diff --git a/changelog.d/1095-cgt-loss-making-landlords.fixed.md b/changelog.d/1095-cgt-loss-making-landlords.fixed.md new file mode 100644 index 0000000000..8fb5dc17f4 --- /dev/null +++ b/changelog.d/1095-cgt-loss-making-landlords.fixed.md @@ -0,0 +1,3 @@ +Loss-making landlords keep the CGT residential stock signal (microcosm#1095). #1081 counts a letting loss (`RENTPROF` 2) as zero `property_income`, and the CGT residential split read `property_income > 0` as one of its two signs that a gainer holds residential property. So landlords whose letting made a loss lost the sign, and the split's stock shift moved residential gains away from them. + +The UK spine now carries an internal flag, `reports_rent_from_other_property`, which holds wherever `ROYYR1` is positive, at a profit or a loss. The CGT stages read it alongside household residential property value and property income. A redrawn SPI row drops its FRS twin's flag, since its property income is an SPI donor's. It is not an engine input, and the release boundary drops it. diff --git a/changelog.d/1095-council-tax-gb-receipts.changed.md b/changelog.d/1095-council-tax-gb-receipts.changed.md new file mode 100644 index 0000000000..e681605fbf --- /dev/null +++ b/changelog.d/1095-council-tax-gb-receipts.changed.md @@ -0,0 +1,3 @@ +`obr.council_tax` now binds OBR table 4.1 row 15, total council tax receipts (the sum of the England, Scotland and Wales rows), over Great Britain's households (microcosm#1095). It previously bound row 19, total net council tax receipts. Row 19 adds Northern Ireland domestic rates, the council tax accruals adjustment and the high-value council tax surcharge, which no household's council tax carries: £1.34bn, 2.7% of the calendar-2025 window. The target moves from £50.12bn to £48.77bn. `obr.domestic_rates` stays held. + +Chronicle stamps the row K02000001, since OBR's table is UK-wide. A new exact-id geography pin keeps it there: the generator's substring rule would otherwise read "scotland" in the concept and pin Scotland. The country legs keep their own nations' pins. diff --git a/changelog.d/1095-looked-after-children.added.md b/changelog.d/1095-looked-after-children.added.md new file mode 100644 index 0000000000..013d3c521c --- /dev/null +++ b/changelog.d/1095-looked-after-children.added.md @@ -0,0 +1,3 @@ +The UK spine now sets policyengine-uk's `is_looked_after_by_local_authority` for children placed in foster care (microcosm#1095). The FRS 2024-25 question instructions put foster children under 16 in the carer's benefit unit. They make a foster child aged 16 or over a separate benefit unit where a local authority maintenance allowance covers them, so one who stays in the carer's unit has no such allowance and is not marked. Nothing set the flag before, so the engine counted them as the carer's own children in its child tests: Universal Credit's child element and youngest-child rule, Child Tax Credit, Pension Credit, Tax-Free Childcare, extended childcare, the Working Tax Credit childcare element and the legacy benefits. + +The `frs_relationships` stage sets the flag for a child-table member under 16 whose household-grid code toward an adult in their own benefit unit is foster child (5), or toward whom that adult records foster parent (9). It follows the relationship code alone: few such children are recorded, and the carer's local-authority fostering allowance (`ALLOW3`) covers fewer still. diff --git a/changelog.d/1095-ni-total-diagnostic.changed.md b/changelog.d/1095-ni-total-diagnostic.changed.md new file mode 100644 index 0000000000..46bfa28b28 --- /dev/null +++ b/changelog.d/1095-ni-total-diagnostic.changed.md @@ -0,0 +1,5 @@ +OBR's total National Insurance contributions (`obr.ni`, table 3.8 row 12) is now a diagnostic beside the three bound class rows, `obr.ni_employee`, `obr.ni_employer` and `obr.ni_self_employed` (María's ruling of 9 October, microcosm#1095; uk-data#537 drops the row). The engine's `total_national_insurance` is the sum of its class columns, and at the calendar-2025 window OBR's total sits about 1.8% above the three class rows. + +The binding note had put the gap down to Class 1A and 1B alone. It is wider than that. OBR's total is a cash figure while the class rows are accrued, and it carries statutory-payment recoveries, Class 1A, 1B and 3, and settlements, none of which any household carries. + +The exact FY2025-26 forecast (£200.08bn) is retained on `obr.ni_employee` and never fitted. The incumbent row `obr/ni` moves from mapped to excluded. diff --git a/changelog.d/1095-pension-credit-take-up.changed.md b/changelog.d/1095-pension-credit-take-up.changed.md new file mode 100644 index 0000000000..58b29a79a3 --- /dev/null +++ b/changelog.d/1095-pension-credit-take-up.changed.md @@ -0,0 +1,7 @@ +Pension Credit take-up now follows uk-data#510 (microcosm#1095): + +- Each component band's residual rate is solved over Great Britain, which DWP's take-up rates cover, and drawn for every entitled non-reporter, Northern Ireland's included. Before, Northern Ireland's units entered the solve, so the rate held UK-wide rather than in Great Britain. +- A benefit unit with no entitlement that does not report Pension Credit claims at DWP's Savings Credit-only caseload take-up, 37% in FYE 2024, on the same draw (new contract key `pension_credit_newly_entitled`). Before, such a unit never claimed, so a reform or a later year that entitled it paid it nothing. +- Entitlement is read at the release's calibration year, as uk-data reads it. The frame is stamped with its survey year, so policyengine-uk projects its incomes, pensions and capital to that year as it does the released file. The UK engine adapter gains a `dataset_period` argument for this, named by the `ProjectingRulesEngine` protocol, and the stage refuses a frame whose period is not the declared survey year. The mixed-age saving is still evaluated at the survey year, and the entitlement read takes it as stored. + +SPI-channel reporters stay anchored, since `spi_benefit_coherence` makes their reports coherent; uk-data draws them instead. The stage gate measures each band in Great Britain and fails a receipt measured over any other scope. By channel, the receipt also records the stage's Pension Credit assessable capital and deemed income, and its Guarantee and Savings Credit entitlement. diff --git a/changelog.d/1095-pip-caseload-scope.changed.md b/changelog.d/1095-pip-caseload-scope.changed.md new file mode 100644 index 0000000000..13ab9f0c7c --- /dev/null +++ b/changelog.d/1095-pip-caseload-scope.changed.md @@ -0,0 +1,5 @@ +The two PIP daily-living caseload rows, `dwp.pip.daily_living_standard_claimants` and `dwp.pip.daily_living_enhanced_claimants`, are now England-and-Wales diagnostics beside `obr.pip` (uk-data#490, microcosm#1095). DWP has reported PIP for England and Wales only since executive competence moved to the Scottish Government on 1 April 2020, and Chronicle stamps its January 2025 caseload K04000001. Under the Great Britain default pin the rows matched no fact, so they were dormant. + +Scoped to England and Wales, the rows resolve to 1.283m standard-rate and 1.608m enhanced-rate claimants, and a calibration fits both within 0.1%. But fitting them takes the calibration over the `uk_weight_ratio` limit. So they leave the fit. Each is a diagnostic reference on `obr.pip`, pinned to its exact January 2025 fact: checked, and never fitted. Diagnostic references now accept a fact dated by month. + +The contract holds 388 targets and 1,229 active references. diff --git a/changelog.d/1095-recorded-capital-property.fixed.md b/changelog.d/1095-recorded-capital-property.fixed.md new file mode 100644 index 0000000000..7c098635e3 --- /dev/null +++ b/changelog.d/1095-recorded-capital-property.fixed.md @@ -0,0 +1,15 @@ +Universal Credit and Pension Credit recorded capital now include property (uk-data#495, microcosm#1095). policyengine-uk replaces every capital source with a recorded figure of 0 or more and takes it as countable. The FRS benefit-unit capital (TOTCAPB4) counts accounts and assets but no property, so the engine assessed no unit on its household's second homes, buy-to-let property, other buildings or land. + +`uc_capital_coherence` now records each capital as the financial carrier plus the household's countable property, for the unit that holds it: + +- **Who holds it.** The household reference person's benefit unit holds all of the household's property, and no other unit records any (María's ruling of 12 October). WAS records property for the household, and each means test counts the claimant's and partner's own capital. The reference person is the one in whose name the home is owned or rented, the best single reading of the owner. policyengine-uk's own proxies share household capital over every claimant or partner, which puts a parent's property on an adult child's claim; they apply only to a unit on the -1 sentinel. +- **At what value.** The property counts at 90% of its value: market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19). Debt secured on the property is not on the frame and is not netted (pe-uk#1969). +- **Which property.** Universal Credit counts other residential and non-residential property. Pension Credit counts those and owned land. + +The carrier's -1 sentinel passes through to both. + +A unit that reports Universal Credit keeps its receipt, and records the carrier alone for Universal Credit. DWP assessed its capital to pay it, and the property is imputed without that receipt, since the WAS file has no Universal Credit column. + +`uc_reporter_redraw` screens SPI units on the carrier plus the property they hold, so a drawn reporter does not land on a unit whose property is over the capital limit. It screens an FRS unit that reports Universal Credit on its carrier alone, so that unit stays in the training set. The `uk_uc_capital_coherence` gate re-derives both capitals from the carrier, the declared rule and the reporter rule, within £0.01 plus one float32 step, and now checks Pension Credit capital too. + +policyengine-uk 2.123.0 does not uprate `uc_reported_capital`, so its property stays at survey-year values in later model years. `pension_credit_reported_capital` and the household property columns uprate by GDP per head, so after the survey year the same property counts at a lower value for Universal Credit than for Pension Credit. policyengine-uk 2.124.0 adds `uc_reported_capital` to the dataset projection (pe-uk#2201); the next engine bump closes the gap. diff --git a/changelog.d/1095-salary-sacrifice-rate-bands.added.md b/changelog.d/1095-salary-sacrifice-rate-bands.added.md new file mode 100644 index 0000000000..4f50ca1e09 --- /dev/null +++ b/changelog.d/1095-salary-sacrifice-rate-bands.added.md @@ -0,0 +1,11 @@ +The three HMRC salary-sacrifice income tax relief bands now bind: basic, higher and additional rate, from Table 6.1 for 2024-25 (uk-data#533, microcosm#1095). They had been held out of the fit since microcosm#1069, because their band test compared adjusted net income with after-allowance thresholds and ignored the Scottish bands. + +The measure resolver gains a declared rate-class allocation. One counterfactual simulation per substitution pair returns the sacrifice to pay, as uk-data does, and is cached. Each person's relief is the rise in tax on `earned_taxable_income`, bracket by bracket, under their rest-of-UK or Scottish schedule (`pays_scottish_income_tax`). The schedule is read from the simulation's own fiscal-year parameters, so the Scottish bands are the ones in force from 6 April. The brackets group as follows: + +- a bracket taxed below 30% is basic; +- the schedule's top rate is additional, where a higher rate sits below it; +- the rest are higher, so the Scottish advanced rate counts as higher. + +A contribution that straddles a boundary is relieved at each rate, and the personal allowance taper lands in the brackets the extra pay reaches. The three bands sum to the change in the engine's `earned_income_tax` for every person, and the measure refuses if they stop doing so. The tolerance is £1 plus four float32 steps at the person's own amounts, since the engine computes in float32. The resolver's receipt records the gap to the change in `income_tax` (for example, dividends pushed into the higher dividend band). A binding that declares anything other than the exact allocation is refused. uk-data's worked examples at 2025 reproduce exactly: £454 basic and £692 higher, Scotland's £349.02 and £981.96, and £6,000 all higher where the taper applies. + +policyengine-uk puts the Scottish top-rate threshold at £112,570 rather than the statutory £125,140 (pe-uk#2130), so it files some advanced-rate relief as additional. The Table 6.2 all-rates total (£8.8bn) becomes a diagnostic on the higher-rate band, since the bands carry the same relief. The three measure exclusions that held the bands out, due to expire on 25 November, are retired. diff --git a/changelog.d/1095-savings-interest-index.changed.md b/changelog.d/1095-savings-interest-index.changed.md new file mode 100644 index 0000000000..0495b96ebc --- /dev/null +++ b/changelog.d/1095-savings-interest-index.changed.md @@ -0,0 +1,3 @@ +The 13 HMRC SPI savings-interest band targets now reach the 2025 calibration year in two legs (uk-data#541, microcosm#1095, María's ruling of 9 October). They move to the dataset's year, 2024, by ONS household interest income, the index of the variable's history. From there they move by GDP per head, the index policyengine-uk's dataset projection grows `savings_interest_income` with. The SPI donors take the same path: the stage rebases them to 2024 by the history index, and the engine projects the frame to 2025. + +The band targets' 2023-24 to 2025 factor moves from 1.182 to 1.192. The donor rebase is unchanged. An engine lockstep test holds every indexed SPI component's rebasing index, and every HMRC SPI amount family's projection index, to the engine's projection table, and names savings interest's history index apart. diff --git a/changelog.d/1095-uc-award-band-edges.changed.md b/changelog.d/1095-uc-award-band-edges.changed.md new file mode 100644 index 0000000000..b2740f52b0 --- /dev/null +++ b/changelog.d/1095-uc-award-band-edges.changed.md @@ -0,0 +1,12 @@ +The four UC payment-distribution families now read DWP's monthly award bands in whole pence (uk-data#530, microcosm#1095). + +**Band edges.** DWP publishes the bands as "£0.01 to £100.00", "£100.01 to £200.00", up to "£2,400.01 to £2,500.00", with each band starting a penny above its predecessor's top. Each binding now declares `band_semantics: whole_pence_upper_closed`, so a band is the upper-closed interval (label lower − £0.01, next label lower − £0.01] × 12, compared in whole pence. The half-open reading was float-fragile on those edges: + +- an award of a penny a month could fall below the bottom band; +- £100.01 a month (£1,200.12 a year) stayed in the bottom band, because 100.01 × 12 rounds just above 1,200.12. + +The semantics are opt-in, and an unknown value is refused. The other 28 banded bindings keep the half-open reading. + +**The top band stays unbound.** `band_upper_bound` closes the last bound band at £2,500.00 a month, so it never absorbs the separately published "£2,500.01 or over" category. Binding that category was measured and left out. With it unbound, the calibration holds 2.6 times DWP's count of lone parents in it and 2.0 times its count of couples with children, and reaches OBR's Universal Credit total. Bound to DWP's counts, the total falls 5.6% short, and couples without children have fewer than 10 households in the band. The awards below the top band are too low for DWP's counts and OBR's total to fit together. + +The generator now counts the fan-out facts it leaves unnamed, rather than declaring 12. The pinned feed leaves 8: one "No payment" and one "or over" fact for each of the four family types. The declared 12 counted eight overlapping "or over" facts, which an earlier feed carried. diff --git a/changelog.d/1095-uc-gainful-self-employment.added.md b/changelog.d/1095-uc-gainful-self-employment.added.md new file mode 100644 index 0000000000..8cf87ca4b8 --- /dev/null +++ b/changelog.d/1095-uc-gainful-self-employment.added.md @@ -0,0 +1,10 @@ +The UK spine now sets policyengine-uk's `uc_is_in_gainful_self_employment` (uk-data#525, microcosm#1095). Nothing set it before, so the engine's own rule applied: any nonzero `self_employment_income` counted as gainful self-employment. Because the spine floors losses at zero, a trader at a loss or breaking even never met the Universal Credit minimum income floor, while an employee with a small side trade did. The input's only reader is `uc_mif_applies`. + +The rule follows UC Regs 2013 reg 64(a), which asks whether the person carries on a trade as their main employment, and DWP's Advice for Decision Making. ADM starts from hours (H4031) but lets earnings outweigh them (H4034). A person is in gainful self-employment when either holds: + +- their main job is self-employed (`EMPSTATI` 3 or 4), whatever its profit, since a trade at a loss or breaking even is still carried on (H4013, H4054, H4503); +- their side trade's profit (`SEINCAM2`) is above zero and above their employment income (`INEARNS`). + +SPI-channel rows keep their donor's employment status but take new imputed pay and profit, and the draw does not read the status. So after the redraw they take the earnings route alone: a profit above zero and above pay. The main-job route returns when the SPI draw follows employment status (uk-data#529, microcosm#840). + +The flag is fixed at the survey year's incomes. Uprating reprices pay and profit by different indices, so in a projected year a flagged side trade can earn less than the job, or the reverse, and the flag does not follow. diff --git a/changelog.d/1095-uc-take-up-population.changed.md b/changelog.d/1095-uc-take-up-population.changed.md new file mode 100644 index 0000000000..06d402ab39 --- /dev/null +++ b/changelog.d/1095-uc-take-up-population.changed.md @@ -0,0 +1,8 @@ +The Universal Credit take-up draw now runs over the benefit units policyengine-uk can pay Universal Credit to (uk-data#486, uk-data#524, microcosm#1095). These are units with a UC claimant or partner (`is_uc_claimant`) aged 18 or over and under Pension Credit qualifying age. Before, the draw took any unit with a member aged 18 to under State Pension age. That included a pensioner unit whose only younger member was an 18- or 19-year-old dependant. A dependant is neither claimant nor partner, so the engine never pays such a unit Universal Credit, yet the contract rate was spread over it. + +The draw, its SPI-channel redraw in `spi_benefit_coherence` and the `uk_take_up_signal` gate share one helper, so all three use the same population. Both stage manifests declare it as `any_uc_claimant_aged_18_under_pension_credit_age` with `role_column: is_uc_claimant`, and the code refuses a manifest that says otherwise. The take-up contract's population text for Universal Credit is re-signed; the rate is unchanged. + +The qualifying age is the State Pension age the engine's timetable sets for the cohort reaching it in the build year. That equals the engine's Pension Credit qualifying age at 2024 and 2025. The engine lockstep test compares the population with `is_uc_eligible`, with reported capital set to zero, for one-person units aged 14 to 90. It names the two engine cases the population does not mirror: + +- 16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, whom the engine includes; +- mixed-age couples keeping Pension Credit under the SI 2019/37 saving, whom it excludes. diff --git a/docs/uk-income-anchors-280.md b/docs/uk-income-anchors-280.md index 4d7cd4e463..bf0997193b 100644 --- a/docs/uk-income-anchors-280.md +++ b/docs/uk-income-anchors-280.md @@ -62,11 +62,15 @@ membership carries the uprated value with its receipt. others take `policyengine_uk_parameter:`, the ratio of the pinned engine's parameter between 1 January of the fact's opening year and 1 January of the calibration year, the instants the SPI donor rebasing already - uses: dividends `obr.per_capita.gdp`, savings interest - `ons.household_interest_income`; the state pension follows + uses: dividends `obr.per_capita.gdp`. Savings interest takes two legs + (microcosm#1095): `ons.household_interest_income`, the index of its + history, to the dataset's year, then `obr.per_capita.gdp`, the index the + engine's dataset projection applies, to the calibration year (1.1420 x + 1.0438 = 1.1920 on the pinned engine values). The state pension follows `gov.dwp.state_pension.new_state_pension.amount`, the rate the engine pays (203.85 to 230.25 a week, 1.1295). On policyengine-uk 2.98.0 and 2.100.0 the - 2023 to 2025 factors are 1.1067, 1.0344, 1.0826, 1.0877 and 1.1819. The compile + 2023 to 2025 factors are 1.1067, 1.0344, 1.0826, 1.0877 and, for household + interest alone, 1.1819. The compile reads the values from the vendored `hmrc_uprating_engine_pins.json` (the six parameters at every 1 January 2019 to 2026, written from the installed engine by `tools/pin_uk_uprating_engine_values.py`, whose `--check` refuses drift), so @@ -197,8 +201,9 @@ before this stage the spine, like the enhanced FRS, carried no record above Two uprating conventions sit side by side by design. The national SPI amount rows move by the engine's own component indices (earnings, mixed income, GDP -per head, the private-pension index, household interest, the new State -Pension rate), because each row is one income component and the engine uprates +per head, the private-pension index, the new State Pension rate, and for +savings interest household interest to the dataset's year and GDP per head +after it), because each row is one income component and the engine uprates that component by that index: the target and the model then agree on what "2025 employment income" means. The regional Table 3.11 rows are totals and tax by band with no component split, so no engine index applies; they move by diff --git a/experiments/1095-uk-data-ports-2-receipts.md b/experiments/1095-uk-data-ports-2-receipts.md index d2ffc7a6cb..aa26cf4ccb 100644 --- a/experiments/1095-uk-data-ports-2-receipts.md +++ b/experiments/1095-uk-data-ports-2-receipts.md @@ -146,8 +146,20 @@ floored), which a calibration of the current head would include. corrected upstream to include other investment income (pe-uk#2174, filed 2026-10-07). - **Y excludes the two UC bands.** Replaying the target-fit gate on the head arm's evidence without them passes, with X in force, and the other six seam gates passed on that arm. The replay keeps - the head arm's weights, which were solved with both bands bound; a calibration of the new head, - the national build and the certifier have not been run. + the head arm's weights, which were solved with both bands bound; arm head-r3 below solves without + them. +- **Arm head-r3 (2026-10-08, built as `c7fe76e8a`, the tree of the pushed head).** Loss 0.00712 + (A9 0.00715), 98.3% of 1,167 targets within 10%, ESS 4,874. With X and Y in force, 7 of 7 + calibrated-seam gates pass, and so do the exported file's terminal gates. + - The certifier's input-mass gate (`input_mass_check.py`) passes, with no stale, expired or + dormant exclusion. + - The West Midlands cell, deferred by X, is at +28.9%. `obr.vat` is at +24.1%, under its 25% + bound. `dwp.hb.amount_pension_age` calibrates to 0.0%. + - C6's weighted-share rewrite leaves `uc_is_in_startup_period` unchanged: no value differs on the + 142,129 person rows this spine shares with arm head's. + - The ledger is feed 825406f, rebuilt with the original's manifest after the original's fact file + was lost on 2026-10-08. + - The national build and the certifier follow the merge. ## Verification @@ -179,8 +191,8 @@ floored), which a calibration of the current head would include. `MicroSeries`, because microdf reaches an environment only with an engine and the FRS spine runs in the engine-free lane. B0's forward-year note cites pe-uk#2173. The FRS spine tests pass (101), and no generated surface moved. -- Head calibration: queued on 2026-10-08 at tree `c7fe76e8a`, which is this head's tree. Its results - are owed before merge. +- Head calibration (2026-10-08, tree `c7fe76e8a`, this head's tree): 7 of 7 seam gates and the + certifier's input-mass gate pass (Measurement, arm head-r3). ## Signature drafts diff --git a/experiments/1095-uk-data-ports-3-receipts.md b/experiments/1095-uk-data-ports-3-receipts.md new file mode 100644 index 0000000000..db92d476e2 --- /dev/null +++ b/experiments/1095-uk-data-ports-3-receipts.md @@ -0,0 +1,495 @@ +# microcosm#1095 receipts: the third ports PR + +Plan: `repos/microcosm-1095-ports-3-plan.md` (approved 2026-10-09). Branch `uk-data-ports-1095-3`, off +main `d50220d1f` (after #1121 and #1160). It was rebased on 2026-10-09 onto main `a224dc110` (which +merged #1087, #1162, #1144 and #1147) and replayed on 2026-10-10 onto main `7639ef8b0` (which merged +#1161, #1169, #1171, #1172 and #1180 to #1182), with María's rulings of that day folded into D1, D3 and +T1. Each replay regenerated the coverage manifest, the stage digests and the H2 fixture after every +commit, and every other change is line-for-line its pre-replay original. Each target commit carries the +national references and the compile-parity receipts regenerated against Chronicle feed 825406f. + +On 2026-10-12 the branch was rebased a fourth time, onto main `977ac983a`, which merged #1167 +(consumption-tax levels), #1188, #1145 (property income, policyengine-uk 2.123.0) and #1198 (the +target surface, Chronicle feed 942a1fa). This replay used another method. The commits were replayed +as they were, and one commit, `4dac8a31d`, sets the tree to a merge that was regenerated and tested as +a whole. So the commits before `4dac8a31d` do not each regenerate on the new base, and the branch +does not bisect across it. The commit ids in the measurement sections below name the trees that were +measured at the time. + +Commits, in order: + +- R0 `13d46988c`: #1121's head calibration (arm head-r3) recorded in its receipts. +- D1 `88cebb064`: `uc_is_in_gainful_self_employment` from the FRS main job and earnings (uk-data#525). +- D5 `867f88a17`: `is_looked_after_by_local_authority` from the FRS foster links. +- D6 `1b0ed5822`: loss-making landlords stay in the CGT residential signal. +- D2 `f5f6fcce1`: the UC take-up population on policyengine-uk's age rule (uk-data#486, #524). +- D3 `80a56b46d`: property in UC and Pension Credit recorded capital, with UC reporters keeping their + receipt. +- D4 `cc78eaab1`: Pension Credit take-up solved over Great Britain, with newly entitled units drawn + (uk-data#510). +- T5 `b19fdbe6c`: SPI savings interest and its band targets at the engine's projection index (uk-data#541). +- T6 `cf7ab87fe`: the PIP daily-living caseload rows scoped to England and Wales. +- T3 `0b3cedbb0`: `obr.council_tax` on OBR table 4.1 row 15, over Great Britain. +- T4 `849b04711`: `obr.ni` a diagnostic beside the class rows (uk-data#537). +- T2 `6ea7dcd75`: the salary-sacrifice relief bands by income tax rate class (uk-data#533). +- T1 `733caba7a`: UC award bands in whole pence (uk-data#530). +- T6b `d240102df`: the two PIP caseload rows held as diagnostics on `obr.pip`. +- W `26d0082a3`: the West Midlands low-band income-tax deferral retired. Review round 1 reverted it. +- R `4f31c6eb7`: this file. +- Review round 1 (below): `396dde568` the stage fixes, `f3988e792` and `a741745ef` savings interest and + its parity receipts, `356f491a4` the rate-class check, `0a5b68995` the deferral kept. +- `61062bc73`: María's D3 ruling of 2026-10-12, the reference person's unit holds the property at 90%. +- `4dac8a31d`: the rebased branch reconciled with main `977ac983a` (below). +- The re-review of 2026-10-12 (below): `c0d4c6740` the rate-class tolerance, `cee4f4ec8` the sentinel + tests, `e87e2ce7d` the Pension Credit stage's declared year, `4d9e5ed22` text, `4718cc6f6` a + formatting slip of the merge, `30ef5546f` main's pins. + +## María's rulings of 2026-10-10 + +The first head (below) failed two calibrated-seam gates. After its attribution she ruled: + +- **D1.** SPI-channel rows take the earnings route alone: a profit above zero and above pay. The SPI + draw does not read the donor's employment status, so on those rows the status says nothing about + the drawn trade. The main-job route returns when the SPI draw follows employment status + (uk-data#529, microcosm#840). +- **D3.** A unit that reports Universal Credit keeps its receipt and records no UC property share. + Pension Credit keeps property. +- **T6.** The two PIP daily-living rows are diagnostics on `obr.pip`: checked, never fitted (T6b). +- **T1.** The whole-pence band edges stay. The "£2,500.01 or over" band stays unbound, as on main. +- **The support floor** for sparse UC bands moves to its own PR. This PR no longer needs it to pass. + +W is not one of her rulings. The gate requires it on this head (see Measurement), and it follows the +earlier retirements of stale deferrals. + +## Where the plan changed during implementation + +- **D5, the allowance.** The plan asked for the carer's fostering allowance (ALLOW3) as well, if the + counts supported it. FRS 2024-25 records few children under 16 as looked after (10–29 child + records, in fewer than 10 households), and ALLOW3 covers fewer still. So the rule reads the + relationship code alone: foster child (5) toward an + adult in the child's own benefit unit, or foster parent (9) from that adult toward the child. +- **D2, Max's scaffolding.** The plan reused Max's test scaffolding + (`test_support/microcosm_build/uk_take_up_population.py`). It is not on main, so D2 uses none of it. + His `uk-take-up-per-person-spa` branch solves a different part, the State Pension age by date of + birth that builds from 2026-27 need, and it will need a rebase onto D2. +- **D3, one share helper.** The plan pointed the UC share at `household_family_role_counts`. That + helper counts claimants and partners only, and the Pension Credit share needs members at or over its + qualifying age. So one helper, `_household_property_share`, takes each proxy's owner mask and serves + both. +- **D4, the entitlement year.** The stage stores the mixed-age saving at the survey year. It then reads + entitlement, Pension Credit capital and deemed income at the calibration year through a second engine + read, as uk-data does. +- **T3, the geography pin.** The generator's substring rule reads "scotland" in the row's concept, + `obr.council_tax_receipts_england_scotland_wales`, and would pin Scotland. A new exact-id pin keeps + the row at the UK stamp Chronicle gives OBR's table. +- **T3 and T4, the parity rationales.** Their compile-parity rows carry their own reasons: a different + series for council tax, and the ruling for the NICs total, as `obr.state_pension` has. +- **T2, the total.** The Table 6.2 all-rates total became a diagnostic on the higher-rate band, the + band that carries most of the relief. +- **T6, diagnostics by month.** The caseload facts are dated by month, so diagnostic references now + accept a month-dated fact. +- **T1, the top band and the floor.** The plan bound the top band and added a support-floor rule. The + measurement below changed both. + +## Measurement + +Each arm built the spine from its commit in the measurement tree and calibrated it on the national +role, with local staging only and Chronicle feed 825406f (rebuilt with the original's manifest). The +control is main `d50220d1f`. Figures are aggregates; cells under 10 records are suppressed. A count +of survey records or units behind a small group is given as a band (`<10`, `10–29` or `30+`), and a +group that rests on fewer than 10 survey households gets no weighted figure. +`uk_weight_ratio` holds the largest support-family weight over the positive median against its June +fence. That statistic is a per-record extreme, so this file gives the gate's verdict and not its value. + +### Before the rulings + +D1 and D3 were as first built here: SPI rows flagged by the donor's status, and property counted for +every unit. + +- **Control (`d50220d1f`).** Loss 0.00712, 98.3% of 1,167 targets within 10%, ESS 4,874. All 7 + calibrated-seam gates and the certifier's input-mass gate pass. +- **Arm 1 (built as `341a02bd4`: D1, D5, D6).** Loss 0.00701, 98.5% within 10%, ESS 4,846. All 7 + gates and input mass pass. Design UC falls from £52.55bn to £51.96bn. + - D1, at the arm's calibrated weights in 2025: the minimum income floor applies to 1.66m FRS-channel + people (control 1.84m) and 2.49m SPI-channel people (control 1.73m). + - D1, the SPI channel: 1.67m weighted SPI-channel people are flagged gainfully self-employed with no + self-employment income. This led to the D1 ruling. + - D5: the flag is set on 10–29 FRS-channel child records, in fewer than 10 survey households, so + no weighted figure is given. The SPI channel holds fewer than 10 records. + - D6: residential CGT gains move from £5.906bn to £5.898bn in the FRS channel and from £6.842bn to + £6.858bn in the SPI channel. +- **D2, on #1121's head-r3 export.** The new population and the old differ by fewer than 10 benefit + units. Against the engine's age half of `is_uc_eligible`, two named classes remain: reg 8 claimants + aged 16-17, whom the engine includes, and mixed-age couples on the Pension Credit route, whom it + excludes. Both are small (each 30+ rows on that export, which counts household copies), so no + weighted figure is given. Disagreement with the engine falls by fewer than 10 units. +- **D3, on #1121's head-r3 export, before recalibration.** The property shares add £1,247bn of UC + capital over 5.18m benefit units, and £427bn of Pension Credit capital over 1.18m. Counted for every + unit, UC falls £2.78bn (3.7%) and about 329k units lose paid UC; about 157k weighted UC reporters + move above £16,000 on imputed property. With UC reporters keeping their receipt, UC falls £0.80bn, + and the 179k units that lose UC are all non-reporters. Pension Credit falls £0.18bn either way. +- **Arm 2 (built as `ae9795d7c`: D2, D3 for every unit, D4).** Loss 0.00708, 98.4% within 10%, ESS 4,875. + All 7 gates and input mass pass. Design UC falls to £50.65bn and design Pension + Credit to £4.26bn; both calibrate to target. + - On the arm's spine, 30+ FRS-channel rows that report UC go above £16,000 through property alone. + About three quarters of them are in households with no property income, and half are in + households with two or more benefit units, where the engine's proxy shares property over every + claimant or partner. + - D4: newly entitled units are flagged at a share of 0.3695 against the 0.37 rate. + - The arm pipeline leaves two gates this PR changes to the release-cut certifier, so they were run + on the arm's exported file with their own evaluators (`scripts/certifier_gates_check.py`). + `uk_uc_capital_coherence` and `uk_take_up_signal` pass, and the capital gate fails on the control's + export, which has no property shares. +- **The first head (built as `a42edaa9e`: T5, T6, T3, T4, T2, and T1 with the top band bound).** Loss + 0.00726, 98.2% of 1,174 targets within 10%, ESS 4,822. Two gates fail: + - `uk_target_fit`: couples without children have fewer than 10 households in the top UC band, and + that row is far outside the bound; + - `uk_weight_ratio`. +- **Attribution, calibrate-only on that head's spine** (T6 to T1 change no stage): + - T5 alone: all 7 gates pass, loss 0.00716. It moves the top income cells, + inside the 25% gate: the £500,000–1,000,000 employment-income band from −2.5% to −13.3%, and + Scotland's £200,000-and-over total income from −8.3% to −15.7%. + - T5 and T6: the two PIP rows fit within 0.1%, but `uk_weight_ratio` fails with them and passes + without them. + - Adding T3 and T4: the gate still fails. `obr.council_tax` moves from −9.8% to −7.3%. + - Adding T2: it still fails. The three salary-sacrifice bands fit within 0.1%. Their design values are basic + £1.05bn against £1.6bn, higher £2.94bn against £5.5bn, additional £2.46bn against £1.8bn. + - Adding T1's top band: `obr.universal_credit` falls from 0.0% to −5.6% (£71.8bn against £76.1bn). + Unbound, the calibration holds 2.57 times DWP's count of lone parents in the band (224k against + 87k) and 1.99 times for couples with children, £13.1bn of UC. Bound, the band holds £5.5bn + (`scripts/uc_top_band_check.py`). + - A measurement-only support floor of 10 holds out 17 bound rows with fewer than 10 source households. + `uk_target_fit` then passes and loss is 0.00687, but `uk_weight_ratio` still fails and the UC + total is at −4.6%. + +### The head after the rulings + +Main's evidence reader refuses real run manifests (#1187), so a national build from main stops before +its first phase. This head was therefore calibrated with #1188's fix on top, for measurement only; +the fix touches no calibration code. + +- **Final head (`a7e5bc56f`).** The spine was built from `0324e9e84`, whose tree differs from the head + only in compile-parity receipts and W's register entry, and calibrated from the head plus #1188's + one commit. Loss 0.00694 (control 0.00712), 98.3% of 1,168 targets within 10%, ESS 4,843. All 7 + calibrated-seam gates pass, and so does the certifier's input-mass gate, with no + stale, expired or dormant exclusion. + - `obr.universal_credit` and `obr.pip` calibrate to 0.0%, and 109 of 112 UC cells are within 10% + (at most 14.6%). Design UC is £51.78bn and design Pension Credit £4.27bn. + - `obr.council_tax` is at −7.0% and `obr.vat` at +22.7%. The three salary-sacrifice bands are within + 0.3%. T5's top income cells sit at −11.5% and −15.2%. + - **W.** The West Midlands £12,570–15,000 income-tax cell calibrates to +23.9%, inside the 25% + bound. With its deferral still in the register, the same weights failed `uk_target_fit` on that + one point: a stale deferral. W retires it and the gate passes. The cell sat at +28.9% on the + control and +26.8% on the first head. Its design value is still 6.2 times its target + (pe-uk#2174), so a later change can push it back over the bound. + - D1, at calibrated weights in 2025: the minimum income floor applies to 1.63m FRS-channel people + (control 1.84m) and 1.24m SPI-channel people (control 1.73m). 1.58m SPI-channel people are flagged, + against 3.35m when the flag followed the donor's status. + - D3, the stage's receipt: 30+ FRS-channel benefit units that report UC keep their receipt, and + most of them would have gone above £16,000 with their property share. Fewer than 10 SPI-channel + reporter units are affected. 10–29 FRS-channel reporter units hold financial capital above + £16,000 as surveyed. + - D4: newly entitled units are flagged at a share of 0.3695 against the 0.37 rate. + - D5: no weighted figure is given (fewer than 10 survey households). + - On the exported file, `uk_uc_capital_coherence` passes with the reporter rule, and + `uk_take_up_signal` passes: `would_claim_uc` sits at 0.560 against 0.55, over 55,763 units. + +## Review round 1 (2026-10-11) + +An adversarial review of the head `cf42d8a7a` is on the PR. Each claim was checked against the code, +policyengine-uk 2.122.2 (the lock at that time; 2.123.0 since the rebase below) and the data. This section records what changed and the new measurement. Where +it differs from the sections above, this section is the current one. + +### What changed + +- **D4 read survey-year values against calibration-year rules.** The engine adapter stamped the frame + with the entitlement year, so nothing was projected. The adapter now takes a `dataset_period`, the + stage stamps the frame with its survey year, and policyengine-uk projects it to 2025, as uk-data#510 + reads it. Units entitled to Guarantee Credit fall from 1,354k to 1,247k and its residual rate from + 0.213 to 0.176; Savings Credit-only units are 371k (369k). An engine test pins the case: a pensioner + on the 2024-25 full new State Pension is entitled when the values are read as 2025's, and not when + they are projected (£230.25 a week). +- **T5 dropped a real rise.** The port rebased the 2022-23 SPI donors to 2024 by GDP per head (x1.09). + The engine projects from the dataset's year on; the years before it are history, and ONS household + interest income is x1.99 from 2022 to 2023. At prior weights, mean interest per saver against the + target's mean fell from between 1.02 and 2.66 in the control's top four bands to between 0.40 and + 0.76. The donor rebase is main's again, and the 13 band targets take two legs: household interest + income to 2024, then GDP per head to 2025 (factor 1.192; main 1.182; as first built 1.083). This + was the cause of the top-income pull the attribution above assigns to T5. +- **D3, the redraw's screen.** An FRS unit that reports UC is screened on its carrier alone, as the + capital stage records it. Training units rise from 3,991 to 4,029. +- **D6 on SPI rows.** A redrawn row kept its FRS twin's landlord flag, which alone turned the CGT + residential signal on for 695k weighted SPI-channel people (35k in the FRS channel, the intended + group). Redrawn rows now drop it. +- **T2.** The measure compares each person's classes with the change in the engine's + `earned_income_tax` and refuses beyond £1. A schedule with one rate above the basic class files it + as higher. +- **W is reverted.** See the measurement. +- **Checked and kept.** D5's limit of 16: the FRS makes a foster child aged 16 or over a separate + benefit unit where a local authority allowance covers them, and fewer than 10 child records aged 16 + to 19 are fostered within the carer's unit. D4's draw cannot bar Income Support: a mixed-age couple + that reports Income Support has the SI 2019/37 saving only if it reports Pension Credit, and then + it is a reporter (an engine test pins it). + +### Measurement + +The control above (`d50220d1f`) predates #1087, so its deltas to the head also carried #1087's effect. +The control is now the branch's base, main `7639ef8b0`. Both runs carry #1188's commit on top, which +changes the evidence reader and its tests only; spine and calibration come from the same tree. + +- **Control (main `7639ef8b0`).** Loss 0.00702, 98.5% of 1,167 targets within 10%, ESS 4,840. + `uk_weight_ratio` fails; it passed on `d50220d1f`. The other six gates pass. The West Midlands + cell is at +26.5%, + `obr.council_tax` at −9.5% and `obr.vat` at +24.1%. +- **Main at #1087's merge commit (`a469ebe3d`).** The same figures as the control to every digit + shown: loss 0.00702, the same failing weight ratio, West Midlands +26.5%. So #1087, BRMA + private-rented weights, is the change that took main over the fence, and no later merge moves the + calibration. +- **Head (`2792b21cd`, then `78ea6b695` on the same spine).** Loss 0.00691, 98.5% of 1,168 targets + within 10%, ESS 4,849. Six of the 7 calibrated-seam gates pass, and so does the certifier's + input-mass gate, with no stale, expired or dormant exclusion. + - `uk_weight_ratio` fails, by less than the control does. The earlier head passed with the T5 + error in place. + - The West Midlands cell is at +26.9%, above the bound, so `uk_target_fit` failed on W at + `2792b21cd`. With the deferral restored (`78ea6b695`) the same weights pass it. Across this PR's + arms the cell ranged from +23.9% to +29.7%. + - `obr.council_tax` is at −7.2% and `obr.vat` at +23.5%. 108 of 111 UC cells are within 10% + (control 106). The three salary-sacrifice bands are within 0.3%. + - The £500,000–1,000,000 employment-income band is at −1.9% (control −3.4%; −11.5% with the T5 + error), and Scotland's £200,000-and-over total income at −8.6% (control −8.2%; −15.2%). + - Design UC is £51.70bn (control £52.69bn) and design Pension Credit £4.21bn (control £4.39bn). + - D1, at calibrated weights in 2025: the minimum income floor applies to 1.62m FRS-channel people + (control 1.86m) and 1.24m SPI-channel people (control 1.73m). + - D5: no weighted figure is given (fewer than 10 survey households). + - D6: residential CGT gains are £5.99bn in the FRS channel (control £5.98bn) and £6.78bn in the SPI + channel (control £6.80bn). + - D3, the stage's receipt: 30+ FRS-channel benefit units that report UC keep their receipt, and + most of them would have gone above £16,000 with their property share. Fewer than 10 SPI-channel + reporter units are affected. + - D4: newly entitled units are flagged at a share of 0.3699 against the 0.37 rate. + - A blocked run writes no export, so the certifier-owned gates were run on the head's spine at the + solution's weights: `uk_uc_capital_coherence` passes, and `uk_take_up_signal` passes with + `would_claim_uc` at 0.561 against 0.55, over 55,830 units. + +### What the review left open + +- **D3, the owner and the value of the property.** The −1 sentinel is on fewer than 10 benefit units, + so the recorded figure decides alone. Shared pro rata, £104bn of property sat on 0.99m benefit + units that are not the household reference person's. On that head's calibrated weights at 2025, + before recalibration: with the reference person's unit holding it all, at 90% of its value, UC was + £0.65bn higher and 170k more benefit units were paid; the 10% alone moved UC by £0.003bn. On the + WAS donor file, mortgages on other property are 26% of its value, and countable value less 10% and + secured debt is 64% of gross. +- **The signed differences.** The committed `uc-reported-capital-net-new-column` text predates D3; + its redraft, and drafts for D1, D5, D4 and Pension Credit capital, wait for María's signature. + +## María's rulings of 2026-10-12 + +1. **D3, who holds the property.** The household reference person's benefit unit holds all of the + household's property, and no other unit records any. WAS records property for the household, each + means test counts the claimant's and partner's own capital, and the reference person is the best + single reading of the owner. +2. **D3, the value.** The property counts at 90% of its value, market value less 10% for the costs of + sale. Secured debt goes with the WAS property draw: `was_wealth` draws the mortgages on other + property but does not emit them (pe-uk#1969 is the engine side). +3. **Main's weight-ratio breach.** This PR proceeds, and the breach is resolved apart from it. + +Commit `cf91835c9` applies 1 and 2. The Pension Credit share no longer reads the qualifying age, so +the capital stage and its gate take no population policy. The engine reads +`pension_credit_reported_capital` only for a unit with a member at that age. + +### What the ruling changes + +Built and calibrated as above (`cf91835c9` plus #1188's commit, spine and calibration from the same +tree). "Before" is the review-round-1 head. + +- **The spine, at design weights.** UC property is £931bn across 3.21m benefit units (£1,032bn across + 4.21m before). Pension Credit property on units with a member at qualifying age is £285bn across + 0.97m units (£338bn across 1.02m before). +- **The stage's receipt.** 10–29 FRS-channel benefit units that report UC keep their receipt (30+ + before), and most of them would have gone above £16,000. The redraw trains on 4,104 units (4,029 + before; 4,191 on main). +- **The calibration.** Loss 0.00685 (before 0.00691; control 0.00702), 98.5% of 1,168 targets within + 10% (1,151; before 1,150), ESS 4,809 (before 4,849). Six of the 7 calibrated-seam gates and the + input-mass gate pass. + - `uk_weight_ratio` fails, by more than before the ruling and by less than the control. + - The West Midlands cell is at +25.3% (before +26.9%), under its deferral. + - Design UC is £51.90bn (before £51.70bn) and design Pension Credit £4.24bn (before £4.21bn); + both calibrate to target. 108 of 111 UC cells are within 10%, as before. +- **Who is paid UC, at each run's calibrated weights in 2025.** The total is on target both times. + - Units over the capital limit through property: 1.50m (before 2.65m). Of those, units that are + not the reference person's: fewer than 10 (before 1.16m). + - In households with other property, units that are not the reference person's are paid £1.16bn + of UC across 216k units (before £0.60bn across 85k). + - `uk_uc_capital_coherence` passes with the new rule, and `uk_take_up_signal` passes with + `would_claim_uc` at 0.557 against 0.55. + +## The rebase onto main `977ac983a` and the re-review (2026-10-12) + +A re-review of `bcbe274b2` is in the PR's comments. It asked for the rebase, a measurement on the new +base, and five smaller changes. + +### What main changed under this branch + +- **#1167** adds the consumption-tax facts and four measure exclusions, `obr.vat` among them. +- **#1145** moves the lock to policyengine-uk 2.123.0, adds the landlord stages, and empties the + target-fit register: it retired the West Midlands deferral that this branch had restored. +- **#1198** reconciles the national register across grains, adds the uprating-holds register, and moves + the feed to Chronicle 942a1fa. +- **#1188** is the evidence-reader fix the earlier arms carried on top. It is on main, so the arms + below need nothing on top. + +### The merge + +- 15 files conflicted. The eight hand-written ones were merged by hand. The seven generated ones were + regenerated on the merged tree: the national references and membership, the coverage manifest, the + H2 fixture, the gate digests and the two national compile-parity receipts. The local census and + the local references regenerate identically to main's. +- **The merged surface:** 1,279 active references, main's 1,281 less `obr.ni` and the all-rates + salary-sacrifice relief, which this branch turns into diagnostics. Against main, 13 rows change + their index (savings interest) and one its selector (`obr.council_tax`). The contract has 396 + targets and the measure register 65 exclusions: main's four new ones, less the three this branch + retires. +- **The salary-sacrifice hold.** #1198's signed hold named `hmrc.salary_sacrifice.it_relief_total`. + The row is a diagnostic here, and the compile refuses a hold that matches no held row, so the id + leaves the hold. The hold keeps the three rate bands and the employee NICs relief. +- **W.** The target-fit register is main's, empty. The West Midlands £12,570–15,000 income-tax cell + needs no deferral on the new base (below). +- **The savings-interest index.** The two-leg factor is still 1.192 on policyengine-uk 2.123.0. + +### What the re-review changed + +- **N3 and N4, D3's reference-person rule.** Two engine tests hold the cases the rule leaves, and + the module text says both: + - where the reference person is of working age, the property of a pension-age unit in the household + is recorded on the reference person's unit, and no Pension Credit assessment counts it. On the + head's spine at design weights, 0.04m pension-age benefit units are in such a household, and those + households hold £21bn of this property; + - a unit on the unavailable sentinel stays on the engine's proxies. Beside a recording unit, the UC + proxy leaves it the 10% that unit did not record, and the Pension Credit proxy, which subtracts + nothing, assesses a pension-age unit the gross property a second time. Fewer than 10 units are on + the sentinel. +- **N5, T2's tolerance.** The engine computes in float32, whose step is £1 at £10 million. The + refusal is now beyond £1 plus four float32 steps at the person's own amounts. +- **The Pension Credit stage's declared year.** The stage resolves its two declared `survey_year` + rules and refuses a frame whose period is another year. The adapter's `dataset_period` argument + has a named protocol, `ProjectingRulesEngine`, declared beside the UK adapter. +- **Main's new pins.** Two of main's checks caught the rebased branch, and `30ef5546f` answers both. + The protocol was first added to `microcosm.frame.rules`, which is inside the H1 parity kernels' + implementation hash, so it moved a pinned case; `rules.py` is main's again. And `hmrc_uprating` + reads the release's survey year for the savings-interest pivot, so `uk_runtime.frs_release` joins + the UK target compile modules (microcosm#1123's coverage test). +- **Text.** The contract's two PIP diagnostic notes, two changelog fragments and this file no longer + give the value of the weight-ratio statistic, which is a per-record extreme, or two counts whose + difference is under 10. +- **Kept.** The landlord-carrier refresh still skips a frame without the column, as D1's refresh + does: the root stage always writes it and the graph checks its cells. The all-rates relief + diagnostic is checked by a feed-gated test, as main's other diagnostics are. + +### Measurement on the new base + +Both arms were built and calibrated locally on Chronicle 942a1fa with policyengine-uk 2.123.0. The +control is main `977ac983a`, the branch's base. The head arm ran on the tree of `e87e2ce7d`. The +commits after it change text, formatting, the place of a typing protocol and the compile-module +list; none changes a stage or a target. + +- **Control (main `977ac983a`).** Loss 0.00611, 98.6% of 1,213 targets within 10% (1,196), ESS 5,195. + All 7 calibrated-seam gates pass. So main's `uk_weight_ratio` failure, which the earlier control + showed, is gone since #1167, #1145 and #1198. +- **Head.** Loss 0.00586, 98.8% of 1,214 targets within 10% (1,199), ESS 5,129. All 7 + calibrated-seam gates pass, and so does the certifier's input-mass gate, with no stale, expired or + dormant exclusion. + - The West Midlands £12,570–15,000 income-tax cell is at +0.2% (control −0.2%). #1198 reconciles + the regional bands with the national ones, so the cell no longer sits near the bound. + - `obr.council_tax` is at −7.2% (control −9.7%). `obr.vat` is out of the fit on main since #1167. + - 108 of 111 UC cells are within 10% (control 107). `dwp.uc.households_children_2` moves the other + way, from −9.7% to −12.4%. + - The three salary-sacrifice bands are within 0.1%. `obr.universal_credit` and `obr.pip` are within + 0.1%. + - The £500,000–1,000,000 employment-income band is at −3.4% (control −5.1%), and Scotland's + £200,000-and-over total income at −8.1% (control −8.2%). + - The largest error among the fitted cells is 15.9% (control 16.5%). + - Design UC is £51.90bn (control £52.69bn) and design Pension Credit £4.24bn (control £4.39bn). + - The stage receipts are those of the head before the rebase: 1,255k units entitled to Guarantee + Credit, 4,104 training units in the reporter redraw, and 10–29 FRS-channel benefit units that + report UC keeping their receipt. + - D3, the spine at design weights: UC property is £931bn across 3.21m benefit units, and Pension + Credit property on units with a member at qualifying age is £286bn across 0.97m units. + - D3, at calibrated weights in 2025: 1.53m benefit units are over the UC capital limit through + property, and fewer than 10 of them are not the reference person's. In households with other + property, units that are not the reference person's are paid £1.22bn of UC across 219k units. + - On the exported file, `uk_uc_capital_coherence` passes, and `uk_take_up_signal` passes with + `would_claim_uc` at 0.551 against 0.55, over 55,713 units. + +## Verification + +- Touched-surface tests per commit (counts in each commit's run). +- Each replay left every commit's hand-written change line-for-line identical; only the regenerated + surfaces differ. +- Final head `a7e5bc56f`: + - the `engine-free` lane's 534 test files, with `policyengine_uk` blocked as in CI: 16,146 passed + and 139 skipped. The three failures are environmental: + - the live Hugging Face pointer test fails on main too; + - the telemetry ambient-token exchange needs the hosted service; + - main's new US head-to-head scorer test needs Python 3.14.7, and this machine has 3.14.6; + - the `engine-uk` lane's 40 test files, run in full: 174 passed; + - against the pinned feed, 12 passed: the national compile-parity receipts regenerate identically + and pass their gate, the committed national and local references regenerate, and the NICs and PIP + diagnostics resolve their exact facts. +- `ruff check` and `ruff format --check` pass on the 61 changed Python files, and so do + `tools/ci_test_plan.py verify` and `tools/graph_acceptance_burndown.py --verify`. +- After review round 1, head `78ea6b695`: + - the `engine-free` lane's 534 test files, with `policyengine_uk` blocked: 16,152 passed and 140 + skipped, with the same three environmental failures; + - the `engine-uk` lane's 41 test files: 179 passed; + - against the pinned feed, 45 passed: the national compile-parity receipts regenerate identically + and pass their gate, the committed national and local references regenerate, and the NICs, PIP + and salary-sacrifice diagnostics resolve their exact facts; + - `ruff` on the 66 changed Python files, `tools/ci_test_plan.py verify` and + `tools/graph_acceptance_burndown.py --verify` pass. +- With the D3 ruling, head `cf91835c9`: + - the `engine-free` lane's 534 test files: 16,152 passed and 141 skipped, with the same three + environmental failures; + - the `engine-uk` lane's 41 test files: 179 passed; + - the commit changes no target, reference or parity file, so the pinned-feed run above stands; + - `ruff`, `tools/ci_test_plan.py verify` and `tools/graph_acceptance_burndown.py --verify` pass. +- After the rebase onto main `977ac983a`, head `30ef5546f`: + - the `engine-free` lane's 558 test files, with `policyengine_uk` blocked: 16,708 passed and 148 + skipped, with the same three environmental failures; + - the `engine-uk` lane's 43 test files: 191 passed; + - against the pinned feed 942a1fa, 59 passed and 2 skipped: the national compile-parity receipts + regenerate identically, the committed references regenerate, and the NICs, PIP, salary-sacrifice, + State Pension and Winter Fuel diagnostics resolve their exact facts. One test fails, and it fails + on main `977ac983a` in the same way: after its receipts compare equal, its gate call reads three + 2023 land rows as `fixture_only` where the receipt signs them `ledger_absent`. That is main's, + from #1198, and CI skips the test; + - `ruff` on the 67 changed Python files, `tools/ci_test_plan.py verify` and + `tools/graph_acceptance_burndown.py --verify` pass. + +## Signature drafts + +`data/ukds/acceptance/1095-uk-data-ports-3/signed-differences-draft-d1-d5.md` (D1 and D5, net-new +columns), `signed-differences-draft-d3.md` (D3's redrafted UC entry and the new Pension Credit +entry) and `signed-differences-draft-d4.md` (`would_claim_pc`). They are not committed until signed. +The measurement scripts are in the same directory, outside the repository beside the licensed data, +as for #1100 and #1121. + +## Left for later + +- The sparse-band support floor, as its own PR. +- SPI incomes drawn by employment status (uk-data#529), with microcosm#840. It removes D1's SPI rule. +- The WAS property draw: a landlord stratum, a benefit-income predictor, and the mortgages on + other property carried to the frame so that recorded capital nets them. +- The low UC awards below the top band, which the unbound band hides. +- The weight year, and the renewals of the measure exclusions that expire from 15 October. +- **D3 for Universal Credit after the survey year, at the next engine bump.** policyengine-uk 2.123.0 + holds `uc_reported_capital` flat in its dataset projection, while `pension_credit_reported_capital` + and the household property columns project by GDP per head. So in 2025 and later the same property + counts at a lower value for Universal Credit than for Pension Credit. policyengine-uk 2.124.0 + projects `uc_reported_capital` too (pe-uk#2201). The D3 figures at 2025, such as the units over the + UC capital limit through property, will move at that bump. 2.124.0 also adds + `benunit_reported_capital`; D3 records two different figures (land counts for Pension Credit only, + and UC reporters keep their receipt), so the spine keeps writing both columns. +- **uk-data#547, not covered.** No child aged 9 to 11 months reaches the working-parent childcare + entitlement. María ruled on 2026-10-09 that the fix belongs in policyengine-uk, whose childcare + test should read age in months. No issue is filed for it yet. 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 6a60a14662..f775b4895e 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 @@ -2072,6 +2072,29 @@ "full_time_education_through_age" ] }, + "looked_after_child": { + "type": "object", + "additionalProperties": false, + "properties": { + "foster_child_code": { + "type": "integer", + "minimum": 1 + }, + "foster_parent_code": { + "type": "integer", + "minimum": 1 + }, + "below_age": { + "type": "integer", + "minimum": 0 + } + }, + "required": [ + "foster_child_code", + "foster_parent_code", + "below_age" + ] + }, "one_person_age_split": { "type": "integer", "minimum": 0 @@ -2145,6 +2168,7 @@ "head_label", "family_link_codes", "dependent_child", + "looked_after_child", "one_person_age_split", "family_role_values", "household_type_values", @@ -6321,6 +6345,12 @@ "survey_year", "calibration_year" ] + }, + "dataset_year_rule": { + "type": "string", + "enum": [ + "survey_year" + ] } } }, @@ -6383,7 +6413,9 @@ "anchor", "bands", "weight_mapping", - "residual_rate" + "residual_rate", + "solve_scope", + "newly_entitled_rate_key" ], "properties": { "kind": { @@ -6429,6 +6461,15 @@ }, "residual_rate": { "type": "string" + }, + "solve_scope": { + "type": "string", + "enum": [ + "great_britain" + ] + }, + "newly_entitled_rate_key": { + "type": "string" } } }, @@ -6810,6 +6851,9 @@ "additionalProperties": { "type": "string" } + }, + "role_column": { + "type": "string" } } }, diff --git a/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py b/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py index 0d33a0f805..c7441b557d 100644 --- a/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py +++ b/packages/microcosm-build/src/microcosm/build/stochastic_assignment.py @@ -64,8 +64,8 @@ def assign_binary_with_anchored_residual( 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 + programme can reach (a benefit unit with no claimant or partner of working + 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. diff --git a/packages/microcosm-build/src/microcosm/build/target_materialization.py b/packages/microcosm-build/src/microcosm/build/target_materialization.py index 8f11e64a09..b58528dfb2 100644 --- a/packages/microcosm-build/src/microcosm/build/target_materialization.py +++ b/packages/microcosm-build/src/microcosm/build/target_materialization.py @@ -50,6 +50,16 @@ re.IGNORECASE, ) _UNDER_LABEL = re.compile(r"^\s*under\s*£\s*([\d,]+(?:\.\d+)?)\s*$", re.IGNORECASE) +#: An opt-in reading of whole-pence amount bands. DWP publishes UC award bands +#: as "£0.01 to £100.00", "£100.01 to £200.00", ..., "£2,500.01 or over": each +#: band starts a penny above its predecessor's top, so the band is the +#: upper-closed interval (label lower - £0.01, next label lower - £0.01] in +#: source units, and model values are compared in whole pence, so an award of +#: a penny a month lands in the bottom band (microcosm#1095). Where the top +#: category is not bound, ``band_upper_bound`` closes the last bound band. +WHOLE_PENCE_UPPER_CLOSED = "whole_pence_upper_closed" +BAND_SEMANTICS = frozenset({WHOLE_PENCE_UPPER_CLOSED}) +_PENNY = 0.01 _MISSING_COLUMN = re.compile(r"^'(?:([a-z_0-9]+)\.)?([A-Za-z_0-9]+)'$") #: Provider kinds whose values are additionally masked by the binding's @@ -574,6 +584,29 @@ def _band_bounds( if edge > lower: upper = edge break + semantics = _band_semantics(binding) + if semantics == WHOLE_PENCE_UPPER_CLOSED: + if "band_upper_bound_inclusive" in binding: + raise ValueError( + "whole_pence_upper_closed bands are closed above; declare no " + "band_upper_bound_inclusive" + ) + factor = float(binding.get("band_period_factor", 1) or 1) + step = _PENNY * factor + closed_upper = upper - step + # A consumer may bind only the finite portion of a published + # histogram. band_upper_bound is then the closed top of the last bound + # band in source units, so that band does not absorb a separately + # published, unbound top-coded category. + if "band_upper_bound" in binding: + declared_upper = float(binding["band_upper_bound"]) * factor + if not math.isfinite(declared_upper) or declared_upper <= lower - step: + raise ValueError( + "band_upper_bound requires a finite bound above the band's " + "lower edge" + ) + closed_upper = min(closed_upper, declared_upper) + return lower - step, closed_upper own_upper = _band_label_upper_edge(spec, binding) if own_upper is not None: if not own_upper > lower: @@ -872,10 +905,30 @@ def _binding_mask( if band is not None: lower, upper = band banded = _column(adapter, entity, binding["groupby_variable"]).astype(float) - mask &= (banded >= lower) & (banded < upper) + if _band_semantics(binding) == WHOLE_PENCE_UPPER_CLOSED: + pence = np.rint(banded * 100.0) + mask &= pence > np.rint(lower * 100.0) + if math.isfinite(upper): + mask &= pence <= np.rint(upper * 100.0) + else: + mask &= (banded >= lower) & (banded < upper) return mask +def _band_semantics(binding: Mapping[str, Any]) -> str | None: + """A binding's declared band reading; an undeclared value refuses.""" + + semantics = binding.get("band_semantics") + if semantics is None: + return None + if semantics not in BAND_SEMANTICS: + raise ValueError( + f"unknown band_semantics {semantics!r}; expected one of " + f"{sorted(BAND_SEMANTICS)}" + ) + return str(semantics) + + def _entity_reduction(adapter: Any, reduction: Mapping[str, Any]) -> np.ndarray: """Reduce a member-level variable to the target entity's grain. 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 c34a1576cc..913ec73e06 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json @@ -1,33 +1,6 @@ { "schema_version": 2, "exclusions": [ - { - "name": "hmrc.salary_sacrifice.it_relief_basic_rate", - "reason": "Deferred inside microcosm#1069 c11 (R7): the counterfactual route now resolves the salary-sacrifice relief, but the rate-band test compares adjusted net income with the after-allowance thresholds (0 / 37,700 / 125,140) and ignores the Scottish bands, so the three bands wait for a band-test fix; the all-rates total binds.", - "tracking": "microcosm#1069", - "approved_by": "juaristi22", - "adjudication": "microcosm#1069 plan R7, approved 2026-09-30: the relief rows bind through the counterfactual route, the rate bands after the band-test fix", - "approved_on": "2026-09-30", - "expires_on": "2026-11-25" - }, - { - "name": "hmrc.salary_sacrifice.it_relief_higher_rate", - "reason": "Deferred inside microcosm#1069 c11 (R7): the counterfactual route now resolves the salary-sacrifice relief, but the rate-band test compares adjusted net income with the after-allowance thresholds (0 / 37,700 / 125,140) and ignores the Scottish bands, so the three bands wait for a band-test fix; the all-rates total binds.", - "tracking": "microcosm#1069", - "approved_by": "juaristi22", - "adjudication": "microcosm#1069 plan R7, approved 2026-09-30: the relief rows bind through the counterfactual route, the rate bands after the band-test fix", - "approved_on": "2026-09-30", - "expires_on": "2026-11-25" - }, - { - "name": "hmrc.salary_sacrifice.it_relief_additional_rate", - "reason": "Deferred inside microcosm#1069 c11 (R7): the counterfactual route now resolves the salary-sacrifice relief, but the rate-band test compares adjusted net income with the after-allowance thresholds (0 / 37,700 / 125,140) and ignores the Scottish bands, so the three bands wait for a band-test fix; the all-rates total binds.", - "tracking": "microcosm#1069", - "approved_by": "juaristi22", - "adjudication": "microcosm#1069 plan R7, approved 2026-09-30: the relief rows bind through the counterfactual route, the rate bands after the band-test fix", - "approved_on": "2026-09-30", - "expires_on": "2026-11-25" - }, { "name": "hmrc/employment_income_income_band_1_000_000_to_inf", "reason": "Zero-support channel on total income (ITA 2007 s.23, which excludes capital gains): zero in-band carrier records with total income above 1m, under both the SPI assessable-income concept and the component-sum proxy, measured identically on spine-i and spine-j (microcosm#807, receipts uk-candidate-eval out/excluded_cell_support_spine_{i,j}.json). The spine carries capital gains to 20.4m and thousands of records above 1m wealth; the premise is total-income support, never a wealth or gains cap, and a zero-support cell cannot be reached by reweighting.", diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 86fa1db761..2cd436f9cd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -858,9 +858,30 @@ "threshold": 0.0, "capital_column": "uc_reported_capital", "carrier_column": "frs_benunit_capital", - "sentinel": -1.0 + "sentinel": -1.0, + "property_shares": { + "uc_reported_capital": { + "sources": [ + "other_residential_property_value", + "non_residential_property_value" + ], + "owners": "household_reference_person_unit", + "valuation": 0.9, + "units_reporting_universal_credit": "no_share" + }, + "pension_credit_reported_capital": { + "sources": [ + "owned_land", + "other_residential_property_value", + "non_residential_property_value" + ], + "owners": "household_reference_person_unit", + "valuation": 0.9 + } + }, + "sum_tolerance": 0.01 }, - "notes": "Benunit-aggregated reported Universal Credit must imply would_claim_uc. The reported-capital engine carrier is bounded below by its named -1 unavailable sentinel, may use that sentinel only where the FRS carrier is unavailable, and otherwise remains the exact same source value." + "notes": "Benunit-aggregated reported Universal Credit must imply would_claim_uc. Both recorded capitals, uc_reported_capital and pension_credit_reported_capital, are bounded below by the named -1 unavailable sentinel and may use it only where the FRS carrier frs_benunit_capital is unavailable. Otherwise each equals the carrier plus the household property the unit holds, re-derived here with the stage's own rule (microcosm#1095): the household reference person's benefit unit holds the household's property at 90% of its value, market value less 10% for the costs of sale, and no other unit holds any. UC counts other residential and non-residential property, and a unit that reports Universal Credit keeps its receipt and carries none; Pension Credit counts those and owned land. The sum may differ by GBP 0.01 plus one float32 step of the expected value, so a float32 round-trip of the export does not fail it." }, { "id": "uk_support", @@ -1015,6 +1036,8 @@ "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", "person.uc_is_in_startup_period", + "person.uc_is_in_gainful_self_employment", + "person.is_looked_after_by_local_authority", "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 18ceb0e1a2..06eab16f39 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 @@ -1,25 +1,25 @@ { - "compiled_count": 1281, + "compiled_count": 1279, "counts_by_kind": { - "calibration_drift": 569, - "fixture_only": 42, - "ledger_only": 686 + "calibration_drift": 568, + "fixture_only": 43, + "ledger_only": 685 }, - "difference_count": 1297, + "difference_count": 1296, "differences": [ { "fixture_value": 1608000.0, "kind": "fixture_only", "name": "dwp.pip.daily_living_enhanced_claimants", "period": 2025, - "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." + "reason": "microcosm#1095 (María's ruling of 2026-10-10) keeps DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) out of the fit: the two rows resolve and fit, but they push the heaviest support family's weight over the uk_weight_ratio fence. The exact figures are retained as diagnostic provenance on obr.pip, so the frozen fixture's fitted rows are fixture-only on the current surface." }, { "fixture_value": 1283000.0, "kind": "fixture_only", "name": "dwp.pip.daily_living_standard_claimants", "period": 2025, - "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." + "reason": "microcosm#1095 (María's ruling of 2026-10-10) keeps DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) out of the fit: the two rows resolve and fit, but they push the heaviest support family's weight over the uk_weight_ratio fence. The exact figures are retained as diagnostic provenance on obr.pip, so the frozen fixture's fitted rows are fixture-only on the current surface." }, { "fixture_value": 320866000.0, @@ -154,6 +154,13 @@ "period": 2025, "reason": "Fixture row has no Ledger-compiled counterpart at this comparison period." }, + { + "fixture_value": 200081395082.7125, + "kind": "fixture_only", + "name": "obr.ni", + "period": 2025, + "reason": "microcosm#1095 (María's ruling of 2026-10-09; uk-data#537 drops the row) moves OBR's total NICs receipts out of the fit: a cash total against the three accrued class rows, carrying statutory-payment recoveries, Class 1A, 1B and 3 and settlements that no household carries. The exact FY2025-26 figure is retained as diagnostic provenance on obr.ni_employee, so the frozen fixture's fitted row is fixture-only on the current surface." + }, { "fixture_value": 146185954351.57382, "kind": "fixture_only", @@ -1748,399 +1755,399 @@ "ledger_value": 15500.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000001", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 63250.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000002", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 41000.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000003", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 43750.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000004", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 48750.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000005", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 122250.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000006", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 345500.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000007", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 232250.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000008", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 62750.0, "name": "hmrc.itl.additional_rate_taxpayers@E12000009", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 10750.0, "name": "hmrc.itl.additional_rate_taxpayers@N92000002", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 184500.0, "name": "hmrc.itl.additional_rate_taxpayers@S92000003", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 17500.0, "name": "hmrc.itl.additional_rate_taxpayers@W92000004", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 197000.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000001", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 645500.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000002", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 434500.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000003", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 436750.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000004", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 518500.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000005", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 766000.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000006", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 1305000.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000007", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 1255000.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000008", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 574250.0, "name": "hmrc.itl.higher_rate_taxpayers@E12000009", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 121500.0, "name": "hmrc.itl.higher_rate_taxpayers@N92000002", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 593250.0, "name": "hmrc.itl.higher_rate_taxpayers@S92000003", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 238750.0, "name": "hmrc.itl.higher_rate_taxpayers@W92000004", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 11550000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_1000000_2000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 38000000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_100000_150000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 650750000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_12570_15000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 20000000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_150000_200000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 4532500000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_15000_20000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 21800000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_2000000_plus", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 41075000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_200000_500000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 23025000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_20000_30000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 57925000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_30000_50000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 18100000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_500000_1000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 86025000000.0, "name": "hmrc.itl.income_tax_by_total_income_band.band_50000_100000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 20750.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_1000000_2000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 1092500.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_100000_150000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 2972500.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_12570_15000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 342750.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_150000_200000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 5412500.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_15000_20000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 10750.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_2000000_plus", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 372500.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_200000_500000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 10225000.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_20000_30000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 12125000.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_30000_50000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 65500.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_500000_1000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 6950000.0, "name": "hmrc.itl.taxpayers_by_total_income_band.band_50000_100000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 28425000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_1000000_2000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 129750000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_100000_150000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 40950000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_12570_15000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 58525000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_150000_200000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 94200000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_15000_20000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 55175000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_2000000_plus", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 108250000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_200000_500000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 253750000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_20000_30000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 465500000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_30000_50000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 44350000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_500000_1000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 455500000000.0, "name": "hmrc.itl.total_income_by_total_income_band.band_50000_100000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", @@ -2165,192 +2172,185 @@ }, { "kind": "ledger_only", - "ledger_value": 8800000000.0, - "name": "hmrc.salary_sacrifice.it_relief_total", - "period": 2025, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." - }, - { - "kind": "ledger_only", - "ledger_value": 1489138606.9960644, + "ledger_value": 1501942263.1528072, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_100000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 1465501486.2500951, + "ledger_value": 1478101909.7694294, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_1000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 323828554.21977913, + "ledger_value": 326612841.3522771, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_12570", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 1619142771.0988955, + "ledger_value": 1633064206.7613857, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_15000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 768206424.2440015, + "ledger_value": 774811484.9597815, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_150000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 3699209396.744192, + "ledger_value": 3731015304.49864, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_20000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 866300475.339774, + "ledger_value": 873748951.5007998, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_200000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 2990095774.3651137, + "ledger_value": 3015804702.9973035, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_30000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 795389113.1018662, + "ledger_value": 802227891.350666, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_300000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 2210070789.7481275, + "ledger_value": 2229073041.345833, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_40000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 2765543127.2784057, + "ledger_value": 2789321345.8552136, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_50000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 771751992.355897, + "ledger_value": 778387537.9672881, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_500000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", - "ledger_value": 1914606780.4235115, + "ledger_value": 1931068624.0536094, "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_70000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 608465.2619589977, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_100000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 24500.0, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_1000000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 1303728.0701754387, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_12570", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 2779656.419529837, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_15000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 170209.1836734694, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_150000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 5262867.647058823, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_20000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 132950.16077170416, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_200000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 4142708.333333333, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_30000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 81446.94533762058, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_300000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 2660763.8888888885, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_40000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 2714273.204903678, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_50000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 46092.59259259259, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_500000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", "ledger_value": 1302364.2732049038, "name": "hmrc.spi.savings_interest_income.count_by_total_income_band.band_70000", "period": 2025, - "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band uprated by the household interest index; the incumbent registry binds neither (chronicle#282)." + "reason": "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax liabilities Table 2.5 taxpayers, total income and tax by band as the calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI 2023-24 savings-interest rows by band, moved to the dataset's year by the household interest index and from there to the calibration year by GDP per head, the index the engine's dataset projection applies (microcosm#1095); the incumbent registry binds neither (chronicle#282)." }, { "kind": "ledger_only", @@ -8138,10 +8138,10 @@ { "fixture_value": 50925163826.30539, "kind": "calibration_drift", - "ledger_value": 50116358662.27284, + "ledger_value": 48774253146.51462, "name": "obr.council_tax", "period": 2025, - "reason": "Period basis (PolicyEngine/chronicle#280 lane, María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year): ours binds the OBR March 2026 line at the calendar-2025 window, three twelfths of FY2024-25 and nine twelfths of FY2025-26, on the same series; the frozen incumbent fixture holds the FY2025-26 value alone." + "reason": "Series and period basis (microcosm#1095, María's ruling of 2026-10-09): ours binds OBR table 4.1 row 15, the council tax receipts of England, Scotland and Wales, over Great Britain's households, at the calendar-2025 window (three twelfths of FY2024-25 and nine twelfths of FY2025-26, María's rule of 2026-09-22, PolicyEngine/chronicle#280 lane); the frozen incumbent fixture holds the FY2025-26 value of row 19 alone, which adds Northern Ireland domestic rates, the council tax accruals adjustment and the high-value council tax surcharge." }, { "fixture_value": 7206287181.538654, @@ -8175,14 +8175,6 @@ "period": 2025, "reason": "Period basis (PolicyEngine/chronicle#280 lane, María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year): ours binds the OBR March 2026 line at the calendar-2025 window, three twelfths of FY2024-25 and nine twelfths of FY2025-26, on the same series; the frozen incumbent fixture holds the FY2025-26 value alone." }, - { - "fixture_value": 200081395082.7125, - "kind": "calibration_drift", - "ledger_value": 193190546312.03436, - "name": "obr.ni", - "period": 2025, - "reason": "Period basis (PolicyEngine/chronicle#280 lane, María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year): ours binds the OBR March 2026 line at the calendar-2025 window, three twelfths of FY2024-25 and nine twelfths of FY2025-26, on the same series; the frozen incumbent fixture holds the FY2025-26 value alone." - }, { "fixture_value": 49544363784.26465, "kind": "calibration_drift", 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 2cdc088cc6..1911b241d3 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 @@ -1,12 +1,12 @@ { - "compiled_count": 939, + "compiled_count": 938, "counts_by_kind": { "calibration_drift": 118, "fixture_only": 17, "ledger_absent": 3, - "ledger_only": 810 + "ledger_only": 809 }, - "difference_count": 948, + "difference_count": 947, "differences": [ { "kind": "ledger_absent", @@ -911,13 +911,6 @@ "period": 2023, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, - { - "kind": "ledger_only", - "ledger_value": 7200000000.0, - "name": "hmrc.salary_sacrifice.it_relief_total", - "period": 2023, - "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." - }, { "kind": "ledger_only", "ledger_value": 1200000000.0, 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 fb8d3f0272..dd71a05857 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json +++ b/packages/microcosm-build/src/microcosm/build/uk/release_input_coverage_manifest.json @@ -470,7 +470,7 @@ "required_mass_change_reason": "Capital-gains incidence anchor moves the mass of non-liable clone households back to their paired originals until the sub-exempt and loss-making clone mass match the Advani-Summers reporter composition at the redrawn liable mass; every pair's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, the HMRC Table 3 redrawn gains on the spine and Advani-Summers capital-gains incidence" @@ -493,7 +493,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, and Advani-Summers capital-gains incidence" @@ -516,7 +516,7 @@ "required_mass_change_reason": "Capital-gains residential split divides every household carrying liable gainers into arms weighted by the solved residential probabilities, one arm per subset of residential gainers; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative) on the amounts the Table 3 redraw placed" @@ -538,7 +538,7 @@ "required_mass_change_reason": "Capital-gains support split divides the wealthiest households of each HMRC Table 3 income band into light copies at equal weight; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2026 release, Table 3 for 2024-25, via the vendored conditioning facts", "survey": "Family Resources Survey 2024-25, SPI synthetic support and HMRC Capital Gains Tax statistics Table 3 (2024-25 individuals by size of gain and taxable income)" @@ -567,7 +567,7 @@ "required_mass_change_reason": "ETB public-services imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab, DfT rail fare index, and public NHS activity/cost table.", "survey": "Effects of Taxes and Benefits 1977-2024 and NHS age-gender public table" @@ -587,7 +587,7 @@ "required_mass_change_reason": "ETB VAT expenditure-rate imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "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" @@ -608,7 +608,7 @@ "required_mass_change_reason": "CGT asset-type assignment on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative), Table 4.1 (individuals claiming Business Asset Disposal Relief or Investors' Relief by band of qualifying gain, 2024-25, administrative) and Table 7 (disposals, proceeds and gains by asset type, 2023-24, sample-based), vendored from the pinned Chronicle feed" @@ -632,7 +632,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "417d662bb58a25db06dcd542a83553a6775bbc12a7c3a995f695b091483215ed", @@ -721,7 +721,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -762,7 +762,7 @@ "required_mass_change_reason": "LCFS consumption imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "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 2024 mean kWh, DESNZ Energy Trends and QEP average prices paid for calendar 2024, road-fuel outturn (DESNZ, HMRC, ONS Consumer Trends) and road fuel by vehicle type (DESNZ), ONS Consumer Trends household spending by COICOP division, the DfT VEH1103 licensed-car stock, NTS bus use, and the DfT and devolved bus finance tables and fares indices.", "survey": "Living Costs and Food Survey 2023-24" @@ -788,7 +788,7 @@ "required_mass_change_reason": "NTS bus-travel imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "Department for Transport National Travel Survey, UK Data Service SN 5340 (End User Licence, 19th edition, November 2025), DOI 10.5255/UKDA-SN-5340-19; local licensed tab files (household, individual, trip); England residents only from 2013.", "survey": "National Travel Survey 2002-2024" @@ -811,7 +811,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "https://www.gov.uk/government/statistics/property-rental-income-statistics", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Property Rental Income Statistics 2026 (tax year 2024-25)" @@ -835,7 +835,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "MHCLG dwellings and ONS UK House Price Index December 2025 regional average prices.", "survey": "Public regional property reference" @@ -862,7 +862,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "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" @@ -895,7 +895,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "Reserved SPI support households for the HMRC Table 2.5 total-income bands from GBP 200,000, stacked at the published band taxpayer count over the donors per band (PolicyEngine/chronicle#280 lane).", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Income Tax liabilities statistics July 2026 Table 2.5" @@ -917,7 +917,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "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" @@ -939,7 +939,7 @@ "required_mass_change_reason": "WAS Lifetime ISA imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "source_vintages": { "source": "Office for National Statistics Wealth and Assets Survey, UK Data Service SN 7215, DOI 10.5255/UKDA-SN-7215-20; local licensed round-8 person and household tabs (interviews April 2020 to March 2022, Great Britain).", "survey": "Wealth and Assets Survey round 8" @@ -974,7 +974,7 @@ "required_mass_change_reason": "WAS wealth imputation on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "18a1d8f03569f90821e601056476bee872b1956fee985bce0e3ef0953c6cebb8", + "source_manifest_sha256": "f5fb7c4e6d153595e7a7dbb3a107aeec60be6b3e1732d340eaafde46b666b1c4", "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 fe59f8f8b5..83f3a125a7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -229,7 +229,7 @@ }, { "kind": "map_columns", - "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" }, { "kind": "map_coded_amounts", @@ -266,6 +266,7 @@ "is_claimant_or_partner", "is_hbai_dependent_child", "uc_is_in_startup_period", + "uc_is_in_gainful_self_employment", "rent_paid_as_boarder", "rent_paid_as_lodger", "employment_income", @@ -275,6 +276,7 @@ "savings_interest_income", "dividend_income", "property_income", + "reports_rent_from_other_property", "maintenance_income", "miscellaneous_income", "private_transfer_income", @@ -612,6 +614,11 @@ "unconditional_below_age": 16, "full_time_education_through_age": 18 }, + "looked_after_child": { + "foster_child_code": 5, + "foster_parent_code": 9, + "below_age": 16 + }, "one_person_age_split": 65, "family_role_values": [ "COUPLE_PARTNER", @@ -644,12 +651,13 @@ "relationship_to_head", "ons_family_role", "ons_family_index", + "is_looked_after_by_local_authority", "ons_household_type" ], "nonnegative_outputs": [ "ons_family_index" ], - "notes": "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review." + "notes": "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review. It also sets is_looked_after_by_local_authority for a child-table member under 16 whose grid code toward an adult in their own benefit unit is foster child (5), or toward whom that adult records foster parent (9). The FRS 2024-25 question instructions (Benefit Unit Allocation) put foster children under 16 in the carer's benefit unit, so without the flag policyengine-uk's child tests (Universal Credit, Child Tax Credit, Pension Credit, Tax-Free Childcare, extended childcare and the legacy benefits) count them as the carer's own children. The same instructions make a foster child aged 16 or over a separate benefit unit where a local authority maintenance allowance covers them, so one who stays in the carer's unit has no such allowance and is not marked. The flag follows the relationship code alone: few such children are recorded, and the carer's local-authority fostering allowance (ALLOW3) covers fewer still." }, { "stage": "frs_employment", @@ -965,11 +973,12 @@ }, { "kind": "aggregate_person_to_benunit", - "method": "any_adult_under_state_pension_age", + "method": "any_uc_claimant_aged_18_under_pension_credit_age", "consumed_only": true, "aggregates": { "uc_age_eligible": "age" - } + }, + "role_column": "is_uc_claimant" }, { "kind": "assign_binary_with_anchored_residual", @@ -1070,7 +1079,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with 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)." + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095)." }, { "stage": "frs_person_draws", @@ -4682,7 +4691,8 @@ "kind": "derive", "derived": { "is_uc_couple": "count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2", - "uc_reported_capital": "frs_benunit_capital", + "uc_property_capital_share": "0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "uc_reported_capital": "frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital; a base FRS-channel unit with universal_credit_reported_amount > 0 takes frs_benunit_capital alone", "positive_pre_takeup_uc_award": "max(0, uc_maximum_amount - uc_income_reduction) > 0", "has_non_child_member": "any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members", "claimable_screen": "positive_pre_takeup_uc_award AND has_non_child_member" @@ -4793,11 +4803,12 @@ }, { "kind": "aggregate_person_to_benunit", - "method": "any_adult_under_state_pension_age", + "method": "any_uc_claimant_aged_18_under_pension_credit_age", "consumed_only": true, "aggregates": { "uc_age_eligible": "age" - } + }, + "role_column": "is_uc_claimant" }, { "kind": "assign_binary_with_anchored_residual", @@ -4829,12 +4840,12 @@ "receives_benefits_in_own_right", "would_claim_uc" ], - "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in the Universal Credit age population claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps." + "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps." }, { "stage": "uc_capital_coherence", "survey": "Family Resources Survey 2024-25", - "source": "Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel.", + "source": "Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel, plus the household's Wealth and Assets Survey property imputation for the unit that holds it.", "grain": "benunit", "artifacts": [], "operations": [ @@ -4883,8 +4894,10 @@ { "kind": "derive", "derived": { - "uc_reported_capital": "frs_benunit_capital", - "pension_credit_reported_capital": "frs_benunit_capital", + "uc_property_capital_share": "0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "pension_credit_property_capital_share": "0.9 x (owned_land + other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "uc_reported_capital": "frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0 and NOT universal_credit_reported_anchor, else frs_benunit_capital", + "pension_credit_reported_capital": "frs_benunit_capital + pension_credit_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital", "would_claim_uc": "would_claim_uc OR universal_credit_reported_anchor" } } @@ -4897,7 +4910,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh." + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both." }, { "stage": "pension_credit_take_up", @@ -4910,16 +4923,21 @@ "kind": "materialize_rules_engine_predictors", "predictors": [ "has_mixed_age_couple_pension_credit_saving" - ] + ], + "year_rule": "survey_year" }, { "kind": "materialize_rules_engine_predictors", "predictors": [ "guarantee_credit", "savings_credit", - "is_pension_credit_eligible" + "is_pension_credit_eligible", + "pension_credit_assessable_capital", + "pension_credit_deemed_income" ], - "consumed_only": true + "consumed_only": true, + "year_rule": "calibration_year", + "dataset_year_rule": "survey_year" }, { "kind": "aggregate_person_to_benunit", @@ -4948,7 +4966,9 @@ } ], "weight_mapping": "household_to_benunit", - "residual_rate": "(rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1]" + "residual_rate": "(rate * entitled - reporters) / (entitled - reporters), weighted, over Great Britain, clipped to [0, 1]; drawn for every entitled non-reporter", + "solve_scope": "great_britain", + "newly_entitled_rate_key": "pension_credit_newly_entitled" } ], "outputs": [ @@ -4957,7 +4977,7 @@ "rewrites": [ "would_claim_pc" ], - "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible." + "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). A second temporary engine materialization, at the release's calibration year on the frame stamped with its survey year, so the engine projects its incomes, pensions and capital forward as it does the released file and as uk-data reads it (uk-data#510), gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible) and, for the receipt only, its assessable capital and deemed income (microcosm#1095); all are consumed only. Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. DWP's rates cover Great Britain, so E and R are Great Britain's and Northern Ireland's entitled units claim at the same r (uk-data#510). A unit with no entitlement claims if it reports, and otherwise at DWP's Savings Credit-only caseload take-up (37%, pension_credit_newly_entitled), the rate at which a unit claims once a reform or a later year entitles it (uk-data#510). The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces." }, { "stage": "child_benefit_take_up", @@ -5592,7 +5612,7 @@ "population": "persons with net capital gains above the annual exempt amount (the national taxpayer proxy)", "model": "logistic probability in centred log gains with a stock shift, p = 1 / (1 + exp(-(a + b (log g - c) + s h))) with c the weighted mean log gain of the population, s the stock log-odds and h one where the stock signal holds, zero otherwise", "stock_log_odds": 1.6, - "stock_signal": "household other residential property value above zero, or the person's property income above zero", + "stock_signal": "household other residential property value above zero, or the person's property income above zero, or the person reports rent from other property at a profit or a loss", "parameter_solver": "nested bisection; the intercept matches the expected weighted count at each trial slope, the slope matches the expected weighted gains", "count_target": "Table 8a 2024-25 total taxpayers reporting residential property disposals times the Table 8b 2024-25 individuals/all taxpayer share on the UK Property service", "gains_target": "Table 8a 2024-25 total residential property gains times the Table 8b 2024-25 individuals/all gains share on the UK Property service", 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 6c33096e8d..e271e33274 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -368,7 +368,7 @@ stages: person_table: adult union child household_sort: set_index('household_id').sort_index() before positional household reads - kind: map_columns - scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit''s UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer''s property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)' + scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit''s UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer''s property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it' - kind: map_coded_amounts scope: region, tenure, accommodation, council tax band, benefit-code amount mappings (Scotland's Adult and Child Disability Payment codes 117/118 and 121/122 into the PIP and DLA columns they replace; benefit recovery codes 69 and 70 unmapped); adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance - kind: annualize_periodic_amounts @@ -397,6 +397,7 @@ stages: - is_claimant_or_partner - is_hbai_dependent_child - uc_is_in_startup_period + - uc_is_in_gainful_self_employment - rent_paid_as_boarder - rent_paid_as_lodger - employment_income @@ -406,6 +407,7 @@ stages: - savings_interest_income - dividend_income - property_income + - reports_rent_from_other_property - maintenance_income - miscellaneous_income - private_transfer_income @@ -643,6 +645,10 @@ stages: dependent_child: unconditional_below_age: 16 full_time_education_through_age: 18 + looked_after_child: + foster_child_code: 5 + foster_parent_code: 9 + below_age: 16 one_person_age_split: 65 family_role_values: [COUPLE_PARTNER, LONE_PARENT, DEPENDENT_CHILD, NON_DEPENDENT_CHILD, INDIVIDUAL] household_type_values: @@ -665,10 +671,11 @@ stages: - relationship_to_head - ons_family_role - ons_family_index + - is_looked_after_by_local_authority - ons_household_type nonnegative_outputs: - ons_family_index - notes: "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review." + notes: "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review. It also sets is_looked_after_by_local_authority for a child-table member under 16 whose grid code toward an adult in their own benefit unit is foster child (5), or toward whom that adult records foster parent (9). The FRS 2024-25 question instructions (Benefit Unit Allocation) put foster children under 16 in the carer's benefit unit, so without the flag policyengine-uk's child tests (Universal Credit, Child Tax Credit, Pension Credit, Tax-Free Childcare, extended childcare and the legacy benefits) count them as the carer's own children. The same instructions make a foster child aged 16 or over a separate benefit unit where a local authority maintenance allowance covers them, so one who stays in the carer's unit has no such allowance and is not marked. The flag follows the relationship code alone: few such children are recorded, and the carer's local-authority fostering allowance (ALLOW3) covers fewer still." - stage: frs_employment survey: Family Resources Survey 2024-25 source: Department for Work and Pensions Family Resources Survey 2024-25, UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1; local licensed 2024_25 tabs. @@ -900,10 +907,11 @@ stages: 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 + method: any_uc_claimant_aged_18_under_pension_credit_age consumed_only: true aggregates: uc_age_eligible: age + role_column: is_uc_claimant - kind: assign_binary_with_anchored_residual output: would_claim_child_benefit draw: would_claim_child_benefit @@ -975,7 +983,7 @@ stages: - maximum_extended_childcare_hours_usage nonnegative_outputs: - maximum_extended_childcare_hours_usage - notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with 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). + notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). - stage: frs_person_draws survey: Family Resources Survey 2024-25 source: Sourced UK take-up contract rates and identity-keyed deterministic draws. @@ -3793,7 +3801,8 @@ stages: - kind: derive derived: is_uc_couple: count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2 - uc_reported_capital: frs_benunit_capital + uc_property_capital_share: 0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0 + uc_reported_capital: frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital; a base FRS-channel unit with universal_credit_reported_amount > 0 takes frs_benunit_capital alone positive_pre_takeup_uc_award: max(0, uc_maximum_amount - uc_income_reduction) > 0 has_non_child_member: any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members claimable_screen: positive_pre_takeup_uc_award AND has_non_child_member @@ -3874,10 +3883,11 @@ stages: aggregates: universal_credit_reported_anchor: universal_credit_reported - kind: aggregate_person_to_benunit - method: any_adult_under_state_pension_age + method: any_uc_claimant_aged_18_under_pension_credit_age consumed_only: true aggregates: uc_age_eligible: age + role_column: is_uc_claimant - kind: assign_binary_with_anchored_residual output: would_claim_uc draw: would_claim_uc @@ -3904,10 +3914,10 @@ stages: - is_severely_disabled_for_benefits - receives_benefits_in_own_right - would_claim_uc - notes: 'Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult''s benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit''s entitlement or claim history. This stage zeroes the SPI rows'' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household''s new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in the Universal Credit age population claim as the residual of the contract''s universal_credit rate on draws keyed on the SPI unit''s own identity. Contributory ESA, PIP, DLA, AA, Carer''s Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.' + notes: 'Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult''s benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit''s entitlement or claim history. This stage zeroes the SPI rows'' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household''s new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up''s Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract''s universal_credit rate on draws keyed on the SPI unit''s own identity. Contributory ESA, PIP, DLA, AA, Carer''s Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.' - stage: uc_capital_coherence survey: Family Resources Survey 2024-25 - source: Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel. + source: Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel, plus the household's Wealth and Assets Survey property imputation for the unit that holds it. grain: benunit artifacts: [] operations: @@ -3947,8 +3957,10 @@ stages: salt: frs_benunit_capital - kind: derive derived: - uc_reported_capital: frs_benunit_capital - pension_credit_reported_capital: frs_benunit_capital + uc_property_capital_share: 0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0 + pension_credit_property_capital_share: 0.9 x (owned_land + other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0 + uc_reported_capital: frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0 and NOT universal_credit_reported_anchor, else frs_benunit_capital + pension_credit_reported_capital: frs_benunit_capital + pension_credit_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital would_claim_uc: would_claim_uc OR universal_credit_reported_anchor outputs: - uc_reported_capital @@ -3956,7 +3968,7 @@ stages: rewrites: - frs_benunit_capital - would_claim_uc - notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. + notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. - stage: pension_credit_take_up survey: Family Resources Survey 2024-25 source: 'DWP Income-related benefits: estimates of take-up, financial year ending 2024 (Pension Credit caseload take-up by component), applied to PolicyEngine-UK pre-take-up Pension Credit entitlement after the SPI income chain.' @@ -3966,12 +3978,17 @@ stages: - kind: materialize_rules_engine_predictors predictors: - has_mixed_age_couple_pension_credit_saving + year_rule: survey_year - kind: materialize_rules_engine_predictors predictors: - guarantee_credit - savings_credit - is_pension_credit_eligible + - pension_credit_assessable_capital + - pension_credit_deemed_income consumed_only: true + year_rule: calibration_year + dataset_year_rule: survey_year - kind: aggregate_person_to_benunit method: any_positive consumed_only: true @@ -3990,12 +4007,14 @@ stages: rule: is_pension_credit_eligible AND guarantee_credit <= 0 AND savings_credit > 0 rate_key: pension_credit_savings_credit_only weight_mapping: household_to_benunit - residual_rate: (rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1] + residual_rate: (rate * entitled - reporters) / (entitled - reporters), weighted, over Great Britain, clipped to [0, 1]; drawn for every entitled non-reporter + solve_scope: great_britain + newly_entitled_rate_key: pension_credit_newly_entitled outputs: - has_mixed_age_couple_pension_credit_saving rewrites: - would_claim_pc - notes: Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible. + notes: Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). A second temporary engine materialization, at the release's calibration year on the frame stamped with its survey year, so the engine projects its incomes, pensions and capital forward as it does the released file and as uk-data reads it (uk-data#510), gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible) and, for the receipt only, its assessable capital and deemed income (microcosm#1095); all are consumed only. Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. DWP's rates cover Great Britain, so E and R are Great Britain's and Northern Ireland's entitled units claim at the same r (uk-data#510). A unit with no entitlement claims if it reports, and otherwise at DWP's Savings Credit-only caseload take-up (37%, pension_credit_newly_entitled), the rate at which a unit claims once a reform or a later year entitles it (uk-data#510). The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces. - stage: child_benefit_take_up survey: Family Resources Survey 2024-25 source: HMRC Child Benefit statistics, annual release at August 2025 (percentage of eligible children for whom Child Benefit is claimed by single year of age; families registered and families opted out of payment), applied to PolicyEngine-UK Child Benefit eligibility and adjusted net income after the SPI income chain. @@ -4500,7 +4519,7 @@ stages: population: persons with net capital gains above the annual exempt amount (the national taxpayer proxy) model: logistic probability in centred log gains with a stock shift, p = 1 / (1 + exp(-(a + b (log g - c) + s h))) with c the weighted mean log gain of the population, s the stock log-odds and h one where the stock signal holds, zero otherwise stock_log_odds: 1.6 - stock_signal: household other residential property value above zero, or the person's property income above zero + stock_signal: household other residential property value above zero, or the person's property income above zero, or the person reports rent from other property at a profit or a loss parameter_solver: nested bisection; the intercept matches the expected weighted count at each trial slope, the slope matches the expected weighted gains count_target: Table 8a 2024-25 total taxpayers reporting residential property disposals times the Table 8b 2024-25 individuals/all taxpayer share on the UK Property service gains_target: Table 8a 2024-25 total residential property gains times the Table 8b 2024-25 individuals/all gains share on the UK Property service 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 2cd674dec3..b5fce6143e 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 @@ -89,6 +89,21 @@ "status": "sourced_administrative" } }, + { + "key": "pension_credit_newly_entitled", + "output": "would_claim_pc", + "entity": "benunit", + "values": { + "2023-01-01": 0.37 + }, + "source": { + "source": "Income-related benefits: estimates of take-up, financial year ending 2024", + "agency": "DWP", + "citation": "Pension Credit caseload take-up, Savings Credit only: 37% in FYE 2024, the component with the smallest awards. Applied by pension_credit_take_up to a benefit unit with no Pension Credit entitlement at the calibration year that does not report it: the rate at which such a unit claims once a reform or a later year entitles it, as uk-data#510 sets it (microcosm#1095).", + "url": "https://www.gov.uk/government/statistics/income-related-benefits-estimates-of-take-up-financial-year-ending-2024/income-related-benefits-estimates-of-take-up-financial-year-ending-2024", + "status": "sourced_administrative" + } + }, { "key": "universal_credit", "output": "would_claim_uc", @@ -99,14 +114,14 @@ "source": { "source": "U8 adjudication and upstream issue uk-data#452; lever run receipts in data/ukds/acceptance/757-swap (2026-08-26)", "agency": "PolicyEngine", - "citation": "Frozen at 0.55 by adjudication for parity with the incumbent UK pipeline. The pre-registered U8 lever (a raise to 0.85 at build year 2024) was run and measured on 2026-08-26 per the #757 uk_target_fit dispositions and REVERTED on the receipts: the raise left every failing UC caseload cell unchanged (dwp.uc.households -44.9% at both rates) while destroying the legacy housing-benefit surface (obr.housing_benefit -0.0% at 0.55, -52.4% at 0.85 - the raise moves claimants off legacy benefits) and polluting the QRF predictor surface through engine-computed household_net_income. The binding constraint on UC caseload is capital-test support (uk-data#452 mechanism 2, microcosm#750), not take-up support. Re-signed 2026-09-16 (#882, microcosm#867): the rate is a share of the benefit units with an adult under State Pension age; units with every adult at or over State Pension age cannot claim Universal Credit (the engine's is_uc_eligible requires a working-age adult) and are no longer drawn, which restores the reported Housing Benefit the draw had zeroed for 407k pension-age units on spine-q; those units have no Universal Credit either way, while the working-age draw moves with the population definition (see below). The 0.55 value is unchanged. Measured on the control and repaired twins at design weights (experiments/882-uc-repairs-receipts.md R5): the old all-units draw realized a 0.582 share among units with a working-age adult, because the reported-UC anchors sit almost entirely there while the count target was taken over every unit; the population draw realizes 0.559 among them and paid UC moves -1.8 percent at design weights. María's ruling of 2026-09-16: keep the rate over the eligible population; the calibrated UC rows absorb the level.", + "citation": "Frozen at 0.55 by adjudication for parity with the incumbent UK pipeline. The pre-registered U8 lever (a raise to 0.85 at build year 2024) was run and measured on 2026-08-26 per the #757 uk_target_fit dispositions and REVERTED on the receipts: the raise left every failing UC caseload cell unchanged (dwp.uc.households -44.9% at both rates) while destroying the legacy housing-benefit surface (obr.housing_benefit -0.0% at 0.55, -52.4% at 0.85 - the raise moves claimants off legacy benefits) and polluting the QRF predictor surface through engine-computed household_net_income. The binding constraint on UC caseload is capital-test support (uk-data#452 mechanism 2, microcosm#750), not take-up support. Re-signed 2026-09-16 (#882, microcosm#867): the rate is a share of the benefit units with an adult under State Pension age; units with every adult at or over State Pension age cannot claim Universal Credit (the engine's is_uc_eligible requires a working-age adult) and are no longer drawn, which restores the reported Housing Benefit the draw had zeroed for 407k pension-age units on spine-q; those units have no Universal Credit either way, while the working-age draw moves with the population definition (see below). The 0.55 value is unchanged. Measured on the control and repaired twins at design weights (experiments/882-uc-repairs-receipts.md R5): the old all-units draw realized a 0.582 share among units with a working-age adult, because the reported-UC anchors sit almost entirely there while the count target was taken over every unit; the population draw realizes 0.559 among them and paid UC moves -1.8 percent at design weights. María's ruling of 2026-09-16: keep the rate over the eligible population; the calibrated UC rows absorb the level. Re-signed 2026-10-09 (microcosm#1095, the third ports PR, approved in María's plan of 2026-10-09): the population follows policyengine-uk's is_uc_eligible (2.122.2 when signed; the same in the locked 2.123.0), whose age conditions read the benefit unit's UC claimant or partner (is_uc_claimant) rather than any adult, so a unit whose only member aged 18 to under Pension Credit qualifying age is a dependant is no longer drawn. The 0.55 value is unchanged.", "status": "frozen_by_adjudication", "freeze": { - "decision": "U8 (re-opened and re-frozen by the 2026-08-26 lever run; population re-signed 2026-09-16 to benefit units with an adult under State Pension age, rate unchanged)", + "decision": "U8 (re-opened and re-frozen by the 2026-08-26 lever run; population re-signed 2026-09-16 to benefit units with an adult under State Pension age, and 2026-10-09 to benefit units with a UC claimant or partner aged 18 to under Pension Credit qualifying age, rate unchanged)", "followup": "uk-data#452" } }, - "population": "benefit units with an adult under State Pension age" + "population": "benefit units with a UC claimant or partner aged 18 or over and under Pension Credit qualifying age" }, { "key": "marriage_allowance", 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 0760067755..9f53ca6edd 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 @@ -1,12 +1,12 @@ { "source_fact_feed": "chronicle-uk-942a1fa0-consumer-facts", "target_period": 2025, - "candidate_count": 1303, - "contract_target_count": 348, - "active_reference_count": 1281, + "candidate_count": 1299, + "contract_target_count": 344, + "active_reference_count": 1279, "status_counts": { - "active": 1281, - "no_fact_at_or_before_period": 7, + "active": 1279, + "no_fact_at_or_before_period": 5, "signed_excluded": 15 }, "geography_pins": { @@ -14,10 +14,6 @@ "geography_level": "country", "geography_id": "K02000001" }, - "obr.ni": { - "geography_level": "country", - "geography_id": "K02000001" - }, "obr.ni_employee": { "geography_level": "country", "geography_id": "K02000001" @@ -210,14 +206,6 @@ "geography_level": "country", "geography_id": "K03000001" }, - "dwp.pip.daily_living_standard_claimants": { - "geography_level": "country", - "geography_id": "K03000001" - }, - "dwp.pip.daily_living_enhanced_claimants": { - "geography_level": "country", - "geography_id": "K03000001" - }, "dwp.esa_claimants": { "geography_level": "country", "geography_id": "K03000001" @@ -4235,10 +4223,6 @@ "dwp.hb.amount_pension_age": { "geography_level": "country", "geography_id": "K03000001" - }, - "hmrc.salary_sacrifice.it_relief_total": { - "geography_level": "country", - "geography_id": "K02000001" } }, "genuine_sum_residue": [ @@ -7435,104 +7419,104 @@ "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_100000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1260000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_1000000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1240000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_12570", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "274000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_15000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1370000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_150000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "650000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_20000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "3130000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_200000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "733000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_30000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "2530000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_300000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "673000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_40000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1870000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_50000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "2340000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_500000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "653000000.0" }, { "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_70000", "from": "2023", "to": "2025", - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1620000000.0" }, { @@ -10518,11 +10502,6 @@ "index": "hmrc.itl_2026.total_tax_growth_by_total_income_band", "factor": "1.21312442637997", "value_before_uprating": "522000000.0" - }, - { - "name": "hmrc.salary_sacrifice.it_relief_total", - "from": "2024", - "to": "2025" } ], "targets": { @@ -10541,21 +10520,6 @@ } ] }, - "obr.ni": { - "status": "active", - "candidates": [ - { - "name": "obr.ni", - "status": "active", - "matched_fact_count_overall": 7, - "matched_fact_count_at_or_before_period": 2, - "resolved_period": 2025, - "resolved_value": 193190546312.03436, - "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_eccab3ee763d68bb782276a7" - } - ] - }, "obr.ni_employee": { "status": "active", "candidates": [ @@ -10712,9 +10676,9 @@ "matched_fact_count_overall": 7, "matched_fact_count_at_or_before_period": 2, "resolved_period": 2025, - "resolved_value": 50116358662.27284, + "resolved_value": 48774253146.51462, "resolved_fact_period": "2025", - "resolved_fact_key": "ledger_aggregate_fact_v2_16bcd3e2a16bfa44c0b1ea74" + "resolved_fact_key": "ledger_aggregate_fact_v2_bd6caaecbca3ab1bebe5d2d8" } ] }, @@ -13486,30 +13450,6 @@ } ] }, - "dwp.pip.daily_living_standard_claimants": { - "status": "no_fact_at_or_before_period", - "candidates": [ - { - "name": "dwp.pip.daily_living_standard_claimants", - "status": "no_fact_at_or_before_period", - "matched_fact_count_overall": 0, - "matched_fact_count_at_or_before_period": 0, - "error": "ledger_reference_compile_status_no_fact_at_or_before_period" - } - ] - }, - "dwp.pip.daily_living_enhanced_claimants": { - "status": "no_fact_at_or_before_period", - "candidates": [ - { - "name": "dwp.pip.daily_living_enhanced_claimants", - "status": "no_fact_at_or_before_period", - "matched_fact_count_overall": 0, - "matched_fact_count_at_or_before_period": 0, - "error": "ledger_reference_compile_status_no_fact_at_or_before_period" - } - ] - }, "dwp.esa_claimants": { "status": "active", "candidates": [ @@ -21503,12 +21443,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 1489138606.9960644, + "resolved_value": 1501942263.1528072, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_0ef4a95b830d9e4e41b15e02", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1260000000.0" } }, @@ -21518,12 +21458,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 1465501486.2500951, + "resolved_value": 1478101909.7694294, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_4f8e330da2fcd6774dd7ea1a", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1240000000.0" } }, @@ -21533,12 +21473,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 323828554.21977913, + "resolved_value": 326612841.3522771, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_bd65aa884ad89d9837551b0e", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "274000000.0" } }, @@ -21548,12 +21488,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 1619142771.0988955, + "resolved_value": 1633064206.7613857, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_34ecf84ec234f3525db59f91", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1370000000.0" } }, @@ -21563,12 +21503,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 768206424.2440015, + "resolved_value": 774811484.9597815, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_89bdbe74eda3cee8844ad898", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "650000000.0" } }, @@ -21578,12 +21518,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 3699209396.744192, + "resolved_value": 3731015304.49864, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_752ea2329d313046c61bf1e2", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "3130000000.0" } }, @@ -21593,12 +21533,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 866300475.339774, + "resolved_value": 873748951.5007998, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_02f3a7f7b7097a3fd455b702", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "733000000.0" } }, @@ -21608,12 +21548,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 2990095774.3651137, + "resolved_value": 3015804702.9973035, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_d341b6820714e38a12ac4b1c", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "2530000000.0" } }, @@ -21623,12 +21563,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 795389113.1018662, + "resolved_value": 802227891.350666, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_8fc8bdb298f62c90d7af57b8", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "673000000.0" } }, @@ -21638,12 +21578,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 2210070789.7481275, + "resolved_value": 2229073041.345833, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_367d1c49bfab3c640463ded8", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1870000000.0" } }, @@ -21653,12 +21593,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 2765543127.2784057, + "resolved_value": 2789321345.8552136, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_819d2f77e45b3ba5a7907a4a", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "2340000000.0" } }, @@ -21668,12 +21608,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 771751992.355897, + "resolved_value": 778387537.9672881, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_5d68b2314b5c54a09f274733", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "653000000.0" } }, @@ -21683,12 +21623,12 @@ "matched_fact_count_overall": 1, "matched_fact_count_at_or_before_period": 1, "resolved_period": 2025, - "resolved_value": 1914606780.4235115, + "resolved_value": 1931068624.0536094, "resolved_fact_period": "2023", "resolved_fact_key": "ledger_aggregate_fact_v2_ab8a5ac6dd4ba75e194133f1", "uprating": { - "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "factor": "1.18185603729846", + "index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "factor": "1.19201766916889", "value_before_uprating": "1620000000.0" } } @@ -29333,21 +29273,6 @@ "resolved_fact_key": "ledger_aggregate_fact_v2_3631e3adc6a555d6845a1252" } ] - }, - "hmrc.salary_sacrifice.it_relief_total": { - "status": "active", - "candidates": [ - { - "name": "hmrc.salary_sacrifice.it_relief_total", - "status": "active", - "matched_fact_count_overall": 2, - "matched_fact_count_at_or_before_period": 2, - "resolved_period": 2025, - "resolved_value": 8800000000.0, - "resolved_fact_period": "2024", - "resolved_fact_key": "ledger_aggregate_fact_v2_ab19a7f36038519a7f359742" - } - ] } }, "fanout_family_outcomes": [ @@ -29379,8 +29304,8 @@ "family": "dwp_universal_credit", "status": "active_with_unmapped_vintage_residue_skipped", "active_reference_count": 100, - "skipped_unmapped_fact_count": 12, - "signed_rationale": "UC payment-distribution targets fan out over family_type and monthly_award_amount_bands into incumbent-compatible annual-payment rows. The four source-only 'No payment' facts and eight overlapping source-only 'or over' facts are left out; no active reference may use the legacy nan_to_nan band name." + "skipped_unmapped_fact_count": 8, + "signed_rationale": "UC payment-distribution targets fan out over family_type and monthly_award_amount_bands into incumbent-compatible annual-payment rows. The source-only 'No payment' and top-coded 'or over' facts are left out, and the skipped count is the generator's own (microcosm#1095); no active reference may use the legacy nan_to_nan band name." }, { "family": "council_tax_stock", diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json index 808af6149d..678a75424b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -374,7 +374,6 @@ "hmrc.salary_sacrifice.it_relief_additional_rate": "hmrc.salary_sacrifice_income_tax_relief", "hmrc.salary_sacrifice.it_relief_basic_rate": "hmrc.salary_sacrifice_income_tax_relief", "hmrc.salary_sacrifice.it_relief_higher_rate": "hmrc.salary_sacrifice_income_tax_relief", - "hmrc.salary_sacrifice.it_relief_total": "hmrc.salary_sacrifice_income_tax_relief", "hmrc.salary_sacrifice.nics_relief_employee": "hmrc.salary_sacrifice_national_insurance_relief", "hmrc.salary_sacrifice.nics_relief_employer": "hmrc.salary_sacrifice_national_insurance_relief", "hmrc.salary_sacrifice.users_above_cap": "hmrc.salary_sacrifice_pension_users", @@ -447,7 +446,6 @@ "obr.housing_benefit": "obr.efo_expenditure", "obr.income_tax": "obr.efo_receipts", "obr.jobseekers_allowance": "obr.efo_expenditure", - "obr.ni": "obr.efo_receipts", "obr.ni_employee": "obr.efo_receipts", "obr.ni_employer": "obr.efo_receipts", "obr.ni_self_employed": "obr.efo_receipts", @@ -710,7 +708,6 @@ "hmrc.salary_sacrifice.it_relief_additional_rate": "It relief additional rate", "hmrc.salary_sacrifice.it_relief_basic_rate": "It relief basic rate", "hmrc.salary_sacrifice.it_relief_higher_rate": "It relief higher rate", - "hmrc.salary_sacrifice.it_relief_total": "Income Tax relief total", "hmrc.salary_sacrifice.nics_relief_employee": "National Insurance contributions relief employee", "hmrc.salary_sacrifice.nics_relief_employer": "National Insurance contributions relief employer", "hmrc.salary_sacrifice.users_above_cap": "Users above cap", @@ -783,7 +780,6 @@ "obr.housing_benefit": "Housing benefit", "obr.income_tax": "Income tax", "obr.jobseekers_allowance": "Jobseekers allowance", - "obr.ni": "National Insurance contributions", "obr.ni_employee": "Employee National Insurance contributions", "obr.ni_employer": "Employer National Insurance contributions", "obr.ni_self_employed": "Self-employed National Insurance contributions", @@ -944,26 +940,6 @@ "assertion_policy": "allow_source_projection", "value_operation": "calendar_year_window" }, - { - "name": "obr.ni", - "ledger_selector": { - "source_name": "obr", - "source_concept": "obr.ni_total", - "entity_name": "person", - "geography_level": "country", - "geography_id": "K02000001" - }, - "entity": "person", - "measure": "obr/ni", - "family": "obr", - "period": 2025, - "metadata": { - "contract_target_id": "obr.ni", - "measure_kind": "prepared_column" - }, - "assertion_policy": "allow_source_projection", - "value_operation": "calendar_year_window" - }, { "name": "obr.ni_employee", "ledger_selector": { @@ -1155,7 +1131,7 @@ "name": "obr.council_tax", "ledger_selector": { "source_name": "obr", - "source_concept": "obr.council_tax_total", + "source_concept": "obr.council_tax_receipts_england_scotland_wales", "geography_level": "country", "geography_id": "K02000001" }, @@ -30126,7 +30102,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30151,7 +30127,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30176,7 +30152,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30201,7 +30177,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30226,7 +30202,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30251,7 +30227,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30276,7 +30252,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30301,7 +30277,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30326,7 +30302,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30351,7 +30327,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30376,7 +30352,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30401,7 +30377,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -30426,7 +30402,7 @@ "contract_target_id": "hmrc.spi.savings_interest_income.amount_by_total_income_band", "measure_kind": "prepared_column" }, - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", "uprating_from_period": "2023", "uprating_to_period": 2025 }, @@ -46049,25 +46025,6 @@ }, "assertion_policy": "allow_source_projection", "value_operation": "calendar_year_window" - }, - { - "name": "hmrc.salary_sacrifice.it_relief_total", - "ledger_selector": { - "source_name": "hmrc", - "source_concept": "hmrc.ss_income_tax_relief_total", - "geography_level": "country", - "geography_id": "K02000001" - }, - "entity": "person", - "measure": "hmrc/salary_sacrifice_it_relief_total", - "family": "hmrc_salary_sacrifice", - "period": 2025, - "metadata": { - "contract_target_id": "hmrc.salary_sacrifice.it_relief_total", - "measure_kind": "prepared_column" - }, - "uprating_from_period": "2024", - "uprating_to_period": 2025 } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json b/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json index 6a5bc45910..7aab195eb6 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_data_target_parity.json @@ -953,7 +953,7 @@ "covers": [ "policyengine_uk_data.targets.sources.obr" ], - "evidence": "uk-data targets/sources/obr.py:284; Microcosm target ids obr.council_tax and country legs obr.council_tax_{england,scotland,wales}.", + "evidence": "uk-data targets/sources/obr.py:284; Microcosm target ids obr.council_tax (OBR table 4.1 row 15, England, Scotland and Wales receipts over Great Britain's households, microcosm#1095) and country legs obr.council_tax_{england,scotland,wales}.", "status": "ported_national" }, { @@ -962,7 +962,7 @@ "covers": [ "policyengine_uk_data.targets.sources.obr" ], - "evidence": "uk-data targets/sources/obr.py:348; Microcosm target ids obr.ni, obr.ni_employee, obr.ni_employer, and obr.ni_self_employed.", + "evidence": "uk-data targets/sources/obr.py:348; Microcosm target ids obr.ni_employee, obr.ni_employer, and obr.ni_self_employed; OBR's cash total obr.ni is a diagnostic on obr.ni_employee (microcosm#1095).", "status": "ported_national" }, { 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 0ed9ec4068..aca40d4624 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json @@ -25,16 +25,14 @@ "pinned_ref": "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3", "pinned_version": "1.56.16", "registry_rows": 652, - "mapped_rows": 625, - "excluded_rows": 27, + "mapped_rows": 622, + "excluded_rows": 30, "mapped": { "dwp/benefit_capped_households": "dwp.benefit_cap.capped_households", "dwp/esa_claimants": "dwp.esa_claimants", "dwp/esa_contrib_claimants": "dwp.esa_contrib_claimants", "dwp/esa_income_claimants": "dwp.esa_income_claimants", "dwp/jsa_claimants": "dwp.jsa_claimants", - "dwp/pip_dl_enhanced_claimants": "dwp.pip.daily_living_enhanced_claimants", - "dwp/pip_dl_standard_claimants": "dwp.pip.daily_living_standard_claimants", "dwp/scotland_uc_households_child_under_1": "dwp.uc.scotland_households_child_under_1", "dwp/uc/claimants_couple_no_children": "dwp.uc.households_couple_no_children", "dwp/uc/claimants_couple_with_children": "dwp.uc.households_couple_with_children", @@ -354,7 +352,6 @@ "obr/housing_benefit": "obr.housing_benefit", "obr/income_tax": "obr.income_tax", "obr/jobseekers_allowance": "obr.jobseekers_allowance", - "obr/ni": "obr.ni", "obr/ni_employee": "obr.ni_employee", "obr/ni_employer": "obr.ni_employer", "obr/ni_self_employed": "obr.ni_self_employed", @@ -681,7 +678,10 @@ "ons/tenure_england_total": "England tenure counts come from an LA-grain workbook uk-data code-sums; ruled non-portable as national facts on chronicle#133 - moved to the wave-3 LA package (chronicle#134).", "obr/capital_gains_tax": "Observed FY2024-25 treatment (#875): the incumbent OBR cash forecast is retained only as diagnostic provenance. It has no same-basis liability mapping and is removed from fitting; no source value is adjusted.", "obr/state_pension": "Resident treatment (microcosm#1069 R4): the incumbent OBR FY State Pension forecast counts Great Britain plus pensioners paid abroad and leaves out Northern Ireland. The DWP Stat-Xplore and DfC resident level is bound instead and the exact OBR FY2025-26 figure is retained only as diagnostic provenance on dwp.state_pension.amount; no source value is adjusted.", - "obr/salary_sacrifice_users_total": "R10 (microcosm#1069 c9): the salary-sacrifice users total contradicts the staged support and the two headcounts either side of £2,000 already bind it; it leaves the fit." + "obr/salary_sacrifice_users_total": "R10 (microcosm#1069 c9): the salary-sacrifice users total contradicts the staged support and the two headcounts either side of £2,000 already bind it; it leaves the fit.", + "obr/ni": "Diagnostic only (María's ruling of 2026-10-09, microcosm#1095; uk-data#537 drops the row): OBR's total NICs receipts (table 3.8 row 12) is a cash total, while the three bound class rows are accrued, and it carries statutory-payment recoveries, Class 1A, 1B and 3 and settlements, which no household carries; at the calendar-2025 window it sits about 1.8% above the class rows. The exact OBR FY2025-26 figure is retained only as diagnostic provenance on obr.ni_employee; no source value is adjusted.", + "dwp/pip_dl_standard_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted.", + "dwp/pip_dl_enhanced_claimants": "Diagnostic only (María's ruling of 2026-10-10, microcosm#1095): DWP's England-and-Wales PIP daily living caseload (FOI2025/24990, end of January 2025) resolves at K04000001 and fits within 0.1%, but fitting the two rows takes the calibration over the uk_weight_ratio fence. The exact figure is retained only as diagnostic provenance on obr.pip; no source value is adjusted." }, "unmapped_declarations": { "hmrc.salary_sacrifice.it_relief_basic_rate": "New declaration backed by chronicle#139 salary-sacrifice facts; the incumbent source CSV is still 410-Gone at 12a1e028 and its rows are silently suppressed by the bare-except drop in build_loss_matrix.py:376-394.", @@ -835,7 +835,6 @@ "dwp.hb.households_pension_age_social_rented": "Post-freeze fact family (DWP Stat-Xplore Housing Benefit, pension age), no uk-data metric (microcosm#1069 c10).", "dwp.hb.households_pension_age_private_rented": "Post-freeze fact family (DWP Stat-Xplore Housing Benefit, pension age), no uk-data metric (microcosm#1069 c10).", "dwp.hb.amount_pension_age": "Post-freeze fact family (DWP benefit expenditure and caseload tables, pension-age Housing Benefit spending), no uk-data metric (microcosm#1095).", - "hmrc.salary_sacrifice.it_relief_total": "New declaration backed by HMRC Table 6.2 (PolicyEngine/chronicle#302 via #305): the all-rates income-tax relief on salary sacrifice, resolved by the live counterfactual route (microcosm#1069 c11).", "ons.households.english_regions": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", "nrs.households.scotland": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", "welshgov.households.wales": "Post-freeze fact family (each nation's official household estimate: LFS 2025 for England's regions, NRS 2025, Welsh Government rolled to 2025, Census 2021 rolled by LPS stock for Northern Ireland), no uk-data metric (microcosm#1123, nations win).", @@ -874,7 +873,6 @@ }, "scope_target_ids": [ "obr.income_tax", - "obr.ni", "obr.ni_employee", "obr.ni_employer", "obr.ni_self_employed", @@ -923,8 +921,6 @@ "hmrc.cgt.taxpayers_total", "hmrc.cgt.gains_total", "dwp.benefit_cap.capped_households", - "dwp.pip.daily_living_standard_claimants", - "dwp.pip.daily_living_enhanced_claimants", "dwp.esa_claimants", "dwp.esa_contrib_claimants", "dwp.esa_income_claimants", @@ -1219,8 +1215,7 @@ "dwp.hb.households_pension_age", "dwp.hb.households_pension_age_social_rented", "dwp.hb.households_pension_age_private_rented", - "dwp.hb.amount_pension_age", - "hmrc.salary_sacrifice.it_relief_total" + "dwp.hb.amount_pension_age" ] }, "profile_parity": { @@ -1881,41 +1876,6 @@ "label": "Income tax", "value_operation": "calendar_year_window" }, - { - "target_id": "obr.ni", - "family": "obr", - "geography_levels": [ - "country" - ], - "measurement": { - "entity": "person", - "concept": "uk.tax.national_insurance.amount", - "map_to": "household" - }, - "bindings": { - "policyengine": { - "metric_name": "obr/ni", - "value_variable": "total_national_insurance", - "from_entity": "person", - "map_to": "household", - "notes": "The EFO line is total NICs receipts; binds total_national_insurance (class 1 employee + employer + classes 2-4). The prior ni_employee-only binding was the uk-data defect carried for parity — final estimate bit-identical to obr.ni_employee — fixed under the microcosm#757 audit partition (2026-08-26). Nearly redundant with the three separately-calibrated component lines by construction; kept so the published total binds honestly. Residual gap is Class 1A/1B on benefits in kind, which the model does not carry. Bound at the calendar-2025 window of the OBR fiscal years (three twelfths of FY2024-25, nine twelfths of FY2025-26; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year (PolicyEngine/chronicle#280 lane)." - }, - "axiom": { - "metric_name": "obr/ni", - "status": "pending", - "value_rule": "uk.tax.national_insurance.amount" - } - }, - "assertion_policy": "allow_source_projection", - "ledger_selector": { - "source_name": "obr", - "source_concept": "obr.ni_total", - "entity_name": "person" - }, - "category_id": "obr.efo_receipts", - "label": "National Insurance contributions", - "value_operation": "calendar_year_window" - }, { "target_id": "obr.ni_employee", "family": "obr", @@ -2316,13 +2276,46 @@ ], "measurement": { "entity": "household", - "concept": "uk.tax.council_tax.net_amount" + "concept": "uk.tax.council_tax.net_amount", + "filters": [ + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] + } + ] }, "bindings": { "policyengine": { "metric_name": "obr/council_tax", "value_variable": "council_tax_less_benefit", - "notes": "OBR Table 4.1 reports net council tax receipts (net of council tax reduction), so the binding is council_tax_less_benefit, not gross council_tax (compute/council_tax.py:21-43). Bound at the calendar-2025 window of the OBR fiscal years (three twelfths of FY2024-25, nine twelfths of FY2025-26; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year (PolicyEngine/chronicle#280 lane)." + "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": "household" + } + ], + "notes": "OBR Table 4.1 reports net council tax receipts (net of council tax reduction), so the binding is council_tax_less_benefit, not gross council_tax (compute/council_tax.py:21-43). It binds row 15 ('Total council tax receipts'), the sum of the England, Scotland and Wales rows, over Great Britain's households (microcosm#1095). Row 19 ('Total net council tax receipts', obr.council_tax_total, bound before) adds Northern Ireland domestic rates, the council tax accruals adjustment and the high-value council tax surcharge: GBP 1.34bn, 2.7% of the calendar-2025 window, which no household's council tax carries (domestic rates are obr.domestic_rates, held). Chronicle stamps the row K02000001, as OBR's table is UK-wide, and the selector pins it there. Bound at the calendar-2025 window of the OBR fiscal years (three twelfths of FY2024-25, nine twelfths of FY2025-26; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year (PolicyEngine/chronicle#280 lane)." }, "axiom": { "metric_name": "obr/council_tax", @@ -2333,7 +2326,7 @@ "assertion_policy": "allow_source_projection", "ledger_selector": { "source_name": "obr", - "source_concept": "obr.council_tax_total" + "source_concept": "obr.council_tax_receipts_england_scotland_wales" }, "category_id": "obr.efo_expenditure", "label": "Council tax", @@ -3204,16 +3197,22 @@ "zeroed_input": "pension_contributions_via_salary_sacrifice", "folded_into": "employment_income", "output_delta": "counterfactual_minus_baseline", - "output_variable": "income_tax", - "band": { - "variable": "adjusted_net_income", - "under": "counterfactual", - "above_parameter": "gov.hmrc.income_tax.rates.uk[0].threshold", - "at_or_below_parameter": "gov.hmrc.income_tax.rates.uk[1].threshold" + "output_variable": "earned_taxable_income", + "allocation": { + "kind": "income_tax_rate_class", + "taxable_income": "earned_taxable_income", + "schedules": { + "rest_of_uk": "gov.hmrc.income_tax.rates.uk", + "scotland": "gov.hmrc.income_tax.rates.scotland.rates" + }, + "schedule_flag": "pays_scottish_income_tax", + "basic_below_rate": 0.3, + "additional": "the schedule's top rate", + "rate_class": "basic" }, "from_entity": "person", "map_to": "household", - "notes": "Income-tax relief attributed to salary sacrifice, banded on counterfactual adjusted net income against the live parameter thresholds (compute/income.py:23-50)." + "notes": "HMRC Private pension statistics Table 6.1, 2024-25: income-tax relief on salary-sacrificed pension contributions at the basic rate (microcosm#1095, uk-data#533). One counterfactual simulation from the same frame at the calibration year returns the sacrifice to pay, as uk-data does, and the relief is the rise in tax on earned_taxable_income, bracket by bracket, under each person's rest-of-UK or Scottish schedule (pays_scottish_income_tax), read from the simulation's own fiscal-year parameters. A bracket taxed below 30% is basic, the schedule's top rate additional and the rest higher, so the Scottish advanced rate counts as higher; a contribution that straddles a boundary is relieved at each rate, and the personal allowance taper falls in the brackets the extra pay reaches. policyengine-uk puts the Scottish top-rate threshold at GBP 112,570 rather than the statutory 125,140 (pe-uk#2130), so it files some advanced-rate relief as additional. The three bands sum to the change in tax on earned income; the measure's receipt records the gap to the change in income_tax." }, "axiom": { "metric_name": "hmrc/salary_sacrifice_it_relief_basic_rate", @@ -3246,16 +3245,22 @@ "zeroed_input": "pension_contributions_via_salary_sacrifice", "folded_into": "employment_income", "output_delta": "counterfactual_minus_baseline", - "output_variable": "income_tax", - "band": { - "variable": "adjusted_net_income", - "under": "counterfactual", - "above_parameter": "gov.hmrc.income_tax.rates.uk[1].threshold", - "at_or_below_parameter": "gov.hmrc.income_tax.rates.uk[2].threshold" + "output_variable": "earned_taxable_income", + "allocation": { + "kind": "income_tax_rate_class", + "taxable_income": "earned_taxable_income", + "schedules": { + "rest_of_uk": "gov.hmrc.income_tax.rates.uk", + "scotland": "gov.hmrc.income_tax.rates.scotland.rates" + }, + "schedule_flag": "pays_scottish_income_tax", + "basic_below_rate": 0.3, + "additional": "the schedule's top rate", + "rate_class": "higher" }, "from_entity": "person", "map_to": "household", - "notes": "Income-tax relief attributed to salary sacrifice, banded on counterfactual adjusted net income against the live parameter thresholds (compute/income.py:23-50)." + "notes": "HMRC Private pension statistics Table 6.1, 2024-25: income-tax relief on salary-sacrificed pension contributions at the higher rate (microcosm#1095, uk-data#533). One counterfactual simulation from the same frame at the calibration year returns the sacrifice to pay, as uk-data does, and the relief is the rise in tax on earned_taxable_income, bracket by bracket, under each person's rest-of-UK or Scottish schedule (pays_scottish_income_tax), read from the simulation's own fiscal-year parameters. A bracket taxed below 30% is basic, the schedule's top rate additional and the rest higher, so the Scottish advanced rate counts as higher; a contribution that straddles a boundary is relieved at each rate, and the personal allowance taper falls in the brackets the extra pay reaches. policyengine-uk puts the Scottish top-rate threshold at GBP 112,570 rather than the statutory 125,140 (pe-uk#2130), so it files some advanced-rate relief as additional. The three bands sum to the change in tax on earned income; the measure's receipt records the gap to the change in income_tax." }, "axiom": { "metric_name": "hmrc/salary_sacrifice_it_relief_higher_rate", @@ -3288,15 +3293,22 @@ "zeroed_input": "pension_contributions_via_salary_sacrifice", "folded_into": "employment_income", "output_delta": "counterfactual_minus_baseline", - "output_variable": "income_tax", - "band": { - "variable": "adjusted_net_income", - "under": "counterfactual", - "above_parameter": "gov.hmrc.income_tax.rates.uk[2].threshold" + "output_variable": "earned_taxable_income", + "allocation": { + "kind": "income_tax_rate_class", + "taxable_income": "earned_taxable_income", + "schedules": { + "rest_of_uk": "gov.hmrc.income_tax.rates.uk", + "scotland": "gov.hmrc.income_tax.rates.scotland.rates" + }, + "schedule_flag": "pays_scottish_income_tax", + "basic_below_rate": 0.3, + "additional": "the schedule's top rate", + "rate_class": "additional" }, "from_entity": "person", "map_to": "household", - "notes": "Income-tax relief attributed to salary sacrifice, banded on counterfactual adjusted net income against the live parameter thresholds (compute/income.py:23-50)." + "notes": "HMRC Private pension statistics Table 6.1, 2024-25: income-tax relief on salary-sacrificed pension contributions at the additional rate (microcosm#1095, uk-data#533). One counterfactual simulation from the same frame at the calibration year returns the sacrifice to pay, as uk-data does, and the relief is the rise in tax on earned_taxable_income, bracket by bracket, under each person's rest-of-UK or Scottish schedule (pays_scottish_income_tax), read from the simulation's own fiscal-year parameters. A bracket taxed below 30% is basic, the schedule's top rate additional and the rest higher, so the Scottish advanced rate counts as higher; a contribution that straddles a boundary is relieved at each rate, and the personal allowance taper falls in the brackets the extra pay reaches. policyengine-uk puts the Scottish top-rate threshold at GBP 112,570 rather than the statutory 125,140 (pe-uk#2130), so it files some advanced-rate relief as additional. The three bands sum to the change in tax on earned income; the measure's receipt records the gap to the change in income_tax." }, "axiom": { "metric_name": "hmrc/salary_sacrifice_it_relief_additional_rate", @@ -4325,100 +4337,6 @@ "category_id": "dwp.benefit_cap", "label": "Capped households" }, - { - "target_id": "dwp.pip.daily_living_standard_claimants", - "family": "dwp_pip", - "geography_levels": [ - "country" - ], - "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.person.count", - "filters": [ - { - "concept": "uk.benefits.pip.daily_living_category", - "equals": "standard" - } - ] - }, - "bindings": { - "policyengine": { - "metric_name": "dwp/pip_dl_standard_claimants", - "filters": [ - { - "variable": "pip_dl_category", - "equals": "STANDARD" - } - ], - "value_variable": "person_count", - "from_entity": "person", - "map_to": "household", - "notes": "True source is DWP FOI2025/24990 (caseload at end January 2025), not the routine PIP publication tables." - }, - "axiom": { - "metric_name": "dwp/pip_dl_standard_claimants", - "status": "pending", - "value_rule": "uk.person.count" - } - }, - "ledger_selector": { - "source_name": "dwp", - "source_concept": "dwp.pip_daily_living_claimants", - "dimension_values": { - "pip_daily_living_award_rate": "standard" - } - }, - "category_id": "dwp.pip", - "label": "Daily living standard claimants" - }, - { - "target_id": "dwp.pip.daily_living_enhanced_claimants", - "family": "dwp_pip", - "geography_levels": [ - "country" - ], - "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.person.count", - "filters": [ - { - "concept": "uk.benefits.pip.daily_living_category", - "equals": "enhanced" - } - ] - }, - "bindings": { - "policyengine": { - "metric_name": "dwp/pip_dl_enhanced_claimants", - "filters": [ - { - "variable": "pip_dl_category", - "equals": "ENHANCED" - } - ], - "value_variable": "person_count", - "from_entity": "person", - "map_to": "household", - "notes": "True source is DWP FOI2025/24990 (caseload at end January 2025), not the routine PIP publication tables." - }, - "axiom": { - "metric_name": "dwp/pip_dl_enhanced_claimants", - "status": "pending", - "value_rule": "uk.person.count" - } - }, - "ledger_selector": { - "source_name": "dwp", - "source_concept": "dwp.pip_daily_living_claimants", - "dimension_values": { - "pip_daily_living_award_rate": "enhanced" - } - }, - "category_id": "dwp.pip", - "label": "Daily living enhanced claimants" - }, { "target_id": "dwp.esa_claimants", "family": "dwp_legacy_benefits", @@ -6015,7 +5933,9 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at £2,500 monthly, inclusive; the separately published £2,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", + "band_semantics": "whole_pence_upper_closed", + "band_upper_bound": 2500, + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98). Each band starts a penny above its predecessor's top, so it reads as the upper-closed interval (label lower - GBP 0.01, next label lower - GBP 0.01] x 12, compared in whole pence (band_semantics whole_pence_upper_closed), and an award of a penny a month lands in the bottom band (microcosm#1095). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The separately published £2,500.01 or over band stays unbound, and band_upper_bound closes the last bound band at £2,500.00 a month, so it never absorbs that category. Bound to DWP's counts, the top band left OBR's Universal Credit total 5.6% short on the measured head (experiments/1095-uk-data-ports-3-receipts.md), so it waits until the awards below it are understood. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", "household_conditions": [ { "variable": "region", @@ -6037,9 +5957,7 @@ ], "map_to": "benunit" } - ], - "band_upper_bound": 2500, - "band_upper_bound_inclusive": true + ] }, "axiom": { "metric_name": "dwp/uc_payment_dist/SINGLE", @@ -6107,7 +6025,9 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at £2,500 monthly, inclusive; the separately published £2,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", + "band_semantics": "whole_pence_upper_closed", + "band_upper_bound": 2500, + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98). Each band starts a penny above its predecessor's top, so it reads as the upper-closed interval (label lower - GBP 0.01, next label lower - GBP 0.01] x 12, compared in whole pence (band_semantics whole_pence_upper_closed), and an award of a penny a month lands in the bottom band (microcosm#1095). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The separately published £2,500.01 or over band stays unbound, and band_upper_bound closes the last bound band at £2,500.00 a month, so it never absorbs that category. Bound to DWP's counts, the top band left OBR's Universal Credit total 5.6% short on the measured head (experiments/1095-uk-data-ports-3-receipts.md), so it waits until the awards below it are understood. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", "household_conditions": [ { "variable": "region", @@ -6129,9 +6049,7 @@ ], "map_to": "benunit" } - ], - "band_upper_bound": 2500, - "band_upper_bound_inclusive": true + ] }, "axiom": { "metric_name": "dwp/uc_payment_dist/LONE_PARENT", @@ -6199,7 +6117,9 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at £2,500 monthly, inclusive; the separately published £2,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", + "band_semantics": "whole_pence_upper_closed", + "band_upper_bound": 2500, + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98). Each band starts a penny above its predecessor's top, so it reads as the upper-closed interval (label lower - GBP 0.01, next label lower - GBP 0.01] x 12, compared in whole pence (band_semantics whole_pence_upper_closed), and an award of a penny a month lands in the bottom band (microcosm#1095). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The separately published £2,500.01 or over band stays unbound, and band_upper_bound closes the last bound band at £2,500.00 a month, so it never absorbs that category. Bound to DWP's counts, the top band left OBR's Universal Credit total 5.6% short on the measured head (experiments/1095-uk-data-ports-3-receipts.md), so it waits until the awards below it are understood. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", "household_conditions": [ { "variable": "region", @@ -6221,9 +6141,7 @@ ], "map_to": "benunit" } - ], - "band_upper_bound": 2500, - "band_upper_bound_inclusive": true + ] }, "axiom": { "metric_name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN", @@ -6291,7 +6209,9 @@ "from_entity": "benunit", "map_to": "household", "band_period_factor": 12, - "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98; half-open [lower, upper)). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The last bound source band ends at £2,500 monthly, inclusive; the separately published £2,500.01 or over category remains unbound. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", + "band_semantics": "whole_pence_upper_closed", + "band_upper_bound": 2500, + "notes": "Benunit counts by annual UC award band: publisher bands are monthly award amounts, multiplied by 12 to band annual model UC (compute/benefits.py:84-98). Each band starts a penny above its predecessor's top, so it reads as the upper-closed interval (label lower - GBP 0.01, next label lower - GBP 0.01] x 12, compared in whole pence (band_semantics whole_pence_upper_closed), and an award of a penny a month lands in the bottom band (microcosm#1095). Administrative family classification uses model standard allowance above the model-year maximum single rate for couples, separately from retained claimant roles; zero allowance is UNKNOWN. Child presence retains own-benefit-unit reported-under-20/qualifying-child proxy and does not establish DWP declared-child attachment or ineligible-partner exceptions. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Family%20Type.html The separately published £2,500.01 or over band stays unbound, and band_upper_bound closes the last bound band at £2,500.00 a month, so it never absorbs that category. Bound to DWP's counts, the top band left OBR's Universal Credit total 5.6% short on the measured head (experiments/1095-uk-data-ports-3-receipts.md), so it waits until the awards below it are understood. Source: https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html 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", "household_conditions": [ { "variable": "region", @@ -6313,9 +6233,7 @@ ], "map_to": "benunit" } - ], - "band_upper_bound": 2500, - "band_upper_bound_inclusive": true + ] }, "axiom": { "metric_name": "dwp/uc_payment_dist/COUPLE_WITH_CHILDREN", @@ -19388,8 +19306,8 @@ }, "category_id": "hmrc.survey_of_personal_incomes", "label": "Amount by total income band", - "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.ons.household_interest_income", - "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.ons.household_interest_income between 1 January 2023 and 1 January 2025, the index the engine uprates savings_interest_income with. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane). The ONS UKEA HAXV row (ons.savings_interest_income) is a national-accounts D.41 resources concept and stays a measured diagnostic on the exclusion register (microcosm#866). Taxpayers only (income_tax > 0), as SPI Table 3.6 and the area Tables 3.14 and 3.15 are: the tables cover individuals liable to UK income tax (microcosm#1123, taxpayer basis everywhere)." + "uprating_index": "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp", + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period in two legs on the pinned engine's parameters: gov.economic_assumptions.indices.ons.household_interest_income between 1 January 2023 and 1 January 2024, the index of the variable's history up to the dataset's year, then gov.economic_assumptions.indices.obr.per_capita.gdp between 1 January 2024 and 1 January 2025, the index policyengine-uk's dataset projection grows savings_interest_income with (policyengine_uk/data/uprating_indices.yaml; uk-data#541). The SPI donors take the same path: rebased to the dataset's year by the history index, then projected by the engine. Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane), with the projection leg moved to the engine's index by her ruling of 2026-10-09 (microcosm#1095). The ONS UKEA HAXV row (ons.savings_interest_income) is a national-accounts D.41 resources concept and stays a measured diagnostic on the exclusion register (microcosm#866)." }, { "target_id": "hmrc.spi.savings_interest_income.count_by_total_income_band", @@ -27595,42 +27513,6 @@ "label": "Housing Benefit spending, pension age", "value_operation": "calendar_year_window" }, - { - "target_id": "hmrc.salary_sacrifice.it_relief_total", - "family": "hmrc_salary_sacrifice", - "geography_levels": [ - "country" - ], - "measurement": { - "entity": "person", - "map_to": "household", - "concept": "uk.tax.salary_sacrifice.income_tax_relief" - }, - "bindings": { - "policyengine": { - "metric_name": "hmrc/salary_sacrifice_it_relief_total", - "kind": "input_substitution_counterfactual", - "zeroed_input": "pension_contributions_via_salary_sacrifice", - "folded_into": "employment_income", - "output_delta": "counterfactual_minus_baseline", - "output_variable": "income_tax", - "from_entity": "person", - "map_to": "household", - "notes": "HMRC Private pension statistics Table 6.2, 2024-25: income-tax relief on salary-sacrificed pension contributions, all rates, as the rise in income tax when the sacrifice is returned to pay (one counterfactual simulation from the same frame at the calibration year, uk-data's recipe; microcosm#1069 c11, R7). income_tax carries the High Income Child Benefit charge and the annual-allowance charge, so the delta includes their response to the returned pay (measured on the c11 arm). The rate-band rows wait for a band test that reads after-allowance thresholds and the Scottish bands." - }, - "axiom": { - "metric_name": "hmrc/salary_sacrifice_it_relief_total", - "status": "pending", - "value_rule": "uk.tax.salary_sacrifice.income_tax_relief" - } - }, - "ledger_selector": { - "source_name": "hmrc", - "source_concept": "hmrc.ss_income_tax_relief_total" - }, - "category_id": "hmrc.salary_sacrifice_income_tax_relief", - "label": "Income Tax relief total" - }, { "target_id": "hmrc.self_employment_income.amount", "family": "hmrc", @@ -30996,6 +30878,124 @@ "ledger_fact_key": "ledger.aggregate_fact.v2:31256472aba8a0b143c30697" }, "notes": "DWP Winter Fuel Payment statistics, winter 2025/26 recipients in England and Wales (11.0m), retained as diagnostic provenance beside the bound OBR line (microcosm#1069 c10): DWP counts everyone paid and HMRC recovers the payment above £35,000 later, while the engine withholds the household payment above £35,000 (PolicyEngine/policyengine-uk#1760), so the count measures a different quantity until the engine separates payment from recovery. Never insert it into the fitting registry." + }, + "obr.ni": { + "attach_to_target": "obr.ni_employee", + "metadata_prefix": "obr_ni_total_diagnostic", + "value_unit": "gbp", + "required_period_type": "fiscal_year", + "required_assertion": "source_projection", + "reference": { + "name": "obr.ni", + "ledger_selector": { + "source_name": "obr", + "source_concept": "obr.ni_total", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001" + }, + "entity": "person", + "measure": "obr/ni", + "family": "obr", + "period": 2025, + "metadata": { + "diagnostic_role": "cash_total_beside_accrued_class_rows" + }, + "assertion_policy": "allow_source_projection", + "period_match_policy": "exact", + "ledger_fact_key": "ledger.aggregate_fact.v2:eccab3ee763d68bb782276a7" + }, + "notes": "March 2026 OBR FY2025-26 total National Insurance contributions forecast (table 3.8 row 12), retained as diagnostic provenance beside the bound class rows obr.ni_employee, obr.ni_employer and obr.ni_self_employed (María's ruling of 2026-10-09, microcosm#1095; uk-data#537 drops it). The engine's total_national_insurance is the sum of its class columns, and at the calendar-2025 window OBR's total sits about 1.8% above the three class rows: it is a cash total against accrued class rows, and it carries statutory-payment recoveries, Class 1A, 1B and 3 and settlements, which no household carries. Never insert it into the fitting registry." + }, + "hmrc.salary_sacrifice.it_relief_total": { + "attach_to_target": "hmrc.salary_sacrifice.it_relief_higher_rate", + "metadata_prefix": "salary_sacrifice_it_relief_total_diagnostic", + "value_unit": "gbp", + "required_period_type": "tax_year", + "required_assertion": "observation", + "reference": { + "name": "hmrc.salary_sacrifice.it_relief_total", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.ss_income_tax_relief_total", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001" + }, + "entity": "person", + "measure": "hmrc/salary_sacrifice_it_relief_total", + "family": "hmrc_salary_sacrifice", + "period": 2024, + "metadata": { + "diagnostic_role": "all_rates_total_beside_bound_rate_bands" + }, + "assertion_policy": "observed_only", + "period_match_policy": "exact", + "ledger_fact_key": "ledger.aggregate_fact.v2:ab19a7f36038519a7f359742" + }, + "notes": "HMRC Private pension statistics Table 6.2, 2024-25: income-tax relief on salary-sacrificed pension contributions at all rates (GBP 8.8bn), retained as diagnostic provenance beside the three bound rate bands of Table 6.1 (María's ruling of 2026-10-09, microcosm#1095). The bands carry the same relief by rate; binding the total as well would fit it twice. Never insert it into the fitting registry." + }, + "dwp.pip.daily_living_standard_claimants": { + "attach_to_target": "obr.pip", + "metadata_prefix": "dwp_pip_daily_living_standard_diagnostic", + "value_unit": "count", + "required_period_type": "month", + "required_assertion": "observation", + "reference": { + "name": "dwp.pip.daily_living_standard_claimants", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.pip_daily_living_claimants", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K04000001", + "dimension_values": { + "pip_daily_living_award_rate": "standard" + } + }, + "entity": "person", + "measure": "dwp/pip_dl_standard_claimants", + "family": "dwp_pip", + "period": "2025-01", + "metadata": { + "diagnostic_role": "caseload_beside_bound_spending" + }, + "assertion_policy": "observed_only", + "period_match_policy": "exact", + "ledger_fact_key": "ledger.aggregate_fact.v2:7e25a3faa6fdceb4f5ee5868" + }, + "notes": "DWP FOI2025/24990: PIP daily living standard-rate claimants in England and Wales at the end of January 2025 (1.283m), retained as diagnostic provenance beside the bound OBR line obr.pip (María's ruling of 2026-10-10, microcosm#1095). DWP has reported PIP for England and Wales only since executive competence moved to the Scottish Government on 1 April 2020, so Chronicle stamps the fact K04000001 (uk-data#490); under the Great Britain default pin the row matched no fact. Fitted with its sibling it calibrates to within 0.1%, but the two rows push the heaviest support family's weight from 1,061 to 1,241 times the positive median, over the uk_weight_ratio fence of 1,151 (experiments/1095-uk-data-ports-3-receipts.md). Never insert it into the fitting registry." + }, + "dwp.pip.daily_living_enhanced_claimants": { + "attach_to_target": "obr.pip", + "metadata_prefix": "dwp_pip_daily_living_enhanced_diagnostic", + "value_unit": "count", + "required_period_type": "month", + "required_assertion": "observation", + "reference": { + "name": "dwp.pip.daily_living_enhanced_claimants", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.pip_daily_living_claimants", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K04000001", + "dimension_values": { + "pip_daily_living_award_rate": "enhanced" + } + }, + "entity": "person", + "measure": "dwp/pip_dl_enhanced_claimants", + "family": "dwp_pip", + "period": "2025-01", + "metadata": { + "diagnostic_role": "caseload_beside_bound_spending" + }, + "assertion_policy": "observed_only", + "period_match_policy": "exact", + "ledger_fact_key": "ledger.aggregate_fact.v2:8d850c8ffb6eae8f4c71e611" + }, + "notes": "DWP FOI2025/24990: PIP daily living enhanced-rate claimants in England and Wales at the end of January 2025 (1.608m), retained as diagnostic provenance beside the bound OBR line obr.pip (María's ruling of 2026-10-10, microcosm#1095). DWP has reported PIP for England and Wales only since executive competence moved to the Scottish Government on 1 April 2020, so Chronicle stamps the fact K04000001 (uk-data#490); under the Great Britain default pin the row matched no fact. Fitted with its sibling it calibrates to within 0.1%, but the two rows push the heaviest support family's weight from 1,061 to 1,241 times the positive median, over the uk_weight_ratio fence of 1,151 (experiments/1095-uk-data-ports-3-receipts.md). Never insert it into the fitting registry." } }, "hierarchy": { diff --git a/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json b/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json index d962587176..ee23dbfa03 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json +++ b/packages/microcosm-build/src/microcosm/build/uk/uprating_holds.json @@ -237,7 +237,6 @@ "hmrc.salary_sacrifice.it_relief_additional_rate", "hmrc.salary_sacrifice.it_relief_basic_rate", "hmrc.salary_sacrifice.it_relief_higher_rate", - "hmrc.salary_sacrifice.it_relief_total", "hmrc.salary_sacrifice.nics_relief_employee" ], "reason": "HMRC Table 6.2's 2024-25 salary-sacrifice income-tax and employee NICs reliefs, the latest edition. They follow the employer NICs relief row (microcosm#1069 c11), which anchors the sacrificed amount at its 2024-25 level and restates only the rate it is relieved at; the income tax rates and the employee Class 1 main rate (8%) are unchanged from 2024-25 to 2025-26, so no restatement applies. Growing all the reliefs with earnings instead is an open ruling (microcosm#1123).", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py index b4ac74b7e0..bddd28089e 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/battery_bindings.py @@ -480,64 +480,96 @@ def _evaluate_column_implication( capital_column = str(parameters["capital_column"]) carrier_column = str(parameters["carrier_column"]) sentinel = float(parameters.get("sentinel", -1.0)) - missing_capital = sorted({capital_column, carrier_column} - set(target.columns)) + property_shares = _declared_property_shares(parameters) + capital_columns = tuple(dict.fromkeys((capital_column, *property_shares))) + missing_capital = sorted({*capital_columns, carrier_column} - set(target.columns)) if missing_capital: raise ValueError( f"column_implication {target_entity} capital evidence is missing " f"columns {missing_capital}." ) - capital = pd.to_numeric(target[capital_column], errors="coerce").to_numpy( - dtype=float - ) carrier = pd.to_numeric(target[carrier_column], errors="coerce").to_numpy( dtype=float ) - nonfinite = ~np.isfinite(capital) | ~np.isfinite(carrier) - # The -1 contract reserves exactly one negative: a value is either the - # sentinel or nonnegative. A bare floor (`< sentinel`) would admit the - # open interval between them — the one region the contract does not - # define (adversarial-review verification residual 1). Sentinel equality - # is exact (#833): every producer writes the -1.0 literal or a - # nonnegative amount, and a tolerance band would silently reclassify a - # corrupted near-sentinel value as a declared absence. - out_of_domain = np.isfinite(capital) & ~((capital == sentinel) | (capital >= 0.0)) + shares = _capital_property_shares(frame, target, property_shares) + # A declared tolerance admits GBP sum_tolerance plus one float32 step of + # the expected value, so the carrier-plus-share sum survives a float32 + # round-trip of the export; without one the capital must equal it exactly. + sum_tolerance = parameters.get("sum_tolerance") carrier_out_of_domain = np.isfinite(carrier) & ~( (carrier == sentinel) | (carrier >= 0.0) ) - sentinel_mismatch = (capital == sentinel) != (carrier == sentinel) - same_source_mismatch = ( - np.isfinite(capital) & np.isfinite(carrier) & (capital != carrier) - ) failures = list(result.failures) - if nonfinite.any(): - failures.append( - f"{target_entity}.{capital_column}/{carrier_column}: " - f"{int(nonfinite.sum())} row(s) have non-finite carrier evidence." - ) - if out_of_domain.any(): - failures.append( - f"{target_entity}.{capital_column}: {int(out_of_domain.sum())} " - f"value(s) outside the declared domain (exactly the {sentinel:g} " - "sentinel or >= 0)." - ) if carrier_out_of_domain.any(): failures.append( f"{target_entity}.{carrier_column}: " f"{int(carrier_out_of_domain.sum())} value(s) outside the declared " f"domain (exactly the {sentinel:g} sentinel or >= 0)." ) - if sentinel_mismatch.any(): - failures.append( - f"{target_entity}.{capital_column}: sentinel {sentinel:g} is allowed " - f"only where {carrier_column} has the same sentinel; " - f"{int(sentinel_mismatch.sum())} mismatch(es)." + counts: dict[str, dict[str, int]] = {} + for column in capital_columns: + capital = pd.to_numeric(target[column], errors="coerce").to_numpy(dtype=float) + expected = np.where(carrier >= 0.0, carrier + shares.get(column, 0.0), carrier) + if sum_tolerance is None: + tolerance = np.zeros_like(expected) + else: + tolerance = float(sum_tolerance) + np.spacing( + np.abs(np.nan_to_num(expected)).astype(np.float32) + ).astype(float) + nonfinite = ~np.isfinite(capital) | ~np.isfinite(carrier) + # The -1 contract reserves exactly one negative: a value is either the + # sentinel or nonnegative. A bare floor (`< sentinel`) would admit the + # open interval between them — the one region the contract does not + # define (adversarial-review verification residual 1). Sentinel + # equality is exact (#833): every producer writes the -1.0 literal or + # a nonnegative amount, and a tolerance band would silently reclassify + # a corrupted near-sentinel value as a declared absence. + out_of_domain = np.isfinite(capital) & ~( + (capital == sentinel) | (capital >= 0.0) ) - if same_source_mismatch.any(): - failures.append( - f"{target_entity}.{capital_column} must equal {carrier_column}; " - f"{int(same_source_mismatch.sum())} mismatch(es)." + sentinel_mismatch = (capital == sentinel) != (carrier == sentinel) + # Where the carrier is available, the capital is it plus the unit's + # declared property share (microcosm#1095). + sum_mismatch = ( + np.isfinite(capital) + & np.isfinite(carrier) + & (carrier >= 0.0) + & ~(np.abs(capital - expected) <= tolerance) ) + if nonfinite.any(): + failures.append( + f"{target_entity}.{column}/{carrier_column}: " + f"{int(nonfinite.sum())} row(s) have non-finite carrier evidence." + ) + if out_of_domain.any(): + failures.append( + f"{target_entity}.{column}: {int(out_of_domain.sum())} " + f"value(s) outside the declared domain (exactly the {sentinel:g} " + "sentinel or >= 0)." + ) + if sentinel_mismatch.any(): + failures.append( + f"{target_entity}.{column}: sentinel {sentinel:g} is allowed " + f"only where {carrier_column} has the same sentinel; " + f"{int(sentinel_mismatch.sum())} mismatch(es)." + ) + if sum_mismatch.any(): + share_text = ( + " plus the unit's declared property share" + if column in property_shares + else "" + ) + failures.append( + f"{target_entity}.{column} must equal {carrier_column}{share_text} " + f"where {carrier_column} >= 0; {int(sum_mismatch.sum())} mismatch(es)." + ) + counts[column] = { + "capital_domain_violation_count": int(out_of_domain.sum()), + "sentinel_mismatch_count": int(sentinel_mismatch.sum()), + "same_source_mismatch_count": int(sum_mismatch.sum()), + "nonfinite_capital_count": int(nonfinite.sum()), + } return GateResult( name="column_implication", passed=not failures, @@ -548,15 +580,90 @@ def _evaluate_column_implication( "capital_column": f"{target_entity}.{capital_column}", "carrier_column": f"{target_entity}.{carrier_column}", "sentinel": sentinel, - "capital_domain_violation_count": int(out_of_domain.sum()), + **counts[capital_column], "carrier_domain_violation_count": int(carrier_out_of_domain.sum()), - "sentinel_mismatch_count": int(sentinel_mismatch.sum()), - "same_source_mismatch_count": int(same_source_mismatch.sum()), - "nonfinite_capital_count": int(nonfinite.sum()), + "sum_tolerance": sum_tolerance, + "property_shares": { + column: dict(rule) for column, rule in property_shares.items() + }, + "capital_columns": { + f"{target_entity}.{column}": counts[column] + for column in capital_columns + }, }, ) +def _declared_property_shares( + parameters: Mapping[str, Any], +) -> dict[str, dict[str, Any]]: + """The declared property-share rules, refused unless they are the code's.""" + + from microcosm.build.uk_runtime.uc_capital_coherence import ( + PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES, + PROPERTY_CAPITAL_OWNERS, + PROPERTY_CAPITAL_VALUATION, + UC_PROPERTY_CAPITAL_REPORTER_RULE, + UC_PROPERTY_CAPITAL_SOURCES, + ) + + rules = { + "uc_reported_capital": { + "sources": list(UC_PROPERTY_CAPITAL_SOURCES), + "owners": PROPERTY_CAPITAL_OWNERS, + "valuation": PROPERTY_CAPITAL_VALUATION, + # A unit that reports Universal Credit keeps its receipt. + "units_reporting_universal_credit": UC_PROPERTY_CAPITAL_REPORTER_RULE, + }, + "pension_credit_reported_capital": { + "sources": list(PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES), + "owners": PROPERTY_CAPITAL_OWNERS, + "valuation": PROPERTY_CAPITAL_VALUATION, + }, + } + declared = { + str(column): {**dict(rule), "sources": list(rule.get("sources") or ())} + for column, rule in dict(parameters.get("property_shares") or {}).items() + } + drifted = sorted( + column for column, rule in declared.items() if rules.get(column) != rule + ) + if drifted: + raise ValueError( + f"column_implication property shares for {drifted} are not the " + f"recorded-capital rules uc_capital_coherence applies: {rules}." + ) + return declared + + +def _capital_property_shares( + frame: Frame, + target: pd.DataFrame, + property_shares: Mapping[str, Mapping[str, Any]], +) -> dict[str, np.ndarray]: + """Each declared capital column's property share, in target row order.""" + + from microcosm.build.uk_runtime.uc_capital_coherence import ( + pension_credit_property_capital_share, + uc_recorded_property_share, + ) + + shares: dict[str, np.ndarray] = {} + if not property_shares: + return shares + person = frame.table("person") + household = frame.table("household") + if "uc_reported_capital" in property_shares: + shares["uc_reported_capital"] = uc_recorded_property_share( + person, target, household + ) + if "pension_credit_reported_capital" in property_shares: + shares["pension_credit_reported_capital"] = ( + pension_credit_property_capital_share(person, target, household) + ) + return shares + + def _evaluate_take_up_signal( context: EvidenceContext, parameters: Mapping[str, Any] ) -> GateResult: @@ -1840,6 +1947,9 @@ def _ledger_compile_parity_required_artifacts( "capital_column", "carrier_column", "sentinel", + # The recorded capitals' property shares (microcosm#1095). + "property_shares", + "sum_tolerance", } ), ), diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_asset_type.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_asset_type.py index 204875111a..0cba0eff28 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_asset_type.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_asset_type.py @@ -687,7 +687,8 @@ def _logistic( CGT_RESIDENTIAL_STOCK_SIGNAL = ( "household other residential property value above zero, or the person's " - "property income above zero" + "property income above zero, or the person reports rent from other " + "property at a profit or a loss" ) CGT_BUSINESS_STOCK_SIGNAL = ( "household corporate wealth above zero, or the person's self-employment " @@ -709,6 +710,7 @@ def _logistic( ) CGT_STOCK_PERSON_COLUMNS: tuple[str, ...] = ( "property_income", + "reports_rent_from_other_property", "self_employment_income", "dividend_income", ) @@ -749,8 +751,12 @@ def pp(column: str) -> np.ndarray: column, pd.to_numeric(person[column], errors="raise").to_numpy(dtype=float) ) - residential = (hh("other_residential_property_value") > 0) | ( - pp("property_income") > 0 + # property_income counts a letting loss as zero, so the landlord carrier + # keeps loss-making landlords in the signal (#1081). + residential = ( + (hh("other_residential_property_value") > 0) + | (pp("property_income") > 0) + | (pp("reports_rent_from_other_property") > 0) ) business = (hh("corporate_wealth") > 0) | (pp("self_employment_income") > 0) by_type = { diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py index 5544008876..f5eb74a30d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/data_target_parity.py @@ -184,7 +184,7 @@ def _fence(origin: str, purpose: str, verdict_basis: str) -> dict[str, str]: "concern_id": "national_obr_national_insurance", "status": "ported_national", "classification": "red_line_national_family", - "evidence": "uk-data targets/sources/obr.py:348; Microcosm target ids obr.ni, obr.ni_employee, obr.ni_employer, and obr.ni_self_employed.", + "evidence": "uk-data targets/sources/obr.py:348; Microcosm target ids obr.ni_employee, obr.ni_employer, and obr.ni_self_employed; OBR's cash total obr.ni is a diagnostic on obr.ni_employee (microcosm#1095).", }, { "concern_id": "national_obr_national_insurance_class_3", @@ -202,7 +202,7 @@ def _fence(origin: str, purpose: str, verdict_basis: str) -> dict[str, str]: "concern_id": "national_obr_council_tax", "status": "ported_national", "classification": "red_line_national_family", - "evidence": "uk-data targets/sources/obr.py:284; Microcosm target ids obr.council_tax and country legs obr.council_tax_{england,scotland,wales}.", + "evidence": "uk-data targets/sources/obr.py:284; Microcosm target ids obr.council_tax (OBR table 4.1 row 15, England, Scotland and Wales receipts over Great Britain's households, microcosm#1095) and country legs obr.council_tax_{england,scotland,wales}.", }, { "concern_id": "national_obr_welfare", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_relationships.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_relationships.py index 8745abe3eb..1c06a56c22 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_relationships.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_relationships.py @@ -57,6 +57,7 @@ "FRS_RELATIONSHIPS_SOURCE_COLUMNS", "FRS_RELATIONSHIPS_STAGE_NAME", "FRS_RELATIONSHIPS_TABLES", + "LOOKED_AFTER_CHILD_RULE", "ONS_DEPENDENT_CHILD_RULE", "ONS_FAMILY_LINK_CODES", "ONS_FAMILY_ROLE_VALUES", @@ -79,12 +80,14 @@ "relationship_to_head", "ons_family_role", "ons_family_index", + "is_looked_after_by_local_authority", "ons_household_type", ) FRS_RELATIONSHIPS_PERSON_OUTPUT_COLUMNS = ( "relationship_to_head", "ons_family_role", "ons_family_index", + "is_looked_after_by_local_authority", ) FRS_RELATIONSHIPS_HOUSEHOLD_OUTPUT_COLUMNS = ("ons_household_type",) @@ -178,6 +181,21 @@ #: the parent records as non-relative or sibling (step-0 probe, microcosm#791). #: Either side's declaration establishes the link; any other count refuses. FRS_RELATIONSHIPS_RECIPROCITY_MISMATCH_TOLERANCE = 3 +#: A child placed by a local authority (policyengine-uk's +#: ``is_looked_after_by_local_authority``): a child-table member under 16 whose +#: grid code toward an adult in their own benefit unit is foster child (5), or +#: toward whom that adult carries the reciprocal foster parent code (9). The +#: FRS puts foster children under 16 in the carer's benefit unit, so without +#: the flag the engine counts them as the carer's own children. At 16 or over +#: the FRS makes a foster child a local authority maintains a separate benefit +#: unit (Benefit Unit Allocation, rule d), so one who stays in the carer's +#: unit has no such allowance and is not marked. +LOOKED_AFTER_CHILD_RULE: Mapping[str, int] = { + "foster_child_code": 5, + "foster_parent_code": 9, + "below_age": 16, +} + FRS_RELATIONSHIPS_DOCUMENTATION: Mapping[str, str] = { "frs_codes": ( "UK Data Service SN 9563 (DOI 10.5255/UKDA-SN-9563-1), FRS 2024-25 " @@ -219,6 +237,7 @@ def frs_relationships_operation_parameters() -> dict[str, Any]: key: list(codes) for key, codes in ONS_FAMILY_LINK_CODES.items() }, "dependent_child": dict(ONS_DEPENDENT_CHILD_RULE), + "looked_after_child": dict(LOOKED_AFTER_CHILD_RULE), "one_person_age_split": ONS_ONE_PERSON_AGE_SPLIT, "family_role_values": list(ONS_FAMILY_ROLE_VALUES), "household_type_values": list(CHRONICLE_ONS_HOUSEHOLD_TYPE_VALUE_IDS), @@ -387,14 +406,15 @@ def derive_frs_relationships( """Derive the relationship, family and household-type columns. ``person`` and ``household`` are frame tables (``person_id``, - ``person_household_id``, ``age``, ``is_household_head``; - ``household_id``). ``raw_adult`` / ``raw_child`` / ``raw_household`` are + ``person_benunit_id``, ``person_household_id``, ``age``, + ``is_household_head``; ``household_id``). ``raw_adult`` / ``raw_child`` / ``raw_household`` are the id-normalised FRS tabs. Pure pandas/numpy; no engine. """ raw = _aligned_raw_person(person, raw_adult, raw_child) person_ids = person["person_id"].to_numpy(dtype="int64") household_ids = person["person_household_id"].to_numpy(dtype="int64") + benunit_ids = person["person_benunit_id"].to_numpy(dtype="int64") age_values = pd.to_numeric(person["age"], errors="coerce") if age_values.isna().any(): # A blank age would read as 0 and silently make a dependent child. @@ -448,6 +468,7 @@ def derive_frs_relationships( family_index = np.zeros(len(person), dtype="int64") role = np.full(len(person), "INDIVIDUAL", dtype=object) + looked_after = np.zeros(len(person), dtype=bool) household_type: dict[int, str] = {} counters = { @@ -485,6 +506,16 @@ def derive_frs_relationships( partner = np.isin(matrix, partner_codes) child_of = np.isin(matrix, child_of_codes) parent_of = np.isin(matrix, parent_of_codes) + foster_child_of = (matrix == LOOKED_AFTER_CHILD_RULE["foster_child_code"]) | ( + matrix.T == LOOKED_AFTER_CHILD_RULE["foster_parent_code"] + ) + local_benunit = benunit_ids[rows] + same_unit = local_benunit[:, None] == local_benunit[None, :] + looked_after[rows] = ( + from_child_tab[rows] + & (ages[rows] < LOOKED_AFTER_CHILD_RULE["below_age"]) + & (foster_child_of & same_unit).any(axis=1) + ) counters["grid_reciprocity_mismatches"] += int( np.triu(partner != partner.T, 1).sum() ) + int((child_of != parent_of.T).sum()) @@ -644,6 +675,7 @@ def derive_frs_relationships( "family_index_violations": family_index_violations, "domain_violations": domain_violations, "dependency_counts": dependency_counts, + "looked_after_children": int(looked_after.sum()), "household_type_counts": type_counts, "household_type_weighted": type_weighted, "partition_closes": sum(type_counts.values()) == int(len(household_table_ids)), @@ -656,6 +688,7 @@ def derive_frs_relationships( "relationship_to_head": relationship_to_head, "ons_family_role": role, "ons_family_index": family_index, + "is_looked_after_by_local_authority": looked_after, }, index=person.index, ) 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 c05c46c500..3fa8860fd6 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 @@ -199,6 +199,7 @@ "is_claimant_or_partner", "is_hbai_dependent_child", "uc_is_in_startup_period", + "uc_is_in_gainful_self_employment", "rent_paid_as_boarder", "rent_paid_as_lodger", "employment_income", @@ -208,6 +209,7 @@ "savings_interest_income", "dividend_income", "property_income", + "reports_rent_from_other_property", "maintenance_income", "miscellaneous_income", "private_transfer_income", @@ -563,6 +565,11 @@ def _assemble_frame( job=frs["job"], benefits=frs["benefits"], ) + pe_person["uc_is_in_gainful_self_employment"] = uc_gainful_self_employment( + _number(person, "empstati").isin(FRS_SELF_EMPLOYED_EMPSTATI).to_numpy(), + pe_person["self_employment_income"], + pe_person["employment_income"], + ) _add_household_columns(pe_household, household, frs) @@ -651,6 +658,9 @@ def _add_person_income( evidence: dict[str, object] | None = None, ) -> None: pe_person["property_income"] = frs_property_income(person, household) + pe_person[FRS_LANDLORD_CARRIER_COLUMN] = frs_reports_rent_from_other_property( + person + ) maintenance_to_self = np.maximum( np.where( _number(person, "mntus1") == 2, @@ -696,6 +706,24 @@ def _add_person_income( pe_person["free_school_meals"] = _positive(person, "fsmval") * WEEKS_IN_YEAR +#: The internal landlord carrier ``frs_reports_rent_from_other_property`` writes. +FRS_LANDLORD_CARRIER_COLUMN = "reports_rent_from_other_property" + + +def frs_reports_rent_from_other_property(person: pd.DataFrame) -> np.ndarray: + """Whether the person reports rent from other property, at a profit or a loss. + + ROYYR1 is entered as a positive amount whether the letting made a profit or + a loss (RENTPROF 2 marks the loss), so a positive ROYYR1 marks a landlord + either way. ``property_income`` counts a loss as zero + (``frs_property_income``), so this internal carrier keeps the landlord + signal the CGT stages read for loss-making landlords too. It is not an + engine input and does not leave the release. + """ + + return (_positive(person, "royyr1") > 0).to_numpy(dtype=bool) + + def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.ndarray: """Annual property income each person reports in the FRS. @@ -1380,6 +1408,29 @@ def frs_uc_start_up_period( return self_employed & (claim_in_window[person_benunit] | (trade_years < 1)) +def uc_gainful_self_employment( + self_employed_main_job: Any, self_employment_income: Any, employment_income: Any +) -> np.ndarray: + """Whether each person is in gainful self-employment for Universal Credit. + + UC Regs 2013 reg 64(a) asks whether the person carries on a trade as their + main employment. DWP's Advice for Decision Making starts from hours (H4031) + but lets earnings outweigh them (H4034). So the flag holds for a + self-employed main job (FRS EMPSTATI 3 or 4) whatever its profit, since a + trade at a loss or breaking even is still carried on (H4013, H4054, + H4503), and for a side trade whose profit is above the person's employment + income (uk-data#525). It overrides the engine's default, which reads any + nonzero self-employment income as gainful. The flag is fixed at the + survey-year (or SPI-drawn) incomes: uprating reprices the two incomes by + different indices, and the flag does not follow. + """ + + profit = np.asarray(self_employment_income, dtype=float) + pay = np.asarray(employment_income, dtype=float) + main_job = np.asarray(self_employed_main_job, dtype=bool) + return main_job | ((profit > 0) & (profit > pay)) + + #: FRS yes code for the local-authority registration questions (SPCREG1-3). FRS_REGISTERED_YES = 1 #: FRS period codes (SN 9563 code frame) for an amount reported per calendar 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 731ac93410..2925c400ac 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 @@ -48,21 +48,28 @@ "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`` timetable 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. +# Universal Credit is claimable only by a benefit unit whose claimant or +# partner meets its age conditions: policyengine-uk 2.123.0's ``is_uc_eligible`` +# (capital aside) needs a member with ``is_uc_claimant`` who meets UC's minimum +# age and has not reached Pension Credit qualifying age. The draw's population +# is therefore the units with a UC claimant or partner aged 18 or over and +# under that age; any other unit is never drawn (#882, microcosm#867, +# microcosm#1095). The bounds come from the engine at the build instant through +# ``uk_take_up_population_policy``: the qualifying age is the State Pension age +# the ``gov.dwp.state_pension.age`` timetable sets for the cohort reaching it +# in the build year, which equals the engine's Pension Credit qualifying age at +# 2024 and 2025, and the minimum age is UC's 18. Two engine cases are not +# mirrored, and the lockstep test names them: 16- and 17-year-old claimants +# under the UC Regs 2013 reg 8 exceptions, whom the engine includes, and +# mixed-age couples on the Pension Credit route, whom it excludes. 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_METHOD = "any_uc_claimant_aged_18_under_pension_credit_age" UK_UC_AGE_ELIGIBLE_SOURCE = "age" +UK_UC_AGE_ELIGIBLE_ROLE = "is_uc_claimant" 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 @@ -102,11 +109,11 @@ 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`` the age its ``gov.dwp.state_pension.age`` timetable - sets for the cohort attaining it in the build year, so a unit is in the - population exactly when the engine's ``is_WA_adult`` is true for one of its - members (the lockstep test checks every age). + ``adult_age`` is UC's minimum age and ``state_pension_age`` the age the + engine's ``gov.dwp.state_pension.age`` timetable sets for the cohort + attaining it in the build year, which equals its Pension Credit qualifying + age at 2024 and 2025. ``working_age`` is the age half of the population; + ``uc_age_eligible_benunits`` adds the claimant role. """ adult_age: int @@ -175,6 +182,7 @@ def assert_take_up_stage_population_declaration(stage: SourceStageSpec) -> None: 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} + and op.parameters.get("role_column") == UK_UC_AGE_ELIGIBLE_ROLE for op in stage.operations ) declared_population = any( @@ -193,7 +201,8 @@ def assert_take_up_stage_population_declaration(stage: SourceStageSpec) -> None: 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"{UK_UC_AGE_ELIGIBLE_SOURCE}, role {UK_UC_AGE_ELIGIBLE_ROLE}) and " + f"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})" @@ -273,15 +282,21 @@ def uc_age_eligible_benunits( benunit: pd.DataFrame, policy: UKTakeUpPopulationPolicy, ) -> np.ndarray: - """True where the benefit unit has a working-age adult under ``policy``.""" + """True where a UC claimant or partner of the unit is within ``policy``'s ages.""" - if "age" not in person.columns: + missing = [ + column + for column in (UK_UC_AGE_ELIGIBLE_SOURCE, UK_UC_AGE_ELIGIBLE_ROLE) + if column not in person.columns + ] + if missing: raise KeyError( - "person.age is missing; the Universal Credit take-up population " - "cannot be formed without it" + f"person columns {missing} are missing; the Universal Credit take-up " + "population cannot be formed without them" ) - age = pd.to_numeric(person["age"], errors="coerce").fillna(0) - eligible_adult = policy.working_age(age) + age = pd.to_numeric(person[UK_UC_AGE_ELIGIBLE_SOURCE], errors="coerce").fillna(0) + claimant = person[UK_UC_AGE_ELIGIBLE_ROLE].to_numpy(dtype=bool) + eligible_adult = policy.working_age(age) & claimant eligible_ids = set(person.loc[eligible_adult, "person_benunit_id"]) return benunit["benunit_id"].isin(eligible_ids).to_numpy(dtype=bool) @@ -387,9 +402,9 @@ def uk_take_up_signal_gate( 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. + # with a UC claimant or partner within the engine's age bounds at + # the build instant. The gate measures the realized share on the + # same population. if policy is None: policy = uk_take_up_population_policy(uk_time_period(frame)) population = uc_age_eligible_benunits(frame.table("person"), table, policy) 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 9991e4e7f7..52202d7d5a 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -225,9 +225,11 @@ "child_benefit_reported", "pension_credit_reported", "universal_credit_reported", - # The Universal Credit draw's population: units with an adult - # under State Pension age (#882). + # The Universal Credit draw's population: units with a UC + # claimant or partner aged 18 to under Pension Credit qualifying + # age (#882, microcosm#1095). "age", + "is_uc_claimant", ) ) | frozenset({("benunit", "is_married")}), @@ -344,6 +346,8 @@ "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits", "receives_benefits_in_own_right", + # The UC draw's population (frs_take_up's rule). + "is_uc_claimant", ) ), ("benunit", "benunit_support_channel"), @@ -360,6 +364,12 @@ ("person", "savings_interest_income"), ("person", "dividend_income"), ("person", "other_investment_income"), + # The recorded capitals' property: the household reference + # person's unit holds it (microcosm#1095). + ("person", "is_household_head"), + ("household", "owned_land"), + ("household", "other_residential_property_value"), + ("household", "non_residential_property_value"), ("benunit", "benunit_support_channel"), ("benunit", "dependent_children"), ("benunit", "frs_benunit_capital"), @@ -422,6 +432,7 @@ for column in ( "capital_gains", "property_income", + "reports_rent_from_other_property", "self_employment_income", "dividend_income", ) @@ -513,6 +524,8 @@ def owned(self, *, rewrite: bool = False) -> Owned: "is_claimant_or_partner", "is_hbai_dependent_child", "uc_is_in_startup_period", + "uc_is_in_gainful_self_employment", + "reports_rent_from_other_property", "would_claim_carers_allowance", } _ROOT_PERSON_INT = {"age"} @@ -599,6 +612,7 @@ def _cells( _Cell("person", "relationship_to_head", "string"), _Cell("person", "ons_family_role", "string"), _Cell("person", "ons_family_index", "int64"), + _Cell("person", "is_looked_after_by_local_authority", "bool"), _Cell("household", "ons_household_type", "string"), ), "frs_employment": ( @@ -1031,6 +1045,12 @@ def _cells( # uk-data#527: the UC start-up period holds on a redrawn row only while it # stays self-employed. "uc_is_in_startup_period", + # uk-data#525: a redrawn row's gainful self-employment follows its drawn + # incomes. + "uc_is_in_gainful_self_employment", + # A redrawn row drops its FRS twin's landlord carrier: its property income + # is an SPI donor's (microcosm#1095). + "reports_rent_from_other_property", ) _STAGE_CELLS = { **_STAGE_CELLS, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py index dcedf1b8aa..e92bf2563c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_uprating.py @@ -12,6 +12,10 @@ * amount rows: the ratio of a policyengine-uk parameter between the fact's opening-year instant and the calibration year's, the same calendar-year instants the SPI donor rebasing uses (``spi_income._spi_income_uprating_factors``). + Savings interest takes two legs, since the index of its history is not the + one policyengine-uk's dataset projection applies: ONS household interest + income to the frame's survey year, then GDP per head to the calibration year + (``UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT``). The OBR indices are calendar-year series keyed at 1 January, so the engine's period 2025 is calendar 2025 for incomes; the state pension rate is the tax-year rate the engine pays for the whole year, so its rows move by that @@ -43,6 +47,7 @@ LedgerTargetReference, TargetRegistry, ) +from microcosm.build.uk_runtime.frs_release import resolve_uk_year_rule from microcosm.build.uk_runtime.ledger_fact_vendoring import vendored_rows UK_ENGINE_PARAMETER_INDEX_PREFIX = "policyengine_uk_parameter:" @@ -154,6 +159,30 @@ #: described different populations. UK_SPI_STATE_PENSION_AMOUNT_INDEX_CONCEPT = "hmrc.itl_2026.taxpayer_count_growth_times_new_state_pension_rate_by_total_income_band" UK_SPI_STATE_PENSION_RATE_PARAMETER = "gov.dwp.state_pension.new_state_pension.amount" +#: The SPI savings interest amount by band (microcosm#1095, uk-data#541). +#: policyengine-uk's dataset projection grows ``savings_interest_income`` by +#: GDP per head, but the variable's own index, ONS household interest income, +#: is what records its history: x1.99 from 2022 to 2023 as rates rose, against +#: GDP per head's x1.05. The frame holds survey-year values (the SPI donors are +#: rebased to that year by the history index) and the engine projects them to +#: the calibration year. So a fact moves to the survey year by the history +#: index and from there by the projection's, the path the data takes. +UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT = ( + "policyengine_uk.household_interest_income_to_dataset_year_then_per_capita_gdp" +) +UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER = ( + "gov.economic_assumptions.indices.ons.household_interest_income" +) +UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER = ( + "gov.economic_assumptions.indices.obr.per_capita.gdp" +) +UK_DATASET_YEAR_RULE = "survey_year" +UK_HISTORY_THEN_PROJECTION_BASIS = ( + "policyengine-uk parameter ratios in two legs: {history} between 1 January of " + "the fact's opening year and 1 January of the dataset's year, the index of " + "the variable's history, then {projection} from there to 1 January of the " + "calibration year, the index the engine's dataset projection applies" +) UK_RECIPIENT_AMOUNT_GROWTH_BASIS = ( "recipients times the amount per recipient: the band's recipients grow like its " "taxpayers (the count row's index, {count_basis}) and the amount per recipient by " @@ -449,6 +478,79 @@ def align_hmrc_row_by_engine_index( return TargetRegistry(aligned, country="uk") +def align_hmrc_row_by_history_then_projection( + reference: LedgerTargetReference, + registry: TargetRegistry, + *, + index_concept: str, + history_path: str, + projection_path: str, + dataset_year: int | None = None, + parameter_value: ParameterValue = engine_parameter_value, +) -> TargetRegistry: + """Move an amount row by its history index, then the dataset projection's. + + factor = ``history(pivot) / history(opening)`` x ``projection(target) / + projection(pivot)``, the pivot being the dataset's year held between the + fact's opening year and the calibration year. Both legs' values and + instants are written on the spec. + """ + + if reference.uprating_index != index_concept: + return registry + if dataset_year is None: + dataset_year = resolve_uk_year_rule(UK_DATASET_YEAR_RULE) + target_year = _target_year(reference) + aligned = [] + for spec in registry.specs: + opening_year = _opening_year(spec, reference) + _refuse_fact_after_target(spec, opening_year, target_year) + if opening_year == target_year: + aligned.append(_identity_aligned(spec, reference, opening_year)) + continue + pivot_year = min(max(int(dataset_year), opening_year), target_year) + instants = { + "from": f"{opening_year}-01-01", + "pivot": f"{pivot_year}-01-01", + "to": f"{target_year}-01-01", + } + history = { + key: parameter_value(history_path, instants[key]) + for key in ("from", "pivot") + } + projection = { + key: parameter_value(projection_path, instants[key]) + for key in ("pivot", "to") + } + history_factor = history["pivot"] / history["from"] + projection_factor = projection["to"] / projection["pivot"] + factor = history_factor * projection_factor + metadata = { + **spec.metadata, + "uprating_index": index_concept, + "uprating_index_basis": UK_HISTORY_THEN_PROJECTION_BASIS.format( + history=history_path, projection=projection_path + ), + "uprating_index_engine": f"policyengine-uk {_engine_version()}", + "uprating_index_from_instant": instants["from"], + "uprating_index_pivot_instant": instants["pivot"], + "uprating_index_to_instant": instants["to"], + "uprating_history_parameter": history_path, + "uprating_history_from_value": f"{history['from']:.15g}", + "uprating_history_to_value": f"{history['pivot']:.15g}", + "uprating_history_factor": f"{history_factor:.15g}", + "uprating_projection_parameter": projection_path, + "uprating_projection_from_value": f"{projection['pivot']:.15g}", + "uprating_projection_to_value": f"{projection['to']:.15g}", + "uprating_projection_factor": f"{projection_factor:.15g}", + "uprating_factor": f"{factor:.15g}", + "ledger_value_before_alignment": f"{spec.value:.15g}", + "uprating_adjudication": UK_INCOME_UPRATING_ADJUDICATION, + } + aligned.append(replace(spec, value=spec.value * factor, metadata=metadata)) + return TargetRegistry(aligned, country="uk") + + def _engine_rate_year(month: str) -> int: """The engine year whose 1 January value is the rate paid in ``month``.""" @@ -869,6 +971,12 @@ def hmrc_uprating_appliers() -> dict[str, Any]: index_concept=UK_SPI_STATE_PENSION_AMOUNT_INDEX_CONCEPT, parameter_path=UK_SPI_STATE_PENSION_RATE_PARAMETER, ) + appliers[UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT] = partial( + align_hmrc_row_by_history_then_projection, + index_concept=UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, + history_path=UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, + projection_path=UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, + ) return appliers @@ -892,7 +1000,11 @@ def align_hmrc_count_row_by_taxpayer_growth( "SPI_BAND_TO_ITL_BANDS", "UK_SPI_STATE_PENSION_AMOUNT_INDEX_CONCEPT", "UK_SPI_STATE_PENSION_RATE_PARAMETER", + "UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER", + "UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT", + "UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER", "align_hmrc_amount_by_recipient_growth_and_rate", + "align_hmrc_row_by_history_then_projection", "UK_ENGINE_INDEX_CONCEPTS", "UK_ENGINE_INDEX_PARAMETERS", "UK_ENGINE_PARAMETER_INDEX_PREFIX", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py new file mode 100644 index 0000000000..ab498abd78 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/income_tax_rate_classes.py @@ -0,0 +1,137 @@ +"""Income tax by HMRC rate category, for salary-sacrifice relief (microcosm#1095). + +HMRC's private pension statistics (Table 6.1) report the income tax relief on +salary-sacrificed pension contributions by marginal rate: basic, higher and +additional. HMRC applies income tax rates to employees' pay, so a person's +relief is the rise in tax on their earned income when the sacrifice is paid as +salary instead, under their rest-of-UK or Scottish schedule, and a contribution +that straddles a band boundary is relieved partly at each rate (uk-data#533). + +The categories group a schedule's brackets: a bracket taxed below 30% is basic +(HMRC counts the Scottish starter, basic and intermediate rates there), the +schedule's top rate is additional where a higher rate sits below it (the +rest-of-UK additional rate and the Scottish top rate), and every other bracket +is higher (the Scottish higher and advanced rates, which span the rest-of-UK +higher-rate income range). Where the +returned pay tapers the personal allowance, the allowance it withdraws raises +taxable income too, so that tax lands in the brackets it reaches, as +uk-data#533 reads it. policyengine-uk puts the Scottish top-rate threshold at +GBP 112,570 rather than the statutory GBP 125,140 (pe-uk#2130), so it files +some advanced-rate relief as additional. +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence + +import numpy as np + +UK_RATE_CLASSES = ("basic", "higher", "additional") +#: A bracket taxed below this rate is basic rate. +UK_BASIC_RATE_CLASS_CEILING = 0.3 +#: The declared allocation a salary-sacrifice relief band binds; the measure +#: refuses a binding that declares anything else. +UK_RATE_CLASS_ALLOCATION: Mapping[str, object] = { + "kind": "income_tax_rate_class", + "taxable_income": "earned_taxable_income", + "schedules": { + "rest_of_uk": "gov.hmrc.income_tax.rates.uk", + "scotland": "gov.hmrc.income_tax.rates.scotland.rates", + }, + "schedule_flag": "pays_scottish_income_tax", + "basic_below_rate": UK_BASIC_RATE_CLASS_CEILING, + "additional": "the schedule's top rate", +} + + +#: The engine variable the allocation re-implements: the schedule applied to +#: earned taxable income. A person's classes must add up to its rise within +#: the tolerance, or the measure refuses. +UK_RATE_CLASS_ENGINE_VARIABLE = "earned_income_tax" +UK_RATE_CLASS_ENGINE_TOLERANCE_GBP = 1.0 +#: The engine computes in float32, whose step grows with the amount (GBP 1 at +#: GBP 10 million, GBP 8 at GBP 100 million). The tolerance adds this many +#: steps at the person's own largest amount to the GBP 1, so rounding alone +#: never refuses a schedule that still matches. +UK_RATE_CLASS_ENGINE_FLOAT32_STEPS = 4 + + +def rate_class_engine_tolerance(*amounts: np.ndarray) -> np.ndarray: + """Each person's tolerance against the engine variable, in GBP. + + GBP 1 plus ``UK_RATE_CLASS_ENGINE_FLOAT32_STEPS`` float32 steps at the + largest of the person's ``amounts`` (the incomes and taxes the comparison + reads). Below about GBP 2 million the steps add less than GBP 1. + """ + + largest = np.maximum.reduce( + [np.abs(np.asarray(amount, dtype=float)) for amount in amounts] + ) + step = np.spacing(largest.astype(np.float32)).astype(float) + return ( + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP + UK_RATE_CLASS_ENGINE_FLOAT32_STEPS * step + ) + + +def tax_by_rate_class( + income: np.ndarray, + thresholds: Sequence[float], + rates: Sequence[float | None], +) -> dict[str, np.ndarray]: + """Tax on ``income`` within each of HMRC's rate categories. + + The categories sum to the schedule's tax on ``income``. A bracket whose + rate is ``None`` (not in force at the instant) is skipped. + """ + + values = np.asarray(income, dtype=float) + brackets = [ + (float(threshold), float(rate)) + for threshold, rate in zip(thresholds, rates, strict=True) + if rate is not None + ] + if not brackets: + raise ValueError("an income tax schedule needs at least one bracket.") + # The top rate is the additional rate only where a higher rate sits below + # it; a schedule with one rate above the basic class has no additional rate. + above_basic = {rate for _, rate in brackets if rate >= UK_BASIC_RATE_CLASS_CEILING} + top = brackets[-1][1] if len(above_basic) > 1 else None + uppers = [threshold for threshold, _ in brackets[1:]] + [np.inf] + classes = {name: np.zeros_like(values) for name in UK_RATE_CLASSES} + for (lower, rate), upper in zip(brackets, uppers, strict=True): + if rate < UK_BASIC_RATE_CLASS_CEILING: + name = "basic" + elif rate == top: + name = "additional" + else: + name = "higher" + classes[name] = classes[name] + rate * np.clip( + values - lower, 0.0, upper - lower + ) + return classes + + +def relief_by_rate_class( + *, + baseline_income: np.ndarray, + counterfactual_income: np.ndarray, + scottish: np.ndarray, + schedules: Mapping[str, tuple[Sequence[float], Sequence[float | None]]], +) -> dict[str, np.ndarray]: + """Each person's rise in tax on earned income, by rate category. + + ``schedules`` maps ``rest_of_uk`` and ``scotland`` to their (thresholds, + rates); ``scottish`` picks each person's schedule. + """ + + scottish = np.asarray(scottish, dtype=bool) + relief = {name: np.zeros(scottish.shape, dtype=float) for name in UK_RATE_CLASSES} + for schedule, members in (("rest_of_uk", ~scottish), ("scotland", scottish)): + thresholds, rates = schedules[schedule] + after = tax_by_rate_class(counterfactual_income, thresholds, rates) + before = tax_by_rate_class(baseline_income, thresholds, rates) + for name in UK_RATE_CLASSES: + relief[name] = relief[name] + np.where( + members, after[name] - before[name], 0.0 + ) + return relief diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py index 053ae0a5b1..c236c269c9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/ledger_targets.py @@ -754,9 +754,28 @@ def _assert_region_facts_resolved_at_region( # it above GBP 35,000, where the engine withholds it; the recipient count # sits beside the bound OBR line until payment and recovery separate. "obr.winter_fuel_allowance": ("dwp.winter_fuel_payment.recipients",), + # microcosm#1095 (María, 2026-10-09): OBR's cash NICs total sits beside + # the accrued class rows it does not equal; the class rows are bound. + "obr.ni_employee": ("obr.ni",), + # microcosm#1095 (María, 2026-10-09): the three Table 6.1 rate bands + # bind, and the Table 6.2 total sits beside the higher-rate band. + "hmrc.salary_sacrifice.it_relief_higher_rate": ( + "hmrc.salary_sacrifice.it_relief_total", + ), + # microcosm#1095 (María, 2026-10-10): DWP's England-and-Wales daily + # living caseload sits beside the bound spending line; fitting the two + # rows breaks the weight-ratio fence. + "obr.pip": ( + "dwp.pip.daily_living_standard_claimants", + "dwp.pip.daily_living_enhanced_claimants", + ), } ) -_DIAGNOSTIC_PERIOD_TYPES = frozenset(("fiscal_year", "tax_year", "calendar_year")) +#: A diagnostic is one exactly dated publisher figure; a caseload snapshot is +#: dated by its month (microcosm#1095). +_DIAGNOSTIC_PERIOD_TYPES = frozenset( + ("fiscal_year", "tax_year", "calendar_year", "month") +) _DIAGNOSTIC_ASSERTION_POLICIES = MappingProxyType( {"source_projection": "allow_source_projection", "observation": "observed_only"} ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py index a2c715b6dc..fde52659d5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/measure_simulation.py @@ -17,6 +17,15 @@ from microcosm.build.uk_runtime.atomic_area_support import ( without_uk_native_alias_columns, ) +from microcosm.build.uk_runtime.income_tax_rate_classes import ( + UK_RATE_CLASS_ALLOCATION, + UK_RATE_CLASS_ENGINE_FLOAT32_STEPS, + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP, + UK_RATE_CLASS_ENGINE_VARIABLE, + UK_RATE_CLASSES, + rate_class_engine_tolerance, + relief_by_rate_class, +) from microcosm.build.uk_runtime.national_frame import ( UK_RELEASE_EXPORT_DROPPED_COLUMNS, load_uk_national_frame, @@ -634,16 +643,22 @@ def counterfactual_delta( amount added to the folded-into input (salary sacrifice returned to pay, uk-data's recipe), and the output's delta against this resolver's baseline comes back for the binding's entity (microcosm#1069 c11). - Banded deltas are refused: their band test compares adjusted net income - with after-allowance thresholds and ignores the Scottish bands. + + A binding that declares ``allocation`` splits the delta by HMRC rate + category instead (microcosm#1095, uk-data#533): its output is the rise + in tax on the declared taxable income under each person's schedule, + computed once per substitution pair and cached. The old adjusted-net- + income ``band`` test stays refused: it compared after-allowance + thresholds and ignored the Scottish bands. """ self.validate_period(period) if binding.get("band"): raise ValueError( - "banded counterfactual measures are deferred: the band test " + "banded counterfactual measures are refused: the band test " "compares adjusted net income with after-allowance thresholds " - "and ignores the Scottish bands (microcosm#1069 c11)" + "and ignores the Scottish bands; declare a rate-class " + "allocation instead (microcosm#1095)" ) if binding.get("kind") != "input_substitution_counterfactual": raise ValueError(f"unsupported counterfactual kind {binding.get('kind')!r}") @@ -658,6 +673,27 @@ def counterfactual_delta( "baseline_minus_counterfactual", ): raise ValueError(f"unsupported output_delta {direction!r}") + metric = str(binding.get("metric_name") or output) + allocation = binding.get("allocation") + if allocation is not None: + rate_class = _declared_rate_class(allocation, output, direction, entity) + delta = self._rate_class_relief(zeroed, folded)[rate_class] + self._counterfactual_measures[metric] = { + "entity": entity, + "zeroed_input": zeroed, + "folded_into": folded, + "output_variable": output, + "output_delta": direction, + "allocation": str(allocation["kind"]), + "rate_class": rate_class, + "rows_nonzero": int(np.count_nonzero(delta)), + } + route = ( + f"policyengine-uk counterfactual: {zeroed} set to zero" + + (f" and folded into {folded}" if folded else "") + + f"; rise in tax on {entity}.{output} at the {rate_class} rate" + ) + return delta, route counterfactual = self._counterfactual_simulation(zeroed, folded) baseline_values, _ = compute_uk_measure_input( self.frame, self.simulation, entity, output, self.year @@ -670,7 +706,6 @@ def counterfactual_delta( ) if direction == "baseline_minus_counterfactual": delta = -delta - metric = str(binding.get("metric_name") or output) self._counterfactual_measures[metric] = { "entity": entity, "zeroed_input": zeroed, @@ -686,6 +721,118 @@ def counterfactual_delta( ) return delta, route + def _rate_class_relief( + self, zeroed: str, folded: str | None + ) -> Mapping[str, np.ndarray]: + """Each person's rise in tax on earned income by rate class, cached.""" + + cache = getattr(self, "_rate_class_reliefs", None) + if cache is None: + cache = self._rate_class_reliefs = {} + self._rate_class_receipts = {} + key = (zeroed, folded) + if key in cache: + return cache[key] + counterfactual = self._counterfactual_simulation(zeroed, folded) + income = str(UK_RATE_CLASS_ALLOCATION["taxable_income"]) + flag = str(UK_RATE_CLASS_ALLOCATION["schedule_flag"]) + baseline_income, _ = compute_uk_measure_input( + self.frame, self.simulation, "person", income, self.year + ) + counterfactual_income, _ = compute_uk_measure_input( + self.frame, counterfactual, "person", income, self.year + ) + scottish, _ = compute_uk_measure_input( + self.frame, self.simulation, "person", flag, self.year + ) + # The simulation's own parameters at the measurement year are its + # fiscal-year values, so the Scottish bands are the ones in force from + # 6 April, as the engine taxes them. + rates = self.simulation.tax_benefit_system.parameters(self.year) + schedules = {} + for name, path in dict(UK_RATE_CLASS_ALLOCATION["schedules"]).items(): + node = rates + for part in str(path).split("."): + node = getattr(node, part) + schedules[name] = (list(node.thresholds), list(node.rates)) + relief = relief_by_rate_class( + baseline_income=np.asarray(baseline_income, dtype=float), + counterfactual_income=np.asarray(counterfactual_income, dtype=float), + scottish=np.asarray(scottish, dtype=bool), + schedules=schedules, + ) + # The allocation re-implements the engine's earned_income_tax, the + # schedule applied to earned taxable income. Its classes must add up + # to that variable's rise, or the schedule has moved away from the + # brackets read here. + engine_variable = UK_RATE_CLASS_ENGINE_VARIABLE + baseline_earned, _ = compute_uk_measure_input( + self.frame, self.simulation, "person", engine_variable, self.year + ) + counterfactual_earned, _ = compute_uk_measure_input( + self.frame, counterfactual, "person", engine_variable, self.year + ) + earned_tax_change = np.asarray(counterfactual_earned, dtype=float) - np.asarray( + baseline_earned, dtype=float + ) + earned_gap = np.abs(earned_tax_change - sum(relief.values())) + # GBP 1 plus a few float32 steps at the person's own amounts: the + # engine rounds a very large income to more than GBP 1. + tolerance = rate_class_engine_tolerance( + baseline_income, + counterfactual_income, + baseline_earned, + counterfactual_earned, + ) + beyond = earned_gap > tolerance + if beyond.any(): + raise ValueError( + f"The {UK_RATE_CLASS_ALLOCATION['kind']} allocation no longer adds " + f"up to the engine's {engine_variable}: {int(beyond.sum())} " + f"person(s) differ by more than GBP " + f"{UK_RATE_CLASS_ENGINE_TOLERANCE_GBP:g} and " + f"{UK_RATE_CLASS_ENGINE_FLOAT32_STEPS} float32 steps, by up to " + f"GBP {float(earned_gap[beyond].max()):.2f}. " + "Review the income tax schedule it reads." + ) + baseline_tax, _ = compute_uk_measure_input( + self.frame, self.simulation, "person", "income_tax", self.year + ) + counterfactual_tax, _ = compute_uk_measure_input( + self.frame, counterfactual, "person", "income_tax", self.year + ) + income_tax_change = np.asarray(counterfactual_tax, dtype=float) - np.asarray( + baseline_tax, dtype=float + ) + classes_total = float(sum(np.sum(values) for values in relief.values())) + self._rate_class_receipts[f"{zeroed}->{folded}"] = { + "allocation": str(UK_RATE_CLASS_ALLOCATION["kind"]), + "relief_by_class_unweighted_total": { + name: float(np.sum(values)) for name, values in relief.items() + }, + "engine_variable": engine_variable, + "engine_variable_change_unweighted_total": float(np.sum(earned_tax_change)), + "largest_gap_to_engine_variable": float(earned_gap.max()) + if earned_gap.size + else 0.0, + "income_tax_change_unweighted_total": float(np.sum(income_tax_change)), + "gap_to_income_tax_change_unweighted": float( + np.sum(income_tax_change) - classes_total + ), + "persons_with_gap": int( + np.count_nonzero( + ~np.isclose( + income_tax_change, + sum(relief.values()), + rtol=0.0, + atol=0.01, + ) + ) + ), + } + cache[key] = relief + return relief + def _counterfactual_simulation(self, zeroed: str, folded: str | None) -> Any: key = (zeroed, folded) if key in self._counterfactuals: @@ -719,6 +866,11 @@ def receipt(self) -> dict[str, Any]: name: dict(entry) for name, entry in sorted(counterfactual_measures.items()) } + rate_class_receipts = getattr(self, "_rate_class_receipts", None) + if rate_class_receipts: + receipt["rate_class_reliefs"] = { + name: dict(entry) for name, entry in sorted(rate_class_receipts.items()) + } if self._uc_tcl_measures_used: receipt["uc_tcl_comparison_contract"] = { **uc_tcl_comparison_contract(self.year), @@ -921,3 +1073,29 @@ def _uk_contract_targets() -> dict[str, Any]: for target in contract["targets"] if set(target.get("geography_levels") or ()) <= national_geography_levels } + + +def _declared_rate_class( + allocation: object, output: str, direction: str, entity: str +) -> str: + """The rate class a binding's allocation names, refused unless declared.""" + + if not isinstance(allocation, Mapping): + raise ValueError("a counterfactual allocation must be a mapping") + rate_class = allocation.get("rate_class") + expected = {**UK_RATE_CLASS_ALLOCATION, "rate_class": rate_class} + if ( + rate_class not in UK_RATE_CLASSES + or dict(allocation) != expected + or output != UK_RATE_CLASS_ALLOCATION["taxable_income"] + or direction != "counterfactual_minus_baseline" + or entity != "person" + ): + raise ValueError( + "a rate-class allocation must declare " + f"{dict(UK_RATE_CLASS_ALLOCATION)} with a rate_class in " + f"{UK_RATE_CLASSES}, on person {UK_RATE_CLASS_ALLOCATION['taxable_income']} " + f"counterfactual_minus_baseline; got {allocation!r} on {entity}.{output} " + f"{direction}" + ) + return str(rate_class) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py index 3d7e3a9c76..798f5d2739 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py @@ -488,6 +488,10 @@ def _write_uk_single_year_tables( # the identity receipts can reverse the stage's in-place rewrite of a # converted record's pay, and is no engine input. "salary_sacrifice_pre_conversion_pay", + # The landlord carrier (frs_spine.frs_reports_rent_from_other_property) + # that keeps loss-making landlords in the CGT residential signal; it is + # no engine input. + "reports_rent_from_other_property", ), "household": ("property_wealth",), } diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py index 265c2235d7..9fdcba7457 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py @@ -3,20 +3,23 @@ ``frs_take_up`` draws ``would_claim_pc`` early, on FRS incomes, so the engine simulations of the intermediate stages see a value. This stage redraws it once the SPI income chain has set the incomes the engine assesses. One temporary -engine materialization gives each benefit unit's pre-take-up Pension Credit -entitlement; the entitled units fall into two component bands, as DWP reports -take-up: Guarantee Credit (with or without Savings Credit) and Savings Credit -only. Within a band a reporter always claims and the other entitled units claim -at the residual rate +engine materialization, at the release's calibration year as uk-data reads it, +gives each benefit unit's pre-take-up Pension Credit entitlement; the entitled +units fall into two component bands, as DWP reports take-up: Guarantee Credit +(with or without Savings Credit) and Savings Credit only. Within a band a +reporter always claims and the other entitled units claim at the residual rate r = (t * E - R) / (E - R), with t the band's DWP caseload take-up rate, E the band's entitled units and R -the reporters among them, both weighted at the stage's household weights, so -reporters plus drawn claimants make up t of the band (or more, where reporters -alone exceed it; the receipt says so). A unit with no entitlement claims only -if it reports. The uniform draw is the ``would_claim_pc`` identity stream -``frs_take_up`` uses. +the reporters among them, both weighted at the stage's household weights and +both over Great Britain, which DWP's rates cover. Reporters plus drawn claimants +make up t of the band in Great Britain (or more, where reporters alone exceed +it; the receipt says so), and Northern Ireland's entitled units claim at the +same r (uk-data#510). A unit with no entitlement claims if it reports, and +otherwise at DWP's Savings Credit-only take-up, the rate at which a unit claims +once a reform or a later year entitles it (uk-data#510). The uniform draw is the +``would_claim_pc`` identity stream ``frs_take_up`` uses. """ from __future__ import annotations @@ -29,20 +32,24 @@ from microcosm.build.source_manifest import SourceStageSpec from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.frs_release import resolve_uk_year_rule from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, uk_national_frame, uk_time_period, validate_uk_national_frame, ) +from microcosm.build.uk_runtime.spi_support import support_channel_column from microcosm.build.uk_runtime.take_up_contract import ( UKTakeUpContract, load_uk_take_up_contract, ) from microcosm.build.uk_runtime.uc_capital_coherence import ( + _benunit_households, _household_to_benunit_weights, ) from microcosm.frame import Frame +from microcosm.frame.adapters.policyengine_uk import ProjectingRulesEngine from microcosm.frame.rules import assert_rules_engine_country PENSION_CREDIT_TAKE_UP_STAGE_NAME = "pension_credit_take_up" @@ -53,6 +60,25 @@ "savings_credit", "is_pension_credit_eligible", ) +#: Read beside the entitlement for the receipt only: the capital the engine +#: assesses and the income it deems from it (microcosm#1095). +PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES = ( + "pension_credit_assessable_capital", + "pension_credit_deemed_income", +) +#: The saving is stored at the survey year; entitlement is read at the +#: release's calibration year on the frame the engine projects there, as +#: uk-data reads it (uk-data#510). +PENSION_CREDIT_SAVING_YEAR_RULE = "survey_year" +PENSION_CREDIT_ENTITLEMENT_YEAR_RULE = "calibration_year" +PENSION_CREDIT_ENTITLEMENT_DATASET_YEAR_RULE = "survey_year" +#: DWP's take-up rates cover Great Britain, so each band's residual is solved +#: there and applied to Northern Ireland's entitled units as well. +PENSION_CREDIT_SOLVE_SCOPE = "great_britain" +PENSION_CREDIT_OUTSIDE_SOLVE_REGION = "NORTHERN_IRELAND" +#: The take-up of a unit with no entitlement once a reform or a later year +#: entitles it (uk-data#510). +PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY = "pension_credit_newly_entitled" #: The SI 2019/37 saving for a mixed-age couple (pe-uk#1940), stored at the #: survey year so later model years do not re-derive it from a birth year #: that moves with the model year (uk-data#519). @@ -76,7 +102,10 @@ "rate_key": "pension_credit_savings_credit_only", }, ) -PENSION_CREDIT_RESIDUAL_RATE = "(rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1]" +PENSION_CREDIT_RESIDUAL_RATE = ( + "(rate * entitled - reporters) / (entitled - reporters), weighted, over " + "Great Britain, clipped to [0, 1]; drawn for every entitled non-reporter" +) @dataclass(frozen=True) @@ -87,15 +116,22 @@ class UKPensionCreditTakeUpResult: bands: tuple[Mapping[str, object], ...] reporters_without_entitlement: Mapping[str, float] changed_units: int + newly_entitled: Mapping[str, object] = field(default_factory=dict) + entitlement_year: int | None = None + capital: Mapping[str, object] = field(default_factory=dict) def evidence(self) -> dict[str, object]: return { "stage": PENSION_CREDIT_TAKE_UP_STAGE_NAME, "seed": PENSION_CREDIT_TAKE_UP_SEED, "residual_rate": PENSION_CREDIT_RESIDUAL_RATE, + "solve_scope": PENSION_CREDIT_SOLVE_SCOPE, + "entitlement_year": self.entitlement_year, "bands": [dict(band) for band in self.bands], + "newly_entitled": dict(self.newly_entitled), "reporters_without_entitlement": dict(self.reporters_without_entitlement), "changed_units": self.changed_units, + "capital": dict(self.capital), } @@ -131,7 +167,7 @@ def checkpoint_metadata(self) -> dict[str, object]: def redraw_pension_credit_take_up( frame: Frame, *, - engine: object, + engine: ProjectingRulesEngine, contract: UKTakeUpContract, ) -> UKPensionCreditTakeUpResult: """Redraw ``would_claim_pc`` by component band from engine entitlement.""" @@ -147,7 +183,16 @@ def redraw_pension_credit_take_up( ("person_benunit_id", "person_household_id", "pension_credit_reported"), "person", ), - (benunit, ("benunit_id", PENSION_CREDIT_TAKE_UP_OUTPUT), "benunit"), + ( + benunit, + ( + "benunit_id", + PENSION_CREDIT_TAKE_UP_OUTPUT, + support_channel_column("benunit"), + ), + "benunit", + ), + (household, ("household_id", "region"), "household"), ): missing = sorted(set(columns) - set(table.columns)) if missing: @@ -155,23 +200,63 @@ def redraw_pension_credit_take_up( f"Pension Credit take-up {label} columns missing: {missing}." ) period = uk_time_period(frame) + entitlement_year = resolve_uk_year_rule(PENSION_CREDIT_ENTITLEMENT_YEAR_RULE) + # The declared rules name the year the frame's values belong to. The stage + # reads the frame's own period for it, so a frame from another year is + # refused rather than projected from the wrong one. + for rule in ( + PENSION_CREDIT_SAVING_YEAR_RULE, + PENSION_CREDIT_ENTITLEMENT_DATASET_YEAR_RULE, + ): + declared_year = resolve_uk_year_rule(rule) + if int(period) != declared_year: + raise ValueError( + f"Pension Credit take-up reads the frame as {rule} " + f"{declared_year} values, but the frame's period is {period}." + ) # The engine's own default for the mixed-age saving, evaluated once at the # frame's survey-year period on the post-SPI receipts and stored, so the # entitlement below and every later model year read the same answer: a # mixed-age couple whose older member was born by 5 February 1954 and who # report Pension Credit, or pension-age Housing Benefit without an # income-related legacy benefit, and no Universal Credit (uk-data#519). - requested = [PENSION_CREDIT_MIXED_AGE_SAVING, *PENSION_CREDIT_ENTITLEMENT_VARIABLES] - materialized = engine.materialize(frame, requested, period) - missing = sorted(set(requested) - set(materialized)) - if missing: - raise ValueError(f"Pension Credit take-up engine outputs missing: {missing}.") count = len(benunit) + saving = engine.materialize(frame, [PENSION_CREDIT_MIXED_AGE_SAVING], period) + if PENSION_CREDIT_MIXED_AGE_SAVING not in saving: + raise ValueError( + "Pension Credit take-up engine outputs missing: " + f"{[PENSION_CREDIT_MIXED_AGE_SAVING]}." + ) benunit[PENSION_CREDIT_MIXED_AGE_SAVING] = _aligned( - materialized[PENSION_CREDIT_MIXED_AGE_SAVING], + saving[PENSION_CREDIT_MIXED_AGE_SAVING], count, PENSION_CREDIT_MIXED_AGE_SAVING, ).astype(bool) + with_saving = uk_national_frame( + person=person, + benunit=benunit, + household=household, + time_period=period, + weight_kind=uk_household_weight_kind(frame), + household_weights=frame.weights_for("household").values, + mass_log=frame.mass_log, + ) + requested = [ + *PENSION_CREDIT_ENTITLEMENT_VARIABLES, + *PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES, + ] + # The frame holds survey-year values. Stamped with its own period, the + # engine projects them to the entitlement year as it does the released + # file, so the entitled set is the one a calibration-year run finds + # (uk-data#510 reads it the same way). Read as entitlement-year inputs + # instead, a survey-year State Pension would sit against the next year's + # guarantee. + materialized = engine.materialize( + with_saving, requested, entitlement_year, dataset_period=period + ) + missing = sorted(set(requested) - set(materialized)) + if missing: + raise ValueError(f"Pension Credit take-up engine outputs missing: {missing}.") guarantee = _aligned(materialized["guarantee_credit"], count, "guarantee_credit") savings = _aligned(materialized["savings_credit"], count, "savings_credit") eligible = _aligned( @@ -189,12 +274,15 @@ def redraw_pension_credit_take_up( household=household, household_weights=frame.weights_for("household").values, ) + great_britain = _benunit_in_great_britain( + benunit, person=person, household=household + ) draws = stable_identity_uniforms( benunit["benunit_id"].to_numpy(), seed=PENSION_CREDIT_TAKE_UP_SEED, salt=PENSION_CREDIT_TAKE_UP_OUTPUT, ) - would_claim, bands = assign_component_take_up( + would_claim, bands, newly_entitled = assign_component_take_up( guarantee=guarantee, savings=savings, eligible=eligible, @@ -205,11 +293,31 @@ def redraw_pension_credit_take_up( band["rate_key"]: contract.rate(band["rate_key"]) for band in PENSION_CREDIT_TAKE_UP_BANDS }, + great_britain=great_britain, + newly_entitled_rate=contract.rate(PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY), ) entitled = eligible & ((guarantee > 0.0) | (savings > 0.0)) outside = reporter & ~entitled previous = benunit[PENSION_CREDIT_TAKE_UP_OUTPUT].fillna(False).to_numpy(dtype=bool) benunit[PENSION_CREDIT_TAKE_UP_OUTPUT] = would_claim + channel = benunit[support_channel_column("benunit")].astype(str).to_numpy() + capital_receipt = { + name: { + str(value): float(np.sum((weights * amounts)[channel == value])) + for value in sorted(set(channel)) + } + for name, amounts in ( + ("guarantee_credit", guarantee), + ("savings_credit", savings), + *( + ( + variable, + _aligned(materialized[variable], count, variable).astype(float), + ) + for variable in PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES + ), + ) + } result = uk_national_frame( person=person, benunit=benunit, @@ -228,9 +336,22 @@ def redraw_pension_credit_take_up( "weighted_units": float(weights[outside].sum()), }, changed_units=int((previous != would_claim).sum()), + newly_entitled=newly_entitled, + entitlement_year=entitlement_year, + capital=capital_receipt, ) +def _benunit_in_great_britain( + benunit: pd.DataFrame, *, person: pd.DataFrame, household: pd.DataFrame +) -> np.ndarray: + region = household.set_index("household_id")["region"].astype(str) + regions = pd.Series(_benunit_households(benunit, person=person)).map(region) + if regions.isna().any(): + raise ValueError("Every benefit unit's household must carry a region.") + return regions.ne(PENSION_CREDIT_OUTSIDE_SOLVE_REGION).to_numpy(dtype=bool) + + def assign_component_take_up( *, guarantee: np.ndarray, @@ -240,8 +361,15 @@ def assign_component_take_up( weights: np.ndarray, draws: np.ndarray, rates: Mapping[str, float], -) -> tuple[np.ndarray, tuple[dict[str, object], ...]]: - """Reporters claim; entitled non-reporters claim at their band's residual rate.""" + great_britain: np.ndarray, + newly_entitled_rate: float, +) -> tuple[np.ndarray, tuple[dict[str, object], ...], dict[str, object]]: + """Reporters claim; entitled non-reporters claim at their band's residual rate. + + Each band's residual is solved over its Great Britain units and drawn for + every entitled non-reporter, Northern Ireland's included. A non-reporter + with no entitlement claims at ``newly_entitled_rate`` on the same draw. + """ guarantee = np.asarray(guarantee, dtype=np.float64) savings = np.asarray(savings, dtype=np.float64) @@ -249,6 +377,7 @@ def assign_component_take_up( reporter = np.asarray(reporter, dtype=bool) weights = np.asarray(weights, dtype=np.float64) draws = np.asarray(draws, dtype=np.float64) + great_britain = np.asarray(great_britain, dtype=bool) masks = { "guarantee_credit": eligible & (guarantee > 0.0), "savings_credit_only": eligible & (guarantee <= 0.0) & (savings > 0.0), @@ -257,13 +386,10 @@ def assign_component_take_up( receipts = [] for band in PENSION_CREDIT_TAKE_UP_BANDS: mask = masks[band["name"]] - rate = float(rates[band["rate_key"]]) - if not 0.0 <= rate <= 1.0: - raise ValueError( - f"{band['rate_key']} must be a rate in [0, 1], got {rate}." - ) - entitled = float(weights[mask].sum()) - reporting = float(weights[mask & reporter].sum()) + rate = _rate(band["rate_key"], rates[band["rate_key"]]) + solve = mask & great_britain + entitled = float(weights[solve].sum()) + reporting = float(weights[solve & reporter].sum()) residual = 0.0 if entitled > reporting: residual = float( @@ -274,7 +400,7 @@ def assign_component_take_up( drawn = mask & ~reporter & (draws < residual) would_claim |= drawn realized = ( - float(weights[mask & would_claim].sum()) / entitled + float(weights[solve & would_claim].sum()) / entitled if entitled > 0.0 else None ) @@ -284,19 +410,46 @@ def assign_component_take_up( "rule": band["rule"], "rate_key": band["rate_key"], "rate": rate, - "entitled_units": int(mask.sum()), + "scope": PENSION_CREDIT_SOLVE_SCOPE, + "entitled_units": int(solve.sum()), "entitled_weighted": entitled, - "reporter_units": int((mask & reporter).sum()), + "reporter_units": int((solve & reporter).sum()), "reporter_weighted": reporting, "residual_rate": residual, - "drawn_units": int(drawn.sum()), + "drawn_units": int((drawn & great_britain).sum()), "realized_take_up": realized, "reporters_exceed_rate": bool( entitled > 0.0 and reporting > rate * entitled ), + "outside_scope_entitled_units": int((mask & ~great_britain).sum()), + "outside_scope_drawn_units": int((drawn & ~great_britain).sum()), } ) - return would_claim, tuple(receipts) + rate = _rate(PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY, newly_entitled_rate) + not_entitled = ~(masks["guarantee_credit"] | masks["savings_credit_only"]) + newly = not_entitled & ~reporter & (draws < rate) + would_claim |= newly + not_entitled_weighted = float(weights[not_entitled].sum()) + newly_entitled = { + "rate_key": PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY, + "rate": rate, + "units": int(not_entitled.sum()), + "weighted": not_entitled_weighted, + "drawn_units": int(newly.sum()), + "flagged_share": ( + float(weights[not_entitled & would_claim].sum()) / not_entitled_weighted + if not_entitled_weighted > 0.0 + else None + ), + } + return would_claim, tuple(receipts), newly_entitled + + +def _rate(key: str, value: float) -> float: + rate = float(value) + if not 0.0 <= rate <= 1.0: + raise ValueError(f"{key} must be a rate in [0, 1], got {rate}.") + return rate def _aligned(values: object, expected: int, label: str) -> np.ndarray: @@ -328,10 +481,18 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None: ) saving, materialize, aggregate, assign = stage.operations expected = { - "saving": {"predictors": [PENSION_CREDIT_MIXED_AGE_SAVING]}, + "saving": { + "predictors": [PENSION_CREDIT_MIXED_AGE_SAVING], + "year_rule": PENSION_CREDIT_SAVING_YEAR_RULE, + }, "materialize": { - "predictors": list(PENSION_CREDIT_ENTITLEMENT_VARIABLES), + "predictors": [ + *PENSION_CREDIT_ENTITLEMENT_VARIABLES, + *PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES, + ], "consumed_only": True, + "year_rule": PENSION_CREDIT_ENTITLEMENT_YEAR_RULE, + "dataset_year_rule": PENSION_CREDIT_ENTITLEMENT_DATASET_YEAR_RULE, }, "aggregate": { "method": "any_positive", @@ -346,6 +507,8 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None: "bands": [dict(band) for band in PENSION_CREDIT_TAKE_UP_BANDS], "weight_mapping": "household_to_benunit", "residual_rate": PENSION_CREDIT_RESIDUAL_RATE, + "solve_scope": PENSION_CREDIT_SOLVE_SCOPE, + "newly_entitled_rate_key": PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY, }, } actual = { @@ -369,7 +532,9 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None: __all__ = [ + "PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES", "PENSION_CREDIT_ENTITLEMENT_VARIABLES", + "PENSION_CREDIT_NEWLY_ENTITLED_RATE_KEY", "PENSION_CREDIT_MIXED_AGE_SAVING", "PENSION_CREDIT_TAKE_UP_BANDS", "PENSION_CREDIT_TAKE_UP_STAGE_NAME", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py index 0c6c72791a..16f0da3bae 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_benefit_coherence.py @@ -69,6 +69,7 @@ from microcosm.build.uk_runtime.frs_take_up import ( UK_UC_AGE_ELIGIBLE_AGGREGATE, UK_UC_AGE_ELIGIBLE_METHOD, + UK_UC_AGE_ELIGIBLE_ROLE, UK_UC_AGE_ELIGIBLE_SOURCE, UK_UC_TAKE_UP_OUTPUT, UKTakeUpPopulationPolicy, @@ -195,6 +196,7 @@ def spi_benefit_coherence_operations() -> tuple[tuple[str, dict[str, object]], . "method": UK_UC_AGE_ELIGIBLE_METHOD, "consumed_only": True, "aggregates": {UK_UC_AGE_ELIGIBLE_AGGREGATE: UK_UC_AGE_ELIGIBLE_SOURCE}, + "role_column": UK_UC_AGE_ELIGIBLE_ROLE, }, ), ( 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 2c37156c3c..8f51da50c0 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 @@ -27,6 +27,10 @@ FRS_HMRC_SRP_REGULAR_CODE5_COLUMN, FRS_HMRC_UBISJA_COLUMN, ) +from microcosm.build.uk_runtime.frs_spine import ( + FRS_LANDLORD_CARRIER_COLUMN, + uc_gainful_self_employment, +) from microcosm.build.uk_runtime.spi_support import ( BASE_FRS_SUPPORT_CHANNEL, FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS, @@ -82,6 +86,20 @@ #: ``uc_relationships.frs_uc_claimant_mask``. SPI_RECIPIENT_ROLE_COLUMN = "is_uc_claimant" SPI_DONOR_INCOME_YEAR = 2022 +#: The donor rebase moves each SPI component from the tape's year to the +#: frame's survey year. Those years are history, so a component moves by the +#: index its engine variable names (``Variable.uprating``). For every +#: component but savings interest that is also the index policyengine-uk's +#: dataset projection applies from the survey year on (``policyengine_uk/data/ +#: uprating_indices.yaml``). Savings interest's own index is ONS household +#: interest income, x2.27 from 2022 to 2024 as rates rose, where the +#: projection's GDP per head gives x1.09. The projection's index applies from +#: the survey year on, where the engine runs it and the SPI band targets follow +#: it (``hmrc_uprating.UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT``, uk-data#541, +#: microcosm#1095). The engine lockstep test holds both statements. +SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION = { + "savings_interest_income": "gov.economic_assumptions.indices.obr.per_capita.gdp", +} # Use the pinned engine's variable-specific indices. None explicitly retains # nominal amounts where no same-scope indexed mapping has been reviewed; # broader indexed benefit inputs do not establish a taxable SPI crosswalk. @@ -992,6 +1010,8 @@ def impute_uk_spi_income_support( person = _refresh_disability_derived_inputs(person, spi_people=spi_people) person = _refresh_carer_take_up_input(person, spi_people=spi_people) person = _refresh_uc_start_up_period(person, spi_people=spi_people) + person = _refresh_uc_gainful_self_employment(person, spi_people=spi_people) + person = _refresh_landlord_carrier(person, spi_people=spi_people) return UKSPIIncomeImputationResult( person=person, fit_weight_records=( @@ -1363,6 +1383,10 @@ def _spi_income_uprating_factors(year: int): "factor": factor, "basis": "model_index" if path else "held_nominal", } + if variable_name in SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION: + columns[column]["dataset_projection_index"] = ( + SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION[variable_name] + ) return factors, { "from_period": SPI_DONOR_INCOME_YEAR, "to_period": year, @@ -2114,6 +2138,61 @@ def _refresh_uc_start_up_period( return person +def _refresh_uc_gainful_self_employment( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Derive a redrawn row's UC gainful self-employment from its drawn incomes. + + A redrawn row keeps the donor's employment status but takes new imputed + pay and profit, and the draw does not read the status (uk-data#529, + microcosm#840): on such a row the status says nothing about the drawn + trade. So the row takes the earnings route of the root's rule alone, a + profit above zero and above its pay (uk-data#525, ADM H4034). The main-job + route returns when the SPI draw follows employment status. + """ + + if "uc_is_in_gainful_self_employment" not in person.columns: + return person + rows = person.loc[spi_people] + _assign_spi_values( + person, + spi_people, + "uc_is_in_gainful_self_employment", + uc_gainful_self_employment( + np.zeros(len(rows), dtype=bool), + pd.to_numeric(rows["self_employment_income"], errors="coerce").fillna(0.0), + pd.to_numeric(rows["employment_income"], errors="coerce").fillna(0.0), + ), + default=False, + ) + return person + + +def _refresh_landlord_carrier( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Clear the FRS landlord carrier on a redrawn row. + + The carrier records that the FRS respondent reported rent, at a profit or + a loss. A redrawn row takes its property income from an SPI donor that did + not read it, so the twin's answer says nothing about the row's letting, + and the tape has no letting-loss field to rebuild it from. The CGT stages + read the row's drawn ``property_income`` instead (microcosm#1095). + """ + + if FRS_LANDLORD_CARRIER_COLUMN not in person.columns: + return person + rows = person.loc[spi_people] + _assign_spi_values( + person, + spi_people, + FRS_LANDLORD_CARRIER_COLUMN, + np.zeros(len(rows), dtype=bool), + 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/stage_health.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py index 1860a3ba39..b8f3fb57a3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/stage_health.py @@ -979,7 +979,9 @@ def _pension_credit_take_up_gate( A band whose reporters alone exceed the rate realizes their share instead; the receipt flags it and the gate requires the realized share to be at - least the rate there. + least the rate there. DWP's rates cover Great Britain, so each band is + measured there, and a receipt measured over any other scope fails + (uk-data#510). """ check = "pension_credit_take_up" @@ -1000,7 +1002,18 @@ def _pension_credit_take_up_gate( rate = _finite_number(band.get("rate"), label=f"{stage}.{name}.rate") units = int(band.get("entitled_units", 0)) realized = band.get("realized_take_up") - details[name] = {"rate": rate, "realized_take_up": realized, "units": units} + scope = band.get("scope") + details[name] = { + "rate": rate, + "realized_take_up": realized, + "units": units, + "scope": scope, + } + if scope != "great_britain": + failures.append( + f"{stage}: band {name} is measured over {scope!r}, not Great Britain." + ) + continue if units < minimum_units or realized is None: failures.append(f"{stage}: band {name} has {units} entitled units.") continue diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/target_compile_modules.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/target_compile_modules.py index 5481b00754..48e8f1e026 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/target_compile_modules.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/target_compile_modules.py @@ -29,6 +29,7 @@ "microcosm.build.uk_runtime.cgt_calibration", "microcosm.build.uk_runtime.chronicle_feed", "microcosm.build.uk_runtime.cross_grain_declarations", + "microcosm.build.uk_runtime.frs_release", "microcosm.build.uk_runtime.geography_ladder", "microcosm.build.uk_runtime.hmrc_uprating", "microcosm.build.uk_runtime.ledger_fact_vendoring", 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 1c4bf81c85..7d65674e48 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 @@ -291,6 +291,8 @@ def __post_init__(self) -> None: "person.student_loan_plan", "person.tax_free_childcare_spend_routed_share", "person.uc_is_in_startup_period", + "person.uc_is_in_gainful_self_employment", + "person.is_looked_after_by_local_authority", "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_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py index 1eef83a387..471411d8ad 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py @@ -74,6 +74,95 @@ _UC_CAPITAL_LEVELS = ("exact_cell", *UC_CAPITAL_COARSENING) #: The UC capital limits the receipt measures the redraw against. _UC_CAPITAL_RECEIPT_LIMITS = (6_000.0, 16_000.0) +#: policyengine-uk 2.123.0 counts these household property values as Universal +#: Credit and Pension Credit capital (the ``sources`` lists under +#: ``gov.dwp.universal_credit.means_test.capital`` and +#: ``gov.dwp.pension_credit.income.capital``, beside ``savings`` and +#: ``corporate_wealth``). A recorded figure of 0 or more replaces every source, +#: and the engine takes it as already countable, but TOTCAPB4 counts accounts +#: and assets and no property. So the recorded figures add the household's +#: property for the unit that holds it (uk-data#495, microcosm#1095). +UC_PROPERTY_CAPITAL_SOURCES = ( + "other_residential_property_value", + "non_residential_property_value", +) +PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES = ("owned_land", *UC_PROPERTY_CAPITAL_SOURCES) +#: Who holds a household's property. WAS records it for the household, and +#: each means test counts the claimant's and partner's own capital (UC Regs +#: 2013 reg 18; SPCA 2002 s.5). The household reference person, in whose name +#: the home is owned or rented, is the best single reading of the owner, so +#: that person's benefit unit holds all of it and no other unit records a +#: share (María's ruling of 2026-10-12, microcosm#1095). policyengine-uk's +#: proxies share household capital over every claimant or partner instead, +#: which puts a parent's property on an adult child's claim; they apply only +#: to a unit on the unavailable sentinel. Such a unit takes the gross value, +#: with no sale costs. Beside a recording unit, the Universal Credit proxy +#: shares only what that unit did not record. The Pension Credit proxy +#: subtracts nothing, so a pension-age unit on the sentinel is assessed a +#: share of property the reference person's unit also records. +#: +#: A consequence of the ruling: where the reference person is of working age, +#: the property of a pension-age unit in the household is recorded on the +#: reference person's unit. policyengine-uk reads Pension Credit capital only +#: for a unit with a member at the qualifying age, so no Pension Credit +#: assessment counts that property. +PROPERTY_CAPITAL_OWNERS = "household_reference_person_unit" +PROPERTY_CAPITAL_OWNER_COLUMN = "is_household_head" +#: Capital counts at its market value less 10% for the costs of sale (UC Regs +#: 2013 reg 49(1)(a); SPC Regs 2002 reg 19(a)). Debt secured on the property +#: is not on the frame and is not netted here (pe-uk#1969). +PROPERTY_CAPITAL_VALUATION = 0.9 +UC_PROPERTY_CAPITAL_SHARE = "uc_property_capital_share" +PENSION_CREDIT_PROPERTY_CAPITAL_SHARE = "pension_credit_property_capital_share" + + +def _property_share_definition(sources: tuple[str, ...]) -> str: + return ( + f"{PROPERTY_CAPITAL_VALUATION:g} x ({' + '.join(sources)}) of the household " + "where the unit holds the household reference person " + f"({PROPERTY_CAPITAL_OWNER_COLUMN}), else 0" + ) + + +def _recorded_capital_definition(share: str) -> str: + return ( + f"{UC_CAPITAL_REDRAW_OUTPUT} + {share} where {UC_CAPITAL_REDRAW_OUTPUT} " + f">= 0, else {UC_CAPITAL_REDRAW_OUTPUT}" + ) + + +UC_PROPERTY_CAPITAL_SHARE_DEFINITION = _property_share_definition( + UC_PROPERTY_CAPITAL_SOURCES +) +#: The carrier plus the unit's property share: what a unit records when it +#: reports no Universal Credit, and what the reporter redraw screens on. +UC_CAPITAL_WITH_PROPERTY_DEFINITION = _recorded_capital_definition( + UC_PROPERTY_CAPITAL_SHARE +) +#: A unit that reports Universal Credit keeps its receipt: DWP assessed its +#: capital to pay it, and the property is imputed without that receipt (the +#: WAS file has no Universal Credit column). So such a unit records the +#: carrier alone, the take-up stages' rule that a reported receipt is a fact +#: (microcosm#1095). +UC_PROPERTY_CAPITAL_REPORTER_RULE = "no_share" +UC_REPORTED_CAPITAL_DEFINITION = ( + f"{UC_CAPITAL_REDRAW_OUTPUT} + {UC_PROPERTY_CAPITAL_SHARE} where " + f"{UC_CAPITAL_REDRAW_OUTPUT} >= 0 and NOT {UC_CAPITAL_REPORTER_STATUS}, " + f"else {UC_CAPITAL_REDRAW_OUTPUT}" +) +#: The stage's declared derivations; ``_assert_stage_parameters`` refuses a +#: manifest that says otherwise. +UC_CAPITAL_COHERENCE_DERIVED = { + UC_PROPERTY_CAPITAL_SHARE: UC_PROPERTY_CAPITAL_SHARE_DEFINITION, + PENSION_CREDIT_PROPERTY_CAPITAL_SHARE: _property_share_definition( + PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES + ), + "uc_reported_capital": UC_REPORTED_CAPITAL_DEFINITION, + "pension_credit_reported_capital": _recorded_capital_definition( + PENSION_CREDIT_PROPERTY_CAPITAL_SHARE + ), + "would_claim_uc": "would_claim_uc OR universal_credit_reported_anchor", +} @dataclass(frozen=True) @@ -89,6 +178,7 @@ class UKUCCapitalCoherenceResult: capital_against_investment_income: Mapping[str, object] = field( default_factory=dict ) + property_shares: Mapping[str, object] = field(default_factory=dict) def evidence(self) -> dict[str, object]: """Return JSON-safe stage evidence.""" @@ -106,6 +196,7 @@ def evidence(self) -> dict[str, object]: "capital_against_investment_income": dict( self.capital_against_investment_income ), + "property_shares": dict(self.property_shares), } @@ -138,7 +229,9 @@ def checkpoint_metadata(self) -> dict[str, object]: def cohere_uc_capital( - frame: Frame, *, minimum_cell_donors: int = UC_CAPITAL_MINIMUM_CELL_DONORS + frame: Frame, + *, + minimum_cell_donors: int = UC_CAPITAL_MINIMUM_CELL_DONORS, ) -> UKUCCapitalCoherenceResult: """Make late SPI UC receipt, FRS capital, and take-up flags coherent.""" @@ -152,6 +245,7 @@ def cohere_uc_capital( "person_benunit_id", "person_household_id", "universal_credit_reported", + PROPERTY_CAPITAL_OWNER_COLUMN, *UC_FINANCIAL_INVESTMENT_INCOME_COLUMNS, ), label="person", @@ -167,7 +261,11 @@ def cohere_uc_capital( ), label="benunit", ) - _require_columns(household, ("household_id",), label="household") + _require_columns( + household, + ("household_id", *PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES), + label="household", + ) reporter = _post_fill_reporter_anchor(person, benunit) capital = pd.to_numeric( @@ -222,13 +320,37 @@ def cohere_uc_capital( previous_would_claim = _boolean_values(benunit["would_claim_uc"]) refreshed_would_claim = previous_would_claim | reporter benunit[UC_CAPITAL_REDRAW_OUTPUT] = capital - benunit["uc_reported_capital"] = capital.copy() - # Pension Credit reads the same recorded capital (pe-uk#2018 and #2070, - # uk-data#513): 0 or more replaces every capital source in its assessable - # capital, and the unavailable sentinel -1 is the engine's own default, so - # the household proxy applies there. - benunit["pension_credit_reported_capital"] = capital.copy() + # Both programmes read a recorded capital (pe-uk#2018 and #2070, + # uk-data#513): 0 or more replaces every capital source in the assessable + # capital, so the household reference person's unit adds the household's + # property to its financial carrier. The unavailable sentinel -1 is the + # engine's own default, so the household proxy applies there and the + # sentinel passes through unchanged. A unit that reports Universal Credit + # keeps its receipt and records no UC share. + uc_unreported_share = uc_property_capital_share(person, benunit, household) + uc_share = uc_recorded_property_share(person, benunit, household) + pension_credit_share = pension_credit_property_capital_share( + person, benunit, household + ) + uc_capital = recorded_capital_with_property(capital, uc_share) + pension_credit_capital = recorded_capital_with_property( + capital, pension_credit_share + ) + benunit["uc_reported_capital"] = uc_capital + benunit["pension_credit_reported_capital"] = pension_credit_capital benunit["would_claim_uc"] = refreshed_would_claim + property_receipt = _property_share_receipt( + weights=weights, + base=base, + spi=spi, + reporter=reporter, + carrier=capital, + shares={ + "universal_credit": (uc_share, uc_capital), + "pension_credit": (pension_credit_share, pension_credit_capital), + }, + uc_unreported_share=uc_unreported_share, + ) result_frame = uk_national_frame( person=person, @@ -251,7 +373,168 @@ def cohere_uc_capital( "before": receipt_before, "after": receipt_after, }, + property_shares=property_receipt, + ) + + +def uc_property_capital_share( + person: pd.DataFrame, benunit: pd.DataFrame, household: pd.DataFrame +) -> np.ndarray: + """The household property each unit holds for Universal Credit. + + The household's ``UC_PROPERTY_CAPITAL_SOURCES`` at + ``PROPERTY_CAPITAL_VALUATION`` for the household reference person's unit, + and nothing for any other unit. In benefit-unit row order. + """ + + return _reference_person_unit_property( + person, benunit, household, sources=UC_PROPERTY_CAPITAL_SOURCES + ) + + +def uc_recorded_property_share( + person: pd.DataFrame, benunit: pd.DataFrame, household: pd.DataFrame +) -> np.ndarray: + """The UC property share a unit's recorded capital carries. + + :func:`uc_property_capital_share`, and none for a unit that reports + Universal Credit (``UC_PROPERTY_CAPITAL_REPORTER_RULE``): its receipt is a + fact, and the imputed property would end it. No other unit takes the + share it does not record. A unit on the unavailable sentinel takes the + engine's household residual, which subtracts recorded figures only. In + benefit-unit row order. + """ + + share = uc_property_capital_share(person, benunit, household) + return np.where(_post_fill_reporter_anchor(person, benunit), 0.0, share) + + +def pension_credit_property_capital_share( + person: pd.DataFrame, benunit: pd.DataFrame, household: pd.DataFrame +) -> np.ndarray: + """The household property each unit holds for Pension Credit. + + The household's ``PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES`` at + ``PROPERTY_CAPITAL_VALUATION`` for the household reference person's unit, + and nothing for any other unit. In benefit-unit row order. + """ + + return _reference_person_unit_property( + person, benunit, household, sources=PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES + ) + + +def recorded_capital_with_property( + carrier: np.ndarray, share: np.ndarray +) -> np.ndarray: + """The carrier plus the property share; the -1 sentinel passes through.""" + + carrier = np.asarray(carrier, dtype=float) + return np.where(carrier >= 0.0, carrier + np.asarray(share, dtype=float), carrier) + + +def _reference_person_unit_property( + person: pd.DataFrame, + benunit: pd.DataFrame, + household: pd.DataFrame, + *, + sources: tuple[str, ...], +) -> np.ndarray: + values = household[list(sources)].apply(pd.to_numeric, errors="coerce") + property_value = values.sum(axis=1, min_count=len(sources)).to_numpy( + dtype=float, na_value=np.nan + ) + if not np.isfinite(property_value).all() or (property_value < 0.0).any(): + raise ValueError(f"household {list(sources)} must be finite and nonnegative.") + reference_person = person[PROPERTY_CAPITAL_OWNER_COLUMN] + if not pd.api.types.is_bool_dtype(reference_person.dtype): + raise ValueError(f"{PROPERTY_CAPITAL_OWNER_COLUMN} must be a boolean column.") + reference_person = reference_person.to_numpy(dtype=bool) + per_household = ( + pd.Series(reference_person.astype(int)) + .groupby(person["person_household_id"].to_numpy(), sort=False) + .sum() + .reindex(household["household_id"].to_numpy(), fill_value=0) ) + if (per_household != 1).any(): + raise ValueError( + f"{int((per_household != 1).sum())} household(s) do not have exactly " + "one household reference person to hold their property." + ) + benunit_household = _benunit_households(benunit, person=person) + household_value = ( + pd.Series(property_value, index=household["household_id"].to_numpy()) + .reindex(benunit_household) + .to_numpy(dtype=float) + ) + if np.isnan(household_value).any(): + raise ValueError("Household property does not cover every benefit unit.") + holds = ( + benunit["benunit_id"] + .isin(person.loc[reference_person, "person_benunit_id"]) + .to_numpy(dtype=bool) + ) + return np.where(holds, PROPERTY_CAPITAL_VALUATION * household_value, 0.0) + + +def _property_share_receipt( + *, + weights: np.ndarray, + base: np.ndarray, + spi: np.ndarray, + reporter: np.ndarray, + carrier: np.ndarray, + shares: Mapping[str, tuple[np.ndarray, np.ndarray]], + uc_unreported_share: np.ndarray, +) -> dict[str, object]: + """Aggregate receipt of the property the recorded capitals now carry.""" + + available = carrier >= 0.0 + limit = _UC_CAPITAL_RECEIPT_LIMITS[-1] + receipt: dict[str, object] = { + "owners": PROPERTY_CAPITAL_OWNERS, + "valuation": PROPERTY_CAPITAL_VALUATION, + } + for programme, (share, recorded) in shares.items(): + with_share = available & (share > 0.0) + receipt[programme] = { + name: { + "benefit_units_with_share": int((rows & with_share).sum()), + "weighted_share_total": float( + (weights * share)[rows & available].sum() + ), + "units_moved_above_16000": int( + (rows & available & (carrier <= limit) & (recorded > limit)).sum() + ), + } + for name, rows in (("frs", base), ("spi", spi)) + } + uc_recorded = shares["universal_credit"][1] + receipt["uc_reporters_recorded_capital_above_16000"] = { + name: int((rows & reporter & (uc_recorded > limit)).sum()) + for name, rows in (("frs", base), ("spi", spi)) + } + # The units the reporter rule leaves without the share they would hold, + # and that share. + withheld = available & reporter & (uc_unreported_share > 0.0) + receipt["uc_reporters_keeping_receipt"] = { + name: { + "benefit_units": int((rows & withheld).sum()), + "weighted_share_withheld": float( + (weights * uc_unreported_share)[rows & withheld].sum() + ), + "units_kept_at_or_below_16000": int( + ( + rows + & withheld + & (carrier <= limit) + & (carrier + uc_unreported_share > limit) + ).sum() + ), + } + for name, rows in (("frs", base), ("spi", spi)) + } + return receipt def _post_fill_reporter_anchor( @@ -419,21 +702,13 @@ def _household_to_benunit_weights( household: pd.DataFrame, household_weights: np.ndarray, ) -> np.ndarray: - placements = person[["person_benunit_id", "person_household_id"]].drop_duplicates() - counts = placements.groupby("person_benunit_id", sort=False)[ - "person_household_id" - ].nunique() - if (counts != 1).any(): - raise ValueError("Every benefit unit must map to exactly one household.") - household_by_benunit = placements.set_index("person_benunit_id")[ - "person_household_id" - ] weight_by_household = pd.Series( np.asarray(household_weights, dtype=float), index=household["household_id"], ) - mapped_households = benunit["benunit_id"].map(household_by_benunit) - weights = mapped_households.map(weight_by_household) + weights = pd.Series(_benunit_households(benunit, person=person)).map( + weight_by_household + ) if weights.isna().any(): raise ValueError("Household weights do not cover every benefit unit.") values = weights.to_numpy(dtype=float) @@ -442,6 +717,19 @@ def _household_to_benunit_weights( return values +def _benunit_households(benunit: pd.DataFrame, *, person: pd.DataFrame) -> np.ndarray: + """Each benefit unit's household id, in benefit-unit row order.""" + + placements = person[["person_benunit_id", "person_household_id"]].drop_duplicates() + if placements["person_benunit_id"].duplicated().any(): + raise ValueError("Every benefit unit must map to exactly one household.") + return ( + benunit["benunit_id"] + .map(placements.set_index("person_benunit_id")["person_household_id"]) + .to_numpy() + ) + + def _dependent_children_band(values: pd.Series) -> np.ndarray: numeric = pd.to_numeric(values, errors="coerce").to_numpy( dtype=float, na_value=np.nan @@ -465,6 +753,16 @@ def _boolean_values(values: pd.Series) -> np.ndarray: def _assert_stage_parameters(stage: SourceStageSpec) -> None: + derived = [ + operation.parameters.get("derived") + for operation in stage.operations + if operation.kind == "derive" + ] + if derived != [UC_CAPITAL_COHERENCE_DERIVED]: + raise ValueError( + "uc_capital_coherence must declare one derive operation with the " + f"code's derivations {UC_CAPITAL_COHERENCE_DERIVED}, got {derived}." + ) redraw = [ operation for operation in stage.operations diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py index 054b4c8353..4cd18ec7e9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py @@ -30,8 +30,15 @@ support_clone_index_column, ) from microcosm.build.uk_runtime.uc_capital_coherence import ( + PROPERTY_CAPITAL_OWNER_COLUMN, + UC_CAPITAL_WITH_PROPERTY_DEFINITION, + UC_PROPERTY_CAPITAL_SHARE, + UC_PROPERTY_CAPITAL_SHARE_DEFINITION, + UC_PROPERTY_CAPITAL_SOURCES, _boolean_values, _household_to_benunit_weights, + recorded_capital_with_property, + uc_property_capital_share, ) from microcosm.build.uk_runtime.uc_relationships import ( UC_COUPLE_DEFINITION, @@ -69,9 +76,20 @@ predictor for predictor in UC_REPORTER_PREDICTORS if predictor != "region" ) UC_REPORTER_TARGET = "universal_credit_reported_amount" +UC_REPORTER_SCREEN_CAPITAL_DEFINITION = ( + f"{UC_CAPITAL_WITH_PROPERTY_DEFINITION}; a base FRS-channel unit with " + f"{UC_REPORTER_TARGET} > 0 takes frs_benunit_capital alone" +) UC_REPORTER_TEMPORARY_DERIVED = { "is_uc_couple": UC_COUPLE_DEFINITION, - "uc_reported_capital": "frs_benunit_capital", + # The screen reads the capital uc_capital_coherence records. An FRS unit + # that reports Universal Credit is screened on its carrier alone, so the + # training set keeps the reporters whose imputed property would take them + # over the capital limit. Every other unit is screened on the carrier plus + # its property share, so a drawn SPI reporter does not land on a unit its + # property takes over the limit (microcosm#1095). + UC_PROPERTY_CAPITAL_SHARE: UC_PROPERTY_CAPITAL_SHARE_DEFINITION, + "uc_reported_capital": UC_REPORTER_SCREEN_CAPITAL_DEFINITION, "positive_pre_takeup_uc_award": ( "max(0, uc_maximum_amount - uc_income_reduction) > 0" ), @@ -184,6 +202,7 @@ def redraw_spi_reported_uc( "is_benunit_head", "is_parent", "age", + PROPERTY_CAPITAL_OWNER_COLUMN, UC_REPORTER_REDRAW_OUTPUT, *_PERSON_INCOME_COLUMNS, ), @@ -202,7 +221,7 @@ def redraw_spi_reported_uc( ) _require_columns( household, - ("household_id", "region"), + ("household_id", "region", *UC_PROPERTY_CAPITAL_SOURCES), label="household", ) @@ -364,9 +383,21 @@ def _materialize_screen_inputs( household: pd.DataFrame, ) -> Mapping[str, np.ndarray]: temporary_benunit = benunit.copy() - temporary_benunit["uc_reported_capital"] = pd.to_numeric( - temporary_benunit["frs_benunit_capital"], errors="coerce" - ).to_numpy(dtype=np.float64, na_value=np.nan) + frs_reporter = temporary_benunit[support_channel_column("benunit")].astype(str).eq( + BASE_FRS_SUPPORT_CHANNEL + ).to_numpy(dtype=bool) & ( + _benefit_unit_reporter_amounts(person, temporary_benunit) > 0.0 + ) + temporary_benunit["uc_reported_capital"] = recorded_capital_with_property( + pd.to_numeric( + temporary_benunit["frs_benunit_capital"], errors="coerce" + ).to_numpy(dtype=np.float64, na_value=np.nan), + np.where( + frs_reporter, + 0.0, + uc_property_capital_share(person, temporary_benunit, household), + ), + ) temporary = uk_national_frame( person=person, benunit=temporary_benunit, diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py index 1c2022ef13..996735e9ba 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_frs_stochastic.py @@ -4,12 +4,36 @@ from test_support.microcosm_build.uk_frs_stochastic import * -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.""" +def _engine_uc_eligible(people: dict, benunits: dict) -> list[bool]: + """policyengine-uk's is_uc_eligible at 2025, with zero reported capital.""" import importlib policyengine_uk = importlib.import_module("policyengine_uk") + situation = { + "people": people, + "benunits": { + name: {**unit, "uc_reported_capital": {2025: 0}} + for name, unit in benunits.items() + }, + "households": { + f"h_{name}": {"members": unit["members"]} for name, unit in benunits.items() + }, + } + sim = policyengine_uk.Simulation(situation=situation) + return np.asarray(sim.calculate("is_uc_eligible", 2025), dtype=bool).tolist() + + +def test_uc_take_up_population_mirrors_the_engines_eligibility_rule() -> None: + """The population is policyengine-uk's is_uc_eligible, capital aside. + + The take-up population holds a unit with a UC claimant or partner aged 18 + to under Pension Credit qualifying age. One-person units aged 0 to 100 + agree with the engine; the two cases below are engine conditions the + population does not read, named so that an engine change to either fails + here. + """ + policy = uk_take_up_population_policy(2025) assert policy == UKTakeUpPopulationPolicy( adult_age=18, @@ -19,14 +43,33 @@ def test_uc_take_up_population_policy_matches_the_engine_working_age_test() -> N ) 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}, - } + engine = _engine_uc_eligible( + {f"p{age}": {"age": {2025: age}} for age in ages}, + {f"b{age}": {"members": [f"p{age}"]} for age in ages}, ) - is_wa_adult = np.asarray(sim.calculate("is_WA_adult", 2025), dtype=bool) + # A one-person unit's member is its claimant. + assert engine == policy.working_age(np.array(ages)).tolist() + + # Named exception (ii): a 16- or 17-year-old claimant under the UC Regs + # 2013 reg 8 exceptions (here a carer) meets UC's reduced minimum age. + # The population leaves them out. + assert _engine_uc_eligible( + {"carer": {"age": {2025: 17}, "care_hours": {2025: 40}}}, + {"b_carer": {"members": ["carer"]}}, + ) == [True] + assert not policy.working_age(np.array([17])).any() - assert is_wa_adult.tolist() == policy.working_age(np.array(ages)).tolist() + # Named exception (iii): a mixed-age couple keeping the SI 2019/37 saving + # takes the Pension Credit route, which the population does not read: its + # younger partner is a claimant or partner of working age. + couple = {"older": {"age": {2025: 70}}, "younger": {"age": {2025: 60}}} + assert _engine_uc_eligible( + couple, + { + "b_couple": { + "members": ["older", "younger"], + "has_mixed_age_couple_pension_credit_saving": {2025: True}, + } + }, + ) == [False] + assert policy.working_age(np.array([70, 60])).tolist() == [False, True] diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_uprating.py b/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_uprating.py index d3968782e1..6c01439e03 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_uprating.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_uprating.py @@ -19,3 +19,82 @@ def factor(path: str) -> float: assert factor("gov.dwp.state_pension.new_state_pension.amount") == pytest.approx( 230.25 / 203.85, abs=1e-6 ) + + +def test_spi_band_targets_move_by_the_engines_dataset_projection() -> None: + """An HMRC SPI amount family projects its value by the index + policyengine-uk's dataset projection grows the variable with, so the + targets move with the data the engine runs on (microcosm#1095, + uk-data#541). Savings interest reaches the dataset's year by the index of + its own history first.""" + + import json + from importlib.resources import files + from pathlib import Path + + import policyengine_uk + import yaml + + from microcosm.build.uk_runtime.hmrc_uprating import ( + UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, + UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, + UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, + ) + + table = yaml.safe_load( + ( + Path(policyengine_uk.__file__).parent / "data" / "uprating_indices.yaml" + ).read_text() + ) + projection = { + variable: path.replace(".yoy_growth.", ".indices.") + for path, variables in table.items() + for variable in variables + } + variables = policyengine_uk.CountryTaxBenefitSystem().variables + contract = json.loads( + files("microcosm.build.uk").joinpath("uk_population_targets.json").read_text() + ) + checked = [] + for family in contract["targets"]: + index = str(family.get("uprating_index") or "") + variable = family["bindings"]["policyengine"].get("value_variable") + if family.get("family") != "hmrc_spi": + continue + if index == UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT: + assert variable == "savings_interest_income" + assert UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER == projection[variable] + assert UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER == ( + variables[variable].uprating + ) + checked.append(family["target_id"]) + continue + path = index.removeprefix(UK_ENGINE_PARAMETER_INDEX_PREFIX) + if path == index or not path.startswith("gov.economic_assumptions.indices."): + continue + assert path == projection[variable], family["target_id"] + checked.append(family["target_id"]) + assert "hmrc.spi.savings_interest_income.amount_by_total_income_band" in checked + assert len(checked) > 1 + + +def test_savings_interest_targets_take_the_donors_path() -> None: + """The band targets reach 2025 by the path the SPI donors take: the + history index to the frame's year, then the engine's projection.""" + + history = "gov.economic_assumptions.indices.ons.household_interest_income" + projection = "gov.economic_assumptions.indices.obr.per_capita.gdp" + + def ratio(path: str, start: int, end: int) -> float: + return engine_parameter_value(path, f"{end}-01-01") / engine_parameter_value( + path, f"{start}-01-01" + ) + + target_factor = ratio(history, 2023, 2024) * ratio(projection, 2024, 2025) + donor_path = ratio(history, 2022, 2024) * ratio(projection, 2024, 2025) + + assert target_factor == pytest.approx(1.1920, abs=1e-4) + # The donors start a year earlier and so carry one more year of history. + assert donor_path / target_factor == pytest.approx( + ratio(history, 2022, 2023), rel=1e-12 + ) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_pension_credit_take_up.py b/packages/microcosm-build/tests/engine/uk/test_uk_pension_credit_take_up.py new file mode 100644 index 0000000000..627194764a --- /dev/null +++ b/packages/microcosm-build/tests/engine/uk/test_uk_pension_credit_take_up.py @@ -0,0 +1,106 @@ +"""Engine reads behind the Pension Credit take-up stage (microcosm#1095).""" + +from __future__ import annotations + +import numpy as np +import pandas as pd + +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind +from microcosm.frame.adapters.policyengine_uk import PolicyEngineUKEngine + +#: The full new State Pension in 2024-25, a week. +_NEW_STATE_PENSION_2024 = 221.20 + + +def _frame(): + """A single pensioner on the full new State Pension, and a mixed-age + couple whose younger member cares and reports Income Support.""" + + person = pd.DataFrame( + { + "person_id": [11, 21, 22], + "person_benunit_id": [1, 2, 2], + "person_household_id": [1, 2, 2], + "age": [70.0, 72.0, 58.0], + "state_pension_reported": [_NEW_STATE_PENSION_2024 * 52, 0.0, 0.0], + "income_support_reported": [0.0, 0.0, 3_000.0], + "carers_allowance_reported": [0.0, 0.0, 4_000.0], + "is_carer_for_benefits": [False, False, True], + } + ) + benunit = pd.DataFrame({"benunit_id": [1, 2], "would_claim_pc": [True, True]}) + household = pd.DataFrame( + { + "household_id": [1, 2], + "region": ["WALES", "WALES"], + "council_tax": [0.0, 0.0], + "tenure_type": ["OWNED_OUTRIGHT", "OWNED_OUTRIGHT"], + "rent": [0.0, 0.0], + } + ) + return uk_national_frame( + person=person, + benunit=benunit, + household=household, + household_weights=np.ones(2), + weight_kind=WeightKind.DESIGN, + time_period="2024", + ) + + +def test_entitlement_is_read_on_the_frame_the_engine_projects() -> None: + # The stage reads calibration-year entitlement on a survey-year frame + # (uk-data#510). Read as 2025 inputs, a 2024-25 full new State Pension sits + # under the 2025-26 standard minimum guarantee and the pensioner is + # entitled. Stamped with its survey year, the engine uprates the pension + # as it does on the released file, and the pensioner is not. + engine = PolicyEngineUKEngine() + names = ["state_pension", "guarantee_credit"] + + relabelled = engine.materialize(_frame(), names, 2025) + projected = engine.materialize(_frame(), names, 2025, dataset_period=2024) + + survey_pension = _NEW_STATE_PENSION_2024 * 52 + assert relabelled["state_pension"][0] == np.float32(survey_pension) + assert relabelled["guarantee_credit"][0] > 0 + assert projected["state_pension"][0] > survey_pension * 1.03 + assert projected["guarantee_credit"][0] == 0 + + +def test_a_dataset_period_after_the_period_is_refused() -> None: + engine = PolicyEngineUKEngine() + + try: + engine.materialize(_frame(), ["state_pension"], 2024, dataset_period=2025) + except ValueError as error: + assert "projects a dataset forward only" in str(error) + else: + raise AssertionError("a later dataset period must be refused") + + +def test_drawing_pension_credit_take_up_leaves_income_support_in_place() -> None: + # policyengine-uk bars Income Support where a unit meets the Pension + # Credit age conditions and would claim Pension Credit, with no means + # test. The stage draws would_claim_pc for units without entitlement. A + # mixed-age couple that reports Income Support and no Pension Credit has + # no SI 2019/37 saving, so it does not meet those conditions and the draw + # cannot take its Income Support away. + engine = PolicyEngineUKEngine() + + values = engine.materialize( + _frame(), + [ + "is_mixed_age_couple", + "has_mixed_age_couple_pension_credit_saving", + "meets_pension_credit_age_conditions", + "income_support_eligible", + ], + 2025, + dataset_period=2024, + ) + + assert bool(values["is_mixed_age_couple"][1]) + assert not bool(values["has_mixed_age_couple_pension_credit_saving"][1]) + assert not bool(values["meets_pension_credit_age_conditions"][1]) + assert bool(values["income_support_eligible"][1]) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py index 8b3194d3f6..68b9fbaf3d 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_salary_sacrifice_counterfactual.py @@ -4,7 +4,12 @@ import numpy as np import pandas as pd +import pytest +from microcosm.build.uk_runtime.income_tax_rate_classes import ( + UK_RATE_CLASS_ALLOCATION, + UK_RATE_CLASSES, +) from microcosm.build.uk_runtime.measure_simulation import UKMeasureResolver from microcosm.build.uk_runtime.national_frame import uk_national_frame from microcosm.frame import WeightKind @@ -85,3 +90,127 @@ def test_returned_sacrifice_is_taxed_at_the_basic_rate_and_main_nics_rates(tmp_p "hmrc/salary_sacrifice_ni_employee_relief", "hmrc/salary_sacrifice_ni_employer_relief", } + + +def _band_frame(): + """uk-data#533's three examples at 2025 (pay after the sacrifice), plus the + first with GBP 10,000 of dividends.""" + + ids = np.arange(4, dtype=np.int64) + return uk_national_frame( + person=pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": [40, 40, 40, 40], + "employment_income": [48_000.0, 42_000.0, 100_000.0, 48_000.0], + "pension_contributions_via_salary_sacrifice": [ + 4_000.0, + 4_000.0, + 10_000.0, + 4_000.0, + ], + "dividend_income": [0.0, 0.0, 0.0, 10_000.0], + } + ), + benunit=pd.DataFrame({"benunit_id": ids}), + household=pd.DataFrame( + { + "household_id": ids, + "household_weight": [1.0, 1.0, 1.0, 1.0], + "region": ["LONDON", "SCOTLAND", "LONDON", "LONDON"], + "council_tax": 0.0, + "rent": 0.0, + "tenure_type": "OWNED_OUTRIGHT", + } + ), + time_period="2025", + weight_kind=WeightKind.DESIGN, + ) + + +def _band_binding(rate_class: str) -> dict: + return { + "metric_name": f"hmrc/salary_sacrifice_it_relief_{rate_class}_rate", + "kind": "input_substitution_counterfactual", + "zeroed_input": "pension_contributions_via_salary_sacrifice", + "folded_into": "employment_income", + "output_delta": "counterfactual_minus_baseline", + "output_variable": "earned_taxable_income", + "allocation": {**UK_RATE_CLASS_ALLOCATION, "rate_class": rate_class}, + "from_entity": "person", + } + + +def test_relief_bands_relieve_each_slice_at_its_rate(tmp_path): + """uk-data#533's examples (microcosm#1095): rest of the UK, GBP 4,000 on + GBP 48,000 is 454 basic and 692 higher; Scotland, GBP 4,000 on GBP 42,000 + is 349.02 basic and 981.96 higher; GBP 10,000 on GBP 100,000 withdraws + GBP 5,000 of personal allowance, so 6,000, all higher.""" + + resolver = UKMeasureResolver( + simulation_source=None, frame=_band_frame(), scratch_dir=tmp_path, year=2025 + ) + + relief = { + rate_class: resolver.counterfactual_delta(_band_binding(rate_class), 2025)[0] + for rate_class in UK_RATE_CLASSES + } + + expected = { + "basic": [454.0, 349.02, 0.0, 454.0], + "higher": [692.0, 981.96, 6_000.0, 692.0], + "additional": [0.0, 0.0, 0.0, 0.0], + } + for rate_class in UK_RATE_CLASSES: + np.testing.assert_allclose(relief[rate_class], expected[rate_class], atol=0.01) + # With pay the only income the bands add up to the rise in income tax. The + # returned pay also pushes the fourth person's dividends into the higher + # dividend band: that tax is not salary-sacrifice relief, and the receipt + # records the gap. + income_tax, _ = resolver.counterfactual_delta(_relief_binding("income_tax"), 2025) + total = sum(relief.values()) + np.testing.assert_allclose(total[:3], income_tax[:3], atol=0.01) + assert income_tax[3] > total[3] + 100.0 + (receipt,) = resolver.receipt()["rate_class_reliefs"].values() + # The classes add up to the rise in the engine's own earned_income_tax + # for every person, the fourth included: a drifted schedule fails there. + earned, _ = resolver.counterfactual_delta( + _relief_binding("earned_income_tax"), 2025 + ) + np.testing.assert_allclose(total, earned, atol=0.01) + assert receipt["engine_variable"] == "earned_income_tax" + assert receipt["largest_gap_to_engine_variable"] < 0.01 + assert receipt["engine_variable_change_unweighted_total"] == pytest.approx( + float(np.sum(total)), abs=0.01 + ) + assert receipt["persons_with_gap"] == 1 + assert receipt["gap_to_income_tax_change_unweighted"] == pytest.approx( + float(np.sum(income_tax) - np.sum(total)), abs=0.01 + ) + # One counterfactual simulation serves the three bands and the total. + assert len(resolver._counterfactuals) == 1 + + +def test_relief_bands_refuse_when_they_leave_the_engines_earned_income_tax( + tmp_path, monkeypatch +): + """The allocation re-implements earned_income_tax; a schedule it no longer + follows refuses the measure instead of binding a drifted band.""" + + from microcosm.build.uk_runtime import measure_simulation + + real = measure_simulation.relief_by_rate_class + + def drifted(**kwargs): + relief = real(**kwargs) + return {**relief, "basic": relief["basic"] + 5.0} + + monkeypatch.setattr(measure_simulation, "relief_by_rate_class", drifted) + resolver = UKMeasureResolver( + simulation_source=None, frame=_band_frame(), scratch_dir=tmp_path, year=2025 + ) + + with pytest.raises(ValueError, match="no longer adds up to the engine's"): + resolver.counterfactual_delta(_band_binding("basic"), 2025) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py b/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py index c8e61e6bff..625035022d 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_spi_income.py @@ -558,6 +558,8 @@ def test_spi_uprating_uses_actual_engine_indices_and_explicit_nominal_holdouts() "obr.per_capita.mixed_income", target.obr.per_capita.mixed_income / source.obr.per_capita.mixed_income, ), + # The index of the variable's history; the dataset projection's index + # applies from the frame's year on (microcosm#1095). "savings_interest_income": ( "ons.household_interest_income", target.ons.household_interest_income / source.ons.household_interest_income, @@ -625,3 +627,53 @@ def test_spi_uprating_uses_actual_engine_indices_and_explicit_nominal_holdouts() assert source_receipt["from_period"] == source_receipt["to_period"] == 2022 with pytest.raises(ValueError, match="before its source year"): spi_income._spi_income_uprating_factors.__wrapped__(2021) + + +def _engine_projection_indices() -> dict[str, str]: + """Each variable's index in policyengine-uk's dataset projection, as a level.""" + + import policyengine_uk + import yaml + + table = yaml.safe_load( + ( + Path(policyengine_uk.__file__).parent / "data" / "uprating_indices.yaml" + ).read_text() + ) + return { + variable: path.replace(".yoy_growth.", ".indices.") + for path, variables in table.items() + for variable in variables + } + + +def test_spi_rebasing_index_is_the_projections_except_where_named() -> None: + """The donor rebase moves each component by its variable's own index. + + For every indexed component but savings interest that is also the index + policyengine-uk's dataset projection applies. Savings interest's history + index is named apart, with the projection's index the SPI band targets + take from the frame's year on. An engine that moves either fails here + (microcosm#1095, uk-data#541). + """ + + projection = _engine_projection_indices() + _, receipt = spi_income._spi_income_uprating_factors.__wrapped__(2024) + + indexed = { + column: row for column, row in receipt["columns"].items() if row["index"] + } + assert indexed + differs = spi_income.SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION + for column, row in indexed.items(): + if row["variable"] in differs: + assert row["index"] != projection[row["variable"]], column + assert row["dataset_projection_index"] == projection[row["variable"]] + else: + assert row["index"] == projection[row["variable"]], column + assert "dataset_projection_index" not in row + savings = receipt["columns"]["savings_interest_income"] + assert savings["index"] == ( + "gov.economic_assumptions.indices.ons.household_interest_income" + ) + assert savings["factor"] == pytest.approx(2.2692, abs=1e-4) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py index f385b20b53..364e4e174a 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_uc_capital_coherence.py @@ -50,3 +50,201 @@ def test_engine_uses_reported_capital_and_sentinel_routes_to_residual_proxy() -> ] np.testing.assert_array_equal(result, np.asarray([0.0, 16_000.0, 7_000.0])) + + +def _capital_frame(uc_capital, pension_credit_capital, *, child_heads=False): + """Pensioner parents with their working-age child's unit, and a lone + parent with a 17-year-old dependant. One of the parents is the household + reference person, or the child where ``child_heads``.""" + + person = pd.DataFrame( + { + "person_id": [1001, 1002, 1003, 2001, 2002], + "person_benunit_id": [101, 101, 102, 201, 201], + "person_household_id": [1, 1, 1, 2, 2], + "age": [70, 68, 30, 40, 17], + "is_benunit_head": [True, False, True, True, False], + "is_household_head": [not child_heads, False, child_heads, True, False], + "is_uc_claimant": [True, True, True, True, False], + "universal_credit_reported": 0.0, + } + ) + benunit = pd.DataFrame( + { + "benunit_id": [101, 102, 201], + "uc_reported_capital": uc_capital, + "pension_credit_reported_capital": pension_credit_capital, + "would_claim_uc": True, + } + ) + household = pd.DataFrame( + { + "household_id": [1, 2], + "region": ["LONDON", "LONDON"], + "council_tax": [0.0, 0.0], + "tenure_type": ["OWNED_OUTRIGHT", "OWNED_OUTRIGHT"], + "rent": [0.0, 0.0], + "savings": [0.0, 0.0], + "corporate_wealth": [0.0, 0.0], + "other_residential_property_value": [90_000.0, 50_000.0], + "non_residential_property_value": [10_000.0, 0.0], + "owned_land": [30_000.0, 5_000.0], + } + ) + return uk_national_frame( + person=person, + benunit=benunit, + household=household, + household_weights=np.ones(2), + weight_kind=WeightKind.DESIGN, + time_period="2024", + ) + + +def _assessable_capital(frame): + values = PolicyEngineUKEngine().materialize( + frame, ["uc_assessable_capital", "pension_credit_assessable_capital"], 2024 + ) + return ( + np.asarray(values["uc_assessable_capital"], dtype=float), + np.asarray(values["pension_credit_assessable_capital"], dtype=float), + ) + + +def test_the_engine_assesses_the_recorded_property_and_no_other_unit_takes_it() -> None: + """The household reference person's unit records the household's property + at 90% of its value, and policyengine-uk takes each recorded figure as the + unit's assessable capital. Its own proxies, which apply only to a unit on + the sentinel, would put a third of the parents' property on their child's + unit (microcosm#1095).""" + + unavailable = [-1.0, -1.0, -1.0] + proxied = _capital_frame(unavailable, unavailable) + tables = { + entity: proxied.table(entity) for entity in ("person", "benunit", "household") + } + uc_share = uc_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + pension_credit_share = pension_credit_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + # UC counts other property; Pension Credit adds land. The parents' unit + # and the lone parent's hold their households' property; the child's unit + # holds none. + np.testing.assert_allclose(uc_share, [90_000.0, 0.0, 45_000.0], rtol=1e-12) + np.testing.assert_allclose( + pension_credit_share, [117_000.0, 0.0, 49_500.0], rtol=1e-12 + ) + + recorded = _capital_frame( + recorded_capital_with_property(np.zeros(3), uc_share), + recorded_capital_with_property(np.zeros(3), pension_credit_share), + ) + uc_recorded, pension_credit_recorded = _assessable_capital(recorded) + np.testing.assert_allclose(uc_recorded, uc_share, rtol=1e-6) + # The engine assesses Pension Credit capital only for a unit with a member + # at its qualifying age, so the lone parent's recorded figure is not read. + np.testing.assert_allclose( + pension_credit_recorded, [pension_credit_share[0], 0.0, 0.0], rtol=1e-6 + ) + + # The engine's UC proxy shares household 1's gross property over its three + # claimants and partners, so the child's unit would be assessed a third. + uc_proxy, _ = _assessable_capital(proxied) + np.testing.assert_allclose( + uc_proxy, [200_000.0 / 3, 100_000.0 / 3, 50_000.0], rtol=1e-6 + ) + + # A unit that reports Universal Credit keeps its receipt: it records the + # carrier alone, the engine assesses it no property, and its child's unit, + # which records its own carrier, takes none of it. + reporter_keeps = _capital_frame( + [0.0, 0.0, uc_share[2]], + recorded_capital_with_property(np.zeros(3), pension_credit_share), + ) + uc_reporter_keeps, _ = _assessable_capital(reporter_keeps) + np.testing.assert_allclose(uc_reporter_keeps, [0.0, 0.0, uc_share[2]], rtol=1e-6) + + +def test_a_unit_on_the_sentinel_beside_a_recording_unit_takes_the_engines_proxy() -> ( + None +): + """A unit on the unavailable sentinel stays on policyengine-uk's proxies, + which share the household's gross property. Beside a recording reference + person's unit, the Universal Credit proxy leaves it the 10% that unit did + not record, and the Pension Credit proxy, which subtracts nothing, counts + the property a second time (microcosm#1095).""" + + unavailable = [-1.0, -1.0, -1.0] + tables = { + entity: _capital_frame(unavailable, unavailable).table(entity) + for entity in ("person", "benunit", "household") + } + uc_share = uc_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + pension_credit_share = pension_credit_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + + # The parents record and their child's unit is on the sentinel: the + # Universal Credit residual gives the child the 10,000 of sale costs the + # parents' 90,000 left out of household 1's 100,000. + uc_mixed, pension_credit_mixed = _assessable_capital( + _capital_frame( + [uc_share[0], -1.0, uc_share[2]], + [pension_credit_share[0], -1.0, pension_credit_share[2]], + ) + ) + np.testing.assert_allclose(uc_mixed, [90_000.0, 10_000.0, 45_000.0], rtol=1e-6) + np.testing.assert_allclose(pension_credit_mixed, [117_000.0, 0.0, 0.0], rtol=1e-6) + + +def test_property_held_by_a_working_age_reference_person_leaves_pension_credit() -> ( + None +): + """Where the working-age child is the household reference person, the + child's unit records the household's property and the parents record none. + policyengine-uk reads Pension Credit capital only for a unit with a member + at the qualifying age, so no Pension Credit assessment counts it. Parents + on the sentinel take the engine's gross proxy instead, beside the child's + recorded figure (microcosm#1095).""" + + unavailable = [-1.0, -1.0, -1.0] + tables = { + entity: _capital_frame(unavailable, unavailable, child_heads=True).table(entity) + for entity in ("person", "benunit", "household") + } + uc_share = uc_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + pension_credit_share = pension_credit_property_capital_share( + tables["person"], tables["benunit"], tables["household"] + ) + np.testing.assert_allclose(uc_share, [0.0, 90_000.0, 45_000.0], rtol=1e-12) + np.testing.assert_allclose( + pension_credit_share, [0.0, 117_000.0, 49_500.0], rtol=1e-12 + ) + + uc_recorded, pension_credit_recorded = _assessable_capital( + _capital_frame( + recorded_capital_with_property(np.zeros(3), uc_share), + recorded_capital_with_property(np.zeros(3), pension_credit_share), + child_heads=True, + ) + ) + np.testing.assert_allclose(uc_recorded, uc_share, rtol=1e-6) + np.testing.assert_array_equal(pension_credit_recorded, [0.0, 0.0, 0.0]) + + # The parents on the sentinel: Pension Credit assesses them the gross + # 130,000 (property and land), which the child's unit also records at 90%. + uc_mixed, pension_credit_mixed = _assessable_capital( + _capital_frame( + [-1.0, uc_share[1], uc_share[2]], + [-1.0, pension_credit_share[1], pension_credit_share[2]], + child_heads=True, + ) + ) + np.testing.assert_allclose(uc_mixed, [10_000.0, 90_000.0, 45_000.0], rtol=1e-6) + np.testing.assert_allclose(pension_credit_mixed, [130_000.0, 0.0, 0.0], rtol=1e-6) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_uc_reporter_redraw.py b/packages/microcosm-build/tests/engine/uk/test_uk_uc_reporter_redraw.py index e920a569b8..9c875dbd8e 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_uc_reporter_redraw.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_uc_reporter_redraw.py @@ -4,50 +4,58 @@ from test_support.microcosm_build.uk_uc_reporter_redraw import * -def test_live_engine_screen_reads_frs_capital() -> None: +def test_live_engine_screen_reads_the_recorded_capital_with_property() -> None: + """The screen's capital is the FRS carrier plus the property the unit + holds, as uc_capital_coherence records it: unit 103 holds no financial + capital but its household's other property is over the UC limit + (microcosm#1095).""" + person = pd.DataFrame( { - "person_id": [1001, 1002], - "person_benunit_id": [101, 102], - "person_household_id": [1, 2], - "age": [40, 40], - "is_benunit_head": [True, True], - UC_REPORTER_REDRAW_OUTPUT: [0.0, 0.0], - "employment_income": [0.0, 0.0], - "self_employment_income": [0.0, 0.0], - "savings_interest_income": [0.0, 0.0], - "dividend_income": [0.0, 0.0], - "property_income": [0.0, 0.0], - "other_investment_income": [0.0, 0.0], + "person_id": [1001, 1002, 1003], + "person_benunit_id": [101, 102, 103], + "person_household_id": [1, 2, 3], + "age": [40, 40, 40], + "is_benunit_head": [True, True, True], + "is_household_head": [True, True, True], + "is_uc_claimant": [True, True, True], + UC_REPORTER_REDRAW_OUTPUT: [0.0, 0.0, 0.0], + "employment_income": [0.0, 0.0, 0.0], + "self_employment_income": [0.0, 0.0, 0.0], + "savings_interest_income": [0.0, 0.0, 0.0], + "dividend_income": [0.0, 0.0, 0.0], + "property_income": [0.0, 0.0, 0.0], + "other_investment_income": [0.0, 0.0, 0.0], } ) benunit = pd.DataFrame( { - "benunit_id": [101, 102], - "frs_benunit_capital": [0.0, 16_001.0], - "is_married": [False, False], - support_channel_column("benunit"): ["frs", "frs"], - support_clone_index_column("benunit"): [0, 0], + "benunit_id": [101, 102, 103], + "frs_benunit_capital": [0.0, 16_001.0, 0.0], + "is_married": [False, False, False], + support_channel_column("benunit"): ["frs", "frs", "frs"], + support_clone_index_column("benunit"): [0, 0, 0], } ) household = pd.DataFrame( { - "household_id": [1, 2], - "region": ["LONDON", "LONDON"], - "council_tax": [0.0, 0.0], - "tenure_type": ["OWNED_OUTRIGHT", "OWNED_OUTRIGHT"], - "rent": [0.0, 0.0], - "savings": [100_000.0, 100_000.0], - "other_residential_property_value": [0.0, 0.0], - "non_residential_property_value": [0.0, 0.0], - "corporate_wealth": [0.0, 0.0], + "household_id": [1, 2, 3], + "region": ["LONDON", "LONDON", "LONDON"], + "council_tax": [0.0, 0.0, 0.0], + "tenure_type": ["OWNED_OUTRIGHT", "OWNED_OUTRIGHT", "OWNED_OUTRIGHT"], + "rent": [0.0, 0.0, 0.0], + "savings": [100_000.0, 100_000.0, 0.0], + # At 90% of its value, GBP 18,000 of property is GBP 16,200. + "other_residential_property_value": [0.0, 0.0, 18_000.0], + "non_residential_property_value": [0.0, 0.0, 0.0], + "corporate_wealth": [0.0, 0.0, 0.0], } ) frame = uk_national_frame( person=person, benunit=benunit, household=household, - household_weights=np.ones(2), + household_weights=np.ones(3), weight_kind=WeightKind.DESIGN, time_period="2024", ) @@ -61,7 +69,7 @@ def test_live_engine_screen_reads_frs_capital() -> None: screen = _positive_pre_takeup_award_screen( materialized["uc_maximum_amount"], materialized["uc_income_reduction"], - expected=2, + expected=3, ) - np.testing.assert_array_equal(screen, [True, False]) + np.testing.assert_array_equal(screen, [True, False, False]) diff --git a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py index 4b3301d951..348bcb2a9c 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py @@ -1147,7 +1147,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: references = {reference.name: reference for reference in spec.target_references} assert ( - len(references) == 1281 + len(references) == 1279 ) # microcosm#1106: + 13 SPI property amounts; microcosm#1113: + 1 ONS 07.2.2 household road fuel; PolicyEngine/chronicle#280 lane: 705 + 33 HMRC liabilities rows (Table 2.5, three # measures by eleven bands) + 26 SPI savings-interest rows (two measures by # thirteen bands) + 360 SPI Table 3.11 region-tier rows (three measures by @@ -1166,7 +1166,10 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None: # total (the Table 3.8 rows and the DWP employee total left the fit); two # Attendance Allowance and three pension-age Housing Benefit rows; the # income-tax relief total on salary sacrifice in place of the amount row; - # microcosm#1095: Great Britain pension-age Housing Benefit spending; + # microcosm#1095: Great Britain pension-age Housing Benefit spending, + # less OBR's NICs total and the all-rates salary-sacrifice relief, now + # diagnostics beside the bound rows they repeat; the two + # England-and-Wales PIP daily-living caseload rows are diagnostics too; # microcosm#1123: the 12 nation household control cells and the 24 # ITL Table 2.2 marginal-rate cells; # microcosm#1106: the 13 SPI Table 3.7 net property-income amounts by diff --git a/packages/microcosm-build/tests/engine_free/shared/test_target_materialization.py b/packages/microcosm-build/tests/engine_free/shared/test_target_materialization.py index 2bb3d6d8aa..f081fbda55 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_target_materialization.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_target_materialization.py @@ -1401,3 +1401,151 @@ class NoRouteProvider(StubMeasureProvider): NoRouteProvider(), period=2025, ) + + +def _whole_pence_award_bands(binding_extra=None, *, top_band=True): + labels = { + "award_low": "£0.01 to £100.00", + "award_mid": "£100.01 to £200.00", + } + if top_band: + labels["award_top"] = "£200.01 or over" + registry = TargetRegistry( + [ + TargetSpec( + name=name, + entity="person", + measure=name, + value=1.0, + source="test", + family="dwp_universal_credit", + metadata={ + "contract_target_id": "uc.award_bands", + "ledger_filter_monthly_award_amount_bands": label, + }, + ) + for name, label in labels.items() + ], + country="uk", + ) + contract = { + "uc.award_bands": { + "bindings": { + "policyengine": { + "value_variable": "person_count", + "groupby_variable": "income", + "from_entity": "person", + "band_period_factor": 12, + **(binding_extra or {}), + } + } + } + } + adapter = StubAdapter() + # Annual awards: none; a penny a month, exact and with float noise; + # GBP 100.00 a month; a sub-penny GBP 100.005 a month; GBP 100.01; + # GBP 200.00; GBP 200.01; a large award. + adapter.tables["person"] = { + "income": np.array( + [ + 0.0, + 0.12, + 0.12 - 1e-9, + 1_200.0, + 1_200.06, + 1_200.12, + 2_400.0, + 2_400.12, + 1e6, + ] + ) + } + return adapter, registry, contract + + +def test_whole_pence_bands_close_at_the_next_band_less_a_penny(): + """microcosm#1095: DWP's award bands read as (lower - £0.01, next lower - + £0.01] in whole pence, so a penny a month lands in the bottom band.""" + + adapter, registry, contract = _whole_pence_award_bands( + {"band_semantics": "whole_pence_upper_closed"} + ) + + result = materialize_target_bindings(adapter, registry, contract, period=2025) + + assert result.skipped == () + person = adapter.tables["person"] + assert list(person["award_low"]) == [0, 1, 1, 1, 0, 0, 0, 0, 0] + assert list(person["award_mid"]) == [0, 0, 0, 0, 1, 1, 1, 0, 0] + assert list(person["award_top"]) == [0, 0, 0, 0, 0, 0, 0, 1, 1] + + +def test_undeclared_bands_keep_their_half_open_reading(): + """Without band_semantics the same bands stay half-open [lower, upper), + the reading every other banded binding keeps. It is float-fragile on + pence edges: the penny-a-month award with float noise falls below the + bottom band, and GBP 100.01 a month (1,200.12 a year) stays in the bottom + band because 100.01 x 12 rounds just above 1,200.12.""" + + adapter, registry, contract = _whole_pence_award_bands() + + result = materialize_target_bindings(adapter, registry, contract, period=2025) + + assert result.skipped == () + person = adapter.tables["person"] + assert list(person["award_low"]) == [0, 1, 0, 1, 1, 1, 0, 0, 0] + assert list(person["award_mid"]) == [0, 0, 0, 0, 0, 0, 1, 0, 0] + assert list(person["award_top"]) == [0, 0, 0, 0, 0, 0, 0, 1, 1] + + +@pytest.mark.parametrize( + "extra, message", + [ + ({"band_semantics": "whole_pounds"}, "unknown band_semantics"), + ( + { + "band_semantics": "whole_pence_upper_closed", + "band_upper_bound": 200, + "band_upper_bound_inclusive": True, + }, + "declare no band_upper_bound_inclusive", + ), + ], +) +def test_whole_pence_bands_refuse_undeclared_readings(extra, message): + adapter, registry, contract = _whole_pence_award_bands(extra) + + result = materialize_target_bindings(adapter, registry, contract, period=2025) + + assert len(result.skipped) == 3 + assert all(message in skip.reason for skip in result.skipped) + + +def test_whole_pence_bands_close_the_last_bound_band_at_its_declared_top(): + """Where the top-coded category is not bound, band_upper_bound closes the + last bound band, so it does not absorb the awards above it.""" + + semantics = {"band_semantics": "whole_pence_upper_closed"} + adapter, registry, contract = _whole_pence_award_bands( + {**semantics, "band_upper_bound": 200}, top_band=False + ) + + result = materialize_target_bindings(adapter, registry, contract, period=2025) + + assert result.skipped == () + person = adapter.tables["person"] + assert list(person["award_low"]) == [0, 1, 1, 1, 0, 0, 0, 0, 0] + # GBP 200.00 a month is the last award in the band. + assert list(person["award_mid"]) == [0, 0, 0, 0, 1, 1, 1, 0, 0] + + # Without the bound, the last band runs on and takes the top awards. + adapter, registry, contract = _whole_pence_award_bands(semantics, top_band=False) + materialize_target_bindings(adapter, registry, contract, period=2025) + assert list(adapter.tables["person"]["award_mid"]) == [0, 0, 0, 0, 1, 1, 1, 1, 1] + + # A bound at or below a band's lower edge is refused. + adapter, registry, contract = _whole_pence_award_bands( + {**semantics, "band_upper_bound": 100}, top_band=False + ) + result = materialize_target_bindings(adapter, registry, contract, period=2025) + assert [skip.reason for skip in result.skipped if "band_upper_bound" in skip.reason] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py index 357f15355f..d494e6f931 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py @@ -104,6 +104,16 @@ def _tables(*, n: int = 4, weights=None): if weights is None: weights = np.ones(n, dtype=float) household_ids = np.arange(1, n + 1, dtype=np.int64) + # One person per benefit unit and household, so each person is the + # household reference person and the unit holds the household's property + # at 90% of its value: other property for UC, and that with land for + # Pension Credit (microcosm#1095). The units that report Universal Credit + # hold none here. + claimant = np.arange(n) % 3 != 2 + other_property = 1_000.0 * (np.arange(n) % 2) + non_residential = 500.0 * (np.arange(n) % 2) + land = 250.0 * (np.arange(n) % 2) + carrier = np.arange(n, dtype=float) person = pd.DataFrame( { "person_id": np.arange(101, 101 + n, dtype=np.int64), @@ -114,14 +124,19 @@ def _tables(*, n: int = 4, weights=None): # Distinct sub-exempt gains: not degenerate, and none crosses the # frozen 3,000 under the fake projection's pinned growth path. "capital_gains": np.linspace(500.0, 2_000.0, n), + "age": 30 + 10 * np.arange(n), + "is_uc_claimant": claimant, + "is_household_head": True, } ) benunit = pd.DataFrame( { "benunit_id": np.arange(201, 201 + n, dtype=np.int64), "would_claim_uc": np.asarray([True, False] * n)[:n], - "frs_benunit_capital": np.arange(n, dtype=float), - "uc_reported_capital": np.arange(n, dtype=float), + "frs_benunit_capital": carrier, + "uc_reported_capital": carrier + 0.9 * (other_property + non_residential), + "pension_credit_reported_capital": carrier + + 0.9 * (land + other_property + non_residential), } ) household = pd.DataFrame( @@ -130,6 +145,9 @@ def _tables(*, n: int = 4, weights=None): "household_weight": np.asarray(weights, dtype=float), "household_is_spi_synthetic": np.arange(n) % 2 == 1, "household_is_capital_gains_clone": np.arange(n) % 4 >= 2, + "owned_land": land, + "other_residential_property_value": other_property, + "non_residential_property_value": non_residential, } ) return person, benunit, household @@ -250,6 +268,19 @@ def _run_battery( extra_artifacts=None, ): person, benunit, household = tables + # A dependant in the first household, so is_household_head varies as it + # does in a real frame: the degenerate-surface gate refuses a constant + # column. The dependant adds no income, receipt or taxable gain. + dependant = person.iloc[[0]].assign( + person_id=int(person["person_id"].max()) + 1, + employment_income=0.0, + universal_credit_reported=0.0, + capital_gains=250.0, + age=5, + is_uc_claimant=False, + is_household_head=False, + ) + person = pd.concat([person, dependant], ignore_index=True) frame = uk_national_frame( person=person, benunit=benunit, household=household, time_period="2023" ) @@ -447,6 +478,133 @@ def test_uc_column_implication_binding_refuses_near_sentinel_values( assert failing.details["sentinel_mismatch_count"] == 0 assert failing.details["same_source_mismatch_count"] == 0 + @staticmethod + def _capital_gate(): + return next( + gate + for gate in load_country_spec("uk").gates.gates + if gate.id == "uk_uc_capital_coherence" + ) + + def test_uc_column_implication_binding_checks_both_capitals_with_property( + self, + ) -> None: + # microcosm#1095: each recorded capital is the carrier plus the + # household's property, at 90% of its value, for the household + # reference person's unit: other property for UC, and that with land + # for Pension Credit. + person, benunit, household = _tables() + # Unit 0 reports no Universal Credit here, so it records the property. + person.loc[0, "universal_credit_reported"] = 0.0 + household.loc[0, "other_residential_property_value"] = 40_000.0 + household.loc[0, "owned_land"] = 10_000.0 + benunit.loc[0, "uc_reported_capital"] = 36_000.0 + benunit.loc[0, "pension_credit_reported_capital"] = 45_000.0 + frame = uk_national_frame( + person=person, benunit=benunit, household=household, time_period="2023" + ) + entry = self._capital_gate() + evaluate = UK_GATE_REGISTRY["column_implication"].evaluate + context = EvidenceContext(frame=frame) + + passing = evaluate(context, entry.parameters) + assert passing.passed, passing.failures + for rule in passing.details["property_shares"].values(): + assert rule["owners"] == "household_reference_person_unit" + assert rule["valuation"] == 0.9 + + # The carrier alone, without the property, fails on both columns. + frame.table("benunit").loc[0, "uc_reported_capital"] = 0.0 + frame.table("benunit").loc[0, "pension_credit_reported_capital"] = 0.0 + failing = evaluate(context, entry.parameters) + assert not failing.passed + share_failures = [ + failure + for failure in failing.failures + if "plus the unit's declared property share" in failure + ] + assert len(share_failures) == 2 + columns = failing.details["capital_columns"] + for column in ("uc_reported_capital", "pension_credit_reported_capital"): + assert columns[f"benunit.{column}"]["same_source_mismatch_count"] == 1 + + def test_uc_column_implication_binding_keeps_a_reporters_receipt( + self, + ) -> None: + # microcosm#1095: a unit that reports Universal Credit records the + # carrier alone, whatever property its household holds. + person, benunit, household = _tables() + assert person.loc[0, "universal_credit_reported"] > 0 + household.loc[0, "other_residential_property_value"] = 40_000.0 + benunit.loc[0, "pension_credit_reported_capital"] = 36_000.0 + frame = uk_national_frame( + person=person, benunit=benunit, household=household, time_period="2023" + ) + entry = self._capital_gate() + evaluate = UK_GATE_REGISTRY["column_implication"].evaluate + context = EvidenceContext(frame=frame) + + passing = evaluate(context, entry.parameters) + assert passing.passed, passing.failures + + # The property it would hold is refused on a reporting unit. + frame.table("benunit").loc[0, "uc_reported_capital"] = 36_000.0 + failing = evaluate(context, entry.parameters) + assert not failing.passed + columns = failing.details["capital_columns"] + assert columns["benunit.uc_reported_capital"]["same_source_mismatch_count"] == 1 + assert ( + columns["benunit.pension_credit_reported_capital"][ + "same_source_mismatch_count" + ] + == 0 + ) + + def test_uc_column_implication_binding_tolerates_a_float32_round_trip( + self, + ) -> None: + person, benunit, household = _tables() + household.loc[1, "other_residential_property_value"] = 654_321.09 + household.loc[1, "non_residential_property_value"] = 0.0 + benunit.loc[1, "frs_benunit_capital"] = 123_456.78 + benunit.loc[1, "pension_credit_reported_capital"] = 123_456.78 + 0.9 * ( + household.loc[1, "owned_land"] + 654_321.09 + ) + expected = 123_456.78 + 0.9 * 654_321.09 + benunit.loc[1, "uc_reported_capital"] = float(np.float32(expected)) + frame = uk_national_frame( + person=person, benunit=benunit, household=household, time_period="2023" + ) + entry = self._capital_gate() + evaluate = UK_GATE_REGISTRY["column_implication"].evaluate + context = EvidenceContext(frame=frame) + + assert evaluate(context, entry.parameters).passed + frame.table("benunit").loc[1, "uc_reported_capital"] = expected + 1.0 + assert not evaluate(context, entry.parameters).passed + + def test_uc_column_implication_binding_refuses_a_drifted_property_rule( + self, + ) -> None: + person, benunit, household = _tables() + frame = uk_national_frame( + person=person, benunit=benunit, household=household, time_period="2023" + ) + entry = self._capital_gate() + parameters = { + **dict(entry.parameters), + "property_shares": { + "uc_reported_capital": { + "sources": ["owned_land"], + "owners": "is_uc_claimant", + } + }, + } + with pytest.raises(ValueError, match="not the recorded-capital rules"): + UK_GATE_REGISTRY["column_implication"].evaluate( + EvidenceContext(frame=frame), parameters + ) + def test_nonnegative_binding_requires_scheduled_stage_columns(self) -> None: # frs_employment declares sic_industry_division nonnegative; a build # that scheduled the stage but lost the column must fail — the @@ -663,7 +821,9 @@ def test_support_binding_passes_in_range_was_outputs(self) -> None: ) assert result.passed is True - assert result.details["columns_checked"] == 3 + # The fixture's other residential and non-residential property are + # WAS outputs too. + assert result.details["columns_checked"] == 5 def test_support_binding_fails_out_of_range_was_outputs(self) -> None: person, benunit, household = _tables(n=1) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py index 57acf809cf..451ba0ccec 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_benefit_target_scope.py @@ -10,10 +10,14 @@ from __future__ import annotations import json +import os from pathlib import Path +import pytest + import microcosm.build from microcosm.build.uk_runtime.local_targets import load_uk_population_contract +from test_support.paths import paths_for GB_REGIONS = { "NORTH_EAST", @@ -50,6 +54,12 @@ "obr.pip", "obr.winter_fuel_allowance", ) +# DWP's PIP daily-living caseload (FOI2025/24990), which Chronicle stamps +# K04000001: diagnostics on obr.pip, not targets (microcosm#1095). +PIP_CASELOAD_ROWS = ( + "dwp.pip.daily_living_standard_claimants", + "dwp.pip.daily_living_enhanced_claimants", +) # OBR table 4.9 concepts reported inside "DWP social security". DWP_SOCIAL_SECURITY_CONCEPTS = { "obr.attendance_allowance", @@ -186,3 +196,66 @@ def test_obr_pip_binds_dla_and_pip_together() -> None: binding = _targets()["obr.pip"]["bindings"]["policyengine"] assert binding["value_expression"] == "pip + dla" assert "value_variable" not in binding + + +def test_pip_caseload_rows_are_england_and_wales_diagnostics_on_obr_pip() -> None: + """The two rows resolve at K04000001, the geography Chronicle stamps DWP's + caseload with, and fit; fitting them breaks the weight-ratio fence, so + they sit beside obr.pip as diagnostics (María's ruling of 2026-10-10, + microcosm#1095).""" + + from microcosm.build.uk_runtime.ledger_targets import ( + UK_REQUIRED_TARGET_DIAGNOSTICS, + ) + + contract = load_uk_population_contract() + target_ids = {target["target_id"] for target in contract["targets"]} + assert not target_ids.intersection(PIP_CASELOAD_ROWS) + assert UK_REQUIRED_TARGET_DIAGNOSTICS["obr.pip"] == PIP_CASELOAD_ROWS + parity = contract["registry_parity"] + for target_id in PIP_CASELOAD_ROWS: + assert target_id not in parity["scope_target_ids"] + declaration = contract["diagnostic_references"][target_id] + assert declaration["attach_to_target"] == "obr.pip" + assert declaration["required_period_type"] == "month" + assert declaration["required_assertion"] == "observation" + reference = declaration["reference"] + assert reference["period"] == "2025-01" + assert reference["period_match_policy"] == "exact" + assert reference["ledger_selector"]["geography_id"] == "K04000001" + assert reference["ledger_fact_key"] + assert reference["measure"] in parity["excluded"] + assert reference["measure"] not in parity["mapped"] + + +def test_pip_caseload_diagnostics_carry_the_january_2025_caseload() -> None: + """Feed-gated: each diagnostic resolves its exact January 2025 fact.""" + + from microcosm.build.uk_runtime.ledger_targets import ( + _attached_diagnostic_metadata, + ) + + root = paths_for("microcosm-build").repository + configured = os.environ.get("CHRONICLE_UK_FACTS") + feed = ( + Path(configured) if configured else root / ".codex-work/consumer_facts_uk.jsonl" + ) + if feed.is_dir(): + feed = feed / "consumer_facts.jsonl" + if not feed.is_file(): + pytest.skip("pinned UK Chronicle consumer feed is not present") + with feed.open(encoding="utf-8") as handle: + facts = tuple( + json.loads(line) + for line in handle + if '"dwp.pip_daily_living_claimants"' in line + ) + + metadata = _attached_diagnostic_metadata(facts, "obr.pip") + + for rate, value in (("standard", 1_283_000.0), ("enhanced", 1_608_000.0)): + prefix = f"dwp_pip_daily_living_{rate}_diagnostic" + assert metadata[f"{prefix}_role"] == "diagnostic_only_not_in_fit" + assert metadata[f"{prefix}_status"] == "available" + assert float(metadata[f"{prefix}_value_count"]) == value + assert metadata[f"{prefix}_period"] == "2025-01" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_asset_type.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_asset_type.py index 4e1709609e..7ce8f6a20e 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_asset_type.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_cgt_asset_type.py @@ -38,6 +38,7 @@ HMRCCGTBADRBand, HMRCCGTTable7Type, UKCGTBADRParameters, + _stock_signals, assign_uk_cgt_asset_types, cgt_asset_type_operation_parameters, claimant_status_share_targets, @@ -177,7 +178,7 @@ def _frame(gains, *, weights=None, time_period: str = "2024", stocks=None) -> Fr "age": np.full(rows, 45, dtype="int64"), } ) - for column in UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS: + for column in (*UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS, *CGT_STOCK_PERSON_COLUMNS): person[column] = 0.0 household = pd.DataFrame( { @@ -710,6 +711,21 @@ def test_the_open_top_band_leans_without_a_slope(self) -> None: business = person["corporate_wealth"].to_numpy(dtype=float) > 0 assert business[at_limit].mean() > 0.65 + def test_a_loss_making_landlord_shows_the_residential_stock(self) -> None: + # property_income counts a letting loss as zero (#1081); the landlord + # carrier keeps the person in the residential signal. + frame = _frame( + np.full(3, 5_000.0), + stocks={ + "property_income": np.array([1.0, 0.0, 0.0]), + "reports_rent_from_other_property": np.array([1.0, 1.0, 0.0]), + }, + ) + + signals = _stock_signals(frame.table("person"), frame.table("household")) + + assert signals["residential"].tolist() == [True, True, False] + def test_refuses_a_frame_without_a_stock_column(self) -> None: gains = _synthetic_gains(3_000) frame = _frame(gains) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_relationships.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_relationships.py index a76b3300e9..373b7a9a72 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_relationships.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_relationships.py @@ -46,6 +46,7 @@ def _person( hrp: bool = False, fte: bool | None = None, rel: dict[int, int] | None = None, + benunit: int = 1, ) -> dict: return { "number": number, @@ -54,6 +55,7 @@ def _person( "hrp": hrp, "fte": fte, "rel": dict(rel or {}), + "benunit": benunit, } @@ -92,7 +94,7 @@ def _tables(*households: list[dict]): person_rows.append( { "person_id": sernum * 1000 + p["number"], - "person_benunit_id": sernum * 100 + 1, + "person_benunit_id": sernum * 100 + p["benunit"], "person_household_id": sernum, "age": p["age"], "is_household_head": bool(p["hrp"]), @@ -395,6 +397,54 @@ def test_civil_partners_and_step_children_form_one_family() -> None: ] +def _looked_after(result) -> list[bool]: + return result.person_values["is_looked_after_by_local_authority"].tolist() + + +def test_a_foster_child_under_16_in_the_carers_unit_is_looked_after() -> None: + result = _derive( + [ + _person(1, age=45, hrp=True, rel={2: FOSTER_PARENT, 3: PARENT}), + _person(2, age=8, table="child", rel={1: FOSTER_CHILD}), + _person(3, age=10, table="child", rel={1: CHILD}), + ] + ) + + assert _looked_after(result) == [False, True, False] + assert result.evidence["looked_after_children"] == 1 + + +def test_either_side_of_the_foster_link_marks_the_child() -> None: + # The carer's foster parent code marks the child even where the child's + # own grid entry reads non-relative; foster codes never form a family. + result = _derive( + [ + _person(1, age=45, hrp=True, rel={2: FOSTER_PARENT}), + _person(2, age=8, table="child", rel={1: NON_RELATIVE}), + ] + ) + + assert _looked_after(result) == [False, True] + assert _roles(result)[1] == "INDIVIDUAL" + + +def test_older_foster_children_and_other_units_are_not_marked() -> None: + result = _derive( + [ + _person(1, age=45, hrp=True, rel={2: FOSTER_PARENT, 3: FOSTER_PARENT}), + # Sixteen or over: the FRS gives a foster child with a local + # authority allowance their own benefit unit, so one in the + # carer's unit has no such allowance. + _person(2, age=16, table="child", rel={1: FOSTER_CHILD}), + # A foster link to an adult outside the child's own benefit unit. + _person(3, age=8, table="child", rel={1: FOSTER_CHILD}, benunit=2), + ] + ) + + assert _looked_after(result) == [False, False, False] + assert result.evidence["looked_after_children"] == 0 + + def test_weighted_cells_and_domains_in_evidence() -> None: person, household, adult, child, raw_household = _tables( [_person(1, age=30, hrp=True)], diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py index b7669e8151..75618b0dc9 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py @@ -49,6 +49,7 @@ completed_months, parse_frs_uc_claim_start, scottish_water_and_sewerage_weekly, + uc_gainful_self_employment, uc_trade_years, uk_frs_spine_seed_frame, ) @@ -725,9 +726,12 @@ def source_stage( {"kind": "aggregate_person_to_benunit"}, { "kind": "aggregate_person_to_benunit", - "method": "any_adult_under_state_pension_age", + "method": ( + "any_uc_claimant_aged_18_under_pension_credit_age" + ), "consumed_only": True, "aggregates": {"uc_age_eligible": "age"}, + "role_column": "is_uc_claimant", }, { "kind": "assign_binary_with_anchored_residual", @@ -1214,6 +1218,16 @@ def amount() -> float: built = person.set_index("person_id")["property_income"] np.testing.assert_allclose(built.reindex(expected.index), expected) assert (built.drop(expected.index) == 0).all() + # The landlord carrier holds wherever ROYYR1 is positive, a loss included, + # though property_income counts that loss as zero. + landlord = pd.Series( + (raw.ROYYR1 > 0).to_numpy(), + index=(raw.SERNUM * 1000 + raw.PERSON).astype(int), + ) + carrier = person.set_index("person_id")["reports_rent_from_other_property"] + assert carrier.dtype == bool + assert carrier.reindex(landlord.index).tolist() == landlord.tolist() + assert not carrier.drop(landlord.index).any() @pytest.mark.parametrize("code", [-1.0, -9.0]) @@ -3382,6 +3396,72 @@ def test_without_the_raw_date_columns_the_spine_refuses( build_uk_frs_spine_frame(tmp_path, stage=stage) +def _gainful_flags(tmp_path: Path, tables) -> dict[int, bool]: + stage = _write_fixture(tmp_path, tables) + person = build_uk_frs_spine_frame(tmp_path, stage=stage).table("person") + assert person["uc_is_in_gainful_self_employment"].dtype == bool + return person.set_index("person_id")["uc_is_in_gainful_self_employment"].to_dict() + + +class TestUCGainfulSelfEmployment: + """UC gainful self-employment from the main job and earnings (uk-data#525).""" + + @staticmethod + def _oracle(main_job: bool, profit: float, pay: float) -> bool: + # Written out per person, independently of the vectorised rule. + if main_job: + return True + return profit > 0 and profit > pay + + @pytest.mark.parametrize("main_job", [True, False]) + @pytest.mark.parametrize( + ("profit", "pay"), + [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0), (2.0, 1.0), (1.0, 2.0)], + ) + def test_the_rule_matches_its_truth_table( + self, main_job: bool, profit: float, pay: float + ) -> None: + flag = uc_gainful_self_employment([main_job], [profit], [pay]) + + assert flag.tolist() == [self._oracle(main_job, profit, pay)] + + def test_a_side_trade_counts_only_when_it_out_earns_pay(self) -> None: + # ADM H4034 example 1: fewer self-employed hours but more profit. + # ADM H4035 example 2: the job earns more. A self-employed main job + # is gainful at a loss, which reaches this build floored at zero. + flags = uc_gainful_self_employment( + [False, False, False, True, True], + [140 * 52, 40 * 52, 10_000.0, 0.0, 0.0], + [80 * 52, 49.6 * 52, 10_000.0, 0.0, 50_000.0], + ) + + assert flags.tolist() == [True, False, False, True, True] + + def test_the_spine_sets_it_from_the_main_job_and_a_side_trade( + self, tmp_path: Path + ) -> None: + tables = _fixture_tables() + # Both fixture adults are neither employed nor self-employed (EMPSTATI + # 5), with SEINCAM2 3 below INEARNS 10. + assert _gainful_flags(tmp_path / "base", tables) == { + 1001: False, + 1002: False, + 2001: False, + } + adult_1 = next(row for row in tables["adult"] if row["SERNUM"] == 1) + adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2) + # Household 1: an employee whose side trade earns more than the job. + adult_1.update({"EMPSTATI": 1, "SEINCAM2": 20.0}) + # Household 2: a self-employed main job at no profit. + adult_2.update({"EMPSTATI": 3, "SEINCAM2": 0.0}) + + assert _gainful_flags(tmp_path / "changed", tables) == { + 1001: True, + 1002: False, + 2001: True, + } + + def test_in_kind_benefits_map_from_the_raw_person_tapes(tmp_path: Path) -> None: """The four in-kind benefit columns, ported at #686. diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py index 854cc74a95..be1fb4c8fa 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py @@ -134,14 +134,15 @@ def test_brma_missing_cell_fails_closed() -> None: 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.""" + """A unit with no UC claimant or partner of working age is never drawn.""" 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. + # The age bounds: 17 is out, 18 in, and 66, Pension Credit qualifying age + # for the 2025 cohort, out. assert _POLICY.working_age(np.array([17, 18, 65, 66])).tolist() == [ False, True, @@ -166,8 +167,39 @@ def test_uc_take_up_population_excludes_units_without_a_working_age_adult() -> N derive_frs_take_up( benunit, anchors=anchors, contract=_Contract(), uc_age_eligible=eligible[:2] ) - with pytest.raises(KeyError, match="person.age is missing"): + with pytest.raises(KeyError, match=r"\['age'\] are missing"): uc_age_eligible_benunits(person.drop(columns=["age"]), benunit, _POLICY) + with pytest.raises(KeyError, match=r"\['is_uc_claimant'\] are missing"): + uc_age_eligible_benunits( + person.drop(columns=["is_uc_claimant"]), benunit, _POLICY + ) + + +def test_uc_take_up_population_needs_a_claimant_or_partner_of_working_age() -> None: + """A working-age dependant does not bring a unit into the UC population. + + policyengine-uk 2.123.0's is_uc_eligible reads the age of the unit's UC + claimant or partner (is_uc_claimant), not of any adult: a pensioner unit + with an 18-year-old dependant cannot claim Universal Credit. + """ + + frame = _frame() + person, benunit = frame.table("person").copy(), frame.table("benunit") + # Unit 30 (a 70-year-old) gains an 18-year-old dependant; unit 20's + # 40-year-old stops being a claimant or partner. + dependant = person.iloc[[3]].assign(person_id=302, age=18, is_uc_claimant=False) + person = pd.concat([person, dependant], ignore_index=True) + person.loc[person["person_id"] == 201, "is_uc_claimant"] = False + + eligible = uc_age_eligible_benunits(person, benunit, _POLICY) + + assert eligible.tolist() == [False, False, False] + person.loc[person["person_id"] == 302, "is_uc_claimant"] = True + assert uc_age_eligible_benunits(person, benunit, _POLICY).tolist() == [ + False, + False, + True, + ] def test_take_up_stage_refuses_a_manifest_that_drops_the_population_declaration() -> ( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_uprating.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_uprating.py index 25957a2311..18b4f44f7a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_uprating.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_hmrc_uprating.py @@ -14,10 +14,12 @@ def test_every_declared_engine_index_and_the_count_index_have_an_applier() -> No "hmrc.itl_2026.taxpayer_count_growth_all_bands", "hmrc.itl_2026.total_income_growth_all_bands", UK_SPI_STATE_PENSION_AMOUNT_INDEX_CONCEPT, + UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, } assert set(appliers) <= set(UK_UPRATING_APPLIERS) assert len(UK_ENGINE_INDEX_PARAMETERS) == 7 + def test_engine_index_moves_the_amount_by_the_parameter_ratio_and_receipts_it() -> None: registry = TargetRegistry( ( @@ -48,6 +50,7 @@ def test_engine_index_moves_the_amount_by_the_parameter_ratio_and_receipts_it() assert spec.metadata["uprating_index_engine"].startswith("policyengine-uk ") assert "2026-09-22" in spec.metadata["uprating_adjudication"] + def test_engine_index_applier_passes_through_a_reference_declaring_another_index() -> ( None ): @@ -66,6 +69,7 @@ def test_engine_index_applier_passes_through_a_reference_declaring_another_index is registry ) + def test_a_fact_opening_in_the_calibration_year_binds_as_published_with_a_receipt() -> ( None ): @@ -89,6 +93,7 @@ def test_a_fact_opening_in_the_calibration_year_binds_as_published_with_a_receip ).specs assert count.value == 7.0 and count.metadata["uprating_factor"] == "1" + def test_a_fact_opening_after_the_calibration_year_is_refused() -> None: registry = TargetRegistry( (_spec(name="x", value=1.0, lower_bound=20_000, period="2026"),), country="uk" @@ -102,6 +107,7 @@ def test_a_fact_opening_after_the_calibration_year_is_refused() -> None: parameter_value=_fake_parameter, ) + def test_count_growth_uses_the_containing_table_2_5_band_and_the_calendar_window() -> ( None ): @@ -141,6 +147,7 @@ def test_count_growth_uses_the_containing_table_2_5_band_and_the_calendar_window assert million.metadata["uprating_index_itl_bands"] == "1000000-2000000;2000000-inf" assert CALENDAR_YEAR_WINDOW_WEIGHTS == {-1: 0.25, 0: 0.75} + def test_count_growth_refuses_a_band_without_a_vendored_count() -> None: registry = TargetRegistry( (_spec(name="x", value=1.0, lower_bound=12_570),), country="uk" @@ -153,6 +160,7 @@ def test_count_growth_refuses_a_band_without_a_vendored_count() -> None: count_rows=_count_rows(), ) + def test_every_spi_band_maps_to_table_2_5_bands_that_tile_the_range() -> None: assert sorted(SPI_BAND_TO_ITL_BANDS) == [ 12_570, @@ -174,6 +182,7 @@ def test_every_spi_band_maps_to_table_2_5_bands_that_tile_the_range() -> None: for (_a_low, a_high), (b_low, _) in zip(bands, bands[1:], strict=False): assert a_high == b_low + def test_itl_bands_spanned_tiles_every_spi_band_and_the_regional_open_top_band() -> ( None ): @@ -205,6 +214,7 @@ def test_itl_bands_spanned_tiles_every_spi_band_and_the_regional_open_top_band() with pytest.raises(ValueError, match="no declared Table 2.5 band"): itl_bands_spanned(250_000, None) + def test_a_regional_open_top_band_sums_every_table_2_5_band_above_it() -> None: rows = _count_rows() for year, value in ((2023, 100.0), (2024, 110.0), (2025, 120.0)): @@ -232,6 +242,7 @@ def test_a_regional_open_top_band_sums_every_table_2_5_band_above_it() -> None: "200000-500000;500000-1000000;1000000-2000000;2000000-inf" ) + def test_state_pension_amount_moves_with_its_count_rows_recipients_and_the_rate() -> ( None ): @@ -307,6 +318,91 @@ def rate(path: str, instant: str) -> float: assert "uprating_rate_from_value" not in own_year.metadata +def _two_leg_parameter(path: str, instant: str) -> float: + return { + (UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, "2023-01-01"): 5.0, + (UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, "2024-01-01"): 6.0, + (UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, "2024-01-01"): 2.0, + (UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, "2025-01-01"): 2.1, + }[(path, instant)] + + +def _align_two_legs(registry, *, period: int = 2025, dataset_year: int = 2024): + return align_hmrc_row_by_history_then_projection( + _reference(UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, period=period), + registry, + index_concept=UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, + history_path=UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, + projection_path=UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, + dataset_year=dataset_year, + parameter_value=_two_leg_parameter, + ) + + +def test_savings_interest_moves_by_its_history_then_the_dataset_projection() -> None: + # A 2023 fact reaches the dataset's year (2024) by the history index and + # the calibration year by the projection's (microcosm#1095). + registry = TargetRegistry( + (_spec(name="x", value=100.0, lower_bound=20_000),), country="uk" + ) + + (spec,) = _align_two_legs(registry).specs + + assert spec.value == pytest.approx(100.0 * (6.0 / 5.0) * (2.1 / 2.0)) + assert spec.metadata["uprating_index"] == UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT + assert spec.metadata["uprating_index_from_instant"] == "2023-01-01" + assert spec.metadata["uprating_index_pivot_instant"] == "2024-01-01" + assert spec.metadata["uprating_index_to_instant"] == "2025-01-01" + assert float(spec.metadata["uprating_history_factor"]) == pytest.approx(1.2) + assert float(spec.metadata["uprating_projection_factor"]) == pytest.approx(1.05) + assert float(spec.metadata["uprating_factor"]) == pytest.approx(1.26) + assert spec.metadata["ledger_value_before_alignment"] == "100" + assert spec.metadata["uprating_history_parameter"] == ( + UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER + ) + assert spec.metadata["uprating_projection_parameter"] == ( + UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER + ) + + +def test_the_two_leg_index_keeps_to_one_leg_outside_the_datasets_year() -> None: + # A fact that opens in the dataset's year takes the projection leg alone. + registry = TargetRegistry( + (_spec(name="x", value=100.0, lower_bound=20_000, period="2024"),), + country="uk", + ) + (spec,) = _align_two_legs(registry).specs + assert spec.value == pytest.approx(105.0) + assert float(spec.metadata["uprating_history_factor"]) == 1.0 + + # A fact compiled at its own year binds as published. + registry = TargetRegistry( + (_spec(name="x", value=7.0, lower_bound=20_000),), country="uk" + ) + (spec,) = _align_two_legs(registry, period=2023).specs + assert spec.value == 7.0 + assert spec.metadata["uprating_factor"] == "1" + + # Another index passes through, and a fact after the target is refused. + assert ( + align_hmrc_row_by_history_then_projection( + _reference(EARNINGS_INDEX), + registry, + index_concept=UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, + history_path=UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, + projection_path=UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, + dataset_year=2024, + ) + is registry + ) + late = TargetRegistry( + (_spec(name="x", value=7.0, lower_bound=20_000, period="2026"),), + country="uk", + ) + with pytest.raises(ValueError, match="never moves a value backwards"): + _align_two_legs(late) + + def test_all_band_growth_moves_an_unbanded_area_row_by_the_all_ranges_window() -> None: """The SPI income-by-area cells carry no band: they move by HMRC's projected growth of all taxpayers (counts) and their total income diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py new file mode 100644 index 0000000000..7c44edb9e8 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_income_tax_rate_classes.py @@ -0,0 +1,196 @@ +"""Income tax by HMRC rate category (microcosm#1095, uk-data#533).""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import numpy as np +import pytest + +from microcosm.build.uk_runtime.income_tax_rate_classes import ( + UK_RATE_CLASS_ENGINE_FLOAT32_STEPS, + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP, + UK_RATE_CLASSES, + rate_class_engine_tolerance, + relief_by_rate_class, + tax_by_rate_class, +) +from test_support.paths import paths_for + +#: Rest-of-UK and Scottish schedules shaped like policyengine-uk's 2025 ones +#: (taxable-income thresholds, after the personal allowance). +REST_OF_UK = ([0.0, 37_700.0, 125_140.0], [0.20, 0.40, 0.45]) +SCOTLAND = ( + [0.0, 2_827.0, 14_921.0, 31_092.0, 62_430.0, 112_570.0], + [0.19, 0.20, 0.21, 0.42, 0.45, 0.48], +) + + +def _schedule_tax(income: np.ndarray, thresholds, rates) -> np.ndarray: + uppers = list(thresholds[1:]) + [np.inf] + return sum( + rate * np.clip(income - lower, 0.0, upper - lower) + for lower, upper, rate in zip(thresholds, uppers, rates, strict=True) + ) + + +@pytest.mark.parametrize("schedule", [REST_OF_UK, SCOTLAND], ids=["ruk", "scotland"]) +def test_rate_classes_add_up_to_the_schedule_and_rise_with_income(schedule) -> None: + rng = np.random.default_rng(0) + income = np.concatenate( + [rng.uniform(-1_000.0, 200_000.0, 5_000), np.asarray(schedule[0][1:])] + ) + + classes = tax_by_rate_class(income, *schedule) + more = tax_by_rate_class( + income + rng.uniform(0.0, 20_000.0, income.size), *schedule + ) + + assert set(classes) == set(UK_RATE_CLASSES) + np.testing.assert_allclose( + sum(classes.values()), _schedule_tax(income, *schedule), atol=1e-6 + ) + for name in UK_RATE_CLASSES: + assert np.all(more[name] >= classes[name] - 1e-9) + + +def test_scottish_rates_group_into_hmrc_categories() -> None: + """Starter, basic and intermediate are basic; higher and advanced are + higher; top is additional.""" + + _, _, _, higher, advanced, top = SCOTLAND[0] + income = np.array( + [higher - 1.0, (higher + advanced) / 2, (advanced + top) / 2, top + 10_000.0] + ) + + classes = tax_by_rate_class(income, *SCOTLAND) + + assert classes["higher"][0] == 0.0 and classes["additional"][0] == 0.0 + assert classes["higher"][1] == pytest.approx(0.42 * (income[1] - higher)) + assert classes["higher"][2] == pytest.approx( + 0.42 * (advanced - higher) + 0.45 * (income[2] - advanced) + ) + assert classes["additional"][2] == 0.0 + assert classes["additional"][3] == pytest.approx(0.48 * 10_000.0) + + +def test_a_bracket_not_in_force_is_skipped() -> None: + classes = tax_by_rate_class( + np.array([50_000.0]), [0.0, 2_000.0, 37_700.0], [0.20, None, 0.40] + ) + + # With the starter bracket skipped, 40% is the only rate above the basic + # class, so it is the higher rate. + assert classes["basic"][0] == pytest.approx(0.20 * 37_700.0) + assert classes["higher"][0] == pytest.approx(0.40 * 12_300.0) + assert classes["additional"][0] == 0.0 + + +def test_relief_takes_each_persons_own_schedule() -> None: + # The same GBP 4,000 returned on GBP 29,430 of taxable income: the rest of + # the UK relieves it at 20%, Scotland at 21% to 31,092 and 42% above. + relief = relief_by_rate_class( + baseline_income=np.array([29_430.0, 29_430.0]), + counterfactual_income=np.array([33_430.0, 33_430.0]), + scottish=np.array([False, True]), + schedules={"rest_of_uk": REST_OF_UK, "scotland": SCOTLAND}, + ) + + assert relief["basic"].tolist() == pytest.approx([800.0, 0.21 * 1_662.0]) + assert relief["higher"].tolist() == pytest.approx([0.0, 0.42 * 2_338.0]) + assert relief["additional"].tolist() == [0.0, 0.0] + + +def test_a_schedule_without_an_additional_rate_files_its_top_rate_as_higher() -> None: + # The top rate is the additional rate only where a higher rate sits below + # it. With one rate above the basic class, that rate is the higher rate. + income = np.array([20_000.0, 60_000.0]) + + classes = tax_by_rate_class(income, [0.0, 37_700.0], [0.20, 0.40]) + + assert classes["basic"].tolist() == pytest.approx([4_000.0, 7_540.0]) + assert classes["higher"].tolist() == pytest.approx([0.0, 8_920.0]) + assert classes["additional"].tolist() == [0.0, 0.0] + + +def test_the_engine_tolerance_adds_float32_steps_at_each_persons_amounts() -> None: + # The engine computes in float32: its step is under a penny at GBP 60,000, + # GBP 1 at GBP 10 million and GBP 8 at GBP 100 million. The GBP 1 + # tolerance grows by a few steps at the person's largest amount, so + # rounding alone does not refuse a schedule that still matches. + tolerance = rate_class_engine_tolerance( + np.array([50_000.0, 10_000_000.0, 100_000_000.0]), + np.array([60_000.0, 1_000.0, -5.0]), + ) + + steps = UK_RATE_CLASS_ENGINE_FLOAT32_STEPS + assert tolerance.tolist() == [ + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP + steps * 2.0**-8, + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP + steps * 1.0, + UK_RATE_CLASS_ENGINE_TOLERANCE_GBP + steps * 8.0, + ] + # A drift of GBP 5 at ordinary pay is still refused. + assert tolerance[0] < 1.02 + + +def test_the_all_rates_total_is_a_diagnostic_beside_the_higher_rate_band() -> None: + from microcosm.build.uk_runtime.ledger_targets import ( + UK_REQUIRED_TARGET_DIAGNOSTICS, + ) + from microcosm.build.uk_runtime.local_targets import load_uk_population_contract + + contract = load_uk_population_contract() + target_ids = {target["target_id"] for target in contract["targets"]} + + assert "hmrc.salary_sacrifice.it_relief_total" not in target_ids + for rate_class in UK_RATE_CLASSES: + assert f"hmrc.salary_sacrifice.it_relief_{rate_class}_rate" in target_ids + declaration = contract["diagnostic_references"][ + "hmrc.salary_sacrifice.it_relief_total" + ] + assert declaration["attach_to_target"] == ( + "hmrc.salary_sacrifice.it_relief_higher_rate" + ) + assert UK_REQUIRED_TARGET_DIAGNOSTICS[ + "hmrc.salary_sacrifice.it_relief_higher_rate" + ] == ("hmrc.salary_sacrifice.it_relief_total",) + + +def test_the_all_rates_total_diagnostic_carries_the_2024_25_relief() -> None: + """Feed-gated: the diagnostic resolves its exact tax-year 2024-25 fact. + + A selector or fact-key mismatch reads as ``unavailable`` and does not fail + the compile, so this test holds the declaration to the pinned feed. + """ + + from microcosm.build.uk_runtime.ledger_targets import ( + _attached_diagnostic_metadata, + ) + + root = paths_for("microcosm-build").repository + configured = os.environ.get("CHRONICLE_UK_FACTS") + feed = ( + Path(configured) if configured else root / ".codex-work/consumer_facts_uk.jsonl" + ) + if feed.is_dir(): + feed = feed / "consumer_facts.jsonl" + if not feed.is_file(): + pytest.skip("pinned UK Chronicle consumer feed is not present") + with feed.open(encoding="utf-8") as handle: + facts = tuple( + json.loads(line) + for line in handle + if '"hmrc.ss_income_tax_relief_total"' in line + ) + + metadata = _attached_diagnostic_metadata( + facts, "hmrc.salary_sacrifice.it_relief_higher_rate" + ) + + prefix = "salary_sacrifice_it_relief_total_diagnostic" + assert metadata[f"{prefix}_role"] == "diagnostic_only_not_in_fit" + assert metadata[f"{prefix}_status"] == "available" + assert float(metadata[f"{prefix}_value_gbp"]) == pytest.approx(8.8e9) + assert metadata[f"{prefix}_period"] == "2024" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py index e9d2697dd5..d9a8610b54 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_measure_simulation.py @@ -10,6 +10,7 @@ import pytest from microcosm.build.uk_runtime import measure_simulation +from microcosm.build.uk_runtime.income_tax_rate_classes import UK_RATE_CLASS_ALLOCATION from microcosm.build.uk_runtime.measure_simulation import ( UKMeasureResolver, apply_uk_calibration_measure_exclusions, @@ -570,10 +571,10 @@ def test_exclusion_applier_warns_within_week_of_expiry(): #: a drifting count is a register change that must be re-adjudicated, #: never absorbed. _PACKAGED_EXCLUSION_CENSUS = { - # microcosm#1069 c11: the live counterfactual route binds the two NICs - # relief rows and the income-tax relief total; the three rate bands wait - # for the band-test fix. - "hmrc.salary_sacrifice.": 3, + # microcosm#1069 c11 held the three salary-sacrifice rate bands out until + # a band test that reads the Scottish bands; microcosm#1095 binds them by + # rate class, so no salary-sacrifice entry remains. + "hmrc.salary_sacrifice.": 0, "_1_000_000_to_inf": 10, "slc.": 5, # microcosm#1095 (2026-10-07): two bands whose few supporting awards @@ -644,7 +645,7 @@ def test_exclusion_applier_warns_within_week_of_expiry(): def test_packaged_exclusions_load(): exclusions = load_uk_calibration_measure_exclusions() names = [entry["name"] for entry in exclusions] - assert len(names) == len(set(names)) == 68 + assert len(names) == len(set(names)) == 65 band_h_region_cells = [ entry for entry in exclusions @@ -1184,11 +1185,27 @@ def test_counterfactual_delta_refuses_bands_and_other_periods(monkeypatch, tmp_p year=2025, frame=FrameStub(), ) - with pytest.raises(ValueError, match="banded counterfactual measures are deferred"): + with pytest.raises(ValueError, match="banded counterfactual measures are refused"): resolver.counterfactual_delta( _relief_binding("income_tax", band={"variable": "adjusted_net_income"}), 2025, ) + # A rate-class allocation must be exactly the declared one (microcosm#1095). + allocation = {**UK_RATE_CLASS_ALLOCATION, "rate_class": "basic"} + for drifted in ( + {**allocation, "basic_below_rate": 0.25}, + {**allocation, "rate_class": "starter"}, + {**allocation, "taxable_income": "adjusted_net_income"}, + ): + with pytest.raises(ValueError, match="rate-class allocation must declare"): + resolver.counterfactual_delta( + _relief_binding("earned_taxable_income", allocation=drifted), + 2025, + ) + with pytest.raises(ValueError, match="rate-class allocation must declare"): + resolver.counterfactual_delta( + _relief_binding("income_tax", allocation=allocation), 2025 + ) with pytest.raises(ValueError, match="does not match target period"): resolver.counterfactual_delta(_relief_binding("income_tax"), 2024) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py index 4717ee9eab..790b6d0274 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py @@ -53,7 +53,7 @@ from microcosm.calibrate.artifacts import decode_problem from microcosm.frame import EntitySchema, Frame, MassChange, WeightKind, Weights -ACTIVE_REFERENCE_COUNT = 1281 +ACTIVE_REFERENCE_COUNT = 1279 def _uc_reference(**overrides) -> LedgerTargetReference: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_insurance_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_insurance_targets.py new file mode 100644 index 0000000000..66f7466e6e --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_insurance_targets.py @@ -0,0 +1,67 @@ +"""OBR's NICs total sits beside the bound class rows (microcosm#1095).""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import pytest + +from microcosm.build.uk_runtime.local_targets import load_uk_population_contract +from test_support.paths import paths_for + +_TEST_PATHS = paths_for("microcosm-build") +STABLE_UK_FACT_FEED_NAME = ".codex-work/consumer_facts_uk.jsonl" +CLASS_ROWS = ("obr.ni_employee", "obr.ni_employer", "obr.ni_self_employed") + + +def test_the_nics_total_is_a_diagnostic_beside_the_class_rows() -> None: + """OBR's table 3.8 total is cash, the class rows accrued, and it carries + statutory-payment recoveries, Class 1A, 1B and 3 and settlements no + household carries, so it leaves the fit (María's ruling of 2026-10-09).""" + + from microcosm.build.uk_runtime.ledger_targets import ( + UK_REQUIRED_TARGET_DIAGNOSTICS, + ) + + contract = load_uk_population_contract() + target_ids = {target["target_id"] for target in contract["targets"]} + assert "obr.ni" not in target_ids + assert set(CLASS_ROWS) <= target_ids + parity = contract["registry_parity"] + assert "obr/ni" in parity["excluded"] + assert "obr/ni" not in parity["mapped"] + assert "obr.ni" not in parity["scope_target_ids"] + declaration = contract["diagnostic_references"]["obr.ni"] + assert declaration["attach_to_target"] == "obr.ni_employee" + assert UK_REQUIRED_TARGET_DIAGNOSTICS["obr.ni_employee"] == ("obr.ni",) + reference = declaration["reference"] + assert reference["period_match_policy"] == "exact" + assert reference["ledger_selector"]["source_concept"] == "obr.ni_total" + assert reference["ledger_fact_key"] + + +def test_the_nics_diagnostic_carries_the_fy2025_26_forecast() -> None: + """Feed-gated: the diagnostic resolves its exact FY2025-26 fact.""" + + from microcosm.build.uk_runtime.ledger_targets import ( + _attached_diagnostic_metadata, + ) + + root = _TEST_PATHS.repository + configured = os.environ.get("CHRONICLE_UK_FACTS") + feed = Path(configured) if configured else root / STABLE_UK_FACT_FEED_NAME + if feed.is_dir(): + feed = feed / "consumer_facts.jsonl" + if not feed.is_file(): + pytest.skip("pinned UK Chronicle consumer feed is not present") + with feed.open(encoding="utf-8") as handle: + facts = tuple(json.loads(line) for line in handle if '"obr.ni_total"' in line) + + metadata = _attached_diagnostic_metadata(facts, "obr.ni_employee") + + prefix = "obr_ni_total_diagnostic" + assert metadata[f"{prefix}_role"] == "diagnostic_only_not_in_fit" + assert metadata[f"{prefix}_status"] == "available" + assert float(metadata[f"{prefix}_value_gbp"]) == pytest.approx(200_081_395_082.71) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py index 57a0bba014..bf59e3e94a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py @@ -13,6 +13,7 @@ from microcosm.build.uk_runtime.frs_take_up import UK_TAKE_UP_SIGNAL_OUTPUTS from microcosm.build.uk_runtime.national_frame import uk_national_frame from microcosm.build.uk_runtime.pension_credit_take_up import ( + PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES, PENSION_CREDIT_ENTITLEMENT_VARIABLES, PENSION_CREDIT_MIXED_AGE_SAVING, PENSION_CREDIT_TAKE_UP_STAGE_NAME, @@ -21,6 +22,7 @@ assign_component_take_up, redraw_pension_credit_take_up, ) +from microcosm.build.uk_runtime.spi_support import support_channel_column from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract from microcosm.frame import WeightKind @@ -54,7 +56,7 @@ def test_each_component_band_claims_at_its_rate_with_reporters_first() -> None: guarantee, savings, eligible, reporter, draws, band = _population() weights = np.ones(len(band)) - would_claim, receipts = assign_component_take_up( + would_claim, receipts, _ = assign_component_take_up( guarantee=guarantee, savings=savings, eligible=eligible, @@ -62,6 +64,8 @@ def test_each_component_band_claims_at_its_rate_with_reporters_first() -> None: weights=weights, draws=draws, rates=RATES, + great_britain=np.ones(len(band), dtype=bool), + newly_entitled_rate=0.0, ) assert would_claim[reporter].all() @@ -85,7 +89,7 @@ def test_reporters_above_the_rate_leave_no_residual_draw() -> None: gc_reporter_share=0.8 ) - would_claim, receipts = assign_component_take_up( + would_claim, receipts, _ = assign_component_take_up( guarantee=guarantee, savings=savings, eligible=eligible, @@ -93,6 +97,8 @@ def test_reporters_above_the_rate_leave_no_residual_draw() -> None: weights=np.ones(len(band)), draws=draws, rates=RATES, + great_britain=np.ones(len(band), dtype=bool), + newly_entitled_rate=0.0, ) gc = receipts[0] @@ -107,7 +113,7 @@ def test_reporters_above_the_rate_leave_no_residual_draw() -> None: def test_the_residual_is_weighted() -> None: # Two entitled Guarantee Credit units: a reporter of weight 1 and a # non-reporter of weight 3, so r = (0.5 * 4 - 1) / (4 - 1) = 1 / 3. - _, receipts = assign_component_take_up( + _, receipts, _ = assign_component_take_up( guarantee=np.asarray([10.0, 10.0]), savings=np.zeros(2), eligible=np.asarray([True, True]), @@ -115,6 +121,8 @@ def test_the_residual_is_weighted() -> None: weights=np.asarray([1.0, 3.0]), draws=np.asarray([0.9, 0.9]), rates={**RATES, "pension_credit_guarantee_credit": 0.5}, + great_britain=np.asarray([True, True]), + newly_entitled_rate=0.37, ) assert receipts[0]["residual_rate"] == pytest.approx(1 / 3) @@ -124,22 +132,34 @@ class _StubEngine: def __init__(self) -> None: self.calls: list[tuple[tuple[str, ...], str]] = [] + self.dataset_periods: list[str | None] = [] - def materialize(self, frame, variables, period): + def materialize(self, frame, variables, period, *, dataset_period=None): self.calls.append((tuple(variables), str(period))) + self.dataset_periods.append( + None if dataset_period is None else str(dataset_period) + ) benunit = frame.table("benunit") ids = benunit["benunit_id"].to_numpy() + self.saving_seen = ( + benunit[PENSION_CREDIT_MIXED_AGE_SAVING].tolist() + if PENSION_CREDIT_MIXED_AGE_SAVING in benunit + else None + ) # 1: Guarantee Credit; 2: Savings Credit only; 3: not entitled. # Unit 2 is a mixed-age couple keeping the SI 2019/37 saving. - return { + values = { PENSION_CREDIT_MIXED_AGE_SAVING: ids == 2, "guarantee_credit": np.where(ids == 1, 40.0, 0.0), "savings_credit": np.where(ids == 2, 15.0, 0.0), "is_pension_credit_eligible": ids != 3, + "pension_credit_assessable_capital": np.where(ids == 2, 12_000.0, 0.0), + "pension_credit_deemed_income": np.where(ids == 2, 208.0, 0.0), } + return {name: values[name] for name in variables} -def _frame(): +def _frame(time_period: str = "2024"): person = pd.DataFrame( { "person_id": [11, 21, 31], @@ -150,7 +170,11 @@ def _frame(): } ) benunit = pd.DataFrame( - {"benunit_id": [1, 2, 3], "would_claim_pc": [True, True, False]} + { + "benunit_id": [1, 2, 3], + "would_claim_pc": [True, True, False], + support_channel_column("benunit"): ["frs", "frs", "spi"], + } ) household = pd.DataFrame({"household_id": [1, 2, 3], "region": ["WALES"] * 3}) return uk_national_frame( @@ -159,10 +183,23 @@ def _frame(): household=household, household_weights=np.asarray([10.0, 10.0, 10.0]), weight_kind=WeightKind.IMPORTANCE, - time_period="2024", + time_period=time_period, ) +def test_redraw_refuses_a_frame_outside_the_declared_dataset_year() -> None: + # The stage declares the frame's values as survey-year values and has the + # engine project them from that year. A frame stamped with another year + # would be projected from the wrong one. + engine = _StubEngine() + + with pytest.raises(ValueError, match="survey_year 2024 values"): + redraw_pension_credit_take_up( + _frame("2025"), engine=engine, contract=load_uk_take_up_contract() + ) + assert engine.calls == [] + + def test_redraw_reads_engine_entitlement_once_and_rewrites_only_would_claim_pc() -> ( None ): @@ -173,13 +210,23 @@ def test_redraw_reads_engine_entitlement_once_and_rewrites_only_would_claim_pc() frame, engine=engine, contract=load_uk_take_up_contract() ) - # One materialization at the frame's survey-year period, the saving first. + # The saving at the frame's survey year, then the entitlement and the + # capital receipt at the release's calibration year, reading the stored + # saving (uk-data#510, uk-data#519). assert engine.calls == [ + ((PENSION_CREDIT_MIXED_AGE_SAVING,), "2024"), ( - (PENSION_CREDIT_MIXED_AGE_SAVING, *PENSION_CREDIT_ENTITLEMENT_VARIABLES), - "2024", - ) + ( + *PENSION_CREDIT_ENTITLEMENT_VARIABLES, + *PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES, + ), + "2025", + ), ] + # The entitlement read stamps the frame with its own survey year, so the + # engine projects its values to the calibration year (uk-data#510). + assert engine.dataset_periods == [None, "2024"] + assert engine.saving_seen == [False, True, False] after = result.frame.table("benunit") # Unit 3 reports Pension Credit the engine finds it is not entitled to: it # keeps claiming, and the receipt counts it. @@ -208,6 +255,77 @@ def test_redraw_reads_engine_entitlement_once_and_rewrites_only_would_claim_pc() "savings_credit_only", ] assert evidence["bands"][0]["entitled_units"] == 1 + assert evidence["entitlement_year"] == 2025 + assert evidence["solve_scope"] == "great_britain" + assert evidence["capital"]["pension_credit_assessable_capital"] == { + "frs": 120_000.0, + "spi": 0.0, + } + for name in PENSION_CREDIT_CAPITAL_RECEIPT_VARIABLES: + assert name not in after + + +def test_bands_are_solved_over_great_britain_and_drawn_in_northern_ireland() -> None: + # Four Guarantee Credit units: a reporter and a non-reporter in Great + # Britain, and two Northern Ireland non-reporters of large weight. Solved + # over Great Britain, r = (0.5 * 2 - 1) / (2 - 1) = 0, and Northern + # Ireland draws at that rate too; solved UK-wide it would be 0.33. + would_claim, receipts, _ = assign_component_take_up( + guarantee=np.full(4, 10.0), + savings=np.zeros(4), + eligible=np.ones(4, dtype=bool), + reporter=np.asarray([True, False, False, False]), + weights=np.asarray([1.0, 1.0, 5.0, 5.0]), + draws=np.asarray([0.9, 0.1, 0.1, 0.1]), + rates={**RATES, "pension_credit_guarantee_credit": 0.5}, + great_britain=np.asarray([True, True, False, False]), + newly_entitled_rate=0.37, + ) + gc = receipts[0] + assert gc["scope"] == "great_britain" + assert gc["residual_rate"] == 0.0 + assert gc["entitled_units"] == 2 + assert gc["outside_scope_entitled_units"] == 2 + assert gc["realized_take_up"] == pytest.approx(0.5) + assert would_claim.tolist() == [True, False, False, False] + + would_claim, receipts, _ = assign_component_take_up( + guarantee=np.full(4, 10.0), + savings=np.zeros(4), + eligible=np.ones(4, dtype=bool), + reporter=np.asarray([True, False, False, False]), + weights=np.asarray([1.0, 3.0, 5.0, 5.0]), + draws=np.asarray([0.9, 0.1, 0.1, 0.9]), + rates={**RATES, "pension_credit_guarantee_credit": 0.5}, + great_britain=np.asarray([True, True, False, False]), + newly_entitled_rate=0.37, + ) + # r = (0.5 * 4 - 1) / (4 - 1) = 1/3: Northern Ireland's unit drawing 0.1 + # claims, the one drawing 0.9 does not. + assert receipts[0]["residual_rate"] == pytest.approx(1 / 3) + assert receipts[0]["outside_scope_drawn_units"] == 1 + assert would_claim.tolist() == [True, True, True, False] + + +def test_units_without_entitlement_claim_at_the_newly_entitled_rate() -> None: + # Not entitled: a reporter (claims), and non-reporters drawing either side + # of 0.37 on the same stream. + would_claim, _, newly = assign_component_take_up( + guarantee=np.zeros(3), + savings=np.zeros(3), + eligible=np.zeros(3, dtype=bool), + reporter=np.asarray([True, False, False]), + weights=np.ones(3), + draws=np.asarray([0.99, 0.36, 0.38]), + rates=RATES, + great_britain=np.ones(3, dtype=bool), + newly_entitled_rate=0.37, + ) + assert would_claim.tolist() == [True, True, False] + assert newly["rate_key"] == "pension_credit_newly_entitled" + assert newly["units"] == 3 + assert newly["drawn_units"] == 1 + assert newly["flagged_share"] == pytest.approx(2 / 3) def test_committed_manifest_declares_the_stage_and_its_rewrite() -> None: @@ -246,6 +364,7 @@ def test_contract_carries_the_fye2024_component_rates() -> None: assert contract.rate("pension_credit_guarantee_credit") == 0.69 assert contract.rate("pension_credit_savings_credit_only") == 0.37 + assert contract.rate("pension_credit_newly_entitled") == 0.37 def test_terminal_signal_gate_leaves_would_claim_pc_to_the_stage_gate() -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py index e8623c4b99..50bf4714c8 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py @@ -77,9 +77,11 @@ } POLICYENGINE_BINDING_KEYS = { "affected_flag_variable", + "allocation", "band", "band_filter_dimension", "band_period_factor", + "band_semantics", "band_upper_bound", "band_upper_bound_inclusive", "count_of", @@ -215,10 +217,13 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: # contribution targets and the DWP employee total leave the fit again (their # relief-mechanism and gross definitions do not match the engine's columns; # microcosm#1069 c9 arm); microcosm#1095 adds DWP's Great Britain - # pension-age Housing Benefit spending; microcosm#1113 adds ONS 07.2.2 - # household road fuel; microcosm#1123 adds the four nation household - # controls (LFS English regions, NRS, Welsh Government, NI). - assert len(resource["targets"]) == 400 + # pension-age Housing Benefit spending, and OBR's NICs total and the + # all-rates salary-sacrifice relief become diagnostics beside the bound + # rows they repeat, as do the two PIP daily-living caseload rows; + # microcosm#1113 adds ONS 07.2.2 household road fuel; microcosm#1123 adds + # the four nation household controls (LFS English regions, NRS, Welsh + # Government, NI). + assert len(resource["targets"]) == 396 providers = resource["hierarchy"]["providers"] categories = resource["hierarchy"]["categories"] @@ -232,10 +237,10 @@ 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) == 348 + assert len(registry_scope) == 344 assert len(profile_scope) == 52 - assert target_ids[:348] == registry_scope - assert target_ids[348:] == profile_scope + assert target_ids[:344] == registry_scope + assert target_ids[344:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -248,8 +253,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: assert set(mapped_target_ids).isdisjoint(unmapped_declarations) assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope) assert all(reason for reason in unmapped_declarations.values()) - assert len(mapped_target_ids) == 192 - assert len(unmapped_declarations) == 156 + # microcosm#1095: obr/ni and the two PIP daily-living caseload rows leave + # the mapped rows for diagnostics. + assert len(mapped_target_ids) == 189 + assert len(unmapped_declarations) == 155 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 assert set(suppressed_ancestors).isdisjoint(parity["mapped"]) @@ -368,7 +375,7 @@ 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) == 400 + assert len(target_ids) == 396 assert len(target_ids) == len(set(target_ids)) @@ -840,12 +847,16 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - target["bindings"]["policyengine"]["filters"][0]["variable"] == "uc_calibration_administrative_family_type" ) - assert target["bindings"]["policyengine"]["band_upper_bound"] == 2500 + binding = target["bindings"]["policyengine"] + # microcosm#1095: whole-pence bands, closed at GBP 2,500.00 a month. + assert binding["band_semantics"] == "whole_pence_upper_closed" + assert binding["band_upper_bound"] == 2500 + assert "band_upper_bound_inclusive" not in binding def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 400 + assert len(targets) == 396 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets @@ -895,8 +906,8 @@ def test_uc_gb_bindings_match_the_committed_source_geography_and_finite_bands(): "from_entity", "household" ) if target_id.startswith("dwp.uc.payment_distribution_"): + assert binding["band_semantics"] == "whole_pence_upper_closed" assert binding["band_upper_bound"] == 2500 - assert binding["band_upper_bound_inclusive"] is True assert binding["band_period_factor"] == 12 # The omitted source top-coded row is not introduced or merged into a # finite reference by these measurement-only changes. @@ -1091,3 +1102,35 @@ def test_household_composition_cells_do_not_share_the_household_total_signature( assert by_id[target_id]["measurement"]["filters"] == [ {"concept": "uk.housing.council_tax_band", "operator": "in", "value": bands} ], target_id + + +def test_only_the_uc_award_bands_read_whole_pence() -> None: + """microcosm#1095: the four UC payment families opt in to whole-pence, + upper-closed bands; the other 28 banded bindings keep the half-open + reading.""" + + banded = { + target["target_id"]: target["bindings"]["policyengine"] + for target in _load()["targets"] + if target["bindings"]["policyengine"].get("groupby_variable") + } + whole_pence = { + target_id + for target_id, binding in banded.items() + if binding.get("band_semantics") == "whole_pence_upper_closed" + } + assert len(banded) == 32 + assert whole_pence == { + f"dwp.uc.payment_distribution_{family}" + for family in ( + "single", + "lone_parent", + "couple_no_children", + "couple_with_children", + ) + } + assert not [ + target_id + for target_id, binding in banded.items() + if target_id not in whole_pence and "band_semantics" in binding + ] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_benefit_coherence.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_benefit_coherence.py index d809b73237..cbdf5c0e37 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_benefit_coherence.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_benefit_coherence.py @@ -77,6 +77,8 @@ def _person( "person_benunit_id": benunit_id, "person_household_id": household_id, "age": age, + # The fixtures' adults are their units' UC claimants or partners. + "is_uc_claimant": age >= 18, support_channel_column("person"): channel, support_source_id_column("person"): source_id, } diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py index 5411643d57..bb3017225d 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py @@ -94,6 +94,80 @@ def test_spi_carer_take_up_refresh_follows_the_refilled_receipt() -> None: ) +def test_spi_uc_gainful_self_employment_follows_the_rows_drawn_incomes() -> None: + """A redrawn row's gainful self-employment reads its drawn incomes alone.""" + + person = pd.DataFrame( + { + "employment_status": [ + "FT_EMPLOYED", + "FT_SELF_EMPLOYED", + "PT_EMPLOYED", + "FT_SELF_EMPLOYED", + "FT_SELF_EMPLOYED", + "FT_EMPLOYED", + ], + # Row 10's drawn profit no longer out-earns its pay. Rows 20 and 40 + # keep a self-employed status the draw did not read: no profit, and + # a profit below pay, so neither is flagged. Rows 30 and 50 draw a + # profit above their pay, whatever the status. Row 60 is a base row + # whose flag stays. + "self_employment_income": [500.0, 0.0, 9_000.0, 3_000.0, 8_000.0, 0.0], + "employment_income": [20_000.0, 0.0, 4_000.0, 25_000.0, 1_000.0, 30_000.0], + "uc_is_in_gainful_self_employment": [True, True, False, True, False, True], + }, + index=[10, 20, 30, 40, 50, 60], + ) + spi_people = pd.Series([True, True, True, True, True, False], index=person.index) + + result = spi_income._refresh_uc_gainful_self_employment( + person, spi_people=spi_people + ) + + assert result["uc_is_in_gainful_self_employment"].tolist() == [ + False, + False, + True, + False, + True, + True, + ] + assert result["uc_is_in_gainful_self_employment"].dtype == bool + bare = pd.DataFrame({"employment_status": ["FT_EMPLOYED"]}, index=[1]) + assert ( + "uc_is_in_gainful_self_employment" + not in spi_income._refresh_uc_gainful_self_employment( + bare, spi_people=pd.Series([True], index=[1]) + ) + ) + + +def test_a_redrawn_row_drops_its_frs_twins_landlord_carrier() -> None: + """The carrier is the FRS respondent's answer; a redrawn row's letting is its donor's.""" + + person = pd.DataFrame( + { + "property_income": [0.0, 6_000.0, 0.0], + "reports_rent_from_other_property": [True, True, True], + }, + index=[10, 20, 30], + ) + spi_people = pd.Series([True, True, False], index=person.index) + + result = spi_income._refresh_landlord_carrier(person, spi_people=spi_people) + + # Row 30 is a base row and keeps its answer. + assert result["reports_rent_from_other_property"].tolist() == [False, False, True] + assert result["reports_rent_from_other_property"].dtype == bool + assert result["property_income"].tolist() == [0.0, 6_000.0, 0.0] + bare = pd.DataFrame({"property_income": [0.0]}, index=[1]) + assert "reports_rent_from_other_property" not in ( + spi_income._refresh_landlord_carrier( + bare, spi_people=pd.Series([True], index=[1]) + ) + ) + + def test_spi_uc_start_up_period_holds_only_while_the_row_stays_self_employed() -> None: """A redrawn row keeps its donor's start-up period only while self-employed.""" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py index 74ec34ab0d..8cb4b266db 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py @@ -2196,13 +2196,14 @@ def gate(evidence, parameters): def test_pension_credit_take_up_checks_each_band_against_its_rate() -> None: - def band(name, rate, realized, *, exceed=False, units=10): + def band(name, rate, realized, *, exceed=False, units=10, scope="great_britain"): return { "band": name, "rate": rate, "realized_take_up": realized, "reporters_exceed_rate": exceed, "entitled_units": units, + "scope": scope, } parameters = { @@ -2231,6 +2232,9 @@ def gate(*bands): assert not gate(band("guarantee_credit", 0.69, 0.50, exceed=True)).passed assert not gate(band("guarantee_credit", 0.69, None, units=0)).passed assert not gate().passed + # DWP's rates cover Great Britain: a band measured UK-wide fails even at + # its rate (uk-data#510). + assert not gate(band("guarantee_credit", 0.69, 0.69, scope="united_kingdom")).passed # The realised rates are population facts: a synthetic smoke fixture records # the failure without blocking, every other posture blocks on it. committed = _gate_parameters("uk_stage_pension_credit_take_up") diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py index fd06fea55f..2fee79f4d6 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_stochastic_tools.py @@ -187,6 +187,7 @@ def continuous_entry(self, key: str): "person_benunit_id": [10, 10, 20, 30], "person_household_id": [1, 1, 1, 2], "age": [5, 6, 40, 70], + "is_uc_claimant": [False, False, True, True], "child_benefit_reported": [0.0, 10.0, 0.0, 0.0], "pension_credit_reported": [0.0, 0.0, 0.0, 5.0], "universal_credit_reported": [0.0, 0.0, 20.0, 0.0], diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py index 4901cb7053..eab98eb41f 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_contract.py @@ -18,6 +18,7 @@ def test_uk_contract_loads_and_selects_build_year_rates() -> None: "pension_credit", "pension_credit_guarantee_credit", "pension_credit_savings_credit_only", + "pension_credit_newly_entitled", "universal_credit", "marriage_allowance", "tax_free_childcare", @@ -35,6 +36,7 @@ def test_uk_contract_loads_and_selects_build_year_rates() -> None: 0.7, 0.69, 0.37, + 0.37, 0.55, 0.5, 0.88, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py index 6e3339327d..26a0170830 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_take_up_gate.py @@ -54,6 +54,7 @@ def _frame(*, brma_values=("LONDON_A", "LONDON_B")): "person_benunit_id": np.arange(201, 201 + n), "person_household_id": household_ids, "age": [30, 40] * 5, + "is_uc_claimant": [True] * n, } ) benunit = pd.DataFrame( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py index 6d1e9600d9..43a79ab2d0 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py @@ -54,6 +54,7 @@ from tools.generate_uk_local_target_references import _support_floor_register_scope from tools.generate_uk_target_references import ( POLICYENGINE_BINDING_KEYS, + TARGET_ID_GEOGRAPHY_PINS, _annual_uc_award_band_token, _fanout_name, _geography_pins, @@ -64,7 +65,7 @@ _TEST_PATHS = paths_for("microcosm-build") -ACTIVE_REFERENCE_COUNT = 1281 +ACTIVE_REFERENCE_COUNT = 1279 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} @@ -701,9 +702,10 @@ def test_prefix_geography_pins_carry_scotgov_and_england_scoped_slc_families() - England-scoped too — the borrower bindings filter country == ENGLAND explicitly and the support model variables are England-gated by construction — while the GB default pin could never match. - slc.repayments.devolved_total and the dwp.pip claimant counts keep the GB - pin and stay held: activating them needs contract redesign (per-nation - repayment rows; an England-and-Wales PIP binding), not a pin change. + slc.repayments.devolved_total keeps the GB pin and stays held: activating + it needs per-nation repayment rows, not a pin change. The dwp.pip + claimant counts are no targets now: they are England-and-Wales diagnostics + on obr.pip, pinned K04000001 in their own declarations (microcosm#1095). """ contract = _load_uk_resource("uk_population_targets.json") pins = _geography_pins(contract) @@ -742,12 +744,7 @@ def test_prefix_geography_pins_carry_scotgov_and_england_scoped_slc_families() - "E92000001" } assert pins["slc.repayments.devolved_total"]["geography_id"] == "K03000001" - assert ( - pins["dwp.pip.daily_living_standard_claimants"]["geography_id"] == "K03000001" - ) - assert ( - pins["dwp.pip.daily_living_enhanced_claimants"]["geography_id"] == "K03000001" - ) + assert not [target_id for target_id in pins if target_id.startswith("dwp.pip.")] def haystack(target: dict) -> str: selector = target.get("ledger_selector") or {} @@ -759,12 +756,16 @@ def haystack(target: dict) -> str: ) ) + # An exact-id pin overrides the substring rule: OBR's council tax row + # sums England, Scotland and Wales and carries Chronicle's UK stamp + # (microcosm#1095). substring_scotland = { str(target["target_id"]) for target in contract["targets"] if "scotland" in haystack(target) and "northern" not in haystack(target) and "domestic_rates" not in haystack(target) + and str(target["target_id"]) not in TARGET_ID_GEOGRAPHY_PINS } # The by_area council-tax cells carry the same prefix pin, but they sit on # the local surface, where the roster (not the pin) fixes the geography. @@ -787,14 +788,15 @@ def haystack(target: dict) -> str: for target_id in sorted(england_slc_ids): assert membership["geography_pins"][target_id]["geography_id"] == "E92000001" assert membership["targets"][target_id]["status"] == "active" - for target_id in ( - "slc.repayments.devolved_total", - "dwp.pip.daily_living_standard_claimants", - "dwp.pip.daily_living_enhanced_claimants", - ): - assert ( - membership["targets"][target_id]["status"] == "no_fact_at_or_before_period" - ) + assert ( + membership["targets"]["slc.repayments.devolved_total"]["status"] + == "no_fact_at_or_before_period" + ) + assert not [ + target_id + for target_id in membership["targets"] + if target_id.startswith("dwp.pip.") + ] def test_uk_target_reference_membership_report_is_packaged() -> None: @@ -803,8 +805,8 @@ 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": 1281, - "no_fact_at_or_before_period": 7, + "active": 1279, + "no_fact_at_or_before_period": 5, "signed_excluded": 15, } assert membership["genuine_sum_residue"] @@ -889,14 +891,14 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: "family": "dwp_universal_credit", "status": "active_with_unmapped_vintage_residue_skipped", "active_reference_count": 100, - "skipped_unmapped_fact_count": 12, + "skipped_unmapped_fact_count": 8, "signed_rationale": ( "UC payment-distribution targets fan out over family_type and " "monthly_award_amount_bands into incumbent-compatible " - "annual-payment rows. The four source-only 'No payment' facts " - "and eight overlapping source-only 'or over' facts are left " - "out; no active reference may use the legacy nan_to_nan band " - "name." + "annual-payment rows. The source-only 'No payment' and " + "top-coded 'or over' facts are left out, and the skipped count " + "is the generator's own (microcosm#1095); no active reference " + "may use the legacy nan_to_nan band name." ), }, { @@ -1022,6 +1024,16 @@ def test_uk_fixture_b_signed_differences_carry_ruled_rationales() -> None: "historical forecast/uprated" in differences["hmrc.cgt.gains_total"]["reason"] ) assert differences["obr.capital_gains_tax"]["fixture_value"] == 21_801_546_197.09165 + council_tax = differences["obr.council_tax"] + assert council_tax["kind"] == "calibration_drift" + assert "row 15" in council_tax["reason"] and "row 19" in council_tax["reason"] + national_insurance = differences["obr.ni"] + assert national_insurance["kind"] == "fixture_only" + assert "diagnostic provenance on obr.ni_employee" in national_insurance["reason"] + for rate in ("standard", "enhanced"): + caseload = differences[f"dwp.pip.daily_living_{rate}_claimants"] + assert caseload["kind"] == "fixture_only" + assert "diagnostic provenance on obr.pip" in caseload["reason"] assert "single-age-90 share" in differences["ons.population.female_85_89"]["reason"] assert "single-age-90 share" in differences["ons.population.male_85_89"]["reason"] assert ( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py index daef0aded3..934ba203a5 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py @@ -411,11 +411,12 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: assert exported.weights_for("household").values.tolist() == [1.0, 2.0] assert exported.mass_log == frame.mass_log # The boundary drops the reviewed export exclusions, the internal - # disability carriers and the salary-sacrifice pre-conversion pay - # carrier, and nothing else. + # disability carriers, the salary-sacrifice pre-conversion pay carrier + # and the landlord carrier, and nothing else. from microcosm.build.uk_runtime.frs_disability import ( UK_INTERNAL_DISABILITY_REPORTED_COLUMNS, ) + from microcosm.build.uk_runtime.frs_spine import FRS_LANDLORD_CARRIER_COLUMN from microcosm.build.uk_runtime.salary_sacrifice import ( SALSAC_PRE_CONVERSION_PAY_COLUMN, ) @@ -429,6 +430,7 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: set(UK_REVIEWED_EXPORT_EXCLUSIONS) | {f"person.{column}" for column in UK_INTERNAL_DISABILITY_REPORTED_COLUMNS} | {f"person.{SALSAC_PRE_CONVERSION_PAY_COLUMN}"} + | {f"person.{FRS_LANDLORD_CARRIER_COLUMN}"} ) # A frame without the column passes through untouched. assert uk_release_export_frame(exported) is exported diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py index c496b77045..f35d06b770 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py @@ -3,7 +3,7 @@ # ruff: noqa: F403, F405 from test_support.microcosm_build.uk_uc_capital_coherence import * -RECEIPTS = ("coarsening_levels", "capital_against_investment_income") +RECEIPTS = ("coarsening_levels", "capital_against_investment_income", "property_shares") def test_manifest_declares_exact_redraw_seed_and_output() -> None: @@ -356,3 +356,239 @@ def test_receipt_counts_capital_below_what_the_income_implies() -> None: # 1005 now draws from its income band's donor, 101 (GBP 100). assert after["spi"]["benefit_units"] == 1 assert receipt["after"]["spi_reporters_capital_above_16000"] == 0 + + +def _property_tables(): + """Four households covering the shared-household cases (microcosm#1095). + + 1: one couple unit. 2: pensioner parents, one of them the household + reference person, with their working-age child's unit. 3: a lone parent + with a 17-year-old dependant. 4: two single adults, the second of them the + household reference person. + """ + + members = [ + # (person, benunit, household, age, is_household_head) + (1, 11, 1, 40, True), + (2, 11, 1, 38, False), + (3, 21, 2, 70, True), + (4, 21, 2, 68, False), + (5, 22, 2, 30, False), + (6, 31, 3, 35, True), + (7, 31, 3, 17, False), + (8, 41, 4, 50, False), + (9, 42, 4, 45, True), + ] + person = pd.DataFrame( + members, + columns=[ + "person_id", + "person_benunit_id", + "person_household_id", + "age", + "is_household_head", + ], + ) + benunit = pd.DataFrame({"benunit_id": [11, 21, 22, 31, 41, 42]}) + household = pd.DataFrame( + { + "household_id": [1, 2, 3, 4], + "other_residential_property_value": [ + 100_000.0, + 90_000.0, + 40_000.0, + 10_000.0, + ], + "non_residential_property_value": [20_000.0, 0.0, 0.0, 2_000.0], + "owned_land": [5_000.0, 30_000.0, 0.0, 0.0], + } + ) + return person, benunit, household + + +def test_the_reference_persons_unit_holds_the_households_property() -> None: + person, benunit, household = _property_tables() + + uc = uc_property_capital_share(person, benunit, household) + pension_credit = pension_credit_property_capital_share(person, benunit, household) + + # UC counts other residential and non-residential property at 90% of its + # value, market value less 10% for the costs of sale. The household + # reference person's unit holds all of it: the parents' unit and not their + # child's in household 2, and the second adult's in household 4. + np.testing.assert_allclose( + uc, [108_000.0, 81_000.0, 0.0, 36_000.0, 0.0, 10_800.0], rtol=1e-12 + ) + # Pension Credit counts land as well, for the same unit. + np.testing.assert_allclose( + pension_credit, + [112_500.0, 108_000.0, 0.0, 36_000.0, 0.0, 10_800.0], + rtol=1e-12, + ) + # No household's units hold more than its property. + by_household = pd.Series(uc, index=[1, 2, 2, 3, 4, 4]).groupby(level=0).sum() + np.testing.assert_allclose( + by_household, [108_000.0, 81_000.0, 36_000.0, 10_800.0], rtol=1e-12 + ) + + +def test_a_household_needs_exactly_one_reference_person() -> None: + person, benunit, household = _property_tables() + + none = person.assign( + is_household_head=person["is_household_head"] & (person["person_id"] != 9) + ) + with pytest.raises(ValueError, match="exactly one household reference person"): + uc_property_capital_share(none, benunit, household) + two = person.assign( + is_household_head=person["is_household_head"] | (person["person_id"] == 8) + ) + with pytest.raises(ValueError, match="exactly one household reference person"): + uc_property_capital_share(two, benunit, household) + with pytest.raises(ValueError, match="must be a boolean column"): + uc_property_capital_share( + person.assign(is_household_head=1.0), benunit, household + ) + + +def test_recorded_capital_keeps_zero_and_the_unavailable_sentinel_distinct() -> None: + share = np.asarray([0.0, 40_000.0, 6_000.0]) + carrier = np.asarray([0.0, 0.0, -1.0]) + + np.testing.assert_array_equal( + recorded_capital_with_property(carrier, share), [0.0, 40_000.0, -1.0] + ) + + +def test_a_unit_reporting_universal_credit_records_no_uc_property_share() -> None: + person, benunit, household = _property_tables() + # The parents' unit in household 2 and the lone parent report Universal + # Credit. Each is its household reference person's unit. + person["universal_credit_reported"] = np.where( + person["person_id"].isin([3, 6]), 4_000.0, 0.0 + ) + + recorded = uc_recorded_property_share(person, benunit, household) + + # A reporting unit keeps its receipt, and no other unit of its household + # takes the property it does not record. + np.testing.assert_allclose( + recorded, [108_000.0, 0.0, 0.0, 0.0, 0.0, 10_800.0], rtol=1e-12 + ) + np.testing.assert_allclose( + uc_property_capital_share(person, benunit, household), + [108_000.0, 81_000.0, 0.0, 36_000.0, 0.0, 10_800.0], + rtol=1e-12, + ) + + +def test_stage_records_capital_with_property_on_both_channels() -> None: + frame = _frame() + household = frame.table("household") + # Base non-reporter 501 and SPI non-reporter 1006 hold other property. So + # do base reporter 101 and SPI reporter 1005, who keep their receipt. Base + # non-reporter 401 holds land, which is Pension Credit capital only. + for household_id, value in ( + (5, 80_000.0), + (7, 20_000.0), + (1, 50_000.0), + (6, 20_000.0), + ): + household.loc[ + household["household_id"].eq(household_id), + "other_residential_property_value", + ] = value + household.loc[household["household_id"].eq(4), "owned_land"] = 30_000.0 + + result = cohere_uc_capital(frame, minimum_cell_donors=1) + benunit = result.frame.table("benunit").set_index("benunit_id") + carrier = benunit["frs_benunit_capital"] + + # Each unit is its household reference person's, and the property counts + # at 90% of its value. + assert benunit.loc[501, "uc_reported_capital"] == carrier.loc[501] + 72_000.0 + assert benunit.loc[1006, "uc_reported_capital"] == carrier.loc[1006] + 18_000.0 + # Land is no UC capital. + assert benunit.loc[401, "uc_reported_capital"] == carrier.loc[401] + assert ( + benunit.loc[401, "pension_credit_reported_capital"] + == carrier.loc[401] + 27_000.0 + ) + # A reporter keeps its receipt for UC; its Pension Credit capital still + # holds the property. + assert ( + benunit.loc[101, "pension_credit_reported_capital"] + == carrier.loc[101] + 45_000.0 + ) + # A unit that reports Universal Credit records the carrier alone. + unchanged = ~benunit.index.isin([501, 1006]) + np.testing.assert_array_equal( + benunit.loc[unchanged, "uc_reported_capital"], carrier[unchanged] + ) + receipt = result.property_shares + assert receipt["owners"] == "household_reference_person_unit" + assert receipt["valuation"] == 0.9 + assert receipt["universal_credit"]["frs"]["benefit_units_with_share"] == 1 + assert receipt["universal_credit"]["spi"]["benefit_units_with_share"] == 1 + # Pension Credit capital holds the property of every unit above, the two + # reporters' included, and 401's land. + assert receipt["pension_credit"]["frs"]["benefit_units_with_share"] == 3 + assert receipt["pension_credit"]["spi"]["benefit_units_with_share"] == 2 + # 101 holds GBP 100 and 1005 draws a reporter's GBP 100 or 200; the + # property each would hold would take it over GBP 16,000. + assert receipt["uc_reporters_keeping_receipt"] == { + "frs": { + "benefit_units": 1, + "weighted_share_withheld": 45_000.0, + "units_kept_at_or_below_16000": 1, + }, + "spi": { + "benefit_units": 1, + "weighted_share_withheld": 9_000.0, + "units_kept_at_or_below_16000": 1, + }, + } + assert receipt["uc_reporters_recorded_capital_above_16000"] == {"frs": 0, "spi": 0} + + +def test_stage_passes_the_unavailable_sentinel_through_property() -> None: + frame = _frame() + benunit_table = frame.table("benunit") + benunit_table.loc[benunit_table["benunit_id"].eq(501), "frs_benunit_capital"] = -1.0 + household = frame.table("household") + household.loc[ + household["household_id"].eq(5), "other_residential_property_value" + ] = 80_000.0 + + benunit = ( + cohere_uc_capital(frame, minimum_cell_donors=1) + .frame.table("benunit") + .set_index("benunit_id") + ) + + assert benunit.loc[501, "uc_reported_capital"] == -1.0 + assert benunit.loc[501, "pension_credit_reported_capital"] == -1.0 + + +def test_stage_refuses_a_drifted_property_share_declaration() -> None: + stage = _stage() + operations = tuple( + replace( + operation, + parameters={ + **operation.parameters, + "derived": { + **operation.parameters["derived"], + "uc_reported_capital": "frs_benunit_capital", + }, + }, + ) + if operation.kind == "derive" + else operation + for operation in stage.operations + ) + transform = UKUCCapitalCoherenceStageTransform( + stage=replace(stage, operations=operations) + ) + with pytest.raises(ValueError, match="derive operation"): + transform(_frame()) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py index 725768878e..5b44083621 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py @@ -237,8 +237,9 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case ], ), ] - # Match the published decimal lower edges, independently of _band_bounds. - # DWP's last included band is £2,400.01–£2,500/month, inclusive: + # Award values around the published decimal lower edges, independently of + # _band_bounds. DWP's bands are whole pence, "£0.01 to £100.00" up to + # "£2,400.01 to £2,500.00" and "£2,500.01 or over": # https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Households/Monthly%20Award%20Amount%20(bands).html # The separately published £2,500.01+ category remains unbound. It must # never be silently absorbed into the last finite reference. @@ -376,18 +377,16 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case uc = bu.universal_credit.to_numpy() # Synthetic ids encode the independent fixture's two-benefit-unit dwelling. bu_household = bu.benunit_id.to_numpy() // 2 + # microcosm#1095: band i is (GBP 1,200 i, 1,200 (i + 1)] a year compared in + # whole pence, and an award above GBP 30,000 is in no bound band. + pence = np.rint(uc * 100.0) for spec, name, row in zip( active, problem.names, problem.matrix.toarray(), strict=True ): family_name, band_name = spec.name.split("/")[-1].split("_annual_payment_") band_index = int(band_name.split("_to_")[0].replace("_", "")) // 1200 - lower = edges[band_index] - upper = ( - edges[band_index + 1] if band_index < 24 else np.nextafter(30_000.0, np.inf) - ) - mask = ( - (expected_family == family_name) & (uc > 0) & (uc >= lower) & (uc < upper) - ) + in_band = (pence > 120_000 * band_index) & (pence <= 120_000 * (band_index + 1)) + mask = (expected_family == family_name) & (uc > 0) & in_band expected = np.array( [mask[bu_household == hid].sum() for hid in hh.household_id] ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_reporter_redraw.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_reporter_redraw.py index 2fcb87e6b2..66d7aa29de 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_reporter_redraw.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_reporter_redraw.py @@ -65,6 +65,59 @@ def test_stub_engine_fast_lane_reads_frs_capital_once_and_consumes_outputs() -> assert "uc_reported_capital" not in result.frame.table("benunit") +def test_screen_capital_adds_the_units_uc_property_share() -> None: + # The screen reads the recorded capital uc_capital_coherence writes: the + # carrier plus the household's other residential and non-residential + # property, at 90% of its value, for the household reference person's + # unit. Land is Pension Credit capital only (microcosm#1095). + frame = _frame() + household = frame.table("household") + rows = household["household_id"].eq(7) + household.loc[rows, "other_residential_property_value"] = 50_000.0 + household.loc[rows, "non_residential_property_value"] = 10_000.0 + household.loc[rows, "owned_land"] = 99_999.0 + engine = _StubEngine() + + redraw_spi_reported_uc(frame, engine=engine, qrf_factory=_StubQRFFactory()) + + screened = ( + engine.calls[0][0] + .table("benunit") + .set_index("benunit_id")["uc_reported_capital"] + ) + carrier = _frame().table("benunit").set_index("benunit_id")["frs_benunit_capital"] + # Benefit unit 201 is household 7's only unit and holds its reference + # person. + assert screened.loc[201] == carrier.loc[201] + 54_000.0 + pd.testing.assert_series_equal( + screened.drop(201), carrier.drop(201), check_names=False + ) + + +def test_an_frs_unit_reporting_universal_credit_is_screened_on_its_carrier() -> None: + # uc_capital_coherence records an FRS reporter's carrier alone, so the + # screen does too and the reporter stays a training row whatever property + # its household is imputed. A unit that does not report takes its share, + # and so does an SPI unit, whose report is the one being redrawn. + frame = _frame() + household = frame.table("household") + rows = household["household_id"].isin([1, 2, 7]) + household.loc[rows, "other_residential_property_value"] = 50_000.0 + engine = _StubEngine() + + redraw_spi_reported_uc(frame, engine=engine, qrf_factory=_StubQRFFactory()) + + screened = ( + engine.calls[0][0] + .table("benunit") + .set_index("benunit_id")["uc_reported_capital"] + ) + carrier = _frame().table("benunit").set_index("benunit_id")["frs_benunit_capital"] + assert screened.loc[101] == carrier.loc[101] + assert screened.loc[102] == carrier.loc[102] + 45_000.0 + assert screened.loc[201] == carrier.loc[201] + 45_000.0 + + def test_training_mask_channel_containment_screen_and_landing_rule() -> None: before = _frame() result, _, record = _stub_run() diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_support_diagnostics.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_support_diagnostics.py index 5551c6ffdb..04271fb3af 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_support_diagnostics.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_support_diagnostics.py @@ -206,7 +206,9 @@ def write_saved_run(tmp_path, **overrides): *sorted(support_tool.FIVE_PLUS), *sorted(support_tool.KNOWN_ZERO_ROWS), "obr.income_tax", - "obr.ni", + "obr.ni_employee", + "obr.ni_employer", + "obr.ni_self_employed", "dwp.state_pension.amount", "obr.child_benefit", "hmrc/employment_income_count_income_band_0_to_5000", diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 2020986555..623f38ed4e 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 = ( - "1d6e112daf3125733c57003d97d1f2d7b96929d248a00b8868d58e182572af6a" + "75ec46b57d350ba65aad808e09ab4c90d02d86f50a4fdf2ce10e5058d220e52b" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "d8db3502536ab016beede21b7cab29db67ad8ec0eca1d634aaabcca03a9b3810" + "eef1dd46db8f32c5c1bd7ae508475b72b0987df3ba4a68d273a7f86631371f76" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "f8d7dfff9664fcf9b2919ab15ac918ee2222545c03f42ae4ba5e7ac53d0179f0" + "fb8b643927d6fddb046234488ad140d7505c4bf6d46fae5280d461d3eedea246" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -882,10 +882,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "b1d9f2478256f4e011e96484e39fa8dcbdc6425bb64b8ee38d8997035518a10c" + "6e55a4c1c28d0e0edeaf84ed0ca98b748cce697ef5bd471737ce7cccaee93083" ), "policy_sha256": ( - "33e4a58e75c11989c937089416f9d9e42c274bcbda15427d2f1b093c2d1102f1" + "d5bbcf2a3b07984691b77e89d3ca5d7967221e90fb4b8c7e4ad0ac94a76aeb3b" ), }, } diff --git a/packages/microcosm-data/tests/engine_free/shared/test_contract.py b/packages/microcosm-data/tests/engine_free/shared/test_contract.py index 0170fa28e3..61b0a36bb9 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -134,13 +134,13 @@ 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 = ( - "1d6e112daf3125733c57003d97d1f2d7b96929d248a00b8868d58e182572af6a" + "75ec46b57d350ba65aad808e09ab4c90d02d86f50a4fdf2ce10e5058d220e52b" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "d8db3502536ab016beede21b7cab29db67ad8ec0eca1d634aaabcca03a9b3810" + "eef1dd46db8f32c5c1bd7ae508475b72b0987df3ba4a68d273a7f86631371f76" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "f8d7dfff9664fcf9b2919ab15ac918ee2222545c03f42ae4ba5e7ac53d0179f0" + "fb8b643927d6fddb046234488ad140d7505c4bf6d46fae5280d461d3eedea246" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "6f0243bcda09dad26945376230c44ec3cf55d4e417c3a25e29bae8c59bc1a69d" 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 283ac6288e..571284b193 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py @@ -7,10 +7,10 @@ from __future__ import annotations -from collections.abc import Sequence +from collections.abc import Mapping, Sequence from importlib import metadata from pathlib import Path -from typing import Any +from typing import Any, Protocol import numpy as np @@ -20,12 +20,13 @@ from microcosm.frame.bundle import Frame from microcosm.frame.concept_mapping import ConceptMapping from microcosm.frame.materialize import engine_tables -from microcosm.frame.rules import ExportContract +from microcosm.frame.rules import ExportContract, RulesEngine from microcosm.frame.schema import EntitySchema, VariableMetadata __all__ = [ "POLICYENGINE_UK_CONCEPT_MAPPING", "PolicyEngineUKEngine", + "ProjectingRulesEngine", "UK_SCHEMA", "validate_uc_claimant_input", ] @@ -42,6 +43,31 @@ _PERIOD_BY_DEFINITION: dict[str, str] = {"year": "year", "month": "month"} +class ProjectingRulesEngine(RulesEngine, Protocol): + """A rules engine that can read a bundle from an earlier year. + + ``RulesEngine.materialize`` reads the bundle's values as ``period`` + inputs. An engine that projects a dataset forward itself accepts + ``dataset_period``: the year the bundle's values belong to. It stamps the + dataset with that year and computes ``variables`` at ``period`` on its own + projection, as it does for a released file. A stage that needs this names + the protocol. It is declared beside the adapter that implements it: + ``microcosm.frame.rules`` is inside pinned kernel hashes, and a + typing-only addition there would move them. + """ + + def materialize( + self, + bundle: Frame, + variables: Sequence[str], + period: int | str, + *, + dataset_period: int | str | None = None, + ) -> Mapping[str, np.ndarray]: + """Compute ``variables`` for ``period`` on a ``dataset_period`` bundle.""" + ... + + class PolicyEngineUKEngine: """RulesEngine adapter backed by ``policyengine_uk``.""" @@ -87,11 +113,26 @@ def materialize( bundle: Frame, variables: Sequence[str], period: int | str, + *, + dataset_period: int | str | None = None, ) -> dict[str, np.ndarray]: - """Compute ``variables`` for ``period`` with a UK Microsimulation.""" + """Compute ``variables`` for ``period`` with a UK Microsimulation. + + The bundle's values are read as ``period`` inputs. Pass + ``dataset_period`` where they belong to an earlier year: the dataset is + then stamped with that year and policyengine-uk projects it forward to + ``period`` as it does a released file (``extend_single_year_dataset``). + """ microsimulation_class = self._import_policyengine_uk().Microsimulation - dataset = self._build_dataset(bundle, period) + if dataset_period is not None and int(dataset_period) > int(period): + raise ValueError( + f"dataset_period {dataset_period} is after period {period}; " + "policyengine-uk projects a dataset forward only." + ) + dataset = self._build_dataset( + bundle, period if dataset_period is None else dataset_period + ) simulation = microsimulation_class(dataset=dataset) validate_uc_claimant_input(simulation, bundle.table("person"), period) results: dict[str, np.ndarray] = {} 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 9fe0519391..29d875fa3c 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 @@ -315,7 +315,7 @@ "realization": "probability carried as weight, no draw: a household carrying k liable gainers becomes 2^k arms, one per subset of residential gainers, at the household's weight times the product of p over the residential gainers and 1 - p over the others; the arm with no residential gainer keeps the household's ids; at design weights the residential count and gains equal the solved expectations sum(p w) and sum(p w g) in every gain band", "reason": "Capital-gains residential split divides every household carrying liable gainers into arms weighted by the solved residential probabilities, one arm per subset of residential gainers; every household's mass and the total household mass are conserved.", "stock_log_odds": 1.6, - "stock_signal": "household other residential property value above zero, or the person's property income above zero", + "stock_signal": "household other residential property value above zero, or the person's property income above zero, or the person reports rent from other property at a profit or a loss", "values": "every other column copied unchanged; no draw, no seed, no salt", "weight_kind_out": "importance" } @@ -1643,7 +1643,7 @@ "nonnegative_outputs": [ "ons_family_index" ], - "notes": "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review.", + "notes": "Derives the ONS Families and households Table 7 household type from the FRS household grid (microcosm#791). Every person's relhrp code becomes relationship_to_head (HEAD for the household reference person, whose relhrp is blank and whose hrpid is 1; hrpnum on the househol tab is the independent check). The pairwise R01-R14 matrix builds ONS families: any partner pair (spouse, cohabitee, civil partner) is a couple; a person with no partner and no own child in the household joins a parent's family through a natural/adopted or step child link (natural before step, then the lowest person number, on ties); foster, in-law, grandparent and sibling links never form a family, so foster children and grandchildren without a parent present stay individuals, as ONS treats them. A child is dependent under 16, or 16-18 in full-time education (child-tab members are in full-time non-advanced education by construction; adults use educft, fted on earlier tapes); 19 and over is non-dependent whatever the education status - the FRS child tab runs to 19, ONS stops at 18, so the two definitions are translated here rather than assumed equal. Household type: one person (split at 65), no family present (two or more unrelated adults, which counts related non-family households such as two siblings), exactly one family classified by that family whatever other individuals live there (a couple plus a lodger is a couple household - the class the #757 person-count restrictions could not place), or two or more families. The ten values are the Chronicle ons-families-households-2025 ons.household_type value ids verbatim, so the frame value, this declaration, the contract row and the published fact share one string. The reciprocity tolerance is the 2024-25 tape's three ordered pairs (two households) where a child records a parent link the parent records as non-relative or sibling; either side's declaration establishes the link, and any other count refuses the build for review. It also sets is_looked_after_by_local_authority for a child-table member under 16 whose grid code toward an adult in their own benefit unit is foster child (5), or toward whom that adult records foster parent (9). The FRS 2024-25 question instructions (Benefit Unit Allocation) put foster children under 16 in the carer's benefit unit, so without the flag policyengine-uk's child tests (Universal Credit, Child Tax Credit, Pension Credit, Tax-Free Childcare, extended childcare and the legacy benefits) count them as the carer's own children. The same instructions make a foster child aged 16 or over a separate benefit unit where a local authority maintenance allowance covers them, so one who stays in the carer's unit has no such allowance and is not marked. The flag follows the relationship code alone: few such children are recorded, and the carer's local-authority fostering allowance (ALLOW3) covers fewer still.", "operations": [ { "delimiter": "\t", @@ -1777,6 +1777,11 @@ "multi_family_households" ], "kind": "derive_ons_household_composition", + "looked_after_child": { + "below_age": 16, + "foster_child_code": 5, + "foster_parent_code": 9 + }, "one_person_age_split": 65, "reciprocity_mismatch_tolerance": 3, "source_columns": { @@ -1810,6 +1815,7 @@ "relationship_to_head", "ons_family_role", "ons_family_index", + "is_looked_after_by_local_authority", "ons_household_type" ], "rewrites": [], @@ -2027,7 +2033,7 @@ }, { "kind": "map_columns", - "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)" + "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512); uc_is_in_gainful_self_employment holds for a self-employed main job (EMPSTATI 3 or 4) whatever its profit, and for a side trade whose SEINCAM2 profit is above 0 and above INEARNS pay (UC Regs 2013 reg 64, ADM H4034, uk-data#525); reports_rent_from_other_property is an internal carrier, a positive ROYYR1 whether the letting made a profit or a loss (RENTPROF 2), which property_income counts as zero; the CGT stages read it as the landlord signal and the release drops it" }, { "kind": "map_coded_amounts", @@ -2064,6 +2070,7 @@ "is_claimant_or_partner", "is_hbai_dependent_child", "uc_is_in_startup_period", + "uc_is_in_gainful_self_employment", "rent_paid_as_boarder", "rent_paid_as_lodger", "employment_income", @@ -2073,6 +2080,7 @@ "savings_interest_income", "dividend_income", "property_income", + "reports_rent_from_other_property", "maintenance_income", "miscellaneous_income", "private_transfer_income", @@ -2156,7 +2164,7 @@ "nonnegative_outputs": [ "maximum_extended_childcare_hours_usage" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with 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).", + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.123.0's is_uc_eligible; the engine lockstep test pins it and names the two engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095).", "operations": [ { "aggregates": { @@ -2174,7 +2182,8 @@ }, "consumed_only": true, "kind": "aggregate_person_to_benunit", - "method": "any_adult_under_state_pension_age" + "method": "any_uc_claimant_aged_18_under_pension_credit_age", + "role_column": "is_uc_claimant" }, { "anchor": "child_benefit_reported_anchor", @@ -4503,22 +4512,27 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible.", + "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). A second temporary engine materialization, at the release's calibration year on the frame stamped with its survey year, so the engine projects its incomes, pensions and capital forward as it does the released file and as uk-data reads it (uk-data#510), gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible) and, for the receipt only, its assessable capital and deemed income (microcosm#1095); all are consumed only. Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. DWP's rates cover Great Britain, so E and R are Great Britain's and Northern Ireland's entitled units claim at the same r (uk-data#510). A unit with no entitlement claims if it reports, and otherwise at DWP's Savings Credit-only caseload take-up (37%, pension_credit_newly_entitled), the rate at which a unit claims once a reform or a later year entitles it (uk-data#510). The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces.", "operations": [ { "kind": "materialize_rules_engine_predictors", "predictors": [ "has_mixed_age_couple_pension_credit_saving" - ] + ], + "year_rule": "survey_year" }, { "consumed_only": true, + "dataset_year_rule": "survey_year", "kind": "materialize_rules_engine_predictors", "predictors": [ "guarantee_credit", "savings_credit", - "is_pension_credit_eligible" - ] + "is_pension_credit_eligible", + "pension_credit_assessable_capital", + "pension_credit_deemed_income" + ], + "year_rule": "calibration_year" }, { "aggregates": { @@ -4544,9 +4558,11 @@ ], "draw": "would_claim_pc", "kind": "assign_component_take_up_residual", + "newly_entitled_rate_key": "pension_credit_newly_entitled", "output": "would_claim_pc", - "residual_rate": "(rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1]", + "residual_rate": "(rate * entitled - reporters) / (entitled - reporters), weighted, over Great Britain, clipped to [0, 1]; drawn for every entitled non-reporter", "seed": 0, + "solve_scope": "great_britain", "weight_mapping": "household_to_benunit" } ], @@ -4799,7 +4815,7 @@ "artifacts": [], "grain": "person+benunit", "nonnegative_outputs": [], - "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in the Universal Credit age population claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.", + "notes": "Runs on SPI-channel rows after uc_reporter_redraw, the last writer of Universal Credit reports, and before uc_capital_coherence and the Pension Credit and Child Benefit take-up stages (microcosm#1095, uk-data#514). The stage-2 forests of hmrc_spi_income_spine re-impute each SPI adult's benefit reports from age, gender, region and the new incomes, with nothing of the benefit unit's entitlement or claim history. This stage zeroes the SPI rows' contributory and income-based JSA, income-related ESA, Income Support, Working and Child Tax Credit, Severe Disablement Allowance and Sure Start Maternity Grant reports, whose award needs an existing legacy claim, an out-of-work test, or a claim the copied household's new incomes cannot carry (policyengine-uk pays income-related ESA as reported behind a capital test alone); restores Industrial Injuries Disablement Benefit, the Armed Forces Compensation Scheme and bereavement support from the FRS twin (person_source_id), since they follow an injury, service or a death; re-derives the disability flags (the categories, which read only AA, DLA and PIP, are asserted unchanged) and receives_benefits_in_own_right from the final reports; and redraws would_claim_uc for SPI benefit units as frs_take_up draws it for FRS units: a unit with a Universal Credit reporter claims, and the other units in frs_take_up's Universal Credit population (a UC claimant or partner aged 18 to under Pension Credit qualifying age) claim as the residual of the contract's universal_credit rate on draws keyed on the SPI unit's own identity. Contributory ESA, PIP, DLA, AA, Carer's Allowance, State Pension and Winter Fuel Payment keep their draws, and FRS rows are never modified; contributory ESA is open to new claims and not income-tested, and the incapacity it needs follows the person, whose health and disability reports the SPI copy keeps.", "operations": [ { "columns": [ @@ -4850,7 +4866,8 @@ }, "consumed_only": true, "kind": "aggregate_person_to_benunit", - "method": "any_adult_under_state_pension_age" + "method": "any_uc_claimant_aged_18_under_pension_credit_age", + "role_column": "is_uc_claimant" }, { "anchor": "universal_credit_reported_anchor", @@ -5243,7 +5260,7 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh.", + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.123.0 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine's proxies share household capital over every claimant or partner instead, which puts a parent's property on an adult child's claim; they apply only to a unit on the unavailable sentinel. The property counts at 90% of its value, market value less 10% for the costs of sale (UC Regs 2013 reg 49; SPC Regs 2002 reg 19); debt secured on it is not on the frame and is not netted (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both.", "operations": [ { "aggregates": { @@ -5289,8 +5306,10 @@ }, { "derived": { - "pension_credit_reported_capital": "frs_benunit_capital", - "uc_reported_capital": "frs_benunit_capital", + "pension_credit_property_capital_share": "0.9 x (owned_land + other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "pension_credit_reported_capital": "frs_benunit_capital + pension_credit_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital", + "uc_property_capital_share": "0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "uc_reported_capital": "frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0 and NOT universal_credit_reported_anchor, else frs_benunit_capital", "would_claim_uc": "would_claim_uc OR universal_credit_reported_anchor" }, "kind": "derive" @@ -5304,7 +5323,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "source": "Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel.", + "source": "Family Resources Survey 2024-25 benefit-unit TOTCAPB4 observations carried through the SPI support channel, plus the household's Wealth and Assets Survey property imputation for the unit that holds it.", "stage": "uc_capital_coherence", "survey": "Family Resources Survey 2024-25" }, @@ -5386,7 +5405,8 @@ "has_non_child_member": "any(NOT is_child_or_qualifying_young_person_for_universal_credit) over benefit-unit members", "is_uc_couple": "count(is_benunit_head OR is_parent OR (dependent_children == 0)) over benefit-unit members == 2", "positive_pre_takeup_uc_award": "max(0, uc_maximum_amount - uc_income_reduction) > 0", - "uc_reported_capital": "frs_benunit_capital" + "uc_property_capital_share": "0.9 x (other_residential_property_value + non_residential_property_value) of the household where the unit holds the household reference person (is_household_head), else 0", + "uc_reported_capital": "frs_benunit_capital + uc_property_capital_share where frs_benunit_capital >= 0, else frs_benunit_capital; a base FRS-channel unit with universal_credit_reported_amount > 0 takes frs_benunit_capital alone" }, "kind": "derive" }, 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 6c041aa791..69bd87ae8a 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,care_hours,is_household_head,is_benunit_head,is_parent,is_blind,employment_income,self_employment_income,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,property_income,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,would_claim_carers_allowance,council_tax_benefit_reported,maintenance_expenses,childcare_expenses,personal_pension_contributions,employee_pension_contributions,pension_contributions_via_salary_sacrifice,salary_sacrifice_reported,salary_sacrifice_asked,is_uc_claimant,rent_paid_as_boarder,rent_paid_as_lodger,is_claimant_or_partner,is_hbai_dependent_child,uc_is_in_startup_period,__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 c4c88701cb..28e82f4053 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 @@ -85,6 +85,7 @@ "property_income": "float64", "rent_paid_as_boarder": "float64", "rent_paid_as_lodger": "float64", + "reports_rent_from_other_property": "bool", "salary_sacrifice_asked": "float64", "salary_sacrifice_reported": "float64", "savings_interest_income": "float64", @@ -97,6 +98,7 @@ "student_loan_repayments": "float64", "student_loans": "float64", "tax_free_savings_income": "float64", + "uc_is_in_gainful_self_employment": "bool", "uc_is_in_startup_period": "bool", "universal_credit_reported": "float64", "winter_fuel_allowance_reported": "float64", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json index 6f1eb7fe89..f6a93a009c 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json @@ -1 +1 @@ -{"country":"uk","nodes":[{"artifact_outputs":[{"name":"stage_evidence","type":{"name":"microcosm.stage-evidence","schema_version":1}}],"base":null,"citation":"","description":"Load the source-bound UK FRS root 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and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/uk_frs_stochastic.py b/test_support/microcosm_build/uk_frs_stochastic.py index b17f7bab40..a677429127 100644 --- a/test_support/microcosm_build/uk_frs_stochastic.py +++ b/test_support/microcosm_build/uk_frs_stochastic.py @@ -116,6 +116,7 @@ def _frame() -> object: "person_benunit_id": [10, 10, 20, 30], "person_household_id": [1, 1, 1, 2], "age": [5, 6, 40, 70], + "is_uc_claimant": [False, False, True, True], "child_benefit_reported": [0, 10, 0, 0], "pension_credit_reported": [0, 0, 0, 5], "universal_credit_reported": [0, 0, 20, 0], diff --git a/test_support/microcosm_build/uk_hmrc_uprating.py b/test_support/microcosm_build/uk_hmrc_uprating.py index df90a072b7..2dc3891a49 100644 --- a/test_support/microcosm_build/uk_hmrc_uprating.py +++ b/test_support/microcosm_build/uk_hmrc_uprating.py @@ -18,11 +18,15 @@ UK_ENGINE_INDEX_PARAMETERS, UK_ENGINE_PARAMETER_INDEX_PREFIX, UK_HMRC_TAXPAYER_GROWTH_INDEX_CONCEPT, + UK_SPI_SAVINGS_INTEREST_HISTORY_PARAMETER, + UK_SPI_SAVINGS_INTEREST_INDEX_CONCEPT, + UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, UK_SPI_STATE_PENSION_AMOUNT_INDEX_CONCEPT, UK_SPI_STATE_PENSION_RATE_PARAMETER, align_hmrc_amount_by_recipient_growth_and_rate, align_hmrc_count_row_by_taxpayer_growth, align_hmrc_row_by_engine_index, + align_hmrc_row_by_history_then_projection, engine_parameter_value, hmrc_uprating_appliers, ) @@ -80,16 +84,6 @@ def _fake_parameter(path: str, instant: str) -> float: return {"2023-01-01": 1.5, "2025-01-01": 1.65}[instant] - - - - - - - - - - def _count_row(*, year: int, lower: int, upper: int | None, value: float): return { "concept": "hmrc.spi_taxpayer_count", @@ -119,4 +113,5 @@ def _count_rows(): rows.append(_count_row(year=year, lower=2_000_000, upper=None, value=two)) return rows + __all__ = [name for name in globals() if not name.startswith("__")] diff --git a/test_support/microcosm_build/uk_uc_capital_coherence.py b/test_support/microcosm_build/uk_uc_capital_coherence.py index ac91f12ddc..6742bb3183 100644 --- a/test_support/microcosm_build/uk_uc_capital_coherence.py +++ b/test_support/microcosm_build/uk_uc_capital_coherence.py @@ -14,6 +14,7 @@ from microcosm.build.uk_runtime.national_frame import uk_national_frame from microcosm.build.uk_runtime.spi_support import support_channel_column from microcosm.build.uk_runtime.uc_capital_coherence import ( + PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES, UC_CAPITAL_COARSENING, UC_CAPITAL_INCOME_BAND_EDGES, UC_CAPITAL_MINIMUM_CELL_DONORS, @@ -27,6 +28,10 @@ _investment_income_band, _redraw_spi_capital, cohere_uc_capital, + pension_credit_property_capital_share, + recorded_capital_with_property, + uc_property_capital_share, + uc_recorded_property_share, ) from microcosm.build.uk_runtime.uc_relationships import ( UC_FINANCIAL_INVESTMENT_INCOME_COLUMNS, @@ -67,7 +72,10 @@ def _frame(): "person_household_id": [row[0] for row in rows], "universal_credit_reported": [row[8] for row in rows], "is_benunit_head": True, + "is_household_head": True, "is_parent": [row[5] > 0 for row in rows], + "age": 40, + "is_uc_claimant": True, } ) other_members = [] @@ -80,7 +88,10 @@ def _frame(): "person_household_id": row[0], "universal_credit_reported": 0.0, "is_benunit_head": False, + "is_household_head": False, "is_parent": row[5] > 0, + "age": 40, + "is_uc_claimant": True, } ) for child in range(row[5]): @@ -91,7 +102,10 @@ def _frame(): "person_household_id": row[0], "universal_credit_reported": 0.0, "is_benunit_head": False, + "is_household_head": False, "is_parent": False, + "age": 5, + "is_uc_claimant": False, } ) person = pd.concat([person, pd.DataFrame(other_members)], ignore_index=True) @@ -108,6 +122,9 @@ def _frame(): } ) household = pd.DataFrame({"household_id": [row[0] for row in rows]}) + # No household holds property here; the property-share tests add it. + for column in PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES: + household[column] = 0.0 return uk_national_frame( person=person, benunit=benunit, diff --git a/test_support/microcosm_build/uk_uc_reporter_redraw.py b/test_support/microcosm_build/uk_uc_reporter_redraw.py index 1a005e91f5..9254e8235d 100644 --- a/test_support/microcosm_build/uk_uc_reporter_redraw.py +++ b/test_support/microcosm_build/uk_uc_reporter_redraw.py @@ -15,7 +15,10 @@ support_channel_column, support_clone_index_column, ) -from microcosm.build.uk_runtime.uc_capital_coherence import cohere_uc_capital +from microcosm.build.uk_runtime.uc_capital_coherence import ( + PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES, + cohere_uc_capital, +) from microcosm.build.uk_runtime.uc_reporter_redraw import ( UC_REPORTER_REDRAW_OUTPUT, UC_REPORTER_REDRAW_SEED, @@ -157,8 +160,10 @@ def _frame(*, child_only: bool = False): "person_household_id": household_id, "age": age, "is_benunit_head": member == 0, + "is_household_head": member == 0, "is_parent": children > 0, "is_uc_child": benunit_id == 204, + "is_uc_claimant": True, "employment_income": 10_000.0 + benunit_id + member, "self_employment_income": 100.0 * member, "savings_interest_income": 10.0, @@ -176,8 +181,10 @@ def _frame(*, child_only: bool = False): "person_household_id": household_id, "age": 10, "is_benunit_head": False, + "is_household_head": False, "is_parent": False, "is_uc_child": True, + "is_uc_claimant": False, "employment_income": 0.0, "self_employment_income": 0.0, "savings_interest_income": 0.0, @@ -206,6 +213,8 @@ def _frame(*, child_only: bool = False): "household_is_capital_gains_clone": [row[4] for row in benunit_rows], } ) + for column in PENSION_CREDIT_PROPERTY_CAPITAL_SOURCES: + household[column] = 0.0 return uk_national_frame( person=person, benunit=benunit, diff --git a/tools/build_uk_ledger_compile_parity_signed_differences.py b/tools/build_uk_ledger_compile_parity_signed_differences.py index 428a4da42a..9efea7f3a0 100644 --- a/tools/build_uk_ledger_compile_parity_signed_differences.py +++ b/tools/build_uk_ledger_compile_parity_signed_differences.py @@ -87,8 +87,10 @@ class ParityReceiptSpec: "Coverage the incumbent lacks (PolicyEngine/chronicle#280 lane): HMRC's Income Tax " "liabilities Table 2.5 taxpayers, total income and tax by band as the " "calendar-2025 window of the 2024-25 and 2025-26 projections, and the SPI " - "2023-24 savings-interest rows by band uprated by the household interest " - "index; the incumbent registry binds neither (chronicle#282)." + "2023-24 savings-interest rows by band, moved to the dataset's year by the " + "household interest index and from there to the calibration year by GDP " + "per head, the index the engine's dataset projection applies " + "(microcosm#1095); the incumbent registry binds neither (chronicle#282)." ) _SPI_REGION_LEDGER_ONLY_RATIONALE = ( @@ -110,6 +112,17 @@ class ParityReceiptSpec: "alone." ) +_OBR_COUNCIL_TAX_GB_RECEIPTS_RATIONALE = ( + "Series and period basis (microcosm#1095, María's ruling of 2026-10-09): ours " + "binds OBR table 4.1 row 15, the council tax receipts of England, Scotland " + "and Wales, over Great Britain's households, at the calendar-2025 window " + "(three twelfths of FY2024-25 and nine twelfths of FY2025-26, María's rule " + "of 2026-09-22, PolicyEngine/chronicle#280 lane); the frozen incumbent " + "fixture holds the FY2025-26 value of row 19 alone, which adds Northern " + "Ireland domestic rates, the council tax accruals adjustment and the " + "high-value council tax surcharge." +) + _ESA_CUBE_DRIFT_RATIONALE = ( "Source class (PolicyEngine/chronicle#280 lane): ours binds DWP's Stat-Xplore ESA " "caseload by payment type as the mean of the four quarterly points inside " @@ -135,6 +148,28 @@ class ParityReceiptSpec: "diagnostic provenance on dwp.state_pension.amount, so the frozen fixture's " "fitted row is fixture-only on the current surface." ) +_OBR_NI_DIAGNOSTIC_RATIONALE = ( + "microcosm#1095 (María's ruling of 2026-10-09; uk-data#537 drops the row) moves " + "OBR's total NICs receipts out of the fit: a cash total against the three " + "accrued class rows, carrying statutory-payment recoveries, Class 1A, 1B and 3 " + "and settlements that no household carries. The exact FY2025-26 figure is " + "retained as diagnostic provenance on obr.ni_employee, so the frozen fixture's " + "fitted row is fixture-only on the current surface." +) +_DWP_PIP_CASELOAD_DIAGNOSTIC_ROWS = frozenset( + { + "dwp.pip.daily_living_standard_claimants", + "dwp.pip.daily_living_enhanced_claimants", + } +) +_DWP_PIP_CASELOAD_DIAGNOSTIC_RATIONALE = ( + "microcosm#1095 (María's ruling of 2026-10-10) keeps DWP's England-and-Wales " + "PIP daily living caseload (FOI2025/24990, end of January 2025) out of the " + "fit: the two rows resolve and fit, but they push the heaviest support " + "family's weight over the uk_weight_ratio fence. The exact figures are " + "retained as diagnostic provenance on obr.pip, so the frozen fixture's " + "fitted rows are fixture-only on the current surface." +) _STATE_PENSION_PREFIXES = ("dwp.state_pension.", "dfc_ni.state_pension.") _STATE_PENSION_LEDGER_ONLY_RATIONALE = ( "microcosm#1069 binds the resident State Pension from DWP Stat-Xplore (Great " @@ -628,6 +663,13 @@ def _add_signed_rationale_notes( row["reason"] = _WELSH_BAND_I_LEDGER_ONLY_RATIONALE elif name == "obr.state_pension" and row.get("kind") == "fixture_only": row["reason"] = _OBR_STATE_PENSION_DIAGNOSTIC_RATIONALE + elif name == "obr.ni" and row.get("kind") == "fixture_only": + row["reason"] = _OBR_NI_DIAGNOSTIC_RATIONALE + elif ( + name in _DWP_PIP_CASELOAD_DIAGNOSTIC_ROWS + and row.get("kind") == "fixture_only" + ): + row["reason"] = _DWP_PIP_CASELOAD_DIAGNOSTIC_RATIONALE elif ( name.startswith(_STATE_PENSION_PREFIXES) and row.get("kind") == "ledger_only" @@ -657,6 +699,8 @@ def _add_signed_rationale_notes( row["reason"] = _ESA_CUBE_DRIFT_RATIONALE elif name.startswith("hmrc.spi_region.") and row.get("kind") == "ledger_only": row["reason"] = _SPI_REGION_LEDGER_ONLY_RATIONALE + elif name == "obr.council_tax" and row.get("kind") == "calibration_drift": + row["reason"] = _OBR_COUNCIL_TAX_GB_RECEIPTS_RATIONALE elif ( name.startswith("obr.") and row.get("kind") == "calibration_drift" diff --git a/tools/diagnose_uk_uc_lp_evidence.py b/tools/diagnose_uk_uc_lp_evidence.py index 8e3312bd22..f06f4ee05a 100644 --- a/tools/diagnose_uk_uc_lp_evidence.py +++ b/tools/diagnose_uk_uc_lp_evidence.py @@ -575,7 +575,9 @@ def bounded_redistribution(run): if row["name"] in [ "obr.income_tax@2025", - "obr.ni@2025", + "obr.ni_employee@2025", + "obr.ni_employer@2025", + "obr.ni_self_employed@2025", "dwp.state_pension.amount@2025", "obr.child_benefit@2025", ] diff --git a/tools/diagnose_uk_uc_support.py b/tools/diagnose_uk_uc_support.py index 8da101ceaa..b039bb4f76 100644 --- a/tools/diagnose_uk_uc_support.py +++ b/tools/diagnose_uk_uc_support.py @@ -256,8 +256,16 @@ def supported_uc_probe(matrix, targets, prior, names, rows, tolerance=0.05): def protected_subset_probe(matrix, targets, prior, names, tolerance=0.05): # The bound State Pension level (microcosm#1069 moved the OBR line to a - # diagnostic). - obr = ["obr.income_tax", "obr.ni", "dwp.state_pension.amount", "obr.child_benefit"] + # diagnostic) and the bound NICs class rows (microcosm#1095 moved OBR's + # total to a diagnostic). + obr = [ + "obr.income_tax", + "obr.ni_employee", + "obr.ni_employer", + "obr.ni_self_employed", + "dwp.state_pension.amount", + "obr.child_benefit", + ] employment = [ str(name).rsplit("@", 1)[0] for name in names diff --git a/tools/generate_uk_target_references.py b/tools/generate_uk_target_references.py index 8b7751667b..83978b6a53 100644 --- a/tools/generate_uk_target_references.py +++ b/tools/generate_uk_target_references.py @@ -44,9 +44,11 @@ POLICYENGINE_BINDING_KEYS = frozenset( { "affected_flag_variable", + "allocation", "band", "band_filter_dimension", "band_period_factor", + "band_semantics", "band_upper_bound", "band_upper_bound_inclusive", "count_of", @@ -111,6 +113,16 @@ def main() -> None: if line.strip() ] inverse_mapping = _registry_inverse(contract) + # The facts each family's fan-out leaves unnamed, counted rather than + # declared, so the membership report states what the feed carried. + skipped_fanout: Counter[str] = Counter() + + def fanout_name(target: Mapping[str, Any], fact: Mapping[str, Any]) -> str | None: + name = _fanout_name(target, fact, inverse_mapping) + if not name: + skipped_fanout[str(target["family"])] += 1 + return name + config = TargetReferenceAuthoringConfig( target_period=args.period, geography_pins=_geography_pins(contract), @@ -118,11 +130,7 @@ def main() -> None: geography_fanout_metadata=_geography_fanout_metadata, geography_composition_by_target_id=_geography_composition(contract), geography_composition_aliases=_geography_composition_aliases(), - fanout_name=lambda target, fact: _fanout_name( - target, - fact, - inverse_mapping, - ), + fanout_name=fanout_name, sum_target_ids=_sum_target_ids(contract), value_operation_by_target_id=_value_operation_by_target_id(contract), selector_pins_by_target_id=_selector_pins(contract), @@ -134,7 +142,11 @@ def main() -> None: uprating_appliers=UK_UPRATING_APPLIERS, ) authored = author_target_references(contract, facts, config) - _add_uk_membership_accounting(authored.membership_report, authored.references) + _add_uk_membership_accounting( + authored.membership_report, + authored.references, + skipped_fanout=skipped_fanout, + ) resource = target_references_resource( country="uk", description=DESCRIPTION, @@ -455,8 +467,21 @@ def _geography_composition( } +#: Target ids pinned one by one, where neither a prefix nor the substring rule +#: gives the geography the publisher's fact is stamped with. +TARGET_ID_GEOGRAPHY_PINS: Mapping[str, str] = { + # OBR table 4.1 row 15 sums the England, Scotland and Wales council tax + # rows; Chronicle stamps it K02000001, as the table is UK-wide, while the + # substring rule would read "scotland" in its concept (microcosm#1095). + # The binding scopes the model column to Great Britain. + "obr.council_tax": "uk", +} + + def _geography_id_for_target(target: Mapping[str, Any]) -> str: target_id = str(target["target_id"]).lower() + if target_id in TARGET_ID_GEOGRAPHY_PINS: + return UK_GEOGRAPHY_IDS[TARGET_ID_GEOGRAPHY_PINS[target_id]] if target_id.startswith("dft."): area = str( (target.get("ledger_selector") or {}).get("layout_groupby_value_id", "") @@ -780,6 +805,9 @@ def _int_token(value: int) -> str: def _annual_uc_award_band_token(value: str) -> str: normalized = value.strip().lower() + # "No payment" and the top-coded "or over" categories stay unbound: bound + # to DWP's counts, the GBP 2,500.01 or over band left OBR's Universal + # Credit total short (microcosm#1095 receipts). if normalized == "no payment" or "or over" in normalized: return "" if " to " not in normalized: @@ -798,6 +826,8 @@ def _annual_uc_award_band_token(value: str) -> str: def _add_uk_membership_accounting( report: dict[str, Any], references: tuple[dict[str, Any], ...], + *, + skipped_fanout: Mapping[str, int], ) -> None: fanout_counts = Counter( reference["family"] @@ -887,14 +917,16 @@ def _add_uk_membership_accounting( "family": "dwp_universal_credit", "status": "active_with_unmapped_vintage_residue_skipped", "active_reference_count": fanout_counts.get("dwp_universal_credit", 0), - "skipped_unmapped_fact_count": 12, + "skipped_unmapped_fact_count": int( + skipped_fanout.get("dwp_universal_credit", 0) + ), "signed_rationale": ( "UC payment-distribution targets fan out over family_type and " "monthly_award_amount_bands into incumbent-compatible " - "annual-payment rows. The four source-only 'No payment' facts " - "and eight overlapping source-only 'or over' facts are left " - "out; no active reference may use the legacy nan_to_nan band " - "name." + "annual-payment rows. The source-only 'No payment' and " + "top-coded 'or over' facts are left out, and the skipped count " + "is the generator's own (microcosm#1095); no active reference " + "may use the legacy nan_to_nan band name." ), }, {