leaver's last job. Measured job by job on one job per + person, so a second job's pay never enters it. + """ + + weight_by_person = pd.Series(weights, index=person_ids) + working_by_person = pd.Series(np.asarray(working, dtype=bool), index=person_ids) + jobs = job.loc[ + (_numeric(job, "jobtype") == 1) + & (_numeric(job, "etype") == ETYPE_EMPLOYEE_CODE) + & (_numeric(job, "ugross") > 0) + & (_numeric(job, "ugrspay") > 0) + ] + jobs = jobs.loc[ + working_by_person.reindex(jobs["person_id"].to_numpy()) + .fillna(False) + .to_numpy(dtype=bool) + ] + job_weights = weight_by_person.reindex(jobs["person_id"].to_numpy()).to_numpy( + dtype=float + ) + usual = _numeric(jobs, "ugross") + dv = _numeric(jobs, "ugrspay") + support = distinct_carriers(jobs["household_id"].to_numpy(), job_weights) + denominator = float(np.nansum(job_weights * usual)) + if support < 10 or denominator <= 0: + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} cannot measure the usual-to-DV pay " + "ratio: fewer than ten households report both." + ) + ratio = float(np.nansum(job_weights * dv)) / denominator + lower, upper = PAY_CONCEPT_RATIO_BOUNDS + if not lower <= ratio <= upper: + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME}: the DV-to-usual pay ratio {ratio:.4f} " + f"lies outside {PAY_CONCEPT_RATIO_BOUNDS}." + ) + return ratio, support + + +def _donor_rates( + needs_donor: np.ndarray, + *, + donor: np.ndarray, + current_weekly: np.ndarray, + weights: np.ndarray, + age: np.ndarray, + male: np.ndarray, + part_time: np.ndarray, + donor_part_time: np.ndarray, + draws: np.ndarray, +) -> tuple[np.ndarray, dict[str, int]]: + """A donor's weekly pay for each recipient, by weighted inverse CDF.""" + + rates = np.zeros(len(needs_donor)) + band = np.digitize(np.nan_to_num(age), DONOR_AGE_EDGES) + cells = {"cells": 0, "cells_without_sex": 0} + if not needs_donor.any(): + return rates, cells + if not donor.any(): + raise ValueError(f"{FRS_ANNUAL_INCOME_STAGE_NAME} has no pay donors.") + keys = { + (int(b), bool(m), bool(p)) + for b, m, p in zip( + band[needs_donor], male[needs_donor], part_time[needs_donor], strict=True + ) + } + for age_band, is_male, is_part_time in sorted(keys): + recipients = ( + needs_donor + & (band == age_band) + & (male == is_male) + & (part_time == is_part_time) + ) + pool = donor & (band == age_band) & (donor_part_time == is_part_time) + with_sex = pool & (male == is_male) + cells["cells"] += 1 + if with_sex.sum() >= MINIMUM_DONORS: + pool = with_sex + else: + cells["cells_without_sex"] += 1 + if pool.sum() < MINIMUM_DONORS: + pool = donor & (donor_part_time == is_part_time) + if not pool.any(): + pool = donor + order = np.flatnonzero(pool) + cumulative = np.cumsum(weights[order]) + cumulative /= cumulative[-1] + picks = np.searchsorted(cumulative, draws[recipients], side="right") + picks = np.minimum(picks, len(order) - 1) + rates[recipients] = current_weekly[order[picks]] + return rates, cells + + +def _check_descriptors(descriptors: pd.DataFrame) -> None: + values = descriptors.to_numpy(dtype=float) + if not np.isfinite(values).all(): + raise ValueError(f"{FRS_ANNUAL_INCOME_STAGE_NAME} wrote a non-finite value.") + for column in ANNUAL_SHARE_COLUMNS: + share = descriptors[column].to_numpy(dtype=float) + if ((share < 0) | (share > 1)).any(): + raise ValueError(f"{column} must lie in [0, 1].") + for column in OTHER_SPELLS_COLUMNS: + if (descriptors[column].to_numpy(dtype=float) < 0).any(): + raise ValueError(f"{column} must not be negative.") + + +def _receipt( + person: pd.DataFrame, + *, + weights: np.ndarray, + descriptors: pd.DataFrame, + current: np.ndarray, + se_current: np.ndarray, + pay: np.ndarray, + source: np.ndarray, + observed: np.ndarray, + needs_donor: np.ndarray, + donor_cells: Mapping[str, int], + ratio: float, + ratio_support: int, + earnings_growth: float, + employment_share: np.ndarray, +) -> dict[str, object]: + households = person["person_household_id"].to_numpy() + + def carriers(mask: np.ndarray) -> int: + return distinct_carriers(households[mask], weights[mask]) + + def weighted(values: np.ndarray, mask: np.ndarray) -> float | str: + return small_cell_share(float((weights * values)[mask].sum()), carriers(mask)) + + other = descriptors[EMPLOYMENT_INCOME_OTHER_SPELLS].to_numpy(dtype=float) + annual = current * descriptors[EMPLOYMENT_INCOME_ANNUAL_SHARE].to_numpy() + other + se_annual = se_current * descriptors[ + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE + ].to_numpy() + descriptors[SELF_EMPLOYMENT_INCOME_OTHER_SPELLS].to_numpy( + dtype=float + ) + annual_earner = annual > 0 + outside = (other > 0) & ~pay + # Each source gives its support band only: weighted counts by source beside + # the totals below would give back a suppressed source by difference. + sources = { + label: support_band(carriers(source == label)) for label in SPELL_SOURCES + } + # Structural checks the stage gate reads: each is zero by construction, + # and a count of rows that break one is banded as the other stages band + # theirs (zero exact), so a failing build never records 1 to 9. + full_year = source == SOURCE_FULL_YEAR + return { + "window_months": WINDOW_MONTHS, + "weights": "household weights at the stage, carried by each person", + "pay_concept_ratio": ratio, + "pay_concept_ratio_support": support_band(ratio_support), + "earnings_growth": float(earnings_growth), + "spell_sources": sources, + "donor_cells": dict(donor_cells), + "annual_employee_earners": weighted(np.ones(len(pay)), annual_earner), + "current_employee_earners": weighted(np.ones(len(pay)), pay), + "annual_over_current_earners": ( + "<10" + if carriers(annual_earner) < 10 or carriers(pay) < 10 + else float(weights[annual_earner].sum()) / float(weights[pay].sum()) + ), + "annual_over_current_employment_income": ( + float((weights * annual).sum()) / float((weights * current).sum()) + if carriers(pay) >= 10 + else "<10" + ), + "annual_over_current_self_employment_income": ( + float((weights * se_annual).sum()) / float((weights * se_current).sum()) + if carriers(se_current > 0) >= 10 + else "<10" + ), + "other_spells_pay_observed_share": ( + "<10" + if carriers(outside) < 10 + else float(weights[outside & observed].sum()) + / float(weights[outside].sum()) + ), + "other_spells_pay_donor_support": support_band(carriers(needs_donor)), + "full_year_rows_not_whole": extreme_count( + int((full_year & (employment_share != 1.0)).sum()) + ), + "rows_outside_share_bounds": extreme_count( + int( + sum( + ((descriptors[column] < 0) | (descriptors[column] > 1)) + .to_numpy() + .sum() + for column in ANNUAL_SHARE_COLUMNS + ) + ) + ), + "rows_negative_other_spells": extreme_count( + int( + sum( + (descriptors[column] < 0).to_numpy().sum() + for column in OTHER_SPELLS_COLUMNS + ) + ) + ), + } + + +def _artifact_by_table(stage: SourceStageSpec) -> dict[str, Mapping[str, Any]]: + by_table = { + str(artifact.get("table")): artifact + for artifact in stage.artifacts + if artifact.get("table") + } + missing = [name for name in _FRS_TABLES if name not in by_table] + if missing: + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} manifest is missing the FRS tables " + f"{missing}." + ) + return by_table + + +def _assert_stage_parameters(stage: SourceStageSpec) -> None: + if stage.stage != FRS_ANNUAL_INCOME_STAGE_NAME: + raise ValueError( + f"FRS annual income received stage {stage.stage!r}, expected " + f"{FRS_ANNUAL_INCOME_STAGE_NAME!r}." + ) + kinds = tuple(operation.kind for operation in stage.operations) + expected = frs_annual_income_operation_parameters() + if kinds != tuple(kind for kind, _ in expected): + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} operation order drifted: expected " + f"{tuple(kind for kind, _ in expected)}, got {kinds}." + ) + for operation, (kind, parameters) in zip(stage.operations, expected, strict=True): + if dict(operation.parameters) != parameters: + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} operation {kind!r} parameters " + "drifted from the reviewed values." + ) + if ( + tuple(stage.outputs) != FRS_ANNUAL_INCOME_OUTPUT_COLUMNS + or tuple(stage.rewrites) != () + ): + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} must declare exactly the outputs " + f"{FRS_ANNUAL_INCOME_OUTPUT_COLUMNS} and no rewrites." + ) + if stage.grain != "person": + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} is person grain, got {stage.grain!r}." + ) + resources = { + str(artifact["resource"]) + for artifact in stage.artifacts + if "resource" in artifact + } + if resources != {EARNINGS_INDEX_RESOURCE}: + raise ValueError( + f"{FRS_ANNUAL_INCOME_STAGE_NAME} must declare exactly the resource " + f"{EARNINGS_INDEX_RESOURCE!r}." + ) + + +def _numeric(table: pd.DataFrame, column: str) -> np.ndarray: + """A raw FRS column as floats; blanks, text and negative codes are missing.""" + + if column not in table.columns: + raise KeyError(f"{FRS_ANNUAL_INCOME_STAGE_NAME} needs FRS column {column!r}.") + values = pd.to_numeric(table[column], errors="coerce").to_numpy(dtype=float) + return np.where(values < 0, np.nan, values) + + +def _amount(table: pd.DataFrame, column: str) -> np.ndarray: + if column not in table.columns: + raise KeyError(f"{FRS_ANNUAL_INCOME_STAGE_NAME} needs spine column {column!r}.") + values = pd.to_numeric(table[column], errors="coerce").to_numpy(dtype=float) + if not np.isfinite(values).all(): + raise ValueError(f"spine column {column!r} must be finite.") + return values + + +__all__ = [ + "ANNUAL_SHARE_COLUMNS", + "CONTRIBUTORY_BENEFITS_ANNUAL_SHARE", + "CURRENT_INCOME_COLUMNS", + "EMPLOYMENT_INCOME_ANNUAL_SHARE", + "EMPLOYMENT_INCOME_OTHER_SPELLS", + "FRS_ANNUAL_INCOME_OPERATION_KIND", + "FRS_ANNUAL_INCOME_OUTPUT_COLUMNS", + "FRS_ANNUAL_INCOME_STAGE_NAME", + "OTHER_SPELLS_COLUMNS", + "PRIVATE_PENSION_INCOME_ANNUAL_SHARE", + "SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE", + "SELF_EMPLOYMENT_INCOME_OTHER_SPELLS", + "SPELL_SOURCES", + "UKFRSAnnualIncomeResult", + "UKFRSAnnualIncomeStageTransform", + "add_frs_annual_income_spells", + "derive_frs_annual_income_spells", + "frs_annual_income_operation_parameters", + "load_earnings_growth", + "pay_concept_ratio", +] 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 9181cebea..75332118b 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 @@ -462,8 +462,16 @@ def _read_pinned_tab( raw = pd.read_csv(path, sep="\t") else: wanted = {str(column).lower() for column in columns} + # One pass over the selected columns: pandas' chunked parser + # mis-indexes a column whose type changes between chunks under a + # callable column filter, and raises IndexError on a long tab (FRS + # 2024-25 adult.tab). The values after the numeric conversion below + # are the chunked read's wherever that read succeeds. raw = pd.read_csv( - path, sep="\t", usecols=lambda column: str(column).lower() in wanted + path, + sep="\t", + usecols=lambda column: str(column).lower() in wanted, + low_memory=False, ) raw.columns = raw.columns.str.lower() if columns is not None: 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 c4f76cf19..63491a383 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 @@ -148,9 +148,11 @@ # would_claim_child_benefit and child_benefit_opts_out are redrawn by # ``child_benefit_take_up`` against HMRC's claim rates of eligible children and # opted-out share of registered families; its stage gate measures them -# (microcosm#1063). +# (microcosm#1063). would_claim_uc is redrawn after the SPI chain by +# ``uc_take_up`` by award band and family type against DWP's caseloads; its +# stage gate measures it, so it is no longer one share of the age population +# (microcosm#1334). UK_TAKE_UP_SIGNAL_OUTPUTS = ( - ("benunit", "would_claim_uc", "universal_credit"), ("benunit", "would_claim_tfc", "tax_free_childcare"), ("benunit", "would_claim_extended_childcare", "extended_childcare"), ("benunit", "would_claim_universal_childcare", "universal_childcare"), @@ -614,14 +616,12 @@ def uk_take_up_signal_gate( *, contract: UKTakeUpContract | None = None, maximum_share_deviation: float = 0.05, - population_policy: UKTakeUpPopulationPolicy | None = None, ) -> GateResult: """Require non-constant UK stochastic flags near contract target shares.""" resolved = contract if contract is not None else load_uk_take_up_contract() failures: list[str] = [] details: dict[str, object] = {} - policy: UKTakeUpPopulationPolicy | None = population_policy for entity, output, key in UK_TAKE_UP_SIGNAL_OUTPUTS: table = frame.table(entity) if output not in table.columns: @@ -631,20 +631,6 @@ def uk_take_up_signal_gate( unique_count = int(pd.Series(values).nunique(dropna=False)) weights = np.asarray(frame.resolve_weights(entity).values, dtype=np.float64) population = np.ones(values.shape, dtype=bool) - if key == "universal_credit": - # The contract rate is a share of the units that can claim: those - # with a UC claimant or partner within the engine's age bounds at - # the build instant. The gate measures the realized share on the - # same population. - if policy is None: - policy = uk_take_up_population_policy(uk_time_period(frame)) - population = uc_age_eligible_benunits(frame.table("person"), table, policy) - details["universal_credit_population_policy"] = { - "adult_age": policy.adult_age, - "state_pension_age": policy.state_pension_age, - "instant": policy.instant, - "source": policy.source, - } if key == UK_UNDER_TWO_USAGE_SIGNAL_KEY: # The rate is a share of the children under 2 in units that claim # the working-parent hours; every other person keeps 30 hours. 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 080ae6f41..ac3791a67 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -119,6 +119,9 @@ # would_claim_uc (microcosm#1095). "spi_benefit_coherence", "uc_capital_coherence", + # The Universal Credit redraw rewrites ``would_claim_uc``, which the + # open-surface readers before it bind (microcosm#1334). + "uc_take_up", # The Pension Credit redraw rewrites ``frs_take_up``'s would_claim_pc, # which the open-surface readers before it bind (microcosm#1069). "pension_credit_take_up", @@ -169,6 +172,7 @@ "etb_vat": ("etb",), "etb_services": ("etb",), "frs_hmrc_spine_leaves": ("frs",), + "frs_annual_income_spells": ("frs",), "spi_income_band_donors": ("spi",), "hmrc_spi_income_spine": ("spi", "hmrc_income"), } @@ -321,6 +325,18 @@ "etb_services": None, # The employer pension draw applies an ASHE rate to pay (microcosm#1069). "frs_hmrc_spine_leaves": frozenset({("person", "employment_income")}), + # The annual income descriptors re-read the pinned adult, job and household + # tabs; the frame reads are the current income columns they qualify and + # the donor cells' age and sex. + "frs_annual_income_spells": frozenset( + ("person", column) + for column in ( + "employment_income", + "self_employment_income", + "age", + "gender", + ) + ), "spi_support_channel": None, "spi_income_band_donors": None, "hmrc_spi_income_spine": None, @@ -347,6 +363,10 @@ # Runs one temporary engine materialization over the whole frame for its # award screen, so its input surface is genuinely open. "uc_reporter_redraw": None, + # One temporary engine materialization of the Universal Credit award over + # the whole frame, which also hands the stage the source lineage its pools + # and receipt count by: an open surface (microcosm#1334). + "uc_take_up": None, # One temporary engine materialization of Pension Credit entitlement over # the whole frame: an open surface, like the UC award screen. "pension_credit_take_up": None, @@ -924,6 +944,17 @@ def _cells( "employer_pension_contributions", ), ), + "frs_annual_income_spells": _cells( + "person", + ( + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share", + ), + ), "spi_support_channel": ( _Cell("person", "person_source_id", "int64"), _Cell("person", "person_support_channel", "string"), @@ -1019,6 +1050,7 @@ def _cells( _Cell("benunit", "frs_benunit_capital", "float64"), _Cell("benunit", "would_claim_uc", "bool"), ), + "uc_take_up": (_Cell("benunit", "would_claim_uc", "bool"),), "uc_legacy_closure_take_up": ( _Cell("benunit", "would_claim_uc_at_legacy_closure", "bool"), ), @@ -1161,6 +1193,13 @@ def _cells( "hmrc_spi_pay", "hmrc_spi_unemployment_benefit_income", "hmrc_spi_incapacity_benefit_income", + # A drawn row's annual income descriptors follow its drawn incomes. + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share", ) _HMRC_SPI_HIDDEN_STRING = ( "aa_category", 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 882eaf979..c7ca55964 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 @@ -54,12 +54,14 @@ hmrc_property_rental, household_lineage, ledger_fact_vendoring, + legacy_closure_take_up, property_localisation, receipt_counts, rowwise_geography, spi_income, spi_support, uc_capital_coherence, + uc_deduction_attributes, uc_relationships, was_wealth, ) @@ -103,6 +105,7 @@ "etb_vat": "etb_vat", "etb_services": "etb_services", "frs_hmrc_spine_leaves": "spi_spine", + "frs_annual_income_spells": "frs_annual_income", "spi_support_channel": "spi_spine", "spi_income_band_donors": "spi_band_donors", "hmrc_spi_income_spine": "spi_spine", @@ -110,6 +113,7 @@ "uc_reporter_redraw": "uc_reporter_redraw", "spi_benefit_coherence": "spi_benefit_coherence", "uc_capital_coherence": "uc_capital_coherence", + "uc_take_up": "uc_take_up", "uc_legacy_closure_take_up": "legacy_closure_take_up", "pension_credit_take_up": "pension_credit_take_up", "child_benefit_take_up": "child_benefit_take_up", @@ -134,6 +138,9 @@ # helpers and the adapter's input-retention checks into every consuming stage. _STAGE_HELPER_MODULES = { "frs_hmrc_spine_leaves": (frs_hmrc_source,), + # The annual income descriptors' receipt follows the shared disclosure rules + # (microcosm#1164). + "frs_annual_income_spells": (receipt_counts,), # The SPI income stage's imputation, band-donor resample and receipt live # in spi_income, so an edit there moves the stage's hash (microcosm#1278). "hmrc_spi_income_spine": (spi_income,), @@ -172,6 +179,20 @@ uc_capital_coherence, ), "uc_capital_coherence": (uc_relationships,), + # The Universal Credit redraw reads the award through the adapter, the + # vendored DWP counts, the closure stage's rule and rates, the deduction + # attributes, the take-up population and the family-type roles, and counts + # its pools and receipt on the source lineage (microcosm#1334). + "uc_take_up": ( + uk_engine_adapter, + ledger_fact_vendoring, + legacy_closure_take_up, + uc_deduction_attributes, + frs_take_up, + uc_relationships, + uc_capital_coherence, + receipt_counts, + ), # The Move to UC claim draw reuses the UC stage's household-to-benefit-unit # weight mapping and reads the vendored DWP rows (microcosm#1095). "uc_legacy_closure_take_up": (uc_capital_coherence, ledger_fact_vendoring), @@ -501,6 +522,7 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: from .childcare_copy_take_up import UKChildcareCopyTakeUpStageTransform from .etb_services import UKETBServicesStageTransform from .etb_vat import UKETBVATStageTransform + from .frs_annual_income import UKFRSAnnualIncomeStageTransform from .frs_brma import UKFRSBRMAStageTransform from .frs_council_tax import UKFRSCouncilTaxStageTransform from .frs_disability import UKFRSDisabilityStageTransform @@ -539,6 +561,7 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: from .uc_capital_coherence import UKUCCapitalCoherenceStageTransform from .uc_deduction_attributes import UKUCDeductionAttributesStageTransform from .uc_reporter_redraw import UKUCReporterRedrawStageTransform + from .uc_take_up import UKUCTakeUpStageTransform from .was_lisa import UKWASLISAStageTransform from .was_wealth import UKWASWealthStageTransform @@ -699,6 +722,9 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: "frs_hmrc_spine_leaves": UKFRSHMRCSpineLeavesStageTransform( raw_dir, stage=stages["frs_hmrc_spine_leaves"] ), + "frs_annual_income_spells": UKFRSAnnualIncomeStageTransform( + raw_dir, stage=stages["frs_annual_income_spells"] + ), "spi_support_channel": UKSPISupportChannelStageTransform( stage=stages["spi_support_channel"], sample_fraction=sample_fraction, @@ -731,6 +757,9 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: "uc_capital_coherence": UKUCCapitalCoherenceStageTransform( stage=stages["uc_capital_coherence"] ), + "uc_take_up": UKUCTakeUpStageTransform( + stage=stages["uc_take_up"], engine=engine + ), "uc_legacy_closure_take_up": UKLegacyClosureTakeUpStageTransform( stage=stages["uc_legacy_closure_take_up"] ), 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 b1e19b250..b87480dd0 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 @@ -22,6 +22,15 @@ DFE_SPRING_TERM_MEASURES, dfe_spring_term_status, ) +from microcosm.build.uk_runtime.frs_annual_income import ( + CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, + EMPLOYMENT_INCOME_ANNUAL_SHARE, + EMPLOYMENT_INCOME_OTHER_SPELLS, + FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, + PRIVATE_PENSION_INCOME_ANNUAL_SHARE, + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE, + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, +) from microcosm.build.uk_runtime.high_value_homes import ( high_value_home_mask, uk_high_value_home_thresholds, @@ -247,6 +256,78 @@ def household_high_value_home(frame: Any, variable: str) -> np.ndarray: ) +#: HMRC's income statistics count the income each taxpayer received over a +#: tax year, banded by what they received; they cover individuals liable to UK +#: income tax. The frame's incomes are current, annualised ones, and +#: ``frs_annual_income_spells`` stores beside them what each adult received +#: over the 12 months before the interview. Each annual measure is its current +#: measure computed on a second simulation whose inputs are each person's year, +#: so the engine's own rules give the tax on it. That year is a proxy for the +#: tax year: the 12 months before the interview, applied to the engine's +#: amounts at the calibration year (the stage module says how). Poverty, +#: benefits and the engine's published tax keep reading current income. +UK_ANNUAL_INCOME_MEASURES: Mapping[str, str] = { + "employment_income_annual": "employment_income", + "self_employment_income_after_loss_relief_annual": ( + UK_SELF_EMPLOYMENT_INCOME_AFTER_LOSS_RELIEF + ), + "private_pension_income_annual": "private_pension_income", + "state_pension_annual": "state_pension", + "total_income_after_loss_relief_annual": UK_TOTAL_INCOME_AFTER_LOSS_RELIEF, + "income_tax_annual": "income_tax", + "tax_band_annual": "tax_band", +} +#: The inputs the annual simulation replaces at the measurement year: the +#: engine's current amount times its annual share, plus its other spells, +#: carried from the dataset's year by the engine's own factor for the input. +_ANNUAL_INCOME_INPUTS: tuple[tuple[str, str, str | None], ...] = ( + ( + "employment_income_before_lsr", + EMPLOYMENT_INCOME_ANNUAL_SHARE, + EMPLOYMENT_INCOME_OTHER_SPELLS, + ), + ( + "self_employment_income", + SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE, + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, + ), + ("private_pension_income", PRIVATE_PENSION_INCOME_ANNUAL_SHARE, None), + ("jsa_contrib_reported", CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, None), + ("esa_contrib_reported", CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, None), + ("incapacity_benefit_reported", CONTRIBUTORY_BENEFITS_ANNUAL_SHARE, None), +) +#: Taxable benefits whose entitlement reads earnings, held at their baseline +#: values: a carer was paid in the year at the earnings of the time, which the +#: current concept records, not at the year's total. +_ANNUAL_INCOME_HELD_VARIABLES = ( + "carers_allowance", + "carer_support_payment", + "scottish_carer_supplement", +) +#: ``months_since_state_pension_age`` counts to the middle of the fiscal year +#: (6 October), so someone attaining State Pension age within the year was +#: paid for that many months plus six of it. +_FISCAL_HALF_YEAR_MONTHS = 6.0 + + +def state_pension_annual_share(months_since_state_pension_age: Any) -> np.ndarray: + """The share of the fiscal year a State Pension starting in it was paid. + + Only someone attaining State Pension age within the year, less than six + months either side of its middle, is paid for part of it. Anyone else + keeps the whole year, an amount reported below the age included: the + current concept carries it, and the descriptors say nothing about it. + """ + + months = np.asarray(months_since_state_pension_age, dtype=float) + in_year = np.isfinite(months) & (np.abs(months) < _FISCAL_HALF_YEAR_MONTHS) + share = np.ones(months.shape) + share[in_year] = (months[in_year] + _FISCAL_HALF_YEAR_MONTHS) / ( + 2.0 * _FISCAL_HALF_YEAR_MONTHS + ) + return share + + #: 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). @@ -409,22 +490,7 @@ def compute_uk_measure_input( ), "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", - ) + return _loss_relief_measure_from_engine(frame, simulation, variable, year) if variable in UC_TARGET_VARIABLES: if entity != "benunit": @@ -501,6 +567,29 @@ def compute_uk_measure_input( return values, route +def _loss_relief_measure_from_engine( + frame: Any, simulation: Any, variable: str, year: int +) -> tuple[np.ndarray, str]: + """A loss-relief measure from the engine's components at ``year``.""" + + 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 + ) + return ( + np.asarray(total, dtype=float) - np.asarray(profit, dtype=float) + after, + "engine_components:total_income_with_profit_after_loss_relief", + ) + + def compute_uc_paid_diagnostic_masks( frame: Any, simulation: Any, year: int ) -> dict[str, np.ndarray]: @@ -914,6 +1003,37 @@ def __init__( self._uc_tcl_measures_used: set[str] = set() self._uc_paid_measures_used: set[str] = set() + self._annual_income_measures_used: set[str] = set() + self._annual_income_fallback: str | None = None + self._annual_income_simulation_cache: Any | None = None + self._annual_income_factors: dict[str, float] = {} + + def use_current_income_for_annual_measures(self, *, reason: str) -> None: + """Let a frame without annual income descriptors read current values. + + Only a file built before the descriptors existed (the incumbent a + candidate is scored against) takes this: its annual measures are its + current ones, a share of 1 and no other spells. A frame that carries + the descriptors still reads them, and any other frame without them + refuses every annual measure. + """ + + if not str(reason).strip(): + raise ValueError("the current-income fallback needs a reason.") + self._annual_income_fallback = str(reason) + + def _carries_annual_income_descriptors(self) -> bool: + person = self.frame.table("person") + present = [ + column for column in FRS_ANNUAL_INCOME_OUTPUT_COLUMNS if column in person + ] + if present and len(present) != len(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS): + missing = sorted(set(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS) - set(present)) + raise ValueError( + f"The frame carries only some annual income descriptors; missing " + f"{missing}." + ) + return bool(present) def validate_period(self, period: int | str) -> None: """Refuse thresholds and simulated inputs from different periods.""" @@ -955,6 +1075,12 @@ def knows(self, entity: str, variable: str) -> bool: return entity == "person" and all( self.knows("person", name) for name in _LOSS_RELIEF_MEASURE_COMPONENTS ) + if variable in UK_ANNUAL_INCOME_MEASURES: + readable = ( + self._carries_annual_income_descriptors() + or self._annual_income_fallback is not None + ) + return readable and self.knows(entity, UK_ANNUAL_INCOME_MEASURES[variable]) definition = self.simulation.tax_benefit_system.variables.get(variable) if definition is None or entity not in _ENTITY_ID: return False @@ -977,6 +1103,8 @@ def entity_for(self, variable: str) -> str | None: return "person" if variable in _LOSS_RELIEF_MEASURES or variable in DFE_SPRING_TERM_MEASURES: return "person" + if variable in UK_ANNUAL_INCOME_MEASURES: + return self.entity_for(UK_ANNUAL_INCOME_MEASURES[variable]) if ( variable in _UC_CALIBRATION_VARIABLES @@ -990,6 +1118,8 @@ def entity_for(self, variable: str) -> str | None: return str(definition.entity.key) def compute(self, entity: str, variable: str) -> tuple[np.ndarray, str]: + if variable in UK_ANNUAL_INCOME_MEASURES: + return self._annual_income_measure(entity, variable) result = compute_uk_measure_input( self.frame, self.simulation, entity, variable, self.year ) @@ -1212,6 +1342,138 @@ def _rate_class_relief( cache[key] = relief return relief + def _annual_income_measure( + self, entity: str, variable: str + ) -> tuple[np.ndarray, str]: + """An annual measure: its current measure on the annual simulation.""" + + current = UK_ANNUAL_INCOME_MEASURES[variable] + if not self._carries_annual_income_descriptors(): + if self._annual_income_fallback is None: + raise KeyError( + f"{variable} reads the annual income descriptors " + f"(frs_annual_income_spells), which this frame does not carry." + ) + self._annual_income_measures_used.add(variable) + values, route = compute_uk_measure_input( + self.frame, self.simulation, entity, current, self.year + ) + return values, f"current_values_without_annual_income_descriptors:{route}" + simulation = self._annual_income_simulation() + self._annual_income_measures_used.add(variable) + if current in _LOSS_RELIEF_MEASURES: + if entity != "person": + raise KeyError(f"{variable} is person-only: {entity}") + # Always the engine's components: the frame's stored columns are + # the current year's, never the annual one. + values, route = _loss_relief_measure_from_engine( + self.frame, simulation, current, self.year + ) + else: + values, route = compute_uk_measure_input( + self.frame, simulation, entity, current, self.year + ) + return values, f"annual_income_simulation:{route}" + + def _dataset_year(self) -> int: + metadata = getattr(self.frame, "metadata", None) or {} + period = metadata.get(UK_TIME_PERIOD_METADATA_KEY) + if isinstance(period, str) and period.strip(): + return int(period) + return self.year + + def _annual_income_simulation(self) -> Any: + """The simulation of each person's year, built once from the same source. + + The engine projects a dataset's inputs to its later years as it loads + it, so the year's inputs are set at the measurement year: pay, profit, + private pensions and the contributory benefits to the engine's current + amount times its annual share, plus the other spells carried from the + dataset's year by the factor the engine carries the current amount by. + The State Pension is set to the share of it paid in the fiscal year, + and the earnings-tested carer benefits to their baseline values. + Pension contributions and salary sacrifice keep their current values: + for a part-year entrant they can be large against the year's pay, + which lowers the year's income tax a little. + """ + + if self._annual_income_simulation_cache is not None: + return self._annual_income_simulation_cache + person = self.frame.table("person") + simulation = self._factory(dataset=str(self._source_path)) + annual_inputs: dict[str, np.ndarray] = {} + for name, share_column, other_column in _ANNUAL_INCOME_INPUTS: + current = _values(self.simulation.calculate(name, self.year)).astype(float) + share = person[share_column].to_numpy(dtype=float) + other = ( + person[other_column].to_numpy(dtype=float) + if other_column is not None + else np.zeros(len(person)) + ) + if current.shape != share.shape: + raise ValueError( + f"{name} must align with the frame's person table to read " + "its annual income descriptors." + ) + factor = self._engine_projection_factor(name) if other.any() else 1.0 + annual = current * share + other * factor + if not np.isfinite(annual).all(): + raise ValueError(f"The annual {name} must be finite.") + simulation.set_input(name, self.year, annual) + annual_inputs[name] = annual + months = _values( + self.simulation.calculate("months_since_state_pension_age", self.year) + ) + pension = _values(self.simulation.calculate("state_pension", self.year)) + annual_pension = pension.astype(float) * state_pension_annual_share(months) + simulation.set_input("state_pension", self.year, annual_pension) + annual_inputs["state_pension"] = annual_pension + for name in _ANNUAL_INCOME_HELD_VARIABLES: + held = _values(self.simulation.calculate(name, self.year)).astype(float) + simulation.set_input(name, self.year, held) + annual_inputs[name] = held + for name, expected in annual_inputs.items(): + read = _values(simulation.calculate(name, self.year)).astype(float) + tolerance = 1e-6 * np.maximum(np.abs(expected), 1.0) + 0.01 + if ( + read.shape != expected.shape + or (np.abs(read - expected) > tolerance).any() + ): + raise ValueError( + f"The annual simulation does not read back the {name} set " + f"for {self.year}." + ) + self._annual_income_simulation_cache = simulation + return simulation + + def _engine_projection_factor(self, name: str) -> float: + """The factor the engine carries ``name`` by from the dataset's year. + + Read off the engine's own projection, so the other spells grow by the + series the current amount grows by; a projection that is not one + factor for every row refuses, since no single factor then carries the + other spells as the engine carries pay. + """ + + dataset_year = self._dataset_year() + then = _values(self.simulation.calculate(name, dataset_year)).astype(float) + now = _values(self.simulation.calculate(name, self.year)).astype(float) + carried = np.abs(then) > 0 + if not carried.any(): + raise ValueError( + f"No row carries {name} in {dataset_year}, so the engine's factor " + f"to {self.year} for the other spells cannot be read." + ) + factors = now[carried] / then[carried] + factor = float(np.median(factors)) + if (np.abs(factors - factor) > 1e-5 * max(abs(factor), 1.0)).any(): + raise ValueError( + f"The engine does not carry {name} from {dataset_year} to " + f"{self.year} by one factor, so the other spells cannot follow it." + ) + self._annual_income_factors[name] = factor + return factor + def _counterfactual_simulation(self, zeroed: str, folded: str | None) -> Any: key = (zeroed, folded) if key in self._counterfactuals: @@ -1317,8 +1579,42 @@ def receipt(self) -> dict[str, Any]: **uc_paid_comparison_contract(self.year), "computed_measures": sorted(self._uc_paid_measures_used), } + if getattr(self, "_annual_income_measures_used", None): + receipt["annual_income_measures"] = self._annual_income_receipt() return receipt + def _annual_income_receipt(self) -> dict[str, Any]: + measures = { + name: UK_ANNUAL_INCOME_MEASURES[name] + for name in sorted(self._annual_income_measures_used) + } + if not self._carries_annual_income_descriptors(): + return { + "computed_measures": measures, + "route": "current_values_without_annual_income_descriptors", + "reason": self._annual_income_fallback, + } + return { + "computed_measures": measures, + "route": "annual_income_simulation", + "descriptors": list(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS), + "dataset_year": self._dataset_year(), + "measurement_year": self.year, + "inputs": { + name: {"annual_share": share, "other_spells": other} + for name, share, other in _ANNUAL_INCOME_INPUTS + }, + "other_spells_engine_factors": dict( + sorted(self._annual_income_factors.items()) + ), + "state_pension": ( + "state_pension at the measurement year times the share of the " + "fiscal year paid: (months_since_state_pension_age + 6) / 12 " + "within six months of the year's middle, else 1" + ), + "held_at_baseline": list(_ANNUAL_INCOME_HELD_VARIABLES), + } + #: Every field a reviewed measure exclusion must carry (the #: ``weighted_integrity`` reviewed-exclusion record shape, microcosm#757 / diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py index 7b20158b8..80cdf624e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py @@ -39,6 +39,10 @@ it for the FRS units: a unit with a Universal Credit reporter claims, and the other units in the Universal Credit age population claim as the residual of the take-up contract's rate, on draws keyed on the SPI unit's own identity. + That draw stands for a unit with no award: ``uc_take_up`` redraws every + entitled unit of both channels by award band and family type once + ``uc_capital_coherence`` has set the capital the engine assesses + (microcosm#1334). Contributory ESA, the disability benefits, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS base rows are never 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 c784a7bba..f88a6937c 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 @@ -19,6 +19,12 @@ integer_record_keys, stable_identity_uniforms, ) +from microcosm.build.uk_runtime.frs_annual_income import ( + ANNUAL_SHARE_COLUMNS, + EMPLOYMENT_INCOME_OTHER_SPELLS, + FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, +) from microcosm.build.uk_runtime.frs_disability import ( UKDWPDisabilityCategoryRates, UKDWPDisabilityFlagRates, @@ -1291,6 +1297,7 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): person, spi_people=spi_people ) person = _refresh_landlord_carrier(person, spi_people=spi_people) + person = _refresh_annual_income_descriptors(person, spi_people=spi_people) # The flag is the stage's last new column, where the graph's typed cells # place it (the legacy oracle and the graph must agree on column order). person[SPI_INCOME_RECIPIENT_COLUMN] = ( @@ -2943,6 +2950,58 @@ def _refresh_uc_start_up_period( return person +#: The drawn income whose absence keeps a twin's other-spell amount. +_OTHER_SPELLS_DRAWN_INCOME = { + EMPLOYMENT_INCOME_OTHER_SPELLS: "employment_income", + SELF_EMPLOYMENT_INCOME_OTHER_SPELLS: "self_employment_income", +} + + +def _refresh_annual_income_descriptors( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Hold a drawn row's annual income descriptors to its drawn incomes. + + The tape records income received over the tax year, so a drawn row's pay, + profit and private pension already cover the whole year and each share is + 1. Its contributory benefits are stage-2 draws on the current concept with + no spell of their own, so their share is 1 too. A drawn row keeps its + twin's other-spell pay only where it drew no pay, and its twin's ceased + self-employment only where it drew no profit: the bridge draws a record + without pay for a twin who left a job in the year, and the twin's spell is + then the only record of that year's pay. An undrawn copy keeps every + descriptor of its twin, with its twin's incomes. + """ + + present = [ + column for column in FRS_ANNUAL_INCOME_OUTPUT_COLUMNS if column in person + ] + if not present: + return person + if len(present) != len(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS): + missing = sorted(set(FRS_ANNUAL_INCOME_OUTPUT_COLUMNS) - set(present)) + raise ValueError( + f"SPI rows carry only some annual income descriptors; missing {missing}." + ) + rows = person.loc[spi_people] + for column in ANNUAL_SHARE_COLUMNS: + _assign_spi_values(person, spi_people, column, np.ones(len(rows)), default=1.0) + for column, drawn_column in _OTHER_SPELLS_DRAWN_INCOME.items(): + drew_income = ( + pd.to_numeric(rows[drawn_column], errors="coerce").fillna(0.0).to_numpy() + != 0.0 + ) + twin_spells = rows[column].to_numpy(dtype=np.float64) + _assign_spi_values( + person, + spi_people, + column, + np.where(drew_income, 0.0, twin_spells), + default=0.0, + ) + return person + + def _refresh_uc_gainful_self_employment( person: pd.DataFrame, *, spi_people: pd.Series ) -> tuple[pd.DataFrame, dict[str, object] | 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 410e6cdc9..ff115781b 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 @@ -17,6 +17,9 @@ employer_scheme_and_sector, load_ashe_employer_rates, ) +from microcosm.build.uk_runtime.frs_annual_income import ( + FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, +) from microcosm.build.uk_runtime.frs_hmrc_source import ( FRS_HMRC_INCPBEN_COLUMN, FRS_HMRC_PAY_COLUMN, @@ -260,6 +263,10 @@ FRS_HMRC_PAY_COLUMN, FRS_HMRC_UBISJA_COLUMN, FRS_HMRC_INCPBEN_COLUMN, + # The annual income descriptors follow a drawn row's drawn incomes: the + # tape's income is the tax year's, so each share is 1, and the twin's + # other spells stay only where the tape drew none of that income. + *FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, ) # Reviewed constants for every load-bearing manifest parameter the spine @@ -301,6 +308,8 @@ "loss_relief", "ni_class_4_losses_brought_forward", ) + # Held to the drawn incomes by rule, not drawn by stage 2. + and column not in FRS_ANNUAL_INCOME_OUTPUT_COLUMNS ) #: 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/spine_build.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py index c2b917db0..bcc5d2bd8 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 @@ -96,6 +96,9 @@ from microcosm.build.uk_runtime.content_identity import uk_frame_content_identity from microcosm.build.uk_runtime.etb_services import UKETBServicesStageTransform from microcosm.build.uk_runtime.etb_vat import UKETBVATStageTransform +from microcosm.build.uk_runtime.frs_annual_income import ( + UKFRSAnnualIncomeStageTransform, +) from microcosm.build.uk_runtime.frs_brma import UKFRSBRMAStageTransform from microcosm.build.uk_runtime.frs_council_tax import UKFRSCouncilTaxStageTransform from microcosm.build.uk_runtime.frs_disability import UKFRSDisabilityStageTransform @@ -202,6 +205,7 @@ from microcosm.build.uk_runtime.uc_reporter_redraw import ( UKUCReporterRedrawStageTransform, ) +from microcosm.build.uk_runtime.uc_take_up import UKUCTakeUpStageTransform from microcosm.build.uk_runtime.was_lisa import UKWASLISAStageTransform from microcosm.build.uk_runtime.was_wealth import UKWASWealthStageTransform from microcosm.frame import Frame @@ -1688,6 +1692,13 @@ def prepare_uk_spine_execution( sampled_rung=_is_sampled(args), ) ) + if "frs_annual_income_spells" in stage_names: + implementations["frs_annual_income_spells"] = _GraphSourceTransform( + lambda sources: UKFRSAnnualIncomeStageTransform( + sources["frs"], + stage=stages_by_name["frs_annual_income_spells"], + ) + ) implementations["spi_support_channel"] = UKSPISupportChannelStageTransform( stage=stages_by_name["spi_support_channel"], sample_fraction=args.sample_fraction, @@ -1718,6 +1729,11 @@ def prepare_uk_spine_execution( implementations["uc_capital_coherence"] = UKUCCapitalCoherenceStageTransform( stage=stages_by_name["uc_capital_coherence"] ) + if "uc_take_up" in stage_names: + implementations["uc_take_up"] = UKUCTakeUpStageTransform( + stage=stages_by_name["uc_take_up"], + engine=engine, + ) if "uc_legacy_closure_take_up" in stage_names: implementations["uc_legacy_closure_take_up"] = ( UKLegacyClosureTakeUpStageTransform( 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 cea1be7d7..00c63599c 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 @@ -43,8 +43,12 @@ def uk_stage_health_gate( return _spi_support_channel_gate(stage, evidence, parameters) if check == "spi_income_spine": return _spi_income_spine_gate(stage, evidence, parameters) + if check == "frs_annual_income_spells": + return _frs_annual_income_spells_gate(stage, evidence, parameters) if check == "pension_credit_take_up": return _pension_credit_take_up_gate(stage, evidence, parameters) + if check == "uc_take_up": + return _uc_take_up_gate(stage, evidence, parameters) if check == "child_benefit_take_up": return _child_benefit_take_up_gate(stage, evidence, parameters) if check == "spi_benefit_coherence": @@ -1070,6 +1074,78 @@ def _child_benefit_take_up_gate( ) +#: The structural counts the annual income receipt records, banded by +#: ``receipt_counts.extreme_count`` with zero exact; each must be zero. +_ANNUAL_INCOME_STRUCTURAL_ZEROS = ( + "full_year_rows_not_whole", + "rows_outside_share_bounds", + "rows_negative_other_spells", +) + + +def _frs_annual_income_spells_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """The annual income descriptors keep their invariants and plausible sizes. + + Every share lies in [0, 1], no other-spell amount is negative and every + full-year earner keeps the whole year, exactly. The usual-to-DV pay ratio + the stage measures lies in its bounds, and so does the ratio of annual to + current employee earners: the survey's spells can add part-year earners + but cannot remove a current one. Where the receipt measures it, at least + the declared share of the pay outside the current job is observed rather + than drawn from a donor; a share carried by fewer than ten households is + reported unmeasured. + """ + + check = "frs_annual_income_spells" + failures: list[str] = [] + for key in _ANNUAL_INCOME_STRUCTURAL_ZEROS: + value = evidence.get(key) + if value != 0: + failures.append(f"{stage}: {key} is {value!r}, not 0.") + details: dict[str, object] = {} + for key, bounds_key in ( + ("pay_concept_ratio", "pay_concept_ratio_bounds"), + ("annual_over_current_earners", "annual_over_current_earners_bounds"), + ): + bounds = parameters[bounds_key] + if not isinstance(bounds, list | tuple) or len(bounds) != 2: + raise ValueError(f"{stage}.{bounds_key} must be a [lower, upper] pair.") + lower = _finite_number(bounds[0], label=f"{stage}.{bounds_key}[0]") + upper = _finite_number(bounds[1], label=f"{stage}.{bounds_key}[1]") + value = evidence.get(key) + details[key] = value + if isinstance(value, bool) or not isinstance(value, int | float): + failures.append(f"{stage}: {key} is {value!r}, not a measured ratio.") + elif not lower <= float(value) <= upper: + failures.append( + f"{stage}: {key} {float(value):.4f} lies outside [{lower}, {upper}]." + ) + minimum = _finite_number( + parameters["minimum_observed_pay_share"], + label=f"{stage}.minimum_observed_pay_share", + ) + observed = evidence.get("other_spells_pay_observed_share") + details["other_spells_pay_observed_share"] = observed + if observed == "<10": + details["observed_pay_share_measured"] = False + elif isinstance(observed, bool) or not isinstance(observed, int | float): + failures.append(f"{stage}: other_spells_pay_observed_share is {observed!r}.") + elif float(observed) < minimum: + failures.append( + f"{stage}: only {float(observed):.4f} of the pay outside the current " + f"job is observed, below {minimum}." + ) + return ( + _fail(stage, check, failures, details) + if failures + else _pass(stage, check, details) + ) + + def _pension_credit_take_up_gate( stage: str, evidence: Mapping[str, object], @@ -1206,6 +1282,82 @@ def _spi_benefit_coherence_gate( ) +def _uc_take_up_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """Each family type's reachable pools realize DWP's caseload (microcosm#1334). + + Every reporter claims and no unit outside the draw changes. Over a family + type's pools whose target the draw can reach, the realized Great Britain + caseload is within ``maximum_caseload_deviation`` of DWP's count, and the + family type's whole caseload is no further from DWP's than before the + redraw, give or take that share of DWP's count. + """ + + check = "uc_take_up" + tolerance = _finite_number( + parameters["maximum_caseload_deviation"], + label=f"{stage}.maximum_caseload_deviation", + ) + failures: list[str] = [] + details: dict[str, object] = {} + for key in ("reporters_not_claiming", "changed_outside_draw"): + value = evidence.get(key) + details[key] = value + if value != 0: + failures.append(f"{stage}: {key} is {value!r}, not 0.") + families = evidence.get("families") + if not isinstance(families, Mapping) or not families: + failures.append(f"{stage}: the receipt carries no family types.") + families = {} + for name, family in families.items(): + family = _mapping(family, label=f"{stage}.families.{name}") + values = { + key: family.get(key) + for key in ( + "dwp_caseload", + "caseload_before", + "caseload_after", + "reachable_pools_dwp_caseload", + "reachable_pools_caseload_after", + ) + } + details[str(name)] = values + if any(not isinstance(value, int | float) for value in values.values()): + failures.append( + f"{stage}: family type {name} carries a caseload resting on fewer " + "than ten sources, or none." + ) + continue + dwp = _finite_number(values["dwp_caseload"], label=f"{stage}.{name}.dwp") + before = _finite_number(values["caseload_before"], label=f"{stage}.{name}") + after = _finite_number(values["caseload_after"], label=f"{stage}.{name}") + target = _finite_number( + values["reachable_pools_dwp_caseload"], label=f"{stage}.{name}" + ) + realized = _finite_number( + values["reachable_pools_caseload_after"], label=f"{stage}.{name}" + ) + if target > 0.0 and abs(realized - target) > tolerance * target: + failures.append( + f"{stage}: family type {name} realizes {realized:,.0f} against " + f"DWP's {target:,.0f} over its reachable pools (tolerance " + f"{tolerance})." + ) + if abs(after - dwp) > abs(before - dwp) + tolerance * dwp: + failures.append( + f"{stage}: family type {name} moves from {before:,.0f} to " + f"{after:,.0f}, further from DWP's {dwp:,.0f}." + ) + return ( + _fail(stage, check, failures, details) + if failures + else _pass(stage, check, details) + ) + + def _spi_income_spine_gate( stage: str, evidence: Mapping[str, object], 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 5fdd426d6..bf18e42b5 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 @@ -269,6 +269,12 @@ def __post_init__(self) -> None: "person.dla_m_category", "person.dla_sc_category", "person.employment_sector", + "person.contributory_benefits_annual_share", + "person.employment_income_annual_share", + "person.employment_income_other_spells", + "person.private_pension_income_annual_share", + "person.self_employment_income_annual_share", + "person.self_employment_income_other_spells", "person.esa_health_condition_proxy", "person.esa_includes_support_component", "person.esa_support_group_proxy", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py new file mode 100644 index 000000000..f32d32d57 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py @@ -0,0 +1,1005 @@ +"""Universal Credit take-up by award band and family type (microcosm#1334). + +``frs_take_up`` draws ``would_claim_uc`` early, before the SPI chain, and +``spi_benefit_coherence`` redraws the SPI-channel units on the same rule: a +reporter claims, and the rest of the take-up contract's one rate times the age +population is drawn uniformly over the non-reporting units of that population, +whatever their award. Real non-take-up is concentrated in small awards, so at +the spine's design weights the claimants overshot DWP's caseload for single +people without children, couples and the smallest awards and fell short for +lone parents, and the national solve met DWP's caseloads by reweighting +low-income families. + +This stage redraws the flag once the SPI chain and ``uc_capital_coherence`` +have set the incomes and capital the engine assesses, for both channels. One +temporary engine materialization, at the release's calibration year on the +frame stamped with its survey year (as ``pension_credit_take_up`` reads Pension +Credit), gives each benefit unit the award it would be paid if it claimed: the +engine's ``universal_credit`` on a copy where every unit claims and carries the +deduction attributes ``uc_deduction_attributes`` will assign it (they read only +the unit's identity and region), so after the benefit cap and deductions, the +award the registry's ``dwp/uc_payment_dist`` rows band. It also gives whether +the engine moves the unit off its legacy benefits that year +(``legacy_benefits_closed``) and the UC child status the calibration's family +type reads. A unit with an award falls into a cell of DWP's family type (the +calibration measure's composition) by DWP's GBP 100 monthly award band, banded +as the registry rows band an award, with one more band above GBP 2,500 a +month. Within a +family type, adjacent bands pool until the pool's drawable units rest on at +least 30 distinct source households. In each pool a reporter claims, and every +other entitled unit of the age population that the engine does not move off +its legacy benefits, and that reports none it keeps (policyengine-uk ends a +kept legacy benefit once the unit claims UC), claims at + + r = (T - F) / D, + +clipped to [0, 1]: ``T`` is DWP's calendar-2025 mean count of the pool's +bands (Stat-Xplore Households on Universal Credit, April to December 2025, +the facts the registry rows average), ``D`` the weight of the units drawn, and +``F`` the expected claims of the pool's other entitled units: 1 for a +reporter, the claim probability ``uc_legacy_closure_take_up``'s rule gives a +unit the engine moves off its legacy benefits (its draw is left alone, so that +probability does not move), and its stored flag for any other. ``T``, ``F`` +and ``D`` are Great Britain's, at the stage's household weights, which DWP's +counts cover. The drawn units claim in the order of their uniform until their +weight is nearest ``T - F``, so a pool realizes ``r`` to within one unit's +weight rather than only in expectation, and Northern Ireland's units claim +below the same uniform. A pool where +``F`` alone reaches ``T``, or where ``T`` exceeds every entitled unit, cannot +meet DWP's count; the receipt flags it. A unit with no award, or outside the +age population, keeps its stored flag, the single-rate draw. The uniform is +the ``would_claim_uc`` identity stream ``frs_take_up`` and +``spi_benefit_coherence`` use, so a unit whose pool's rate falls below the +early draw's keeps its claim only if it had one. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from dataclasses import dataclass, field +from typing import Any + +import numpy as np +import pandas as pd + +from microcosm.build.source_manifest import SourceStageSpec +from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.cgt_structure import _assert_closed_world_operations +from microcosm.build.uk_runtime.frs_release import resolve_uk_year_rule +from microcosm.build.uk_runtime.frs_take_up import ( + UK_UC_AGE_ELIGIBLE_AGGREGATE, + UK_UC_AGE_ELIGIBLE_METHOD, + UK_UC_AGE_ELIGIBLE_ROLE, + UK_UC_AGE_ELIGIBLE_SOURCE, + UK_UC_TAKE_UP_OUTPUT, + UKTakeUpPopulationPolicy, + uc_age_eligible_benunits, + uk_take_up_population_policy, +) +from microcosm.build.uk_runtime.legacy_closure_take_up import ( + LEGACY_CLOSURE_RESOURCE, + legacy_benefit_combinations, + legacy_closure_claim_probabilities, + load_move_to_uc_claim_rates, +) +from microcosm.build.uk_runtime.national_frame import ( + uk_household_weight_kind, + uk_national_frame, + uk_time_period, + validate_uk_national_frame, +) +from microcosm.build.uk_runtime.receipt_counts import ( + distinct_count, + extreme_count, + rounded_count, + small_cell_share, + source_household_ids, + support_band, +) +from microcosm.build.uk_runtime.uc_capital_coherence import ( + _benunit_households, + _household_to_benunit_weights, +) +from microcosm.build.uk_runtime.uc_deduction_attributes import ( + assign_uc_deduction_attributes, + load_uc_deduction_distributions, +) +from microcosm.build.uk_runtime.uc_relationships import ( + frs_uc_claimant_mask, + uc_family_child_member, +) +from microcosm.frame import Frame +from microcosm.frame.adapters.policyengine_uk import ProjectingRulesEngine +from microcosm.frame.rules import assert_rules_engine_country + +__all__ = [ + "UC_AWARD_BAND_LABELS", + "UC_FAMILY_TYPES", + "UC_TAKE_UP_ENTITLEMENT_VARIABLES", + "UC_TAKE_UP_MINIMUM_POOL_SOURCES", + "UC_TAKE_UP_OPERATION_KIND", + "UC_TAKE_UP_RECORD_SETS", + "UC_TAKE_UP_RESOURCE", + "UC_TAKE_UP_STAGE_NAME", + "UKUCTakeUpResult", + "UKUCTakeUpStageTransform", + "assign_award_band_take_up", + "load_uc_award_band_targets", + "redraw_uc_take_up", + "uc_award_band_targets_from_rows", + "uc_award_bands", + "uc_family_types", + "uc_take_up_operation_parameters", + "working_age_housing_benefit_receipt", +] + +UC_TAKE_UP_STAGE_NAME = "uc_take_up" +UC_TAKE_UP_OPERATION_KIND = "assign_award_band_take_up_residual" +UC_TAKE_UP_SEED = 0 +UC_TAKE_UP_RESOURCE = "dwp_uc_award_band_facts.json" +#: The months DWP's Stat-Xplore counts cover, whose mean is the registry rows' +#: calendar-2025 value (their ``calendar_year_average``). +UC_TAKE_UP_MONTHS: tuple[str, ...] = tuple( + f"2025-{month:02d}" for month in range(4, 13) +) +UC_TAKE_UP_RECORD_SETS: tuple[str, ...] = tuple( + "dwp.statx.uc_benefit_units.award_band_family_type.month" + month.replace("-", "_") + for month in UC_TAKE_UP_MONTHS +) +_CONCEPT = "dwp.uc_households" +_MEASURE = "benefit_units" +UC_TAKE_UP_ENTITLEMENT_VARIABLES = ( + "universal_credit", + "legacy_benefits_closed", + "is_child_or_qualifying_young_person_for_universal_credit", +) +#: Entitlement is read at the release's calibration year on the frame the +#: engine projects there from its survey year, as Pension Credit's is. +UC_TAKE_UP_ENTITLEMENT_YEAR_RULE = "calibration_year" +UC_TAKE_UP_DATASET_YEAR_RULE = "survey_year" +UC_TAKE_UP_SOLVE_SCOPE = "great_britain" +_OUTSIDE_SOLVE_REGION = "NORTHERN_IRELAND" +#: The calibration measure's family types with DWP's Stat-Xplore labels. +UC_FAMILY_TYPES: tuple[tuple[str, str], ...] = ( + ("SINGLE", "Single, no children"), + ("LONE_PARENT", "Single, with children"), + ("COUPLE_NO_CHILDREN", "Couple, no children"), + ("COUPLE_WITH_CHILDREN", "Couple, with children"), +) +#: DWP's GBP 100 monthly bands as annual awards, (1,200 i, 1,200 (i + 1)] in +#: whole pence for i < 25 as the registry rows band them, and the band above. +_BAND_PENCE = 120_000 +_BOUND_BANDS = 25 +UC_AWARD_BAND_LABELS: tuple[str, ...] = ( + *(f"£{100 * index}.01 to £{100 * (index + 1)}.00" for index in range(_BOUND_BANDS)), + f"£{100 * _BOUND_BANDS}.01 or over", +) +UC_TAKE_UP_MINIMUM_POOL_SOURCES = 30 +UC_TAKE_UP_RESIDUAL_RATE = ( + "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the " + "pool's bands, D the weight of the pool's drawn units and F the expected " + "claims of its other entitled units, over Great Britain at the stage's " + "household weights; realized by the drawn units claiming in the order of " + "their uniform until their weight is nearest T - F, and applied at that " + "uniform to Northern Ireland's drawn units too" +) + + +def uc_award_bands(award: np.ndarray) -> np.ndarray: + """Each unit's award band index, or -1 where it has no award. + + Band ``i`` below 25 is (GBP 1,200 i, 1,200 (i + 1)] a year compared in + whole pence, as the registry's payment rows band the engine's award; band + 25 is everything above GBP 30,000, DWP's "GBP 2,500.01 or over". + """ + + pence = np.rint(np.asarray(award, dtype=np.float64) * 100.0) + if not np.isfinite(pence).all(): + raise ValueError("Universal Credit awards must be finite.") + band = np.full(pence.shape, -1, dtype=np.int64) + positive = pence > 0 + band[positive] = np.minimum( + np.ceil(pence[positive] / _BAND_PENCE).astype(np.int64) - 1, _BOUND_BANDS + ) + return band + + +def uc_family_types( + person: pd.DataFrame, benunit: pd.DataFrame, qualifying: np.ndarray +) -> np.ndarray: + """DWP's family type per benefit unit, as the calibration measure reads it. + + Two FRS claimant or partner roles make a couple; a member counts as a + child where ``uc_family_child_member`` says so (a UC child or qualifying + young person, or anyone else under 20 who is not a claimant). The rule is + ``measure_simulation``'s ``uc_calibration_family_type``. + """ + + claimant = frs_uc_claimant_mask(person, benunit) + qualifying = np.asarray(qualifying, dtype=bool) + if qualifying.shape != (len(person),): + raise ValueError("UC child status must align with the person table.") + members = person["person_benunit_id"].to_numpy() + ids = benunit["benunit_id"].to_numpy() + claimants = pd.Series(claimant).groupby(members).sum().reindex(ids).to_numpy() + children = uc_family_child_member( + claimant, qualifying, person["age"].to_numpy(dtype=float) + ) + child_count = pd.Series(children).groupby(members).sum().reindex(ids).to_numpy() + couple = claimants == 2 + has_children = child_count > 0 + return np.select( + [couple & has_children, couple, has_children], + ["COUPLE_WITH_CHILDREN", "COUPLE_NO_CHILDREN", "LONE_PARENT"], + default="SINGLE", + ).astype(object) + + +def uc_award_band_targets_from_rows( + rows: Sequence[Mapping[str, Any]], +) -> dict[tuple[str, int], float]: + """DWP's calendar-2025 mean count by (family type, award band). + + The mean of the nine monthly Stat-Xplore counts, April to December 2025, + as the registry rows' ``calendar_year_average`` reads them. Rows for the + unknown family type and for households with no payment are not cells. + """ + + families = {label: name for name, label in UC_FAMILY_TYPES} + bands = {label: index for index, label in enumerate(UC_AWARD_BAND_LABELS)} + counts: dict[tuple[str, int], dict[str, float]] = {} + for row in rows: + if row.get("concept") != _CONCEPT or row.get("measure_id") != _MEASURE: + continue + dimensions = row.get("dimensions") or {} + family = families.get(str(dimensions.get("family_type"))) + band = bands.get(str(dimensions.get("monthly_award_amount_bands"))) + if family is None or band is None: + continue + month = str((row.get("period") or {}).get("value")) + if month not in UC_TAKE_UP_MONTHS: + raise ValueError(f"UC award band facts carry an unexpected month {month}.") + cell = counts.setdefault((family, band), {}) + if month in cell: + raise ValueError( + f"UC award band facts repeat {family} band {band} in {month}." + ) + cell[month] = float(row["value"]) + expected = { + (family, band) + for family, _ in UC_FAMILY_TYPES + for band in range(len(UC_AWARD_BAND_LABELS)) + } + if set(counts) != expected or any( + set(cell) != set(UC_TAKE_UP_MONTHS) for cell in counts.values() + ): + raise ValueError( + "UC award band facts must give every family type and award band in " + f"each month {UC_TAKE_UP_MONTHS[0]} to {UC_TAKE_UP_MONTHS[-1]}." + ) + return { + key: float(np.mean([cell[month] for month in UC_TAKE_UP_MONTHS])) + for key, cell in sorted(counts.items()) + } + + +def load_uc_award_band_targets() -> dict[tuple[str, int], float]: + """The targets from the committed vendored resource, checked against the pin.""" + + from microcosm.build.uk_runtime.ledger_fact_vendoring import vendored_rows + + return uc_award_band_targets_from_rows(vendored_rows(UC_TAKE_UP_RESOURCE)) + + +def _pools( + *, + band: np.ndarray, + in_family: np.ndarray, + drawable_gb: np.ndarray, + source_ids: np.ndarray, + weights: np.ndarray, + minimum_sources: int, +) -> list[list[int]]: + """Adjacent bands, lowest first, pooled until the drawn units rest on + ``minimum_sources`` distinct source households; a short last pool joins the + one before it.""" + + pools: list[list[int]] = [] + current: list[int] = [] + for index in range(len(UC_AWARD_BAND_LABELS)): + current.append(index) + mask = in_family & drawable_gb & np.isin(band, current) + if distinct_count(source_ids, mask, weights) >= minimum_sources: + pools.append(current) + current = [] + if current: + if pools: + pools[-1].extend(current) + else: + pools.append(current) + return pools + + +def _realized_rate( + residual: float, *, need: float, draws: np.ndarray, weights: np.ndarray +) -> float: + """The uniform below which a pool's drawn units claim. + + ``residual`` clipped to [0, 1] is the share of the drawn weight that + claims. Inside (0, 1) the drawn units claim in the order of their uniform + until their weight is nearest ``need``, so the pool realizes DWP's count + to within one unit's weight rather than only in expectation; no claimant + at all is a candidate too, when ``need`` sits nearer zero than the first + unit's weight. The uniform just above the last claimant's (the lowest + uniform, when none claims) is the rate the pool's Northern Ireland units, + which carry no weight in the solve, are drawn at too. + """ + + if residual <= 0.0: + return 0.0 + if residual >= 1.0: + return 1.0 + order = np.argsort(draws, kind="stable") + cumulative = np.cumsum(weights[order]) + last = min(int(np.searchsorted(cumulative, need)), len(order) - 1) + if last > 0 and abs(cumulative[last - 1] - need) < abs(cumulative[last] - need): + last -= 1 + if last == 0 and abs(need) < abs(cumulative[0] - need): + # No claimant is nearer DWP's count than the first: a unit claims + # below the rate, and no uniform lies below the lowest. + return float(draws[order[0]]) + return float(np.nextafter(draws[order[last]], 2.0)) + + +def assign_award_band_take_up( + *, + award: np.ndarray, + family: np.ndarray, + population: np.ndarray, + reporter: np.ndarray, + moved: np.ndarray, + moved_claim: np.ndarray, + previous: np.ndarray, + weights: np.ndarray, + draws: np.ndarray, + great_britain: np.ndarray, + source_ids: np.ndarray, + targets: Mapping[tuple[str, int], float], + minimum_pool_sources: int = UC_TAKE_UP_MINIMUM_POOL_SOURCES, + legacy_kept: np.ndarray | None = None, +) -> tuple[np.ndarray, dict[str, object]]: + """Reporters claim; drawn units claim at their pool's residual rate. + + The drawn units are the entitled, non-reporting units of the age + population that the engine does not move off their legacy benefits and + that report none (``legacy_kept``: a unit reporting a legacy benefit the + engine leaves in place keeps it, and policyengine-uk ends it once the unit + claims UC, so the draw must not move it; Vahid's review of #1344, item 7). + Every other unit keeps ``previous``, or claims if it reports. In a pool, ``F`` + counts a reporter at 1, a moved unit at ``moved_claim`` (its expected claim + at the legacy closure) and any other undrawn entitled unit at ``previous``. + A drawn unit claims where its uniform falls below the pool's realized rate + (``_realized_rate``), so units claim in the order of their uniform and the + early draw's claimants keep claiming wherever the rate rises. + """ + + award = np.asarray(award, dtype=np.float64) + family = np.asarray(family, dtype=object) + population = np.asarray(population, dtype=bool) + reporter = np.asarray(reporter, dtype=bool) + moved = np.asarray(moved, dtype=bool) + moved_claim = np.asarray(moved_claim, dtype=np.float64) + previous = np.asarray(previous, dtype=bool) + legacy_kept = ( + np.zeros(previous.shape, dtype=bool) + if legacy_kept is None + else np.asarray(legacy_kept, dtype=bool) + ) + weights = np.asarray(weights, dtype=np.float64) + draws = np.asarray(draws, dtype=np.float64) + gb = np.asarray(great_britain, dtype=bool) + source_ids = np.asarray(source_ids) + shape = award.shape + for name, values in ( + ("family", family), + ("population", population), + ("reporter", reporter), + ("moved", moved), + ("moved_claim", moved_claim), + ("previous", previous), + ("legacy_kept", legacy_kept), + ("weights", weights), + ("draws", draws), + ("great_britain", gb), + ("source_ids", source_ids), + ): + if values.shape != shape: + raise ValueError(f"UC take-up {name} must align to the benefit units.") + if not ((moved_claim >= 0.0) & (moved_claim <= 1.0)).all(): + raise ValueError("Expected claims at the legacy closure must lie in [0, 1].") + unknown = sorted(set(family) - {name for name, _ in UC_FAMILY_TYPES}) + if unknown: + raise ValueError(f"UC take-up family types are unknown: {unknown}.") + missing = sorted( + (name, index) + for name, _ in UC_FAMILY_TYPES + for index in range(len(UC_AWARD_BAND_LABELS)) + if (name, index) not in targets + ) + if missing: + raise ValueError(f"UC take-up targets are missing cells: {missing}.") + band = uc_award_bands(award) + entitled = band >= 0 + drawable = entitled & population & ~reporter & ~moved & ~legacy_kept + # Each undrawn entitled unit's expected claim. + fixed = np.where( + reporter, 1.0, np.where(moved, moved_claim, previous.astype(np.float64)) + ) + rates = np.zeros(shape, dtype=np.float64) + + def sources(mask: np.ndarray) -> int: + return distinct_count(source_ids, mask, weights) + + pools: list[dict[str, object]] = [] + for name, _ in UC_FAMILY_TYPES: + in_family = family == name + for bands in _pools( + band=band, + in_family=in_family, + drawable_gb=drawable & gb, + source_ids=source_ids, + weights=weights, + minimum_sources=minimum_pool_sources, + ): + in_pool = in_family & np.isin(band, bands) + solve = in_pool & gb + target = float(sum(targets[(name, index)] for index in bands)) + undrawn = float((weights * fixed)[solve & ~drawable].sum()) + drawn = float(weights[solve & drawable].sum()) + # Where the undrawn claims reach DWP's count, or the count exceeds + # every entitled unit, the clipped rate cannot meet it. + if drawn > 0.0: + residual = (target - undrawn) / drawn + rates[in_pool & drawable] = _realized_rate( + residual, + need=target - undrawn, + draws=draws[solve & drawable], + weights=weights[solve & drawable], + ) + else: + residual = 0.0 if undrawn >= target else 1.0 + unreachable = ( + "undrawn_claims_reach_target" + if residual <= 0.0 + else "target_exceeds_entitled" + if residual >= 1.0 + else None + ) + pools.append( + { + "family_type": name, + "bands": bands, + "mask": solve, + "target": target, + "unreachable": unreachable, + } + ) + would_claim = np.where(drawable, draws < rates, previous | reporter) + expected_before = np.where(drawable, previous.astype(np.float64), fixed) + expected_after = np.where(drawable, would_claim.astype(np.float64), fixed) + + pools_out: list[dict[str, object]] = [] + families_out: dict[str, dict[str, object]] = {} + for name, _ in UC_FAMILY_TYPES: + basis = (family == name) & entitled & gb + reachable = np.zeros(shape, dtype=bool) + reachable_target = 0.0 + for pool in pools: + if pool["family_type"] != name: + continue + solve = pool["mask"] + supported = sources(solve) + pools_out.append( + { + "family_type": name, + "bands": [UC_AWARD_BAND_LABELS[index] for index in pool["bands"]], + "dwp_caseload": pool["target"], + "support": support_band(supported), + "drawn_support": support_band(sources(solve & drawable)), + # One bit on which side of DWP's count the pool's undrawn + # claims lie, given only where ten or more sources carry + # the pool (UKDS disclosure control). + "target_unreachable": ( + pool["unreachable"] if supported >= 10 else "<10" + ), + } + ) + if pool["unreachable"] is None: + reachable |= solve + reachable_target += float(pool["target"]) + supported = sources(basis) + families_out[name] = { + "dwp_caseload": float( + sum( + targets[(name, index)] for index in range(len(UC_AWARD_BAND_LABELS)) + ) + ), + "entitled_weighted": small_cell_share( + float(weights[basis].sum()), supported + ), + "caseload_before": small_cell_share( + float((weights * expected_before)[basis].sum()), supported + ), + "caseload_after": small_cell_share( + float((weights * expected_after)[basis].sum()), supported + ), + "reachable_pools_dwp_caseload": reachable_target, + "reachable_pools_caseload_after": small_cell_share( + float((weights * expected_after)[reachable].sum()), + sources(reachable), + ), + "pools": sum(1 for pool in pools if pool["family_type"] == name), + } + changed = would_claim != previous + receipt: dict[str, object] = { + "families": families_out, + "pools": pools_out, + "minimum_pool_sources": int(minimum_pool_sources), + # Zero exact, as the gate reads it; any other count in bands. + "reporters_not_claiming": extreme_count(int((reporter & ~would_claim).sum())), + "changed_outside_draw": extreme_count( + int((changed & ~drawable & ~reporter).sum()) + ), + "drawn_units": rounded_count(int(drawable.sum())), + "drawn_units_northern_ireland": rounded_count(int((drawable & ~gb).sum())), + "moved_units": rounded_count(int((entitled & moved & ~reporter).sum())), + "legacy_kept_units": rounded_count( + int((entitled & population & legacy_kept & ~reporter & ~moved).sum()) + ), + "set_true": rounded_count(int((changed & would_claim).sum())), + "set_false": rounded_count(int((changed & ~would_claim).sum())), + } + return would_claim.astype(bool), receipt + + +@dataclass(frozen=True) +class UKUCTakeUpResult: + """Output frame and executed-effect receipt for the Universal Credit redraw.""" + + frame: Frame + receipt: Mapping[str, object] + entitlement_year: int + + def evidence(self) -> dict[str, object]: + return { + "stage": UC_TAKE_UP_STAGE_NAME, + "seed": UC_TAKE_UP_SEED, + "residual_rate": UC_TAKE_UP_RESIDUAL_RATE, + "solve_scope": UC_TAKE_UP_SOLVE_SCOPE, + "entitlement_year": self.entitlement_year, + **dict(self.receipt), + } + + +def redraw_uc_take_up( + frame: Frame, + *, + engine: ProjectingRulesEngine, + population_policy: UKTakeUpPopulationPolicy, + targets: Mapping[tuple[str, int], float], + closure_rates: Mapping[str, float], + deduction_resource: Mapping[str, Any] | None = None, + minimum_pool_sources: int = UC_TAKE_UP_MINIMUM_POOL_SOURCES, +) -> UKUCTakeUpResult: + """Redraw ``would_claim_uc`` by award band and family type.""" + + validate_uk_national_frame(frame) + assert_rules_engine_country(engine, "uk") + person = frame.table("person").copy() + benunit = frame.table("benunit").copy() + household = frame.table("household").copy() + for table, columns, label in ( + ( + person, + ( + "person_benunit_id", + "person_household_id", + "universal_credit_reported", + UK_UC_AGE_ELIGIBLE_SOURCE, + UK_UC_AGE_ELIGIBLE_ROLE, + ), + "person", + ), + (benunit, ("benunit_id", UK_UC_TAKE_UP_OUTPUT), "benunit"), + (household, ("household_id", "region"), "household"), + ): + missing = sorted(set(columns) - set(table.columns)) + if missing: + raise ValueError(f"UC take-up {label} columns missing: {missing}.") + period = uk_time_period(frame) + declared = resolve_uk_year_rule(UC_TAKE_UP_DATASET_YEAR_RULE) + if int(period) != declared: + raise ValueError( + f"UC take-up reads the frame as {UC_TAKE_UP_DATASET_YEAR_RULE} " + f"{declared} values, but the frame's period is {period}." + ) + entitlement_year = resolve_uk_year_rule(UC_TAKE_UP_ENTITLEMENT_YEAR_RULE) + # The award a unit would be paid if it claimed: every unit claims on a + # temporary copy that carries the deduction attributes the later stage + # assigns from each unit's identity and region, so the engine applies the + # benefit cap and deductions as it does to a claimant on the released file. + deductions = assign_uc_deduction_attributes( + frame, resource=deduction_resource or load_uc_deduction_distributions() + ).frame + claiming = deductions.table("benunit").copy() + claiming[UK_UC_TAKE_UP_OUTPUT] = True + assessed = uk_national_frame( + person=deductions.table("person"), + benunit=claiming, + household=deductions.table("household"), + time_period=period, + weight_kind=uk_household_weight_kind(frame), + household_weights=frame.weights_for("household").values, + mass_log=frame.mass_log, + ) + # The frame holds survey-year values; stamped with its own period, the + # engine projects them to the entitlement year as it does the released + # file, so the award is the one a calibration-year run pays. + materialized = engine.materialize( + assessed, + list(UC_TAKE_UP_ENTITLEMENT_VARIABLES), + entitlement_year, + dataset_period=period, + ) + missing = sorted(set(UC_TAKE_UP_ENTITLEMENT_VARIABLES) - set(materialized)) + if missing: + raise ValueError(f"UC take-up engine outputs missing: {missing}.") + count = len(benunit) + award = _aligned( + materialized["universal_credit"], count, "universal_credit" + ).astype(float) + moved = _aligned( + materialized["legacy_benefits_closed"], count, "legacy_benefits_closed" + ).astype(bool) + qualifying = _aligned( + materialized["is_child_or_qualifying_young_person_for_universal_credit"], + len(person), + "is_child_or_qualifying_young_person_for_universal_credit", + ).astype(bool) + family = uc_family_types(person, benunit, qualifying) + reported = ( + pd.to_numeric(person["universal_credit_reported"], errors="coerce").fillna(0.0) + > 0.0 + ) + reporter = ( + benunit["benunit_id"] + .isin(set(person.loc[reported, "person_benunit_id"])) + .to_numpy(dtype=bool) + ) + population = uc_age_eligible_benunits(person, benunit, population_policy) + previous = benunit[UK_UC_TAKE_UP_OUTPUT].fillna(False).to_numpy(dtype=bool) + weights = _household_to_benunit_weights( + benunit, + person=person, + household=household, + household_weights=frame.weights_for("household").values, + ) + unit_households = pd.Series(_benunit_households(benunit, person=person)) + regions = unit_households.map(household.set_index("household_id")["region"]) + if regions.isna().any(): + raise ValueError("Every benefit unit's household must carry a region.") + great_britain = regions.astype(str).ne(_OUTSIDE_SOLVE_REGION).to_numpy(dtype=bool) + source_ids = unit_households.map( + pd.Series( + source_household_ids(household, stage="The UC take-up stage"), + index=household["household_id"].to_numpy(), + ) + ).to_numpy() + # A unit the engine moves off its legacy benefits claims at the closure + # with the probability ``uc_legacy_closure_take_up`` gives it; this stage + # leaves its flag alone, so the closure's share already claiming, and so + # that probability, does not move. + combination = legacy_benefit_combinations(person, benunit) + closure_drawn = ~reporter & (combination != "") + closure_probability, _ = legacy_closure_claim_probabilities( + combination=combination, + drawn=closure_drawn, + claims_anyway=previous, + weights=weights, + rates=closure_rates, + great_britain=great_britain, + source_ids=source_ids, + ) + moved_claim = np.where( + previous | ~closure_drawn, previous.astype(float), closure_probability + ) + draws = stable_identity_uniforms( + benunit["benunit_id"].to_numpy(), + seed=UC_TAKE_UP_SEED, + salt=UK_UC_TAKE_UP_OUTPUT, + ) + would_claim, receipt = assign_award_band_take_up( + award=award, + family=family, + population=population, + reporter=reporter, + moved=moved, + moved_claim=moved_claim, + previous=previous, + weights=weights, + draws=draws, + great_britain=great_britain, + source_ids=source_ids, + targets=targets, + minimum_pool_sources=minimum_pool_sources, + legacy_kept=(combination != "") & ~moved & ~reporter, + ) + working_age_hb = working_age_housing_benefit_receipt( + person=person, + benunit=benunit, + population=population, + moved=moved, + uc_before=previous | reporter, + uc_after=would_claim | reporter, + weights=weights, + great_britain=great_britain, + source_ids=source_ids, + ) + benunit[UK_UC_TAKE_UP_OUTPUT] = would_claim + result = uk_national_frame( + person=person, + benunit=benunit, + household=household, + time_period=period, + weight_kind=uk_household_weight_kind(frame), + household_weights=frame.weights_for("household").values, + mass_log=frame.mass_log, + ) + validate_uk_national_frame(result) + return UKUCTakeUpResult( + frame=result, + receipt={ + **receipt, + "population_state_pension_age": int(population_policy.state_pension_age), + "working_age_housing_benefit": working_age_hb, + }, + entitlement_year=entitlement_year, + ) + + +def working_age_housing_benefit_receipt( + *, + person: pd.DataFrame, + benunit: pd.DataFrame, + population: np.ndarray, + moved: np.ndarray, + uc_before: np.ndarray, + uc_after: np.ndarray, + weights: np.ndarray, + great_britain: np.ndarray, + source_ids: np.ndarray, +) -> dict[str, object]: + """Working-age Housing Benefit the redraw keeps, at the stage's weights. + + policyengine-uk continues an existing Housing Benefit award only while the + unit neither claims Universal Credit nor is moved off its legacy benefits, + so a working-age unit reporting Housing Benefit that the redraw sets to + claim UC loses it. This counts, over Great Britain, the benefit units of + the UC age population with a member reporting Housing Benefit that the + engine does not move, and how many of them keep the award before and after + the redraw, so the stage's effect on working-age Housing Benefit can be set + beside DWP's 2025 caseload (Vahid's review of #1344, item 7). Weighted + figures only where ten or more source households carry them. + """ + + column = "housing_benefit_reported" + reported = ( + pd.to_numeric(person[column], errors="coerce").fillna(0.0) > 0.0 + if column in person.columns + else pd.Series(False, index=person.index) + ) + reporting = ( + benunit["benunit_id"] + .isin(set(person.loc[reported.to_numpy(dtype=bool), "person_benunit_id"])) + .to_numpy(dtype=bool) + ) + base = reporting & population & ~moved & great_britain + kept_before = base & ~uc_before + kept_after = base & ~uc_after + drawn_onto_uc = kept_before & uc_after + + def weighted(mask: np.ndarray) -> object: + return small_cell_share( + float(weights[mask].sum()), distinct_count(source_ids, mask, weights) + ) + + return { + "scope": "great_britain", + "reporting_units": weighted(base), + "keeping_housing_benefit_before": weighted(kept_before), + "keeping_housing_benefit_after": weighted(kept_after), + "drawn_onto_universal_credit": weighted(drawn_onto_uc), + "drawn_support": support_band( + distinct_count(source_ids, drawn_onto_uc, weights) + ), + } + + +def _aligned(values: object, expected: int, label: str) -> np.ndarray: + array = np.asarray(values) + if array.shape != (expected,): + raise ValueError(f"{label} must align to its entity table.") + if array.dtype != bool and not np.isfinite(array.astype(np.float64)).all(): + raise ValueError(f"{label} must be finite.") + return array + + +def uc_take_up_operation_parameters() -> tuple[tuple[str, dict[str, object]], ...]: + """The reviewed operations the manifest must carry verbatim.""" + + return ( + ( + "verify_vendored_fact_resource", + { + "artifact_role": "uc_award_band_facts", + "resource": UC_TAKE_UP_RESOURCE, + "feed_pin": "chronicle_feed.json", + "record_sets": list(UC_TAKE_UP_RECORD_SETS), + "source_vintage": "April to December 2025", + "mapped_build_period": 2025, + "period_mapping": "calendar_year_average_of_monthly_counts", + "require_before_source_read": True, + "runtime_sha256_required": True, + "fail_on_mismatch": True, + }, + ), + ( + "materialize_rules_engine_predictors", + { + "predictors": list(UC_TAKE_UP_ENTITLEMENT_VARIABLES), + "consumed_only": True, + "year_rule": UC_TAKE_UP_ENTITLEMENT_YEAR_RULE, + "dataset_year_rule": UC_TAKE_UP_DATASET_YEAR_RULE, + }, + ), + ( + "aggregate_person_to_benunit", + { + "method": "any_positive", + "consumed_only": True, + "aggregates": { + "universal_credit_reported_anchor": "universal_credit_reported" + }, + }, + ), + ( + "aggregate_person_to_benunit", + { + "method": UK_UC_AGE_ELIGIBLE_METHOD, + "consumed_only": True, + "aggregates": {UK_UC_AGE_ELIGIBLE_AGGREGATE: UK_UC_AGE_ELIGIBLE_SOURCE}, + "role_column": UK_UC_AGE_ELIGIBLE_ROLE, + }, + ), + ( + UC_TAKE_UP_OPERATION_KIND, + { + "output": UK_UC_TAKE_UP_OUTPUT, + "draw": UK_UC_TAKE_UP_OUTPUT, + "seed": UC_TAKE_UP_SEED, + "anchor": "universal_credit_reported_anchor", + "population": UK_UC_AGE_ELIGIBLE_AGGREGATE, + "award": ( + "universal_credit on a temporary copy where every unit claims " + "and carries the deduction attributes uc_deduction_attributes " + "assigns from its identity and region" + ), + "family_type": ( + "two FRS claimant or partner roles make a couple; children " + "are UC children or qualifying young people and other " + "non-claimants under 20 (the calibration measure's " + "uc_calibration_family_type)" + ), + "award_bands": ( + "GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, " + "1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000" + ), + "targets": ( + "DWP Stat-Xplore Households on Universal Credit by family type " + "and monthly award band, mean of April to December 2025" + ), + "pooling": ( + "adjacent bands within a family type, lowest first, until the " + "drawn units rest on minimum_pool_sources distinct " + "household_source_id values; a short last pool joins the one " + "before it" + ), + "minimum_pool_sources": UC_TAKE_UP_MINIMUM_POOL_SOURCES, + "drawn_units": ( + "entitled, non-reporting units of the population that the " + "engine does not move off their legacy benefits " + "(legacy_benefits_closed) and that report none, since a " + "legacy benefit the engine leaves in place ends once the " + "unit claims UC" + ), + "undrawn_claims": ( + "a reporter at 1, a moved unit at the claim probability " + "uc_legacy_closure_take_up's rule gives it " + f"({LEGACY_CLOSURE_RESOURCE}), any other at its stored flag" + ), + "weight_mapping": "household_to_benunit", + "residual_rate": UC_TAKE_UP_RESIDUAL_RATE, + "solve_scope": UC_TAKE_UP_SOLVE_SCOPE, + "not_drawn": "keeps its stored flag", + }, + ), + ) + + +def _assert_stage_parameters(stage: SourceStageSpec) -> None: + if stage.stage != UC_TAKE_UP_STAGE_NAME: + raise ValueError( + f"UC take-up received stage {stage.stage!r}, expected " + f"{UC_TAKE_UP_STAGE_NAME!r}." + ) + _assert_closed_world_operations(stage, uc_take_up_operation_parameters()) + if tuple(stage.outputs) or tuple(stage.rewrites) != (UK_UC_TAKE_UP_OUTPUT,): + raise ValueError( + f"{UC_TAKE_UP_STAGE_NAME} must declare no outputs and exactly the " + f"{UK_UC_TAKE_UP_OUTPUT} rewrite." + ) + if stage.grain != "benunit": + raise ValueError( + f"{UC_TAKE_UP_STAGE_NAME} is benunit grain, got {stage.grain!r}." + ) + + +@dataclass(frozen=True) +class UKUCTakeUpStageTransform: + """Whole-stage callable for the post-SPI Universal Credit take-up redraw.""" + + stage: SourceStageSpec + engine: object + population_policy: UKTakeUpPopulationPolicy | None = None + #: DWP's counts and the Move to UC rates; read from the vendored facts + #: when not given. + targets: Mapping[tuple[str, int], float] | None = field(default=None, repr=False) + closure_rates: Mapping[str, float] | None = field(default=None, repr=False) + deduction_resource: Mapping[str, Any] | None = field(default=None, repr=False) + last_result: UKUCTakeUpResult | None = field(default=None, init=False) + + def __post_init__(self) -> None: + _assert_stage_parameters(self.stage) + + def __call__(self, frame: Frame) -> Frame: + _assert_stage_parameters(self.stage) + result = redraw_uc_take_up( + frame, + engine=self.engine, + population_policy=( + self.population_policy + or uk_take_up_population_policy(uk_time_period(frame)) + ), + targets=( + self.targets + if self.targets is not None + else load_uc_award_band_targets() + ), + closure_rates=( + self.closure_rates + if self.closure_rates is not None + else load_move_to_uc_claim_rates() + ), + deduction_resource=self.deduction_resource, + ) + object.__setattr__(self, "last_result", result) + return result.frame + + @staticmethod + def output_columns() -> tuple[str, ...]: + return () + + 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()} diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_annual_income_measures.py b/packages/microcosm-build/tests/engine/uk/test_uk_annual_income_measures.py new file mode 100644 index 000000000..75bb7bbaa --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_annual_income_measures.py @@ -0,0 +1,141 @@ +"""The HMRC measures of each person's year run on policyengine-uk itself. + +HMRC's income statistics count the income received over a tax year, and the +frame carries beside its current incomes the descriptors of each adult's year +(``frs_annual_income_spells``). The annual measures are the current measures +on a second simulation whose inputs are that year: the engine projects a +dataset's inputs to its later years as it loads it, so the year's inputs are +set at the measurement year and the other spells grow by the engine's own +factor. +""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.uk_runtime.measure_simulation import ( + UK_ANNUAL_INCOME_MEASURES, + UKMeasureResolver, + state_pension_annual_share, +) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind + + +def _frame(**descriptors): + ids = np.arange(5, dtype=np.int64) + person = { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + # Person 0 is paid all year; person 1 left a job in the year and + # claims contributory ESA since; person 2 started a job half way + # through; persons 3 and 4 are pensioners, 3 retiring in the year. + "age": [45.0, 40.0, 30.0, 65.75, 75.0], + "employment_income": [30_000.0, 0.0, 24_000.0, 0.0, 0.0], + "self_employment_income": [0.0] * 5, + "private_pension_income": [0.0, 0.0, 0.0, 9_000.0, 6_000.0], + "state_pension_reported": [0.0, 0.0, 0.0, 11_500.0, 11_500.0], + "esa_contrib_reported": [0.0, 4_000.0, 0.0, 0.0, 0.0], + **descriptors, + } + return uk_national_frame( + person=pd.DataFrame(person), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "household_weight": [1.0] * 5, + "region": "LONDON", + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2024", + weight_kind=WeightKind.DESIGN, + ) + + +_DESCRIPTORS = { + "employment_income_annual_share": [1.0, 1.0, 0.5, 1.0, 1.0], + "employment_income_other_spells": [0.0, 15_000.0, 0.0, 20_000.0, 0.0], + "self_employment_income_annual_share": [1.0] * 5, + "self_employment_income_other_spells": [0.0] * 5, + "private_pension_income_annual_share": [1.0, 1.0, 1.0, 0.5, 1.0], + "contributory_benefits_annual_share": [1.0, 0.5, 1.0, 1.0, 1.0], +} + + +def test_the_annual_measures_tax_each_persons_year(tmp_path) -> None: + resolver = UKMeasureResolver( + simulation_source=None, + frame=_frame(**_DESCRIPTORS), + scratch_dir=tmp_path, + year=2025, + ) + pay, _ = resolver.compute("person", "employment_income") + pay = np.asarray(pay, dtype=float) + factor = pay[0] / 30_000.0 + assert factor > 1.0 + + annual_pay, route = resolver.compute("person", "employment_income_annual") + assert route.startswith("annual_income_simulation:") + # The leaver's spell and the pensioner's last job grow by the engine's + # own factor for pay; the entrant keeps half the year at today's rate. + np.testing.assert_allclose( + annual_pay, + [pay[0], 15_000.0 * factor, pay[2] * 0.5, 20_000.0 * factor, 0.0], + rtol=1e-6, + ) + pension, _ = resolver.compute("person", "private_pension_income") + annual_pension, _ = resolver.compute("person", "private_pension_income_annual") + np.testing.assert_allclose( + annual_pension, np.asarray(pension) * [1.0, 1.0, 1.0, 0.5, 1.0], rtol=1e-6 + ) + state_pension, _ = resolver.compute("person", "state_pension") + months, _ = resolver.compute("person", "months_since_state_pension_age") + annual_state_pension, _ = resolver.compute("person", "state_pension_annual") + np.testing.assert_allclose( + annual_state_pension, + np.asarray(state_pension) * state_pension_annual_share(months), + rtol=1e-6, + ) + # Taxed by the engine on the year: the leaver's spell makes them a + # taxpayer, and halving the entrant's pay lowers their tax. + tax, _ = resolver.compute("person", "income_tax") + annual_tax, _ = resolver.compute("person", "income_tax_annual") + tax, annual_tax = np.asarray(tax, dtype=float), np.asarray(annual_tax, dtype=float) + assert tax[1] == 0.0 and annual_tax[1] > 0.0 + assert annual_tax[2] < tax[2] + assert annual_tax[0] == tax[0] and annual_tax[4] == tax[4] + total, _ = resolver.compute("person", "total_income_after_loss_relief_annual") + assert np.asarray(total, dtype=float)[1] > 15_000.0 * factor + profit, route = resolver.compute( + "person", "self_employment_income_after_loss_relief_annual" + ) + np.testing.assert_allclose(profit, 0.0) + assert route.startswith("annual_income_simulation:engine_components:") + band, _ = resolver.compute("person", "tax_band_annual") + assert len(band) == 5 + receipt = resolver.receipt()["annual_income_measures"] + assert receipt["route"] == "annual_income_simulation" + factors = receipt["other_spells_engine_factors"] + assert factors["employment_income_before_lsr"] == pytest.approx(factor, rel=1e-6) + assert set(receipt["computed_measures"]) == set(UK_ANNUAL_INCOME_MEASURES) + + +def test_a_frame_without_descriptors_refuses_unless_it_is_the_incumbent( + tmp_path, +) -> None: + resolver = UKMeasureResolver( + simulation_source=None, frame=_frame(), scratch_dir=tmp_path, year=2025 + ) + assert not resolver.knows("person", "income_tax_annual") + resolver.use_current_income_for_annual_measures(reason="incumbent") + annual_tax, route = resolver.compute("person", "income_tax_annual") + tax, _ = resolver.compute("person", "income_tax") + np.testing.assert_array_equal(annual_tax, tax) + assert route.startswith("current_values_without_annual_income_descriptors:") 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 514bf5087..5326688d9 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 @@ -452,6 +452,7 @@ def test_shipped_manifest_is_current(self) -> None: assert manifest.required_build_stages == frozenset( { "frs_hmrc_spine_leaves", + "frs_annual_income_spells", "spi_support_channel", "spi_income_band_donors", "hmrc_spi_income_spine", 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 f926eaa00..99686a985 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 @@ -934,6 +934,7 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "hmrc_property_rental_income_facts.json", "hmrc_stamp_tax_facts.json", "dwp_move_to_uc_facts.json", + "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", "hmrc_itl_taxpayer_counts.json", "hmrc_uprating_engine_pins.json", @@ -1039,8 +1040,9 @@ def test_uk_source_manifest_contains_only_canonical_spine_stages(self) -> None: # after property_components and uc_legacy_closure_take_up after # uc_capital_coherence; microcosm#1319 carers_allowance_take_up after # child_benefit_take_up; childcare_copy_take_up last, after every copy - # stage. - assert len(spec.sources.stages) == 47 + # stage; microcosm#1334 uc_take_up after uc_capital_coherence and + # frs_annual_income_spells after frs_hmrc_spine_leaves. + assert len(spec.sources.stages) == 49 assert not {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.intersection( stage.stage for stage in spec.sources.stages ) @@ -1084,6 +1086,7 @@ def test_uk_package_loads(self) -> None: "hmrc_property_rental_income_facts.json", "hmrc_stamp_tax_facts.json", "dwp_move_to_uc_facts.json", + "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", "hmrc_itl_taxpayer_counts.json", "hmrc_uprating_engine_pins.json", @@ -1458,9 +1461,11 @@ def test_declares_the_full_june_battery(self, manifest) -> None: "uk_stage_etb_services_support", "uk_stage_etb_services_support_pricing", "uk_stage_frs_hmrc_spine_leaves_signal", + "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", "uk_stage_spi_benefit_coherence", + "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", "uk_stage_child_benefit_take_up", "uk_stage_cgt_incidence_clone_mass", @@ -1581,9 +1586,11 @@ def test_strict_absent_evidence_entries_are_declared(self, manifest) -> None: "uk_stage_etb_services_support", "uk_stage_etb_services_support_pricing", "uk_stage_frs_hmrc_spine_leaves_signal", + "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", "uk_stage_spi_benefit_coherence", + "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", "uk_stage_child_benefit_take_up", "uk_stage_cgt_incidence_clone_mass", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py index 5dc79887d..a6fd6f091 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_candidate_score.py @@ -123,12 +123,21 @@ def _intercept(monkeypatch, *, failing_h5: Path, message: str, receipt: dict): original = candidate_score._scored_frame calls: list[dict] = [] - def fake(h5_path, registry, calibration_year, factory, *, band_edge_registry=None): + def fake( + h5_path, + registry, + calibration_year, + factory, + *, + band_edge_registry=None, + incumbent=False, + ): calls.append( { "h5": Path(h5_path), "targets": [spec.name for spec in registry.specs], "band_edge_registry": band_edge_registry, + "incumbent": incumbent, } ) if Path(h5_path) == failing_h5 and any( @@ -224,6 +233,10 @@ def test_prunes_the_target_the_incumbent_cannot_materialize_and_scores_the_rest( assert scoring_calls and all( call["band_edge_registry"] is full for call in scoring_calls ) + # Only the incumbent may read its current income for the annual measures. + assert calls and all( + call["incumbent"] == (call["h5"] == incumbent) for call in calls + ) warning = pruned_warning(score) assert warning is not None assert MEASURE_B in warning and "1 target(s)" in warning and "target_b" in warning @@ -513,7 +526,15 @@ def test_a_gap_shared_by_both_artifacts_still_refuses_on_the_candidate( original = candidate_score._scored_frame calls: list[tuple[Path, list[str]]] = [] - def fake(h5_path, registry, calibration_year, factory, *, band_edge_registry=None): + def fake( + h5_path, + registry, + calibration_year, + factory, + *, + band_edge_registry=None, + incumbent=False, + ): calls.append((Path(h5_path), [spec.name for spec in registry.specs])) if any(spec.name == "target_b" for spec in registry.specs): raise MeasureResolutionError( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py index 526b2a900..885ba0e2e 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_diagnostics.py @@ -1440,11 +1440,16 @@ def test_a_thin_local_area_row_stays_with_the_area_support_rule() -> None: def _withheld_sets( - rows: dict[str, list[int]], n: int, values: dict[str, float] | None = None + rows: dict[str, list[int]], + n: int, + values: dict[str, float | list[float]] | None = None, + sources: list[float] | None = None, ) -> tuple[set[str], set[str]]: """``uk_withheld_row_sets`` over rows given by the households that carry them. - Every household is its own source household and carries weight one. + Every household is its own source household (or ``sources``) and carries + weight one. A row's entries are one, or its ``values`` (one for all its + households, or one each). """ from types import SimpleNamespace @@ -1469,7 +1474,11 @@ def _withheld_sets( "household": pd.DataFrame( { "household_id": np.arange(n, dtype=np.int64), - "source_household_id": np.arange(n, dtype=np.int64), + "source_household_id": ( + np.arange(n, dtype=np.int64) + if sources is None + else np.asarray(sources) + ), } ), }, @@ -1501,6 +1510,507 @@ def test_a_band_its_total_gives_back_withholds_the_next_smallest_band() -> None: assert (thin, beside) == ({"band_a"}, {"band_b"}) +def test_a_thin_top_band_no_row_carries_withholds_the_next_smallest_band() -> None: + """Vahid's second review of #1344, item 17: a thin top band, no thin bound band. + + The top band (above GBP 2,500 a month) is no row, so no row is thin and + no withheld row is given back, but the total less the bound bands gives + the top band's three households back. The smaller bound band is withheld, + so what the published rows give back rests on fifteen households. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(0, 15)), + "band_b": list(range(15, 27)), + }, + 30, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_a_top_band_resting_on_no_household_withholds_nothing() -> None: + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(0, 15)), + "band_b": list(range(15, 30)), + }, + 30, + ) + assert (thin, beside) == (set(), set()) + + +def test_a_household_in_two_bands_still_leaves_the_bands_one_partition() -> None: + """Two benefit units of one household in two bands: half its total each. + + The bands still partition the total, so its thin rest (the top band's + three households) is found and the smaller band withheld. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(0, 15)), + "band_b": [0, *range(15, 27)], + }, + 30, + values={"total": [2.0, *[1.0] * 29]}, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_bands_beside_regions_of_the_same_total_are_still_checked() -> None: + """The regions partition the total exactly; the bands leave a thin top band. + + Each band crosses both regions, so a band and a region never stand in one + partition, and the bands' rest is measured on its own. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "region_1": list(range(0, 15)), + "region_2": list(range(15, 30)), + "band_a": [*range(0, 10), *range(15, 20)], + "band_b": [*range(10, 15), *range(20, 27)], + }, + 30, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_a_constant_multiple_of_a_part_is_withheld_with_it() -> None: + """A count of people beside the count of their households is that part again. + + The people row is four times the units row on the same households, so + once the units row is withheld, the total less a quarter of the people row + would give the three households back. Both are withheld (the adversarial + review of #1344's item 17 fix, 22 October). + """ + + thin, beside = _withheld_sets( + {"total": list(range(30)), "units": list(range(27)), "people": list(range(27))}, + 30, + values={"people": 4.0}, + ) + assert (thin, beside) == (set(), {"people", "units"}) + + +def test_bands_in_another_unit_than_their_total_are_scaled() -> None: + """Monthly bands under a yearly total: a twelfth of the total on each household.""" + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(15)), + "band_b": list(range(15, 27)), + }, + 30, + values={"total": 1200.0, "band_a": 100.0, "band_b": 100.0}, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_a_band_of_two_unit_households_only_still_partitions_its_total() -> None: + """Every household of band_b holds a second unit in band_a: half the total each.""" + + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(27)), + "band_b": list(range(15, 27)), + }, + 30, + values={"total": [1.0] * 15 + [2.0] * 12 + [1.0] * 3}, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_income_components_less_their_total_band_are_checked() -> None: + """A total-income band less its published components gives back other income. + + The components hold a different share of the total on each household, and + a count of the band's payers sits inside it too; the count is no member. + """ + + employment = [100.5 + h for h in range(40)] + pension = [20.25 + h / 2 for h in range(10, 40)] + total = [ + employment[h] + (pension[h - 10] if h >= 10 else 0.0) + (3.25 if h < 5 else 0.0) + for h in range(40) + ] + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "employment": list(range(40)), + "pension": list(range(10, 40)), + "payers": list(range(40)), + }, + 40, + values={"total": total, "employment": employment, "pension": pension}, + ) + assert (thin, beside) == (set(), {"pension"}) + + +def test_two_overlapping_parts_less_their_total_give_back_the_overlap() -> None: + """Claimants of either benefit less the claimants of each: five on both.""" + + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "contrib": list(range(25)), + "income": list(range(20, 40)), + }, + 40, + ) + assert (thin, beside) == (set(), {"income"}) + + +def test_a_set_smaller_than_the_largest_group_is_checked() -> None: + """Bands a and b and a third part fit pairwise but pass one together. + + The total less bands a and b alone gives back the nine-household top band + and household 0, which the larger set covers. + """ + + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "band_a": list(range(15)), + "band_b": [0, *range(15, 31)], + "c_other": [0, *range(31, 40)], + }, + 40, + values={"total": [2.0] + [1.0] * 39}, + ) + assert (thin, beside) == (set(), {"band_a"}) + + +def test_bands_crossing_larger_regions_are_still_grouped() -> None: + """Each band misses a region larger than it, which a greedy group takes first.""" + + cells = iter(range(39)) + b0r0, b0r1, b1r1, b1r2, b2r0, b2r2 = ( + [next(cells) for _ in range(6)] for _ in range(6) + ) + top = [next(cells) for _ in range(3)] + thin, beside = _withheld_sets( + { + "total": list(range(39)), + "band_0": b0r0 + b0r1, + "band_1": b1r1 + b1r2, + "band_2": b2r0 + b2r2, + "region_0": b0r0 + b2r0 + top[:1], + "region_1": b0r1 + b1r1 + top[1:2], + "region_2": b1r2 + b2r2 + top[2:], + }, + 39, + ) + assert (thin, beside) == (set(), {"band_0"}) + + +def test_a_total_past_the_search_cap_is_grouped_greedily(monkeypatch) -> None: + """With no work allowed, every total is grouped greedily and named in a warning.""" + + from microcosm.build.uk_runtime import diagnostics + + monkeypatch.setattr(diagnostics, "_RESIDUAL_SEARCH_WORK", 0) + with pytest.warns(UserWarning, match="passed its cap"): + thin, beside = _withheld_sets( + { + "total": list(range(120)), + "region_1": list(range(41)), + "region_2": list(range(41, 82)), + "region_3": list(range(82, 120)), + "band_a": [*range(3, 22), *range(41, 53)], + "band_b": [*range(22, 41), *range(82, 94)], + "band_c": list(range(53, 82)), + "band_d": list(range(94, 120)), + }, + 120, + ) + assert (thin, beside) == (set(), {"band_d"}) + + +def test_households_whose_shares_differ_slightly_stay_in_different_atoms() -> None: + """A large share elsewhere must not round band B's last five households away. + + Band B holds 99.999 of the total's 100 on five households, so the total + less bands A and B rests on those five; a part with a share of a million + on another household beside them changes nothing. + """ + + total = [1.0] + [100.0] * 39 + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "band_a": list(range(20)), + "band_b": list(range(20, 40)), + "big": list(range(10)), + }, + 40, + values={ + "total": total, + "band_a": total[:20], + "band_b": [100.0] * 15 + [99.999] * 5, + "big": [1e6] + [100.0] * 9, + }, + ) + assert (thin, beside) == (set(), {"band_a"}) + + +def test_a_component_in_whole_pounds_is_still_searched() -> None: + """An amount component of whole pounds is no count: three components less + their total give back other income on five households.""" + + whole = [100.0 + h for h in range(40)] + first = [10.25 + h for h in range(20)] + second = [20.5 + h for h in range(20)] + other = {3: 7.31, 8: 7.31, 25: 7.31, 30: 7.31, 33: 7.31} + total = [ + whole[h] + (first[h] if h < 20 else second[h - 20]) + other.get(h, 0.0) + for h in range(40) + ] + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "whole": list(range(40)), + "first": list(range(20)), + "second": list(range(20, 40)), + }, + 40, + values={"total": total, "whole": whole, "first": first, "second": second}, + ) + assert (thin, beside) == (set(), {"first"}) + + +def test_a_part_of_constant_negative_share_is_scaled() -> None: + """The total plus twice a part of share minus a half gives back three households.""" + + thin, beside = _withheld_sets( + {"total": list(range(30)), "loss": list(range(27))}, + 30, + values={"total": 100.0, "loss": -50.0}, + ) + assert (thin, beside) == (set(), {"loss"}) + + +def test_three_components_with_no_thin_pair_are_searched_together() -> None: + """Every component sits on every household, so no pair less the total is + thin; all three less it give back other income on five households.""" + + employment = [100.5 + h for h in range(40)] + pension = [20.25 + h / 4 for h in range(40)] + savings = [3.5 + h / 8 for h in range(40)] + total = [ + employment[h] + pension[h] + savings[h] + (2.75 if h < 5 else 0.0) + for h in range(40) + ] + thin, beside = _withheld_sets( + { + "total": list(range(40)), + "employment": list(range(40)), + "pension": list(range(40)), + "savings": list(range(40)), + }, + 40, + values={ + "total": total, + "employment": employment, + "pension": pension, + "savings": savings, + }, + ) + assert (thin, beside) == (set(), {"employment"}) + + +def test_a_part_above_its_total_on_one_household_is_checked_alone() -> None: + """Twice the total's entry on household 0, so no member; the total less it + still rests on four households.""" + + thin, beside = _withheld_sets( + {"total": list(range(30)), "part": list(range(27))}, + 30, + values={"part": [2.0] + [1.0] * 26}, + ) + assert (thin, beside) == (set(), {"part"}) + + +def test_a_missing_source_id_counts_as_a_source_of_its_own() -> None: + sources = [float(h) for h in range(29)] + [float("nan")] + thin, beside = _withheld_sets( + { + "total": list(range(30)), + "band_a": list(range(15)), + "band_b": list(range(15, 27)), + }, + 30, + sources=sources, + ) + assert (thin, beside) == (set(), {"band_b"}) + + +def test_cells_of_ten_households_clear_a_total_without_a_search(monkeypatch) -> None: + """Twelve regions, each split in two cells of ten: every atom rests on ten + households, so no combination at any weights can be thin.""" + + from microcosm.build.uk_runtime import diagnostics + + def refuse(*_args, **_kwargs): + raise AssertionError("the search ran") + + monkeypatch.setattr(diagnostics._ResidualCheck, "_search", refuse) + rows = {"total": list(range(240))} + for g in range(12): + rows[f"region_{g:02d}"] = list(range(20 * g, 20 * g + 20)) + rows[f"region_{g:02d}_a"] = list(range(20 * g, 20 * g + 10)) + rows[f"region_{g:02d}_b"] = list(range(20 * g + 10, 20 * g + 20)) + assert _withheld_sets(rows, 240) == (set(), set()) + + +def test_a_small_atom_in_the_span_of_large_ones_clears_its_total(monkeypatch) -> None: + """Household 20 holds two units, one in each band: its column is the mean of + the two bands' columns, so nothing can isolate it, and no search runs.""" + + from microcosm.build.uk_runtime import diagnostics + + def refuse(*_args, **_kwargs): + raise AssertionError("the search ran") + + monkeypatch.setattr(diagnostics._ResidualCheck, "_search", refuse) + thin, beside = _withheld_sets( + { + "total": list(range(21)), + "band_a": [*range(10), 20], + "band_b": [*range(10, 20), 20], + }, + 21, + values={"total": [1.0] * 20 + [2.0]}, + ) + assert (thin, beside) == (set(), set()) + + +def test_an_outlier_household_does_not_hide_a_gap_from_the_span_test() -> None: + """Employment less a huge offsetting loss on one household of total one. + + The outlier squashes every other share once a row is scaled to its + largest; two households where the parts fall short of the total by a + hundredth still rest on two source households, and the total less both + parts gives them back (the third adversarial review of item 17). + """ + + total, x, y = {}, {}, {} + for h in range(12): + total[h] = x[h] = 30000.0 + 101.0 * h + for h in range(12, 24): + total[h] = y[h] = 30000.0 + 97.0 * h + for h in range(24, 44): + total[h] = 25000.0 + 89.0 * h + x[h], y[h] = 0.4 * total[h], 0.6 * total[h] + total[44], x[44], y[44] = 1.0, 1e6, 1.0 - 1e6 + for h in (45, 46): + total[h] = 40000.0 + h + x[h], y[h] = 0.4 * total[h], 0.59 * total[h] + thin, beside = _withheld_sets( + {"total": sorted(total), "x": sorted(x), "y": sorted(y)}, + 47, + values={ + "total": [total[h] for h in sorted(total)], + "x": [x[h] for h in sorted(x)], + "y": [y[h] for h in sorted(y)], + }, + ) + assert (thin, beside) == (set(), {"x"}) + + +def test_a_large_atom_short_of_one_by_less_than_the_rounding_is_covered() -> None: + """Twelve households hold 0.6 and 0.4 less five ten-millionths: the search + counts that as covered, so the span test must not count it as a direction + that hides the two-household atom.""" + + delta = 5e-7 + shares = ( + [(0.6, 0.4 - delta)] * 12 + + [(0.3, 0.7)] * 12 + + [(1.0, 0.0)] * 12 + + [(0.2, 0.3)] * 2 + ) + first = [h for h, (a, _) in enumerate(shares) if a] + second = [h for h, (_, b) in enumerate(shares) if b] + thin, beside = _withheld_sets( + {"total": list(range(38)), "p1": first, "p2": second}, + 38, + values={ + "total": 1000.0, + "p1": [1000 * shares[h][0] for h in first], + "p2": [1000 * shares[h][1] for h in second], + }, + ) + assert (thin, beside) == (set(), {"p2"}) + + +def test_a_share_exactly_one_rounding_short_of_one_counts_as_covered_everywhere() -> ( + None +): + """A part holding one less a millionth on ten households and a half on one: + the total less it rests on three, whether or not a copy of the total sits + beside it.""" + + almost = 1 - 1e-6 + for rows in ( + {"total": list(range(13)), "copy": list(range(13)), "part": list(range(11))}, + {"total": list(range(13)), "part": list(range(11))}, + ): + thin, beside = _withheld_sets(rows, 13, values={"part": [almost] * 10 + [0.5]}) + assert (thin, beside) == (set(), {"part"}) + + +def test_an_outlier_on_the_gap_household_itself_is_measured_as_stored() -> None: + """Two households where employment is a thousand times the total and a + matching loss falls short of it by a ten-thousandth: as unit columns the + gap looks tiny, but the total less both parts rests on those two.""" + + total, x, y = {}, {}, {} + for h in range(12): + total[h] = x[h] = 30000.0 + 101.0 * h + for h in range(12, 24): + total[h] = y[h] = 30000.0 + 97.0 * h + for h in range(24, 44): + total[h] = 25000.0 + 89.0 * h + x[h], y[h] = 0.4 * total[h], 0.6 * total[h] + for h in (44, 45): + total[h] = 40000.0 + h + x[h], y[h] = 1000.0 * total[h], (1 - 1000.0 - 1e-4) * total[h] + thin, beside = _withheld_sets( + {"total": sorted(total), "x": sorted(x), "y": sorted(y)}, + 46, + values={ + "total": [total[h] for h in sorted(total)], + "x": [x[h] for h in sorted(x)], + "y": [y[h] for h in sorted(y)], + }, + ) + assert (thin, beside) == (set(), {"x"}) + + +def test_a_small_shortfall_beside_many_bands_is_not_cleared() -> None: + """Three hundred bands of ten; two more households in band 1 hold one less + 2.4 millionths of the total, above the rounding share, so the total less + every band rests on those two.""" + + rows = {"total": list(range(3002))} + for k in range(300): + rows[f"band_{k:03d}"] = list(range(10 * k, 10 * k + 10)) + ( + [3000, 3001] if k == 1 else [] + ) + thin, beside = _withheld_sets( + rows, 3002, values={"band_001": [1.0] * 10 + [1 - 2.4e-6] * 2} + ) + assert (thin, beside) == (set(), {"band_000"}) + + def test_a_row_given_back_two_ways_loses_a_giver_from_each() -> None: """Two relations give the thin band back, so one giver of each is withheld. @@ -1557,6 +2067,9 @@ def test_a_row_withheld_beside_a_thin_one_says_so_and_meets_the_schema( ) -> None: """The written diagnostics tell a row withheld beside a thin one from it. + (A row can also be withheld where no row is thin, when a total less its + parts would give back a cell that is no row; it carries the same flag.) + BRMA rows: the thin band fails the ten-household floor; the band withheld beside it meets the floor and is flagged ``complementary_withholding``, as the release contract reads it. The payload, with its null figures, its @@ -1633,6 +2146,7 @@ def test_a_row_withheld_beside_a_thin_one_says_so_and_meets_the_schema( assert beside["model_figures_withheld"].startswith( "model figures withheld: with the published rows" ) + assert "a row or a cell" in beside["model_figures_withheld"] assert rows["band_b"]["final_estimate"] is None assert rows["total"]["final_estimate"] == pytest.approx(30.0) assert document["uk_diagnostics"]["model_figures_withheld"]["targets"] == 2 diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_annual_income.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_annual_income.py new file mode 100644 index 000000000..de19f0b94 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_annual_income.py @@ -0,0 +1,557 @@ +"""The FRS annual income descriptors (frs_annual_income_spells). + +Synthetic adult, job and household tables in the shape the stage reads them +after ``normalize_ids``: one spell rule per person, beside forty donors who +are current employees in their first two years and report usual pay. +""" + +from __future__ import annotations + +import json +from importlib.resources import files + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.source_manifest import SourceOperationSpec, SourceStageSpec +from microcosm.build.uk_runtime import frs_annual_income as fai +from microcosm.build.uk_runtime.frs_spine import WEEKS_IN_YEAR +from microcosm.build.uk_runtime.stage_health import uk_stage_health_gate + +INTERVIEW = pd.Timestamp("2024-10-15") +DONORS = 40 +DONOR_WEEKLY = 500.0 +DONOR_USUAL = 490.0 +RATIO = DONOR_WEEKLY / DONOR_USUAL +EMPLOYEE, UNEMPLOYED, SELF_EMPLOYED = 1, 5, 3 + + +def _calendar(now: int, months: dict[int, int] | None = None) -> dict[str, float]: + """SELFDEMP and SDEMP01-12: ``months`` maps months ago to a code.""" + + row = {"selfdemp": float(now)} + for month in range(1, 13): + row[f"sdemp{month:02d}"] = float((months or {}).get(month, now)) + return row + + +def _population(people: dict[str, dict]) -> tuple: + """Donors in households 1..40 and one household per named person.""" + + adult_rows, job_rows, person_rows = [], [], [] + households: list[int] = [] + + def add(household: int, adult: dict | None, job: dict | None, person: dict) -> None: + person_id = household * 1000 + 1 + households.append(household) + person_rows.append( + { + "person_id": person_id, + "person_household_id": household, + "age": 40, + "gender": "MALE", + "employment_income": 0.0, + "self_employment_income": 0.0, + **person, + } + ) + if adult is not None: + adult_rows.append( + { + "household_id": household, + "benunit_id": household * 100 + 1, + "person_id": person_id, + "samesit": 2.0, + "working": 1.0, + "jobaway": np.nan, + "numjob2": np.nan, + "currjobm": np.nan, + "prevjobm": np.nan, + "lstwrk1": np.nan, + "lstwrk2": np.nan, + "lstyr": np.nan, + "retire": np.nan, + "retire1": np.nan, + "empstati": 1.0, + **_calendar(EMPLOYEE), + **adult, + } + ) + if job is not None: + job_rows.append( + { + "household_id": household, + "benunit_id": household * 100 + 1, + "person_id": person_id, + "jobtype": 1.0, + "etype": 1.0, + "ftpt": 1.0, + "wrkprev": np.nan, + "ugross": np.nan, + "ugrspay": np.nan, + "sejblong": np.nan, + "sejbmths": np.nan, + "selwks": np.nan, + "seincamt": np.nan, + "profit1": np.nan, + "profit2": np.nan, + **job, + } + ) + + for donor in range(1, DONORS + 1): + add( + donor, + {"currjobm": 20.0}, + {"ugross": DONOR_USUAL, "ugrspay": DONOR_WEEKLY}, + {"employment_income": DONOR_WEEKLY * WEEKS_IN_YEAR}, + ) + index: dict[str, int] = {} + for offset, (name, spec) in enumerate(people.items()): + household = 101 + offset + index[name] = household * 1000 + 1 + add(household, spec.get("adult"), spec.get("job"), spec.get("person", {})) + person = pd.DataFrame(person_rows) + adult = pd.DataFrame(adult_rows) + job = pd.DataFrame(job_rows) if job_rows else pd.DataFrame(columns=adult.columns) + interview = pd.Series(INTERVIEW, index=pd.Index(households, name="household_id")) + return person, adult, job, interview, index + + +def _derive(people: dict[str, dict], *, growth: float = 0.0): + person, adult, job, interview, index = _population(people) + descriptors, receipt = fai.derive_frs_annual_income_spells( + person, + person_weights=np.full(len(person), 1000.0), + adult=adult, + job=job, + interview=interview, + earnings_growth=growth, + ) + descriptors.index = person["person_id"].to_numpy() + return descriptors, receipt, index + + +LEAVER = {"working": 2.0, "jobaway": 2.0, "numjob2": 1.0, "empstati": 5.0} + + +def test_full_year_employee_keeps_the_whole_year() -> None: + descriptors, _, index = _derive( + { + "full": { + "adult": {"currjobm": 100.0}, + "job": {}, + "person": {"employment_income": 20000.0}, + } + } + ) + row = descriptors.loc[index["full"]] + assert row[fai.EMPLOYMENT_INCOME_ANNUAL_SHARE] == 1.0 + assert row[fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == 0.0 + + +def test_entrant_is_paid_for_the_months_in_the_job() -> None: + descriptors, _, index = _derive( + { + "entrant": { + # CURRJOBM 5.4 is the sixth month in the job; nothing before it. + "adult": {"currjobm": 5.4}, + "job": {"wrkprev": 3.0}, + "person": {"employment_income": 30000.0}, + } + } + ) + assert descriptors.loc[index["entrant"], fai.EMPLOYMENT_INCOME_ANNUAL_SHARE] == 0.5 + + +def test_job_changer_keeps_the_year_unless_the_calendar_records_a_gap() -> None: + gap = _calendar(EMPLOYEE, {4: UNEMPLOYED, 5: UNEMPLOYED, 6: UNEMPLOYED}) + descriptors, _, index = _derive( + { + "continuous": { + "adult": {"currjobm": 3.0}, + "job": {"wrkprev": 1.0}, + "person": {"employment_income": 30000.0}, + }, + # Four months in the job; the calendar shows three months out of + # work before it and employee months 7 to 11 months ago. + "gap": { + "adult": {"currjobm": 3.0, "samesit": 1.0, **gap}, + "job": {"wrkprev": 1.0}, + "person": {"employment_income": 30000.0}, + }, + } + ) + assert ( + descriptors.loc[index["continuous"], fai.EMPLOYMENT_INCOME_ANNUAL_SHARE] == 1.0 + ) + assert descriptors.loc[index["gap"], fai.EMPLOYMENT_INCOME_ANNUAL_SHARE] == 0.75 + + +def test_leaver_is_paid_the_last_jobs_usual_pay_for_their_weeks() -> None: + descriptors, receipt, index = _derive( + { + "leaver": { + "adult": {**LEAVER, "lstyr": 26.0, "lstwrk2": 2024.0, "lstwrk1": 7.0}, + "job": {"ugross": 300.0}, + } + } + ) + expected = 300.0 * RATIO * WEEKS_IN_YEAR * 6.0 / 12.0 + row = descriptors.loc[index["leaver"]] + assert row[fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == pytest.approx(expected) + # Contributory benefits are in payment for the six months not worked. + assert row[fai.CONTRIBUTORY_BENEFITS_ANNUAL_SHARE] == pytest.approx(0.5) + assert receipt["pay_concept_ratio"] == pytest.approx(RATIO) + + +def test_leaver_pay_is_uprated_for_the_months_since_leaving() -> None: + plain, _, index = _derive( + { + "leaver": { + "adult": {**LEAVER, "lstyr": 26.0, "lstwrk2": 2024.0, "lstwrk1": 4.0}, + "job": {"ugross": 300.0}, + } + } + ) + uprated, _, _ = _derive( + { + "leaver": { + "adult": {**LEAVER, "lstyr": 26.0, "lstwrk2": 2024.0, "lstwrk1": 4.0}, + "job": {"ugross": 300.0}, + } + }, + growth=0.05, + ) + column = fai.EMPLOYMENT_INCOME_OTHER_SPELLS + assert uprated.loc[index["leaver"], column] == pytest.approx( + plain.loc[index["leaver"], column] * 1.05 ** (6 / 12) + ) + + +def test_zero_weeks_of_regular_work_fall_through_to_the_leaving_date() -> None: + descriptors, _, index = _derive( + { + "irregular": { + "adult": {**LEAVER, "lstyr": 0.0, "lstwrk2": 2024.0, "lstwrk1": 8.0}, + "job": {"ugross": 300.0}, + } + } + ) + months = fai.LEAVING_DATE_SHARE * (12 - 2) + assert descriptors.loc[ + index["irregular"], fai.EMPLOYMENT_INCOME_OTHER_SPELLS + ] == pytest.approx(300.0 * RATIO * WEEKS_IN_YEAR * months / 12.0) + + +def test_a_leaving_date_before_the_window_gives_no_months() -> None: + descriptors, _, index = _derive( + { + "long_ago": { + "adult": {**LEAVER, "lstwrk2": 2023.0, "lstwrk1": 3.0}, + "job": {"ugross": 300.0}, + } + } + ) + assert descriptors.loc[index["long_ago"], fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == 0.0 + + +def test_recent_retiree_without_a_leaving_date_uses_the_month_retired() -> None: + descriptors, _, index = _derive( + { + "retired": { + # Retired in April, interviewed in October: six months ago. + "adult": {**LEAVER, "retire": 1.0, "retire1": 4.0, "empstati": 6.0}, + "job": {"ugross": 200.0}, + } + } + ) + row = descriptors.loc[index["retired"]] + months = fai.LEAVING_DATE_SHARE * (12 - 6) + assert row[fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == pytest.approx( + 200.0 * RATIO * WEEKS_IN_YEAR * months / 12.0 + ) + assert row[fai.PRIVATE_PENSION_INCOME_ANNUAL_SHARE] == pytest.approx(6.5 / 12) + + +def test_people_in_work_are_never_leavers() -> None: + switch = _calendar(SELF_EMPLOYED, {m: EMPLOYEE for m in range(6, 12)}) + descriptors, receipt, index = _derive( + { + # Employee six to eleven months ago, self-employed since. + "switcher": { + "adult": {"samesit": 1.0, "empstati": 3.0, **switch}, + "job": {"etype": 3.0, "sejblong": 1.0}, + "person": {"self_employment_income": 15000.0}, + } + } + ) + row = descriptors.loc[index["switcher"]] + # A donor's weekly pay for the six employee months. + assert row[fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == pytest.approx( + DONOR_WEEKLY * WEEKS_IN_YEAR * 6 / 12 + ) + assert row[fai.SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE] == pytest.approx(6 / 12) + # Nothing on a contributory benefit stops: they never left work. + assert row[fai.CONTRIBUTORY_BENEFITS_ANNUAL_SHARE] == 1.0 + + +def test_self_employment_new_and_ceased() -> None: + descriptors, _, index = _derive( + { + "new_business": { + "adult": {"empstati": 3.0, **_calendar(SELF_EMPLOYED)}, + "job": {"etype": 4.0, "sejblong": 0.0, "sejbmths": 5.0}, + "person": {"self_employment_income": 10000.0}, + }, + "ceased": { + "adult": {**LEAVER, **_calendar(UNEMPLOYED)}, + "job": {"etype": 4.0, "seincamt": 250.0, "selwks": 26.0}, + }, + } + ) + assert descriptors.loc[ + index["new_business"], fai.SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE + ] == pytest.approx(6 / 12) + assert descriptors.loc[ + index["ceased"], fai.SELF_EMPLOYMENT_INCOME_OTHER_SPELLS + ] == pytest.approx(250.0 * WEEKS_IN_YEAR * 6 / 12) + # A ceased self-employed person is not an employee leaver. + assert descriptors.loc[index["ceased"], fai.EMPLOYMENT_INCOME_OTHER_SPELLS] == 0.0 + + +def test_the_calendar_bounds_a_business_only_when_it_is_the_main_activity() -> None: + descriptors, _, index = _derive( + { + # Self-employed for the last four months, an employee before. + "moved_into_business": { + "adult": { + "samesit": 1.0, + "empstati": 3.0, + **_calendar( + SELF_EMPLOYED, {month: EMPLOYEE for month in range(4, 13)} + ), + }, + "job": {"etype": 4.0, "sejblong": 1.0}, + "person": {"self_employment_income": 8000.0}, + }, + # An employee all year beside a business the calendar cannot see. + "side_business": { + "adult": {"samesit": 1.0, "currjobm": 30.0}, + "job": {"sejblong": 3.0}, + "person": { + "employment_income": 26000.0, + "self_employment_income": 3000.0, + }, + }, + } + ) + share = fai.SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE + assert descriptors.loc[index["moved_into_business"], share] == pytest.approx(4 / 12) + assert descriptors.loc[index["side_business"], share] == 1.0 + + +def test_people_outside_adult_tab_keep_their_current_columns() -> None: + descriptors, _, index = _derive({"child": {"person": {"age": 8}}}) + row = descriptors.loc[index["child"]] + for column in fai.ANNUAL_SHARE_COLUMNS: + assert row[column] == 1.0 + for column in fai.OTHER_SPELLS_COLUMNS: + assert row[column] == 0.0 + + +def test_draws_are_keyed_on_identity() -> None: + people = { + "calendar_leaver": { + "adult": { + "working": 2.0, + "samesit": 1.0, + "empstati": 5.0, + **_calendar(UNEMPLOYED, {m: EMPLOYEE for m in range(3, 9)}), + } + } + } + first, _, _ = _derive(people) + second, _, _ = _derive(people) + pd.testing.assert_frame_equal(first, second) + + +def test_receipt_gives_bands_and_structural_zeros() -> None: + _, receipt, _ = _derive( + { + "entrant": { + "adult": {"currjobm": 5.4}, + "job": {"wrkprev": 3.0}, + "person": {"employment_income": 30000.0}, + } + } + ) + assert set(receipt["spell_sources"]) == set(fai.SPELL_SOURCES) + # A source carried by one household gives a band, never a count. + assert receipt["spell_sources"][fai.SOURCE_ENTRANT] == "<10" + for key in ( + "full_year_rows_not_whole", + "rows_outside_share_bounds", + "rows_negative_other_spells", + ): + assert receipt[key] == 0 + + +@pytest.mark.parametrize( + ("broken", "count"), [(0, 0), (3, "<10"), (12, "10-49"), (150, 200)] +) +def test_receipt_bands_its_structural_counts(broken: int, count: object) -> None: + # A count of rows that break an invariant says something about the tail, + # so the receipt bands it as the other stages' receipts do, zero exact. + rows = 200 + descriptors = pd.DataFrame( + {column: np.ones(rows) for column in fai.ANNUAL_SHARE_COLUMNS} + | {column: np.zeros(rows) for column in fai.OTHER_SPELLS_COLUMNS} + ) + employment_share = np.ones(rows) + employment_share[:broken] = 0.5 + receipt = fai._receipt( + pd.DataFrame({"person_household_id": np.arange(rows)}), + weights=np.ones(rows), + descriptors=descriptors, + current=np.zeros(rows), + se_current=np.zeros(rows), + pay=np.zeros(rows, dtype=bool), + source=np.full(rows, fai.SOURCE_FULL_YEAR), + observed=np.zeros(rows, dtype=bool), + needs_donor=np.zeros(rows, dtype=bool), + donor_cells={}, + ratio=1.0, + ratio_support=0, + earnings_growth=0.0, + employment_share=employment_share, + ) + assert receipt["full_year_rows_not_whole"] == count + assert receipt["rows_outside_share_bounds"] == 0 + assert receipt["rows_negative_other_spells"] == 0 + + +def _gate(evidence: dict): + """The declared stage-health gate, called as the gate battery calls it.""" + + gates = json.loads(files("microcosm.build.uk").joinpath("gates.json").read_text()) + entry = next( + gate + for gate in gates["gates"] + if gate["id"] == "uk_stage_frs_annual_income_spells" + ) + assert entry["gate"] == "stage_health" + assert entry["evidence_absent_blocks"] is True + parameters = entry["parameters"] + return uk_stage_health_gate( + evidence=evidence, + stage=parameters["stage"], + check=parameters["check"], + parameters=parameters, + ) + + +def _leaver_evidence() -> dict: + _, receipt, _ = _derive( + { + "leaver": { + "adult": {**LEAVER, "lstyr": 26.0, "lstwrk2": 2024.0, "lstwrk1": 7.0}, + "job": {"ugross": 300.0}, + } + } + ) + return {"stage": fai.FRS_ANNUAL_INCOME_STAGE_NAME, **receipt} + + +def test_gate_passes_the_stages_own_receipt() -> None: + gate = _gate(_leaver_evidence()) + assert gate.passed, gate.failures + # One household carries the pay outside the current job: a band, unmeasured. + assert gate.details["other_spells_pay_observed_share"] == "<10" + assert gate.details["observed_pay_share_measured"] is False + + +@pytest.mark.parametrize( + ("key", "value", "message"), + [ + # The receipt bands a count of broken rows (extreme_count). + ("full_year_rows_not_whole", "<10", "full_year_rows_not_whole is '<10'"), + ("rows_outside_share_bounds", 100, "rows_outside_share_bounds is 100"), + ("rows_negative_other_spells", None, "rows_negative_other_spells is None"), + # Weekly pay read as a monthly amount gives a ratio far above one. + ("pay_concept_ratio", 3.58, "pay_concept_ratio 3.5800 lies outside"), + # Spells add part-year earners; they cannot remove a current one. + ("annual_over_current_earners", 0.98, "annual_over_current_earners 0.9800"), + ("annual_over_current_earners", "<10", "not a measured ratio"), + ("other_spells_pay_observed_share", 0.5, "only 0.5000 of the pay"), + ("other_spells_pay_observed_share", None, "observed_share is None"), + ], +) +def test_gate_fails_closed(key: str, value: object, message: str) -> None: + gate = _gate({**_leaver_evidence(), key: value}) + assert not gate.passed + assert any(message in failure for failure in gate.failures), gate.failures + + +def test_gate_refuses_another_stages_receipt() -> None: + gate = _gate({**_leaver_evidence(), "stage": "frs_hmrc_spine_leaves"}) + assert not gate.passed + assert "does not match" in gate.failures[0] + + +def test_pay_concept_ratio_refuses_too_few_reporters() -> None: + person, adult, job, interview, _ = _population({}) + job = job.iloc[:5] + with pytest.raises(ValueError, match="fewer than ten households"): + fai.pay_concept_ratio( + person["person_id"].to_numpy(), + np.ones(len(person)), + job, + np.ones(len(person), dtype=bool), + ) + + +def _stage(**overrides) -> SourceStageSpec: + operations = tuple( + SourceOperationSpec(kind=kind, parameters=parameters) + for kind, parameters in fai.frs_annual_income_operation_parameters() + ) + fields = { + "stage": fai.FRS_ANNUAL_INCOME_STAGE_NAME, + "survey": "Family Resources Survey 2024-25", + "source": "test", + "grain": "person", + "artifacts": ( + {"role": "frs_table", "table": "adult", "locator": "adult.tab"}, + {"role": "frs_table", "table": "job", "locator": "job.tab"}, + {"role": "frs_table", "table": "househol", "locator": "househol.tab"}, + {"role": "earnings_index", "resource": fai.EARNINGS_INDEX_RESOURCE}, + ), + "operations": operations, + "outputs": fai.FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, + } + fields.update(overrides) + return SourceStageSpec(**fields) + + +def test_stage_parameters_are_closed_world() -> None: + fai._assert_stage_parameters(_stage()) + drifted = tuple( + SourceOperationSpec( + kind=operation.kind, + parameters={**operation.parameters, "leaving_date_share": 0.5} + if operation.kind == fai.FRS_ANNUAL_INCOME_OPERATION_KIND + else operation.parameters, + ) + for operation in _stage().operations + ) + with pytest.raises(ValueError, match="drifted"): + fai._assert_stage_parameters(_stage(operations=drifted)) + with pytest.raises(ValueError, match="no rewrites"): + fai._assert_stage_parameters(_stage(rewrites=("employment_income",))) + + +def test_earnings_growth_comes_from_the_pinned_index() -> None: + growth = fai.load_earnings_growth() + assert 0.0 < growth < 0.1 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 42de08b89..1a103006a 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 @@ -3272,6 +3272,36 @@ def test_read_pinned_tab_reads_only_the_declared_columns(tmp_path) -> None: read_pinned_tab(tab, {**pins, "sha256": "f" * 64}, columns=("CASER8",)) +def test_read_pinned_tab_selects_columns_of_a_long_mixed_type_tab(tmp_path) -> None: + """A long tab whose selected column turns from numbers to text reads. + + pandas' chunked parser mis-indexes such a column under a callable column + filter and raises IndexError (FRS 2024-25 adult.tab did), so a selective + read takes one pass. + """ + + import hashlib + + from microcosm.build.uk_runtime.frs_spine import read_pinned_tab + + rows = 300_000 + tab = tmp_path / "adult.tab" + lines = ["SERNUM\tB\tC\tD\tMIXED", *(f"{row}\t1\t2\t3\t1" for row in range(rows))] + lines[-1] = f"{rows - 1}\t1\t2\t3\tx" + tab.write_text("\n".join(lines) + "\n", encoding="utf-8") + pins = { + "sha256": hashlib.sha256(tab.read_bytes()).hexdigest(), + "size_bytes": tab.stat().st_size, + } + + table = read_pinned_tab(tab, pins, columns=("sernum", "mixed")) + + assert list(table.columns) == ["sernum", "mixed"] + assert len(table) == rows + assert table["mixed"].iloc[0] == 1 + assert np.isnan(table["mixed"].iloc[-1]) + + def test_refuses_nan_in_produced_weight_column(tmp_path: Path) -> None: tables = _fixture_tables() tables["househol"][0]["GROSS4"] = "" 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 6da3dfc7a..a1f47ebe7 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 @@ -57,9 +57,10 @@ def test_uk_spine_graph_contains_every_manifest_stage() -> None: # microcosm#1106 property_components after the regional uprating, and # microcosm#1240 high_value_home_support between those two; microcosm#1095 # adds property_purchases and uc_legacy_closure_take_up, microcosm#1112 - # regional_home_values, microcosm#1319 carers_allowance_take_up, and the - # copies' childcare re-draw childcare_copy_take_up. - assert len(expected) == 47 + # regional_home_values, microcosm#1319 carers_allowance_take_up, the + # copies' childcare re-draw childcare_copy_take_up, and microcosm#1334 + # uc_take_up and frs_annual_income_spells. + assert len(expected) == 49 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_hmrc_rate_band_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_rate_band_targets.py index 2c6370998..4f046a34e 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_rate_band_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_rate_band_targets.py @@ -53,8 +53,10 @@ def test_the_national_rows_fan_out_as_the_calendar_2025_window(): assert target["value_operation"] == "calendar_year_window" rate = target_id.split(".")[2].removesuffix("_rate_taxpayers") assert target["ledger_selector"]["dimension_values"] == {"marginal_rate": rate} + # Each person's rate band on their year of income, a proxy for the + # tax year. assert { - "variable": "tax_band", + "variable": "tax_band_annual", "operator": "==", "value": band, } in target["bindings"]["policyengine"]["filters"] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_legacy_closure_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_legacy_closure_take_up.py index ea5369575..1a3836dd9 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_legacy_closure_take_up.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_legacy_closure_take_up.py @@ -336,9 +336,10 @@ def test_the_packaged_stage_carries_the_reviewed_operations_in_order() -> None: lc.UKLegacyClosureTakeUpStageTransform(stage=stage) order = list(stages) position = order.index(lc.LEGACY_CLOSURE_STAGE_NAME) - # After the last writers of would_claim_uc and the UC receipts, before the - # Pension Credit redraw and any CGT clone. - assert order.index("uc_capital_coherence") == position - 1 + # After the last writers of would_claim_uc (uc_take_up, microcosm#1334) and + # the UC receipts, before the Pension Credit redraw and any CGT clone. + assert order.index("uc_take_up") == position - 1 + assert order.index("uc_capital_coherence") == position - 2 assert position < order.index("pension_credit_take_up") assert position < order.index("cgt_support_split") 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 ec5ca8fe1..29febbf37 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 @@ -2022,3 +2022,246 @@ def test_a_persisted_dfe_spring_term_column_is_refused(monkeypatch, tmp_path: Pa year=2025, frame=frame, ) + + +# The annual income measures: each current measure on a simulation of each +# person's year, built from the frs_annual_income_spells descriptors. +_ANNUAL_DESCRIPTORS = { + # Person 1 is paid all year; person 2 left a job and retired in the year, + # on a State Pension that started two months before its middle; person 3 + # started a job half way through and ran a business for half of it. + "employment_income_annual_share": [1.0, 1.0, 0.5], + "employment_income_other_spells": [0.0, 9_000.0, 0.0], + "self_employment_income_annual_share": [1.0, 1.0, 0.5], + "self_employment_income_other_spells": [0.0, 0.0, 1_000.0], + "private_pension_income_annual_share": [1.0, 0.25, 1.0], + "contributory_benefits_annual_share": [1.0, 0.25, 1.0], +} + + +class _AnnualFrameStub(FrameStub): + metadata = {"time_period": "2024"} + + def __init__(self, descriptors=_ANNUAL_DESCRIPTORS): + super().__init__() + for column, values in descriptors.items(): + self._tables["person"][column] = values + + +class _AnnualSimulation: + """A stub engine that uprates stored inputs and taxes total income.""" + + created = 0 + uprating = { + "employment_income_before_lsr": 1.05, + "self_employment_income": 1.03, + "private_pension_income": 1.02, + "jsa_contrib_reported": 1.02, + "esa_contrib_reported": 1.02, + "incapacity_benefit_reported": 1.02, + } + extra_inputs: dict = {} + + def __init__(self, *, dataset): + type(self).created += 1 + self.stored = { + ("employment_income_before_lsr", 2024): np.array([30_000.0, 0.0, 20_000.0]), + ("self_employment_income", 2024): np.array([0.0, 0.0, 6_000.0]), + ("private_pension_income", 2024): np.array([0.0, 8_000.0, 0.0]), + ("jsa_contrib_reported", 2024): np.zeros(3), + ("esa_contrib_reported", 2024): np.array([0.0, 2_000.0, 0.0]), + ("incapacity_benefit_reported", 2024): np.zeros(3), + ("loss_relief", 2024): np.zeros(3), + **type(self).extra_inputs, + } + names = ( + *self.uprating, + "employment_income", + "loss_relief", + "state_pension", + "months_since_state_pension_age", + "carers_allowance", + "carer_support_payment", + "scottish_carer_supplement", + "total_income", + "income_tax", + ) + variables = { + name: SimpleNamespace( + entity=SimpleNamespace(key="person"), value_type=float + ) + for name in names + } + variables["tax_band"] = SimpleNamespace( + entity=SimpleNamespace(key="person"), value_type=str + ) + self.tax_benefit_system = SimpleNamespace(variables=variables) + + def set_input(self, variable, year, values): + self.stored[(variable, int(year))] = np.asarray(values, dtype=float) + + def calculate(self, variable, year, map_to=None): + year = int(year) + if (variable, year) in self.stored: + return self.stored[(variable, year)] + if variable in self.uprating or variable == "loss_relief": + source = max(y for v, y in self.stored if v == variable and y <= year) + factor = self.uprating.get(variable, 1.0) ** (year - source) + return self.stored[(variable, source)] * factor + pay = self.calculate("employment_income_before_lsr", year) + if variable == "employment_income": + return pay + if variable == "months_since_state_pension_age": + return np.array([-200.0, 2.0, -100.0]) + if variable == "state_pension": + return np.array([0.0, 11_000.0, 0.0]) + if variable == "carers_allowance": + # Earnings-tested: the year's pay must not take it away. + return np.where(pay < 5_000.0, 4_000.0, 0.0) + if variable in ("carer_support_payment", "scottish_carer_supplement"): + return np.zeros(3) + total = sum( + self.calculate(name, year) + for name in ( + "employment_income", + "self_employment_income", + "private_pension_income", + "state_pension", + "jsa_contrib_reported", + "esa_contrib_reported", + "incapacity_benefit_reported", + "carers_allowance", + ) + ) + if variable == "total_income": + return total + if variable == "income_tax": + return 0.2 * np.maximum(total - 12_570.0, 0.0) + if variable == "tax_band": + return np.where( + total > 50_270.0, "HIGHER", np.where(total > 12_570.0, "BASIC", "NONE") + ) + raise KeyError(variable) + + +def _annual_resolver(monkeypatch, tmp_path, frame=None): + _AnnualSimulation.created = 0 + monkeypatch.setitem( + sys.modules, + "policyengine_uk", + SimpleNamespace(__version__="9.9.9", Microsimulation=_AnnualSimulation), + ) + return UKMeasureResolver( + simulation_source=tmp_path / "input.h5", + scratch_dir=tmp_path, + year=2025, + frame=_AnnualFrameStub() if frame is None else frame, + ) + + +def test_state_pension_annual_share_counts_the_months_paid_in_the_year(): + share = measure_simulation.state_pension_annual_share( + [-12.0, -6.0, -5.5, 0.0, 2.0, 6.0, 30.0, np.nan] + ) + np.testing.assert_allclose(share, [1.0, 1.0, 0.5 / 12, 0.5, 8 / 12, 1.0, 1.0, 1.0]) + + +def test_annual_measures_read_each_persons_year(monkeypatch, tmp_path): + resolver = _annual_resolver(monkeypatch, tmp_path) + + pay, route = resolver.compute("person", "employment_income_annual") + # The engine carries the year's pay from 2024 at its own index. + np.testing.assert_allclose(pay, [31_500.0, 9_450.0, 10_500.0]) + assert route == "annual_income_simulation:native" + profit, route = resolver.compute( + "person", "self_employment_income_after_loss_relief_annual" + ) + np.testing.assert_allclose(profit, [0.0, 0.0, 4_120.0]) + assert route.startswith("annual_income_simulation:engine_components") + pension, _ = resolver.compute("person", "private_pension_income_annual") + np.testing.assert_allclose(pension, [0.0, 2_040.0, 0.0]) + state_pension, _ = resolver.compute("person", "state_pension_annual") + np.testing.assert_allclose(state_pension, [0.0, 11_000.0 * 8 / 12, 0.0]) + total, _ = resolver.compute("person", "total_income_after_loss_relief_annual") + # Person 2 keeps the Carer's Allowance paid at the earnings of the time. + annual_total = np.array( + [31_500.0, 9_450.0 + 2_040.0 + 11_000.0 * 8 / 12 + 510.0 + 4_000.0, 14_620.0] + ) + np.testing.assert_allclose(total, annual_total) + tax, _ = resolver.compute("person", "income_tax_annual") + np.testing.assert_allclose(tax, 0.2 * np.maximum(annual_total - 12_570.0, 0.0)) + band, _ = resolver.compute("person", "tax_band_annual") + assert list(band) == ["BASIC", "BASIC", "BASIC"] + # The current measures are untouched, and one annual simulation serves all. + current_tax, _ = resolver.compute("person", "income_tax") + assert current_tax[1] == pytest.approx( + 0.2 * (8_160.0 + 11_000.0 + 2_040.0 + 4_000.0 - 12_570.0) + ) + assert _AnnualSimulation.created == 2 + receipt = resolver.receipt()["annual_income_measures"] + assert receipt["route"] == "annual_income_simulation" + assert receipt["dataset_year"] == 2024 + assert receipt["computed_measures"]["income_tax_annual"] == "income_tax" + assert receipt["held_at_baseline"] == [ + "carers_allowance", + "carer_support_payment", + "scottish_carer_supplement", + ] + for name in measure_simulation.UK_ANNUAL_INCOME_MEASURES: + assert resolver.knows("person", name) + assert resolver.entity_for(name) == "person" + + +def test_annual_measures_refuse_a_frame_without_descriptors(monkeypatch, tmp_path): + frame = _AnnualFrameStub(descriptors={}) + resolver = _annual_resolver(monkeypatch, tmp_path, frame=frame) + assert not resolver.knows("person", "income_tax_annual") + with pytest.raises(KeyError, match="annual income descriptors"): + resolver.compute("person", "income_tax_annual") + # The incumbent alone reads its current values. + resolver.use_current_income_for_annual_measures(reason="incumbent") + assert resolver.knows("person", "income_tax_annual") + tax, route = resolver.compute("person", "income_tax_annual") + current, _ = resolver.compute("person", "income_tax") + np.testing.assert_allclose(tax, current) + assert route.startswith("current_values_without_annual_income_descriptors:") + assert _AnnualSimulation.created == 1 + receipt = resolver.receipt()["annual_income_measures"] + assert receipt["route"] == "current_values_without_annual_income_descriptors" + assert receipt["reason"] == "incumbent" + + +def test_the_incumbent_fallback_never_hides_descriptors(monkeypatch, tmp_path): + resolver = _annual_resolver(monkeypatch, tmp_path) + resolver.use_current_income_for_annual_measures(reason="incumbent") + pay, route = resolver.compute("person", "employment_income_annual") + np.testing.assert_allclose(pay, [31_500.0, 9_450.0, 10_500.0]) + assert route.startswith("annual_income_simulation:") + partial = dict(_ANNUAL_DESCRIPTORS) + partial.pop("contributory_benefits_annual_share") + resolver = _annual_resolver( + monkeypatch, tmp_path, frame=_AnnualFrameStub(descriptors=partial) + ) + with pytest.raises(ValueError, match="only some annual income descriptors"): + resolver.knows("person", "income_tax_annual") + + +def test_other_spells_follow_one_engine_factor(monkeypatch, tmp_path): + # A projection that is not one factor for every row cannot carry the + # other spells as it carries pay. + _AnnualSimulation.extra_inputs = { + ("employment_income_before_lsr", 2025): np.array([31_500.0, 0.0, 22_000.0]) + } + try: + resolver = _annual_resolver(monkeypatch, tmp_path) + with pytest.raises(ValueError, match="by one factor"): + resolver.compute("person", "income_tax_annual") + finally: + _AnnualSimulation.extra_inputs = {} + factors = _annual_resolver(monkeypatch, tmp_path) + factors.compute("person", "employment_income_annual") + receipt = factors.receipt()["annual_income_measures"] + assert receipt["other_spells_engine_factors"] == { + "employment_income_before_lsr": pytest.approx(1.05), + "self_employment_income": pytest.approx(1.03), + } diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_measurement_period.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_measurement_period.py index 54b55332b..51a3fef0b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_measurement_period.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_measurement_period.py @@ -418,6 +418,10 @@ def test_a_frame_without_a_period_label_keeps_the_frame_route(): "uc_deductions", "uc_housing_costs_element", "universal_credit", + # The annual income measures: the frame stores none of them, and the + # resolver computes each on its annual simulation at the calibration + # period. + *measure_simulation.UK_ANNUAL_INCOME_MEASURES, } ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py index f3efb14a5..48080a95a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py @@ -181,6 +181,7 @@ def test_hmrc_stage_is_required_while_the_208_fact_replay_remains_fenced() -> No assert family["spi_prior_national_household_mass_share"] == 0.5 assert family["required_predecessor_stages"] == [ "frs_hmrc_spine_leaves", + "frs_annual_income_spells", "spi_support_channel", "spi_income_band_donors", ] 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 2dd920dc6..3311f3be5 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 @@ -63,6 +63,7 @@ ] E7_STAGE_NAMES = [ "frs_hmrc_spine_leaves", + "frs_annual_income_spells", "spi_support_channel", "spi_income_band_donors", "hmrc_spi_income_spine", @@ -79,6 +80,12 @@ UC_COHERENCE_STAGE_NAMES = [ "uc_capital_coherence", ] +# microcosm#1334: the Universal Credit redraw reads the post-SPI awards and the +# capital UC capital coherence sets, and writes would_claim_uc before the Move +# to UC claim draw reads it. +UC_TAKE_UP_STAGE_NAMES = [ + "uc_take_up", +] # microcosm#1095: the Move to UC claim draw reads the final legacy and UC # receipts and would_claim_uc (uk-data#534). UC_LEGACY_CLOSURE_TAKE_UP_STAGE_NAMES = [ @@ -130,6 +137,7 @@ *UC_REPORTER_REDRAW_STAGE_NAMES, *SPI_BENEFIT_COHERENCE_STAGE_NAMES, *UC_COHERENCE_STAGE_NAMES, + *UC_TAKE_UP_STAGE_NAMES, *UC_LEGACY_CLOSURE_TAKE_UP_STAGE_NAMES, *PENSION_CREDIT_TAKE_UP_STAGE_NAMES, *CHILD_BENEFIT_TAKE_UP_STAGE_NAMES, @@ -203,6 +211,7 @@ def test_e5_and_e6_follow_e7_and_precede_the_uc_rewrites(self) -> None: *UC_REPORTER_REDRAW_STAGE_NAMES, *SPI_BENEFIT_COHERENCE_STAGE_NAMES, *UC_COHERENCE_STAGE_NAMES, + *UC_TAKE_UP_STAGE_NAMES, *UC_LEGACY_CLOSURE_TAKE_UP_STAGE_NAMES, *PENSION_CREDIT_TAKE_UP_STAGE_NAMES, *CHILD_BENEFIT_TAKE_UP_STAGE_NAMES, @@ -276,6 +285,7 @@ def test_country_stage_plan_assembles_spine_plan(self) -> None: "etb_vat": _identity, "etb_services": _identity, "frs_hmrc_spine_leaves": _identity, + "frs_annual_income_spells": _identity, "spi_support_channel": _identity, "spi_income_band_donors": _identity, "hmrc_spi_income_spine": _identity, @@ -283,6 +293,7 @@ def test_country_stage_plan_assembles_spine_plan(self) -> None: "uc_reporter_redraw": _identity, "spi_benefit_coherence": _identity, "uc_capital_coherence": _identity, + "uc_take_up": _identity, "uc_legacy_closure_take_up": _identity, "pension_credit_take_up": _identity, "child_benefit_take_up": _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 51f81804d..56772bd24 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 @@ -326,6 +326,67 @@ def test_spi_uc_start_up_period_holds_only_while_the_row_stays_self_employed() - ) +def test_a_drawn_rows_annual_income_descriptors_follow_its_drawn_incomes() -> None: + """Tape income is the tax year's; a twin's spell stays only beside no draw.""" + + person = pd.DataFrame( + { + # Row 10 drew pay, row 20 drew a record with neither pay nor + # profit, row 30 drew a profit only, row 40 is an undrawn copy and + # row 50 a base row; the last two keep their descriptors. + "employment_income": [30_000.0, 0.0, 0.0, 0.0, 18_000.0], + "self_employment_income": [0.0, 0.0, 12_000.0, 0.0, 0.0], + "employment_income_annual_share": [0.5, 1.0, 1.0, 0.5, 0.25], + "employment_income_other_spells": [4_000.0, 5_000.0, 2_000.0, 6_000.0, 0.0], + "self_employment_income_annual_share": [1.0, 1.0, 0.5, 1.0, 1.0], + "self_employment_income_other_spells": [0.0, 1_000.0, 3_000.0, 0.0, 0.0], + "private_pension_income_annual_share": [1.0, 0.4, 1.0, 0.4, 1.0], + "contributory_benefits_annual_share": [1.0, 0.6, 1.0, 0.6, 1.0], + }, + index=[10, 20, 30, 40, 50], + ) + spi_people = pd.Series([True, True, True, False, False], index=person.index) + + result = spi_income._refresh_annual_income_descriptors( + person.copy(), spi_people=spi_people + ) + + for column in ( + "employment_income_annual_share", + "self_employment_income_annual_share", + "private_pension_income_annual_share", + "contributory_benefits_annual_share", + ): + assert result[column].tolist()[:3] == [1.0, 1.0, 1.0], column + assert result[column].tolist()[3:] == person[column].tolist()[3:], column + assert result["employment_income_other_spells"].tolist() == [ + 0.0, + 5_000.0, + 2_000.0, + 6_000.0, + 0.0, + ] + assert result["self_employment_income_other_spells"].tolist() == [ + 0.0, + 1_000.0, + 0.0, + 0.0, + 0.0, + ] + assert result["employment_income"].tolist() == person["employment_income"].tolist() + bare = pd.DataFrame({"employment_income": [0.0]}, index=[1]) + assert list( + spi_income._refresh_annual_income_descriptors( + bare, spi_people=pd.Series([True], index=[1]) + ).columns + ) == ["employment_income"] + with pytest.raises(ValueError, match="only some annual income descriptors"): + spi_income._refresh_annual_income_descriptors( + person.drop(columns="contributory_benefits_annual_share"), + spi_people=spi_people, + ) + + def test_finite_numeric_diagnostic_names_columns_and_counts() -> None: frame = pd.DataFrame( { 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 edc4b5dba..e3df84d08 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 @@ -99,6 +99,13 @@ def _apply_pending_roster_transformations( del roster[start : start + len(spi_block)] brma = roster.index("frs_brma") roster[brma + 1 : brma + 1] = spi_block + # Annual income re-mint pending: the descriptors of each adult's year are + # read from the raw FRS tabs right after the HMRC leaves, before the SPI + # channel clones the rows that carry them. + assert "frs_annual_income_spells" not in roster + roster.insert( + roster.index("frs_hmrc_spine_leaves") + 1, "frs_annual_income_spells" + ) # SPI housing shell re-mint pending: the SPI households' housing is imputed # from their own incomes right after the SPI income chain. assert "spi_housing_shell" not in roster @@ -135,6 +142,11 @@ def _apply_pending_roster_transformations( roster.insert( roster.index("uc_capital_coherence") + 1, "uc_legacy_closure_take_up" ) + # microcosm#1334 re-mint pending: the Universal Credit redraw reads the + # post-SPI awards right after UC capital coherence, before the Move to UC + # claim draw reads would_claim_uc. + assert "uc_take_up" not in roster + roster.insert(roster.index("uc_capital_coherence") + 1, "uc_take_up") # microcosm#1063 re-mint pending: the Child Benefit redraw reads the same # post-SPI incomes, right after the Pension Credit redraw. assert "child_benefit_take_up" not in roster diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py index 1e1be891c..06a461802 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py @@ -2261,6 +2261,61 @@ def gate(*bands): assert committed["check"] == "pension_credit_take_up" +def test_uc_take_up_gate_holds_each_family_type_to_dwp() -> None: + def family(dwp, before, after, reachable, realized): + return { + "dwp_caseload": dwp, + "caseload_before": before, + "caseload_after": after, + "reachable_pools_dwp_caseload": reachable, + "reachable_pools_caseload_after": realized, + } + + parameters = { + "stage": "uc_take_up", + "check": "uc_take_up", + "maximum_caseload_deviation": 0.05, + } + + def gate(families, **counts): + return uk_stage_health_gate( + evidence={ + "stage": "uc_take_up", + "families": families, + "reporters_not_claiming": counts.get("reporters", 0), + "changed_outside_draw": counts.get("outside", 0), + }, + stage="uc_take_up", + check="uc_take_up", + parameters=parameters, + ) + + good = { + "SINGLE": family(3_000.0, 3_300.0, 3_020.0, 2_500.0, 2_510.0), + # No reachable pool: the realized check does not apply. + "LONE_PARENT": family(2_100.0, 1_400.0, 1_500.0, 0.0, 0.0), + } + assert _passed(gate(good)) + # A reporter not claiming, or a unit outside the draw changed, fails. + assert not gate(good, reporters=1).passed + assert not gate(good, outside="<10").passed + # The reachable pools miss DWP's count by more than the tolerance. + assert not gate( + {**good, "SINGLE": family(3_000.0, 3_300.0, 3_020.0, 2_500.0, 2_700.0)} + ).passed + # The family type moves further from DWP than before. + assert not gate( + {**good, "LONE_PARENT": family(2_100.0, 1_400.0, 1_100.0, 0.0, 0.0)} + ).passed + # A caseload resting on fewer than ten sources cannot be checked. + assert not gate({**good, "SINGLE": family(3_000.0, "<10", 1.0, 0.0, 0.0)}).passed + assert not gate({}).passed + # The caseloads are population facts: a synthetic smoke fixture records the + # failure without blocking, every other posture blocks on it. + committed = _gate_parameters("uk_stage_uc_take_up") + assert committed == parameters + + def test_spi_benefit_coherence_gate_holds_the_structural_zeros() -> None: gates = json.loads( (_TEST_PATHS.package / "src/microcosm/build/uk/gates.json").read_text("utf-8") diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py index 13de2fb0d..894a808fc 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py @@ -11,15 +11,10 @@ ) from microcosm.build.uk_runtime.frs_take_up import ( FRS_TAKE_UP_OUTPUT_COLUMNS, - UKTakeUpPopulationPolicy, uk_take_up_signal_gate, ) from microcosm.build.uk_runtime.national_frame import uk_national_frame -_POLICY = UKTakeUpPopulationPolicy( - adult_age=18, state_pension_age=66, instant="2025-01-01", source="test" -) - class _Contract: def rate(self, key: str, build_year: int | None = None) -> float: @@ -96,25 +91,23 @@ def _frame(*, brma_values=("LONDON_A", "LONDON_B")): def test_take_up_gate_seeded_fixture_passes() -> None: - result = uk_take_up_signal_gate( - _frame(), contract=_Contract(), population_policy=_POLICY - ) + result = uk_take_up_signal_gate(_frame(), contract=_Contract()) assert result.passed is True - assert "benunit.would_claim_uc" in result.details + assert "benunit.would_claim_tfc" in result.details # The Child Benefit flags are measured by their own stage gate - # (microcosm#1063), like would_claim_pc (microcosm#1069). + # (microcosm#1063), like would_claim_pc (microcosm#1069) and + # would_claim_uc (microcosm#1334). assert "benunit.would_claim_child_benefit" not in result.details assert "benunit.child_benefit_opts_out" not in result.details + assert "benunit.would_claim_uc" not in result.details def test_take_up_gate_constant_column_fails() -> None: frame = _frame() - frame.table("benunit")["would_claim_uc"] = True + frame.table("benunit")["would_claim_tfc"] = True - result = uk_take_up_signal_gate( - frame, contract=_Contract(), population_policy=_POLICY - ) + result = uk_take_up_signal_gate(frame, contract=_Contract()) assert result.passed is False assert "constant column" in " ".join(result.failures) @@ -124,9 +117,7 @@ def test_take_up_gate_out_of_band_share_fails() -> None: frame = _frame() frame.table("household")["property_purchased"] = [True] * 9 + [False] - result = uk_take_up_signal_gate( - frame, contract=_Contract(), population_policy=_POLICY - ) + result = uk_take_up_signal_gate(frame, contract=_Contract()) assert result.passed is False assert "property_purchased" in " ".join(result.failures) @@ -176,45 +167,24 @@ def test_gate_registry_vocabulary_round_trip() -> None: assert FRS_BRMA_OUTPUT_COLUMNS == ("brma",) -def test_take_up_gate_measures_uc_share_over_units_with_a_working_age_adult() -> None: - """Units with every adult at or over State Pension age are outside the draw.""" - - frame = _frame() - person = frame.table("person") - # Persons 101-110 sit one per benefit unit; make the last four benefit - # units all-pension-age and mark them as claimants: they must not count. - person.loc[person["person_id"].between(107, 110), "age"] = 70 - benunit = frame.table("benunit") - benunit.loc[benunit["benunit_id"] >= 207, "would_claim_uc"] = True - - result = uk_take_up_signal_gate( - frame, contract=_Contract(), population_policy=_POLICY - ) - - detail = result.details["benunit.would_claim_uc"] - assert detail["population_units"] == 6 - assert detail["weighted_share"] == 0.5 - assert result.passed is True +def test_take_up_gate_leaves_universal_credit_to_its_stage_gate() -> None: + """would_claim_uc is set by award band and family type (microcosm#1334). + Its share of the age population is no longer a contract rate, so even a + constant flag is not this gate's to fail; uk_stage_uc_take_up measures it. + """ -def test_take_up_gate_fails_when_the_uc_population_is_empty() -> None: frame = _frame() - frame.table("person")["age"] = 70 + frame.table("benunit")["would_claim_uc"] = True - result = uk_take_up_signal_gate( - frame, contract=_Contract(), population_policy=_POLICY - ) + result = uk_take_up_signal_gate(frame, contract=_Contract()) - assert result.passed is False - assert "would_claim_uc: no unit in the draw's population" in " ".join( - result.failures - ) + assert result.passed is True + assert "benunit.would_claim_uc" not in result.details def test_take_up_gate_measures_under_two_usage_over_claiming_children() -> None: - result = uk_take_up_signal_gate( - _frame(), contract=_Contract(), population_policy=_POLICY - ) + result = uk_take_up_signal_gate(_frame(), contract=_Contract()) details = result.details["person.max_free_entitlement_hours_used"] # Two children under 2 in claiming units, one of whom uses the hours; the @@ -227,8 +197,6 @@ def test_take_up_gate_measures_under_two_usage_over_claiming_children() -> None: frame.table("person").drop( columns=["max_free_entitlement_hours_used"], inplace=True ) - missing = uk_take_up_signal_gate( - frame, contract=_Contract(), population_policy=_POLICY - ) + missing = uk_take_up_signal_gate(frame, contract=_Contract()) assert missing.passed is False assert "max_free_entitlement_hours_used: missing" in " ".join(missing.failures) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py index 9317d19f2..071139262 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py @@ -63,13 +63,15 @@ def test_stage_orders_after_every_universal_credit_report_writer() -> None: assert reporter_writers assert all(index < coherence_index for index, _ in reporter_writers) - # The Move to UC claim draw (microcosm#1095), the Pension Credit take-up - # redraw (microcosm#1069 c7), the Child Benefit redraw (microcosm#1063 c8) - # and the Carer's Allowance draw (microcosm#1319) sit between the - # coherence stage and the deduction attributes; none writes a UC report. + # The Universal Credit redraw (microcosm#1334), the Move to UC claim draw + # (microcosm#1095), the Pension Credit take-up redraw (microcosm#1069 c7), + # the Child Benefit redraw (microcosm#1063 c8) and the Carer's Allowance + # draw (microcosm#1319) sit between the coherence stage and the deduction + # attributes; none writes a UC report. assert [ - stage.stage for stage in stages[coherence_index + 1 : coherence_index + 6] + stage.stage for stage in stages[coherence_index + 1 : coherence_index + 7] ] == [ + "uc_take_up", "uc_legacy_closure_take_up", "pension_credit_take_up", "child_benefit_take_up", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py new file mode 100644 index 000000000..43ab161ed --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py @@ -0,0 +1,670 @@ +"""Universal Credit take-up by award band and family type (microcosm#1334).""" + +from __future__ import annotations + +from dataclasses import replace +from types import SimpleNamespace + +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 SourceStageSpec +from microcosm.build.uk_runtime.frs_take_up import ( + UK_TAKE_UP_SIGNAL_OUTPUTS, + UKTakeUpPopulationPolicy, +) +from microcosm.build.uk_runtime.measure_simulation import ( + _uc_calibration_composition, +) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract +from microcosm.build.uk_runtime.uc_deduction_attributes import ( + UC_DEDUCTION_OUTPUT_COLUMNS, +) +from microcosm.build.uk_runtime.uc_take_up import ( + UC_AWARD_BAND_LABELS, + UC_FAMILY_TYPES, + UC_TAKE_UP_ENTITLEMENT_VARIABLES, + UC_TAKE_UP_MONTHS, + UC_TAKE_UP_RESOURCE, + UC_TAKE_UP_STAGE_NAME, + UKUCTakeUpStageTransform, + _assert_stage_parameters, + assign_award_band_take_up, + load_uc_award_band_targets, + redraw_uc_take_up, + uc_award_band_targets_from_rows, + uc_award_bands, + uc_family_types, + working_age_housing_benefit_receipt, +) +from microcosm.frame import WeightKind + +_POLICY = UKTakeUpPopulationPolicy( + adult_age=18, state_pension_age=66, instant="2024-01-01", source="test" +) + + +def _targets(**cells: float) -> dict[tuple[str, int], float]: + """Every cell zero but the ones named ``FAMILY_band``.""" + + targets = { + (name, index): 0.0 + for name, _ in UC_FAMILY_TYPES + for index in range(len(UC_AWARD_BAND_LABELS)) + } + for key, value in cells.items(): + name, index = key.rsplit("_", 1) + targets[(name, int(index))] = value + return targets + + +def _assign(**overrides): + count = len(overrides["award"]) + arguments = { + "family": np.full(count, "SINGLE", dtype=object), + "population": np.ones(count, dtype=bool), + "reporter": np.zeros(count, dtype=bool), + "moved": np.zeros(count, dtype=bool), + "moved_claim": np.zeros(count), + "previous": np.zeros(count, dtype=bool), + "weights": np.ones(count), + "draws": np.linspace(0.01, 0.99, count), + "great_britain": np.ones(count, dtype=bool), + "source_ids": np.arange(count), + "minimum_pool_sources": 1, + **overrides, + } + return assign_award_band_take_up(**arguments) + + +def test_award_bands_follow_the_registry_pence_edges() -> None: + # (1,200 i, 1,200 (i + 1)] a year in whole pence, -1 for no award, and one + # open band above GBP 30,000 (DWP's "GBP 2,500.01 or over"). + awards = np.asarray([0.0, 0.004, 0.006, 1200.0, 1200.01, 30_000.0, 30_000.01]) + assert uc_award_bands(awards).tolist() == [-1, -1, 0, 0, 1, 24, 25] + assert UC_AWARD_BAND_LABELS[0] == "£0.01 to £100.00" + assert UC_AWARD_BAND_LABELS[24] == "£2400.01 to £2500.00" + assert UC_AWARD_BAND_LABELS[25] == "£2500.01 or over" + + +def _rows(value_for=lambda family, band, month: 10.0): + rows = [] + for month in UC_TAKE_UP_MONTHS: + for _, family in (*UC_FAMILY_TYPES, (None, "Unknown or missing family type")): + for band in (*UC_AWARD_BAND_LABELS, "No payment"): + rows.append( + { + "concept": "dwp.uc_households", + "measure_id": "benefit_units", + "dimensions": { + "family_type": family, + "monthly_award_amount_bands": band, + }, + "period": {"type": "month", "value": month}, + "value": value_for(family, band, month), + } + ) + return rows + + +def test_targets_are_the_mean_of_the_nine_monthly_counts() -> None: + months = {month: index for index, month in enumerate(UC_TAKE_UP_MONTHS)} + targets = uc_award_band_targets_from_rows( + _rows(lambda family, band, month: 9.0 * months[month]) + ) + # The unknown family type and the households with no payment are no cell. + assert len(targets) == 4 * 26 + assert targets[("SINGLE", 0)] == pytest.approx(36.0) + assert targets[("COUPLE_WITH_CHILDREN", 25)] == pytest.approx(36.0) + + short = [row for row in _rows() if row["period"]["value"] != "2025-12"] + with pytest.raises(ValueError, match="every family type and award band"): + uc_award_band_targets_from_rows(short) + repeated = [*_rows(), _rows()[0]] + with pytest.raises(ValueError, match="repeat"): + uc_award_band_targets_from_rows(repeated) + + +def test_committed_targets_are_the_registry_rows_values() -> None: + targets = load_uc_award_band_targets() + assert len(targets) == 4 * 26 + # The registry's dwp/uc_payment_dist rows average the same months: their + # 2025 values for two cells, as published facts (Stat-Xplore, GB). + assert targets[("SINGLE", 0)] == pytest.approx(805_314 / 9) + assert targets[("COUPLE_NO_CHILDREN", 0)] == pytest.approx(7_506.333, abs=1e-3) + + +def test_family_types_are_the_calibration_measures() -> None: + # Single; lone parent with an under-20 child who is no UC child; couple + # without children; couple with a UC child; a single parent's 20-year-old + # non-dependent who is not a child for DWP. + person = pd.DataFrame( + { + "person_id": range(9), + "person_benunit_id": [1, 2, 2, 3, 3, 4, 4, 4, 5], + "age": [30, 40, 19, 35, 36, 30, 31, 5, 45], + "is_benunit_head": [1, 1, 0, 1, 0, 1, 0, 0, 1], + "is_parent": [0, 1, 0, 0, 0, 1, 1, 0, 0], + } + ) + benunit = pd.DataFrame( + {"benunit_id": [1, 2, 3, 4, 5], "dependent_children": [0, 1, 0, 1, 0]} + ) + qualifying = np.asarray([0, 0, 0, 0, 0, 0, 0, 1, 0], dtype=bool) + frame = SimpleNamespace( + table=lambda name: {"person": person, "benunit": benunit}[name] + ) + simulation = SimpleNamespace(calculate=lambda variable, year: qualifying) + measured, _ = _uc_calibration_composition(frame, simulation, 2025) + + families = uc_family_types(person, benunit, qualifying) + assert families.tolist() == measured.tolist() + assert families.tolist() == [ + "SINGLE", + "LONE_PARENT", + "COUPLE_NO_CHILDREN", + "COUPLE_WITH_CHILDREN", + "SINGLE", + ] + + +def test_a_pool_realizes_dwp_count_with_reporters_first() -> None: + # Ten entitled singles in band 0: two reporters of weight 2, eight + # non-reporters of weight 1. DWP counts 7, so the drawn units must carry 3. + award = np.full(10, 600.0) + reporter = np.asarray([True, True] + [False] * 8) + weights = np.asarray([2.0, 2.0] + [1.0] * 8) + draws = np.asarray([0.9, 0.9, 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75]) + would_claim, receipt = _assign( + award=award, + reporter=reporter, + weights=weights, + draws=draws, + targets=_targets(SINGLE_0=7.0), + ) + + assert would_claim[reporter].all() + # The three drawn units with the lowest uniforms claim, the rest do not. + assert would_claim[2:].tolist() == [True, True, True] + [False] * 5 + assert float(weights[would_claim].sum()) == pytest.approx(7.0) + single = receipt["families"]["SINGLE"] + assert single["caseload_after"] == pytest.approx(7.0) + assert single["reachable_pools_caseload_after"] == pytest.approx(7.0) + assert single["reachable_pools_dwp_caseload"] == pytest.approx(7.0) + assert receipt["reporters_not_claiming"] == 0 + assert receipt["changed_outside_draw"] == 0 + + +def test_the_draw_keeps_earlier_claimants_when_the_rate_rises() -> None: + # The early single-rate draw claimed the two lowest uniforms; a pool that + # needs five claims keeps them and adds the next three. + award = np.full(8, 2_000.0) + draws = np.asarray([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]) + previous = draws < 0.25 + would_claim, _ = _assign( + award=award, draws=draws, previous=previous, targets=_targets(SINGLE_1=5.0) + ) + assert would_claim.tolist() == [True] * 5 + [False] * 3 + assert (would_claim | ~previous).all() + + +def test_a_pool_nearer_zero_than_its_first_unit_draws_no_claimant() -> None: + # Two entitled non-reporters of weight 100 and a DWP count of 10: no + # claimant (0) is nearer the count than one (100), so neither claims + # (Vahid's review of #1344, item 15). + award = np.full(2, 600.0) + would_claim, _ = _assign( + award=award, + weights=np.asarray([100.0, 100.0]), + draws=np.asarray([0.2, 0.7]), + targets=_targets(SINGLE_0=10.0), + ) + assert not would_claim.any() + # One unit's weight past half the count, and the first claimant is nearer. + would_claim, _ = _assign( + award=award, + weights=np.asarray([100.0, 100.0]), + draws=np.asarray([0.2, 0.7]), + targets=_targets(SINGLE_0=60.0), + ) + assert would_claim.tolist() == [True, False] + + +def test_clipped_pools_are_flagged_unreachable() -> None: + # Band 0: reporters alone exceed DWP's count, so no drawn unit claims. + # Band 2: DWP's count exceeds every entitled unit, so all of them claim. + award = np.asarray([100.0] * 4 + [3_000.0] * 4) + reporter = np.asarray([True, True, True, False, False, False, False, False]) + would_claim, receipt = _assign( + award=award, + reporter=reporter, + previous=np.asarray([False, False, False, True, False, False, False, False]), + targets=_targets(SINGLE_0=2.0, SINGLE_2=10.0), + source_ids=np.arange(8), + minimum_pool_sources=1, + ) + assert would_claim.tolist() == [True, True, True, False, True, True, True, True] + flags = { + tuple(pool["bands"]): pool["target_unreachable"] + for pool in receipt["pools"] + if pool["family_type"] == "SINGLE" + } + assert flags[(UC_AWARD_BAND_LABELS[0],)] == "<10" + pools = receipt["pools"] + assert all("rate" not in pool for pool in pools) + # With ten or more sources behind a pool the flag says which side it is on. + many = np.asarray([100.0] * 12 + [3_000.0] * 12) + _, receipt = _assign( + award=many, + reporter=np.asarray([True] * 10 + [False] * 14), + targets=_targets(SINGLE_0=2.0, SINGLE_2=50.0), + minimum_pool_sources=1, + ) + + # Band 1 holds no unit, so it pools with band 2, and the empty bands above + # join that pool. + def flag(label): + return next( + pool["target_unreachable"] + for pool in receipt["pools"] + if pool["family_type"] == "SINGLE" and label in pool["bands"] + ) + + assert flag(UC_AWARD_BAND_LABELS[0]) == "undrawn_claims_reach_target" + assert flag(UC_AWARD_BAND_LABELS[2]) == "target_exceeds_entitled" + single = receipt["families"]["SINGLE"] + # No pool is reachable, so the gate's reachable totals are empty. + assert single["reachable_pools_dwp_caseload"] == 0.0 + + +def test_undrawn_units_keep_their_flag_and_count_at_their_expected_claim() -> None: + # Twice over: a unit with no award (keeps its early claim); one outside + # the age population (keeps no claim); one moved off its legacy benefits, + # with an expected claim of 0.5 at the closure (kept, counted at 0.5); and + # four drawn units. DWP counts 5, so four of the eight drawn units claim. + award = np.tile([0.0, 500.0, 500.0, 500.0, 500.0, 500.0, 500.0], 2) + draws = np.concatenate( + [ + [0.9, 0.1, 0.1, 0.2, 0.4, 0.6, 0.8], + [0.9, 0.1, 0.1, 0.201, 0.401, 0.601, 0.801], + ] + ) + would_claim, receipt = _assign( + award=award, + population=np.tile([True, False, True, True, True, True, True], 2), + moved=np.tile([False, False, True, False, False, False, False], 2), + moved_claim=np.tile([0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0], 2), + previous=np.tile([True, False, False, True, True, True, True], 2), + draws=draws, + targets=_targets(SINGLE_0=5.0), + ) + assert would_claim.tolist() == [True, False, False, True, True, False, False] * 2 + single = receipt["families"]["SINGLE"] + # Before: the moved units at 0.5 each and eight drawn claimants. + assert single["caseload_before"] == pytest.approx(9.0) + assert single["caseload_after"] == pytest.approx(5.0) + assert receipt["changed_outside_draw"] == 0 + assert receipt["moved_units"] == "<10" + + +def test_rates_are_solved_over_great_britain_and_drawn_in_northern_ireland() -> None: + # Four Great Britain units and four heavy Northern Ireland units in one + # pool. DWP counts 2 in Great Britain: its two lowest uniforms claim, and + # Northern Ireland's units claim below the same uniform. + award = np.full(8, 900.0) + gb = np.asarray([True] * 4 + [False] * 4) + draws = np.asarray([0.1, 0.3, 0.5, 0.7, 0.05, 0.25, 0.35, 0.9]) + would_claim, receipt = _assign( + award=award, + great_britain=gb, + weights=np.asarray([1.0] * 4 + [50.0] * 4), + draws=draws, + targets=_targets(SINGLE_0=2.0), + ) + assert would_claim[gb].tolist() == [True, True, False, False] + assert would_claim[~gb].tolist() == [True, True, False, False] + + +def test_bands_pool_until_the_drawn_units_rest_on_enough_sources() -> None: + # Bands 0 and 1 each hold one source household's drawn unit, band 3 two, + # band 5 one: with three sources a pool, bands 0 to 3 pool, and band 5's + # short pool joins them. + award = np.asarray([600.0, 1_800.0, 4_000.0, 4_100.0, 6_500.0]) + _, receipt = _assign( + award=award, + source_ids=np.asarray([1, 2, 3, 4, 5]), + targets=_targets(SINGLE_0=1.0), + minimum_pool_sources=3, + ) + singles = [ + pool["bands"] for pool in receipt["pools"] if pool["family_type"] == "SINGLE" + ] + assert singles == [list(UC_AWARD_BAND_LABELS)] + _, receipt = _assign( + award=award, + source_ids=np.asarray([1, 2, 3, 4, 5]), + targets=_targets(SINGLE_0=1.0), + minimum_pool_sources=2, + ) + singles = [ + pool["bands"] for pool in receipt["pools"] if pool["family_type"] == "SINGLE" + ] + assert singles[0] == list(UC_AWARD_BAND_LABELS[:2]) + assert singles[1] == list(UC_AWARD_BAND_LABELS[2:]) + # Copies of one source household count once. + _, receipt = _assign( + award=award, + source_ids=np.asarray([1, 1, 1, 1, 1]), + targets=_targets(SINGLE_0=1.0), + minimum_pool_sources=2, + ) + assert [ + pool["bands"] for pool in receipt["pools"] if pool["family_type"] == "SINGLE" + ] == [list(UC_AWARD_BAND_LABELS)] + + +def test_the_receipt_bands_counts_and_carries_no_rate() -> None: + award = np.full(40, 1_500.0) + _, receipt = _assign( + award=award, + weights=np.full(40, 3.0), + targets=_targets(SINGLE_1=60.0), + ) + # Counts of records are banded or rounded, never exact (UKDS CD137 cl. 8). + assert receipt["drawn_units"] == 40 + assert receipt["drawn_units_northern_ireland"] == 0 + pool = next( + pool + for pool in receipt["pools"] + if pool["family_type"] == "SINGLE" and pool["support"] != 0 + ) + assert pool["support"] == "30+" + assert pool["drawn_support"] == "30+" + assert set(pool) == { + "family_type", + "bands", + "dwp_caseload", + "support", + "drawn_support", + "target_unreachable", + } + _, small = _assign(award=np.full(5, 1_500.0), targets=_targets(SINGLE_1=2.0)) + assert small["drawn_units"] == "<10" + assert small["families"]["SINGLE"]["caseload_after"] == "<10" + + +def test_inputs_must_align_and_name_known_cells() -> None: + with pytest.raises(ValueError, match="must align"): + _assign(award=np.ones(3), draws=np.ones(2), targets=_targets()) + with pytest.raises(ValueError, match="unknown"): + _assign( + award=np.ones(2), + family=np.asarray(["SINGLE", "TRIPLE"], dtype=object), + targets=_targets(), + ) + with pytest.raises(ValueError, match="missing cells"): + _assign(award=np.ones(2), targets={("SINGLE", 0): 1.0}) + + +class _StubEngine: + country = "uk" + + def __init__(self, awards: dict[int, float]) -> None: + self.awards = awards + self.calls: list[tuple[tuple[str, ...], str, str | None]] = [] + self.claims_seen: list[bool] | None = None + self.deduction_columns_seen: bool | None = None + + def materialize(self, frame, variables, period, *, dataset_period=None): + self.calls.append( + ( + tuple(variables), + str(period), + None if dataset_period is None else str(dataset_period), + ) + ) + benunit = frame.table("benunit") + self.claims_seen = benunit["would_claim_uc"].tolist() + self.deduction_columns_seen = set(UC_DEDUCTION_OUTPUT_COLUMNS) <= set( + benunit.columns + ) + ids = benunit["benunit_id"].to_numpy() + person = frame.table("person") + values = { + "universal_credit": np.asarray([self.awards[int(i)] for i in ids]), + "legacy_benefits_closed": np.zeros(len(ids), dtype=bool), + "is_child_or_qualifying_young_person_for_universal_credit": ( + person["age"].to_numpy() < 16 + ), + } + return {name: values[name] for name in variables} + + +_LEGACY_REPORTS = ( + "child_tax_credit_reported", + "working_tax_credit_reported", + "housing_benefit_reported", + "esa_income_reported", + "income_support_reported", + "jsa_income_reported", +) + + +def _frame(time_period: str = "2024"): + # Units 1 to 3 in Wales, unit 4 in Northern Ireland; unit 1 reports UC, + # unit 3 has no award. + person = pd.DataFrame( + { + "person_id": [11, 21, 31, 41], + "person_benunit_id": [1, 2, 3, 4], + "person_household_id": [1, 2, 3, 4], + "age": [30, 40, 50, 35], + "is_uc_claimant": [True] * 4, + "is_benunit_head": [True] * 4, + "is_parent": [False] * 4, + "universal_credit_reported": [1_000.0, 0.0, 0.0, 0.0], + **{column: [0.0] * 4 for column in _LEGACY_REPORTS}, + } + ) + benunit = pd.DataFrame( + { + "benunit_id": [1, 2, 3, 4], + "dependent_children": [0, 0, 0, 0], + "would_claim_uc": [True, True, True, False], + } + ) + household = pd.DataFrame( + { + "household_id": [1, 2, 3, 4], + "region": ["WALES", "WALES", "WALES", "NORTHERN_IRELAND"], + "household_source_id": [101, 102, 103, 104], + } + ) + return uk_national_frame( + person=person, + benunit=benunit, + household=household, + household_weights=np.asarray([10.0, 10.0, 10.0, 10.0]), + weight_kind=WeightKind.IMPORTANCE, + time_period=time_period, + ) + + +def _redraw(frame, engine): + return redraw_uc_take_up( + frame, + engine=engine, + population_policy=_POLICY, + targets=_targets(SINGLE_0=10.0), + closure_rates={"all": 0.8}, + minimum_pool_sources=1, + ) + + +def test_redraw_reads_the_paid_award_once_and_rewrites_only_would_claim_uc() -> None: + engine = _StubEngine({1: 900.0, 2: 900.0, 3: 0.0, 4: 900.0}) + frame = _frame() + + result = _redraw(frame, engine) + + # One read at the calibration year on the frame stamped with its survey + # year, every unit claiming and carrying its deduction attributes, so the + # award is the one the engine pays a claimant. + assert engine.calls == [(UC_TAKE_UP_ENTITLEMENT_VARIABLES, "2025", "2024")] + assert engine.claims_seen == [True, True, True, True] + assert engine.deduction_columns_seen is True + after = result.frame.table("benunit") + # The reporter's 10 meets DWP's 10, so the drawn unit 2 stops claiming and + # Northern Ireland's unit 4 draws at the same rate; unit 3, with no award, + # keeps its claim. + assert after["would_claim_uc"].tolist() == [True, False, True, False] + for entity in ("person", "household"): + pd.testing.assert_frame_equal( + result.frame.table(entity), frame.table(entity), check_exact=True + ) + pd.testing.assert_frame_equal( + after.drop(columns=["would_claim_uc"]), + frame.table("benunit").drop(columns=["would_claim_uc"]), + ) + evidence = result.evidence() + assert evidence["stage"] == UC_TAKE_UP_STAGE_NAME + assert evidence["entitlement_year"] == 2025 + assert evidence["solve_scope"] == "great_britain" + assert evidence["population_state_pension_age"] == 66 + + +def test_redraw_refuses_a_frame_outside_the_declared_dataset_year() -> None: + engine = _StubEngine({1: 900.0, 2: 900.0, 3: 0.0, 4: 900.0}) + with pytest.raises(ValueError, match="survey_year 2024 values"): + _redraw(_frame("2025"), engine) + assert engine.calls == [] + + +def _stage() -> SourceStageSpec: + spec = load_country_spec("uk") + assert spec.sources is not None + return spec.sources.stage_map()[UC_TAKE_UP_STAGE_NAME] + + +def test_committed_manifest_declares_the_stage_and_its_rewrite() -> None: + stage = _stage() + + assert stage.grain == "benunit" + assert stage.outputs == () + assert stage.rewrites == ("would_claim_uc",) + _assert_stage_parameters(stage) + assert [artifact["resource"] for artifact in stage.artifacts] == [ + UC_TAKE_UP_RESOURCE + ] + transform = UKUCTakeUpStageTransform( + stage=stage, + engine=_StubEngine({1: 900.0, 2: 900.0, 3: 0.0, 4: 900.0}), + population_policy=_POLICY, + targets=_targets(SINGLE_0=10.0), + closure_rates={"all": 0.8}, + ) + transform(_frame()) + assert transform.checkpoint_metadata()["evidence"]["stage"] == UC_TAKE_UP_STAGE_NAME + # The stage runs after the last writer of the capital and UC reports and + # before the Move to UC draw reads would_claim_uc. + order = [stage.stage for stage in load_country_spec("uk").sources.stages] + position = order.index(UC_TAKE_UP_STAGE_NAME) + assert order[position - 1] == "uc_capital_coherence" + assert order[position + 1] == "uc_legacy_closure_take_up" + + +def test_stage_parameters_refuse_drift() -> None: + stage = _stage() + operations = [] + for operation in stage.operations: + payload = {"kind": operation.kind, **dict(operation.parameters)} + if operation.kind == "assign_award_band_take_up_residual": + payload["minimum_pool_sources"] = 10 + operations.append(payload) + drifted = SourceStageSpec.from_mapping({**stage.__dict__, "operations": operations}) + with pytest.raises(ValueError, match="declaration drifted"): + _assert_stage_parameters(drifted) + with pytest.raises(ValueError, match="expected 'uc_take_up'"): + _assert_stage_parameters(replace(stage, stage="frs_take_up")) + + +def test_terminal_signal_gate_leaves_would_claim_uc_to_the_stage_gate() -> None: + assert all(output != "would_claim_uc" for _, output, _ in UK_TAKE_UP_SIGNAL_OUTPUTS) + + +def test_contract_records_the_u8_ruling_and_keeps_the_early_rate() -> None: + # María's ruling of 2026-10-21 lifts U8 for entitled units, which uc_take_up + # draws by award band and family type; 0.55 stays the early draw's rate, + # which a unit with no award keeps (microcosm#1334). + contract = load_uk_take_up_contract() + entry = contract.entry("universal_credit") + assert contract.rate("universal_credit") == 0.55 + assert entry.source["status"] == "frozen_by_adjudication" + freeze = entry.source["freeze"] + assert freeze["approved_by"] == "juaristi22" + assert freeze["approved_on"] == "2026-10-21" + assert "microcosm#1334" in freeze["adjudication"] + assert "uc_take_up" in freeze["decision"] + + +def test_the_receipt_counts_working_age_housing_benefit_kept_and_drawn() -> None: + # Vahid's review of #1344, item 7: 30 units, each its own source; 20 report + # Housing Benefit, 4 of those the engine moves off legacy benefits, and the + # redraw sets 6 of the other 16 to claim UC, so 10 keep the award. + count = 30 + person = pd.DataFrame( + { + "person_id": np.arange(count), + "person_benunit_id": np.arange(count), + "housing_benefit_reported": [100.0] * 20 + [0.0] * 10, + } + ) + benunit = pd.DataFrame({"benunit_id": np.arange(count)}) + moved = np.zeros(count, dtype=bool) + moved[16:20] = True + uc_after = np.zeros(count, dtype=bool) + uc_after[:6] = True + receipt = working_age_housing_benefit_receipt( + person=person, + benunit=benunit, + population=np.ones(count, dtype=bool), + moved=moved, + uc_before=np.zeros(count, dtype=bool), + uc_after=uc_after, + weights=np.ones(count), + great_britain=np.ones(count, dtype=bool), + source_ids=np.arange(count), + ) + assert receipt["reporting_units"] == pytest.approx(16.0) + assert receipt["keeping_housing_benefit_before"] == pytest.approx(16.0) + assert receipt["keeping_housing_benefit_after"] == pytest.approx(10.0) + # Six drawn units rest on fewer than ten sources: no weighted figure. + assert receipt["drawn_onto_universal_credit"] == "<10" + + +def test_a_unit_keeping_a_legacy_benefit_is_not_drawn_onto_uc() -> None: + # Vahid's review of #1344, item 7: a unit reporting a legacy benefit the + # engine leaves in place keeps it, so the pool's draw skips it and counts + # it at its stored flag, however many claims the pool needs. + award = np.full(4, 600.0) + legacy_kept = np.asarray([True, False, False, False]) + draws = np.asarray([0.01, 0.2, 0.3, 0.4]) + would_claim, receipt = _assign( + award=award, + draws=draws, + legacy_kept=legacy_kept, + targets=_targets(SINGLE_0=3.0), + ) + assert would_claim.tolist() == [False, True, True, True] + kept_previous, _ = _assign( + award=award, + draws=draws, + legacy_kept=legacy_kept, + previous=np.asarray([True, False, False, False]), + targets=_targets(SINGLE_0=3.0), + ) + # Counted at its stored flag: the pool then needs only two more claims. + assert kept_previous.tolist() == [True, True, True, False] diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 235dcca56..d99f34065 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -451,13 +451,13 @@ 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 = ( - "f2d7e2ad1476cd25d251903c812a3674af8356d423efd08baeac2ab5fac679bf" + "cf7df0ce75fcb6b66cdba6f706ed777aca9550a966f364cef6c3c2c5f7780813" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "671b2c04a1139cae4afc8043332756fdbbbf2a3aabee90d8f6211f7126d54118" + "cb12e74aed6397667b17d5cf770161cf01df721f796ddc244ae9b6945dcf3605" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "9d335e980b720a477d26805559f766a2f9adcaea06747e13a0801c2b69e0cf55" + "fcebd763a4e9c427615ca935b019de43bc49e7dffbcece54181c21576bdcc372" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -520,12 +520,14 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "stage_health", "transferred", ), + "uk_stage_frs_annual_income_spells": ("stage_health", "transferred"), "uk_stage_spi_support_channel_mass": ("stage_health", "transferred"), "uk_stage_hmrc_spi_income_spine_identity": ( "stage_health", "transferred", ), "uk_stage_spi_benefit_coherence": ("stage_health", "transferred"), + "uk_stage_uc_take_up": ("stage_health", "transferred"), "uk_stage_pension_credit_take_up": ("stage_health", "transferred"), "uk_stage_child_benefit_take_up": ("stage_health", "transferred"), "uk_stage_cgt_incidence_clone_mass": ("stage_health", "transferred"), @@ -633,9 +635,11 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "uk_stage_etb_services_support", "uk_stage_etb_services_support_pricing", "uk_stage_frs_hmrc_spine_leaves_signal", + "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", "uk_stage_spi_benefit_coherence", + "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", "uk_stage_child_benefit_take_up", "uk_stage_cgt_incidence_clone_mass", @@ -867,6 +871,7 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: "uk_stage_etb_services_support_pricing", "uk_stage_etb_vat_support", "uk_stage_frs_hmrc_spine_leaves_signal", + "uk_stage_frs_annual_income_spells", "uk_stage_frs_relationships_composition", "uk_stage_high_value_home_support_mass", "uk_stage_hmrc_cgt_asset_type_spine_summary", @@ -888,6 +893,7 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: "uk_stage_spi_support_channel_mass", "uk_stage_student_loans_realization", "uk_stage_uc_deduction_attributes", + "uk_stage_uc_take_up", "uk_stage_was_lisa_support", "uk_stage_was_wealth_coherence", "uk_stage_was_wealth_support", @@ -931,10 +937,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: _UK_CERTIFICATION_PART_DIGESTS: Mapping[str, Mapping[str, str]] = { "spine": { "gates_manifest_sha256": ( - "39295b40f70be77bb59e2aa70c9429be512e5af4ea78b0189f15687fb8ef3e36" + "985f54966ef25bfd7f778cb33ad501fd2e7d4c036f00245518bd1fd448ef9c7d" ), "policy_sha256": ( - "53a7eb6d9e52eb54fe5d123e1f4393535d0e218d5cf5d9383b9ebf50d367cd1e" + "2b4995c6389870fd54ffd815f40d269facb593f1131c4e3fa3c5fa55e2869d24" ), }, "calibration_seam": { @@ -947,10 +953,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "6093d00bef53a5bd369eb66f45074a97d6bca9b9d02eba96e99e38c3a867d073" + "85aaa1f2a7a9d295c12db01698c4ba0e2c3724ab6051d68966f49e5d8a3427f5" ), "policy_sha256": ( - "623f6300975649f691ce239fd744cbcc05e3ec192a40a8121cc25f7a1b5903c5" + "d7bda5f6771845ffc5bf591ae45f2618885f51ebeb48fcb96538a2a3b0ef6a11" ), }, } 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 8cb54de8b..520cd4e48 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 = ( - "f2d7e2ad1476cd25d251903c812a3674af8356d423efd08baeac2ab5fac679bf" + "cf7df0ce75fcb6b66cdba6f706ed777aca9550a966f364cef6c3c2c5f7780813" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "671b2c04a1139cae4afc8043332756fdbbbf2a3aabee90d8f6211f7126d54118" + "cb12e74aed6397667b17d5cf770161cf01df721f796ddc244ae9b6945dcf3605" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "9d335e980b720a477d26805559f766a2f9adcaea06747e13a0801c2b69e0cf55" + "fcebd763a4e9c427615ca935b019de43bc49e7dffbcece54181c21576bdcc372" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6a179222c6b6058ad9caacfd347f7bdb8cb98a62237b50d1fc1136df09337251" @@ -278,6 +278,7 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: "transferred", None, ), + "uk_stage_frs_annual_income_spells": ("stage_health", "transferred", None), "uk_stage_spi_support_channel_mass": ( "stage_health", "transferred", @@ -289,6 +290,7 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: None, ), "uk_stage_spi_benefit_coherence": ("stage_health", "transferred", None), + "uk_stage_uc_take_up": ("stage_health", "transferred", None), "uk_stage_pension_credit_take_up": ("stage_health", "transferred", None), "uk_stage_child_benefit_take_up": ("stage_health", "transferred", None), "uk_stage_high_value_home_support_mass": ("stage_health", "transferred", None), @@ -1119,7 +1121,7 @@ def _terminal_gate_details(name: str) -> dict: if name == "take_up_signal": # Mirrors uk_take_up_signal_gate's per-column detail block. return { - "benunit.would_claim_uc": { + "benunit.would_claim_tfc": { "weighted_share": 0.55, "target": 0.55, "absolute_deviation": 0.0, @@ -1345,6 +1347,7 @@ def _gate_battery_payload( "hmrc_spi_income_spine", "spi_benefit_coherence", "uc_capital_coherence", + "uc_take_up", "pension_credit_take_up", "child_benefit_take_up", "uc_deduction_attributes", @@ -1375,9 +1378,11 @@ def _gate_battery_payload( "uk_stage_etb_services_support": "etb_services", "uk_stage_etb_services_support_pricing": "etb_services", "uk_stage_frs_hmrc_spine_leaves_signal": "frs_hmrc_spine_leaves", + "uk_stage_frs_annual_income_spells": "frs_annual_income_spells", "uk_stage_spi_support_channel_mass": "spi_support_channel", "uk_stage_hmrc_spi_income_spine_identity": "hmrc_spi_income_spine", "uk_stage_spi_benefit_coherence": "spi_benefit_coherence", + "uk_stage_uc_take_up": "uc_take_up", "uk_stage_pension_credit_take_up": "pension_credit_take_up", "uk_stage_child_benefit_take_up": "child_benefit_take_up", "uk_stage_high_value_home_support_mass": "high_value_home_support", 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 e488f5774..c0c4ba6bb 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 @@ -1144,6 +1144,110 @@ "stage": "etb_vat", "survey": "Effects of Taxes and Benefits 1977-2024" }, + "frs_annual_income_spells": { + "artifacts": [ + { + "format": "tab", + "kind": "licensed_microdata", + "locator": "adult.tab", + "role": "frs_table", + "runtime_sha256_required": true, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, + "table": "adult", + "vintage": "synthetic-h2" + }, + { + "format": "tab", + "kind": "licensed_microdata", + "locator": "job.tab", + "role": "frs_table", + "runtime_sha256_required": true, + "sha256": "cc985ee1ded0af25fb629acd58c31a8ea4e103c0d3bb3b0c35cc52158af17c57", + "size_bytes": 7593, + "table": "job", + "vintage": "synthetic-h2" + }, + { + "format": "tab", + "kind": "licensed_microdata", + "locator": "househol.tab", + "role": "frs_table", + "runtime_sha256_required": true, + "sha256": "7b5fc26572d05bc21cee203b91e20fa9e743c170cabb497a284497b309eb3a89", + "size_bytes": 17554, + "table": "househol", + "vintage": "synthetic-h2" + }, + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "hmrc_uprating_engine_pins.json", + "role": "earnings_index", + "runtime_sha256_required": true + } + ], + "grain": "person", + "nonnegative_outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "notes": "Describes each FRS adult's 12 months before the interview beside the current income columns, which it never changes, so the HMRC income-band measures can read annual income while poverty, benefits and the published engine tax keep current income. HMRC counts income received over a tax year: a person who started, left or changed a job, or had a gap, sits in another HMRC band than current pay puts them in, or in none. Outputs: the share of the year paid at today's employee pay and annual pay from other spells (chiefly a recent leaver's last job), the same pair for self-employment, and the shares of the year a private pension started on retiring and contributory JSA, ESA and Incapacity Benefit started on leaving work were in payment; an annual amount is the current amount times its share plus the other spells. The State Pension needs none: policyengine-uk's months_since_state_pension_age gives its share. Months come from CURRJOBM and JOB.WRKPREV for people in work (a person in work is never a leaver), the SAMESIT/SDEMP calendar only where it records a change, and LSTYR, LSTWRK1/2 or RETIRE1 for people out of work who worked in the last 12 months (NUMJOB2 and their last-job JOB rows); a leaving date 12 months or more before the interview gives none. A leaver's pay is the last job's usual gross pay (JOB.UGROSS, asked of people out of work who last worked in the previous 12 months, in respect of that job), scaled to the DV concept by the weighted job-level UGRSPAY/UGROSS ratio on current employees who report both and uprated by the pinned OBR average earnings index for the months since leaving; otherwise a donor's weekly pay, drawn by GROSS4 weight from current employees in their first 24 months in the job within age band x sex x full- or part-time cells (sex dropped below 30 donors), on an identity-keyed uniform (seed 0, salt employment_income_other_spells). Self-employment keeps the whole year where SEJBLONG is one or more, (SEJBMTHS + 1)/12 under a year and at most the calendar's self-employed months where self-employment is the activity today (the calendar records the main activity only, so a side business beside a job keeps its whole year); a ceased self-employed last job pays SEINCAMT (else PROFIT1, a loss as zero) for its SELWKS weeks; SEINCAM2 is already uprated to the interview by the FRS, so it is not uprated again. The receipt gives each source's support as a band of distinct households, ratios only where ten or more households carry them, and the exact zeros its stage gate reads.", + "operations": [ + { + "delimiter": "\t", + "format": "tab", + "kind": "read_tables", + "lowercase_columns": true, + "numeric_errors": "coerce", + "runtime_sha256_required": true + }, + { + "donor_age_edges": [ + 25, + 35, + 50, + 65 + ], + "donor_recent_start_months": 24, + "earnings_index": "gov.economic_assumptions.indices.obr.average_earnings", + "earnings_index_from": "2023-01-01", + "earnings_index_to": "2024-01-01", + "kind": "derive_annual_income_spells", + "leaving_date_share": 0.75, + "maximum_uprating_months": 24, + "minimum_donors": 30, + "outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "salt": "employment_income_other_spells", + "seed": 0, + "unknown_leaving_months": 6, + "window_months": 12 + } + ], + "outputs": [ + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" + ], + "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 adult, job and household tabs; the OBR average earnings index pinned in hmrc_uprating_engine_pins.json.", + "stage": "frs_annual_income_spells", + "survey": "Family Resources Survey 2024-25" + }, "frs_brma": { "artifacts": [ { @@ -1274,8 +1378,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" }, @@ -1393,8 +1497,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" } @@ -1436,8 +1540,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" }, @@ -1458,8 +1562,8 @@ "locator": "job.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "c364262e4d355a12ac929b9b6d2dac0f40a21aa9e1106895ed603c9e6e3554d5", - "size_bytes": 5405, + "sha256": "cc985ee1ded0af25fb629acd58c31a8ea4e103c0d3bb3b0c35cc52158af17c57", + "size_bytes": 7593, "table": "job", "vintage": "synthetic-h2" }, @@ -1654,8 +1758,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" } @@ -1765,8 +1869,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" }, @@ -1996,8 +2100,8 @@ "locator": "adult.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "5c6e326a755da49737d18b976eb90a3fa1e8fd95984ce6c88a8e8238954b7f31", - "size_bytes": 31756, + "sha256": "8d1d7b4195081f9f24543e051484b563fcbc35044e8f93b39e4887a4d0a54500", + "size_bytes": 33996, "table": "adult", "vintage": "synthetic-h2" }, @@ -2073,8 +2177,8 @@ "locator": "job.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "c364262e4d355a12ac929b9b6d2dac0f40a21aa9e1106895ed603c9e6e3554d5", - "size_bytes": 5405, + "sha256": "cc985ee1ded0af25fb629acd58c31a8ea4e103c0d3bb3b0c35cc52158af17c57", + "size_bytes": 7593, "table": "job", "vintage": "synthetic-h2" }, @@ -2938,7 +3042,7 @@ "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. 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).", + "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). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes.", "operations": [ { "artifact_roles": [ @@ -3731,7 +3835,13 @@ "owned_company_income_share", "hmrc_spi_pay", "hmrc_spi_unemployment_benefit_income", - "hmrc_spi_incapacity_benefit_income" + "hmrc_spi_incapacity_benefit_income", + "employment_income_annual_share", + "employment_income_other_spells", + "self_employment_income_annual_share", + "self_employment_income_other_spells", + "private_pension_income_annual_share", + "contributory_benefits_annual_share" ], "source": "https://assets.publishing.service.gov.uk/media/69f1f12d2fae53a03709682f/Collated_Tables_3_1_to_3_11_2324.ods", "stage": "hmrc_spi_income_spine", @@ -5421,7 +5531,7 @@ "artifacts": [], "grain": "person+benunit", "nonnegative_outputs": [], - "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer's Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer's Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.", + "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit, Child Benefit and Carer's Allowance take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. That draw stands for a unit with no award; uc_take_up redraws every entitled unit of both channels by award band and family type once uc_capital_coherence has set the capital the engine assesses (microcosm#1334). Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws (carers_allowance_take_up later opens the Carer's Allowance claim flag to carers over the earnings limit, microcosm#1319), and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.", "operations": [ { "columns": [ @@ -6023,7 +6133,7 @@ ], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_capital_coherence, the last writer of would_claim_uc and universal_credit_reported, and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; Mar\u00eda's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id.", + "notes": "Ports uk-data#534 (a draft, at c67077a66; uk-data#492) at the spine's design weights (microcosm#1095). policyengine-uk 2.128.0 (pe-uk#2119) moves a family off a legacy benefit DWP has closed (tax credits from 2025-26) and takes its other legacy awards away; the family then claims Universal Credit if would_claim_uc or would_claim_uc_at_legacy_closure is true, and the engine's default for the second, true, put every such family on Universal Credit. The stage runs after uc_take_up, the last writer of would_claim_uc (uc_capital_coherence is the last writer of universal_credit_reported), and before pension_credit_take_up and CGT cloning, so every SPI benefit unit draws on its own final receipts and every clone carries its source's draw. A benefit unit with a member reporting Universal Credit, a unit whose would_claim_uc is true, and a unit reporting none of the six legacy means-tested benefits (child_tax_credit, working_tax_credit, housing_benefit, esa_income, income_support and jsa_income, each read as a member's _reported above 0) is true, the engine's default. Every other unit is drawn at (r - p) / (1 - p), clipped to [0, 1], where r is DWP's claimed / (claimed + did not claim) for the unit's combination of legacy benefits (the all-households rate where DWP reports none; households still in progress are left out) and p is the combination's share of Great Britain's such units whose would_claim_uc is true, at the stage's household weights mapped to benefit units (uk-data#534's p is unweighted, as its draw precedes calibration; Mar\u00eda's rulings of 2026-10-16 and 2026-10-17); the probability applies to the combination's units in Northern Ireland too, since DWP's rates cover Great Britain. So, in expectation, the weighted share of Great Britain's drawn units claiming at the closure in each combination is DWP's rate wherever p is at most r; where p exceeds it the probability is 0 and the receipt flags the combination (rate_unreachable), never p itself. Two limits carry over from uk-data#534. The drawn cohort includes units on the Pension Credit route, whose legacy benefits policyengine-uk does not close, and DWP's rates count every household sent a notice, entitled to Universal Credit or not. The uniform reads an identity-keyed stream on benunit_id.", "operations": [ { "artifact_role": "move_to_uc_facts", @@ -6143,6 +6253,100 @@ "stage": "uc_reporter_redraw", "survey": "Family Resources Survey 2024-25" }, + "uc_take_up": { + "artifacts": [ + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "dwp_uc_award_band_facts.json", + "role": "uc_award_band_facts", + "runtime_sha256_required": true + } + ], + "grain": "benunit", + "nonnegative_outputs": [], + "notes": "Runs after uc_capital_coherence, the last writer of universal_credit_reported and of the capital the engine assesses, and before uc_legacy_closure_take_up, which reads would_claim_uc. frs_take_up draws would_claim_uc early for FRS benefit units and spi_benefit_coherence redraws the SPI-channel units on the same rule: a reporter claims and the rest of the take-up contract's one rate times the age population is drawn uniformly over its non-reporting units, whatever their award. At the spine's design weights that put the caseload above DWP's for single people without children, couples and the smallest awards, and below it for lone parents, so the national solve met DWP's caseloads by reweighting low-income families. This stage redraws the flag for both channels. One temporary engine materialization, at the release's calibration year on the frame stamped with its survey year (as pension_credit_take_up reads Pension Credit), gives each benefit unit the award it would be paid if it claimed: universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes will assign it (they read only the unit's identity and region), so after the benefit cap and deductions, the award the registry's dwp/uc_payment_dist rows band. It also gives whether the engine moves the unit off its legacy benefits that year (legacy_benefits_closed) and the UC child status the calibration's family type reads; all are consumed only. A unit with an award falls into a cell of DWP's family type by DWP's GBP 100 monthly award band, banded as the registry rows band an award, with one more band above GBP 2,500 a month. Within a family type, adjacent bands pool, lowest first, until the drawn units rest on at least 30 distinct source households. In each pool a reporter claims, and each drawn unit (entitled, non-reporting, in frs_take_up's age population and not moved off its legacy benefits) claims at r = (T - F) / D clipped to [0, 1]: T is DWP's count for the pool's bands, the mean of the April to December 2025 Stat-Xplore months (the facts the registry rows average, vendored as dwp_uc_award_band_facts.json), D the weight of the drawn units, and F the expected claims of the other entitled units: 1 for a reporter, the claim probability uc_legacy_closure_take_up's rule gives a moved unit (whose flag this stage leaves alone, so that probability does not move), and the stored flag for any other. T, F and D are Great Britain's at the stage's household weights, which DWP's counts cover. The drawn units claim in the order of their uniform until their weight is nearest T - F, so each pool realizes r to within one unit's weight rather than only in expectation, which a pool resting on a few dozen households can miss by several per cent, and Northern Ireland's drawn units claim below the same uniform. A pool whose other claims reach T, or whose T exceeds every entitled unit, cannot meet DWP's count; the receipt flags it. A unit with no award, or outside the age population, keeps its stored flag, the single-rate draw, which is how a unit claims once a reform or a later year entitles it. The uniform is the would_claim_uc identity stream frs_take_up and spi_benefit_coherence use. The receipt gives each pool's DWP count, its support in bands of distinct source households and its unreachable flag (only where ten or more sources carry the pool), and each family type's weighted caseload before and after; it gives no unit count except in bands and no rate. The uk_stage_uc_take_up gate reads it; the terminal take-up signal gate no longer measures would_claim_uc.", + "operations": [ + { + "artifact_role": "uc_award_band_facts", + "fail_on_mismatch": true, + "feed_pin": "chronicle_feed.json", + "kind": "verify_vendored_fact_resource", + "mapped_build_period": 2025, + "period_mapping": "calendar_year_average_of_monthly_counts", + "record_sets": [ + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_04", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_05", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_06", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_07", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_08", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_09", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_10", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_11", + "dwp.statx.uc_benefit_units.award_band_family_type.month2025_12" + ], + "require_before_source_read": true, + "resource": "dwp_uc_award_band_facts.json", + "runtime_sha256_required": true, + "source_vintage": "April to December 2025" + }, + { + "consumed_only": true, + "dataset_year_rule": "survey_year", + "kind": "materialize_rules_engine_predictors", + "predictors": [ + "universal_credit", + "legacy_benefits_closed", + "is_child_or_qualifying_young_person_for_universal_credit" + ], + "year_rule": "calibration_year" + }, + { + "aggregates": { + "universal_credit_reported_anchor": "universal_credit_reported" + }, + "consumed_only": true, + "kind": "aggregate_person_to_benunit", + "method": "any_positive" + }, + { + "aggregates": { + "uc_age_eligible": "age" + }, + "consumed_only": true, + "kind": "aggregate_person_to_benunit", + "method": "any_uc_claimant_aged_18_under_pension_credit_age", + "role_column": "is_uc_claimant" + }, + { + "anchor": "universal_credit_reported_anchor", + "award": "universal_credit on a temporary copy where every unit claims and carries the deduction attributes uc_deduction_attributes assigns from its identity and region", + "award_bands": "GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, 1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000", + "draw": "would_claim_uc", + "drawn_units": "entitled, non-reporting units of the population that the engine does not move off their legacy benefits (legacy_benefits_closed) and that report none, since a legacy benefit the engine leaves in place ends once the unit claims UC", + "family_type": "two FRS claimant or partner roles make a couple; children are UC children or qualifying young people and other non-claimants under 20 (the calibration measure's uc_calibration_family_type)", + "kind": "assign_award_band_take_up_residual", + "minimum_pool_sources": 30, + "not_drawn": "keeps its stored flag", + "output": "would_claim_uc", + "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it", + "population": "uc_age_eligible", + "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", + "seed": 0, + "solve_scope": "great_britain", + "targets": "DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025", + "undrawn_claims": "a reporter at 1, a moved unit at the claim probability uc_legacy_closure_take_up's rule gives it (dwp_move_to_uc_facts.json), any other at its stored flag", + "weight_mapping": "household_to_benunit" + } + ], + "outputs": [], + "rewrites": [ + "would_claim_uc" + ], + "source": "DWP Stat-Xplore, Households on Universal Credit by family type and monthly award amount band, April to December 2025, applied to PolicyEngine-UK pre-take-up Universal Credit awards after the SPI income chain and UC capital coherence.", + "stage": "uc_take_up", + "survey": "Family Resources Survey 2024-25" + }, "was_lisa": { "artifacts": [ { 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 30b788c71..77ea37406 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 SLOS EDUCLEFT SSPADJ SMPADJ SPPADJ TUBORR ACCSSAMT ACCSSPD SPCREG1 SPCREG2 GRTDIR1 GRTDIR2 HEARTVAL HOURTOT -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 5 -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 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 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 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 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 5 -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 4 -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 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 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 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 5 -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 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2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 0 -115 1 1 0 25 2 35 1 1 7 2 4 2 1 3 3 3 3 3 3 3 3 3 3 3 3 2 45 2 0 8 2 0.0 0.0 215.0 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 19 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 0 -116 1 1 0 26 1 36 1 1 3 5 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 46 2 0 9 2 0.0 0.0 216.0 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 20 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 5 -117 1 1 0 27 2 37 1 1 1 6 2 1 1 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 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 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 4 -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 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 5 -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 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 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 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 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 5 -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 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 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 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 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 5 -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 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 4 -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 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 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 SELFDEMP WORKING JOBAWAY NUMJOB2 CURRJOBM PREVJOBM LSTWRK1 LSTWRK2 LSTYR RETIRE RETIRE1 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 HOURTOT +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 1 1 4 2 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 5 +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 3 1 14 2 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 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 3 1 15 2 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 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 1 16 2 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 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 1 1 8 2 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 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 1 18 2 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 5 +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 1 1 19 2 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 4 +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 1 20 2 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 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 1 1 21 2 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 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 3 1 4 2 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 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 3 1 23 2 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 5 +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 1 24 2 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 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 1 1 25 1 2 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 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 1 8 2 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 2 22 1.0 0.5 0.25 500.0 1.0 52.0 1 2 2.0 3.0 5.0 4 +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 1 1 27 2 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 2 23 1.0 0.5 0.25 500.0 1.0 52.0 2 1 2.0 3.0 5.0 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 1 28 2 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 2 16 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 5 +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 1 1 29 2 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 2 17 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 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 3 1 30 2 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 2 18 1.0 0.5 0.25 500.0 1.0 52.0 2 2 2.0 3.0 5.0 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 3 1 4 2 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 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 1 32 2 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 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 1 1 33 2 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 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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 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 1 1 41 2 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 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 3 1 42 1 11 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 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 3 1 8 2 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 5 +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 1 44 2 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 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 1 1 45 2 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 4 +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 1 46 2 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 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 1 1 47 2 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 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 c692bef14..ec303f3b1 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 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 +SERNUM BENUNIT PERSON DEDUC1 SPNAMT SALSAC JOBSECT ETYPE JOBTYPE SEEND SEJBLONG JOBBUS DIRECTOR NATURE DIRPERC DIVIDEND FTPT WRKPREV UGROSS UGRSPAY SEJBMTHS SELWKS SEINCAMT PROFIT1 PROFIT2 +1 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 2 1 101.0 +2 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 1 +3 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 1 5.0 +4 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 104.0 +5 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 1 105.0 +6 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 103.92 106.0 +7 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 107.0 +8 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 108.0 +9 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 2 106.86 109.0 +10 1 1 2.0 3.0 1 1 4 1 1.0 1 1 4 2 1000.0 1 3 +11 1 1 2.0 3.0 1 2 4 1 2.0 2 2 4 -1 0.0 1 +12 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 109.8 112.0 +13 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 113.0 +14 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 3 114.0 +15 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 112.75 115.0 +16 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 116.0 +17 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 2 117.0 +18 1 1 2.0 3.0 1 1 4 1 0.0 1 2 4 -1 0.0 1 5.0 +19 1 1 2.0 3.0 1 2 4 1 1.0 2 2 4 -1 0.0 1 3 +20 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 1 120.0 +21 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 118.63 121.0 +22 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 122.0 +23 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 3 123.0 +24 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 121.57 124.0 +25 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 2 125.0 +26 1 1 2.0 3.0 1 1 4 1 2.0 1 2 4 -1 0.0 1 +27 1 1 2.0 3.0 1 2 4 1 0.0 2 2 4 -1 0.0 1 5.0 +28 1 1 2.0 3.0 1 1 1 1 1 2 4 -1 0.0 1 1 128.0 +29 1 1 2.0 3.0 1 2 1 1 2 2 4 -1 0.0 1 129.0 +30 1 1 2.0 3.0 1 1 1 1 1 1 4 2 1000.0 1 127.45 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UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/tools/build_uk_release_input_coverage_manifest.py b/tools/build_uk_release_input_coverage_manifest.py index b220dd023..10d6e45b7 100644 --- a/tools/build_uk_release_input_coverage_manifest.py +++ b/tools/build_uk_release_input_coverage_manifest.py @@ -1212,6 +1212,9 @@ def _hmrc_family_coverage_contract( "base_candidate_tier": validate_uk_release_tier(candidate_source["tier"]), "required_predecessor_stages": [ "frs_hmrc_spine_leaves", + # The SPI copies carry the annual income descriptors their twins + # hold, and the income stage holds them to the drawn incomes. + "frs_annual_income_spells", "spi_support_channel", "spi_income_band_donors", ], diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index d22519bae..da4ac8835 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -66,6 +66,9 @@ from microcosm.build.uk_runtime.content_identity import uk_frame_content_identity from microcosm.build.uk_runtime.etb_services import UKETBServicesStageTransform from microcosm.build.uk_runtime.etb_vat import UKETBVATStageTransform +from microcosm.build.uk_runtime.frs_annual_income import ( + UKFRSAnnualIncomeStageTransform, +) from microcosm.build.uk_runtime.frs_brma import UKFRSBRMAStageTransform from microcosm.build.uk_runtime.frs_council_tax import UKFRSCouncilTaxStageTransform from microcosm.build.uk_runtime.frs_disability import UKFRSDisabilityStageTransform @@ -179,6 +182,7 @@ from microcosm.build.uk_runtime.uc_reporter_redraw import ( UKUCReporterRedrawStageTransform, ) +from microcosm.build.uk_runtime.uc_take_up import UKUCTakeUpStageTransform from microcosm.build.uk_runtime.was_lisa import UKWASLISAStageTransform from microcosm.build.uk_runtime.was_wealth import UKWASWealthStageTransform from microcosm.frame import Frame @@ -201,7 +205,7 @@ _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 = 47 +UK_FIXTURE_STAGE_COUNT = 49 _QRF_ESTIMATORS = 4 # These are the complete object-string surface observed in the unchanged @@ -455,6 +459,27 @@ def _frs_tables() -> dict[str, pd.DataFrame]: f"SDEMP{month:02d}": 3 if household_id % 8 in (2, 3) else 1 for month in range(1, 13) }, + # The annual income stage's adult columns: every adult works and + # is paid; every ninth started the job 5 or 9 months before the + # interview, and every thirteenth retired from a job in the last + # 12 months. Nobody is out of work, so the last-job block is blank. + "SELFDEMP": 3 if household_id % 8 in (2, 3) else 1, + "WORKING": 1, + "JOBAWAY": "", + "NUMJOB2": "", + "CURRJOBM": ( + 4 + if household_id % 9 == 1 + else 8 + if household_id % 9 == 5 + else 12 + household_id % 100 + ), + "PREVJOBM": "", + "LSTWRK1": "", + "LSTWRK2": "", + "LSTYR": "", + "RETIRE": 1 if household_id % 13 == 0 else 2, + "RETIRE1": 1 + household_id % 12 if household_id % 13 == 0 else "", "MJOBSECT": 1 + household_id % 2, "SIC": 10 + household_id % 80, "FTED": 2, @@ -635,6 +660,33 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "NATURE": 4, "DIRPERC": 2 if household_id % 10 == 0 else -1, "DIVIDEND": 1000.0 if household_id % 10 == 0 else 0.0, + # The annual income stage's job columns: a job under a year old + # follows paid employment (WRKPREV 1) on two households in + # four; every employee job carries its DV pay (UGRSPAY) and + # every third one a usual gross pay two per cent below it, so + # the stage measures its usual-to-DV pay ratio; a business + # under a year old (SEJBLONG 0) is five months old. + "FTPT": 2 if household_id % 8 == 1 else 1, + "WRKPREV": ( + (1 if household_id % 4 in (0, 1) else 3) + if household_id % 9 in (1, 5) + else "" + ), + "UGROSS": ( + round((100.0 + household_id) / 1.02, 2) + if household_id % 8 not in (2, 3) and household_id % 3 == 0 + else "" + ), + "UGRSPAY": ( + 100.0 + household_id if household_id % 8 not in (2, 3) else "" + ), + "SEJBMTHS": ( + 5.0 if household_id % 8 in (2, 3) and household_id % 3 == 0 else "" + ), + "SELWKS": "", + "SEINCAMT": "", + "PROFIT1": "", + "PROFIT2": "", } ) pensions.append( @@ -2031,6 +2083,9 @@ def capture_root(frame: Frame) -> Frame: "frs_hmrc_spine_leaves": UKFRSHMRCSpineLeavesStageTransform( raw_dir, stage=stages["frs_hmrc_spine_leaves"] ), + "frs_annual_income_spells": UKFRSAnnualIncomeStageTransform( + raw_dir, stage=stages["frs_annual_income_spells"] + ), "spi_support_channel": UKSPISupportChannelStageTransform( stage=stages["spi_support_channel"], sample_fraction=_SPI_SAMPLE_FRACTION, @@ -2063,6 +2118,9 @@ def capture_root(frame: Frame) -> Frame: "uc_capital_coherence": UKUCCapitalCoherenceStageTransform( stage=stages["uc_capital_coherence"] ), + "uc_take_up": UKUCTakeUpStageTransform( + stage=stages["uc_take_up"], engine=engine + ), "uc_legacy_closure_take_up": UKLegacyClosureTakeUpStageTransform( stage=stages["uc_legacy_closure_take_up"] ),