diff --git a/changelog.d/882-uk-benefit-cap-bands.added.md b/changelog.d/882-uk-benefit-cap-bands.added.md new file mode 100644 index 0000000000..07fe2583fd --- /dev/null +++ b/changelog.d/882-uk-benefit-cap-bands.added.md @@ -0,0 +1 @@ +Bind the fourteen DWP benefit-cap amount bands (Table 4, November 2025) at benefit-unit grain, the up-to-£100 band in the objective and the thirteen others measured on the reviewed exclusion register while the capped tail rests on a few cloned households, with `tools/diagnose_uk_benefit_cap.py` as the tail diagnostic (#882). diff --git a/changelog.d/882-uk-carer-element.added.md b/changelog.d/882-uk-carer-element.added.md new file mode 100644 index 0000000000..a4e9d0f1fa --- /dev/null +++ b/changelog.d/882-uk-carer-element.added.md @@ -0,0 +1 @@ +Fill the engine's `care_hours` from the FRS adult HOURTOT band (lower band edge; codes 5, 6, 7 and 10 reach the 35-hour line) and set `would_claim_carers_allowance` from reported receipt in the root spine stage, on policyengine-uk 2.98.0 (floor raised from 2.97.0) whose `is_carer_for_benefits` recognises the hours, so the Universal Credit carer element reaches carers without a Carer's Allowance award while the allowance stays on reported claimants (#882). The carer element register row is retired on the twin evidence (paid-UC carer units +80 percent at design weights). The hours limb counts reported hours alone, with no check that the person cared for receives a qualifying benefit, as the engine documents. diff --git a/changelog.d/882-uk-childcare-takeup.added.md b/changelog.d/882-uk-childcare-takeup.added.md new file mode 100644 index 0000000000..2c53bee084 --- /dev/null +++ b/changelog.d/882-uk-childcare-takeup.added.md @@ -0,0 +1 @@ +Draw `would_claim_uc_childcare` in the `frs_take_up` stage at one contract rate per family type (single 0.441, couple 0.2682, fitted by `tools/fit_uk_uc_childcare_takeup.py` so the expected paid-UC count of the Universal Credit childcare element equals DWP's calendar-2025 single and couple households), carried on the extra export surface as a net-new engine input that policyengine-uk 2.98.0 reads (#882). The childcare element register row is retired on the twin evidence. diff --git a/changelog.d/882-uk-legacy-rows.changed.md b/changelog.d/882-uk-legacy-rows.changed.md new file mode 100644 index 0000000000..66cec153de --- /dev/null +++ b/changelog.d/882-uk-legacy-rows.changed.md @@ -0,0 +1 @@ +Re-adjudicate the A16 measure exclusions `obr.housing_benefit` and `dwp.jsa_claimants` on the mechanism receipts of `tools/diagnose_uk_legacy_benefits.py` (the SPI support channel carries reported Housing Benefit and JSA into synthetic households; the Universal Credit take-up draw zeroed Housing Benefit for pension-age units), gate the `would_claim_uc` draw to benefit units with an adult under State Pension age (the U8 rate is unchanged, its population re-signed), bind the DWP Housing Benefit caseload by tenure as measured diagnostics, and declare the ESA single-type and credits-only reconciliation on the three ESA rows (#882, #867, #869). diff --git a/changelog.d/882-uk-register-names.fixed.md b/changelog.d/882-uk-register-names.fixed.md new file mode 100644 index 0000000000..a0d3705164 --- /dev/null +++ b/changelog.d/882-uk-register-names.fixed.md @@ -0,0 +1 @@ +Key the three UC element rows on the UK measure-exclusion register by their reference names (`dwp.uc.households_{carer,childcare,housing}_element`) instead of their binding metric names, which matched no compiled spec and made the calibration run refuse; a test now requires every register name to resolve to a committed reference (#882). diff --git a/docs/uk-uc-baseline-2026-09-10.md b/docs/uk-uc-baseline-2026-09-10.md index 8cca7ec5b8..ac9a648c3c 100644 --- a/docs/uk-uc-baseline-2026-09-10.md +++ b/docs/uk-uc-baseline-2026-09-10.md @@ -90,3 +90,12 @@ Compile-time target values are calendar-2025 means of the Payment Indicator = Ye Deductions and the two tenure rows are within 6 % on both files and LCWRA within 15 % (Q50f) / 20 % (D2); those four rows are active. LCWRA stays in the objective by the rule its binding note states: a concept mismatch goes to the register only when it puts the row outside the 25 % release bound at the baseline. Carer and childcare are measured but held out of the objective on the reviewed exclusion register until their model gaps are repaired (see the element-support section above). The any-tenure housing row is also measured but held out: DWP's 'Yes' is social plus private plus 'Other or unknown' tenure, the model has no other/unknown tenure, so its any-tenure count is identically the two tenure rows and the published total exceeds what the model can reach by the other/unknown cell (112,518 in calendar 2025, 2.8 %); fitting all three would over-fill the tenure rows. The register entries are signed by María in review of the element PR (2026-09-15) with a one-month window. The paid-claim targets sit below the April–December all-claims means quoted in the earlier section because the calendar window includes the lower January–March 2025 months and excludes nil-payment claims. Paid GB caseload for scale: Q50f 6,416,983, D2 6,428,401; share of paid units with earnings 51.6 % / 49.6 %. + +## Repairs measured against this baseline (16 September 2026, the #882 repairs PR) + +Receipts: `experiments/882-uc-repairs-receipts.md`. Twins built with the licensed recipe at design weights: a control at the branch base (51c31438, engine 2.97.0) and the repaired branch (engine 2.98.0, PolicyEngine/policyengine-uk#1857). Projections onto Q50f use the design-weight ratio repaired / control. + +- Carer element: the engine's carer condition now holds on Carer's Allowance receipt or 35 weekly care hours, and the spine fills `care_hours` from the FRS adult HOURTOT band with `would_claim_carers_allowance` following reported receipt. Paid-UC carer-element units 425,757 → 768,273 (+80 %); projection 1,075,100 against 1,078,514 (−0.3 %). Carer's Allowance spend stays on reported claimants once the take-up flag is re-derived after the SPI fill. The register row is retired. +- Childcare element: `would_claim_uc_childcare` is drawn per family type (single 0.441, couple 0.2682) so the expected paid-UC count equals the published calendar-2025 means at the Q50f weights; on the twin the realized count is 116,324 against 353,470 at rate one, below the expectation because SPI-synthetic units copy their donor's flag on a base of 378 and 201 records; projection 149,950 against 178,667 (−16 %). The register row is retired with that caveat recorded. +- Legacy rows: the A16 exclusions `obr.housing_benefit` and `dwp.jsa_claimants` are re-adjudicated to 2026-12-08 on the mechanism receipts (the SPI support channel carries reported Housing Benefit and JSA into synthetic households; the Universal Credit draw had zeroed Housing Benefit for pension-age units). Gating the draw to units with an adult under State Pension age lifts engine Housing Benefit from 401,489 to 644,183 units at design weights (£2.61bn → £4.11bn) and moves paid UC −1.8 % (the rate now realizes 0.559 among the eligible units where the old all-units draw realized 0.582); three Housing Benefit caseload rows bind as measured diagnostics. +- Benefit-cap amount bands: fourteen DWP Table 4 bands bind at benefit-unit grain; the up-to-£100 band is in the objective and the thirteen others are measured on the register. On Q50f the tail above £1,300 a month is 14.2 % against 0.6 % published; on the spine at design weights it is 6.6 % from four source households (eight records), so the Q50f tail is a selection-and-calibration effect on a thin base. diff --git a/experiments/882-uc-repairs-receipts.md b/experiments/882-uc-repairs-receipts.md new file mode 100644 index 0000000000..a5d99579a1 --- /dev/null +++ b/experiments/882-uc-repairs-receipts.md @@ -0,0 +1,65 @@ +# #882 repairs receipts (2026-09-16 →) + +Receipts for the #882 repairs PR: the A16 legacy rows (#867 Housing Benefit, #869 JSA and the ESA rows), the Universal Credit carer and childcare elements, and the benefit-cap amount bands. Plan: `repos/uk-882-repairs-plan-2026-09-16.md` (María's copy). Inputs unless stated: spine-q (`data/ukds/acceptance/spine-q-355/spine-q.h5`, sha `cf1f9dda…`, design weights), the Q50f candidate (`355-dataset-size/spine-q/f100-k15-h55000-e2000-p50-f001-s42/microcosm_uk_2025_local.h5`, calibrated weights), the Chronicle `474a0ae` artifact (facts `bb12d77a…`), FRS 2024-25 licensed tabs (`data/ukds/frs_2024_25`), policyengine-uk 2.97.0. + +## R0. Tape receipts (FRS 2024-25, gross4 design weights) + +- Care hours: `adult.hourtot` is the banded derived variable (1 = 0–4 h, 2 = 5–9, 3 = 10–19, 4 = 20–34, 5 = 35–49, 6 = 50–99, 7 = 100+, 8 = varies under 20, 9 = varies 20–34, 10 = varies 35+). Unweighted counts: 0: 24,818; 1: 604; 2: 471; 3: 390; 4: 227; 5: 248; 6: 158; 7: 247; 8: 69; 9: 52; 10: 430. Adults at 35 hours or more (codes 5, 6, 7, 10): 1,083 records, 2,121,047 weighted; with Carer's Allowance (benefit code 13) 978,957, without 1,142,090. In benefit units reporting Universal Credit (code 95): 906,396 weighted, of which 300,104 without Carer's Allowance (133 sample benefit units). The per-cared-for-person coding on `care.tab` gives 2,075,045 at 35+ for a single cared-for person; 1,449,436 of them care for a household member on DLA, PIP or Attendance Allowance. +- Housing Benefit (code 94): 1,070 benefit-unit records, 1,559,991 weighted; by household tenure (`ptentyp2`) 40 % council, 43 % housing association, 13 % private unfurnished, 3 % private furnished, one record owner-occupied. +- JSA (code 14): 27 records, 63k weighted (VAR2 1 contributory 37,200 over 17 records; VAR2 2 income-based 23,436 over 9). ESA (code 16): 340,271 contributory and 711,810 income-based weighted. Carer's Allowance (code 13): 495 records, 1,011,960 weighted. +- Childcare (`chldcare.tab`): 5,133 spells; 95 % of weighted weekly cost is at registered providers (`registrd = 1`), which is what the spine already keeps. + +## R1. Legacy-benefit decomposition (`tools/diagnose_uk_legacy_benefits.py`; receipt `data/ukds/acceptance/882-uc-repairs/legacy-spine-q-q50f.json`) + +Spine-q, GB, design weights: Housing Benefit reporters 1,209,057 units (£7.15bn reported); by support channel the `frs` channel carries 758k, all renters, and the `spi` channel 451k of which 363k sit in owner-occupied synthetic households; 407k all-pension-age reporting units were drawn into Universal Credit; 362k reporting units sit in households with zero rent; 20k are non-head units. Household mass by channel: `frs` 14.66m, `spi` 14.58m. JSA reporters: 178k contributory (161k in the `spi` channel), 92k income-based. ESA reporters: 269k contributory, 470k income-related. `would_claim_uc` share 0.556 over all units, 0.582 over units with a working-age adult; 4.32m all-pension-age units drawn. + +Q50f, calibrated weights: engine Housing Benefit 235,482 units, £2.13bn (reporters 1,099,378 units, £7.23bn); by age class the engine pays 209,760 all-pension-age, 11,861 mixed-age and 13,861 working-age units; 352,636 all-pension-age reporters were drawn into Universal Credit, 229,456 renting all-pension-age reporters were not. Engine JSA 33,645 contributory and 32,224 income-based; ESA 852k any, 682k contributory, 170k income-related. Paid UC in all-pension-age units: 0 (the draw is inert for UC). + +## R2. Benefit-cap bands (`tools/diagnose_uk_benefit_cap.py`; receipt `data/ukds/acceptance/882-uc-repairs/benefit-cap-q50f.json`) + +Q50f: 111,397 capped GB units (all with a UC award); mean reduction £502 a month, weighted median £257. Bands (units): up to £100 31,483; £100–200 6,689; £200–300 22,125; £300–400 3,118; £400–500 10,933; £500–600 8,267; £600–700 0; £700–800 6,977; £800–900 3,041; £900–1,000 0; £1,000–1,100 0; £1,100–1,200 2,991; £1,200–1,300 0; above £1,300 15,774 (14.2 %, against 0.6 % published). Tail composition: 4.45 children on average (2.36 across all capped), child element £1,350 and housing element £1,346 a month, South East 77 % and North West 23 %. + +## R3. Childcare take-up fit (`tools/fit_uk_uc_childcare_takeup.py`) + +Published calendar-2025 means: single 144,750, couple 34,167 (chronicle `dwp.uc_households_receiving_childcare_element`). Family type is the FRS couple flag the spine persists as `is_married` (famtypb2 5 or 7), the joint-claim analogue; the engine's `family_type` counts two adults aged 18 or over and so classes a lone parent with an 18-year-old dependent child as a couple, which is why the earlier 31.9 percent couple share differs from the 28 percent below. Weighted paid-UC GB units with a positive childcare element: at spine-q design weights single 224,032 and couple 129,438 (rates 0.6461 / 0.2640, receipt `data/ukds/acceptance/882-uc-repairs/uc-childcare-fit-spine-q.json`); at Q50f calibrated weights single 328,231 and couple 127,403 (rates 0.4410 / 0.2682, receipt `data/ukds/acceptance/882-uc-repairs/uc-childcare-fit-q50f.json`). The landed rates are the Q50f ones: the row is evaluated at calibrated weights, and the solver's UC-family reweighting is what moves the base from the design surface. At those rates the expected total is 178,919 against 178,667 published; the mean award is unchanged by construction (£378 a month on Q50f against £402.50). + +## R4. Twins (licensed, this machine, `build_twin.sh`, engine 2.97.0, `data/ukds/acceptance/882-uc-repairs/`) + +- `repaired`: the branch at 97b37c2f (register fix, Part A, Part D, C1), 29 stages, spine gates 17 of 17, rows 113,590 / 61,213 / 52,846 (identical to spine-q's counts; #791's `frs_relationships` is in both this and the control). Build record `repaired/repaired.build.json`; the take-up gate reports `benunit.would_claim_uc` measured over the units with a working-age adult (share 0.559 against 0.55) and `benunit.would_claim_uc_childcare` at 0.398 against the family-type-weighted target. +- `control`: the branch base 51c31438 built from a scratch worktree with its own `uv sync` environment (the first attempt shared the branch's editable install and reproduced the repaired twin byte for byte; discarded), 29 stages, spine gates 17 of 17, the same row counts. Payload diff (`payload_diff.json`): benunit gains `would_claim_uc_childcare` and `would_claim_uc` moves on 9,962 records; person moves only `student_loan_balance` (34 rows); household moves the QRF-imputed consumption, wealth and energy columns downstream of `household_net_income` (the ripple #834 documented), no cash or reported column. + +## R5. Measurements at design weights (GB; the three diagnostics on each twin) + +Repaired twin: +- Legacy benefits: engine Housing Benefit 644,183 units, £4.11bn (reporters 1,217,763 units, £7.15bn); paid by age class 517,492 all-pension-age, 9,646 mixed-age, 117,045 working-age; social rented 538,480, private rented 105,703. The draw marks 2,306 all-pension-age units (the reported-UC anchors only, against 4.32m on spine-q); the share among units with a working-age adult is 0.559. Engine JSA 178,957 contributory and 57,893 income-based; ESA 630,644 any, 270,481 contributory, 360,163 income-related. +- UC elements (paid UC 5,224,967 units): carer 422,506; childcare 352,184 (£1.57bn; the take-up flag is inert at engine 2.97.0, so this is the unconditioned count); LCWRA 1,457,335; housing 3,001,651 (social 1,738,824, private 1,262,827); deductions 2,528,269. +- Benefit cap: 139,402 capped units, mean reduction £444 a month, tail above £1,300 6.6 %; support (positional join of the engine's household order onto the spine's `household_source_id`, re-measured 2026-09-16 after review): 192 capped records from 93 source households, the tail 8 records from 4 households; two bands are empty (£900–1,000, £1,200–1,300), one (£1,000–1,100) rests on a single source household and the other eleven on at least two. The first measurement (176 and 8) joined on float32-cast ids, which the engine's float32 id arrays make lossy above 2^24, and over-stated the distinct households; the repaired-298b twin measures 182 capped records from 88 households with the same tail. The Q50f tail (15,774 units, 14.2 %, four empty bands) is therefore a selection-and-calibration effect on a thin base, not a source one. + +Control twin (the same three tools): +- Legacy benefits: engine Housing Benefit 401,489 units, £2.61bn; paid by age class 280,597 all-pension-age, 8,895 mixed-age, 111,998 working-age; social rented 327,143, private rented 74,347. The draw marks 4,324,979 all-pension-age units; the share of all units is 0.556 and among units with a working-age adult 0.582. JSA, ESA and the benefit-cap figures are identical to the repaired twin (those stages do not read the draw). +- UC elements (paid UC 5,320,794 units): carer 425,757; childcare 353,470; LCWRA 1,480,246; housing 3,039,243; deductions 2,583,856. + +Repaired against control, design weights: +- Housing Benefit paid units +242,694 (+60 %), of which +236,895 all-pension-age; spend £2.61bn → £4.11bn. This is the pension-age gate alone (the SPI-channel reporters in owner-occupied households are untouched). +- Paid UC units 5,320,794 → 5,224,967 (−1.8 %); the elements move in proportion (carer −0.8 %, LCWRA −1.5 %, housing −1.2 %, deductions −2.2 %). Cause: the re-signed contract draws the 0.55 rate over the units with a working-age adult, whose realized share falls from 0.582 (the anchors concentrate there, so the old all-units draw over-filled them) to 0.559. The plan's "UC output unchanged by construction" held only for the pension-age units themselves; the working-age draw moves with the population definition. María's ruling (2026-09-16): keep the rate over the eligible population. It is not a contract-realization mismatch: the entry states 0.55 of units with a working-age adult and the draw realizes 0.559 among them; the old 0.582 was an artefact of a count target taken over ineligible units, and the calibrated UC rows absorb the level. +- `would_claim_uc` flips on 9,962 benefit-unit records: the all-pension-age units set to false and the working-age residual re-drawn to the smaller target. + +## R6. Part B1 on policyengine-uk 2.98.0 (PolicyEngine/policyengine-uk#1857) + +Engine change: `would_claim_carers_allowance` (person, default true) gates Carer's Allowance and the Carer Support Payment; `would_claim_uc_childcare` (benefit unit, default true) gates the childcare element through `uc_childcare_element_claimed`; `is_carer_for_benefits` also holds at 35 or more weekly care hours (the Pension Credit carer addition and the non-dependant deduction exemption follow it; the severe-disability addition stays on receipt). On a frame without the new columns the engine change is inert (both inputs default true, `care_hours` unfilled), so the 2.97.0 control twin remains the control. + +Spine change: `care_hours` from `adult.hourtot` at the band's lower edge (0, 0, 5, 10, 20, 35, 50, 100, 0, 20, 35 for codes 0–10); `would_claim_carers_allowance` = reported Carer's Allowance > 0; the childcare take-up flag from C1 now reaches the element. + +Activation rule for the two register rows (the #921 rule): project each element row onto the Q50f baseline by the design-weight ratio repaired-2.98.0 / control and retire the register entry where the projection sits inside the 25 percent bound of the calendar-2025 target (carer 1,078,514; childcare 178,667); otherwise re-adjudicate to 2026-12-08 on these receipts. + +Twin `repaired-298` (the B1 working tree that became a158d863, engine 2.98.0; 17 of 17 gates): `care_hours` populated on 4.25m weighted persons (35 hours 1.31m, 50 hours 0.29m, 100 hours 0.47m at design weights); paid-UC GB units with a carer element 642,476 against 425,757 on the control (+50.9 %); childcare element 105,903 units, £0.44bn, against 353,470 at rate one (the flag now reaches the element); LCWRA, housing and deductions within 1.5 % of the control; Housing Benefit 643,927 units (as on the 2.97.0 repaired twin). + +Defect found on that twin and fixed in 36b75bde: Carer's Allowance spend moved £3.625bn → £3.750bn (+3.4 %) and recipients 837k → 866k. The root stage sets `would_claim_carers_allowance` from reported receipt, but `hmrc_spi_income_spine` re-imputes `carers_allowance_reported` on the SPI-redrawn rows: 433k weighted redrawn persons kept a true flag over a refilled zero (415k of them at 35 or more care hours, so the engine paid them the allowance) and 388k kept a false flag over a refilled positive receipt. The stage now refreshes the flag from the refilled amount beside its disability refresh (`_refresh_carer_take_up_input`); the twin `repaired-298b` re-measures it. + +Childcare realization on `repaired-298`: with the flag forced on, the rate-one base is 212,920 single (378 records) and 127,874 couple (201 records) at design weights, from 142 and 94 distinct source households; the realized flag share among the base is 0.380 (single) and 0.195 (couple) against the contract rates 0.441 and 0.268. By channel the `frs` rows realize 0.414 and 0.277, inside sampling noise of the rates, while the `spi` rows realize 0.381 and 0.156: SPI-synthetic units copy their donor's flag rather than drawing afresh, so the realized share on a thin base tracks a few donors. The expected count at the contract rates is the quantity the rates control; the realized draw on this frame sits below it by that mechanism. + +Projections onto the Q50f baseline by the design-weight ratio repaired / control: carer 595,807 × 1.509 = 899k against 1,078,514 (−16.6 %, inside the 25 percent bound); childcare 455,634 × 0.2996 = 136.5k against 178,667 (−23.6 %, inside the bound, with the realization caveat above). + +Twin `repaired-298b` (36b75bde, engine 2.98.0, 17 of 17 gates, `882-uc-repairs/repaired-298b/`): the refreshed flag matches the refilled receipt on every row (0 weighted mismatches); Carer's Allowance spend £3.625bn and 837,002 recipients, identical to the control. Paid-UC carer-element units 768,273 (+80 % on the control's 425,757; the first 2.98.0 twin's 642,476 was short because 388k weighted redrawn persons with a refilled receipt carried a stale false flag and so were not carers). Childcare element 116,324 units (against 353,470 at rate one). LCWRA 1,469,803, housing 3,034,531, deductions 2,537,303 (all within 1.8 % of the control); paid UC 5,231,437 (−1.7 %); engine Housing Benefit 643,927 units, £4.10bn. + +Final projections onto the Q50f baseline (design-weight ratio repaired-298b / control): carer 595,807 × 1.8045 = 1,075,100 against 1,078,514 (−0.3 %); childcare 455,634 × 0.3291 = 149,950 against 178,667 (−16.1 %). Both inside the 25 percent bound; the two register rows are retired (9fea08ff). María ruled on 2026-09-16, in review of #937, that the design-weight projection retires the rows; if the calibrated run on this spine fails either row, they go back on the register. diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index eb0a668e12..74f67dd478 100644 --- a/packages/microcosm-build/pyproject.toml +++ b/packages/microcosm-build/pyproject.toml @@ -39,7 +39,7 @@ us = [ # The UK extra adds the rules engine for local metric generation from a # Microcosm UK H5. Target tables remain explicit inputs, and the base package # still does not import policyengine-uk at import time. -uk = ["policyengine-uk>=2.97.0", "h5py>=3", "tables>=3", "openpyxl>=3.1"] +uk = ["policyengine-uk>=2.98.0", "h5py>=3", "tables>=3", "openpyxl>=3.1"] [project.scripts] microcosm-export-us-l0-refit-h5 = "microcosm.build.us_runtime.l0_refit_export:main" 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 130067f329..a3531b458b 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 @@ -5958,6 +5958,9 @@ "rate_key": { "type": "string" }, + "population": { + "type": "string" + }, "seed": { "type": "integer" } diff --git a/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py b/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py index ef79d204c0..0d33a0f805 100644 --- a/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py +++ b/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py @@ -58,25 +58,36 @@ def assign_binary_with_anchored_residual( draws: Sequence[float] | np.ndarray, rate: float, anchor: Sequence[bool] | np.ndarray | None = None, + population: Sequence[bool] | np.ndarray | None = None, ) -> np.ndarray: """Assign a flag while forcing reported-recipient anchors to true. - The target count is ``int(rate * n_units)`` over the full unweighted - population. Anchored overshoot is accepted; the residual fills only - non-anchored rows. + The target count is ``int(rate * n_units)`` over the unweighted + population; ``population`` restricts that population to the rows the + programme can reach (a benefit unit with no adult under State Pension age + cannot claim Universal Credit), so the rate is a share of the reachable + rows and rows outside it are never drawn. Anchored overshoot is accepted, + an anchor outside the population stays true (reported receipt is a fact), + and the residual fills only non-anchored rows inside the population. """ draws = np.asarray(draws, dtype=np.float64) rate = _validate_rate(rate) + if population is None: + population = np.ones(draws.shape, dtype=bool) + else: + population = np.asarray(population, dtype=bool) + if population.shape != draws.shape: + raise ValueError("population and draws must align") if anchor is None: - return draws < rate + return population & (draws < rate) anchor = np.asarray(anchor, dtype=bool) if anchor.shape != draws.shape: raise ValueError("anchor and draws must align") result = anchor.copy() - target = int(rate * len(draws)) - remaining_needed = max(0, target - int(anchor.sum())) - non_anchored = ~anchor + target = int(rate * int(population.sum())) + remaining_needed = max(0, target - int((anchor & population).sum())) + non_anchored = ~anchor & population if remaining_needed == 0 or not non_anchored.any(): return result adjusted = remaining_needed / int(non_anchored.sum()) diff --git a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json index d423986b14..86ea1d58e2 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json @@ -372,12 +372,12 @@ }, { "name": "obr.housing_benefit", - "reason": "The spine carries £2.7bn of housing benefit against the OBR £12.1bn (-78% at initialization on spine-m). Under grain_equal the solver chases the aggregate by multiplying the few HB households (£9.4bn at 512 epochs, £11.4bn at 1500), which halves the effective sample and doubles Glasgow's social-rent count; under uniform it is ignored. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", + "reason": "Frame-side gap, not a weight question, decomposed on 2026-09-16 (spine-q design weights unless stated). FRS 2024-25 reports 1.56m Housing Benefit benefit units, 99.9 percent renters, near the DWP GB caseload (1.94m in January 2025 falling to 1.46m by December); the spine carries 1.21m GB reporters (GBP 7.15bn reported) because the SPI support channel re-imputes benefit receipt onto its synthetic households without tenure coherence: 363k of its 449k reporting units sit in owner-occupied households and can never be paid. The engine then pays Housing Benefit only to reporting units with a rented tenure, not drawn into Universal Credit, under the capital limit and carrying the household's rent (rent is the household head's benefit unit's alone; claims_all_entitled_benefits collapses to a scalar, so there is no take-up lever): 407k all-pension-age reporting units were drawn into Universal Credit by the 55 percent take-up draw although they cannot claim it, and the working-age remainder is pared by the means test. On the Q50f candidate the engine pays 235k units and GBP 2.13bn against the OBR GBP 12.10bn (-82 percent); the U8 lever receipt already showed the same lever from the other side (0.85 take-up moved this row from -0.0 to -52.4 percent). This PR gates the draw to units with an adult under State Pension age; the SPI-channel tenure defect is a #840/#145 item; the OBR line also carries supported and temporary accommodation the frame has no concept for. Re-measured on every evaluation with the three Housing Benefit caseload rows; renew or retire on the next spine that carries the SPI repair.", "tracking": "microcosm#867", "approved_by": "juaristi22", - "adjudication": "microcosm#762 (A16, ruling 2026-09-03; receipts R8-R10 in experiments/762-uk-rowwise-candidate-receipts.md)", - "approved_on": "2026-09-03", - "expires_on": "2026-10-03" + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R1 and R5, tools/diagnose_uk_legacy_benefits.py on spine-q, the Q50f candidate, FRS 2024-25 and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" }, { "name": "slc.borrowers.plan_2_liable", @@ -399,12 +399,12 @@ }, { "name": "dwp.jsa_claimants", - "reason": "The spine carries 199k JSA claimants against the DWP 71k (2.8x at initialization on spine-m; -13% after the solve): legacy-benefit reporting in the frame after the UC migration. Unreachable by reweighting within the stretch bound: a frame-side gap, not a weight question; while bound it pulls the joint solve toward weight concentration (housing benefit alone triples the max/median ratio under grain_equal). One-month window: correct the spine or re-adjudicate.", + "reason": "Frame-side gap, not a weight question, decomposed on 2026-09-16. DWP's 71,000 claimants at August 2025 is New Style (contributory) JSA after the August 2025 methodology change that removed inactive claims. FRS 2024-25 reports 63k JSA benefit units at design weights (37k contributory, 23k income-based; 27 records), so the source reproduces the DWP count. The spine carries 178k contributory and 92k income-based reporters (GB, design weights) because the SPI support channel re-imputes benefit receipt onto its synthetic households: 161k of the 181k contributory reporters sit there. The stage-6 legacy_jobseeker_proxy (967k) is consumed by nothing. On the Q50f candidate the row measures 34k against 71,000 (-52 percent) after the solve pares the reporters to fit the OBR JSA spend line. The repair is the SPI channel's benefit-receipt fill (#840/#145), not a JSA rule; the income-based reporters are a legacy-scheme residual DWP no longer counts. Renew or retire on the next spine that carries the SPI repair.", "tracking": "microcosm#869", "approved_by": "juaristi22", - "adjudication": "microcosm#762 (A16, ruling 2026-09-03; receipts R8-R10 in experiments/762-uk-rowwise-candidate-receipts.md)", - "approved_on": "2026-09-03", - "expires_on": "2026-10-03" + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R1 and R5, tools/diagnose_uk_legacy_benefits.py on spine-q, the Q50f candidate, FRS 2024-25 and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" }, { "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_27_600_to_28_800", @@ -452,8 +452,8 @@ "expires_on": "2026-11-26" }, { - "name": "dwp/uc/elements/carer", - "reason": "Model concept gap, not a weight question: the engine's carer condition is Carer's Allowance receipt (is_carer_for_benefits = receives_carer_benefit) while the UC carer element needs 35 hours a week of care for a qualifying-benefit recipient with no Carer's Allowance requirement; the spine never fills the engine's care_hours input. Measured at the 10 September 2026 baseline against the bound calendar-2025 paid target: Q50f 595,807 / D2 602,749 paid-UC benefit units against 1,078,514 (-45%). Fitting it would move half the carriers' weight against a definition mismatch. Repair: care hours from the FRS care file and a regulation-based carer condition, then activation.", + "name": "dwp.uc.households_housing_element", + "reason": "Structural bias, not a weight question: DWP's any-tenure housing-entitlement count is social plus private plus 'Other or unknown' tenure, and the model has no other/unknown tenure (uc_housing_costs_element pays only council, housing-association and private renters), so the model's any-tenure count is identically the social plus private rows, which are bound and fit; the row overstates the reachable total by the other/unknown cell, 112,518 of 4,037,650 in calendar 2025 (2.8 percent; the published any-tenure mean, which the three tenure cells reproduce to the unit). Fitting all three housing rows would ask the solver to over-fill the tenure rows by that amount. Measured on every evaluation as the housing-element caseload diagnostic (Q50f -0.8 percent, D2 +0.6 percent at the baseline).", "tracking": "microcosm#882", "approved_by": "juaristi22", "adjudication": "microcosm#882 (element sizing at the 10 September 2026 baseline, docs/uk-uc-baseline-2026-09-10.md; María's sign-off in review of microcosm#921 on 2026-09-15, setting the one-month window)", @@ -461,22 +461,148 @@ "expires_on": "2026-10-15" }, { - "name": "dwp/uc/elements/childcare", - "reason": "Model concept gap, not a weight question: the engine pays the childcare element to every benefit unit where all adults work and childcare costs are reported, with no take-up or incidence lever, while DWP publishes 160-193k households a month receiving it in 2025. Measured at the 10 September 2026 baseline against the bound calendar-2025 target: Q50f 455,634 / D2 451,401 paid-UC benefit units (GBP 2.06bn) against 178,667 published (+155%). Repair: an incidence stage modelled on uc_deduction_attributes drawing to the published count, then activation.", + "name": "dwp.hb.households", + "reason": "Housing Benefit caseload diagnostic (all client types and tenures, calendar-2025 mean of the Stat-Xplore Total items), measured on every evaluation but held out of the objective (#882, microcosm#867): the frame cannot reach the DWP caseload for the mechanisms signed on obr.housing_benefit (SPI-channel reporters in owner-occupied synthetic households, reporters drawn into Universal Credit, head-only rent, the means test). Q50f pays 235k GB units against a calendar-2025 published mean near 1.70m (-86 percent). Fitting it would move weight onto the few reporting units the engine pays. Retire when the row sits inside the 25 percent bound at the baseline.", + "tracking": "microcosm#867", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R1 and R5, tools/diagnose_uk_legacy_benefits.py on spine-q, the Q50f candidate, FRS 2024-25 and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.hb.households_social_rented", + "reason": "Housing Benefit caseload diagnostic (social rented sector), measured on every evaluation but held out of the objective (#882, microcosm#867): the frame cannot reach the DWP caseload for the mechanisms signed on obr.housing_benefit (SPI-channel reporters in owner-occupied synthetic households, reporters drawn into Universal Credit, head-only rent, the means test). Q50f pays 155k social-rented units against a calendar-2025 published mean near 1.39m. Fitting it would move weight onto the few reporting units the engine pays. Retire when the row sits inside the 25 percent bound at the baseline.", + "tracking": "microcosm#867", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R1 and R5, tools/diagnose_uk_legacy_benefits.py on spine-q, the Q50f candidate, FRS 2024-25 and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.hb.households_private_rented", + "reason": "Housing Benefit caseload diagnostic (private rented sector), measured on every evaluation but held out of the objective (#882, microcosm#867): the frame cannot reach the DWP caseload for the mechanisms signed on obr.housing_benefit (SPI-channel reporters in owner-occupied synthetic households, reporters drawn into Universal Credit, head-only rent, the means test). Q50f pays 81k private-rented units against a calendar-2025 published mean near 0.31m. Fitting it would move weight onto the few reporting units the engine pays. Retire when the row sits inside the 25 percent bound at the baseline.", + "tracking": "microcosm#867", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R1 and R5, tools/diagnose_uk_legacy_benefits.py on spine-q, the Q50f candidate, FRS 2024-25 and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_100_01_to_200", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 100 .01 to 200 measures 6,689 against 24,274 (-72 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", "tracking": "microcosm#882", "approved_by": "juaristi22", - "adjudication": "microcosm#882 (element sizing at the 10 September 2026 baseline, docs/uk-uc-baseline-2026-09-10.md; María's sign-off in review of microcosm#921 on 2026-09-15, setting the one-month window)", - "approved_on": "2026-09-15", - "expires_on": "2026-10-15" + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" }, { - "name": "dwp/uc/elements/housing", - "reason": "Structural bias, not a weight question: DWP's any-tenure housing-entitlement count is social plus private plus 'Other or unknown' tenure, and the model has no other/unknown tenure (uc_housing_costs_element pays only council, housing-association and private renters), so the model's any-tenure count is identically the social plus private rows, which are bound and fit; the row overstates the reachable total by the other/unknown cell, 112,518 of 4,037,650 in calendar 2025 (2.8 percent; the published any-tenure mean, which the three tenure cells reproduce to the unit). Fitting all three housing rows would ask the solver to over-fill the tenure rows by that amount. Measured on every evaluation as the housing-element caseload diagnostic (Q50f -0.8 percent, D2 +0.6 percent at the baseline).", + "name": "dwp.benefit_cap.capped_households_200_01_to_300", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 200 .01 to 300 measures 22,125 against 16,240 (+36 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", "tracking": "microcosm#882", "approved_by": "juaristi22", - "adjudication": "microcosm#882 (element sizing at the 10 September 2026 baseline, docs/uk-uc-baseline-2026-09-10.md; María's sign-off in review of microcosm#921 on 2026-09-15, setting the one-month window)", - "approved_on": "2026-09-15", - "expires_on": "2026-10-15" + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_300_01_to_400", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 300 .01 to 400 measures 3,118 against 10,562 (-70 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_400_01_to_500", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 400 .01 to 500 measures 10,933 against 7,813 (+40 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_500_01_to_600", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 500 .01 to 600 measures 8,267 against 4,431 (+87 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_600_01_to_700", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 600 .01 to 700 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_700_01_to_800", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 700 .01 to 800 measures 6,977 against 1,902 (+267 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_800_01_to_900", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 800 .01 to 900 measures 3,041 against 1,328 (+129 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_900_01_to_1000", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 900 .01 to 1000 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 1000 .01 to 1100 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 1100 .01 to 1200 measures 2,991 against 380 (+687 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 1200 .01 to 1300 measures no model unit in the band on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" + }, + { + "name": "dwp.benefit_cap.capped_households_1300_01_and_above", + "reason": "Support, not a weight question (#882): the benefit-cap amount band 1300 .01 and above measures 15,774 against 627 (+2416 percent) on the Q50f candidate at calibrated weights (November 2025 DWP Table 4, GB). The capped population rests on a handful of source households cloned many times: four of the fourteen bands are empty and 14.2 percent of capped units sit above GBP 1,300 a month against 0.6 percent published, a tail of large families outside London (4.45 children on average, full child elements, housing element near GBP 1,350 a month). The bound total (dwp.benefit_cap.capped_households) and the up-to-GBP-100 band, the one band inside the 25 percent bound, stay in the objective; fitting this band would move weight onto those clones (the #762 zero-band lesson). Measured on every evaluation with tools/diagnose_uk_benefit_cap.py; retire when support exists and the band sits inside the bound.", + "tracking": "microcosm#882", + "approved_by": "juaristi22", + "adjudication": "microcosm#882 (the 2026-09-16 status comment, issuecomment-5694598278, whose next-steps list this repair works, and the repairs plan María approved on 2026-09-16 with her population ruling on the U8 take-up entry; approver as author per the register convention). Expiry 2026-12-08 aligns the whole UC-repair tranche with the zero-band clock already on the register (microcosm#877), so the next spine re-adjudicates them together. Receipts: experiments/882-uc-repairs-receipts.md (R2 and R5, tools/diagnose_uk_benefit_cap.py on the Q50f candidate and the twins).", + "approved_on": "2026-09-16", + "expires_on": "2026-12-08" } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 356ac7d426..db07315f2c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -596,6 +596,7 @@ "benunit.uc_deduction_type_random_draw", "benunit.uc_latent_deduction_rate", "benunit.uc_reported_capital", + "benunit.would_claim_uc_childcare", "household.bus_fare_spending", "household.bus_subsidy_spending", "household.cash_isa", @@ -625,6 +626,7 @@ "person.admitted_patient_visits", "person.age_started_or_accepted_current_education_or_training", "person.attends_private_school_random_draw", + "person.care_hours", "person.charitable_investment_gifts", "person.dla_m_category", "person.dla_sc_category", @@ -652,6 +654,7 @@ "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", + "person.would_claim_carers_allowance", "person.would_claim_marriage_allowance", "person.would_claim_scp" ], diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json index 713c2e5192..d3b5b3e9b5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 614, + "compiled_count": 631, "counts_by_kind": { "calibration_drift": 370, "fixture_only": 71, - "ledger_only": 48 + "ledger_only": 65 }, - "difference_count": 489, + "difference_count": 506, "differences": [ { "fixture_value": 1608000.0, @@ -560,6 +560,125 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 497.0, + "name": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 24274.0, + "name": "dwp.benefit_cap.capped_households_100_01_to_200", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 380.0, + "name": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 239.0, + "name": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 627.0, + "name": "dwp.benefit_cap.capped_households_1300_01_and_above", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 16240.0, + "name": "dwp.benefit_cap.capped_households_200_01_to_300", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 10562.0, + "name": "dwp.benefit_cap.capped_households_300_01_to_400", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 7813.0, + "name": "dwp.benefit_cap.capped_households_400_01_to_500", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 4431.0, + "name": "dwp.benefit_cap.capped_households_500_01_to_600", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 3116.0, + "name": "dwp.benefit_cap.capped_households_600_01_to_700", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 1902.0, + "name": "dwp.benefit_cap.capped_households_700_01_to_800", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 1328.0, + "name": "dwp.benefit_cap.capped_households_800_01_to_900", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 824.0, + "name": "dwp.benefit_cap.capped_households_900_01_to_1000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 38402.0, + "name": "dwp.benefit_cap.capped_households_up_to_100", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 1685702.9166666667, + "name": "dwp.hb.households", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 304422.6666666667, + "name": "dwp.hb.households_private_rented", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 1381089.75, + "name": "dwp.hb.households_social_rented", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 1078514.5, diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json index 2188ff0cfe..54378b0dd2 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_production_2023_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 445, + "compiled_count": 448, "counts_by_kind": { "calibration_drift": 120, "fixture_only": 17, - "ledger_only": 313 + "ledger_only": 316 }, - "difference_count": 450, + "difference_count": 453, "differences": [ { "fixture_value": 82000.0, @@ -175,6 +175,27 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 1685702.9166666667, + "name": "dwp.hb.households", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 304422.6666666667, + "name": "dwp.hb.households_private_rented", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 1381089.75, + "name": "dwp.hb.households_social_rented", + "period": 2023, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 6197311.0, diff --git a/packages/microcosm-build/src/microcosm/build/uk/local_target_references.json b/packages/microcosm-build/src/microcosm/build/uk/local_target_references.json index c6b34de00e..d2fc628cc2 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/local_target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/local_target_references.json @@ -198,6 +198,10 @@ "welshgov.council_tax_stock": { "provider_id": "welshgov", "label": "Council Tax dwelling stock" + }, + "dwp.housing_benefit": { + "provider_id": "dwp", + "label": "Housing Benefit" } }, "target_categories": { 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 97e7bcf5c6..06c92806c2 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 @@ -473,7 +473,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2025, Table 2.1a", "survey": "HMRC Capital Gains Tax statistics Table 2.1a and Advani-Summers capital-gains incidence" @@ -496,7 +496,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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" @@ -525,7 +525,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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" @@ -545,7 +545,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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" @@ -576,7 +576,7 @@ "superseded_by": { "reason": "The FRS spine build executes hmrc_cgt_gains_spine, which applies the same HMRC Table 3 amounts redraw directly in source_stages.json before calibration.", "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "stage": "hmrc_cgt_gains_spine" } }, @@ -596,7 +596,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024" @@ -610,7 +610,7 @@ "base_candidate_tier": "frs", "calibration_permitted": false, "canonical_source_manifest": "source_stages.json", - "canonical_source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "canonical_source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "effective_mass_requirements": { "charitable_investment_gifts": { "mass_share_denominator": "all_person_effective_mass", @@ -693,7 +693,7 @@ "superseded_by": { "reason": "The FRS spine build executes hmrc_spi_income_spine, which supersedes the June retained-leaves/hmrc_spi_income pair inside source_stages.json.", "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "stage": "hmrc_spi_income_spine" } }, @@ -727,7 +727,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "source_vintages": { "source": "UK Data Service SN 9468 Living Costs and Food Survey 2023-24 household/person tabs and the vendored Chronicle facts for NEED 2023 mean kWh, the FY2024-25 Ofgem cap levels, road-fuel outturn (DESNZ, HMRC, ONS), the DfT VEH1103 licensed-car stock, NTS bus use and the DfT and devolved bus finance tables.", "survey": "Living Costs and Food Survey 2023-24" @@ -748,7 +748,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "source_vintages": { "source": "MHCLG dwellings and ONS UK House Price Index December 2025 regional average prices.", "survey": "Public regional property reference" @@ -772,7 +772,7 @@ "employee_pension_contributions" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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" @@ -794,7 +794,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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" @@ -829,7 +829,7 @@ "required_mass_change_reason": "E5 source-stage transform preserves household rows and typed household weights; total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "5cfb207204a2ff3c26ac525deeb67f6200d3024ee97e9fc54ab05c6d52ef3fd9", + "source_manifest_sha256": "5689601d1e3656655bc7eadc9c77068928138b03e1a24b171893025a0eee12b4", "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 c70e455bee..584d569c88 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -233,7 +233,7 @@ }, { "kind": "map_coded_amounts", - "scope": "region, tenure, accommodation, council tax band, benefit-code amount mappings" + "scope": "region, tenure, accommodation, council tax band, benefit-code amount mappings; adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance" }, { "kind": "annualize_periodic_amounts", @@ -248,6 +248,7 @@ "gender", "marital_status", "hours_worked", + "care_hours", "is_household_head", "is_benunit_head", "is_parent", @@ -289,6 +290,7 @@ "dla_m_reported", "iidb_reported", "carers_allowance_reported", + "would_claim_carers_allowance", "sda_reported", "afcs_reported", "ssmg_reported", @@ -352,9 +354,10 @@ "mortgage_capital_repayment", "structural_insurance_payments", "housing_service_charges", - "external_child_payments" + "external_child_payments", + "care_hours" ], - "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity." + "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)." }, { "stage": "age_tail", @@ -942,6 +945,14 @@ "universal_credit_reported_anchor": "universal_credit_reported" } }, + { + "kind": "aggregate_person_to_benunit", + "method": "any_adult_under_state_pension_age", + "consumed_only": true, + "aggregates": { + "uc_age_eligible": "age" + } + }, { "kind": "assign_binary_with_anchored_residual", "output": "would_claim_child_benefit", @@ -971,6 +982,7 @@ "draw": "would_claim_uc", "rate_key": "universal_credit", "anchor": "universal_credit_reported_anchor", + "population": "uc_age_eligible", "seed": 0 }, { @@ -1001,6 +1013,22 @@ "rate_key": "targeted_childcare", "seed": 0 }, + { + "kind": "assign_binary_from_banded_rates", + "output": "would_claim_uc_childcare", + "draw": "would_claim_uc_childcare", + "band_column": "is_married", + "bands": [ + { + "upper_exclusive": 1, + "rate_key": "uc_childcare_single" + }, + { + "rate_key": "uc_childcare_couple" + } + ], + "seed": 0 + }, { "kind": "assign_clipped_normal", "output": "maximum_extended_childcare_hours_usage", @@ -1018,12 +1046,13 @@ "would_claim_extended_childcare", "would_claim_universal_childcare", "would_claim_targeted_childcare", + "would_claim_uc_childcare", "maximum_extended_childcare_hours_usage" ], "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only." + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with an adult under State Pension age (66 for everyone who can reach it in this release; the engine lockstep test pins it), so the contract rate is a share of those units, an anchor outside them stays true, and a unit with every adult at or over State Pension age is never drawn; its reported Housing Benefit is no longer zeroed by a claim it cannot make (#882, microcosm#867)." }, { "stage": "frs_person_draws", 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 1b43578443..2b28501787 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -206,7 +206,7 @@ stages: - kind: map_columns scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence)' - kind: map_coded_amounts - scope: region, tenure, accommodation, council tax band, benefit-code amount mappings + scope: region, tenure, accommodation, council tax band, benefit-code amount mappings; adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance - kind: annualize_periodic_amounts weeks_in_year: 52.17857142857143 outputs: @@ -217,6 +217,7 @@ stages: - gender - marital_status - hours_worked + - care_hours - is_household_head - is_benunit_head - is_parent @@ -258,6 +259,7 @@ stages: - dla_m_reported - iidb_reported - carers_allowance_reported + - would_claim_carers_allowance - sda_reported - afcs_reported - ssmg_reported @@ -321,7 +323,8 @@ stages: - structural_insurance_payments - housing_service_charges - external_child_payments - notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. + - care_hours + notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882). - stage: age_tail survey: Family Resources Survey 2024-25 and ONS mid-year population estimates source: Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population). @@ -716,6 +719,11 @@ stages: child_benefit_reported_anchor: child_benefit_reported pension_credit_reported_anchor: pension_credit_reported universal_credit_reported_anchor: universal_credit_reported + - kind: aggregate_person_to_benunit + method: any_adult_under_state_pension_age + consumed_only: true + aggregates: + uc_age_eligible: age - kind: assign_binary_with_anchored_residual output: would_claim_child_benefit draw: would_claim_child_benefit @@ -738,6 +746,7 @@ stages: draw: would_claim_uc rate_key: universal_credit anchor: universal_credit_reported_anchor + population: uc_age_eligible seed: 0 - kind: assign_binary_from_rate output: would_claim_tfc @@ -759,6 +768,15 @@ stages: draw: would_claim_targeted_childcare rate_key: targeted_childcare seed: 0 + - kind: assign_binary_from_banded_rates + output: would_claim_uc_childcare + draw: would_claim_uc_childcare + band_column: is_married + bands: + - upper_exclusive: 1 + rate_key: uc_childcare_single + - rate_key: uc_childcare_couple + seed: 0 - kind: assign_clipped_normal output: maximum_extended_childcare_hours_usage draw: maximum_extended_childcare_hours_usage @@ -773,10 +791,11 @@ stages: - would_claim_extended_childcare - would_claim_universal_childcare - would_claim_targeted_childcare + - would_claim_uc_childcare - maximum_extended_childcare_hours_usage nonnegative_outputs: - maximum_extended_childcare_hours_usage - notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. + notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with an adult under State Pension age (66 for everyone who can reach it in this release; the engine lockstep test pins it), so the contract rate is a share of those units, an anchor outside them stays true, and a unit with every adult at or over State Pension age is never drawn; its reported Housing Benefit is no longer zeroed by a claim it cannot make (#882, microcosm#867). - stage: frs_person_draws survey: Family Resources Survey 2024-25 source: Sourced UK take-up contract rates and identity-keyed deterministic draws. diff --git a/packages/microcosm-build/src/microcosm/build/uk/spine_swap_signed_differences.json b/packages/microcosm-build/src/microcosm/build/uk/spine_swap_signed_differences.json index 0b2b3c8704..4a0131272d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spine_swap_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/spine_swap_signed_differences.json @@ -770,6 +770,81 @@ }, "magnitude_provenance": "max_abs_delta measured 2026-09-05 on the L5 rebuilt twin (sha256 37280e7c99af3f4d4f176c42722c56e1fd2c5170de8d3d97ba767db46de40b08) against the packaged reference, rounded up at 1e-4 grain." } + }, + { + "id": "uc-childcare-take-up-net-new-column", + "class": "net_new_column", + "scope": { + "surface": "nonzero_shares", + "columns": [ + "would_claim_uc_childcare" + ], + "entities": [ + "benunit" + ] + }, + "expectation": "column_missing_in_reference", + "magnitude_evidence": "The frs_take_up stage draws would_claim_uc_childcare, the Universal Credit childcare-element take-up flag, at one contract rate per family type (single 0.441, couple 0.2682, fitted on the Q50f candidate so the expected paid-UC count equals DWP's calendar-2025 households receiving the element). The pinned incumbent has no such column: it pays the element to every eligible unit. The column is present in the candidate and absent from the reference, coverage the spine adds rather than a divergence in a shared column; it is an engine input from policyengine-uk 2.98.0 (PolicyEngine/policyengine-uk#1857), the floor this branch sets (#882). SPI-synthetic benefit units are cloned from donors after the draw and inherit the donor's flag, so the realized count on a thin base sits below the expected count the rates control (receipts R6).", + "evidence": "experiments/882-uc-repairs-receipts.md#r3-childcare-take-up-fit-toolsfit_uk_uc_childcare_takeuppy", + "adjudicator": "juaristi22", + "adjudicated_on": "2026-09-16", + "quantitative": { + "structural": { + "expected_columns": [ + "would_claim_uc_childcare" + ] + } + } + }, + { + "id": "care-hours-net-new-column", + "class": "net_new_column", + "scope": { + "surface": "nonzero_shares", + "columns": [ + "care_hours" + ], + "entities": [ + "person" + ] + }, + "expectation": "column_missing_in_reference", + "magnitude_evidence": "The frs_spine stage fills care_hours, the engine's weekly care-hours input, from the FRS adult HOURTOT band code at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line, which is_carer_for_benefits tests from policyengine-uk 2.98.0). The pinned incumbent never populates it, so the column is present in the candidate and absent from the reference: coverage the spine adds rather than a divergence in a shared column (#882).", + "evidence": "experiments/882-uc-repairs-receipts.md#r0-tape-receipts-frs-2024-25-gross4-design-weights", + "adjudicator": "juaristi22", + "adjudicated_on": "2026-09-16", + "quantitative": { + "structural": { + "expected_columns": [ + "care_hours" + ] + } + } + }, + { + "id": "would-claim-carers-allowance-net-new-column", + "class": "net_new_column", + "scope": { + "surface": "nonzero_shares", + "columns": [ + "would_claim_carers_allowance" + ], + "entities": [ + "person" + ] + }, + "expectation": "column_missing_in_reference", + "magnitude_evidence": "The frs_spine stage sets would_claim_carers_allowance from positive reported Carer's Allowance, so the engine (policyengine-uk 2.98.0) keeps the allowance on reported claimants while care_hours qualifies carers for the Universal Credit carer element. The pinned incumbent has no such column; the engine's default is true, so the incumbent's calculator path is the one the candidate departs from only where receipt is absent (#882).", + "evidence": "experiments/882-uc-repairs-receipts.md#r0-tape-receipts-frs-2024-25-gross4-design-weights", + "adjudicator": "juaristi22", + "adjudicated_on": "2026-09-16", + "quantitative": { + "structural": { + "expected_columns": [ + "would_claim_carers_allowance" + ] + } + } } ] } 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 1a0d2de5f5..d4df820960 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 @@ -69,13 +69,14 @@ "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.", + "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.", "status": "frozen_by_adjudication", "freeze": { - "decision": "U8 (re-opened and re-frozen by the 2026-08-26 lever run)", + "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, rate unchanged)", "followup": "uk-data#452" } - } + }, + "population": "benefit units with an adult under State Pension age" }, { "key": "marriage_allowance", @@ -260,6 +261,106 @@ "status": "frozen parity value" } }, + { + "key": "uc_childcare_single", + "output": "would_claim_uc_childcare", + "entity": "benunit", + "population": "single benefit units (FRS famtypb2 couple flag persisted as is_married)", + "values": { + "2024-04-06": 0.441 + }, + "source": { + "source": "DWP Universal Credit childcare element statistics to May 2026, Table 1 (households receiving the childcare element by family type), calendar-2025 mean via chronicle#260", + "agency": "PolicyEngine", + "citation": "Share of single benefit units that the engine would pay the childcare element (paid UC award, work condition met, registered childcare costs) which claim it, fitted so the expected count equals the published calendar-2025 mean (144,750 households) at the Q50f calibrated weights (#882). Realization caveat: frs_take_up draws the flag at stage 10, and the SPI support channel (stage 20) clones benefit-unit rows wholesale, so synthetic units inherit their donor's flag rather than drawing afresh; a realized count on a thin base can sit below rate times base (experiments/882-uc-repairs-receipts.md R6: 116k realized against 150k expected on the repaired twin), which biases the realized childcare count low and makes the retirement projection conservative.", + "url": "https://www.gov.uk/government/collections/universal-credit-statistics", + "status": "fitted_offline" + }, + "fitting_receipt": { + "tool": "fit_uk_uc_childcare_takeup", + "source": "tools/fit_uk_uc_childcare_takeup.py (microcosm#882, fitted on the Q50f candidate at its calibrated weights, engine 2.98.0; the spine-q design-weight fit is recorded in experiments/882-uc-repairs-receipts.md R3)", + "tool_version": 1, + "receipt_sha256": "dd408687ca3078b36f220154d2f6e6bf4dd46f10833ea409857b3951adc8674a", + "seed": 0, + "objective": "expected_count", + "engine_version": "2.98.0", + "model_period": 2025, + "input_h5": "data/ukds/acceptance/355-dataset-size/spine-q/f100-k15-h55000-e2000-p50-f001-s42/microcosm_uk_2025_local.h5", + "input_sha256": "838d1c4a05fea531b75bd76e89585dd4ac190198808fe665ba14dc4a6e83a510", + "ledger_facts_sha256": "3e7d5a4fe18be9e5a19e5475a6e9f91a5d5b0ab827faedccdc755584957e59e7", + "stochastic_contract_sha256_at_fit": "be07b7af00bfbcbc037a07d63f793add17fbd8bba745c302abc06c6dbaa5d569", + "basis": "rate = published calendar-year mean / paid-UC GB benefit units with a positive childcare element of that family type, weighted as the input H5 carries them (a calibrated candidate fits at its calibrated weights, a spine at design weights); one target per rate, so the expected-count objective is exact and needs no optimizer", + "published_means": { + "couple": 34166.666666666664, + "single": 144750.0 + }, + "achieved": { + "dwp.uc.households_childcare_element_couple": { + "base_at_rate_one": 127403.392, + "ratio": 1.0001, + "target": 34166.667, + "value": 34169.59 + }, + "dwp.uc.households_childcare_element_single": { + "base_at_rate_one": 328230.993, + "ratio": 1.0, + "target": 144750.0, + "value": 144749.868 + } + }, + "status": "fitted at the input's weights; realized counts are measured on the twins in the receipts" + } + }, + { + "key": "uc_childcare_couple", + "output": "would_claim_uc_childcare", + "entity": "benunit", + "population": "couple benefit units (FRS famtypb2 couple flag persisted as is_married)", + "values": { + "2024-04-06": 0.2682 + }, + "source": { + "source": "DWP Universal Credit childcare element statistics to May 2026, Table 1 (households receiving the childcare element by family type), calendar-2025 mean via chronicle#260", + "agency": "PolicyEngine", + "citation": "Share of couple benefit units that the engine would pay the childcare element (paid UC award, work condition met, registered childcare costs) which claim it, fitted so the expected count equals the published calendar-2025 mean (34,167 households) at the Q50f calibrated weights (#882). Realization caveat: frs_take_up draws the flag at stage 10, and the SPI support channel (stage 20) clones benefit-unit rows wholesale, so synthetic units inherit their donor's flag rather than drawing afresh; a realized count on a thin base can sit below rate times base (experiments/882-uc-repairs-receipts.md R6: 116k realized against 150k expected on the repaired twin), which biases the realized childcare count low and makes the retirement projection conservative.", + "url": "https://www.gov.uk/government/collections/universal-credit-statistics", + "status": "fitted_offline" + }, + "fitting_receipt": { + "tool": "fit_uk_uc_childcare_takeup", + "source": "tools/fit_uk_uc_childcare_takeup.py (microcosm#882, fitted on the Q50f candidate at its calibrated weights, engine 2.98.0; the spine-q design-weight fit is recorded in experiments/882-uc-repairs-receipts.md R3)", + "tool_version": 1, + "receipt_sha256": "dd408687ca3078b36f220154d2f6e6bf4dd46f10833ea409857b3951adc8674a", + "seed": 0, + "objective": "expected_count", + "engine_version": "2.98.0", + "model_period": 2025, + "input_h5": "data/ukds/acceptance/355-dataset-size/spine-q/f100-k15-h55000-e2000-p50-f001-s42/microcosm_uk_2025_local.h5", + "input_sha256": "838d1c4a05fea531b75bd76e89585dd4ac190198808fe665ba14dc4a6e83a510", + "ledger_facts_sha256": "3e7d5a4fe18be9e5a19e5475a6e9f91a5d5b0ab827faedccdc755584957e59e7", + "stochastic_contract_sha256_at_fit": "be07b7af00bfbcbc037a07d63f793add17fbd8bba745c302abc06c6dbaa5d569", + "basis": "rate = published calendar-year mean / paid-UC GB benefit units with a positive childcare element of that family type, weighted as the input H5 carries them (a calibrated candidate fits at its calibrated weights, a spine at design weights); one target per rate, so the expected-count objective is exact and needs no optimizer", + "published_means": { + "couple": 34166.666666666664, + "single": 144750.0 + }, + "achieved": { + "dwp.uc.households_childcare_element_couple": { + "base_at_rate_one": 127403.392, + "ratio": 1.0001, + "target": 34166.667, + "value": 34169.59 + }, + "dwp.uc.households_childcare_element_single": { + "base_at_rate_one": 328230.993, + "ratio": 1.0, + "target": 144750.0, + "value": 144749.868 + } + }, + "status": "fitted at the input's weights; realized counts are measured on the twins in the receipts" + } + }, { "key": "scp_under_6", "output": "would_claim_scp", diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json index a9ff3db08e..ca57f9f9e3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json @@ -1,11 +1,11 @@ { "source_fact_feed": "chronicle-uk-ec200856-consumer-facts", "target_period": 2025, - "candidate_count": 629, - "contract_target_count": 230, - "active_reference_count": 614, + "candidate_count": 646, + "contract_target_count": 247, + "active_reference_count": 631, "status_counts": { - "active": 614, + "active": 631, "no_fact_at_or_before_period": 7, "signed_excluded": 8 }, @@ -2027,6 +2027,74 @@ "obr.universal_credit": { "geography_level": "country", "geography_id": "K02000001" + }, + "dwp.hb.households": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.hb.households_social_rented": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.hb.households_private_rented": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_up_to_100": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_100_01_to_200": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_200_01_to_300": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_300_01_to_400": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_400_01_to_500": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_500_01_to_600": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_600_01_to_700": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_700_01_to_800": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_800_01_to_900": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_900_01_to_1000": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_1000_01_to_1100": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_1100_01_to_1200": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_1200_01_to_1300": { + "geography_level": "country", + "geography_id": "K03000001" + }, + "dwp.benefit_cap.capped_households_1300_01_and_above": { + "geography_level": "country", + "geography_id": "K03000001" } }, "genuine_sum_residue": [ @@ -4902,6 +4970,76 @@ "name": "nithc.public_transport_support", "from": "2024", "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_up_to_100", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_100_01_to_200", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_200_01_to_300", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_300_01_to_400", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_400_01_to_500", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_500_01_to_600", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_600_01_to_700", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_700_01_to_800", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_800_01_to_900", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_900_01_to_1000", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "from": "2025-11", + "to": "2025" + }, + { + "name": "dwp.benefit_cap.capped_households_1300_01_and_above", + "from": "2025-11", + "to": "2025" } ], 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9b8c87ae48..cd1e48295d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -198,6 +198,10 @@ "welshgov.council_tax_stock": { "provider_id": "welshgov", "label": "Council Tax dwelling stock" + }, + "dwp.housing_benefit": { + "provider_id": "dwp", + "label": "Housing Benefit" } }, "target_categories": { @@ -210,9 +214,26 @@ "dft.bus_net_support.england": "dft.local_bus_finances", "dft.bus_net_support.london": "dft.local_bus_finances", "dwp.benefit_cap.capped_households": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_1000_01_to_1100": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_100_01_to_200": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_1100_01_to_1200": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_1200_01_to_1300": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_1300_01_and_above": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_200_01_to_300": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_300_01_to_400": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_400_01_to_500": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_500_01_to_600": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_600_01_to_700": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_700_01_to_800": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_800_01_to_900": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_900_01_to_1000": "dwp.benefit_cap", + "dwp.benefit_cap.capped_households_up_to_100": "dwp.benefit_cap", "dwp.esa_claimants": "dwp.legacy_benefits", "dwp.esa_contrib_claimants": "dwp.legacy_benefits", "dwp.esa_income_claimants": "dwp.legacy_benefits", + "dwp.hb.households": "dwp.housing_benefit", + "dwp.hb.households_private_rented": "dwp.housing_benefit", + "dwp.hb.households_social_rented": "dwp.housing_benefit", "dwp.jsa_claimants": "dwp.legacy_benefits", "dwp.uc.households": "dwp.universal_credit", "dwp.uc.households_carer_element": "dwp.universal_credit", @@ -427,9 +448,26 @@ "dft.bus_net_support.england": "England", "dft.bus_net_support.london": "London", "dwp.benefit_cap.capped_households": "Capped households", + "dwp.benefit_cap.capped_households_1000_01_to_1100": "Capped households, \u00a31,000.01 to \u00a31,100 a month", + "dwp.benefit_cap.capped_households_100_01_to_200": "Capped households, \u00a3100.01 to \u00a3200 a month", + "dwp.benefit_cap.capped_households_1100_01_to_1200": "Capped households, \u00a31,100.01 to \u00a31,200 a month", + "dwp.benefit_cap.capped_households_1200_01_to_1300": "Capped households, \u00a31,200.01 to \u00a31,300 a month", + "dwp.benefit_cap.capped_households_1300_01_and_above": "Capped households, \u00a31,300.01 and above a month", + "dwp.benefit_cap.capped_households_200_01_to_300": "Capped households, \u00a3200.01 to \u00a3300 a month", + "dwp.benefit_cap.capped_households_300_01_to_400": "Capped households, \u00a3300.01 to \u00a3400 a month", + "dwp.benefit_cap.capped_households_400_01_to_500": "Capped households, \u00a3400.01 to \u00a3500 a month", + "dwp.benefit_cap.capped_households_500_01_to_600": "Capped households, \u00a3500.01 to \u00a3600 a month", + "dwp.benefit_cap.capped_households_600_01_to_700": "Capped households, \u00a3600.01 to \u00a3700 a month", + "dwp.benefit_cap.capped_households_700_01_to_800": "Capped households, \u00a3700.01 to \u00a3800 a month", + "dwp.benefit_cap.capped_households_800_01_to_900": "Capped households, \u00a3800.01 to \u00a3900 a month", + "dwp.benefit_cap.capped_households_900_01_to_1000": "Capped households, \u00a3900.01 to \u00a31,000 a month", + "dwp.benefit_cap.capped_households_up_to_100": "Capped households, Up to \u00a3100 a month", "dwp.esa_claimants": "Employment and Support Allowance claimants", "dwp.esa_contrib_claimants": "Employment and Support Allowance contrib claimants", "dwp.esa_income_claimants": "Employment and Support Allowance income claimants", + "dwp.hb.households": "Housing Benefit claimants, all tenures", + "dwp.hb.households_private_rented": "Housing Benefit claimants, private rented", + "dwp.hb.households_social_rented": "Housing Benefit claimants, social rented", "dwp.jsa_claimants": "Jobseeker\u2019s Allowance claimants", "dwp.uc.households": "Universal Credit households", "dwp.uc.households_carer_element": "Households with carer entitlement", @@ -23673,6 +23711,407 @@ "assertion_policy": "allow_source_projection", "value_operation": "sum", "expected_member_count": 2 + }, + { + "name": "dwp.hb.households", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" + ], + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "all" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/hb/households", + "family": "dwp_housing_benefit", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.hb.households", + "measure_kind": "prepared_column", + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + }, + "period_match_policy": "source_window", + "value_operation": "monthly_window_average" + }, + { + "name": "dwp.hb.households_social_rented", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" + ], + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "Social Rented Sector" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/hb/households_social_rented", + "family": "dwp_housing_benefit", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.hb.households_social_rented", + "measure_kind": "prepared_column", + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + }, + "period_match_policy": "source_window", + "value_operation": "monthly_window_average" + }, + { + "name": "dwp.hb.households_private_rented", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" + ], + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "Private Rented Sector" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ], + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/hb/households_private_rented", + "family": "dwp_housing_benefit", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.hb.households_private_rented", + "measure_kind": "prepared_column", + "uk_uc_expected_source_months": "[\"2025-01\",\"2025-02\",\"2025-03\",\"2025-04\",\"2025-05\",\"2025-06\",\"2025-07\",\"2025-08\",\"2025-09\",\"2025-10\",\"2025-11\",\"2025-12\"]" + }, + "period_match_policy": "source_window", + "value_operation": "monthly_window_average" + }, + { + "name": "dwp.benefit_cap.capped_households_up_to_100", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_up_to_100", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_up_to_100", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_up_to_100", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_100_01_to_200", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_100_01_to_200", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_100_01_to_200", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_100_01_to_200", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_200_01_to_300", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_200_01_to_300", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_200_01_to_300", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_200_01_to_300", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_300_01_to_400", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_300_01_to_400", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_300_01_to_400", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_300_01_to_400", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_400_01_to_500", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_400_01_to_500", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_400_01_to_500", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_400_01_to_500", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_500_01_to_600", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_500_01_to_600", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_500_01_to_600", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_500_01_to_600", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_600_01_to_700", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_600_01_to_700", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_600_01_to_700", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_600_01_to_700", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_700_01_to_800", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_700_01_to_800", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_700_01_to_800", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_700_01_to_800", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_800_01_to_900", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_800_01_to_900", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_800_01_to_900", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_800_01_to_900", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_900_01_to_1000", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_900_01_to_1000", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_900_01_to_1000", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_900_01_to_1000", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1000_01_to_1100", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_1000_01_to_1100", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1100_01_to_1200", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_1100_01_to_1200", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1200_01_to_1300", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_1200_01_to_1300", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 + }, + { + "name": "dwp.benefit_cap.capped_households_1300_01_and_above", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1300_01_and_above", + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/benefit_cap/capped_households_1300_01_and_above", + "family": "dwp_benefit_cap", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.benefit_cap.capped_households_1300_01_and_above", + "measure_kind": "prepared_column" + }, + "uprating_from_period": "2025-11", + "uprating_to_period": 2025 } ] } 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 9d15c86f9a..d600b73c22 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 @@ -717,7 +717,24 @@ "dwp.uc.households_childcare_element": "#882 UC element target from the chronicle#260 Payment Indicator crosses; the incumbent registry has no element rows.", "dwp.uc.households_with_deduction": "#882 UC element target from the chronicle#260 Payment Indicator crosses; the incumbent registry has no element rows.", "obr.universal_credit": "#882 UC expenditure total over the two OBR welfare-cap cells; the incumbent binds only the split, which is excluded.", - "welshgov.council_tax_stock.band_i": "No incumbent target: uk-data's VOA council-tax rows stop at band H; microcosm#929 binds the Welsh band I stock from StatsWales CT1 (Wales is the only nation with a band I)." + "welshgov.council_tax_stock.band_i": "No incumbent target: uk-data's VOA council-tax rows stop at band H; microcosm#929 binds the Welsh band I stock from StatsWales CT1 (Wales is the only nation with a band I).", + "dwp.hb.households": "#882 Housing Benefit caseload diagnostic from the Stat-Xplore client-type-by-tenure cube (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260); the incumbent registry binds only the OBR spend line.", + "dwp.hb.households_social_rented": "#882 Housing Benefit caseload diagnostic from the Stat-Xplore client-type-by-tenure cube (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260); the incumbent registry binds only the OBR spend line.", + "dwp.hb.households_private_rented": "#882 Housing Benefit caseload diagnostic from the Stat-Xplore client-type-by-tenure cube (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260); the incumbent registry binds only the OBR spend line.", + "dwp.benefit_cap.capped_households_up_to_100": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_100_01_to_200": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_200_01_to_300": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_300_01_to_400": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_400_01_to_500": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_500_01_to_600": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_600_01_to_700": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_700_01_to_800": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_800_01_to_900": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_900_01_to_1000": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_1000_01_to_1100": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_1100_01_to_1200": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_1200_01_to_1300": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total.", + "dwp.benefit_cap.capped_households_1300_01_and_above": "#882 benefit-cap amount band from DWP Table 4; the incumbent registry binds only the capped-household total." }, "suppressed_ancestors": { "hmrc/salary_sacrifice_it_relief_basic_rate": { @@ -979,7 +996,24 @@ "dwp.uc.households_housing_element_private_rented", "dwp.uc.households_childcare_element", "dwp.uc.households_with_deduction", - "obr.universal_credit" + "obr.universal_credit", + "dwp.hb.households", + "dwp.hb.households_social_rented", + "dwp.hb.households_private_rented", + "dwp.benefit_cap.capped_households_up_to_100", + "dwp.benefit_cap.capped_households_100_01_to_200", + "dwp.benefit_cap.capped_households_200_01_to_300", + "dwp.benefit_cap.capped_households_300_01_to_400", + "dwp.benefit_cap.capped_households_400_01_to_500", + "dwp.benefit_cap.capped_households_500_01_to_600", + "dwp.benefit_cap.capped_households_600_01_to_700", + "dwp.benefit_cap.capped_households_700_01_to_800", + "dwp.benefit_cap.capped_households_800_01_to_900", + "dwp.benefit_cap.capped_households_900_01_to_1000", + "dwp.benefit_cap.capped_households_1000_01_to_1100", + "dwp.benefit_cap.capped_households_1100_01_to_1200", + "dwp.benefit_cap.capped_households_1200_01_to_1300", + "dwp.benefit_cap.capped_households_1300_01_and_above" ] }, "profile_parity": { @@ -3612,7 +3646,7 @@ "value_variable": "benunit_count", "from_entity": "benunit", "map_to": "household", - "notes": "uk-data binds the combined count to the benunit-level esa variable." + "notes": "uk-data binds the combined count to the benunit-level esa variable. People on ESA at August 2025 (DWP benefit statistics, February 2026). The published total includes claimants on both contributory and income-related ESA and claimants receiving National Insurance credits only; the model's esa > 0 covers paid ESA of either type, so the total sits above what the frame can carry by the credits-only caseload, which DWP does not publish. The frame also carries fewer ESA reporters than the FRS: 791k on spine-q against 1.06m in FRS 2024-25 at design weights, the SPI support channel under-carrying them (#882, microcosm#869). Bound at benefit-unit grain for incumbent parity." }, "axiom": { "metric_name": "dwp/esa_claimants", @@ -3653,7 +3687,8 @@ ], "value_variable": "person_count", "from_entity": "person", - "map_to": "household" + "map_to": "household", + "notes": "Contributory ESA at August 2025 (DWP benefit statistics, February 2026): a single-type count per the publication, excluding claimants on both contributory and income-related ESA. The model's esa_contrib > 0 includes dual claimants; on Q50f the overlap is about 4k of 707k (0.5 percent), recorded rather than filtered because esa_income is a benefit-unit variable (#882, microcosm#869)." }, "axiom": { "metric_name": "dwp/esa_contrib_claimants", @@ -3694,7 +3729,8 @@ ], "value_variable": "benunit_count", "from_entity": "benunit", - "map_to": "household" + "map_to": "household", + "notes": "Income-related ESA at August 2025 (DWP benefit statistics, February 2026): a single-type count per the publication, excluding claimants on both types. The model's esa_income > 0 includes dual claimants; the overlap is about 4k of 180k on Q50f, recorded rather than filtered (#882, microcosm#869)." }, "axiom": { "metric_name": "dwp/esa_income_claimants", @@ -3736,7 +3772,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "uk-data binds the JSA claimant count to contributory JSA only (jsa_contrib) - carried as-is for parity; adjudicate in microcosm#622." + "notes": "uk-data binds the JSA claimant count to contributory JSA only (jsa_contrib) - carried as-is for parity; adjudicate in microcosm#622. DWP's count (71,000 at August 2025) is New Style, contributory JSA only, after a methodology change from August 2025 that removed inactive claims; not comparable with earlier vintages. FRS 2024-25 reports 63k JSA benefit units at design weights, so the frame's excess is not the source: spine-q carries 181k contributory reporters, 161k of them in the SPI support channel's synthetic households (#882, microcosm#869)." }, "axiom": { "metric_name": "dwp/jsa_claimants", @@ -14695,7 +14731,7 @@ "value": 0 } ], - "notes": "Mean monthly paid GB benefit units over the declared months. Benefit units with a positive carer element among positive UC awards, against DWP's Stat-Xplore element entitlement crossed with Payment Indicator = Yes (chronicle#260), the same paid-claim basis as the family-type rows. The engine's carer condition is Carer's Allowance receipt (is_carer_for_benefits); the UC carer element needs 35 hours a week of care without Carer's Allowance, so the row undercounts by construction until the spine carries care hours (#882). Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/260", + "notes": "Mean monthly paid GB benefit units over the declared months. Benefit units with a positive carer element among positive UC awards, against DWP's Stat-Xplore element entitlement crossed with Payment Indicator = Yes (chronicle#260), the same paid-claim basis as the family-type rows. From policyengine-uk 2.98.0 the carer condition (is_carer_for_benefits) holds on Carer's Allowance receipt or 35 weekly care hours, and the spine fills care_hours from the FRS adult HOURTOT band at the band's lower edge with would_claim_carers_allowance following reported receipt, so 35-hour carers without an award reach the element while the allowance stays on reported claimants; the hours limb does not check that the person cared for is on a qualifying benefit. Activated on the 2026-09-16 twin evidence (paid-UC carer units +80 percent at design weights; Q50f projection -0.3 percent), experiments/882-uc-repairs-receipts.md R6 (#882). Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/260", "household_conditions": [ { "variable": "region", @@ -15189,7 +15225,7 @@ "value": 0 } ], - "notes": "Mean monthly paid GB benefit units over the declared months. Benefit units with a positive childcare element among positive UC awards, against DWP's childcare element statistics Table 1 (chronicle#260): an all-claims count rounded to the nearest 1,000 with no payment-indicator split, so the published side is up to the nil-payment share (about 8 percent) above a paid-claim basis. The engine pays the element to every benefit unit where all adults work and childcare costs are reported, with no take-up lever, so the row overcounts by construction until that lever exists (#882). Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/260", + "notes": "Mean monthly paid GB benefit units over the declared months. Benefit units with a positive childcare element among positive UC awards, against DWP's childcare element statistics Table 1 (chronicle#260): an all-claims count rounded to the nearest 1,000 with no payment-indicator split, so the published side is up to the nil-payment share (about 8 percent) above a paid-claim basis. From policyengine-uk 2.98.0 the element is gated by would_claim_uc_childcare, which frs_take_up draws at one contract rate per family type (single 0.441, couple 0.2682, tools/fit_uk_uc_childcare_takeup.py: the expected paid-UC count equals the published calendar-2025 single and couple means at the Q50f calibrated weights). SPI-synthetic units copy their donor's flag rather than drawing afresh, so a realized count on a thin base can sit below the expectation; activated on the 2026-09-16 twin evidence (Q50f projection -16 percent), experiments/882-uc-repairs-receipts.md R6 (#882). Count only the Great Britain regions, matching K03000001; DWP removes Northern Ireland-office claims from these statistics. Source: https://www.gov.uk/government/publications/universal-credit-statistics-background-information-and-methodology/universal-credit-statistics-background-information-and-methodology#creating-a-great-britain-dataset Source: https://github.com/PolicyEngine/chronicle/pull/260", "household_conditions": [ { "variable": "region", @@ -15395,164 +15431,1569 @@ "label": "Universal Credit expenditure" }, { - "target_id": "hmrc.self_employment_income.amount", - "family": "hmrc", + "target_id": "dwp.hb.households", + "family": "dwp_housing_benefit", "geography_levels": [ - "constituency", - "local_authority" + "country" ], "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.income.self_employment.amount", + "entity": "benunit", + "concept": "uk.benefit_unit.count", "filters": [ { - "concept": "uk.tax.income_tax", + "concept": "uk.benefits.housing_benefit.amount", "operator": ">", "value": 0 } + ], + "source_months": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { "policyengine": { - "metric_name": "hmrc/self_employment_income/amount", - "value_variable": "self_employment_income", - "from_entity": "person", - "map_to": "household", + "metric_name": "dwp/hb/households", + "value_variable": "benunit_count", + "from_entity": "benunit", "filters": [ { - "variable": "income_tax", + "variable": "housing_benefit", "operator": ">", "value": 0 } + ], + "notes": "Mean monthly GB benefit units over the declared months with a positive engine Housing Benefit award, against DWP's Stat-Xplore Housing Benefit caseload by client type and tenure (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260; concept dwp.housing_benefit_claimants). All client types and tenures (the publisher's Total items; DWP's 'Unknown or missing housing sector' cell is inside it). Measured, not fit: the row is on the reviewed measure-exclusion register while the frame cannot reach it (#882, microcosm#867): the SPI support channel carries reported Housing Benefit into owner-occupied synthetic households, the engine pays Housing Benefit only to reporting units not drawn into Universal Credit and with a rented tenure, and rent is carried by the household head's benefit unit alone. Count only the Great Britain regions, matching K03000001; DWP's Housing Benefit statistics cover Great Britain. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/hb_new/Accommodation%20Type.html", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } ] }, "axiom": { - "metric_name": "hmrc/self_employment_income/amount", + "metric_name": "dwp/hb/households", "status": "pending", - "value_rule": "uk.income.self_employment.amount" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { - "source_name": "hmrc", - "source_measure_id": [ - "self_employment_income_count", - "self_employment_income_mean" + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" ], - "record_set_spec_id": [ - "uk.local_geography.spi_income.by_constituency.v1", - "uk.local_geography.spi_income.by_local_authority.v1" + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "all" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, - "selector_note": "No Chronicle amount fact exists; count_x_mean consumes the ordered count and mean facts from the same SPI area record-set spec.", - "category_id": "hmrc.tax_statistics", - "label": "Amount" + "value_operation": "monthly_window_average", + "period_match_policy": "source_window", + "category_id": "dwp.housing_benefit", + "label": "Housing Benefit claimants, all tenures" }, { - "target_id": "hmrc.self_employment_income.count", - "family": "hmrc", + "target_id": "dwp.hb.households_social_rented", + "family": "dwp_housing_benefit", "geography_levels": [ - "constituency", - "local_authority" + "country" ], "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.person.count", + "entity": "benunit", + "concept": "uk.benefit_unit.count", "filters": [ { - "concept": "uk.income.self_employment.amount", - "operator": "!=", + "concept": "uk.benefits.housing_benefit.amount", + "operator": ">", "value": 0 }, { - "concept": "uk.tax.income_tax", - "operator": ">", - "value": 0 + "concept": "uk.household.tenure", + "operator": "in", + "value": [ + "rent_from_council", + "rent_from_housing_association" + ] } + ], + "source_months": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { "policyengine": { - "metric_name": "hmrc/self_employment_income/count", - "value_variable": "person_count", - "from_entity": "person", - "map_to": "household", + "metric_name": "dwp/hb/households_social_rented", + "value_variable": "benunit_count", + "from_entity": "benunit", "filters": [ { - "variable": "self_employment_income", - "operator": "!=", + "variable": "housing_benefit", + "operator": ">", "value": 0 }, { - "variable": "income_tax", - "operator": ">", - "value": 0 + "variable": "benunit_tenure_type", + "operator": "in", + "value": [ + "RENT_FROM_COUNCIL", + "RENT_FROM_HA" + ] + } + ], + "notes": "Mean monthly GB benefit units over the declared months with a positive engine Housing Benefit award, against DWP's Stat-Xplore Housing Benefit caseload by client type and tenure (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260; concept dwp.housing_benefit_claimants). Tenure from the benefit unit's household (benunit_tenure_type): council and housing-association renters. Measured, not fit: the row is on the reviewed measure-exclusion register while the frame cannot reach it (#882, microcosm#867): the SPI support channel carries reported Housing Benefit into owner-occupied synthetic households, the engine pays Housing Benefit only to reporting units not drawn into Universal Credit and with a rented tenure, and rent is carried by the household head's benefit unit alone. Count only the Great Britain regions, matching K03000001; DWP's Housing Benefit statistics cover Great Britain. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/hb_new/Accommodation%20Type.html", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" } ] }, "axiom": { - "metric_name": "hmrc/self_employment_income/count", + "metric_name": "dwp/hb/households_social_rented", "status": "pending", - "value_rule": "uk.person.count" + "value_rule": "uk.benefit_unit.count" } }, "ledger_selector": { - "source_name": "hmrc", - "source_measure_id": "self_employment_income_count", - "record_set_spec_id": [ - "uk.local_geography.spi_income.by_constituency.v1", - "uk.local_geography.spi_income.by_local_authority.v1" + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" + ], + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "Social Rented Sector" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, - "category_id": "hmrc.tax_statistics", - "label": "Count" + "value_operation": "monthly_window_average", + "period_match_policy": "source_window", + "category_id": "dwp.housing_benefit", + "label": "Housing Benefit claimants, social rented" }, { - "target_id": "hmrc.employment_income.amount", - "family": "hmrc", + "target_id": "dwp.hb.households_private_rented", + "family": "dwp_housing_benefit", "geography_levels": [ - "constituency", - "local_authority" + "country" ], "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.income.employment.amount", + "entity": "benunit", + "concept": "uk.benefit_unit.count", "filters": [ { - "concept": "uk.tax.income_tax", + "concept": "uk.benefits.housing_benefit.amount", "operator": ">", "value": 0 + }, + { + "concept": "uk.household.tenure", + "operator": "in", + "value": [ + "rent_privately" + ] } + ], + "source_months": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" ] }, "bindings": { "policyengine": { - "metric_name": "hmrc/employment_income/amount", - "value_variable": "employment_income", - "from_entity": "person", - "map_to": "household", + "metric_name": "dwp/hb/households_private_rented", + "value_variable": "benunit_count", + "from_entity": "benunit", "filters": [ { - "variable": "income_tax", + "variable": "housing_benefit", "operator": ">", "value": 0 + }, + { + "variable": "benunit_tenure_type", + "operator": "in", + "value": [ + "RENT_PRIVATELY" + ] } - ] - }, - "axiom": { - "metric_name": "hmrc/employment_income/amount", - "status": "pending", - "value_rule": "uk.income.employment.amount" - } - }, - "ledger_selector": { - "source_name": "hmrc", - "source_measure_id": [ - "employment_income_count", - "employment_income_mean" - ], + ], + "notes": "Mean monthly GB benefit units over the declared months with a positive engine Housing Benefit award, against DWP's Stat-Xplore Housing Benefit caseload by client type and tenure (asked in PolicyEngine/chronicle#259, delivered by PolicyEngine/chronicle#260; concept dwp.housing_benefit_claimants). Tenure from the benefit unit's household (benunit_tenure_type): private renters. Measured, not fit: the row is on the reviewed measure-exclusion register while the frame cannot reach it (#882, microcosm#867): the SPI support channel carries reported Housing Benefit into owner-occupied synthetic households, the engine pays Housing Benefit only to reporting units not drawn into Universal Credit and with a rented tenure, and rent is carried by the household head's benefit unit alone. Count only the Great Britain regions, matching K03000001; DWP's Housing Benefit statistics cover Great Britain. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/hb_new/Accommodation%20Type.html", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/hb/households_private_rented", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.housing_benefit_claimants", + "groupby_dimension": "dwp.hb_client_type", + "dimensions": [ + "client_type", + "private_or_social_rented" + ], + "dimension_values": { + "client_type": "all", + "private_or_social_rented": "Private Rented Sector" + }, + "source_measure_id": "total_claimants", + "period_type": "month", + "period_value": [ + "2025-01", + "2025-02", + "2025-03", + "2025-04", + "2025-05", + "2025-06", + "2025-07", + "2025-08", + "2025-09", + "2025-10", + "2025-11", + "2025-12" + ] + }, + "value_operation": "monthly_window_average", + "period_match_policy": "source_window", + "category_id": "dwp.housing_benefit", + "label": "Housing Benefit claimants, private rented" + }, + { + "target_id": "dwp.benefit_cap.capped_households_up_to_100", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 0 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 1200 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_up_to_100", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 0 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 1200 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band Up to £100 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_up_to_100", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_up_to_100" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, Up to £100 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_100_01_to_200", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 1200 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 2400 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_100_01_to_200", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 1200 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 2400 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £100.01 to £200 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_100_01_to_200", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_100_01_to_200" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £100.01 to £200 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_200_01_to_300", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 2400 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 3600 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_200_01_to_300", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 2400 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 3600 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £200.01 to £300 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_200_01_to_300", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_200_01_to_300" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £200.01 to £300 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_300_01_to_400", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 3600 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 4800 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_300_01_to_400", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 3600 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 4800 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £300.01 to £400 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_300_01_to_400", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_300_01_to_400" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £300.01 to £400 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_400_01_to_500", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 4800 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 6000 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_400_01_to_500", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 4800 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 6000 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £400.01 to £500 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_400_01_to_500", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_400_01_to_500" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £400.01 to £500 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_500_01_to_600", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 6000 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 7200 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_500_01_to_600", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 6000 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 7200 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £500.01 to £600 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_500_01_to_600", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_500_01_to_600" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £500.01 to £600 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_600_01_to_700", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 7200 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 8400 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_600_01_to_700", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 7200 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 8400 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £600.01 to £700 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_600_01_to_700", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_600_01_to_700" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £600.01 to £700 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_700_01_to_800", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 8400 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 9600 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_700_01_to_800", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 8400 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 9600 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £700.01 to £800 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_700_01_to_800", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_700_01_to_800" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £700.01 to £800 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_800_01_to_900", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 9600 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 10800 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_800_01_to_900", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 9600 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 10800 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £800.01 to £900 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_800_01_to_900", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_800_01_to_900" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £800.01 to £900 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_900_01_to_1000", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 10800 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 12000 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_900_01_to_1000", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 10800 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 12000 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £900.01 to £1,000 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_900_01_to_1000", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_900_01_to_1000" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £900.01 to £1,000 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_1000_01_to_1100", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 12000 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 13200 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_1000_01_to_1100", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 12000 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 13200 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £1,000.01 to £1,100 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_1000_01_to_1100", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1000_01_to_1100" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £1,000.01 to £1,100 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_1100_01_to_1200", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 13200 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 14400 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_1100_01_to_1200", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 13200 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 14400 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £1,100.01 to £1,200 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_1100_01_to_1200", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1100_01_to_1200" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £1,100.01 to £1,200 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_1200_01_to_1300", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 14400 + }, + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": "<=", + "value": 15600 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_1200_01_to_1300", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 14400 + }, + { + "variable": "benefit_cap_reduction", + "operator": "<=", + "value": 15600 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £1,200.01 to £1,300 per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_1200_01_to_1300", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1200_01_to_1300" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £1,200.01 to £1,300 a month" + }, + { + "target_id": "dwp.benefit_cap.capped_households_1300_01_and_above", + "family": "dwp_benefit_cap", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefit_unit.count", + "filters": [ + { + "concept": "uk.benefits.benefit_cap.reduction", + "operator": ">", + "value": 15600 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/benefit_cap/capped_households_1300_01_and_above", + "value_variable": "benunit_count", + "from_entity": "benunit", + "filters": [ + { + "variable": "benefit_cap_reduction", + "operator": ">", + "value": 15600 + } + ], + "notes": "GB benefit units whose benefit-cap reduction falls in the DWP amount band £1,300.01 and above per monthly assessment period, against Benefit cap tables to November 2025, Table 4 (point-in-time UC households capped at November 2025 by amount capped; one concept per band, no band dimension). The publisher band is a monthly amount; the engine's benefit_cap_reduction is annual, so the edges are the monthly edges times 12, half-open on the lower edge and closed on the upper (the last band open). DWP's 'UC household' is the unit of assessment, so the band rows bind at benefit-unit grain while the bound total keeps the incumbent's household indicator (a household with two capped units counts once there). Count only the Great Britain regions, matching K03000001. Source: https://www.gov.uk/government/collections/benefit-cap-statistics", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ] + }, + "axiom": { + "metric_name": "dwp/benefit_cap/capped_households_1300_01_and_above", + "status": "pending", + "value_rule": "uk.benefit_unit.count" + } + }, + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.uc_capped_households_1300_01_and_above" + }, + "category_id": "dwp.benefit_cap", + "label": "Capped households, £1,300.01 and above a month" + }, + { + "target_id": "hmrc.self_employment_income.amount", + "family": "hmrc", + "geography_levels": [ + "constituency", + "local_authority" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.income.self_employment.amount", + "filters": [ + { + "concept": "uk.tax.income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "hmrc/self_employment_income/amount", + "value_variable": "self_employment_income", + "from_entity": "person", + "map_to": "household", + "filters": [ + { + "variable": "income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "axiom": { + "metric_name": "hmrc/self_employment_income/amount", + "status": "pending", + "value_rule": "uk.income.self_employment.amount" + } + }, + "ledger_selector": { + "source_name": "hmrc", + "source_measure_id": [ + "self_employment_income_count", + "self_employment_income_mean" + ], + "record_set_spec_id": [ + "uk.local_geography.spi_income.by_constituency.v1", + "uk.local_geography.spi_income.by_local_authority.v1" + ] + }, + "selector_note": "No Chronicle amount fact exists; count_x_mean consumes the ordered count and mean facts from the same SPI area record-set spec.", + "category_id": "hmrc.tax_statistics", + "label": "Amount" + }, + { + "target_id": "hmrc.self_employment_income.count", + "family": "hmrc", + "geography_levels": [ + "constituency", + "local_authority" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "filters": [ + { + "concept": "uk.income.self_employment.amount", + "operator": "!=", + "value": 0 + }, + { + "concept": "uk.tax.income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "hmrc/self_employment_income/count", + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household", + "filters": [ + { + "variable": "self_employment_income", + "operator": "!=", + "value": 0 + }, + { + "variable": "income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "axiom": { + "metric_name": "hmrc/self_employment_income/count", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "hmrc", + "source_measure_id": "self_employment_income_count", + "record_set_spec_id": [ + "uk.local_geography.spi_income.by_constituency.v1", + "uk.local_geography.spi_income.by_local_authority.v1" + ] + }, + "category_id": "hmrc.tax_statistics", + "label": "Count" + }, + { + "target_id": "hmrc.employment_income.amount", + "family": "hmrc", + "geography_levels": [ + "constituency", + "local_authority" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.income.employment.amount", + "filters": [ + { + "concept": "uk.tax.income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "hmrc/employment_income/amount", + "value_variable": "employment_income", + "from_entity": "person", + "map_to": "household", + "filters": [ + { + "variable": "income_tax", + "operator": ">", + "value": 0 + } + ] + }, + "axiom": { + "metric_name": "hmrc/employment_income/amount", + "status": "pending", + "value_rule": "uk.income.employment.amount" + } + }, + "ledger_selector": { + "source_name": "hmrc", + "source_measure_id": [ + "employment_income_count", + "employment_income_mean" + ], "record_set_spec_id": [ "uk.local_geography.spi_income.by_constituency.v1", "uk.local_geography.spi_income.by_local_authority.v1" @@ -18480,6 +19921,10 @@ "welshgov.council_tax_stock": { "provider_id": "welshgov", "label": "Council Tax dwelling stock" + }, + "dwp.housing_benefit": { + "provider_id": "dwp", + "label": "Housing Benefit" } } } 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 61f139c63c..a0f403059c 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 @@ -151,6 +151,26 @@ "pip_dl": 96, } +# FRS adult HOURTOT: total hours of care provided per week, a banded derived +# variable (FRS derived-variable specification). Each code maps to the lower +# edge of its band in weekly hours; the engine's care_hours input is weekly +# hours, and its only consumer today tests the 35-hour Carer's Allowance line, +# which the codes 5, 6, 7 and 10 (35-49, 50-99, 100 or more, varies at 35 or +# more) reach and the codes 1-4, 8 and 9 do not (#882). +FRS_CARE_HOURS_BY_BAND = { + 0: 0.0, # no care provided + 1: 0.0, # 0-4 hours + 2: 5.0, # 5-9 hours + 3: 10.0, # 10-19 hours + 4: 20.0, # 20-34 hours + 5: 35.0, # 35-49 hours + 6: 50.0, # 50-99 hours + 7: 100.0, # 100 hours or more + 8: 0.0, # varies, under 20 hours + 9: 20.0, # varies, 20-34 hours + 10: 35.0, # varies, 35 hours or more +} + OUTPUT_COLUMNS = ( "person_id", "person_benunit_id", @@ -159,6 +179,7 @@ "gender", "marital_status", "hours_worked", + "care_hours", "is_household_head", "is_benunit_head", "is_parent", @@ -200,6 +221,7 @@ "dla_m_reported", "iidb_reported", "carers_allowance_reported", + "would_claim_carers_allowance", "sda_reported", "afcs_reported", "ssmg_reported", @@ -411,6 +433,7 @@ def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame: pe_person["gender"] = np.where(_number(person, "sex") == 1, "MALE", "FEMALE") pe_person["marital_status"] = _map_codes(person, "marital", MARITAL_MAP, "SINGLE") pe_person["hours_worked"] = _positive(person, "tothours") * WEEKS_IN_YEAR + pe_person["care_hours"] = frs_care_hours(person) pe_person["is_household_head"] = _number(person, "hrpid") == 1 pe_person["is_benunit_head"] = _number(person, "uperson") == 1 dependent_children = _dependent_children(benunit_raw, frs["child"]) @@ -427,6 +450,12 @@ def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame: _add_accounts(pe_person, person, frs["accounts"]) _add_person_income(pe_person, person, household, frs["oddjob"]) _add_benefits(pe_person, person, frs["benefits"]) + # The engine pays Carer's Allowance on hours or receipt; a dataset keeps it + # on reported receipt so that care_hours qualifies carers for the UC carer + # element without paying the allowance to every carer (#882). + pe_person["would_claim_carers_allowance"] = ( + pe_person["carers_allowance_reported"] > 0 + ) _add_person_expenses(pe_person, person, frs) pe_benunit["is_married"] = _number(benunit_raw, "famtypb2").isin([5, 7]) @@ -839,6 +868,24 @@ def _map_codes( return values.map(mapping).fillna(default).astype(object) +def frs_care_hours(person: pd.DataFrame) -> pd.Series: + """Weekly care hours from the banded FRS HOURTOT code (lower band edge). + + Blank or non-numeric codes (children, and adults not asked) map to zero; + an unknown code is a vintage defect and refuses the build. + """ + + codes = _raw_number(person, "hourtot").fillna(0) + if not np.array_equal(codes.to_numpy(), np.floor(codes.to_numpy())): + raise ValueError( + "FRS hourtot codes must be integral to build person.care_hours." + ) + unknown = sorted(set(codes.astype(int)) - set(FRS_CARE_HOURS_BY_BAND)) + if unknown: + raise ValueError(f"FRS hourtot carries unknown band code(s) {unknown}.") + return codes.astype(int).map(FRS_CARE_HOURS_BY_BAND).astype("float64") + + def _number(frame: pd.DataFrame, column: str) -> pd.Series: if column not in frame.columns: return pd.Series(np.zeros(len(frame), dtype="float64"), index=frame.index) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py index b34f8c38f9..4fe2379fdd 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 @@ -3,6 +3,8 @@ from __future__ import annotations from dataclasses import dataclass +from importlib import metadata +from pathlib import Path import numpy as np import pandas as pd @@ -36,6 +38,7 @@ "would_claim_extended_childcare", "would_claim_universal_childcare", "would_claim_targeted_childcare", + "would_claim_uc_childcare", "maximum_extended_childcare_hours_usage", ) FRS_TAKE_UP_NONNEGATIVE_OUTPUT_COLUMNS = ("maximum_extended_childcare_hours_usage",) @@ -45,6 +48,29 @@ "universal_credit_reported_anchor": "universal_credit_reported", } UK_TAKE_UP_DECLARED_SEEDS = {output: 0 for output in FRS_TAKE_UP_OUTPUT_COLUMNS} +# Universal Credit is claimable only by a benefit unit with a working-age +# adult (policyengine-uk ``is_uc_eligible`` requires ``is_WA_adult``, which is +# ``is_adult & ~is_SP_age``). The draw's population is therefore those units; +# a unit with every adult at or over State Pension age is never drawn (#882, +# microcosm#867). The bounds come from the engine at the build instant through +# ``uk_take_up_population_policy``: State Pension age from the +# ``gov.dwp.state_pension.age`` parameters and the adult threshold from the +# engine's ``is_adult`` formula (a constant there, pinned by the lockstep test). +# The stage manifest declares the same population on the ``would_claim_uc`` +# operation; ``assert_take_up_stage_population_declaration`` refuses a manifest +# that stops saying so, so the declaration and the code cannot drift apart. +UK_ENGINE_ADULT_AGE = 18 +UK_UC_AGE_ELIGIBLE_AGGREGATE = "uc_age_eligible" +UK_UC_AGE_ELIGIBLE_METHOD = "any_adult_under_state_pension_age" +UK_UC_AGE_ELIGIBLE_SOURCE = "age" +UK_UC_TAKE_UP_OUTPUT = "would_claim_uc" +# The Universal Credit childcare element is claimed at one rate per family +# type (DWP publishes the single / couple split of the households receiving +# it); the couple rate applies where the benefit unit is a couple (#882). +UK_UC_CHILDCARE_RATE_KEYS = { + "single": "uc_childcare_single", + "couple": "uc_childcare_couple", +} UK_TAKE_UP_SIGNAL_OUTPUTS = ( ("benunit", "would_claim_child_benefit", "child_benefit"), ("benunit", "child_benefit_opts_out", "child_benefit_opts_out_rate"), @@ -54,6 +80,7 @@ ("benunit", "would_claim_extended_childcare", "extended_childcare"), ("benunit", "would_claim_universal_childcare", "universal_childcare"), ("benunit", "would_claim_targeted_childcare", "targeted_childcare"), + ("benunit", "would_claim_uc_childcare", "uc_childcare"), ("person", "would_claim_marriage_allowance", "marriage_allowance"), ("person", "would_claim_scp", "scp"), ("household", "household_owns_tv", "tv_ownership_rate"), @@ -67,26 +94,123 @@ ) +@dataclass(frozen=True) +class UKTakeUpPopulationPolicy: + """Engine bounds of the Universal Credit take-up population at one instant. + + ``adult_age`` is the engine's ``is_adult`` threshold and + ``state_pension_age`` its ``gov.dwp.state_pension.age`` value, so a unit is + in the population exactly when the engine's ``is_WA_adult`` is true for one + of its members. + """ + + adult_age: int + state_pension_age: int + instant: str + source: str + + def working_age(self, age: pd.Series | np.ndarray) -> np.ndarray: + values = np.asarray(age, dtype=float) + return (values >= self.adult_age) & (values < self.state_pension_age) + + +def uk_take_up_population_policy(build_period: int | str) -> UKTakeUpPopulationPolicy: + """Read the working-age bounds from the engine at ``{year}-01-01``.""" + + try: + import policyengine_uk + from policyengine_core.parameters import ParameterNode + except ImportError as exc: + raise ImportError( + "UK take-up population bounds require `uv sync --all-packages --extra uk`." + ) from exc + + parameters = ParameterNode( + directory_path=str(Path(policyengine_uk.__file__).parent / "parameters") + ) + instant = f"{int(build_period)}-01-01" + ages = parameters.gov.dwp.state_pension.age + male, female = float(ages.male(instant)), float(ages.female(instant)) + if male != female or not float(male).is_integer(): + raise ValueError( + "the take-up population needs one State Pension age for every adult " + f"at {instant}; the engine has male {male} and female {female}, so the " + "stage must consume sex before it can form the population" + ) + return UKTakeUpPopulationPolicy( + adult_age=UK_ENGINE_ADULT_AGE, + state_pension_age=int(male), + instant=instant, + source="policyengine-uk parameters " + metadata.version("policyengine-uk"), + ) + + +def assert_take_up_stage_population_declaration(stage: SourceStageSpec) -> None: + """Refuse a manifest whose declared UC population differs from the code's.""" + + declared_aggregate = any( + op.kind == "aggregate_person_to_benunit" + and op.parameters.get("method") == UK_UC_AGE_ELIGIBLE_METHOD + and dict(op.parameters.get("aggregates") or {}) + == {UK_UC_AGE_ELIGIBLE_AGGREGATE: UK_UC_AGE_ELIGIBLE_SOURCE} + for op in stage.operations + ) + declared_population = any( + op.kind == "assign_binary_with_anchored_residual" + and op.parameters.get("output") == UK_UC_TAKE_UP_OUTPUT + and op.parameters.get("population") == UK_UC_AGE_ELIGIBLE_AGGREGATE + for op in stage.operations + ) + if not (declared_aggregate and declared_population): + seen = [ + (op.kind, dict(op.parameters)) + for op in stage.operations + if op.kind == "aggregate_person_to_benunit" + or op.parameters.get("output") == UK_UC_TAKE_UP_OUTPUT + ] + raise ValueError( + f"stage {stage.stage!r} must declare the {UK_UC_AGE_ELIGIBLE_AGGREGATE!r} " + f"aggregate ({UK_UC_AGE_ELIGIBLE_METHOD} over " + f"{UK_UC_AGE_ELIGIBLE_SOURCE}) and population={UK_UC_AGE_ELIGIBLE_AGGREGATE!r} " + f"on the {UK_UC_TAKE_UP_OUTPUT!r} operation; the code draws Universal " + "Credit take-up over that population and refuses a manifest that says " + f"otherwise (declared: {seen!r})" + ) + + @dataclass(frozen=True) class UKFRSTakeUpStageTransform: """Whole-stage callable for UK benefit-unit take-up assignments.""" contract: UKTakeUpContract stage: SourceStageSpec + population_policy: UKTakeUpPopulationPolicy | None = None def __call__(self, frame: Frame) -> Frame: - return add_frs_take_up(frame, contract=self.contract) + assert_take_up_stage_population_declaration(self.stage) + policy = self.population_policy or uk_take_up_population_policy( + uk_time_period(frame) + ) + return add_frs_take_up(frame, contract=self.contract, population_policy=policy) @staticmethod def output_columns() -> tuple[str, ...]: return FRS_TAKE_UP_OUTPUT_COLUMNS -def add_frs_take_up(frame: Frame, *, contract: UKTakeUpContract) -> Frame: +def add_frs_take_up( + frame: Frame, + *, + contract: UKTakeUpContract, + population_policy: UKTakeUpPopulationPolicy, +) -> Frame: person = frame.table("person").copy() benunit = frame.table("benunit").copy() anchors = aggregate_person_reported_to_benunit(person, benunit) - derived = derive_frs_take_up(benunit, anchors=anchors, contract=contract) + uc_age_eligible = uc_age_eligible_benunits(person, benunit, population_policy) + derived = derive_frs_take_up( + benunit, anchors=anchors, contract=contract, uc_age_eligible=uc_age_eligible + ) for column in FRS_TAKE_UP_OUTPUT_COLUMNS: benunit[column] = derived[column].to_numpy() result = uk_national_frame( @@ -122,14 +246,36 @@ def aggregate_person_reported_to_benunit( return grouped.reset_index(drop=True) +def uc_age_eligible_benunits( + person: pd.DataFrame, + benunit: pd.DataFrame, + policy: UKTakeUpPopulationPolicy, +) -> np.ndarray: + """True where the benefit unit has a working-age adult under ``policy``.""" + + if "age" not in person.columns: + raise KeyError( + "person.age is missing; the Universal Credit take-up population " + "cannot be formed without it" + ) + age = pd.to_numeric(person["age"], errors="coerce").fillna(0) + eligible_adult = policy.working_age(age) + eligible_ids = set(person.loc[eligible_adult, "person_benunit_id"]) + return benunit["benunit_id"].isin(eligible_ids).to_numpy(dtype=bool) + + def derive_frs_take_up( benunit: pd.DataFrame, *, anchors: pd.DataFrame, contract: UKTakeUpContract, + uc_age_eligible: np.ndarray, ) -> pd.DataFrame: ids = benunit["benunit_id"].to_numpy() values = pd.DataFrame(index=benunit.index) + population = np.asarray(uc_age_eligible, dtype=bool) + if population.shape != ids.shape: + raise ValueError("uc_age_eligible must align with the benefit-unit table") values["would_claim_child_benefit"] = assign_binary_with_anchored_residual( _draws(ids, "would_claim_child_benefit"), contract.rate("child_benefit"), @@ -148,6 +294,7 @@ def derive_frs_take_up( _draws(ids, "would_claim_uc"), contract.rate("universal_credit"), anchors["universal_credit_reported_anchor"].to_numpy(dtype=bool), + population=population, ) for output, key in ( ("would_claim_tfc", "tax_free_childcare"), @@ -158,6 +305,9 @@ def derive_frs_take_up( values[output] = assign_binary_from_rate( _draws(ids, output), contract.rate(key) ) + values["would_claim_uc_childcare"] = _draws( + ids, "would_claim_uc_childcare" + ) < uc_childcare_rates(benunit, contract) distribution = contract.continuous_entry("maximum_extended_childcare_hours_usage") values["maximum_extended_childcare_hours_usage"] = clipped_normal_from_uniforms( _draws(ids, "maximum_extended_childcare_hours_usage"), @@ -169,6 +319,22 @@ def derive_frs_take_up( return values +def uc_childcare_rates(benunit: pd.DataFrame, contract: UKTakeUpContract) -> np.ndarray: + """Per-unit childcare-element take-up rate: the couple or single contract rate.""" + + if "is_married" not in benunit.columns: + raise KeyError( + "benunit.is_married is missing; the Universal Credit childcare " + "take-up rate is drawn by family type" + ) + couple = benunit["is_married"].fillna(False).to_numpy(dtype=bool) + return np.where( + couple, + contract.rate(UK_UC_CHILDCARE_RATE_KEYS["couple"]), + contract.rate(UK_UC_CHILDCARE_RATE_KEYS["single"]), + ) + + def _draws(ids: np.ndarray, output: str) -> np.ndarray: return stable_identity_uniforms( ids, seed=UK_TAKE_UP_DECLARED_SEEDS[output], salt=output @@ -180,12 +346,14 @@ 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: @@ -194,13 +362,37 @@ def uk_take_up_signal_gate( values = np.asarray(table[output], dtype=bool) unique_count = int(pd.Series(values).nunique(dropna=False)) weights = np.asarray(frame.resolve_weights(entity).values, dtype=np.float64) - share = float(np.average(values.astype(float), weights=weights)) + 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 working-age adult under the engine's 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 not population.any() or float(weights[population].sum()) <= 0.0: + failures.append( + f"{entity}.{output}: no unit in the draw's population carries " + "weight; the take-up share cannot be measured." + ) + continue + share = float( + np.average(values[population].astype(float), weights=weights[population]) + ) target = _target_share(table, key, resolved, weights) details[f"{entity}.{output}"] = { "weighted_share": share, "target": target, "absolute_deviation": abs(share - target), "unique_count": unique_count, + "population_units": int(population.sum()), } if unique_count < 2: failures.append( @@ -227,6 +419,8 @@ def _target_share( contract: UKTakeUpContract, weights: np.ndarray, ) -> float: + if key == "uc_childcare": + return float(np.average(uc_childcare_rates(table, contract), weights=weights)) if key != "scp": return contract.rate(key) age = pd.to_numeric(table["age"], errors="coerce").fillna(0).to_numpy() 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 984adec6cf..e1492609d6 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -175,8 +175,12 @@ "child_benefit_reported", "pension_credit_reported", "universal_credit_reported", + # The Universal Credit draw's population: units with an adult + # under State Pension age (#882). + "age", ) - ), + ) + | frozenset({("benunit", "is_married")}), "frs_person_draws": frozenset({("person", "age")}), "frs_household_draws": frozenset(), "frs_brma": None, @@ -278,6 +282,7 @@ def owned(self, *, rewrite: bool = False) -> Owned: "is_benunit_head", "is_parent", "is_uc_claimant", + "would_claim_carers_allowance", } _ROOT_PERSON_INT = {"age"} _ROOT_PERSON_FLOAT: set[str] = set() @@ -436,6 +441,7 @@ def _cells( "would_claim_extended_childcare", "would_claim_universal_childcare", "would_claim_targeted_childcare", + "would_claim_uc_childcare", ), "bool", ), @@ -655,6 +661,9 @@ def _cells( "is_disabled_for_benefits", "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits", + # #882: the carer take-up flag follows the refilled Carer's Allowance + # receipt on the SPI-redrawn rows. + "would_claim_carers_allowance", ) _STAGE_CELLS = { **_STAGE_CELLS, 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 dc06ec0369..05d7c740a4 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 @@ -658,6 +658,7 @@ def impute_uk_spi_income_support( person.loc[spi_people, "savings_interest_income"] = taxable_interest_draw + tax_free person = derive_hmrc_income_auxiliaries(person, row_mask=spi_people) person = _refresh_disability_derived_inputs(person, spi_people=spi_people) + person = _refresh_carer_take_up_input(person, spi_people=spi_people) return UKSPIIncomeImputationResult( person=person, fit_weight_records=( @@ -1304,6 +1305,34 @@ def _refresh_disability_derived_inputs( return person +def _refresh_carer_take_up_input( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Keep would_claim_carers_allowance coherent with the refilled receipt. + + The root stage sets the flag from reported Carer's Allowance; the SPI + fill re-imputes that receipt on the redrawn rows, so the flag is + re-derived here from the refilled amount. A stale flag would pay the + allowance to a redrawn 35-hour carer whose refilled receipt is zero, or + withhold it from one whose refilled receipt is positive (#882). + """ + + if "would_claim_carers_allowance" not in person.columns: + return person + refilled = ( + pd.to_numeric( + person.loc[spi_people, "carers_allowance_reported"], errors="coerce" + ) + .fillna(0.0) + .to_numpy() + > 0.0 + ) + _assign_spi_values( + person, spi_people, "would_claim_carers_allowance", refilled, default=False + ) + return person + + def _assign_spi_values( person: pd.DataFrame, spi_people: pd.Series, 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 9ca574f75f..edca46004f 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 @@ -168,6 +168,10 @@ def __post_init__(self) -> None: "benunit.uc_deduction_type_random_draw", "benunit.uc_latent_deduction_rate", "benunit.uc_reported_capital", + # #882: engine inputs the incumbent never carried (policyengine-uk 2.98.0 + # reads them); the coverage manifest measures the incumbent surface, so + # net-new inputs are declared here like the deduction draws above. + "benunit.would_claim_uc_childcare", "household.bus_fare_spending", "household.bus_subsidy_spending", "household.cash_isa", @@ -197,6 +201,7 @@ def __post_init__(self) -> None: "person.admitted_patient_visits", "person.age_started_or_accepted_current_education_or_training", "person.attends_private_school_random_draw", + "person.care_hours", "person.charitable_investment_gifts", "person.dla_m_category", "person.dla_sc_category", @@ -224,6 +229,7 @@ def __post_init__(self) -> None: "person.student_loan_balance", "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", + "person.would_claim_carers_allowance", "person.would_claim_marriage_allowance", "person.would_claim_scp", ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py index 3e0d76cf70..7b25169cac 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_source_periods.py @@ -22,6 +22,10 @@ from microcosm.calibrate import TargetRegistry EXPECTED_SOURCE_MONTHS = "uk_uc_expected_source_months" +# The DWP families whose monthly Stat-Xplore series declare their source +# window: Universal Credit (the paid-claim and element rows) and, since #882, +# the Housing Benefit caseload rows bound on the same calendar-2025 window. +SOURCE_MONTH_FAMILIES = frozenset({"dwp_universal_credit", "dwp_housing_benefit"}) def uc_source_month_metadata( @@ -60,12 +64,17 @@ def validate_uc_source_month_coverage( encoded = reference.metadata.get(EXPECTED_SOURCE_MONTHS) if encoded is None: return registry - if reference.family != "dwp_universal_credit" or reference.value_operation not in { - "calendar_year_average", - *MONTHLY_WINDOW_OPERATIONS, - }: + if ( + reference.family not in SOURCE_MONTH_FAMILIES + or reference.value_operation + not in { + "calendar_year_average", + *MONTHLY_WINDOW_OPERATIONS, + } + ): raise ValueError( - "UK UC source-month coverage requires a UC calendar_year_average or explicit monthly window reference." + "UK DWP source-month coverage requires a UC or Housing Benefit " + "calendar_year_average or explicit monthly window reference." ) try: expected = json.loads(encoded) diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py index 6b585d9bcf..5e336b231c 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py @@ -28,7 +28,7 @@ PRIMARY_QRF_WORKER_MODULE = "microcosm.build.us_runtime.puf_qrf_worker" PRIMARY_QRF_INTERPRETER_PLACEHOLDER = "{python_interpreter}" APPROVED_UV_LOCK_SHA256 = ( - "635accf67e2b33ed9a1af306928079d42c0b105d65e030ea597fdb26cd5902a1" + "eb328a5cf6cc0bc567c209fce03294f4f3b0b5e59adf47dfa7ff4173545e03be" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" diff --git a/packages/microcosm-build/tests/test_country_spec.py b/packages/microcosm-build/tests/test_country_spec.py index 21ad5f5b1d..3db6a4a94e 100644 --- a/packages/microcosm-build/tests/test_country_spec.py +++ b/packages/microcosm-build/tests/test_country_spec.py @@ -1098,7 +1098,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: references = {reference.name: reference for reference in spec.target_references} assert ( - len(references) == 614 + len(references) == 631 ) # microcosm#905: 424 - 18 country rows + 189 region-tier cells; # microcosm#929: the 81 VOA region cells become 81 composed MHCLG # cells and Wales gains ten country rows (bands A-I + total) diff --git a/packages/microcosm-build/tests/test_stochastic_assignment.py b/packages/microcosm-build/tests/test_stochastic_assignment.py index f0cad84c5d..ede0c0a063 100644 --- a/packages/microcosm-build/tests/test_stochastic_assignment.py +++ b/packages/microcosm-build/tests/test_stochastic_assignment.py @@ -126,3 +126,37 @@ def test_stable_identity_uniforms_is_permutation_equivariant() -> None: permuted_draws = stable_identity_uniforms(permuted_ids, seed=7, salt="flag") np.testing.assert_array_equal(permuted_draws[[1, 2, 0]], draws) + + +def test_anchored_residual_population_restricts_the_draw_and_the_target() -> None: + """The rate is a share of the population; rows outside it are never drawn.""" + + # 60 population rows with evenly spread draws, then 40 rows outside it. + draws = np.concatenate([np.linspace(0.0, 0.99, 60), np.linspace(0.0, 0.99, 40)]) + population = np.arange(100) < 60 + anchor = np.zeros(100, dtype=bool) + anchor[5] = True # inside the population + anchor[95] = True # outside it: reported receipt stays true + + result = assign_binary_with_anchored_residual( + draws, 0.5, anchor, population=population + ) + + # int(0.5 * 60) = 30 inside the population: the anchor plus 29 residual + # rows at the adjusted threshold 29 / 59 over the evenly spread draws. + assert int(result[population].sum()) == 30 + assert result[95] and int(result[~population].sum()) == 1 + assert not (result & ~population & ~anchor).any() + + +def test_anchored_residual_population_without_anchor_and_shape_refusal() -> None: + draws = np.linspace(0.0, 0.99, 10) + population = np.array([True] * 5 + [False] * 5) + + result = assign_binary_with_anchored_residual(draws, 0.4, population=population) + + assert result.tolist() == [True, True, True, True, False] + [False] * 5 + with pytest.raises(ValueError, match="population and draws must align"): + assign_binary_with_anchored_residual( + draws, 0.4, np.zeros(10, dtype=bool), population=np.ones(3, dtype=bool) + ) diff --git a/packages/microcosm-build/tests/test_uk_frs_spine.py b/packages/microcosm-build/tests/test_uk_frs_spine.py index fdd548b969..2f567c22fe 100644 --- a/packages/microcosm-build/tests/test_uk_frs_spine.py +++ b/packages/microcosm-build/tests/test_uk_frs_spine.py @@ -22,6 +22,7 @@ frs_disability, frs_education_grants, frs_legacy_proxies, + frs_take_up, ) from microcosm.build.uk_runtime.content_identity import uk_frame_content_identity from microcosm.build.uk_runtime.frs_relationships import ( @@ -158,6 +159,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "AGE": 40, "SEX": 1, "TOTHOURS": 40, + # HOURTOT 5 = 35-49 hours of care a week (#882). + "HOURTOT": 5, "HRPID": 1, "UPERSON": 1, # #791 household grid: the HRP carries a blank relhrp and the parent @@ -205,7 +208,15 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: # heartval is on the adult tape too; the three school columns are not. "HEARTVAL": 5.0, } - adult_2 = {**adult_1, "SERNUM": 2, "PERSON": 1, "SEX": 2, "HRPID": 1, "R02": ""} + adult_2 = { + **adult_1, + "SERNUM": 2, + "PERSON": 1, + "SEX": 2, + "HRPID": 1, + "R02": "", + "HOURTOT": "", + } child_1 = { "SERNUM": 1, "BENUNIT": 1, @@ -600,6 +611,12 @@ def source_stage( grain="benunit", operations=[ {"kind": "aggregate_person_to_benunit"}, + { + "kind": "aggregate_person_to_benunit", + "method": "any_adult_under_state_pension_age", + "consumed_only": True, + "aggregates": {"uc_age_eligible": "age"}, + }, { "kind": "assign_binary_with_anchored_residual", "output": "would_claim_child_benefit", @@ -618,6 +635,7 @@ def source_stage( { "kind": "assign_binary_with_anchored_residual", "output": "would_claim_uc", + "population": "uc_age_eligible", "seed": 0, }, { @@ -640,6 +658,11 @@ def source_stage( "output": "would_claim_targeted_childcare", "seed": 0, }, + { + "kind": "assign_binary_from_banded_rates", + "output": "would_claim_uc_childcare", + "seed": 0, + }, { "kind": "assign_clipped_normal", "output": "maximum_extended_childcare_hours_usage", @@ -655,6 +678,7 @@ def source_stage( "would_claim_extended_childcare", "would_claim_universal_childcare", "would_claim_targeted_childcare", + "would_claim_uc_childcare", "maximum_extended_childcare_hours_usage", ), nonnegative_outputs=("maximum_extended_childcare_hours_usage",), @@ -975,6 +999,12 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["is_benunit_head"] assert adult["is_parent"] assert adult["hours_worked"] == pytest.approx(40 * WEEKS_IN_YEAR) + assert adult["care_hours"] == 35.0 + assert bool(adult["would_claim_carers_allowance"]) == bool( + adult["carers_allowance_reported"] > 0 + ) + other = person.loc[person["person_id"] == 2001].iloc[0] + assert other["care_hours"] == 0.0 assert adult["employment_income"] == pytest.approx(10 * WEEKS_IN_YEAR) assert adult["self_employment_income"] == pytest.approx(3 * WEEKS_IN_YEAR) assert adult["private_pension_income"] == pytest.approx(15 * WEEKS_IN_YEAR) @@ -1211,6 +1241,16 @@ def _stub_policy_readers(monkeypatch: pytest.MonkeyPatch) -> None: source="test stub", ), ) + monkeypatch.setattr( + frs_take_up, + "uk_take_up_population_policy", + lambda period: frs_take_up.UKTakeUpPopulationPolicy( + adult_age=18, + state_pension_age=66, + instant=f"{period}-01-01", + source="test stub", + ), + ) monkeypatch.setitem(sys.modules, "policyengine_uk", None) @@ -2347,3 +2387,29 @@ def checkpoint_metadata(self) -> dict[str, object]: stage_names=("early_stage", "late_stage"), implementations=implementations ) assert transferred == {"late_stage": {"stage": "late_stage", "ok": True}} + + +def test_care_hours_map_the_hourtot_band_codes_and_refuse_unknown_codes() -> None: + from microcosm.build.uk_runtime.frs_spine import ( + FRS_CARE_HOURS_BY_BAND, + frs_care_hours, + ) + + person = pd.DataFrame( + {"hourtot": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, "", np.nan, "x"]} + ) + hours = frs_care_hours(person) + assert hours.tolist() == [0, 0, 5, 10, 20, 35, 50, 100, 0, 20, 35, 0, 0, 0] + assert hours.dtype == "float64" + # Only the 35-hour codes reach the Carer's Allowance line the engine tests. + assert [code for code, value in FRS_CARE_HOURS_BY_BAND.items() if value >= 35] == [ + 5, + 6, + 7, + 10, + ] + assert frs_care_hours(pd.DataFrame({"age": [1, 2]})).tolist() == [0.0, 0.0] + with pytest.raises(ValueError, match="unknown band code"): + frs_care_hours(pd.DataFrame({"hourtot": [11]})) + with pytest.raises(ValueError, match="must be integral"): + frs_care_hours(pd.DataFrame({"hourtot": [2.5]})) diff --git a/packages/microcosm-build/tests/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/test_uk_frs_stochastic.py index c64eda93d4..320958753a 100644 --- a/packages/microcosm-build/tests/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/test_uk_frs_stochastic.py @@ -1,11 +1,19 @@ from __future__ import annotations +import json +from dataclasses import replace +from importlib import metadata, resources from types import SimpleNamespace import numpy as np import pandas as pd import pytest +from microcosm.build.source_manifest import ( + SourceManifest, + SourceOperationSpec, + SourceStageSpec, +) from microcosm.build.stochastic_assignment import stable_identity_uniforms from microcosm.build.uk_runtime.frs_brma import ( UKFRSBRMAStageTransform, @@ -24,10 +32,33 @@ from microcosm.build.uk_runtime.frs_take_up import ( FRS_TAKE_UP_OUTPUT_COLUMNS, UKFRSTakeUpStageTransform, + UKTakeUpPopulationPolicy, aggregate_person_reported_to_benunit, + assert_take_up_stage_population_declaration, + derive_frs_take_up, + uc_age_eligible_benunits, + uk_take_up_population_policy, ) from microcosm.build.uk_runtime.national_frame import uk_national_frame +# The engine's 2025 working-age bounds (is_adult at 18, State Pension age 66), +# injected so the hermetic tests need no engine; the lockstep test below checks +# the reader returns exactly this. +_POLICY = UKTakeUpPopulationPolicy( + adult_age=18, state_pension_age=66, instant="2025-01-01", source="test" +) + + +def _take_up_stage() -> SourceStageSpec: + manifest = SourceManifest.from_mapping( + json.loads( + resources.files("microcosm.build.uk") + .joinpath("source_stages.json") + .read_text(encoding="utf-8") + ) + ) + return next(stage for stage in manifest.stages if stage.stage == "frs_take_up") + class _Contract: rates = { @@ -39,6 +70,8 @@ class _Contract: "extended_childcare": 0.5, "universal_childcare": 0.5, "targeted_childcare": 0.5, + "uc_childcare_single": 0.5, + "uc_childcare_couple": 0.0, "marriage_allowance": 0.5, "scp_under_6": 0.97, "scp_6_plus": 0.85, @@ -87,7 +120,9 @@ def _frame() -> object: "universal_credit_reported": [0, 0, 20, 0], } ) - benunit = pd.DataFrame({"benunit_id": [10, 20, 30]}) + benunit = pd.DataFrame( + {"benunit_id": [10, 20, 30], "is_married": [False, True, False]} + ) household = pd.DataFrame( { "household_id": [1, 2], @@ -113,7 +148,9 @@ def test_take_up_anchor_missing_source_column_fails_loud() -> None: def test_take_up_anchors_or_over_persons_and_stage_writes_outputs() -> None: frame = _frame() - transformed = UKFRSTakeUpStageTransform(contract=_Contract(), stage=None)(frame) + transformed = UKFRSTakeUpStageTransform( + contract=_Contract(), stage=_take_up_stage(), population_policy=_POLICY + )(frame) benunit = transformed.table("benunit") anchors = aggregate_person_reported_to_benunit( @@ -228,3 +265,115 @@ def test_brma_missing_cell_fails_closed() -> None: with pytest.raises(KeyError, match="missing BRMA"): assign_brma_by_cell(benunit, count_resource={"cells": {"LONDON": {}}}, seed=0) + + +def test_uc_take_up_population_excludes_units_without_a_working_age_adult() -> None: + """A unit with no adult under State Pension age is never drawn into UC.""" + + frame = _frame() + person, benunit = frame.table("person"), frame.table("benunit") + + eligible = uc_age_eligible_benunits(person, benunit, _POLICY) + assert eligible.tolist() == [False, True, False] # children only / 40 / 70 + # The bounds are the engine's is_WA_adult edges: 17 is out, 18 in, 66 out. + assert _POLICY.working_age(np.array([17, 18, 65, 66])).tolist() == [ + False, + True, + True, + False, + ] + + anchors = aggregate_person_reported_to_benunit(person, benunit) + derived = derive_frs_take_up( + benunit, anchors=anchors, contract=_Contract(), uc_age_eligible=eligible + ) + assert derived["would_claim_uc"].tolist() == [False, True, False] + + # An anchor outside the population stays true: reported receipt is a fact. + anchors.loc[2, "universal_credit_reported_anchor"] = True + derived = derive_frs_take_up( + benunit, anchors=anchors, contract=_Contract(), uc_age_eligible=eligible + ) + assert derived["would_claim_uc"].tolist() == [False, True, True] + + with pytest.raises(ValueError, match="uc_age_eligible must align"): + derive_frs_take_up( + benunit, anchors=anchors, contract=_Contract(), uc_age_eligible=eligible[:2] + ) + with pytest.raises(KeyError, match="person.age is missing"): + uc_age_eligible_benunits(person.drop(columns=["age"]), benunit, _POLICY) + + +def test_take_up_stage_refuses_a_manifest_that_drops_the_population_declaration() -> ( + None +): + """The manifest's population declaration is read, not decorative.""" + + stage = _take_up_stage() + assert_take_up_stage_population_declaration(stage) + + stripped = [ + SourceOperationSpec(op.kind, {**op.parameters, "population": "everyone"}) + if op.parameters.get("output") == "would_claim_uc" + else op + for op in stage.operations + ] + broken = replace(stage, operations=tuple(stripped)) + with pytest.raises(ValueError, match="population='uc_age_eligible'"): + assert_take_up_stage_population_declaration(broken) + with pytest.raises(ValueError, match="population='uc_age_eligible'"): + UKFRSTakeUpStageTransform( + contract=_Contract(), stage=broken, population_policy=_POLICY + )(_frame()) + + +@pytest.mark.requires_uk +def test_uc_take_up_population_policy_matches_the_engine_working_age_test() -> None: + """The policy read from the engine reproduces is_WA_adult for every age.""" + + policyengine_uk = pytest.importorskip("policyengine_uk") + + policy = uk_take_up_population_policy(2025) + assert policy == UKTakeUpPopulationPolicy( + adult_age=18, + state_pension_age=66, + instant="2025-01-01", + source="policyengine-uk parameters " + metadata.version("policyengine-uk"), + ) + + ages = list(range(0, 101)) + people = {f"p{age}": {"age": {2025: age}} for age in ages} + sim = policyengine_uk.Simulation( + situation={ + "people": people, + "benunits": {f"b{age}": {"members": [f"p{age}"]} for age in ages}, + "households": {f"h{age}": {"members": [f"p{age}"]} for age in ages}, + } + ) + is_wa_adult = np.asarray(sim.calculate("is_WA_adult", 2025), dtype=bool) + + assert is_wa_adult.tolist() == policy.working_age(np.array(ages)).tolist() + + +def test_uc_childcare_take_up_is_drawn_by_family_type() -> None: + """The couple rate applies where is_married is set; zero means never drawn.""" + + frame = _frame() + person, benunit = frame.table("person"), frame.table("benunit") + anchors = aggregate_person_reported_to_benunit(person, benunit) + derived = derive_frs_take_up( + benunit, + anchors=anchors, + contract=_Contract(), + uc_age_eligible=uc_age_eligible_benunits(person, benunit, _POLICY), + ) + # Benefit unit 20 is the couple; its rate is 0.0 in the fixture contract. + assert not derived.loc[1, "would_claim_uc_childcare"] + assert derived["would_claim_uc_childcare"].dtype == bool + with pytest.raises(KeyError, match="benunit.is_married is missing"): + derive_frs_take_up( + benunit.drop(columns=["is_married"]), + anchors=anchors, + contract=_Contract(), + uc_age_eligible=uc_age_eligible_benunits(person, benunit, _POLICY), + ) diff --git a/packages/microcosm-build/tests/test_uk_measure_simulation.py b/packages/microcosm-build/tests/test_uk_measure_simulation.py index 297c4837bf..d0f6468ccc 100644 --- a/packages/microcosm-build/tests/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/test_uk_measure_simulation.py @@ -128,7 +128,7 @@ def test_cgt_period_measures_bypass_stored_inputs_without_mutation( monkeypatch.setitem( sys.modules, "policyengine_uk", - SimpleNamespace(__version__="2.97.0", Microsimulation=lambda **kw: sim), + SimpleNamespace(__version__="2.98.0", Microsimulation=lambda **kw: sim), ) resolver = UKMeasureResolver( simulation_source=tmp_path / "input.h5", @@ -235,7 +235,7 @@ def test_resolver_refuses_persisted_cgt_measure_aliases(monkeypatch, tmp_path): sys.modules, "policyengine_uk", SimpleNamespace( - __version__="2.97.0", Microsimulation=lambda **kw: SimulationStub({}) + __version__="2.98.0", Microsimulation=lambda **kw: SimulationStub({}) ), ) with pytest.raises(ValueError, match="must not be persisted"): @@ -589,32 +589,40 @@ def test_exclusion_applier_warns_within_week_of_expiry(): "ons.savings_interest_income": 1, "obr.housing_benefit": 1, "dwp.jsa_claimants": 1, - # microcosm#882 (2026-09-15): the three UC element rows measured but - # held out of the objective — carer and childcare as model concept gaps, - # the any-tenure housing row as a structural bias — signed by María in - # review of microcosm#921 with a one-month window. - "dwp/uc/elements/": 3, + # microcosm#882 (2026-09-15): three UC element rows were held out of the + # objective, signed by María in review of microcosm#921 with a one-month + # window. The carer and childcare rows retired on 2026-09-16 with their + # repairs (care hours and the childcare take-up draw on policyengine-uk + # 2.98.0); the any-tenure housing row stays held out as a structural bias. + "dwp.uc.households_": 1, + # microcosm#882 repairs (2026-09-16): the three Housing Benefit caseload + # rows and the thirteen benefit-cap amount bands outside the 25 percent + # bound are measured on every evaluation but held out of the objective; + # the A16 rows obr.housing_benefit and dwp.jsa_claimants were + # re-adjudicated the same day on the mechanism receipts. + "dwp.hb.": 3, + "dwp.benefit_cap.capped_households_": 13, } -_UC_ELEMENT_REGISTER_ROWS = ( - "dwp/uc/elements/carer", - "dwp/uc/elements/childcare", - "dwp/uc/elements/housing", -) +# The carer and childcare rows were retired on 2026-09-16 with the repairs +# (care hours and the childcare take-up draw on policyengine-uk 2.98.0); +# only the any-tenure housing row remains held out. +_UC_ELEMENT_REGISTER_ROWS = ("dwp.uc.households_housing_element",) _A16_UNREACHABLE_ROWS = ( "ons.savings_interest_income", - "obr.housing_benefit", "slc.borrowers.plan_2_liable", "slc.borrowers.plan_2_above_threshold", + "obr.housing_benefit", "dwp.jsa_claimants", ) +_A16_READJUDICATED_ROWS = ("obr.housing_benefit", "dwp.jsa_claimants") def test_packaged_exclusions_load(): exclusions = load_uk_calibration_measure_exclusions() names = [entry["name"] for entry in exclusions] - assert len(names) == len(set(names)) == 53 + assert len(names) == len(set(names)) == 67 band_h_region_cells = [ entry for entry in exclusions @@ -662,11 +670,10 @@ def test_packaged_exclusions_load(): assert not [n for n in names if n.startswith("ons.household_composition.")] # The 2026-09-03 tranche is #762's A16: five unreachable national rows, - # a one-month window, each row tracked on its spine-defect issue. - a16 = [e for e in exclusions if e["approved_on"] == "2026-09-03"] - assert sorted(e["name"] for e in a16) == sorted(_A16_UNREACHABLE_ROWS) - # Each row points at its own spine-defect issue (the October expiry follows - # the pointer); #736 carries the tranche as a whole. + # a one-month window, each row tracked on its spine-defect issue. Two of + # them (housing benefit, JSA) were re-adjudicated on 2026-09-16 with the + # mechanism receipts (#882) and moved to the 2026-12-08 clock; the other + # three still lapse on 2026-10-03. a16_issues = { "ons.savings_interest_income": "microcosm#866", "obr.housing_benefit": "microcosm#867", @@ -674,18 +681,52 @@ def test_packaged_exclusions_load(): "slc.borrowers.plan_2_above_threshold": "microcosm#868", "dwp.jsa_claimants": "microcosm#869", } + a16 = [e for e in exclusions if e["approved_on"] == "2026-09-03"] + assert sorted(e["name"] for e in a16) == sorted(_A16_UNREACHABLE_ROWS[:3]) for entry in a16: assert entry["expires_on"] == "2026-10-03", entry["name"] assert entry["tracking"] == a16_issues[entry["name"]], entry["name"] assert "A16" in entry["adjudication"], entry["name"] - - # The 2026-09-15 tranche is #882's element rows: carer and childcare are - # model concept gaps (the engine's carer condition is Carer's Allowance - # receipt; the childcare element has no take-up lever) and the any-tenure - # housing row a structural bias (DWP's 'Yes' includes an other/unknown - # tenure the model cannot carry). Each is adjudicated to microcosm#882 - # and the committed baseline doc, signed in review of microcosm#921, and - # windowed to one month so the repair or re-adjudication is forced. + for name in _A16_READJUDICATED_ROWS: + entry = next(e for e in exclusions if e["name"] == name) + assert entry["approved_on"] == "2026-09-16", name + assert entry["expires_on"] == "2026-12-08", name + assert entry["tracking"] == a16_issues[name], name + assert "tools/diagnose_uk_legacy_benefits.py" in entry["adjudication"], name + assert "issuecomment-5694598278" in entry["adjudication"], name + assert "SPI support channel" in entry["reason"], name + + # The 2026-09-16 tranche also carries the Housing Benefit caseload rows + # (tracked on #867 like the spend row) and the benefit-cap amount bands + # outside the bound (tracked on #882); every entry names its tool. + repairs = [ + e + for e in exclusions + if e["approved_on"] == "2026-09-16" and e["name"] not in _A16_READJUDICATED_ROWS + ] + assert len(repairs) == 16 + for entry in repairs: + assert entry["expires_on"] == "2026-12-08", entry["name"] + # Every entry of the tranche points at the ruling that exists (the + # 2026-09-16 status comment) and explains the shared expiry. + assert "issuecomment-5694598278" in entry["adjudication"], entry["name"] + assert "zero-band clock" in entry["adjudication"], entry["name"] + if entry["name"].startswith("dwp.hb."): + assert entry["tracking"] == "microcosm#867", entry["name"] + assert "tools/diagnose_uk_legacy_benefits.py" in entry["adjudication"] + else: + assert entry["name"].startswith("dwp.benefit_cap.capped_households_") + assert entry["tracking"] == "microcosm#882", entry["name"] + assert "tools/diagnose_uk_benefit_cap.py" in entry["adjudication"] + assert "tools/diagnose_uk_benefit_cap.py" in entry["reason"], entry["name"] + assert "dwp.benefit_cap.capped_households_up_to_100" not in names + assert "dwp.benefit_cap.capped_households" not in names + + # The 2026-09-15 tranche was #882's element rows. Carer and childcare + # retired on 2026-09-16 with their repairs; the any-tenure housing row (a + # structural bias: DWP's 'Yes' includes an other/unknown tenure the model + # cannot carry) remains, adjudicated to microcosm#882 and the committed + # baseline doc, signed in review of microcosm#921, windowed to one month. elements = [e for e in exclusions if e["approved_on"] == "2026-09-15"] assert sorted(e["name"] for e in elements) == sorted(_UC_ELEMENT_REGISTER_ROWS) for entry in elements: @@ -697,15 +738,19 @@ def test_packaged_exclusions_load(): assert "docs/uk-uc-baseline-2026-09-10.md" in entry["adjudication"], entry[ "name" ] - housing = next(e for e in elements if e["name"] == "dwp/uc/elements/housing") + housing = next( + e for e in elements if e["name"] == "dwp.uc.households_housing_element" + ) assert "112,518 of 4,037,650" in housing["reason"] # The four element rows that stay in the objective are not on the register. for riding in ( - "dwp/uc/elements/lcwra", - "dwp/uc/elements/housing_social_rented", - "dwp/uc/elements/housing_private_rented", - "dwp/uc/elements/deductions", - "obr/universal_credit", + "dwp.uc.households_carer_element", + "dwp.uc.households_childcare_element", + "dwp.uc.households_lcwra_element", + "dwp.uc.households_housing_element_social_rented", + "dwp.uc.households_housing_element_private_rented", + "dwp.uc.households_with_deduction", + "obr.universal_credit", ): assert riding not in names, riding @@ -835,3 +880,26 @@ def test_exclusion_loader_requires_tracking(tmp_path: Path): {"schema_version": 2, "exclusions": [_entry(tracking="")]}, ) ) + + +def test_packaged_exclusion_names_resolve_to_committed_references(): + """Every register name must be a committed reference name, not a metric. + + The applier matches ``spec.name``, which is the reference name + (``dwp.uc.households_carer_element``), so an entry keyed by the binding's + metric name (``dwp/uc/elements/carer``) matches nothing and the + calibration run refuses with "matched zero registry specs". The three + #882 element rows shipped that way in microcosm#921; this pins the + identity so a register edit cannot drift off the reference surface again. + """ + + from microcosm.build.country_spec import load_country_spec + + reference_names = { + reference.name for reference in load_country_spec("uk").target_references + } + exclusions = load_uk_calibration_measure_exclusions() + unresolved = sorted( + entry["name"] for entry in exclusions if entry["name"] not in reference_names + ) + assert unresolved == [], unresolved diff --git a/packages/microcosm-build/tests/test_uk_national_calibration.py b/packages/microcosm-build/tests/test_uk_national_calibration.py index a9ed655016..8eb2db68af 100644 --- a/packages/microcosm-build/tests/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/test_uk_national_calibration.py @@ -41,7 +41,7 @@ from microcosm.calibrate import TargetRegistry, TargetSpec from microcosm.frame import EntitySchema, Frame, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 614 +ACTIVE_REFERENCE_COUNT = 631 def _uc_reference(**overrides) -> LedgerTargetReference: diff --git a/packages/microcosm-build/tests/test_uk_population_targets.py b/packages/microcosm-build/tests/test_uk_population_targets.py index d14453cd80..b7c685e08a 100644 --- a/packages/microcosm-build/tests/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/test_uk_population_targets.py @@ -182,7 +182,7 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: "operation": "sum", "assertion_policy": "observed_only", } - assert len(resource["targets"]) == 281 + assert len(resource["targets"]) == 298 providers = resource["hierarchy"]["providers"] categories = resource["hierarchy"]["categories"] @@ -196,10 +196,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: target_ids = [target["target_id"] for target in resource["targets"]] registry_scope = resource["registry_parity"]["scope_target_ids"] profile_scope = resource["profile_parity"]["scope_target_ids"] - assert len(registry_scope) == 230 + assert len(registry_scope) == 247 assert len(profile_scope) == 51 - assert target_ids[:230] == registry_scope - assert target_ids[230:] == profile_scope + assert target_ids[:247] == registry_scope + assert target_ids[247:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -213,7 +213,7 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope) assert all(reason for reason in unmapped_declarations.values()) assert len(mapped_target_ids) == 192 - assert len(unmapped_declarations) == 38 + assert len(unmapped_declarations) == 55 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 assert set(suppressed_ancestors).isdisjoint(parity["mapped"]) @@ -332,7 +332,7 @@ def test_uk_population_targets_have_unique_target_ids() -> None: resource = _load() target_ids = [target["target_id"] for target in resource["targets"]] - assert len(target_ids) == 281 + assert len(target_ids) == 298 assert len(target_ids) == len(set(target_ids)) @@ -793,7 +793,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 281 + assert len(targets) == 298 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets diff --git a/packages/microcosm-build/tests/test_uk_source_stages.py b/packages/microcosm-build/tests/test_uk_source_stages.py index f2a447dfd0..964f8a68c0 100644 --- a/packages/microcosm-build/tests/test_uk_source_stages.py +++ b/packages/microcosm-build/tests/test_uk_source_stages.py @@ -592,6 +592,7 @@ def test_e3_operation_kinds_are_declared_in_order(self) -> None: "derive", ] assert [op.kind for op in stages["frs_take_up"].operations] == [ + "aggregate_person_to_benunit", "aggregate_person_to_benunit", "assign_binary_with_anchored_residual", "assign_binary_from_rate", @@ -601,6 +602,7 @@ def test_e3_operation_kinds_are_declared_in_order(self) -> None: "assign_binary_from_rate", "assign_binary_from_rate", "assign_binary_from_rate", + "assign_binary_from_banded_rates", "assign_clipped_normal", ] assert [op.kind for op in stages["frs_person_draws"].operations] == [ diff --git a/packages/microcosm-build/tests/test_uk_spi_income.py b/packages/microcosm-build/tests/test_uk_spi_income.py index 14d2b73144..b43ab43a37 100644 --- a/packages/microcosm-build/tests/test_uk_spi_income.py +++ b/packages/microcosm-build/tests/test_uk_spi_income.py @@ -286,6 +286,38 @@ def test_spi_disability_refresh_reuses_frs_derivation_for_spi_people() -> None: ) +def test_spi_carer_take_up_refresh_follows_the_refilled_receipt() -> None: + """The carer flag is re-derived from the refilled receipt on SPI rows only.""" + + person = pd.DataFrame( + { + "carers_allowance_reported": [0.0, 0.0, 12.0, 5.0], + # Root-stage flags: row 20 kept its donor's True although the fill + # zeroed the receipt; row 30 kept a False although the fill gave + # it one; row 40 is a base row whose stale True must survive. + "would_claim_carers_allowance": [False, True, False, True], + }, + index=[10, 20, 30, 40], + ) + person["would_claim_carers_allowance"] = person[ + "would_claim_carers_allowance" + ].astype(bool) + spi_people = pd.Series([False, True, True, False], index=person.index) + + result = spi_income._refresh_carer_take_up_input(person, spi_people=spi_people) + + assert result["would_claim_carers_allowance"].tolist() == [False, False, True, True] + assert result["would_claim_carers_allowance"].dtype == bool + # A frame without the flag (an older synthetic manifest) passes through. + bare = pd.DataFrame({"carers_allowance_reported": [1.0]}, index=[1]) + assert ( + "would_claim_carers_allowance" + not in spi_income._refresh_carer_take_up_input( + bare, spi_people=pd.Series([True], index=[1]) + ) + ) + + @pytest.mark.requires_uk def test_spi_preserves_observed_children_outside_the_donor_age_domain( monkeypatch, tmp_path diff --git a/packages/microcosm-build/tests/test_uk_stochastic_tools.py b/packages/microcosm-build/tests/test_uk_stochastic_tools.py index 732e20968e..ac75338242 100644 --- a/packages/microcosm-build/tests/test_uk_stochastic_tools.py +++ b/packages/microcosm-build/tests/test_uk_stochastic_tools.py @@ -8,6 +8,7 @@ import pandas as pd import pytest +from microcosm.build.uk_runtime.frs_take_up import UKTakeUpPopulationPolicy 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 @@ -190,7 +191,9 @@ def continuous_entry(self, key: str): ) frame = uk_national_frame( person=person, - benunit=pd.DataFrame({"benunit_id": [10, 20, 30]}), + benunit=pd.DataFrame( + {"benunit_id": [10, 20, 30], "is_married": [False, True, False]} + ), household=pd.DataFrame( { "household_id": [1, 2], @@ -213,6 +216,9 @@ def continuous_entry(self, key: str): count_resource=count_resource, lha_category=["A", "A", "A"], permutation_seed=7, + population_policy=UKTakeUpPopulationPolicy( + adult_age=18, state_pension_age=66, instant="2023-01-01", source="test" + ), ) assert receipt["identical_under_permutation"] is True @@ -279,7 +285,9 @@ def _frame(): "person_household_id": [1, 2, 3], } ) - benunit = pd.DataFrame({"benunit_id": [201, 202, 203]}) + benunit = pd.DataFrame( + {"benunit_id": [201, 202, 203], "is_married": [False, True, False]} + ) household = pd.DataFrame( { "household_id": [1, 2, 3], diff --git a/packages/microcosm-build/tests/test_uk_take_up_contract.py b/packages/microcosm-build/tests/test_uk_take_up_contract.py index dc98ea2a81..0b9ca6f046 100644 --- a/packages/microcosm-build/tests/test_uk_take_up_contract.py +++ b/packages/microcosm-build/tests/test_uk_take_up_contract.py @@ -36,6 +36,8 @@ def test_uk_contract_loads_and_selects_build_year_rates() -> None: "extended_childcare", "universal_childcare", "targeted_childcare", + "uc_childcare_single", + "uc_childcare_couple", "scp_under_6", "scp_6_plus", ] @@ -49,6 +51,8 @@ def test_uk_contract_loads_and_selects_build_year_rates() -> None: 0.6054, 0.4539, 0.597, + 0.441, + 0.2682, 0.97, 0.85, ] diff --git a/packages/microcosm-build/tests/test_uk_take_up_gate.py b/packages/microcosm-build/tests/test_uk_take_up_gate.py index 74f7dc8aed..4a4085f26c 100644 --- a/packages/microcosm-build/tests/test_uk_take_up_gate.py +++ b/packages/microcosm-build/tests/test_uk_take_up_gate.py @@ -11,10 +11,15 @@ ) 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: @@ -27,6 +32,8 @@ def rate(self, key: str, build_year: int | None = None) -> float: "extended_childcare": 0.5, "universal_childcare": 0.5, "targeted_childcare": 0.5, + "uc_childcare_single": 0.5, + "uc_childcare_couple": 0.5, "marriage_allowance": 0.5, "scp_under_6": 0.5, "scp_6_plus": 0.5, @@ -46,10 +53,12 @@ def _frame(*, brma_values=("LONDON_A", "LONDON_B")): "person_id": np.arange(101, 101 + n), "person_benunit_id": np.arange(201, 201 + n), "person_household_id": household_ids, - "age": [5, 6] * 5, + "age": [30, 40] * 5, } ) - benunit = pd.DataFrame({"benunit_id": np.arange(201, 201 + n)}) + benunit = pd.DataFrame( + {"benunit_id": np.arange(201, 201 + n), "is_married": [False, True] * 5} + ) household = pd.DataFrame( { "household_id": household_ids, @@ -75,7 +84,9 @@ 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()) + result = uk_take_up_signal_gate( + _frame(), contract=_Contract(), population_policy=_POLICY + ) assert result.passed is True assert "benunit.would_claim_child_benefit" in result.details @@ -85,7 +96,9 @@ def test_take_up_gate_constant_column_fails() -> None: frame = _frame() frame.table("benunit")["would_claim_uc"] = True - result = uk_take_up_signal_gate(frame, contract=_Contract()) + result = uk_take_up_signal_gate( + frame, contract=_Contract(), population_policy=_POLICY + ) assert result.passed is False assert "constant column" in " ".join(result.failures) @@ -95,7 +108,9 @@ 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()) + result = uk_take_up_signal_gate( + frame, contract=_Contract(), population_policy=_POLICY + ) assert result.passed is False assert "property_purchased" in " ".join(result.failures) @@ -143,3 +158,38 @@ def test_gate_registry_vocabulary_round_trip() -> None: assert "take_up_signal" in UK_GATE_REGISTRY assert "enum_domain" in UK_GATE_REGISTRY 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"] >= 107, "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_fails_when_the_uc_population_is_empty() -> None: + frame = _frame() + frame.table("person")["age"] = 70 + + result = uk_take_up_signal_gate( + frame, contract=_Contract(), population_policy=_POLICY + ) + + assert result.passed is False + assert "would_claim_uc: no unit in the draw's population" in " ".join( + result.failures + ) diff --git a/packages/microcosm-build/tests/test_uk_target_references.py b/packages/microcosm-build/tests/test_uk_target_references.py index 14d1e0f53f..fb0c876714 100644 --- a/packages/microcosm-build/tests/test_uk_target_references.py +++ b/packages/microcosm-build/tests/test_uk_target_references.py @@ -60,7 +60,7 @@ _value_operation_by_target_id, ) -ACTIVE_REFERENCE_COUNT = 614 +ACTIVE_REFERENCE_COUNT = 631 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} UK_DATA_REPO = "policyengine-" + "uk-data" @@ -739,7 +739,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: assert membership["target_period"] == 2025 assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT assert membership["status_counts"] == { - "active": 614, + "active": 631, "no_fact_at_or_before_period": 7, "signed_excluded": 8, } diff --git a/packages/microcosm-build/tests/test_uk_uc_source_periods.py b/packages/microcosm-build/tests/test_uk_uc_source_periods.py index 9f0efed7d8..8deca082ae 100644 --- a/packages/microcosm-build/tests/test_uk_uc_source_periods.py +++ b/packages/microcosm-build/tests/test_uk_uc_source_periods.py @@ -442,7 +442,9 @@ def test_generator_preserves_observation_basis_and_explicit_months(): "uc": {"observation_basis": "monthly_stock", **uc_source_month_metadata(MONTHS)} } contract["targets"][0]["family"] = "other" - with pytest.raises(ValueError, match="UK UC-only"): + with pytest.raises( + ValueError, match="declared only by the UK DWP monthly families" + ): _reference_metadata(contract) @@ -511,8 +513,26 @@ def test_shipped_uc_monthly_references_preserve_each_declared_window(): assert json.loads(reference.metadata[EXPECTED_SOURCE_MONTHS]) == expected_months assert new_paid <= {reference.name for reference in monthly} assert set(element_windows) <= {reference.name for reference in monthly} + # The #882 Housing Benefit caseload rows bind the Stat-Xplore client-type + # by tenure cube on the same calendar-2025 window; no other family + # declares source months. + housing_benefit = [ + reference + for reference in references + if reference.family == "dwp_housing_benefit" + ] + assert sorted(reference.name for reference in housing_benefit) == [ + "dwp.hb.households", + "dwp.hb.households_private_rented", + "dwp.hb.households_social_rented", + ] + for reference in housing_benefit: + assert reference.ledger_selector["period_value"] == calendar_2025 + assert reference.value_operation == "monthly_window_average" + assert reference.period_match_policy == "source_window" + assert json.loads(reference.metadata[EXPECTED_SOURCE_MONTHS]) == calendar_2025 assert not any( EXPECTED_SOURCE_MONTHS in reference.metadata for reference in references - if reference.family != "dwp_universal_credit" + if reference.family not in {"dwp_universal_credit", "dwp_housing_benefit"} ) diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index 379332ddb3..2b5f6d8280 100644 --- a/packages/microcosm-data/pyproject.toml +++ b/packages/microcosm-data/pyproject.toml @@ -31,7 +31,7 @@ us = ["policyengine-us>=2.2.1,<3"] # The UK artifact lives in a PRIVATE repo (UK Data Service licence): loads # require an authenticated HF token with access. The loader surfaces the # 401 with that explanation rather than retrying. -uk = ["policyengine-uk>=2.97.0"] +uk = ["policyengine-uk>=2.98.0"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 543eb0a6db..6ef7ab71bb 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -416,13 +416,13 @@ 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 = ( - "4b86eb7903585e2a2ce9585139901dfa467bf42d2a8019e3985615bb1c89e795" + "4456fa0956cde418ae23a60fe72a414428eefab446fd044aa14adf1b6e084fcd" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "5ab0e3da981c6aa162804971f80b680131350f7c53f7d2a52fbaab639fd769d9" + "ff27efe67f3cdb8292dfe8da20a6a98cc20f4e1f77ba61eff2ba206b8eb2fc19" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "49bc5daf9d1a3fea3385d38d2c956c33931747ffb3fda692e5dcb6b2a8e084e9" + "61758f1d9700dd94564d592fafe36b4fc4881b8b77c785349d1756da0df2b0d4" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -766,10 +766,10 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: }, "release_cut": { "gates_manifest_sha256": ( - "72b7b0459c9b276cf6215efb4335f40aca3991ccbc59afb2586f462b7270ccd1" + "7fbf9963c58a3c4ab165a0c1e1d6753243bf48fb685def436b7d394c8dabe57b" ), "policy_sha256": ( - "e8a6c4f196fd1fa056aa7a6e7c4ad9f854763c1796a1fd0bcd1f7173e9e909e7" + "e4146dd4102bb0371a11913c94b41d9ed0114f65d81ef8e9c431c24549edf1bc" ), }, } diff --git a/packages/microcosm-data/tests/test_contract.py b/packages/microcosm-data/tests/test_contract.py index c69757fb3a..82f892f087 100644 --- a/packages/microcosm-data/tests/test_contract.py +++ b/packages/microcosm-data/tests/test_contract.py @@ -138,13 +138,13 @@ 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 = ( - "4b86eb7903585e2a2ce9585139901dfa467bf42d2a8019e3985615bb1c89e795" + "4456fa0956cde418ae23a60fe72a414428eefab446fd044aa14adf1b6e084fcd" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "5ab0e3da981c6aa162804971f80b680131350f7c53f7d2a52fbaab639fd769d9" + "ff27efe67f3cdb8292dfe8da20a6a98cc20f4e1f77ba61eff2ba206b8eb2fc19" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "49bc5daf9d1a3fea3385d38d2c956c33931747ffb3fda692e5dcb6b2a8e084e9" + "61758f1d9700dd94564d592fafe36b4fc4881b8b77c785349d1756da0df2b0d4" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6f0243bcda09dad26945376230c44ec3cf55d4e417c3a25e29bae8c59bc1a69d" diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 3dd6acb1ed..3aa7e71f1d 100644 --- a/packages/microcosm-frame/pyproject.toml +++ b/packages/microcosm-frame/pyproject.toml @@ -16,7 +16,7 @@ microcosm-graph = { workspace = true } [project.optional-dependencies] us = ["microunit>=0.1.0"] policyengine = ["policyengine-us>=2.2.1,<3", "microunit>=0.1.0"] -uk = ["policyengine-uk>=2.97.0"] +uk = ["policyengine-uk>=2.98.0"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py index 4dcb088f5e..00d134dfc2 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py @@ -178,7 +178,7 @@ def validate_uc_claimant_input(simulation: Any, person: Any, period: int | str) except metadata.PackageNotFoundError: installed = "unavailable" guidance = ( - f"Supplied {name} requires policyengine-uk>=2.97.0 with explicit " + f"Supplied {name} requires policyengine-uk>=2.98.0 with explicit " f"person/Boolean/year input support (installed {installed})." ) definition = simulation.tax_benefit_system.variables.get(name) 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 35377274df..0378986547 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 @@ -1511,9 +1511,10 @@ "mortgage_capital_repayment", "structural_insurance_payments", "housing_service_charges", - "external_child_payments" + "external_child_payments", + "care_hours" ], - "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity.", + "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).", "operations": [ { "delimiter": "\t", @@ -1541,7 +1542,7 @@ }, { "kind": "map_coded_amounts", - "scope": "region, tenure, accommodation, council tax band, benefit-code amount mappings" + "scope": "region, tenure, accommodation, council tax band, benefit-code amount mappings; adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance" }, { "kind": "annualize_periodic_amounts", @@ -1556,6 +1557,7 @@ "gender", "marital_status", "hours_worked", + "care_hours", "is_household_head", "is_benunit_head", "is_parent", @@ -1597,6 +1599,7 @@ "dla_m_reported", "iidb_reported", "carers_allowance_reported", + "would_claim_carers_allowance", "sda_reported", "afcs_reported", "ssmg_reported", @@ -1648,7 +1651,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only.", + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with an adult under State Pension age (66 for everyone who can reach it in this release; the engine lockstep test pins it), so the contract rate is a share of those units, an anchor outside them stays true, and a unit with every adult at or over State Pension age is never drawn; its reported Housing Benefit is no longer zeroed by a claim it cannot make (#882, microcosm#867).", "operations": [ { "aggregates": { @@ -1660,6 +1663,14 @@ "kind": "aggregate_person_to_benunit", "method": "any_positive" }, + { + "aggregates": { + "uc_age_eligible": "age" + }, + "consumed_only": true, + "kind": "aggregate_person_to_benunit", + "method": "any_adult_under_state_pension_age" + }, { "anchor": "child_benefit_reported_anchor", "draw": "would_claim_child_benefit", @@ -1688,6 +1699,7 @@ "draw": "would_claim_uc", "kind": "assign_binary_with_anchored_residual", "output": "would_claim_uc", + "population": "uc_age_eligible", "rate_key": "universal_credit", "seed": 0 }, @@ -1719,6 +1731,22 @@ "rate_key": "targeted_childcare", "seed": 0 }, + { + "band_column": "is_married", + "bands": [ + { + "rate_key": "uc_childcare_single", + "upper_exclusive": 1 + }, + { + "rate_key": "uc_childcare_couple" + } + ], + "draw": "would_claim_uc_childcare", + "kind": "assign_binary_from_banded_rates", + "output": "would_claim_uc_childcare", + "seed": 0 + }, { "distribution_key": "maximum_extended_childcare_hours_usage", "draw": "maximum_extended_childcare_hours_usage", @@ -1736,6 +1764,7 @@ "would_claim_extended_childcare", "would_claim_universal_childcare", "would_claim_targeted_childcare", + "would_claim_uc_childcare", "maximum_extended_childcare_hours_usage" ], "rewrites": [], diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv index 597d8d5105..1c3e1df3b8 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ -person_id,person_benunit_id,person_household_id,age,gender,marital_status,hours_worked,is_household_head,is_benunit_head,is_parent,employment_income,self_employment_income,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,property_income,maintenance_income,miscellaneous_income,private_transfer_income,lump_sum_income,student_loan_repayments,statutory_sick_pay,statutory_maternity_pay,student_loans,access_fund,education_grants,healthy_start_vouchers,free_school_breakfasts,free_school_fruit_veg,free_school_meals,child_benefit_reported,income_support_reported,housing_benefit_reported,attendance_allowance_reported,dla_sc_reported,dla_m_reported,iidb_reported,carers_allowance_reported,sda_reported,afcs_reported,ssmg_reported,pension_credit_reported,child_tax_credit_reported,working_tax_credit_reported,state_pension_reported,winter_fuel_allowance_reported,incapacity_benefit_reported,universal_credit_reported,pip_m_reported,pip_dl_reported,jsa_contrib_reported,jsa_income_reported,esa_contrib_reported,esa_income_reported,bsp_reported,council_tax_benefit_reported,maintenance_expenses,childcare_expenses,personal_pension_contributions,employee_pension_contributions,pension_contributions_via_salary_sacrifice,salary_sacrifice_reported,salary_sacrifice_asked,is_uc_claimant,__stratum__ 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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json index 3129218d8c..c5bfafbdf3 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json @@ -33,6 +33,7 @@ "age": "int64", "attendance_allowance_reported": "float64", "bsp_reported": "float64", + "care_hours": "float64", "carers_allowance_reported": "float64", "child_benefit_reported": "float64", "child_tax_credit_reported": "float64", @@ -92,7 +93,8 @@ "tax_free_savings_income": "float64", "universal_credit_reported": "float64", "winter_fuel_allowance_reported": "float64", - "working_tax_credit_reported": "float64" + "working_tax_credit_reported": "float64", + "would_claim_carers_allowance": "bool" } }, "schema": { diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json index c4eff58635..96e78a59f1 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json @@ -1 +1 @@ -{"country":"uk","nodes":[{"base":null,"citation":"","description":"Load the source-bound UK FRS root 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UK spine stage student_loans.","id":"student_loans","inputs":[{"columns":["frs_benunit_capital"],"entity":"benunit","rows":"all"},{"columns":["region"],"entity":"household","rows":"all"},{"columns":["age","current_education","employee_pension_contributions","highest_education","student_loan_repayments","student_loans"],"entity":"person","rows":"all"}],"kernel":"uk.stage.student_loans@1","mass":"conserve","outputs":[{"column":"student_loan_plan","dtype":"string","entity":"person","ownership":"produced","rows":"all"}],"params":{"stage":"student_loans","stage_contract_sha256":"7c4c76398c2e80b41c7f941a5f06246b57d89b5eb0c36d42e3116ac35e82e203","time_period":"2024"},"population":"hmrc_cgt_gains_spine.boundary","sources":["frs"],"structural":"none","weights":null}],"sources":[{"codec":"csv-tables","description":"Content-bound UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/tools/diagnose_uk_benefit_cap.py b/tools/diagnose_uk_benefit_cap.py new file mode 100644 index 0000000000..33a81d9a68 --- /dev/null +++ b/tools/diagnose_uk_benefit_cap.py @@ -0,0 +1,265 @@ +"""Size the benefit-cap reduction tail on a UK spine or calibrated dataset. + +DWP's benefit-cap Table 4 counts UC households capped by the amount capped +per monthly assessment period. The engine's ``benefit_cap_reduction`` is an +annual amount, so the bands are the monthly edges times twelve, half-open on +the lower edge and closed on the upper. The tool reports the band counts on +the paid-claim GB basis the contract binds, the mean and median reduction, +the composition of the tail (family type, children, elements, regions) and, +on a spine, how many source households stand behind each band. Development +diagnostic: it never recalibrates and writes only an aggregate JSON. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import pandas as pd + +GB_REGIONS = ( + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND", +) +# DWP Table 4 monthly bands: (band id, lower edge exclusive, upper edge inclusive). +BANDS = ( + ("up_to_100", 0, 100), + ("100_01_to_200", 100, 200), + ("200_01_to_300", 200, 300), + ("300_01_to_400", 300, 400), + ("400_01_to_500", 400, 500), + ("500_01_to_600", 500, 600), + ("600_01_to_700", 600, 700), + ("700_01_to_800", 700, 800), + ("800_01_to_900", 800, 900), + ("900_01_to_1000", 900, 1000), + ("1000_01_to_1100", 1000, 1100), + ("1100_01_to_1200", 1100, 1200), + ("1200_01_to_1300", 1200, 1300), + ("1300_01_and_above", 1300, None), +) +TAIL_EDGE = 1300 +COMPONENTS = { + "uc_maximum_amount": "uc_maximum_amount", + "housing_element": "uc_housing_costs_element", + "child_element": "uc_child_element", + "standard_allowance": "uc_standard_allowance", + "child_benefit": "child_benefit", +} + + +def _first_person_label(simulation, variable: str, year: int) -> np.ndarray: + labels = np.asarray(simulation.calculate(variable, year, map_to="person")).astype( + str + ) + codes, uniques = pd.factorize(pd.Series(labels)) + first = simulation.populations["benunit"].value_from_first_person(codes) + return np.asarray(uniques)[first].astype(str) + + +def _weighted_median(values: np.ndarray, weights: np.ndarray) -> float: + order = np.argsort(values) + cumulative = np.cumsum(weights[order]) / weights.sum() + return float(np.interp(0.5, cumulative, values[order])) + + +def measure(dataset: str, year: int, spine: str | None = None) -> dict: + from policyengine_uk import Microsimulation + + simulation = Microsimulation(dataset=dataset) + benunit = simulation.populations["benunit"] + weight = np.asarray( + simulation.calculate("benunit_weight", year).values, dtype=float + ) + region = _first_person_label(simulation, "region", year) + universal_credit = np.asarray( + simulation.calculate("universal_credit", year).values, dtype=float + ) + reduction = np.asarray( + simulation.calculate("benefit_cap_reduction", year).values, dtype=float + ) + family_type = np.asarray(simulation.calculate("family_type", year).values).astype( + str + ) + is_child = np.asarray(simulation.calculate("is_child", year).values, dtype=bool) + children = benunit.sum(is_child.astype(float)) + monthly = reduction / 12.0 + great_britain = np.isin(region, GB_REGIONS) + capped = (reduction > 0) & great_britain + paid = capped & (universal_credit > 0) + + def count(mask: np.ndarray) -> float: + return float(weight[mask].sum()) + + def share_by(mask: np.ndarray, labels: np.ndarray) -> dict[str, float]: + total = count(mask) + return { + str(label): count(mask & (labels == label)) / total if total else 0.0 + for label in sorted(set(labels[mask])) + } + + bands: dict[str, dict[str, float]] = {} + for band, lower, upper in BANDS: + in_band = capped & (monthly > lower) + if upper is not None: + in_band &= monthly <= upper + bands[band] = { + "units_gb": count(in_band), + "paid_uc_gb": count(in_band & paid), + "share_of_capped": count(in_band) / count(capped) if count(capped) else 0.0, + "records": int(in_band.sum()), + } + tail = capped & (monthly > TAIL_EDGE) + components = {} + for name, variable in COMPONENTS.items(): + amount = np.asarray( + simulation.calculate(variable, year, map_to="benunit").values, dtype=float + ) + components[name] = { + "tail_mean_monthly_gbp": float( + np.average(amount[tail] / 12.0, weights=weight[tail]) + ) + if tail.any() + else 0.0, + "capped_mean_monthly_gbp": float( + np.average(amount[capped] / 12.0, weights=weight[capped]) + ) + if capped.any() + else 0.0, + } + result = { + "dataset": str(dataset), + "year": year, + "basis": { + "capped": "benefit_cap_reduction > 0 and first member in a GB region", + "paid_uc_gb": "capped and universal_credit > 0", + "bands": "monthly reduction = annual benefit_cap_reduction / 12; " + "lower edge exclusive, upper edge inclusive, last band open", + }, + "capped_units_gb": count(capped), + "paid_uc_gb": count(paid), + "mean_monthly_reduction_gbp": float( + np.average(monthly[capped], weights=weight[capped]) + ) + if capped.any() + else 0.0, + "median_monthly_reduction_gbp": _weighted_median( + monthly[capped], weight[capped] + ) + if capped.any() + else 0.0, + "bands": bands, + "tail": { + "edge_monthly_gbp": TAIL_EDGE, + "units_gb": count(tail), + "share_of_capped": count(tail) / count(capped) if count(capped) else 0.0, + "records": int(tail.sum()), + "mean_children": float(np.average(children[tail], weights=weight[tail])) + if tail.any() + else 0.0, + "family_type_shares": share_by(tail, family_type), + "region_shares": share_by(tail, region), + }, + "capped": { + "mean_children": float(np.average(children[capped], weights=weight[capped])) + if capped.any() + else 0.0, + "family_type_shares": share_by(capped, family_type), + "region_shares": share_by(capped, region), + }, + "components": components, + } + if spine: + result["support"] = _support(spine, simulation, year, capped, tail, monthly) + return result + + +def _support( + spine: str, simulation, year: int, capped: np.ndarray, tail: np.ndarray, monthly +) -> dict: + """Distinct source households behind the capped units, from the spine tables. + + The join is positional: the engine's household population keeps the spine + household table's row order, so each benefit unit's household position + (through its first member) indexes the spine's ``household_source_id`` + directly. No id is cast, so no two households can collide. + """ + + household = pd.read_hdf(spine, "/household") + if "household_source_id" not in household.columns: + return {"available": False} + household_population = simulation.populations["household"] + person_household_position = np.asarray( + household_population.members_entity_id, dtype=np.int64 + ) + benunit = simulation.populations["benunit"] + positions = np.asarray( + benunit.value_from_first_person(person_household_position), dtype=np.int64 + ) + if len(household) != household_population.count or positions.max() >= len( + household + ): + return { + "available": False, + "reason": "engine household order does not match the spine household table", + "spine_households": int(len(household)), + "engine_households": int(household_population.count), + } + source = household["household_source_id"].to_numpy()[positions] + out = { + "available": True, + "join": "positional (engine household order is spine table order)", + "capped_records": int(capped.sum()), + "capped_source_households": int(len(set(source[capped]))), + "tail_records": int(tail.sum()), + "tail_source_households": int(len(set(source[tail]))), + "bands": {}, + } + for band, lower, upper in BANDS: + in_band = capped & (monthly > lower) + if upper is not None: + in_band &= monthly <= upper + out["bands"][band] = int(len(set(source[in_band]))) + return out + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dataset", required=True, help="an H5 the engine loads") + parser.add_argument( + "--spine", + help="the spine H5 whose household table carries household_source_id " + "(support counts; only meaningful when --dataset is that spine)", + ) + parser.add_argument("--year", type=int, default=2025) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + result = measure(args.dataset, args.year, spine=args.spine) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n") + print( + json.dumps( + { + "capped_units_gb": result["capped_units_gb"], + "mean": result["mean_monthly_reduction_gbp"], + "tail_share": result["tail"]["share_of_capped"], + "bands": {k: v["units_gb"] for k, v in result["bands"].items()}, + }, + indent=2, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/tools/diagnose_uk_legacy_benefits.py b/tools/diagnose_uk_legacy_benefits.py new file mode 100644 index 0000000000..c75e743254 --- /dev/null +++ b/tools/diagnose_uk_legacy_benefits.py @@ -0,0 +1,375 @@ +"""Decompose the UK legacy-benefit rows on a spine or a calibrated dataset. + +Housing Benefit, Jobseeker's Allowance and Employment and Support Allowance +are carried by the frame as FRS-reported receipt. This tool reports where the +reporters sit (support channel, tenure, pension age, the ``would_claim_uc`` +draw) and how much of that receipt the engine pays, against the DWP caseload +facts the contract binds. With ``--frs-raw-dir`` it adds the raw FRS 2024-25 +benchmarks (benefit codes 94 HB, 14 JSA, 16 ESA, 13 Carer's Allowance; VAR2 1/3 +contributory, 2/4 income-based). Development diagnostic: it never recalibrates +and writes only an aggregate JSON. +""" + +from __future__ import annotations + +import argparse +import csv +import json +from collections import defaultdict +from pathlib import Path + +import numpy as np +import pandas as pd + +GB_REGIONS = ( + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND", +) +SOCIAL_TENURES = ("RENT_FROM_COUNCIL", "RENT_FROM_HA") +RENTING_TENURES = SOCIAL_TENURES + ("RENT_PRIVATELY",) +REPORTED = { + "housing_benefit": "housing_benefit_reported", + "jsa_contrib": "jsa_contrib_reported", + "jsa_income": "jsa_income_reported", + "esa_contrib": "esa_contrib_reported", + "esa_income": "esa_income_reported", + "carers_allowance": "carers_allowance_reported", + "universal_credit": "universal_credit_reported", +} +ENGINE = { + "housing_benefit": "housing_benefit", + "jsa_contrib": "jsa_contrib", + "jsa_income": "jsa_income", + "esa_contrib": "esa_contrib", + "esa_income": "esa_income", + "esa": "esa", +} +RAW_BENEFIT_CODES = { + "housing_benefit": 94, + "jsa": 14, + "esa": 16, + "carers_allowance": 13, +} + + +def _spine_tables(path: str) -> dict[str, pd.DataFrame]: + return { + name: pd.read_hdf(path, f"/{name}") + for name in ("person", "benunit", "household") + } + + +def _benunit_frame(tables: dict[str, pd.DataFrame]) -> pd.DataFrame: + """One row per benefit unit with the reporter, age, tenure and channel facts.""" + + person = tables["person"] + household = tables["household"].set_index("household_id") + person = person.assign( + weight=person["person_household_id"].map(household["household_weight"]), + tenure=person["person_household_id"].map(household["tenure_type"]).astype(str), + region=person["person_household_id"].map(household["region"]).astype(str), + channel=person["person_household_id"] + .map(household["household_support_channel"]) + .astype(str), + rent=person["person_household_id"].map(household["rent"]), + ) + adults = person[person["age"] >= 16] + ages = adults.groupby("person_benunit_id")["age"].agg(["min", "max"]) + rows = person.groupby("person_benunit_id").agg( + weight=("weight", "first"), + tenure=("tenure", "first"), + region=("region", "first"), + channel=("channel", "first"), + rent=("rent", "first"), + household_head=("is_household_head", "max"), + **{name: (column, "sum") for name, column in REPORTED.items()}, + ) + rows["all_pension_age"] = ages["min"].reindex(rows.index).fillna(0) >= 66 + rows["any_pension_age"] = ages["max"].reindex(rows.index).fillna(0) >= 66 + benunit = tables["benunit"].set_index("benunit_id") + rows["would_claim_uc"] = ( + benunit["would_claim_uc"].reindex(rows.index).fillna(False).astype(bool) + ) + return rows + + +def _age_class(rows: pd.DataFrame) -> pd.Series: + return pd.Series( + np.where( + rows["all_pension_age"], + "all_pension_age", + np.where(rows["any_pension_age"], "mixed_age", "working_age"), + ), + index=rows.index, + ) + + +def _split(rows: pd.DataFrame, mask: pd.Series, by: list[str]) -> dict: + selected = rows[mask] + if selected.empty: + return {} + grouped = selected.groupby(by, observed=True)["weight"].sum() + return { + " | ".join(map(str, key if isinstance(key, tuple) else (key,))): float(v) + for key, v in grouped.items() + } + + +def measure_spine(path: str) -> dict: + rows = _benunit_frame(_spine_tables(path)) + rows["age_class"] = _age_class(rows) + rows["renting"] = rows["tenure"].isin(RENTING_TENURES) + gb = rows["region"].isin(GB_REGIONS) + result: dict[str, object] = { + "dataset": str(path), + "weights": "design", + "reporters": {}, + } + for name in REPORTED: + mask = (rows[name] > 0) & gb + entry = { + "units": float(rows.loc[mask, "weight"].sum()), + "reported_gbp": float((rows[name] * rows["weight"])[mask].sum()), + "by_channel": _split(rows, mask, ["channel"]), + "by_channel_tenure": _split(rows, mask, ["channel", "tenure"]), + "by_age_class": _split(rows, mask, ["age_class"]), + "by_age_class_would_claim_uc": _split( + rows, mask, ["age_class", "would_claim_uc"] + ), + } + if name == "housing_benefit": + entry["renting_units"] = float( + rows.loc[mask & rows["renting"], "weight"].sum() + ) + entry["renting_not_drawn_into_uc"] = float( + rows.loc[ + mask & rows["renting"] & ~rows["would_claim_uc"], "weight" + ].sum() + ) + entry["non_head_units"] = float( + rows.loc[mask & (rows["household_head"] == 0), "weight"].sum() + ) + entry["household_rent_zero_units"] = float( + rows.loc[mask & (rows["rent"] <= 0), "weight"].sum() + ) + result["reporters"][name] = entry + result["would_claim_uc"] = { + "share_all_units": float( + np.average(rows["would_claim_uc"], weights=rows["weight"]) + ), + "all_pension_age_units": float( + rows.loc[rows["all_pension_age"], "weight"].sum() + ), + "all_pension_age_drawn": float( + rows.loc[rows["all_pension_age"] & rows["would_claim_uc"], "weight"].sum() + ), + "share_among_units_with_a_working_age_adult": float( + np.average( + rows.loc[~rows["all_pension_age"], "would_claim_uc"], + weights=rows.loc[~rows["all_pension_age"], "weight"], + ) + ), + } + result["household_mass_by_channel"] = _split( + rows.drop_duplicates(subset=["channel"]).assign(weight=0), + rows["weight"] >= 0, + ["channel"], + ) + household = _spine_tables(path)["household"] + result["household_mass_by_channel"] = { + str(k): float(v) + for k, v in household.groupby("household_support_channel")["household_weight"] + .sum() + .items() + } + return result + + +def _first_person_label(simulation, variable: str, year: int) -> np.ndarray: + labels = np.asarray(simulation.calculate(variable, year, map_to="person")).astype( + str + ) + codes, uniques = pd.factorize(pd.Series(labels)) + first = simulation.populations["benunit"].value_from_first_person(codes) + return np.asarray(uniques)[first].astype(str) + + +def measure_engine(dataset: str, year: int) -> dict: + """Engine-paid legacy benefits on a dataset the engine can load (spine or calibrated).""" + + from policyengine_uk import Microsimulation + + simulation = Microsimulation(dataset=dataset) + benunit = simulation.populations["benunit"] + weight = np.asarray( + simulation.calculate("benunit_weight", year).values, dtype=float + ) + region = _first_person_label(simulation, "region", year) + tenure = np.asarray( + simulation.calculate("benunit_tenure_type", year).values + ).astype(str) + gb = np.isin(region, GB_REGIONS) + would_claim = np.asarray( + simulation.calculate("would_claim_uc", year).values, dtype=bool + ) + pension_age = np.asarray(simulation.calculate("is_SP_age", year).values, dtype=bool) + adult = np.asarray(simulation.calculate("age", year).values) >= 16 + all_pension_age = benunit.all(pension_age | ~adult) & benunit.any(adult) + any_pension_age = benunit.any(pension_age & adult) + age_class = np.where( + all_pension_age, + "all_pension_age", + np.where(any_pension_age, "mixed_age", "working_age"), + ) + social = np.isin(tenure, SOCIAL_TENURES) + private = tenure == "RENT_PRIVATELY" + + def count(mask: np.ndarray) -> float: + return float(weight[mask].sum()) + + def by(mask: np.ndarray, labels: np.ndarray) -> dict[str, float]: + return { + str(label): count(mask & (labels == label)) + for label in sorted(set(labels[mask])) + } + + result: dict[str, object] = { + "dataset": str(dataset), + "year": year, + "weights": "as loaded", + "engine": {}, + } + for name, variable in ENGINE.items(): + amount = np.asarray( + simulation.calculate(variable, year, map_to="benunit").values, dtype=float + ) + paid = (amount > 0) & gb + entry = { + "variable": variable, + "units_gb": count(paid), + "gbp": float((amount * weight)[paid].sum()), + "by_age_class": by(paid, age_class), + } + if name == "housing_benefit": + reported = benunit.sum( + np.asarray( + simulation.calculate("housing_benefit_reported", year).values, + dtype=float, + ) + ) + reporters = (reported > 0) & gb + entry.update( + { + "social_rented_units": count(paid & social), + "private_rented_units": count(paid & private), + "reporter_units_gb": count(reporters), + "reporter_gbp": float((reported * weight)[reporters].sum()), + "reporters_by_age_class": by(reporters, age_class), + "reporters_drawn_into_uc_by_age_class": by( + reporters & would_claim, age_class + ), + "reporters_renting_not_drawn_by_age_class": by( + reporters & (social | private) & ~would_claim, age_class + ), + } + ) + result["engine"][name] = entry + result["would_claim_uc"] = { + "share_all_units": float(np.average(would_claim, weights=weight)), + "all_pension_age_drawn": count(would_claim & all_pension_age), + "paid_uc_all_pension_age": count( + (np.asarray(simulation.calculate("universal_credit", year).values) > 0) + & all_pension_age + ), + } + return result + + +def measure_raw_frs(raw_dir: str) -> dict: + """Raw FRS 2024-25 reporter benchmarks by tenure and claim type (design weights).""" + + raw = Path(raw_dir) + weight: dict[str, float] = {} + tenure: dict[str, str] = {} + with open(raw / "househol.tab") as handle: + for row in csv.DictReader(handle, delimiter="\t"): + weight[row["SERNUM"]] = float(row.get("gross4") or 0) + tenure[row["SERNUM"]] = row.get("ptentyp2", "").strip() + units: dict[tuple[str, str], set[tuple[str, str]]] = defaultdict(set) + with open(raw / "benefits.tab") as handle: + for row in csv.DictReader(handle, delimiter="\t"): + code = row["BENEFIT"].strip() + for name, expected in RAW_BENEFIT_CODES.items(): + if code == str(expected): + var2 = row.get("VAR2", "").strip() + units[(name, var2)].add((row["SERNUM"], row["BENUNIT"])) + result: dict[str, object] = { + "raw_dir": str(raw), + "weights": "gross4", + "benefit_units": {}, + } + for (name, var2), keys in sorted(units.items()): + total = sum(weight.get(sernum, 0.0) for sernum, _ in keys) + by_tenure: dict[str, float] = defaultdict(float) + for sernum, _ in keys: + by_tenure[tenure.get(sernum, "?")] += weight.get(sernum, 0.0) + result["benefit_units"][f"{name}|VAR2={var2 or 'none'}"] = { + "records": len(keys), + "units": total, + "by_ptentyp2": dict(sorted(by_tenure.items())), + } + return result + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--spine", + help="spine H5 with the person/benunit/household tables (design weights)", + ) + parser.add_argument( + "--dataset", + help="an H5 the engine loads (spine or calibrated) for the engine-paid side", + ) + parser.add_argument( + "--frs-raw-dir", + help="licensed FRS 2024-25 tab directory for the raw benchmarks", + ) + parser.add_argument("--year", type=int, default=2025) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + if not (args.spine or args.dataset or args.frs_raw_dir): + parser.error("give at least one of --spine, --dataset, --frs-raw-dir") + result: dict[str, object] = {"year": args.year} + if args.spine: + result["spine"] = measure_spine(args.spine) + if args.dataset: + result["engine"] = measure_engine(args.dataset, args.year) + if args.frs_raw_dir: + result["raw_frs"] = measure_raw_frs(args.frs_raw_dir) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n") + summary = { + key: { + name: entry.get("units", entry.get("units_gb")) + for name, entry in section.get( + "reporters", section.get("engine", {}) + ).items() + } + for key, section in result.items() + if isinstance(section, dict) and ("reporters" in section or "engine" in section) + } + print(json.dumps(summary, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/tools/fit_uk_uc_childcare_takeup.py b/tools/fit_uk_uc_childcare_takeup.py new file mode 100644 index 0000000000..fd96aecabb --- /dev/null +++ b/tools/fit_uk_uc_childcare_takeup.py @@ -0,0 +1,241 @@ +"""Fit the Universal Credit childcare-element take-up rates by family type. + +The engine pays the childcare element to every benefit unit that meets the +work condition and reports childcare costs; DWP publishes the households that +receive it, split single / couple (Universal Credit childcare element +statistics, Table 1). The spine's ``would_claim_uc_childcare`` flag is drawn +at one rate per family type, and the quantity a rate controls is the +expectation ``rate * sum(weight * paid_uc * element > 0)`` over the units of +that family type, so each rate is the published calendar-year mean divided by +that weighted base at the input's weights (the #834 expected-count objective, here +with one target per rate and no cross-scheme interaction). The receipt lands +in ``take_up_contract.json`` as the entries' ``fitting_receipt``. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from datetime import UTC, datetime +from importlib import metadata +from pathlib import Path + +import numpy as np +import pandas as pd + +from microcosm.build.ledger_artifact import load_ledger_consumer_artifact +from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract + +TOOL_VERSION = 1 +CONCEPT = "dwp.uc_households_receiving_childcare_element" +MEASURES = {"single": "single_benefit_units", "couple": "couple_benefit_units"} +RATE_KEYS = {"single": "uc_childcare_single", "couple": "uc_childcare_couple"} +GB_REGIONS = ( + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND", +) + + +ACCEPTANCE_ROOT_MARKER = "data/ukds/acceptance" + + +def _portable_path(path: Path) -> str: + """Record the input relative to the licensed acceptance root, never a home path.""" + + text = str(path) + marker = text.find(ACCEPTANCE_ROOT_MARKER) + return text[marker:] if marker >= 0 else path.name + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(1 << 20), b""): + digest.update(chunk) + return digest.hexdigest() + + +def published_means( + ledger_facts: Path, *, expected_sha256: str | None, year: int +) -> dict: + """Calendar-year means of the published single and couple counts.""" + + artifact = load_ledger_consumer_artifact( + ledger_facts, expected_facts_sha256=expected_sha256 + ) + months = {f"{year}-{month:02d}" for month in range(1, 13)} + by_measure: dict[str, list[float]] = {measure: [] for measure in MEASURES.values()} + for fact in artifact.facts: + measure = fact.get("observed_measure", {}) + if measure.get("source_concept") != CONCEPT: + continue + if fact.get("period", {}).get("value") not in months: + continue + measure_id = measure.get("source_measure_id") + if measure_id in by_measure: + by_measure[measure_id].append(float(fact["value"])) + means = {} + for family, measure_id in MEASURES.items(): + values = by_measure[measure_id] + if len(values) != 12: + raise ValueError( + f"expected 12 monthly {measure_id} facts for {year}, found {len(values)}" + ) + means[family] = float(np.mean(values)) + return {"means": means, "facts_sha256": artifact.facts_sha256} + + +def weighted_bases(input_h5: Path, year: int) -> dict: + """Weighted paid-UC GB units with a positive childcare element, by family type.""" + + from policyengine_uk import Microsimulation + + simulation = Microsimulation(dataset=str(input_h5)) + benunit = simulation.populations["benunit"] + weight = np.asarray( + simulation.calculate("benunit_weight", year).values, dtype=float + ) + labels = np.asarray(simulation.calculate("region", year, map_to="person")).astype( + str + ) + codes, uniques = pd.factorize(pd.Series(labels)) + region = np.asarray(uniques)[benunit.value_from_first_person(codes)].astype(str) + universal_credit = np.asarray( + simulation.calculate("universal_credit", year).values, dtype=float + ) + element = np.asarray( + simulation.calculate("uc_childcare_element", year).values, dtype=float + ) + couple = np.asarray(simulation.calculate("is_married", year).values, dtype=bool) + paid = (universal_credit > 0) & (element > 0) & np.isin(region, GB_REGIONS) + bases = { + "single": float(weight[paid & ~couple].sum()), + "couple": float(weight[paid & couple].sum()), + } + amount = { + "single": float((element * weight)[paid & ~couple].sum()), + "couple": float((element * weight)[paid & couple].sum()), + } + return { + "bases": bases, + "element_gbp": amount, + "records": { + "single": int((paid & ~couple).sum()), + "couple": int((paid & couple).sum()), + }, + } + + +def fit( + input_h5: Path, + ledger_facts: Path, + *, + ledger_facts_sha256: str | None, + year: int, + generated_at: str | None = None, +) -> dict: + published = published_means( + ledger_facts, expected_sha256=ledger_facts_sha256, year=year + ) + model = weighted_bases(input_h5, year) + rates = {} + achieved = {} + for family in MEASURES: + base = model["bases"][family] + target = published["means"][family] + rate = min(1.0, target / base) if base > 0 else 1.0 + rates[RATE_KEYS[family]] = round(rate, 4) + achieved[f"dwp.uc.households_childcare_element_{family}"] = { + "target": round(target, 3), + "base_at_rate_one": round(base, 3), + "value": round(rates[RATE_KEYS[family]] * base, 3), + "ratio": round(rates[RATE_KEYS[family]] * base / target, 4) + if target + else None, + } + try: + engine_version = metadata.version("policyengine-uk") + except metadata.PackageNotFoundError: + engine_version = None + receipt = { + "tool": "fit_uk_uc_childcare_takeup", + "tool_version": TOOL_VERSION, + "input_h5": _portable_path(input_h5), + "input_sha256": _sha256(input_h5), + "seed": 0, + "objective": "expected_count", + "generated_at": generated_at or datetime.now(UTC).date().isoformat(), + "model_period": year, + "engine_version": engine_version, + "ledger_facts_sha256": published["facts_sha256"], + "stochastic_contract_sha256_at_fit": load_uk_take_up_contract().resource_sha256, + "basis": ( + "rate = published calendar-year mean / paid-UC GB benefit units with a positive " + "childcare element of that family type, weighted as the input H5 carries them (a " + "calibrated candidate fits at its calibrated weights, a spine at design weights); " + "one target per rate, so the expected-count objective is exact and needs no optimizer" + ), + "published_means": published["means"], + "model": model, + "rates": rates, + "achieved": achieved, + "status": "fitted at the input's weights; realized counts are measured on the twins in the receipts", + } + receipt["receipt_sha256"] = hashlib.sha256( + json.dumps( + {k: v for k, v in receipt.items() if k != "receipt_sha256"}, sort_keys=True + ).encode() + ).hexdigest() + return receipt + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--input-h5", + type=Path, + required=True, + help="an H5 the engine loads; the rates are fitted at the weights it carries", + ) + parser.add_argument( + "--ledger-facts", + type=Path, + required=True, + help="Chronicle consumer artifact directory", + ) + parser.add_argument("--ledger-facts-sha256") + parser.add_argument("--year", type=int, default=2025) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + receipt = fit( + args.input_h5, + args.ledger_facts, + ledger_facts_sha256=args.ledger_facts_sha256, + year=args.year, + ) + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") + print( + json.dumps( + { + "rates": receipt["rates"], + "achieved": receipt["achieved"], + "published": receipt["published_means"], + }, + indent=2, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index eeb99dae46..de3f96073b 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -22,7 +22,10 @@ target_references_resource, ) from microcosm.build.uk_runtime.ledger_targets import UK_UPRATING_APPLIERS -from microcosm.build.uk_runtime.uc_source_periods import uc_source_month_metadata +from microcosm.build.uk_runtime.uc_source_periods import ( + SOURCE_MONTH_FAMILIES, + uc_source_month_metadata, +) from microcosm.calibrate.geography_constants import ( UK_REGION_TIER, UK_REGION_TIER_ENUM, @@ -562,8 +565,11 @@ def _reference_metadata(contract: Mapping[str, Any]) -> dict[str, dict[str, str] if binding.get("require_matching_fact_period"): metadata["source_period_policy"] = "exact_observation" if "source_months" in measurement: - if target.get("family") != "dwp_universal_credit": - raise ValueError("source_months is currently a UK UC-only declaration.") + if target.get("family") not in SOURCE_MONTH_FAMILIES: + raise ValueError( + "source_months is declared only by the UK DWP monthly families " + f"{sorted(SOURCE_MONTH_FAMILIES)}." + ) metadata.update( uc_source_month_metadata( measurement["source_months"], diff --git a/tools/verify_uk_identity_stability.py b/tools/verify_uk_identity_stability.py index df686c6c3a..82c0662c4b 100644 --- a/tools/verify_uk_identity_stability.py +++ b/tools/verify_uk_identity_stability.py @@ -30,6 +30,8 @@ from microcosm.build.uk_runtime.frs_take_up import ( aggregate_person_reported_to_benunit, derive_frs_take_up, + uc_age_eligible_benunits, + uk_take_up_population_policy, ) from microcosm.build.uk_runtime.national_frame import ( load_uk_national_frame, @@ -86,9 +88,14 @@ def e4_identity_receipt( count_resource: Mapping[str, object], lha_category: Sequence[object], permutation_seed: int, + population_policy=None, ) -> dict[str, object]: """Recompute every E4 column in original and permuted row order. + ``population_policy`` is the engine's working-age bounds the take-up draw + used (``uk_take_up_population_policy``); it is read from the engine when + not supplied, so a hermetic caller passes one. + Two claims are receipted: a row permutation of the input tables changes no assignment per entity id, and the original-order recomputation equals the columns stored in the artifact (re-derivation identity). @@ -97,6 +104,8 @@ def e4_identity_receipt( person = frame.table("person") benunit = frame.table("benunit").copy() household = frame.table("household") + if population_policy is None: + population_policy = uk_take_up_population_policy(uk_time_period(frame)) if len(lha_category) != len(benunit): raise ValueError("LHA_category materialization must align to benunit rows.") benunit["LHA_category"] = [_enum_name(value) for value in lha_category] @@ -104,7 +113,14 @@ def e4_identity_receipt( def recompute(person_t, benunit_t, household_t) -> dict[str, pd.DataFrame]: anchors = aggregate_person_reported_to_benunit(person_t, benunit_t) - take_up = derive_frs_take_up(benunit_t, anchors=anchors, contract=contract) + take_up = derive_frs_take_up( + benunit_t, + anchors=anchors, + contract=contract, + uc_age_eligible=uc_age_eligible_benunits( + person_t, benunit_t, population_policy + ), + ) take_up.index = benunit_t["benunit_id"].to_numpy() person_draws = derive_frs_person_draws(person_t, contract=contract) person_draws.index = person_t["person_id"].to_numpy() @@ -1062,6 +1078,7 @@ def main() -> int: count_resource=load_brma_count_resource(), lha_category=lha_category, permutation_seed=args.permutation_seed, + population_policy=uk_take_up_population_policy(uk_time_period(frame)), ) ok = bool( receipt["identical_under_permutation"] and receipt["matches_stored_columns"] diff --git a/uv.lock b/uv.lock index 79eb76fc6b..086a7f5e4c 100644 --- a/uv.lock +++ b/uv.lock @@ -782,7 +782,7 @@ requires-dist = [ { name = "numpy", specifier = ">=1.26" }, { name = "openpyxl", marker = "extra == 'uk'", specifier = ">=3.1" }, { name = "pandas", specifier = ">=2" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.97.0" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.98.0" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, { name = "pyarrow", specifier = ">=15" }, { name = "pyyaml", specifier = ">=6" }, @@ -855,7 +855,7 @@ requires-dist = [ { name = "h5py", specifier = ">=3" }, { name = "huggingface-hub", specifier = ">=0.20" }, { name = "packaging", specifier = ">=24" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.97.0" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.98.0" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, ] provides-extras = ["us", "uk"] @@ -933,7 +933,7 @@ 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