diff --git a/changelog.d/1095-hrad-nil-band-receipts.changed.md b/changelog.d/1095-hrad-nil-band-receipts.changed.md new file mode 100644 index 000000000..259809ca4 --- /dev/null +++ b/changelog.d/1095-hrad-nil-band-receipts.changed.md @@ -0,0 +1 @@ +The UK property purchases stage allocates households' additional-dwelling (HRAD) purchases at or below GBP 250,000 from HMRC's Table 8a receipts instead of individuals' share of all transactions (microcosm#1095). The residential nil-rate band was GBP 250,000 for all of 2024-25, so a purchase in that band owes no standard-rate SDLT and other buyers' receipts there are surcharge: at Table 6b's gross HRAD receipts per transaction and per pound, their GBP 265m is about 48,100 HRAD transactions and GBP 7.05bn, against the 13,400 transactions the all-band share left them. Households take the rest of the band; above it they keep uk-data#573's shares, which probably still overstate them, since the standard rates make up most receipts there. Households' 2024-25 target falls from 186,589 transactions and GBP 68.81bn to 151,928 and GBP 63.71bn, both above uk-data#573's lower bounds, and the lowest band's share of their purchases from 54% to 43%. The stage's derivation records the nil-band receipts, the implied HRAD and its shares. diff --git a/changelog.d/1095-spi-forest-age-band.changed.md b/changelog.d/1095-spi-forest-age-band.changed.md new file mode 100644 index 000000000..fe427680f --- /dev/null +++ b/changelog.d/1095-spi-forest-age-band.changed.md @@ -0,0 +1 @@ +The UK SPI income stage's stage-1 tape and reference forests now read the SPI age band, not an age (uk-data#628). The 2022-23 SPI tape gives age only as a band (AGERANGE), so the age the stage draws for each donor inside its band carried no income pattern: the forest learned none, and FRS twins in one band were drawn from conditionals that differed only by that noise. The predictor `spi_age_band` is the tape's own AGERANGE code, with composite records (-1) as a band of their own, and for a frame person the band of their whole-year age. It is one-hot encoded, so the composites, the tape's high-income amalgams, are a category of their own rather than the neighbours of 16-24 on an ordinal scale; `at_or_over_spa` still carries the State Pension split inside the 65-74 band. The cross-fit fold cells key on the band too, since they key on the predictor values. The drawn donor ages are kept for the band pools' State Pension side. The stage receipt's `stage1_age_predictor` names the rule. diff --git a/changelog.d/1095-two-child-limit-cutoff-date.fixed.md b/changelog.d/1095-two-child-limit-cutoff-date.fixed.md new file mode 100644 index 000000000..3b8975a0b --- /dev/null +++ b/changelog.d/1095-two-child-limit-cutoff-date.fixed.md @@ -0,0 +1 @@ +The two-child-limit target rows now date births by policyengine-uk's own cutoff test, `uc_is_child_born_before_child_limit` (a date of birth before 6 April 2017), instead of a birth year of 2017 or later (microcosm#1095). The calendar year counted a child born from January to 5 April 2017 as affected by the limit, although the limit does not apply to them in the engine. diff --git a/changelog.d/1095-two-child-limit-non-parental-care.added.md b/changelog.d/1095-two-child-limit-non-parental-care.added.md new file mode 100644 index 000000000..741587328 --- /dev/null +++ b/changelog.d/1095-two-child-limit-non-parental-care.added.md @@ -0,0 +1,3 @@ +The UK spine now sets policyengine-uk's `child_limit_non_parental_caring_exception`, the two-child limit's exception for non-parental caring arrangements (UC Regs 2013 Sch 12 para 4; CTC Regs 2002 reg 12), for children in kinship or friends care (microcosm#1095). Nothing set it before, so the engine paid no child element for such a third or later child born from 6 April 2017. + +The `frs_relationships` stage marks a child-table member whom no adult of their own benefit unit claims as a natural, adopted or step child, read from either side of the household grid (codes 3 and 4 from the child, 7 and 8 from the adult), and whom no local authority placed there. That covers a child living with grandparents, siblings, other relatives or friends, and a baby whose parent is a child of the claimant's unit. The multiple births, adoption and non-consensual conception exceptions stay at the engine's default: the FRS has no twin field and its birth dates are blank. diff --git a/changelog.d/1095-uk-carers-allowance-after-salary-sacrifice.changed.md b/changelog.d/1095-uk-carers-allowance-after-salary-sacrifice.changed.md new file mode 100644 index 000000000..92740c836 --- /dev/null +++ b/changelog.d/1095-uk-carers-allowance-after-salary-sacrifice.changed.md @@ -0,0 +1 @@ +The UK Carer's Allowance take-up stage now runs after `salary_sacrifice`, so its earnings test reads the pay that salary sacrifice converts into pension contributions (microcosm#1095 B5). That also puts it after the CGT stages' copies, so its draws are keyed on lineage: a member of a CGT support copy, incidence clone or residential arm draws as the member at the same rank of the household it copies (`lineage_roots`), and the balanced placement takes a source and its copies in one stratum as one record of their joint weight (`lineage_balanced_positions`), so the two claim together as before. Original rows keep their draws. The stage spec, the coverage manifest and the H2 fixture follow. diff --git a/changelog.d/1095-uk-scottish-owner-council-tax-bands.changed.md b/changelog.d/1095-uk-scottish-owner-council-tax-bands.changed.md new file mode 100644 index 000000000..15f6be5d9 --- /dev/null +++ b/changelog.d/1095-uk-scottish-owner-council-tax-bands.changed.md @@ -0,0 +1 @@ +The UK dense build's post-geography band node now draws each Scottish owner clone's council-tax band from its council area's chargeable dwellings by band, A to H, at September 2025 (Scottish Government CTAXBASE 2025), at the same uniform as its value, its rank among Scotland's owners (microcosm#1095 B12). The counts are vendored from Chronicle into `uk/scotgov_chargeable_dwellings_by_band.json` and pinned as the graph source `uk_scotgov_band_mix`, beside the VOA mix England and Wales draw from. Renters keep their band, as in England and Wales, and so does Northern Ireland. The band node's receipt reports Scotland apart. The national build does not localise property, so it does not change. diff --git a/changelog.d/1095-uk-uc-take-up-gate-pools.changed.md b/changelog.d/1095-uk-uc-take-up-gate-pools.changed.md new file mode 100644 index 000000000..6fcb84d03 --- /dev/null +++ b/changelog.d/1095-uk-uc-take-up-gate-pools.changed.md @@ -0,0 +1 @@ +The UK Universal Credit take-up stage gate (`uk_stage_uc_take_up`) gives the reachable pools a tolerance of their own, `maximum_reachable_pool_deviation`, beside the family type's caseload's `maximum_caseload_deviation` (Vahid's review of #1426). Both stay at 0.055 of DWP's count. With #1428's pool joining, every family type ends closer to DWP's count after the redraw than before it, the SPI employment pay floor (uk-data#629) included, so the caseload needs no wider allowance (María's ruling of 2026-11-02). The contract re-pins the gate battery's policy, manifest and fingerprint digests and the release certification's spine part. diff --git a/changelog.d/1418-spi-donor-savings-rebase.changed.md b/changelog.d/1418-spi-donor-savings-rebase.changed.md new file mode 100644 index 000000000..ae8822314 --- /dev/null +++ b/changelog.d/1418-spi-donor-savings-rebase.changed.md @@ -0,0 +1 @@ +The SPI income stage now rebases the 2022-23 tape's interest and other income to HMRC's published 2023-24 Table 3.7 amounts band by band (the published rows are vendored as `uk/hmrc_spi_table_3_7_savings_rows.json`), then carries both to the frame's year on the OBR's savings income growth (October 2026 EFO, Table 4.5), the path the Table 3.7 band targets take (#1418 savings follow-up, #1428). Before, each column moved by its engine index from 2022 to 2024: other income by GDP per head (x1.09) where SPI's own other income grew x1.65 in the single year to 2023-24, and interest by ONS household interest (x2.27) where SPI's grew x2.69 in that year. The frame then carried about 70 percent of the Table 3.7 savings rows at design weights, and the solve met them by stretching the weights of the few households that carry them. Each band's factor is the published amount over the FACT-weighted amount of the tape's taxpayers in the band, as Table 3.7 counts taxpayers only. The tape is banded once on its income moved to 2023-24, each income leaf at its own factor and each rebased column at its factor over all bands, so the band-edge migration its consumers re-band on uprated income is counted once; a record whose band's factor differs from that of all bands can still sit across an edge on its rebased income. A taxpayer is a record the tape flags (TAXPAYER 1) or one whose income crosses the Personal Allowance by 2023-24, so a saver who becomes a taxpayer counts once; a flagged taxpayer below the first band sits in it (note 8), and any other record takes the factor of all bands. A band too thin to gross (under a tenth of its published amount) takes the factor of all bands, a column whose tape does not gross to the published amounts is left as it is and grows on its engine index from 2022-23, and a tape whose TAXPAYER codes are not 1 or 2 is refused. On the tape, the £1m-and-over band's other income outweighs its interest, as in the published table, so the rebase also undoes the split the two indices had flipped. diff --git a/changelog.d/1428-dwp-jsa-income-based.added.md b/changelog.d/1428-dwp-jsa-income-based.added.md new file mode 100644 index 000000000..c00627bb8 --- /dev/null +++ b/changelog.d/1428-dwp-jsa-income-based.added.md @@ -0,0 +1 @@ +The national calibration binds DWP's income-based Jobseeker's Allowance spending, `dwp.jsa.amount_income_based` (benefit expenditure and caseload tables, Spring Forecast 2026), at the calendar-2025 window of the fiscal years, as the pension-age Housing Benefit row is (#1428; María's ruling of 2026-10-31). Income-based JSA closes with the move to Universal Credit: £91.0m in FY2024-25 and under £1m from FY2025-26. Without the split, the calibration meets OBR's total JSA by scaling income-based reporters together with contributory claimants, far past DWP's income-based spending. diff --git a/changelog.d/1428-frs-other-income-definitions.changed.md b/changelog.d/1428-frs-other-income-definitions.changed.md new file mode 100644 index 000000000..140b38854 --- /dev/null +++ b/changelog.d/1428-frs-other-income-definitions.changed.md @@ -0,0 +1 @@ +FRS rows now book NS&I income on SPI's definitions (#1428). NS&I Direct Saver and the other NS&I and bank products go to `savings_interest_income`, because SPI has counted all interest paid gross or net as bank interest since 2016-17 (Table 3.7 note 4); gilts stay in `other_investment_income`. Income from an annuity not bought with pension funds or from a trust or covenant (FRS PENTYPE 4 and 5) stays in `private_pension_income`, where policyengine-uk's means tests (Pension Credit, Universal Credit, Housing Benefit, Council Tax Reduction, Income Support), household income and tax read it. Its part is stored in a new calibration-only descriptor, `annuity_and_trust_income`, and SPI Table 3.7's other-income rows read `other_investment_income_spi_concept`: the engine's other income plus that share of the engine's private pension, as the table counts both payments as other income (note 6). A row the SPI stage draws holds the tape's pension, which excludes them, so its descriptor is zero. The FRS spine now refuses a pension tab without PENTYPE. diff --git a/changelog.d/1428-isa-fit-weight-records.fixed.md b/changelog.d/1428-isa-fit-weight-records.fixed.md new file mode 100644 index 000000000..967a5f7d9 --- /dev/null +++ b/changelog.d/1428-isa-fit-weight-records.fixed.md @@ -0,0 +1 @@ +The ISA subscriptions stage no longer declares an empty list of fit-weight records (#1428). It fits no model, but the release-cut weights audit read its empty list as a fitting stage that recorded nothing, so every national certified after #1421 failed `uk_weights_audit`. diff --git a/changelog.d/1428-spi-employment-by-self-employment-band.added.md b/changelog.d/1428-spi-employment-by-self-employment-band.added.md new file mode 100644 index 000000000..980f00f97 --- /dev/null +++ b/changelog.d/1428-spi-employment-by-self-employment-band.added.md @@ -0,0 +1 @@ +The national calibration binds SPI 2023-24 Table 3.10's individuals with employment income by self-employment band, `hmrc.spi.employment_income.count_by_self_employment_income_band`, over the eight bands from £1 to £10,000 that the self-employment counts and amounts already bind (#1418; María's choice of 2026-10-30). With the self-employment counts alone, the solve could meet each band with traders who have no pay, while in Table 3.10 more than half of the traders below £3,000 also have employment income; an employee's side profit is taxed at their marginal rate, so the mix of small traders decides what the Budget's trading allowance measures cost. Each person with pay or company car benefits over the year (`employment_income_with_benefits_in_kind_annual`, the term every HMRC employment income row reads since #1430) counts in their own self-employment band, taxpayer or not. Table 3.10's column is wider, income assessed under ITEPA 2003 plus other miscellaneous income, so like Table 3.6's employment rows the binding leaves out miscellaneous income and the contributory benefits; benefits in kind count on SPI rows, whose pay is the tape's PAY + EPB + TAXTERM, and the company car and car fuel benefits on every row. Counts grow to 2025 with HMRC's projected growth of all taxpayers (Income Tax liabilities statistics Table 2.5), as the self-employment counts do, and the zero band and the bands from £10,000 are signed out row by row. diff --git a/changelog.d/1428-spi-gift-anchor.changed.md b/changelog.d/1428-spi-gift-anchor.changed.md new file mode 100644 index 000000000..7ef70ce5a --- /dev/null +++ b/changelog.d/1428-spi-gift-anchor.changed.md @@ -0,0 +1 @@ +The SPI income stage anchors the drawn copies' Gift Aid and charitable investment gifts to the SPI tape's own weighted totals (#1428; María's ruling of 2026-10-31). Both are sparse leaves that the stage-1 chain draws after, and conditioned on, the income leaves, so a change to the savings draws reshuffles which few donors' gifts the copies draw. Each column's total over the drawn copies, at the stage's household weights, is scaled to the tape's FACT-weighted total. A column that the tape or the copies carry none of is left as drawn. The scaling fixes the totals, not how concentrated each column is. The stage evidence records each column's factor, never a total or a count. diff --git a/changelog.d/1428-spi-other-income-analogue.changed.md b/changelog.d/1428-spi-other-income-analogue.changed.md new file mode 100644 index 000000000..c39db4225 --- /dev/null +++ b/changelog.d/1428-spi-other-income-analogue.changed.md @@ -0,0 +1 @@ +The SPI stage-1 transport now locates each copy's other-income draw at its FRS twin's own other income (gilt interest), as it does for the other investment leaves, instead of an identity uniform that ignored the twin's holdings (#1428). diff --git a/changelog.d/1428-trading-allowance-receipts.added.md b/changelog.d/1428-trading-allowance-receipts.added.md new file mode 100644 index 000000000..2d4174f8f --- /dev/null +++ b/changelog.d/1428-trading-allowance-receipts.added.md @@ -0,0 +1 @@ +A new last spine stage, `trading_allowance_receipts`, stores the self-employed's gross receipts (`self_employment_gross_receipts`), so policyengine-uk applies the trading allowance against receipts as the law does (#1428; María's choice of 2026-10-31). Neither the FRS nor the SPI tape records turnover. Without receipts the engine relieved profits up to the allowance in full and nothing above it, so a rise in the allowance missed its main base: traders who already claim the £1,000 allowance against larger receipts. Under current law a claimant's receipts are their profit plus £1,000 exactly. Each trader with a positive profit is flagged as a claimant with probability 0.72 for profits up to £4,500, 0.14 from £4,500 to £9,000 and none above, drawn on their own `person_id`. A claimant's receipts are their profit plus £1,000; any other trader's are their profit plus £3,000, expenses at the raised allowance, since the costing books traders who switch to the allowance as behaviour. The rates are calibrated to HMRC's policy paper (about 400,000 taxpayers with a lower bill, by about £380 on average), measured on policyengine-uk for the national from 9f70c71e7, until HMRC publishes claimant counts; they drift with each national, so #1439 re-measures them after each build. Every trader with a positive profit is treated as filing, as the Table 3.10 counts the calibration binds are of filers, so a profit of £1,000 or less is now taxed where the engine used to relieve it in full. Two limits remain until policyengine-uk#2765 lets the stage store the claim status itself (#1439). A claimant's £1,000 grows with the mixed income index while the allowance is nominal, so the raise relieves £1,932 more in 2027-28, falling to £1,758 by 2031-32. The other traders' stored £3,000 grows the same way, so allowances above £3,000 are mis-scored. `self_employment_gross_receipts` leaves the register of engine inputs left at their default and joins the export surface's allowed columns. The stage spec and schema, the graph and kernel wiring, the spine build, the coverage manifest, the H2 fixture and the data contract's gate digests follow. diff --git a/changelog.d/1428-uc-take-up-passes-unmet-gaps.changed.md b/changelog.d/1428-uc-take-up-passes-unmet-gaps.changed.md new file mode 100644 index 000000000..f057971ad --- /dev/null +++ b/changelog.d/1428-uc-take-up-passes-unmet-gaps.changed.md @@ -0,0 +1 @@ +The Universal Credit take-up stage now passes the gap of a pool that cannot meet DWP's count to its neighbouring pools, so each family type's caseload lands on DWP's wherever its entitled units allow (#1428; María's rulings of 2026-10-31 and 2026-11-01). The stage pools adjacent award bands within a family type until they rest on enough sources. A pool can't meet DWP's count when its reporters' and other undrawn units' expected claims already exceed it, or when the count exceeds every entitled unit. Such a pool used to keep its clipped rate, which left the family type's caseload off DWP's by the gap, and which bands fell short moved with small upstream changes. Now such a pool is held at its clip and its gap passes on. A shortfall and an excess elsewhere in the family type offset first. What remains goes to the other pools, nearest first, the one with more room first at equal distance and the lower band on a tie, each taking what its room allows. Every band keeps its own rate, so no band's drawn units claim against its own count to meet another's. The stage spec, the coverage manifest, the H2 fixture and the data contract's gate digests follow. diff --git a/changelog.d/uk-spi-employment-pay-floor.fixed.md b/changelog.d/uk-spi-employment-pay-floor.fixed.md new file mode 100644 index 000000000..7cc3a9e78 --- /dev/null +++ b/changelog.d/uk-spi-employment-pay-floor.fixed.md @@ -0,0 +1 @@ +The UK SPI income stage now gives drawn employees current pay consistent with their twin's hours, and keeps the SPI tape's pay as their year of pay (uk-data#629). Stage 1 draws annual tape pay on age, sex, region and the bridge predictors but not on hours, and the tape holds part-year workers, so a copy whose FRS twin reports usual hours in an employee job could draw a year's pay below what those hours earn at the minimum wage and fail the minimum-income tests of the 30 hours' childcare offer, Tax-Free Childcare and the benefit cap more often than FRS rows. A new `floor_spi_employment_pay_at_minimum_wage` operation of `hmrc_spi_income_spine` lifts the drawn `hmrc_spi_pay` leaf to the twin's usual weekly hours (`hours_worked` read back at 52) times 52 weeks times the lower of policyengine-uk's `gov.hmrc.minimum_wage.non_apprentice` rate for the twin's age, read as the engine reads the build year (2024-25: £11.44 from 21, £8.60 from 18, £6.40 below), and the twin's own hourly pay, so no copy is held to a higher hourly rate than the FRS person it copies. The floored pay is the row's current pay; its `employment_income_annual_share` becomes its unfloored over its floored employment income, so the annual measures, which the HMRC income rows read, still read the tape's pay: low tape pay is read as part of a year at the floored rate. The operation runs after the stage-1 draw, the band-donor resample and the State Pension leaf guard, and before `employment_income` is derived from the leaves and stage 2 conditions on it, so the pension contributions (#1332's twin employer rate now applies to the floored pay) and every later stage read the floored current pay; the transport receipt keeps the unfloored draws' uniforms. It touches only drawn recipients other than reserved band carriers, whose twin has an employee main job, no trade and usual hours, and whose drawn pay is positive; FRS rows are never modified. Tape PAY is taxable pay, net of net-pay pension contributions and salary sacrifice, while the minimum wage binds gross pay: PAY alone is compared with the gross minimum, since it is the leaf of the engine's gross `employment_income` input. The stage evidence gains an `employment_pay_floor` receipt with eligible and floored source households (rounded), weighted people, the weighted share floored, the weighted mean uplift and the amount added, and an hourly-pay check: the weighted shares of the drawn employees below the minimum wage for their age before and after the floor beside the FRS employees' share, and the eligible rows still below their floor; every figure on one to nine source households, or beside a side on one to nine, is "<10". A new release-blocking stage gate, `uk_stage_hmrc_spi_income_spine_hourly_pay`, refuses a spine where an eligible drawn employee stays below its floor or where, after the floor, drawn employees sit below the minimum wage more often than FRS employees by more than two points; its details give verdicts only. The gate joins the spine gate scope, so spines saved before it no longer match a build's battery. diff --git a/docs/uk-full-build-graph.md b/docs/uk-full-build-graph.md index af26f78b6..244a753c4 100644 --- a/docs/uk-full-build-graph.md +++ b/docs/uk-full-build-graph.md @@ -24,7 +24,7 @@ uv run --no-sync python tools/build_uk_full.py --release-role dense \ The three atomic-area supports are the artifacts pinned in `uk/uk_atomic_area_supports.provenance.json` and `uk/spec/sources.yaml` (built by `tools/build_uk_atomic_area_supports.py` from the published ONS, NRS and NISRA lookups; publisher registration PolicyEngine/chronicle#269). `--geography-assignment atomic` is the default and requires all three; `--atomic-support-sha256-{ew,scotland,ni}` pin them like `--ladder-sha256`, and a release candidate requires all four pins. `--geography-assignment legacy` keeps the previous sequential ladder draw for measurement builds, takes no supports, and is refused by `--release-candidate`. -After derivation, `--property-localisation price_paid` (the default, atomic assignment only) runs the microcosm#1240 value and band nodes: every owner clone in England and Wales reads its `main_residence_value` off its assigned constituency's uprated Land Registry sale distribution at the household's weighted rank among its FRS region's owners (one keyed uniform per spine household, shared by all of its pool clones; high-value support copies take a keyed rank inside the top stratum), `property_wealth` moves by the same amount, and `council_tax_band` is drawn from the assigned LSOA's VOA band mix at the same uniform (owner-occupiers only: the mix counts every dwelling, so renters keep the spine's band). The two resources default to the committed `uk/high_value_home_value_knots.json` and `uk/voa_council_tax_band_mix_lsoa.json`; `--price-paid-quantiles` and `--voa-band-mix` name other bytes with `--price-paid-quantiles-sha256` and `--voa-band-mix-sha256` pins, and a release candidate must build on the committed resources. `--property-localisation none` keeps the spine's values and bands on every clone, for measurement builds only; `--release-candidate` refuses it. The quantile draw is a typed artifact of the value node that the band node and `uk.full.pool` consume, so the checkpoint binds the draw and its resource digests. The high-value support copies are ranked beside their root, so the owner mass at or above the stratum stays the selected share. The two resources and the derived stock carry the publishers' attribution statements (`licence` block): Price Paid and the UK HPI are HM Land Registry data © Crown copyright and database right 2026 under the Open Government Licence v3.0; the NSPL carries the OS and Royal Mail notices with ONS's OGL statement; the VOA, census and OBR inputs are OGL v3.0. +After derivation, `--property-localisation price_paid` (the default, atomic assignment only) runs the microcosm#1240 value and band nodes: every owner clone in England and Wales reads its `main_residence_value` off its assigned constituency's uprated Land Registry sale distribution at the household's weighted rank among its FRS region's owners (one keyed uniform per spine household, shared by all of its pool clones; high-value support copies take a keyed rank inside the top stratum), `property_wealth` moves by the same amount, and `council_tax_band` is drawn from the assigned LSOA's VOA band mix at the same uniform (owner-occupiers only: the mix counts every dwelling, so renters keep the spine's band). A Scottish owner clone draws its band the same way, at the same uniform, from its council area's chargeable dwellings by band, A to H, at September 2025 (Scottish Government CTAXBASE 2025, vendored from Chronicle into `uk/scotgov_chargeable_dwellings_by_band.json`; microcosm#1095 B12); Scottish renters and Northern Ireland keep the spine's band. The three resources default to the committed `uk/high_value_home_value_knots.json`, `uk/voa_council_tax_band_mix_lsoa.json` and `uk/scotgov_chargeable_dwellings_by_band.json`; `--price-paid-quantiles`, `--voa-band-mix` and `--scotgov-band-mix` name other bytes with `--price-paid-quantiles-sha256`, `--voa-band-mix-sha256` and `--scotgov-band-mix-sha256` pins, and a release candidate must build on the committed resources. `--property-localisation none` keeps the spine's values and bands on every clone, for measurement builds only; `--release-candidate` refuses it. The quantile draw is a typed artifact of the value node that the band node and `uk.full.pool` consume, so the checkpoint binds the draw and its resource digests. The high-value support copies are ranked beside their root, so the owner mass at or above the stratum stays the selected share. The knots and VOA resources and the derived stock carry the publishers' attribution statements (`licence` block): Price Paid and the UK HPI are HM Land Registry data © Crown copyright and database right 2026 under the Open Government Licence v3.0; the NSPL carries the OS and Royal Mail notices with ONS's OGL statement; the VOA, census and OBR inputs are OGL v3.0. After derivation, `--brma-localisation area` (the default, atomic assignment only) runs the microcosm#1262 node `uk.full.geography.brma`: every placed clone in Great Britain takes the Broad Rental Market Area of its assigned area from the committed `uk/brma_area_layer.json` (England and Wales by 2021 LSOA, Scotland by 2022 output area), so its BRMA, Local Housing Allowance rate, local authority and constituency describe one place. The spine's `frs_brma` stage draws a BRMA within the FRS region before any household has an area, so without the node every clone of a household keeps that one draw wherever it lands. Northern Ireland has no open BRMA geography and keeps the spine's draw; so does the national line, which has no areas. The node is deterministic, fails closed on an area the layer does not hold or places in another FRS region, and the terminal gate `uk_local_brma_enum_domain` rechecks the rewritten column against the engine's enum before export. `--brma-area-layer` names other layer bytes with a `--brma-area-layer-sha256` pin; a release candidate must build on the committed layer and refuses `--brma-localisation none`, which is for measurement builds only. `tools/build_uk_brma_area_layer.py` rebuilds the layer from Chronicle's raw registrations (population-weighted centroids in the VOA, Rent Officers Wales and Scottish Government polygons, Scotland corrected by Rent Service Scotland's postcode lookup) and the atomic supports. diff --git a/docs/uk-income-anchors-280.md b/docs/uk-income-anchors-280.md index 323ad55da..d97310040 100644 --- a/docs/uk-income-anchors-280.md +++ b/docs/uk-income-anchors-280.md @@ -80,8 +80,10 @@ 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 takes two legs - (microcosm#1095): `ons.household_interest_income`, the index of its + uses: dividends `obr.per_capita.gdp`. Savings interest outside SPI Table + 3.7's rows takes two legs (microcosm#1095); Table 3.7's interest and other + income rows take the OBR's savings income path from 2023-24 instead, as the + next paragraph says (microcosm#1418): `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 1.0474 = 1.1961 on the pinned engine values; 1.0438 and 1.1920 before @@ -96,6 +98,28 @@ membership carries the uprated value with its receipt. by `tools/pin_uk_uprating_engine_values.py`, whose `--check` refuses drift), so it runs and reproduces without policyengine-uk; a `requires_uk` test holds the pins in lockstep with the installed engine, and an engine bump re-runs the tool. + + The SPI donors reach the frame's year apart from these indices for interest + and other income (microcosm#1418 savings follow-up). The income stage first + scales each donor's two columns to HMRC's published 2023-24 Table 3.7 amounts + in its band of total income (`spi_income.rebase_spi_donor_savings`, the rows + vendored as `uk/hmrc_spi_table_3_7_savings_rows.json`), then grows them on the + OBR's savings income path the Table 3.7 targets take + (`hmrc_uprating.uk_obr_savings_income_level`). Each band's factor is the + published amount over the FACT-weighted amount of the tape's taxpayers in + the band, as Table 3.7 counts taxpayers only (microcosm#1428). The tape is + banded once on its income moved to 2023-24, each income leaf at its own + factor and each rebased column at its factor over all bands, so the + band-edge migration the consumers re-band on uprated income is counted + once. A record whose band's factor differs from that of all bands can still + sit across an edge on its rebased income. A taxpayer is a record the tape flags (TAXPAYER 1) or one whose income + crosses the Personal Allowance by 2023-24; a flagged taxpayer below the first + band sits in it (note 8); any other record takes the factor of all bands. A + band the tape carries none of is left as it is, a band whose tape amount is + under a tenth of its published amount takes the factor of all bands, and a + column whose tape does not gross to the published amounts is left as it is + and grows on its engine index from 2022-23. From the frame's year on, the + engine projects the two columns on their own indices. - Count rows: `hmrc.itl_2026.taxpayer_count_growth_by_total_income_band`, HMRC's own projected growth in Income Tax payers for the Table 2.5 band that contains the SPI band (Income Tax liabilities statistics, July 2026), as the @@ -405,8 +429,9 @@ fold differs from its twin's, and a reference whose sign classes differ from the tape forest's on any located target. One difference between the two forests remains: the reference locates a leaf given the twin's observed earlier analogue leaves, while the tape forest draws it given every earlier -output, the three identity-uniform leaves (`other_investment_income`, -`gift_aid`, `charitable_investment_gifts`) included, so a located quantile is +output, the two identity-uniform leaves (`gift_aid`, +`charitable_investment_gifts`) included, since `other_investment_income` has +located at the twin's own other income from microcosm#1428, so a located quantile is read off a conditional those identity draws also shape; pay, which follows them in the chain, has the weakest rank fidelity, and moving the analogue targets ahead of the identity leaves is the follow-up to measure. Because the diff --git a/packages/microcosm-build/src/microcosm/build/source_manifest.py b/packages/microcosm-build/src/microcosm/build/source_manifest.py index e773bea0f..5bee51d06 100644 --- a/packages/microcosm-build/src/microcosm/build/source_manifest.py +++ b/packages/microcosm-build/src/microcosm/build/source_manifest.py @@ -58,6 +58,8 @@ "assign_student_loan_plan_cohorts", "assign_main_asset_type", "assign_trips_from_band_means", + # microcosm#1428: the self-employed's gross receipts. + "assign_trading_allowance_receipts", "assign_uniform_draw", "aggregate_person_to_benunit", "allocate_per_capita_from_cell_table", @@ -142,6 +144,7 @@ "fit_weighted_qrf_chain", "fit_weighted_qrf_stage1", "fit_weighted_qrf_stage2", + "floor_spi_employment_pay_at_minimum_wage", "fold_into", "gate_distributional_effective_mass", "gate_zero_weight_strata", 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 8c23739b7..89ace677d 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 @@ -2285,6 +2285,30 @@ "below_age" ] }, + "non_parental_care_child": { + "type": "object", + "additionalProperties": false, + "properties": { + "child_codes": { + "type": "array", + "minItems": 1, + "items": { + "type": "integer" + } + }, + "parent_codes": { + "type": "array", + "minItems": 1, + "items": { + "type": "integer" + } + } + }, + "required": [ + "child_codes", + "parent_codes" + ] + }, "one_person_age_split": { "type": "integer", "minimum": 0 @@ -2359,6 +2383,7 @@ "family_link_codes", "dependent_child", "looked_after_child", + "non_parental_care_child", "one_person_age_split", "family_role_values", "household_type_values", @@ -6612,6 +6637,75 @@ } } }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "output", + "profit", + "draw", + "seed", + "claimant_bands", + "claimant_expenses", + "other_expenses", + "calibration", + "rule" + ], + "properties": { + "kind": { + "const": "assign_trading_allowance_receipts" + }, + "output": { + "type": "string" + }, + "profit": { + "type": "string" + }, + "draw": { + "type": "string" + }, + "seed": { + "type": "integer" + }, + "claimant_bands": { + "type": "array", + "minItems": 1, + "items": { + "type": "object", + "additionalProperties": false, + "required": [ + "rate" + ], + "properties": { + "upper_inclusive": { + "type": "number", + "exclusiveMinimum": 0 + }, + "rate": { + "type": "number", + "minimum": 0, + "maximum": 1 + } + } + } + }, + "claimant_expenses": { + "type": "number", + "minimum": 0 + }, + "other_expenses": { + "type": "number", + "minimum": 0 + }, + "calibration": { + "type": "string" + }, + "rule": { + "type": "string" + } + } + }, { "type": "object", "additionalProperties": false, @@ -9780,6 +9874,67 @@ } } }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "column", + "hours_column", + "hours_basis", + "weeks_in_year", + "hourly_rate", + "employee_statuses", + "rows", + "pay_basis", + "reason", + "annual_income", + "receipt" + ], + "properties": { + "kind": { + "const": "floor_spi_employment_pay_at_minimum_wage" + }, + "column": { + "type": "string" + }, + "hours_column": { + "type": "string" + }, + "hours_basis": { + "type": "string" + }, + "weeks_in_year": { + "type": "number", + "exclusiveMinimum": 0 + }, + "hourly_rate": { + "type": "string" + }, + "employee_statuses": { + "type": "array", + "minItems": 1, + "items": { + "type": "string" + } + }, + "rows": { + "type": "string" + }, + "pay_basis": { + "type": "string" + }, + "reason": { + "type": "string" + }, + "annual_income": { + "type": "string" + }, + "receipt": { + "type": "string" + } + } + }, { "type": "object", "additionalProperties": false, 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 e0110781d..c51481ee2 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 @@ -41,7 +41,8 @@ "hmrc.spi.private_pension_income.count_by_total_income_band", "hmrc.spi.state_pension.count_by_total_income_band", "hmrc.spi.savings_interest_income.count_by_total_income_band", - "hmrc.spi.self_employment_income.count_by_self_employment_income_band" + "hmrc.spi.self_employment_income.count_by_self_employment_income_band", + "hmrc.spi.employment_income.count_by_self_employment_income_band" ], "max_dropped_rows": 2, "max_dropped_published_share": 0.01 @@ -76,7 +77,7 @@ "reason": "Zero-support floor: a row in one of the floor's scopes (the UC payment-distribution and benefit-cap amount bands, the HMRC SPI income bands by total income and SPI Table 3.10's self-employment income bands, and the band-H council-tax rows by English region) whose compiled matrix row reaches no source household (household_source_id) with a positive prior weight cannot be fitted by any weights (the solver's initialiser refuses a nonzero target with no support), so it is dropped from the solve, not pooled. A row that some households carry stays bound however few they are; when one or two distinct source households carry it, its model figures are withheld from every written surface (CD171's absolute floor of three, receipt_counts.UK_DISCLOSURE_FLOOR, to which María's ruling of 2026-10-29 moved the floor of ten). Each scope holds one unit, because its share ceiling sums published values. The floor's receipt reports each target's dropped published total and each scope's dropped share, and the run refuses when a scope's drops pass either of its ceilings or when a per-row exclusion names a row the floor drops.", "tracking": "microcosm#1095", "approved_by": "juaristi22", - "adjudication": "microcosm#1095 U3, ruled 2026-10-16 with the fifth ports PR (floor 10) and revised 2026-10-21 with #1333: a cell that rests on fewer than 10 records is no reason to leave a row out, only a measured distortion of the calibration is. The floor drops only rows no household carries, the benefit-cap amount bands join its scope and their per-row entries retire, and the figures of rows under 10 source households are withheld, as the BRMA rows' are (under three since María's ruling of 2026-10-29). The thirteen entries it retired (the eleven benefit-cap amount bands and the two HMRC £500k to £1m cells) and the A/B that retired them: experiments/1095-uk-thin-row-binding-receipts.md. Extended on 2026-10-22 (María's ruling on the next policyengine-uk bump) to the HMRC SPI income-band families and the band-H council-tax rows by region, in scopes of one unit each with their own ceilings. On #1333's policyengine-uk 2.145.0 spine every HMRC £1m-and-over cell and every band-H region cell has carriers. The three band-H region entries retire and their rows bind, with their figures withheld where fewer than 10 source households carry them (fewer than three since María's ruling of 2026-10-29, receipt_counts.UK_DISCLOSURE_FLOOR); the ten HMRC £1m-and-over cells, bound on arm 1, distorted their neighbours and stay held on that measurement, so the HMRC scopes cover the bands below them. Extended on 2026-10-29 in microcosm#1420, in answer to the independent review's finding 7 and pending María's confirmation, to the SPI families microcosm#1418 binds, Table 3.7's other income by total-income band and Table 3.10's self-employment bands from GBP 1 to GBP 10,000: a row of theirs that no household carries is dropped and receipted as its siblings' rows are, not refused. The ceilings are unchanged, so the HMRC amounts scope's 2-row cap, signed for seven families, now covers nine, and the counts scope's, signed for seven, now covers eight; María confirms or re-sizes them (the re-reviews of microcosm#1420, H and 4).", + "adjudication": "microcosm#1095 U3, ruled 2026-10-16 with the fifth ports PR (floor 10) and revised 2026-10-21 with #1333: a cell that rests on fewer than 10 records is no reason to leave a row out, only a measured distortion of the calibration is. The floor drops only rows no household carries, the benefit-cap amount bands join its scope and their per-row entries retire, and the figures of rows under 10 source households are withheld, as the BRMA rows' are (under three since María's ruling of 2026-10-29). The thirteen entries it retired (the eleven benefit-cap amount bands and the two HMRC £500k to £1m cells) and the A/B that retired them: experiments/1095-uk-thin-row-binding-receipts.md. Extended on 2026-10-22 (María's ruling on the next policyengine-uk bump) to the HMRC SPI income-band families and the band-H council-tax rows by region, in scopes of one unit each with their own ceilings. On #1333's policyengine-uk 2.145.0 spine every HMRC £1m-and-over cell and every band-H region cell has carriers. The three band-H region entries retire and their rows bind, with their figures withheld where fewer than 10 source households carry them (fewer than three since María's ruling of 2026-10-29, receipt_counts.UK_DISCLOSURE_FLOOR); the ten HMRC £1m-and-over cells, bound on arm 1, distorted their neighbours and stay held on that measurement, so the HMRC scopes cover the bands below them. Extended on 2026-10-29 in microcosm#1420, in answer to the independent review's finding 7 and pending María's confirmation, to the SPI families microcosm#1418 binds, Table 3.7's other income by total-income band and Table 3.10's self-employment bands from GBP 1 to GBP 10,000: a row of theirs that no household carries is dropped and receipted as its siblings' rows are, not refused. The ceilings are unchanged, so the HMRC amounts scope's 2-row cap, signed for seven families, now covers nine, and the counts scope's, signed for seven, now covers eight; María confirms or re-sizes them (the re-reviews of microcosm#1420, H and 4). Extended on 2026-11-01 in microcosm#1428, in answer to its independent review's finding 11, to Table 3.10's employment-income counts by self-employment band, which #1428 binds over the same bands: a row of theirs no household carries is dropped and receipted, not refused. The counts scope's 2-row cap now covers nine families.", "approved_on": "2026-10-22", "expires_on": "2027-01-08" }, @@ -171,6 +172,15 @@ "approved_on": "2026-10-23", "expires_on": "2027-01-23" }, + { + "name": "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_1000000", + "reason": "Measured distortion, not thin support, as for the ten HMRC £1m-and-over cells (María's rule of 2026-10-21). With the SPI donors' interest and other income rebased to SPI's 2023-24 bands (microcosm#1428), the first national built from f3cb3ddd3 fitted every other interest and other-income band, the £1m-and-over other-income row included, and missed this row outside the uk_target_fit band. Held out of the objective and measured on every evaluation. The row rests on the SPI Public Use Tape, so its figures and its neighbours' stay out of this register.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "adjudication": "María's ruling of 2026-10-30 on the savings follow-up (microcosm#1428): hold the £1m-and-over savings interest amount row as a measured distortion, rebuild the national on the same spine and score it; how the rebase sits in the top bands at design weights is a follow-up.", + "approved_on": "2026-10-30", + "expires_on": "2027-01-30" + }, { "name": "slc.repayments.england_plan_5", "reason": "Structural under-support: the student-loans stage tops Plan 5 up to SLC's liable stock, which binds, and fewer than ten source households carry Plan 5 repayments on #1333's 2.145.0 spine, so no weights reach SLC's repayment figure; the row's model figures are withheld.", diff --git a/packages/microcosm-build/src/microcosm/build/uk/country_package.json b/packages/microcosm-build/src/microcosm/build/uk/country_package.json index 63b49d38e..2dee087e5 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/country_package.json +++ b/packages/microcosm-build/src/microcosm/build/uk/country_package.json @@ -112,6 +112,11 @@ "kind": "legacy_json", "schema_id": "legacy_json" }, + { + "path": "scotgov_chargeable_dwellings_by_band.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, { "path": "dwp_move_to_uc_facts.json", "kind": "legacy_json", @@ -162,6 +167,11 @@ "kind": "legacy_json", "schema_id": "legacy_json" }, + { + "path": "hmrc_spi_table_3_7_savings_rows.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, { "path": "slc_liable_stocks.json", "kind": "legacy_json", diff --git a/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json b/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json index d751cd670..89c17c960 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json +++ b/packages/microcosm-build/src/microcosm/build/uk/cross_grain_declarations.json @@ -1074,10 +1074,11 @@ }, { "targets": [ + "hmrc.spi.employment_income.count_by_self_employment_income_band", "hmrc.spi.self_employment_income.amount_by_self_employment_income_band", "hmrc.spi.self_employment_income.count_by_self_employment_income_band" ], - "reason": "Banded by groupby on the person's own self-employment income after loss relief, SPI Table 3.10's range; the band is the fan-out dimension, not a measurement filter, and Table 3.10 counts taxpayers and non-taxpayers alike. Bound from GBP 1 to GBP 10,000 only, the bands above stay with Table 3.6's self-employment rows by total income, so the two tables do not pull against each other (microcosm#1418)." + "reason": "Banded by groupby on the person's own self-employment income after loss relief, SPI Table 3.10's range; the band is the fan-out dimension, not a measurement filter, and Table 3.10 counts taxpayers and non-taxpayers alike; its employment-income count adds the year's pay with its company car benefits (employment_income_with_benefits_in_kind_annual) as a filter over the same bands. Bound from GBP 1 to GBP 10,000 only, the bands above stay with Table 3.6's self-employment rows by total income, so the two tables do not pull against each other (microcosm#1418)." } ] } diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index bd59208bb..5354c847f 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -596,6 +596,20 @@ }, "notes": "Stage-time SPI redraw identity gate over the HMRC SPI income spine receipt." }, + { + "id": "uk_stage_hmrc_spi_income_spine_hourly_pay", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "population_fact_check": true, + "parameters": { + "stage": "hmrc_spi_income_spine", + "check": "spi_hourly_pay", + "maximum_share_above_frs": 0.02 + }, + "notes": "Stage-time hourly-pay check over the employment pay floor receipt (uk-data#629): no eligible drawn SPI employee keeps pay below its floor (its twin's hours at the lower of the minimum wage for its age and the twin's own hourly pay), and the drawn employees' weighted share below the minimum wage for their age after the floor is at most the FRS employees' own share plus maximum_share_above_frs. A withheld share (one or two source households on a side) is not judged. A synthetic smoke build records a failure without blocking on it (population_fact_check)." + }, { "id": "uk_stage_spi_benefit_coherence", "gate": "stage_health", @@ -635,9 +649,10 @@ "parameters": { "stage": "uc_take_up", "check": "uc_take_up", - "maximum_caseload_deviation": 0.055 + "maximum_caseload_deviation": 0.055, + "maximum_reachable_pool_deviation": 0.055 }, - "notes": "Stage-time gate over the Universal Credit take-up receipt (microcosm#1334): every reporter claims and no unit outside the draw changes; in each DWP family type the pools whose target is reachable realize DWP's calendar-2025 caseload within 0.055 of it at the stage's weights, Great Britain only, counting units the engine moves off their legacy benefits at their expected claim at the closure; and the family type's caseload is no further from DWP's than before the redraw, give or take 0.055 of DWP's count. The tolerance was 0.05 until microcosm#1418: routing gilt interest out of dividend_income reshuffles the SPI channel's draws. On the spine of 2026-10-28 (da944f12c), single people's caseload ends 0.0523 of DWP's count further from it than before the redraw (0.0486 on the spine before, 3c0d11b6's), so 0.055 holds it with about 8,000 to spare; couples without children, lone parents and couples with children end closer than before by 0.53, 0.054 and 0.42 of their counts, and every family type's reachable pools realize DWP's count within 0.0015. The terminal take-up signal gate no longer measures would_claim_uc, whose rate is now set by award band and family type, not as one share of the age population." + "notes": "Stage-time gate over the Universal Credit take-up receipt (microcosm#1334): every reporter claims and no unit outside the draw changes; in each DWP family type the pools whose target is reachable realize DWP's calendar-2025 caseload within 0.055 of it at the stage's weights (maximum_reachable_pool_deviation), Great Britain only, counting units the engine moves off their legacy benefits at their expected claim at the closure; and the family type's caseload is no further from DWP's than before the redraw, give or take 0.055 of DWP's count (maximum_caseload_deviation). The tolerance was 0.05 until microcosm#1418: routing gilt interest out of dividend_income reshuffles the SPI channel's draws. In the savings follow-up (microcosm#1428), small upstream changes moved single people's GBP 1,000 to 1,100 band into a pool whose DWP count exceeds its entitled units, and the family type's caseload ended 0.0568, then 0.0674, of DWP's count further from it than before the redraw. The stage now joins a pool that cannot meet its count to the adjacent pool with more room for its gap, so a family type's caseload lands on DWP's wherever its entitled units allow, and the tolerance stays at 0.055 (María's ruling of 2026-10-31). The SPI employment pay floor (uk-data#629) moves single people's caseload before the redraw close to DWP's; before the pool joining, the redraw's usual downward move then read as about twice 0.055 of DWP's count, so maximum_caseload_deviation was 0.12 from María's ruling of 2026-10-29. With the pool joining every family type ends closer to DWP's count after the redraw than before it (the T1426 spine, #1426 at 97fd4d0e4), so it is 0.055 again (María's ruling of 2026-11-02). The terminal take-up signal gate no longer measures would_claim_uc, whose rate is now set by award band and family type, not as one share of the age population." }, { "id": "uk_stage_pension_credit_take_up", @@ -1347,6 +1362,8 @@ "person.private_pension_income_annual_share", "person.self_employment_income_annual_share", "person.self_employment_income_other_spells", + "person.self_employment_gross_receipts", + "person.annuity_and_trust_income", "person.esa_health_condition_proxy", "person.esa_includes_support_component", "person.esa_support_group_proxy", @@ -1411,6 +1428,7 @@ "person.uc_is_in_startup_period", "person.uc_is_in_gainful_self_employment", "person.is_looked_after_by_local_authority", + "person.child_limit_non_parental_caring_exception", "person.would_claim_carers_allowance", "person.would_claim_marriage_allowance", "person.would_claim_scp", diff --git a/packages/microcosm-build/src/microcosm/build/uk/hmrc_spi_table_3_7_savings_rows.json b/packages/microcosm-build/src/microcosm/build/uk/hmrc_spi_table_3_7_savings_rows.json new file mode 100644 index 000000000..9e2558783 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/hmrc_spi_table_3_7_savings_rows.json @@ -0,0 +1,1217 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "hmrc_spi_table_3_7_savings_rows.json", + "purpose": "HMRC Survey of Personal Incomes 2023-24, Table 3.7 (income by source and range of total income, taxpayers): the United Kingdom interest from banks and building societies and other income amounts in each of the 13 total-income bands. Consumed by the SPI income stage, which rebases each donor's interest and other income from the 2022-23 tape to these 2023-24 amounts band by band before the OBR's savings income growth carries them to the frame's year (microcosm#1418 savings follow-up).", + "consumers": [ + "uk_runtime.spi_income" + ], + "planned_consumers": [], + "policy": "Rows are copied verbatim from the pinned Chronicle consumer feed named in source_fact_feed; every value is a publisher-stated fact. 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"table_record_kind": "detail" + }, + "dimensions": { + "total_income_lower_bound": 50000 + }, + "source": { + "source_name": "hmrc", + "source_table": "SPI collated Tables 3.1 to 3.11, tax year 2023-24: income by range of total income", + "source_file": "Collated_Tables_3_1_to_3_11_2324.ods", + "source_sha256": "ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e", + "vintage": "spi_2023_24", + "raw_r2_key": "raw/hmrc/hmrc-spi-income-bands-2023-24/2024/ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e/Collated_Tables_3_1_to_3_11_2324.ods" + }, + "value": 2340000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_5d68b2314b5c54a09f274733", + "semantic_fact_key": "ledger_semantic_fact_v2_66db03562553db442bf55fd1", + "source_record_id": "hmrc.spi_2324.table_3_7.band_500000.savings_interest_amount", + "concept": "hmrc.spi_savings_interest_income_amount", + "measure_id": "savings_interest_amount", + "unit": "gbp", + "aggregation": "sum", + "entity": { + "name": "person", + "role": "taxpayer" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "tax_year", + "value": 2023 + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "hmrc.spi_2324.table_3_7", + "groupby_dimension": "hmrc.total_income_band", + "groupby_value_id": "band_500000", + "table_record_kind": "detail" + }, + "dimensions": { + "total_income_lower_bound": 500000 + }, + "source": { + "source_name": "hmrc", + "source_table": "SPI collated Tables 3.1 to 3.11, tax year 2023-24: income by range of total income", + "source_file": "Collated_Tables_3_1_to_3_11_2324.ods", + "source_sha256": "ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e", + "vintage": "spi_2023_24", + "raw_r2_key": "raw/hmrc/hmrc-spi-income-bands-2023-24/2024/ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e/Collated_Tables_3_1_to_3_11_2324.ods" + }, + "value": 653000000 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_ab8a5ac6dd4ba75e194133f1", + "semantic_fact_key": "ledger_semantic_fact_v2_0cf642d6a0e4224ef468b28c", + "source_record_id": "hmrc.spi_2324.table_3_7.band_70000.savings_interest_amount", + "concept": "hmrc.spi_savings_interest_income_amount", + "measure_id": "savings_interest_amount", + "unit": "gbp", + "aggregation": "sum", + "entity": { + "name": "person", + "role": "taxpayer" + }, + "geography": { + "id": "K02000001", + "level": "country", + "vintage": "current" + }, + "period": { + "type": "tax_year", + "value": 2023 + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "hmrc.spi_2324.table_3_7", + "groupby_dimension": "hmrc.total_income_band", + "groupby_value_id": "band_70000", + "table_record_kind": "detail" + }, + "dimensions": { + "total_income_lower_bound": 70000 + }, + "source": { + "source_name": "hmrc", + "source_table": "SPI collated Tables 3.1 to 3.11, tax year 2023-24: income by range of total income", + "source_file": "Collated_Tables_3_1_to_3_11_2324.ods", + "source_sha256": "ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e", + "vintage": "spi_2023_24", + "raw_r2_key": "raw/hmrc/hmrc-spi-income-bands-2023-24/2024/ad063b06b2bdeef8600dbbb09d48153337a4966f8c7eea50df7a2e0304ebd73e/Collated_Tables_3_1_to_3_11_2324.ods" + }, + "value": 1620000000 + } + ] +} 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 105de5237..b088b6a9b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json @@ -1,11 +1,11 @@ { - "compiled_count": 1409, + "compiled_count": 1418, "counts_by_kind": { "calibration_drift": 566, "fixture_only": 42, - "ledger_only": 814 + "ledger_only": 823 }, - "difference_count": 1422, + "difference_count": 1431, "differences": [ { "fixture_value": 1608000.0, @@ -637,6 +637,13 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 23377178.788788605, + "name": "dwp.jsa.amount_income_based", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 1390586.25, @@ -2485,6 +2492,62 @@ "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." }, + { + "kind": "ledger_only", + "ledger_value": 136689.37329700272, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1000_2000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 72111.7166212534, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1_500", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 110858.310626703, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_2000_3000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 96866.48501362398, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_3000_4000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 82874.65940054496, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_4000_5000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 159291.553133515, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_5000_7500", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 68882.83378746595, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_500_1000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, + { + "kind": "ledger_only", + "ledger_value": 121621.25340599455, + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_7500_10000", + "period": 2025, + "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." + }, { "kind": "ledger_only", "ledger_value": 617065102.3673128, diff --git a/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json b/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json index a838d64e9..6e1c6e15b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json +++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_fact_vendor_selections.json @@ -1728,6 +1728,33 @@ } ] }, + { + "resource": "hmrc_spi_table_3_7_savings_rows.json", + "purpose": "HMRC Survey of Personal Incomes 2023-24, Table 3.7 (income by source and range of total income, taxpayers): the United Kingdom interest from banks and building societies and other income amounts in each of the 13 total-income bands. Consumed by the SPI income stage, which rebases each donor's interest and other income from the 2022-23 tape to these 2023-24 amounts band by band before the OBR's savings income growth carries them to the frame's year (microcosm#1418 savings follow-up).", + "consumers": [ + "uk_runtime.spi_income" + ], + "planned_consumers": [], + "selections": [ + { + "label": "spi_2023_24_table_3_7_savings_rows", + "selector": { + "source_name": "hmrc", + "source_concept": [ + "hmrc.spi_savings_interest_income_amount", + "hmrc.spi_other_income_amount" + ], + "groupby_dimension": "hmrc.total_income_band", + "geography_id": "K02000001", + "period_type": "tax_year", + "period_value": [ + 2023 + ] + }, + "expected_row_count": 26 + } + ] + }, { "resource": "hmrc_isa_statistics_facts.json", "purpose": "HMRC Annual savings statistics (September 2026 release), the rows the ISA subscriptions stage draws subscribers and amounts from and the Lifetime ISA anchor counts payers against (microcosm#1415): Table 9.7 2023-24 subscribers by total income and size of subscription, and the holders who made no further subscription, for all subscribers and each type paid into; Table 9.8 subscribers by age and sex; Table 9.9 subscribers by region; Table 9.4 accounts and amounts subscribed by component for 2023-24 and 2024-25 (the latest year provisional); Table 9.6 market values at 5 April 2025; the cash ISA limit reduction impact note's shares of under-65 cash subscribers either side of GBP 12,000 in 2022-23; and Lifetime ISA Table 1a withdrawals for 2024-25 and 2025-26. Tables 9.7 to 9.9 count individuals and Table 9.4 accounts. None is a calibration target: the stage's reviewed fence keeps them measured only. No value is derived here.", @@ -1892,6 +1919,40 @@ "expected_row_count": 26 } ] + }, + { + "resource": "scotgov_chargeable_dwellings_by_band.json", + "purpose": "The Scottish Government's chargeable dwellings by council-tax band, A to H, for each of the 32 council areas at September 2025 (CTAXBASE 2025). Consumed by the post-geography band node, which draws each Scottish owner clone's band from its council area's mix at the clone's rank among Scotland's owners (microcosm#1095 B12).", + "consumers": [ + "uk_runtime.property_localisation" + ], + "planned_consumers": [], + "selections": [ + { + "label": "ctaxbase_2025_chargeable_dwellings_by_band", + "selector": { + "source_name": "scotgov", + "source_concept": [ + "scotgov.chargeable_dwellings_band_a", + "scotgov.chargeable_dwellings_band_b", + "scotgov.chargeable_dwellings_band_c", + "scotgov.chargeable_dwellings_band_d", + "scotgov.chargeable_dwellings_band_e", + "scotgov.chargeable_dwellings_band_f", + "scotgov.chargeable_dwellings_band_g", + "scotgov.chargeable_dwellings_band_h" + ], + "period_type": "month", + "period_value": [ + "2025-09" + ], + "geography_level": [ + "local_authority" + ] + }, + "expected_row_count": 256 + } + ] } ] } 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 97debbad8..e1cbbfd01 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 @@ -472,7 +472,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -495,7 +495,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -518,7 +518,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -540,7 +540,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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)" @@ -570,7 +570,7 @@ "uc_is_in_gainful_self_employment" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "The FRS job record's company car answers through frs_spine's carriers (EXPBEN01, EXPBEN03, CARVAL, FUELTYP, CARCON, CARAMT), WAS round-8 company cars' list prices, and HMRC's 2024-25 company car recipients by CO2 band (Table 2A) and fuel (Table 2C), via the vendored facts", "survey": "Family Resources Survey 2024-25 job record; Wealth and Assets Survey round 8; HMRC Benefits in kind statistics (July 2026)" @@ -599,7 +599,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -619,7 +619,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -641,7 +641,7 @@ "required_mass_change_reason": "High-value home support divides every owner household in the top value stratum of its region into light copies at equal weight; every household's mass and the total household mass are conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "Microcosm structural operator (microcosm#1240 fix 1); no publisher input", "survey": "Family Resources Survey 2024-25 and SPI synthetic support, after the WAS wealth draw and the regional property uprating" @@ -662,7 +662,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -686,7 +686,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "6a2ba5e3e43c6d7936416552aadc716729b902dcac24b97093ae8ccf06cf8a8f", @@ -776,7 +776,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -803,7 +803,7 @@ "required_mass_change_reason": "ISA subscriptions per adult from HMRC's ISA tables on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "HMRC Individual Savings Account statistics Tables 9.4, 9.6 and 9.7 to 9.9, the cash ISA limit reduction impact note and Lifetime ISA Table 1a, via the vendored facts; 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": "HMRC Annual savings statistics (September 2026 release); Wealth and Assets Survey round 8" @@ -868,7 +868,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "UK Data Service SN 9603 Living Costs and Food Survey 2024-25 (2nd edition) household/person tabs and the vendored Chronicle facts for NEED 2024 mean kWh, DESNZ Energy Trends and QEP average prices paid for calendar 2024, road-fuel outturn (DESNZ, HMRC, ONS Consumer Trends) and road fuel by vehicle type (DESNZ), ONS Consumer Trends household spending by COICOP division, the DfT VEH1103 licensed-car stock, NTS bus use, and the DfT and devolved bus finance tables and fares indices.", "survey": "Living Costs and Food Survey 2024-25" @@ -894,7 +894,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -914,7 +914,7 @@ "required_mass_change_reason": "Private-school attendance stored on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "policyengine-uk attends_private_school (the gov.simulation.private_school_vat attendance rates and factor), evaluated by the pinned engine", "survey": "policyengine-uk private-school attendance imputation, stored at the spine's weights" @@ -937,7 +937,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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)" @@ -958,7 +958,7 @@ "required_mass_change_reason": "Annual property purchases imputed on the source spine: household weights pass through unchanged and total household mass is conserved.", "rewrites": [], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "https://www.gov.uk/government/statistics/uk-stamp-tax-statistics", "survey": "HMRC Annual Stamp Tax Statistics 2024 to 2025 (corrected 11 March 2026), Tables 6b, 8a and 8b" @@ -981,7 +981,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "uk/high_value_home_value_knots.json, built by tools/build_uk_high_value_home_stock.py from PolicyEngine/chronicle#343's registrations", "survey": "HM Land Registry Price Paid latest sales uprated with the UK House Price Index, owner-weighted by region (the dense value node's knots, region level)" @@ -1005,7 +1005,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "source_vintages": { "source": "MHCLG dwellings and the UK House Price Index regional average price averaged over the twelve months of calendar 2024 from the July 2026 full file, the frame's valuation basis, so the engine's annual uprating from 2024 projects the value once (microcosm#1240 fix 5).", "survey": "Public regional property reference" @@ -1033,7 +1033,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -1066,7 +1066,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -1088,7 +1088,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -1096,6 +1096,26 @@ "stage": "student_loans", "status": "required_at_build" }, + "trading_allowance_receipts": { + "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", + "base_candidate_tier": "frs", + "effective_mass_requirements": {}, + "mass_change_semantics": "mass_conserving", + "output_weight_kind": "importance", + "outputs": [ + "self_employment_gross_receipts" + ], + "required_mass_change_reason": "Self-employed gross receipts stored on the source spine: household weights pass through unchanged and total household mass is conserved.", + "rewrites": [], + "source_manifest": "source_stages.json", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", + "source_vintages": { + "source": "self_employment_income after the SPI income stage; HMRC's published gainers of the raise to GBP 3,000, about 400,000 taxpayers with a lower bill by about GBP 380 on average", + "survey": "The self-employed's own profit on the final spine, and HMRC's policy paper on the Budget 2026 trading allowance raise (calibration)" + }, + "stage": "trading_allowance_receipts", + "status": "required_at_build" + }, "was_lisa": { "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", "base_candidate_tier": "frs", @@ -1110,7 +1130,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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" @@ -1149,7 +1169,7 @@ "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": "895ebc04fb44e7147d04490724990bfd9795cc2095a33c999389f445c0975c47", + "source_manifest_sha256": "639f03365785f3255471cdc98a088d9ebcf849c226d8c9ec3402f99d386a08b2", "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/scotgov_chargeable_dwellings_by_band.json b/packages/microcosm-build/src/microcosm/build/uk/scotgov_chargeable_dwellings_by_band.json new file mode 100644 index 000000000..9b0fe8797 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/scotgov_chargeable_dwellings_by_band.json @@ -0,0 +1,11062 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "scotgov_chargeable_dwellings_by_band.json", + "purpose": "The Scottish Government's chargeable dwellings by council-tax band, A to H, for each of the 32 council areas at September 2025 (CTAXBASE 2025). Consumed by the post-geography band node, which draws each Scottish owner clone's band from its council area's mix at the clone's rank among Scotland's owners (microcosm#1095 B12).", + "consumers": [ + "uk_runtime.property_localisation" + ], + "planned_consumers": [], + "policy": "Rows are copied verbatim from the pinned Chronicle consumer feed named in source_fact_feed; every value is a publisher-stated fact. Consumers declare their own arithmetic on these rows and never edit this file by hand: regenerate it with tools/vendor_uk_ledger_facts.py.", + "generator": { + "tool": "tools/vendor_uk_ledger_facts.py", + "version": 1 + }, + "source_fact_feed": { + "source_repo": "PolicyEngine/chronicle", + "source_commit": "08962f1b893fc1ba3b7bdd3d800c7e867318951a", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "fact_row_count": 1512256, + "facts_sha256": "11b20b4333270e269a5cbfe6302eb4986851990b340ef02d948eb71d62c12d5c", + "manifest_sha256": "96faf0e33686682cf37e8fa9511727571b3f8221f15bbc21591b799af87de722" + }, + "selections": [ + { + "label": "ctaxbase_2025_chargeable_dwellings_by_band", + "selector": { + "source_name": "scotgov", + "source_concept": [ + "scotgov.chargeable_dwellings_band_a", + "scotgov.chargeable_dwellings_band_b", + "scotgov.chargeable_dwellings_band_c", + "scotgov.chargeable_dwellings_band_d", + "scotgov.chargeable_dwellings_band_e", + "scotgov.chargeable_dwellings_band_f", + "scotgov.chargeable_dwellings_band_g", + "scotgov.chargeable_dwellings_band_h" + ], + "period_type": "month", + "period_value": [ + "2025-09" + ], + "geography_level": [ + "local_authority" + ] + }, + "row_count": 256 + } + ], + "row_count": 256, + "rows": [ + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_a64c3ca533b6a878f23d0e1d", + "semantic_fact_key": "ledger_semantic_fact_v2_a896f31eee2b0eb8ffbfdec3", + "source_record_id": "scotgov.ctaxbase2025.chargeable_dwellings.council_area.s12000005.band_a", + "concept": "scotgov.chargeable_dwellings_band_a", + "measure_id": "band_a", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "dwelling", + "role": "chargeable_dwelling" + }, + "geography": { + "id": "S12000005", + "level": "local_authority", + "vintage": "ca_2019" + }, + "period": { + "type": "month", + "value": "2025-09" + }, + 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"scotgov.ctaxbase2025.chargeable_dwellings.council_area.s12000006.band_a", + "concept": "scotgov.chargeable_dwellings_band_a", + "measure_id": "band_a", + "unit": "count", + "aggregation": "sum", + "entity": { + "name": "dwelling", + "role": "chargeable_dwelling" + }, + "geography": { + "id": "S12000006", + "level": "local_authority", + "vintage": "ca_2019" + }, + "period": { + "type": "month", + "value": "2025-09" + }, + "period_coverage": { + "start_date": null, + "end_date": null, + "basis": null + }, + "layout": { + "record_set_id": "scotgov.ctaxbase2025.chargeable_dwellings.council_area", + "groupby_dimension": "geography", + "groupby_value_id": "s12000006", + "table_record_kind": "detail" + }, + "dimensions": {}, + "source": { + "source_name": "scotgov", + "source_table": "Number of chargeable dwellings, September 2025 (CTAXBASE 2025)", + "source_file": "CTAXBASE_2025_Tables_Chargeable_Dwellings.xlsx", + "source_sha256": 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"layout": { + "record_set_id": "scotgov.ctaxbase2025.chargeable_dwellings.council_area", + "groupby_dimension": "geography", + "groupby_value_id": "s12000008", + "table_record_kind": "detail" + }, + "dimensions": {}, + "source": { + "source_name": "scotgov", + "source_table": "Number of chargeable dwellings, September 2025 (CTAXBASE 2025)", + "source_file": "CTAXBASE_2025_Tables_Chargeable_Dwellings.xlsx", + "source_sha256": "8e431837aa37ee298c9a2b9b010b8ecbc7e3a817573a3a84f6251e6db00a591f", + "vintage": "ctaxbase_2025", + "raw_r2_key": "raw/scotgov/scotgov-council-tax-bands-2025/2025/8e431837aa37ee298c9a2b9b010b8ecbc7e3a817573a3a84f6251e6db00a591f/CTAXBASE_2025_Tables_Chargeable_Dwellings.xlsx" + }, + "value": 24798 + }, + { + "aggregate_fact_key": "ledger_aggregate_fact_v2_d849708084a434e7bf4d182a", + "semantic_fact_key": "ledger_semantic_fact_v2_1e443651dd0f9721706888e5", + "source_record_id": "scotgov.ctaxbase2025.chargeable_dwellings.council_area.s12000010.band_a", + "concept": 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"ni_class_4_losses_brought_forward", "private_pension_income", + "annuity_and_trust_income", "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", @@ -383,6 +384,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", + "annuity_and_trust_income", "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", @@ -653,6 +655,16 @@ "foster_parent_code": 9, "below_age": 16 }, + "non_parental_care_child": { + "child_codes": [ + 3, + 4 + ], + "parent_codes": [ + 7, + 8 + ] + }, "one_person_age_split": 65, "family_role_values": [ "COUPLE_PARTNER", @@ -686,12 +698,13 @@ "ons_family_role", "ons_family_index", "is_looked_after_by_local_authority", + "child_limit_non_parental_caring_exception", "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. 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." + "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. It also sets child_limit_non_parental_caring_exception, policyengine-uk's two-child limit exception for non-parental caring arrangements (UC Regs 2013 Sch 12 para 4; CTC Regs 2002 reg 12), for a child-table member whom no adult of their own benefit unit claims as a natural, adopted or step child (the child's grid code toward the adult is child or step child, 3 or 4, or the adult's toward the child is parent or step parent, 7 or 8) and whom no local authority placed there: a child living with kinship or friends carers, or with a claimant whose own child is the child's parent. Nothing set it before, so the engine paid no child element for such a third or later child born from 6 April 2017. The multiple births, adoption and non-consensual conception exceptions stay at the engine's default: the FRS has no twin field and its birth dates are blank (María's ruling of 2 November 2026; microcosm#1095)." }, { "stage": "frs_employment", @@ -1910,7 +1923,7 @@ "spi_losses_brought_forward_draw": "self_employment_income" }, "predictors": [ - "age", + "spi_age_band", "gender", "region", "main_source", @@ -1919,6 +1932,7 @@ "at_or_over_spa" ], "categorical_predictors": [ + "spi_age_band", "gender", "region", "main_source" @@ -1961,7 +1975,7 @@ "dividend_income": "dividend_income", "private_pension_income": "private_pension_income", "property_income": "property_income", - "other_investment_income": null, + "other_investment_income": "other_investment_income", "gift_aid": null, "charitable_investment_gifts": null, "hmrc_spi_pay": "hmrc_spi_pay", @@ -1984,7 +1998,7 @@ "seed": 45, "n_estimators": 100, "folds": 5, - "fold_key": "predictor cell (gender, region, whole-year age, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell's stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold's recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest", + "fold_key": "predictor cell (gender, region, spi_age_band, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell's stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold's recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest", "method": "Rosenblatt transport (microcosm#840): each drawn recipient's twin values are located on the reference forest, the sign uniform in the gate's cumulative class probabilities and the quantile on the sign's magnitude forest, each leaf chained on the twin's observed earlier leaves, and the stage-1 forest draws at those uniforms, so the recipient receives the SPI income of someone in the same position among SPI people like it as the twin holds among FRS people like it. taxable_savings_interest is savings_interest_income, the interest outside ISAs, floored at zero. A leaf without an analogue, or whose analogue is degenerate on the FRS rows, draws at identity-keyed uniforms, as do the placements inside a class or a flat stretch of a conditional; no model random stream is consumed. The reference locates a leaf given the twin's observed earlier analogue leaves, while the stage-1 forest draws it given every earlier output, the identity-uniform leaves included, so the located quantile is read off a conditional the identity draws also shape (microcosm#840 follow-up: the chain order). A full build refuses a reference the predictor cells cannot cross-fit (only a sampled rung or a synthetic fixture may locate in-sample, and its receipt says so), a recipient whose fold differs from its twin's, and a reference whose sign classes differ from the stage-1 forest's on any located target.", "receipt": "per target the analogue, the FRS regime and sign classes, the uniform source and the Kolmogorov-Smirnov distance of the sign uniforms and of the quantiles over positive rows from U[0,1), overall and by main_source over at least 10 rows (a statistical floor, as a distance over fewer says nothing); positive-row counts are rounded as every receipt count is, one or two rows reading <3; the counts in the stage evidence stay with the build and are not published" }, @@ -2171,6 +2185,23 @@ "outputs": "stage-1 outputs, uprated as the stage-1 draws", "seed": 44 }, + { + "kind": "floor_spi_employment_pay_at_minimum_wage", + "column": "hmrc_spi_pay", + "hours_column": "hours_worked", + "hours_basis": "annual usual hours: the FRS adult's usual weekly hours in all jobs (TOTHOURS) times 52, read back to weekly hours", + "weeks_in_year": 52, + "hourly_rate": "the lower of policyengine-uk gov.hmrc.minimum_wage.non_apprentice by the twin's age, read as the engine reads the build year (30 April), and the twin's own hourly pay (its employment income before the draw over its annual hours)", + "employee_statuses": [ + "FT_EMPLOYED", + "PT_EMPLOYED" + ], + "rows": "drawn recipients other than reserved band carriers, whose twin has no trade (has_trade 0), an employee main job and usual hours above zero, and whose drawn PAY is positive; FRS rows, undrawn copies and every other leaf keep their values", + "pay_basis": "tape PAY is taxable pay, net of net-pay pension contributions and salary sacrifice; the minimum wage binds gross pay, and the engine reads employment_income (PAY + EPB + TAXTERM) as gross pay, so PAY alone is compared with the gross minimum and a twin at the minimum wage who pays net-pay contributions can be lifted by up to them", + "reason": "stage 1 draws annual tape pay without hours and the tape holds part-year workers, so a twin reporting usual employee hours could draw a year's pay below what those hours earn at the minimum wage (uk-data#629)", + "annual_income": "the floored pay is the row's current pay; its employment_income_annual_share becomes its unfloored over its floored employment income, so the annual measures read the tape's year of pay", + "receipt": "eligible and floored source households (rounded), weighted people, the weighted share floored, the weighted mean uplift and the amount added; figures on one or two source households are <3, and the floored weight, share and amount are given only where neither the floored nor the unfloored side rests on one or two; the hourly-pay check gives the weighted shares of the drawn employees below the minimum wage for their age before and after the floor, beside the FRS employees' share, each withheld beside a side on one or two, and the eligible rows still below their floor (banded, zero exact)" + }, { "kind": "fit_weighted_qrf_stage2", "training_population": "uk_frs_raw_spine_base_support_channel", @@ -2629,9 +2660,10 @@ "self_employment_income_annual_share", "self_employment_income_other_spells", "private_pension_income_annual_share", - "contributory_benefits_annual_share" + "contributory_benefits_annual_share", + "annuity_and_trust_income" ], - "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes." + "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes. A drawn row's annuity and trust descriptor is zero, since the tape's private pension excludes those payments and books them as other income (microcosm#1428)." }, { "artifacts": [ @@ -3811,7 +3843,7 @@ "seed": 0 } }, - "additional_residential_targets": "Table 6b HRAD transactions and value, 2024-25, times individuals' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined", + "additional_residential_targets": "Table 6b HRAD transactions and value, 2024-25: at or below GBP 250,000, the band less other buyers' HRAD, their Table 8a receipts over Table 6b's gross HRAD receipts per transaction and per pound; above it, times individuals' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined, at the households' counts and values", "non_residential_targets": "Table 8b individuals' transactions, value and bands, 2024-25" }, { @@ -6403,13 +6435,13 @@ "family_type": "two FRS claimant or partner roles make a couple; children are UC children or qualifying young people and other non-claimants under 20 (the calibration measure's uc_calibration_family_type)", "award_bands": "GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, 1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000", "targets": "DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025", - "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it", + "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it. Then, lowest first, a pool that cannot meet DWP's count (its undrawn entitled units' expected claims exceed the count, or the count exceeds them plus the weight of every drawn unit) is held at the nearer of those ends and passes its gap to the other pools, nearest first, the one with more room first at equal distance and the lower on a tie, each taking what its room allows; a gap no pool can take leaves the family type unreachable", "minimum_pool_sources": 30, "drawn_units": "entitled, non-reporting units of the population that the engine does not move off their legacy benefits (legacy_benefits_closed) and that report none, since a legacy benefit the engine leaves in place ends once the unit claims UC", "undrawn_claims": "a reporter at 1, a moved unit at the claim probability uc_legacy_closure_take_up's rule gives it (dwp_move_to_uc_facts.json), a unit keeping income-related ESA at 0, any other at its stored flag", "kept_legacy_award": "a unit whose claimant or partner reports income-related ESA (esa_income_reported), and that neither reports Universal Credit nor is moved off its legacy benefits, claims no Universal Credit, since policyengine-uk ends that award once the family claims it", "weight_mapping": "household_to_benunit", - "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", + "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands after any gaps passed to or from it, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", "solve_scope": "great_britain", "not_drawn": "keeps its stored flag, except a unit keeping income-related ESA, which claims none" } @@ -6618,41 +6650,6 @@ ], "notes": "Runs after the SPI income chain, UC capital coherence and the Pension Credit redraw, before CGT cloning (microcosm#1063). frs_take_up draws would_claim_child_benefit early at one flat rate over every benefit unit with the reporters on top, and child_benefit_opts_out at a flat rate whatever the unit's income; this stage redraws both. One temporary engine materialization gives each person's eligibility as a child or qualifying young person and each person's adjusted net income (consumed only). A family claims for all its children or none, so the draw is by family, keyed on the age of its eldest eligible child: a reporter claims, and the other families claim at rates solved from the eldest age down so that the claimed share of the eligible children of each single year of age is HMRC's published one (Table 15, May 2025, opted-out families included), weighted at the stage's household weights. The published opted-out share of registered families (August 2025) is drawn among claiming non-reporters, fully charged first. Opt-out-aware models restrict both the full-charge and taper pools to a positive charge fraction at least the installed opt_out_charge_share. With the default share of one, only fully charged families can opt out; an insufficient pool is recorded as a shortfall instead of selecting paid taper families. Legacy models retain the original full-charge pool and taper fallback. Models exposing gov.hmrc.child_benefit.opt_out_charge_share (policyengine-uk#2140) receive registered claims, including opted-out families, in would_claim_child_benefit and the separate opt-out in child_benefit_opts_out. Older models receive the legacy claims-and-not-opted-out payment flag because they ignore the opt-out input. Stage evidence records the selected encoding, evaluated opt-out charge share and engine parameter source. The in-payment audit counts claims-and-not-opted-out in the draw, with new candidates aligned to baseline payment suppression; it is not a fresh model run or population calibration evidence. A benefit unit with no eligible child keeps its early draw and is never opted out." }, - { - "stage": "carers_allowance_take_up", - "survey": "Family Resources Survey 2024-25", - "source": "Independent Review of Carer's Allowance Overpayments (July 2025), chapter 7, and DWP benefit statistics, through the UK take-up contract's carers_allowance_newly_entitled rate, applied to PolicyEngine-UK's Carer's Allowance earnings test after the SPI income chain.", - "grain": "person", - "artifacts": [], - "operations": [ - { - "kind": "materialize_rules_engine_predictors", - "predictors": [ - "is_gainfully_employed_for_carers_allowance", - "is_severely_disabled_for_carers_allowance", - "carers_allowance_earnings" - ], - "consumed_only": true, - "year_rule": "survey_year" - }, - { - "kind": "assign_binary_from_rate", - "output": "would_claim_carers_allowance", - "draw": "would_claim_carers_allowance", - "assignment_unit": "person", - "eligibility": "persons whose care_hours reach policyengine-uk's gov.dwp.carers_allowance.min_hours, who care for a severely disabled person, who report no Carer's Allowance, and whom is_gainfully_employed_for_carers_allowance holds gainfully employed at the survey year: the carers the earnings limit alone keeps from the allowance (a carer whose frs_care_hours_in_household reach min_hours cares for a severely disabled person when another member of the household is is_severely_disabled_for_carers_allowance at the survey year; a carer whose largest in-household band does not reach the hours does on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member). In Scotland the flag is Carer Support Payment's, which policyengine-uk pays under its own earnings limit from 2.146.1", - "reported_true_anchor": "carers_allowance_reported > 0", - "rate_key": "carers_allowance_newly_entitled", - "rate_derivation": "the take-up contract's carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool's strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false", - "seed": 0 - } - ], - "outputs": [], - "rewrites": [ - "would_claim_carers_allowance" - ], - "notes": "Runs after the SPI income chain, UC capital coherence and the Pension Credit and Child Benefit redraws, before CGT cloning, so every clone carries its source's draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from £151 to £196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer's Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame's own values, gives each person's is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year's parameters at 30 April, so the limit is 2024-25's £151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (María's decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine's gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract's carers_allowance_newly_entitled rate (0.6, María's ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person's identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk's stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (María's ruling of 2026-10-23): there the flag is Carer Support Payment's, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year's limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum's realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony's reviews of microcosm#1420). The stage has no health gate." - }, { "stage": "uc_deduction_attributes", "survey": "DWP Universal Credit deductions statistics, March 2025 to February 2026", @@ -7533,6 +7530,41 @@ ], "notes": "The QRF trains only on the 2024-25 FRS asked subset with pay. Salary sacrifice is pay given up, so every amount, reported or predicted, is limited to the pay the person still receives, and a person without pay has none (uk-data#518, microcosm#1095); the limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' pension amounts, up to half their pay, into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; what does not move stays an employee contribution, so the sacrifice never exceeds the pay that remains. Before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief. The third arm adds bonus sacrifice beside pay toward the OBR's 2027-28 base above GBP 2,000 (microcosm#1340). A sacrificed bonus is never received, so the FRS records it in neither pay nor its bonus amounts, and pay stays as it is (María's ruling of 2026-10-24 on Vahid's review of microcosm#1383); a received FRS bonus marks a bonus-paying job and its size. An SPI-channel adult's FRS bonus is its twin's, beside the tape's pay, so it draws a bonus share of pay from a base-FRS adult in its pay cell instead." }, + { + "stage": "carers_allowance_take_up", + "survey": "Family Resources Survey 2024-25", + "source": "Independent Review of Carer's Allowance Overpayments (July 2025), chapter 7, and DWP benefit statistics, through the UK take-up contract's carers_allowance_newly_entitled rate, applied to PolicyEngine-UK's Carer's Allowance earnings test after the SPI income chain.", + "grain": "person", + "artifacts": [], + "operations": [ + { + "kind": "materialize_rules_engine_predictors", + "predictors": [ + "is_gainfully_employed_for_carers_allowance", + "is_severely_disabled_for_carers_allowance", + "carers_allowance_earnings" + ], + "consumed_only": true, + "year_rule": "survey_year" + }, + { + "kind": "assign_binary_from_rate", + "output": "would_claim_carers_allowance", + "draw": "would_claim_carers_allowance", + "assignment_unit": "person", + "eligibility": "persons whose care_hours reach policyengine-uk's gov.dwp.carers_allowance.min_hours, who care for a severely disabled person, who report no Carer's Allowance, and whom is_gainfully_employed_for_carers_allowance holds gainfully employed at the survey year: the carers the earnings limit alone keeps from the allowance (a carer whose frs_care_hours_in_household reach min_hours cares for a severely disabled person when another member of the household is is_severely_disabled_for_carers_allowance at the survey year; a carer whose largest in-household band does not reach the hours does on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member). In Scotland the flag is Carer Support Payment's, which policyengine-uk pays under its own earnings limit from 2.146.1", + "reported_true_anchor": "carers_allowance_reported > 0", + "rate_key": "carers_allowance_newly_entitled", + "rate_derivation": "the take-up contract's carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool's strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so the two claim together; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false", + "seed": 0 + } + ], + "outputs": [], + "rewrites": [ + "would_claim_carers_allowance" + ], + "notes": "Runs after the SPI income chain, UC capital coherence, the Pension Credit and Child Benefit redraws and salary_sacrifice, so the earnings test reads the pay salary sacrifice converts (microcosm#1095 B5); a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so every clone carries its source's draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from £151 to £196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer's Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame's own values, gives each person's is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year's parameters at 30 April, so the limit is 2024-25's £151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (María's decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine's gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract's carers_allowance_newly_entitled rate (0.6, María's ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person's identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk's stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (María's ruling of 2026-10-23): there the flag is Carer Support Payment's, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year's limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum's realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony's reviews of microcosm#1420). The stage has no health gate." + }, { "stage": "student_loans", "survey": "Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England", @@ -7926,6 +7958,52 @@ "source": "HMRC Individual Savings Account statistics Tables 9.4, 9.6 and 9.7 to 9.9, the cash ISA limit reduction impact note and Lifetime ISA Table 1a, via the vendored facts; 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).", "stage": "isa_subscriptions", "survey": "HMRC Annual savings statistics (September 2026 release); Wealth and Assets Survey round 8" + }, + { + "stage": "trading_allowance_receipts", + "survey": "The self-employed's own profit on the final spine, and HMRC's policy paper on the Budget 2026 trading allowance raise (calibration)", + "source": "self_employment_income after the SPI income stage; HMRC's published gainers of the raise to GBP 3,000, about 400,000 taxpayers with a lower bill by about GBP 380 on average", + "grain": "person", + "operations": [ + { + "kind": "assign_trading_allowance_receipts", + "output": "self_employment_gross_receipts", + "profit": "self_employment_income", + "draw": "trading_allowance_claimant", + "seed": 0, + "claimant_bands": [ + { + "upper_inclusive": 4500.0, + "rate": 0.72 + }, + { + "upper_inclusive": 9000.0, + "rate": 0.14 + }, + { + "rate": 0.0 + } + ], + "claimant_expenses": 1000.0, + "other_expenses": 3000.0, + "calibration": "HMRC's policy paper on the Budget 2026 trading allowance raise: about 400,000 taxpayers with a lower bill, by about GBP 380 on average, measured on policyengine-uk for the microcosm#1428 national from 9f70c71e7 at 2028-29 prices; the rates drift with each national (microcosm#1439), until HMRC publishes claimant counts (María's choices of 2026-10-31 and 2026-11-01)", + "rule": "a person with a positive self_employment_income claims the allowance where their uniform falls below their profit band's rate; a claimant's receipts are their profit plus claimant_expenses, any other trader's their profit plus other_expenses, and a person with no positive profit keeps zero" + }, + { + "kind": "record_mass_conservation_receipt", + "entity": "household", + "reason": "Self-employed gross receipts stored on the source spine: household weights pass through unchanged and total household mass is conserved.", + "declared_factor": 1.0, + "gate_coupling": "The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason." + } + ], + "outputs": [ + "self_employment_gross_receipts" + ], + "nonnegative_outputs": [ + "self_employment_gross_receipts" + ], + "notes": "The last spine stage (microcosm#1428). policyengine-uk applies the trading allowance against self_employment_gross_receipts, which neither the FRS nor the SPI tape records; without receipts a rise in the allowance relieves only profits up to the new allowance, in full, and misses its main base, the traders who already claim the GBP 1,000 allowance against larger receipts. Under current law a claimant's receipts are their profit plus GBP 1,000 exactly. Each trader with a positive profit is flagged a claimant at their profit band's rate, drawn on their own person_id: a claimant's receipts are their profit plus GBP 1,000, any other trader's their profit plus GBP 3,000 (expenses at the raised allowance, so the raise does not relieve them; the costing books switchers as behaviour), and a person with no positive profit keeps zero, which the engine reads as unknown. Every trader with a positive profit is treated as filing, as SPI Table 3.10's counts, which bind the frame's traders, are of filers, so a profit of GBP 1,000 or less is now taxed where the engine used to relieve it in full. The rates are calibrated to HMRC's policy paper until HMRC publishes claimant counts. The engine uprates receipts with profit, so a claimant's GBP 1,000 grows with the mixed income index while the allowance is nominal: the raise relieves GBP 1,932 more in 2027-28, falling to GBP 1,758 by 2031-32, 3.4% to 12.1% below GBP 2,000. The other traders' stored GBP 3,000 grows the same way, so an allowance above GBP 3,000 times that index relieves them from the stored expense, not from data: allowances above GBP 3,000 are mis-scored until policyengine-uk#2765 lets the stage store the claim status (microcosm#1439; María's ruling of 2026-11-01)." } ] } 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 3dd4eb043..536af9887 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -408,6 +408,7 @@ stages: - loss_relief - ni_class_4_losses_brought_forward - private_pension_income + - annuity_and_trust_income - individual_savings_account_interest_income - savings_interest_income - dividend_income @@ -513,6 +514,7 @@ stages: - loss_relief - ni_class_4_losses_brought_forward - private_pension_income + - annuity_and_trust_income - individual_savings_account_interest_income - savings_interest_income - dividend_income @@ -683,6 +685,9 @@ stages: foster_child_code: 5 foster_parent_code: 9 below_age: 16 + non_parental_care_child: + child_codes: [3, 4] + parent_codes: [7, 8] one_person_age_split: 65 family_role_values: [COUPLE_PARTNER, LONE_PARENT, DEPENDENT_CHILD, NON_DEPENDENT_CHILD, INDIVIDUAL] household_type_values: @@ -706,10 +711,11 @@ stages: - ons_family_role - ons_family_index - is_looked_after_by_local_authority + - child_limit_non_parental_caring_exception - 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. 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." + 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. It also sets child_limit_non_parental_caring_exception, policyengine-uk's two-child limit exception for non-parental caring arrangements (UC Regs 2013 Sch 12 para 4; CTC Regs 2002 reg 12), for a child-table member whom no adult of their own benefit unit claims as a natural, adopted or step child (the child's grid code toward the adult is child or step child, 3 or 4, or the adult's toward the child is parent or step parent, 7 or 8) and whom no local authority placed there: a child living with kinship or friends carers, or with a claimant whose own child is the child's parent. Nothing set it before, so the engine paid no child element for such a third or later child born from 6 April 2017. The multiple births, adoption and non-consensual conception exceptions stay at the engine's default: the FRS has no twin field and its birth dates are blank (María's ruling of 2 November 2026; microcosm#1095)." - 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. @@ -1654,7 +1660,7 @@ stages: spi_profit_before_losses_draw: self_employment_income spi_losses_brought_forward_draw: self_employment_income predictors: - - age + - spi_age_band - gender - region - main_source @@ -1662,6 +1668,7 @@ stages: - has_trade - at_or_over_spa categorical_predictors: + - spi_age_band - gender - region - main_source @@ -1697,7 +1704,7 @@ stages: dividend_income: dividend_income private_pension_income: private_pension_income property_income: property_income - other_investment_income: null + other_investment_income: other_investment_income gift_aid: null charitable_investment_gifts: null hmrc_spi_pay: hmrc_spi_pay @@ -1719,7 +1726,7 @@ stages: seed: 45 n_estimators: 100 folds: 5 - fold_key: 'predictor cell (gender, region, whole-year age, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell''s stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold''s recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest' + fold_key: 'predictor cell (gender, region, spi_age_band, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell''s stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold''s recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest' method: 'Rosenblatt transport (microcosm#840): each drawn recipient''s twin values are located on the reference forest, the sign uniform in the gate''s cumulative class probabilities and the quantile on the sign''s magnitude forest, each leaf chained on the twin''s observed earlier leaves, and the stage-1 forest draws at those uniforms, so the recipient receives the SPI income of someone in the same position among SPI people like it as the twin holds among FRS people like it. taxable_savings_interest is savings_interest_income, the interest outside ISAs, floored at zero. A leaf without an analogue, or whose analogue is degenerate on the FRS rows, draws at identity-keyed uniforms, as do the placements inside a class or a flat stretch of a conditional; no model random stream is consumed. The reference locates a leaf given the twin''s observed earlier analogue leaves, while the stage-1 forest draws it given every earlier output, the identity-uniform leaves included, so the located quantile is read off a conditional the identity draws also shape (microcosm#840 follow-up: the chain order). A full build refuses a reference the predictor cells cannot cross-fit (only a sampled rung or a synthetic fixture may locate in-sample, and its receipt says so), a recipient whose fold differs from its twin''s, and a reference whose sign classes differ from the stage-1 forest''s on any located target.' receipt: per target the analogue, the FRS regime and sign classes, the uniform source and the Kolmogorov-Smirnov distance of the sign uniforms and of the quantiles over positive rows from U[0,1), overall and by main_source over at least 10 rows (a statistical floor, as a distance over fewer says nothing); positive-row counts are rounded as every receipt count is, one or two rows reading <3; the counts in the stage evidence stay with the build and are not published identity_uniforms: @@ -1866,6 +1873,20 @@ stages: salt: spi_income_band_donor_leaf_draw outputs: stage-1 outputs, uprated as the stage-1 draws seed: 44 + - kind: floor_spi_employment_pay_at_minimum_wage + column: hmrc_spi_pay + hours_column: hours_worked + hours_basis: "annual usual hours: the FRS adult's usual weekly hours in all jobs (TOTHOURS) times 52, read back to weekly hours" + weeks_in_year: 52 + hourly_rate: "the lower of policyengine-uk gov.hmrc.minimum_wage.non_apprentice by the twin's age, read as the engine reads the build year (30 April), and the twin's own hourly pay (its employment income before the draw over its annual hours)" + employee_statuses: + - FT_EMPLOYED + - PT_EMPLOYED + rows: "drawn recipients other than reserved band carriers, whose twin has no trade (has_trade 0), an employee main job and usual hours above zero, and whose drawn PAY is positive; FRS rows, undrawn copies and every other leaf keep their values" + pay_basis: "tape PAY is taxable pay, net of net-pay pension contributions and salary sacrifice; the minimum wage binds gross pay, and the engine reads employment_income (PAY + EPB + TAXTERM) as gross pay, so PAY alone is compared with the gross minimum and a twin at the minimum wage who pays net-pay contributions can be lifted by up to them" + reason: "stage 1 draws annual tape pay without hours and the tape holds part-year workers, so a twin reporting usual employee hours could draw a year's pay below what those hours earn at the minimum wage (uk-data#629)" + annual_income: "the floored pay is the row's current pay; its employment_income_annual_share becomes its unfloored over its floored employment income, so the annual measures read the tape's year of pay" + receipt: "eligible and floored source households (rounded), weighted people, the weighted share floored, the weighted mean uplift and the amount added; figures on one or two source households are <3, and the floored weight, share and amount are given only where neither the floored nor the unfloored side rests on one or two; the hourly-pay check gives the weighted shares of the drawn employees below the minimum wage for their age before and after the floor, beside the FRS employees' share, each withheld beside a side on one or two, and the eligible rows still below their floor (banded, zero exact)" - kind: fit_weighted_qrf_stage2 training_population: uk_frs_raw_spine_base_support_channel target_population: spi_synthetic_support_channel claimants and partners (is_uc_claimant) aged 16 and over @@ -2255,7 +2276,8 @@ stages: - self_employment_income_other_spells - private_pension_income_annual_share - contributory_benefits_annual_share - notes: Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes. + - annuity_and_trust_income + notes: Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes. A drawn row's annuity and trust descriptor is zero, since the tape's private pension excludes those payments and books them as other income (microcosm#1428). - stage: company_cars survey: Family Resources Survey 2024-25 job record; Wealth and Assets Survey round 8; HMRC Benefits in kind statistics (July 2026) source: The FRS job record's company car answers through frs_spine's carriers (EXPBEN01, EXPBEN03, CARVAL, FUELTYP, CARCON, CARAMT), WAS round-8 company cars' list prices, and HMRC's 2024-25 company car recipients by CO2 band (Table 2A) and fuel (Table 2C), via the vendored facts @@ -3169,7 +3191,7 @@ stages: event_salt: non_residential_property_purchased price_salt: non_residential_property_purchased_price seed: 0 - additional_residential_targets: Table 6b HRAD transactions and value, 2024-25, times individuals' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined + additional_residential_targets: 'Table 6b HRAD transactions and value, 2024-25: at or below GBP 250,000, the band less other buyers'' HRAD, their Table 8a receipts over Table 6b''s gross HRAD receipts per transaction and per pound; above it, times individuals'' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined, at the households'' counts and values' non_residential_targets: Table 8b individuals' transactions, value and bands, 2024-25 - kind: record_mass_conservation_receipt entity: household @@ -5158,13 +5180,13 @@ stages: family_type: two FRS claimant or partner roles make a couple; children are UC children or qualifying young people and other non-claimants under 20 (the calibration measure's uc_calibration_family_type) award_bands: GBP 1,200 a year (DWP's GBP 100 a month), (1,200 i, 1,200 (i + 1)] in whole pence for i < 25, and above GBP 30,000 targets: DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025 - pooling: adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it + pooling: adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it. Then, lowest first, a pool that cannot meet DWP's count (its undrawn entitled units' expected claims exceed the count, or the count exceeds them plus the weight of every drawn unit) is held at the nearer of those ends and passes its gap to the other pools, nearest first, the one with more room first at equal distance and the lower on a tie, each taking what its room allows; a gap no pool can take leaves the family type unreachable minimum_pool_sources: 30 drawn_units: entitled, non-reporting units of the population that the engine does not move off their legacy benefits (legacy_benefits_closed) and that report none, since a legacy benefit the engine leaves in place ends once the unit claims UC undrawn_claims: a reporter at 1, a moved unit at the claim probability uc_legacy_closure_take_up's rule gives it (dwp_move_to_uc_facts.json), a unit keeping income-related ESA at 0, any other at its stored flag kept_legacy_award: a unit whose claimant or partner reports income-related ESA (esa_income_reported), and that neither reports Universal Credit nor is moved off its legacy benefits, claims no Universal Credit, since policyengine-uk ends that award once the family claims it weight_mapping: household_to_benunit - residual_rate: (T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too + residual_rate: (T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands after any gaps passed to or from it, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too solve_scope: great_britain not_drawn: keeps its stored flag, except a unit keeping income-related ESA, which claims none outputs: [] @@ -5316,32 +5338,6 @@ stages: - would_claim_child_benefit - child_benefit_opts_out notes: 'Runs after the SPI income chain, UC capital coherence and the Pension Credit redraw, before CGT cloning (microcosm#1063). frs_take_up draws would_claim_child_benefit early at one flat rate over every benefit unit with the reporters on top, and child_benefit_opts_out at a flat rate whatever the unit''s income; this stage redraws both. One temporary engine materialization gives each person''s eligibility as a child or qualifying young person and each person''s adjusted net income (consumed only). A family claims for all its children or none, so the draw is by family, keyed on the age of its eldest eligible child: a reporter claims, and the other families claim at rates solved from the eldest age down so that the claimed share of the eligible children of each single year of age is HMRC''s published one (Table 15, May 2025, opted-out families included), weighted at the stage''s household weights. The published opted-out share of registered families (August 2025) is drawn among claiming non-reporters, fully charged first. Opt-out-aware models restrict both the full-charge and taper pools to a positive charge fraction at least the installed opt_out_charge_share. With the default share of one, only fully charged families can opt out; an insufficient pool is recorded as a shortfall instead of selecting paid taper families. Legacy models retain the original full-charge pool and taper fallback. Models exposing gov.hmrc.child_benefit.opt_out_charge_share (policyengine-uk#2140) receive registered claims, including opted-out families, in would_claim_child_benefit and the separate opt-out in child_benefit_opts_out. Older models receive the legacy claims-and-not-opted-out payment flag because they ignore the opt-out input. Stage evidence records the selected encoding, evaluated opt-out charge share and engine parameter source. The in-payment audit counts claims-and-not-opted-out in the draw, with new candidates aligned to baseline payment suppression; it is not a fresh model run or population calibration evidence. A benefit unit with no eligible child keeps its early draw and is never opted out.' -- stage: carers_allowance_take_up - survey: Family Resources Survey 2024-25 - source: 'Independent Review of Carer''s Allowance Overpayments (July 2025), chapter 7, and DWP benefit statistics, through the UK take-up contract''s carers_allowance_newly_entitled rate, applied to PolicyEngine-UK''s Carer''s Allowance earnings test after the SPI income chain.' - grain: person - artifacts: [] - operations: - - kind: materialize_rules_engine_predictors - predictors: - - is_gainfully_employed_for_carers_allowance - - is_severely_disabled_for_carers_allowance - - carers_allowance_earnings - consumed_only: true - year_rule: survey_year - - kind: assign_binary_from_rate - output: would_claim_carers_allowance - draw: would_claim_carers_allowance - assignment_unit: person - eligibility: 'persons whose care_hours reach policyengine-uk''s gov.dwp.carers_allowance.min_hours, who care for a severely disabled person, who report no Carer''s Allowance, and whom is_gainfully_employed_for_carers_allowance holds gainfully employed at the survey year: the carers the earnings limit alone keeps from the allowance (a carer whose frs_care_hours_in_household reach min_hours cares for a severely disabled person when another member of the household is is_severely_disabled_for_carers_allowance at the survey year; a carer whose largest in-household band does not reach the hours does on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member). In Scotland the flag is Carer Support Payment''s, which policyengine-uk pays under its own earnings limit from 2.146.1' - reported_true_anchor: carers_allowance_reported > 0 - rate_key: carers_allowance_newly_entitled - rate_derivation: 'the take-up contract''s carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool''s strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false' - seed: 0 - outputs: [] - rewrites: - - would_claim_carers_allowance - notes: 'Runs after the SPI income chain, UC capital coherence and the Pension Credit and Child Benefit redraws, before CGT cloning, so every clone carries its source''s draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from £151 to £196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer''s Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame''s own values, gives each person''s is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year''s parameters at 30 April, so the limit is 2024-25''s £151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (María''s decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine''s gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract''s carers_allowance_newly_entitled rate (0.6, María''s ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person''s identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk''s stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (María''s ruling of 2026-10-23): there the flag is Carer Support Payment''s, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors'' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year''s limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum''s realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony''s reviews of microcosm#1420). The stage has no health gate.' - stage: uc_deduction_attributes survey: DWP Universal Credit deductions statistics, March 2025 to February 2026 source: Department for Work and Pensions supplementary Tables 2, 5 and 6, sha-pinned by Chronicle package dwp-uc-deductions-march-2025-february-2026. @@ -6054,6 +6050,32 @@ stages: - employee_pension_contributions - employment_income notes: The QRF trains only on the 2024-25 FRS asked subset with pay. Salary sacrifice is pay given up, so every amount, reported or predicted, is limited to the pay the person still receives, and a person without pay has none (uk-data#518, microcosm#1095); the limit bites above all on SPI-channel rows, which keep their FRS twin's answer beside pay redrawn from the SPI. The second arm creates support toward the reviewed 5.4m staging target by moving contributors' pension amounts, up to half their pay, into salary sacrifice and lowering their pay by the same amount, since the engine reads employment_income after sacrifice; what does not move stays an employee contribution, so the sacrifice never exceeds the pay that remains. Before microcosm#1069 c9 the pay stayed whole and the converted record lost its net-pay relief. The third arm adds bonus sacrifice beside pay toward the OBR's 2027-28 base above GBP 2,000 (microcosm#1340). A sacrificed bonus is never received, so the FRS records it in neither pay nor its bonus amounts, and pay stays as it is (María's ruling of 2026-10-24 on Vahid's review of microcosm#1383); a received FRS bonus marks a bonus-paying job and its size. An SPI-channel adult's FRS bonus is its twin's, beside the tape's pay, so it draws a bonus share of pay from a base-FRS adult in its pay cell instead. +- stage: carers_allowance_take_up + survey: Family Resources Survey 2024-25 + source: 'Independent Review of Carer''s Allowance Overpayments (July 2025), chapter 7, and DWP benefit statistics, through the UK take-up contract''s carers_allowance_newly_entitled rate, applied to PolicyEngine-UK''s Carer''s Allowance earnings test after the SPI income chain.' + grain: person + artifacts: [] + operations: + - kind: materialize_rules_engine_predictors + predictors: + - is_gainfully_employed_for_carers_allowance + - is_severely_disabled_for_carers_allowance + - carers_allowance_earnings + consumed_only: true + year_rule: survey_year + - kind: assign_binary_from_rate + output: would_claim_carers_allowance + draw: would_claim_carers_allowance + assignment_unit: person + eligibility: 'persons whose care_hours reach policyengine-uk''s gov.dwp.carers_allowance.min_hours, who care for a severely disabled person, who report no Carer''s Allowance, and whom is_gainfully_employed_for_carers_allowance holds gainfully employed at the survey year: the carers the earnings limit alone keeps from the allowance (a carer whose frs_care_hours_in_household reach min_hours cares for a severely disabled person when another member of the household is is_severely_disabled_for_carers_allowance at the survey year; a carer whose largest in-household band does not reach the hours does on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member). In Scotland the flag is Carer Support Payment''s, which policyengine-uk pays under its own earnings limit from 2.146.1' + reported_true_anchor: carers_allowance_reported > 0 + rate_key: carers_allowance_newly_entitled + rate_derivation: 'the take-up contract''s carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool''s strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so the two claim together; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false' + seed: 0 + outputs: [] + rewrites: + - would_claim_carers_allowance + notes: 'Runs after the SPI income chain, UC capital coherence, the Pension Credit and Child Benefit redraws and salary_sacrifice, so the earnings test reads the pay salary sacrifice converts (microcosm#1095 B5); a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so every clone carries its source''s draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from £151 to £196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer''s Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame''s own values, gives each person''s is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year''s parameters at 30 April, so the limit is 2024-25''s £151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (María''s decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine''s gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract''s carers_allowance_newly_entitled rate (0.6, María''s ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person''s identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk''s stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (María''s ruling of 2026-10-23): there the flag is Carer Support Payment''s, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors'' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year''s limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum''s realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony''s reviews of microcosm#1420). The stage has no health gate.' - stage: student_loans survey: Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England source: Explore Education Statistics Table 6a, Higher education total @@ -6361,3 +6383,33 @@ stages: - stocks_and_shares_isa_subscriptions - lifetime_isa_payments notes: 'Imputes ISA subscriptions per adult by type, and the per-person ISA balances, on the final spine (microcosm#1415). Each household''s was_wealth cash and stocks-and-shares ISA totals are split across its adults by the holding pattern of a WAS round-8 donor household with the same number of adults, so person balances sum exactly to the household totals, which are unchanged. Subscribers in 2023-24 are adults aged 18 or over in households holding the type, raked by class (cash only, stocks and shares only, both) to Table 9.7''s income bands, Table 9.8''s age and sex and Table 9.9''s regions and selected by keyed walks, the both class first. Amounts follow Table 9.7''s subscription bands within each income band and class, uniform inside a band and exactly GBP 20,000 at the top; the both class splits each person''s amount by their balance shares, shifted so the class''s stocks-and-shares total is Table 9.4''s 2023-24 stocks-and-shares amount less the stocks-and-shares-only payments. People grow to 2024-25 by Table 9.4''s account growth (an upper bound on people, as from 6 April 2024 a person may pay into several ISAs of one type), and amounts scale to Table 9.4''s 2024-25 amounts per type, holding subscribers already at the GBP 20,000 overall limit and moving combined amounts between GBP 18,000 (Table 9.7''s last band floor below the limit) and the limit on a line up to it, so a factor below one leaves no empty range under the limit (María''s rulings of 29 October 2026): Table 9.7''s cash bands sit above Table 9.4, and growing that excess by Table 9.4''s ratio against the limit pushed the subscribers below it far up. Every Lifetime ISA holder aged 18 to 47 (the ages that can hold one in 2024-25) with room under the overall limit pays in, amounts in (0, GBP 4,000] drawn to Table 9.4''s 2024-25 Lifetime ISA amount. No ISA fact is a calibration target: binding the cells the stage draws to would be circular, so the reviewed fence keeps them measured only.' +- stage: trading_allowance_receipts + survey: The self-employed's own profit on the final spine, and HMRC's policy paper on the Budget 2026 trading allowance raise (calibration) + source: self_employment_income after the SPI income stage; HMRC's published gainers of the raise to GBP 3,000, about 400,000 taxpayers with a lower bill by about GBP 380 on average + grain: person + operations: + - kind: assign_trading_allowance_receipts + output: self_employment_gross_receipts + profit: self_employment_income + draw: trading_allowance_claimant + seed: 0 + claimant_bands: + - upper_inclusive: 4500.0 + rate: 0.72 + - upper_inclusive: 9000.0 + rate: 0.14 + - rate: 0.0 + claimant_expenses: 1000.0 + other_expenses: 3000.0 + calibration: 'HMRC''s policy paper on the Budget 2026 trading allowance raise: about 400,000 taxpayers with a lower bill, by about GBP 380 on average, measured on policyengine-uk for the microcosm#1428 national from 9f70c71e7 at 2028-29 prices; the rates drift with each national (microcosm#1439), until HMRC publishes claimant counts (María''s choices of 2026-10-31 and 2026-11-01)' + rule: a person with a positive self_employment_income claims the allowance where their uniform falls below their profit band's rate; a claimant's receipts are their profit plus claimant_expenses, any other trader's their profit plus other_expenses, and a person with no positive profit keeps zero + - kind: record_mass_conservation_receipt + entity: household + reason: 'Self-employed gross receipts stored on the source spine: household weights pass through unchanged and total household mass is conserved.' + declared_factor: 1.0 + gate_coupling: The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason. + outputs: + - self_employment_gross_receipts + nonnegative_outputs: + - self_employment_gross_receipts + notes: 'The last spine stage (microcosm#1428). policyengine-uk applies the trading allowance against self_employment_gross_receipts, which neither the FRS nor the SPI tape records; without receipts a rise in the allowance relieves only profits up to the new allowance, in full, and misses its main base, the traders who already claim the GBP 1,000 allowance against larger receipts. Under current law a claimant''s receipts are their profit plus GBP 1,000 exactly. Each trader with a positive profit is flagged a claimant at their profit band''s rate, drawn on their own person_id: a claimant''s receipts are their profit plus GBP 1,000, any other trader''s their profit plus GBP 3,000 (expenses at the raised allowance, so the raise does not relieve them; the costing books switchers as behaviour), and a person with no positive profit keeps zero, which the engine reads as unknown. Every trader with a positive profit is treated as filing, as SPI Table 3.10''s counts, which bind the frame''s traders, are of filers, so a profit of GBP 1,000 or less is now taxed where the engine used to relieve it in full. The rates are calibrated to HMRC''s policy paper until HMRC publishes claimant counts. The engine uprates receipts with profit, so a claimant''s GBP 1,000 grows with the mixed income index while the allowance is nominal: the raise relieves GBP 1,932 more in 2027-28, falling to GBP 1,758 by 2031-32, 3.4% to 12.1% below GBP 2,000. The other traders'' stored GBP 3,000 grows the same way, so an allowance above GBP 3,000 times that index relieves them from the stored expense, not from data: allowances above GBP 3,000 are mis-scored until policyengine-uk#2765 lets the stage store the claim status (microcosm#1439; María''s ruling of 2026-11-01).' 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 436464821..76d5f8ef7 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-08962f1-consumer-facts", "target_period": 2025, - "candidate_count": 1439, - "contract_target_count": 396, - "active_reference_count": 1409, + "candidate_count": 1455, + "contract_target_count": 398, + "active_reference_count": 1418, "status_counts": { - "active": 1409, - "signed_excluded": 30 + "active": 1418, + "signed_excluded": 37 }, "geography_pins": { "obr.income_tax": { @@ -161,6 +161,10 @@ "geography_level": "country", "geography_id": "K02000001" }, + "hmrc.spi.employment_income.count_by_self_employment_income_band": { + "geography_level": "country", + "geography_id": "K02000001" + }, "hmrc.spi.dividend_income.amount_by_total_income_band": { "geography_level": "country", "geography_id": "K02000001" @@ -4491,6 +4495,10 @@ "geography_level": "country", "geography_id": "K03000001" }, + "dwp.jsa.amount_income_based": { + "geography_level": "country", + "geography_id": "K03000001" + }, "microcosm.high_value_homes.over_1m_by_region": { "geography_fanout": [ { @@ -5408,6 +5416,70 @@ "factor": "1.11152010143825", "value_before_uprating": "3470000000.0" }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1_500", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "67000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1000_2000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "127000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_2000_3000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "103000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_3000_4000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "90000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_4000_5000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "77000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_500_1000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "64000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_5000_7500", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "148000.0" + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_7500_10000", + "from": "2023", + "to": "2025", + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "113000.0" + }, { "name": "hmrc/dividend_income_income_band_100_000_to_150_000", "from": "2023", @@ -13016,6 +13088,208 @@ } ] }, + "hmrc.spi.employment_income.count_by_self_employment_income_band": { + "status": "active", + "candidates": [ + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_0_1", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 0 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's zero band counts people with a self-employment source and no profit after loss relief. The binding bands people on their self-employment income alone, so a band from 0 to 1 would count every employee without self-employment income. It also lies outside the GBP 1 to GBP 10,000 range Mar\u00eda's ruling of 2026-10-29 binds, which her choice of 2026-10-30 extends to Table 3.10's employment-income counts. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1_500", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 72111.7166212534, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_a8a50ebacc90efdf051f460b", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "67000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1000_2000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 136689.37329700272, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_e07428dae7ed2be1a4b3d793", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "127000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_10000_15000", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 10000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_100000_plus", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 100000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_15000_20000", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 15000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_2000_3000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 110858.310626703, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_8f608695430804bf74928876", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "103000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_20000_30000", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 20000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_3000_4000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 96866.48501362398, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_0814147e65171246cce164c0", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "90000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_30000_50000", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 30000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_4000_5000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 82874.65940054496, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_5e447c3f14dc230f46520c1e", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "77000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_500_1000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 68882.83378746595, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_1f44d42f7a3ba77a7f872437", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "64000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_5000_7500", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 159291.553133515, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_535bdfcf3c4c862eee078139", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "148000.0" + } + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_50000_100000", + "status": "signed_excluded", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "signed_row": { + "dimension": "self_employment_income_lower_bound", + "value": 50000 + }, + "signed_rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: Mar\u00eda's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound." + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_7500_10000", + "status": "active", + "matched_fact_count_overall": 1, + "matched_fact_count_at_or_before_period": 1, + "resolved_period": 2025, + "resolved_value": 121621.25340599455, + "resolved_fact_period": "2023", + "resolved_fact_key": "ledger_aggregate_fact_v2_52b23e53122bfddd68b77ca8", + "uprating": { + "index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "factor": "1.07629427792916", + "value_before_uprating": "113000.0" + } + } + ] + }, "hmrc.spi.dividend_income.amount_by_total_income_band": { "status": "active", "candidates": [ @@ -31645,6 +31919,21 @@ } ] }, + "dwp.jsa.amount_income_based": { + "status": "active", + "candidates": [ + { + "name": "dwp.jsa.amount_income_based", + "status": "active", + "matched_fact_count_overall": 2, + "matched_fact_count_at_or_before_period": 2, + "resolved_period": 2025, + "resolved_value": 23377178.788788605, + "resolved_fact_period": "2025", + "resolved_fact_key": "ledger_aggregate_fact_v2_6d0a1e9a4006a42d57b637a2" + } + ] + }, "microcosm.high_value_homes.over_1m_by_region": { "status": "active", "candidates": [ @@ -32113,7 +32402,7 @@ { "family": "hmrc_spi", "status": "active_strict_band_pins", - "active_reference_count": 211, + "active_reference_count": 219, "signed_rationale": "SPI income-band targets fan out by strict total-income-band dimension pins, and SPI Table 3.10's by self-employment band, its bands outside GBP 1 to GBP 10,000 signed out row by row (microcosm#1418). The 13 property-income amount rows bind the SPI Table 3.7 net concept unscaled: landlords' profit after allowable expenses and before residential finance costs, which is what policyengine-uk's property_income means. The incumbent's x1.9 scaling (uk-data#311, uk-data#230) set the Property Rental Income Statistics' receipts before expenses against this net amount, so it is retired; the receipts are constructed on property_rental_income by the property_components stage and PRIS stays a diagnostic (microcosm#1106, Mar\u00eda's rulings of 2026-10-05 and 2026-10-08)." }, { diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json index aa48ed2e9..f5be4e0dc 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_signed_exclusions.json @@ -272,6 +272,90 @@ "value": 100000 } }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "zero_band_counts_everyone_without_the_income", + "rationale": "SPI 2023-24 Table 3.10's zero band counts people with a self-employment source and no profit after loss relief. The binding bands people on their self-employment income alone, so a band from 0 to 1 would count every employee without self-employment income. It also lies outside the GBP 1 to GBP 10,000 range María's ruling of 2026-10-29 binds, which her choice of 2026-10-30 extends to Table 3.10's employment-income counts. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 0 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 10000 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 15000 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 20000 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 30000 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 50000 + } + }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "reason_id": "bound_by_table_3_6_from_10000", + "rationale": "SPI 2023-24 Table 3.10's self-employment bands from GBP 10,000 stay with Table 3.6's rows by total income, so the two tables do not pull against each other: María's ruling of 2026-10-29 binds Table 3.10 from GBP 1 to GBP 10,000, the small-profit end of the Budget's trading allowance measures (Table 6.1 line 15), and her choice of 2026-10-30 adds its employment-income counts over the same bands. Signed out of the band fan-out; the eight bands from GBP 1 stay bound.", + "tracking": "microcosm#1418", + "approved_by": "juaristi22", + "approved_on": "2026-10-30", + "row": { + "dimension": "self_employment_income_lower_bound", + "value": 100000 + } + }, { "target_id": "hmrc.spi.self_employment_income.amount_by_self_employment_income_band", "reason_id": "bound_by_table_3_6_from_10000", 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 2c5ae87c3..338b645d4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json +++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json @@ -310,6 +310,7 @@ "dwp.hb.households_pension_age_social_rented": "dwp.legacy_benefits", "dwp.hb.households_private_rented": "dwp.housing_benefit", "dwp.hb.households_social_rented": "dwp.housing_benefit", + "dwp.jsa.amount_income_based": "dwp.legacy_benefits", "dwp.jsa_claimants": "dwp.legacy_benefits", "dwp.pension_credit.benefit_units": "dwp.pension_credit", "dwp.pension_credit.benefit_units_65_69": "dwp.pension_credit", @@ -426,6 +427,7 @@ "hmrc.spi.dividend_income.amount_by_total_income_band": "hmrc.survey_of_personal_incomes", "hmrc.spi.dividend_income.count_by_total_income_band": "hmrc.survey_of_personal_incomes", "hmrc.spi.employment_income.amount_by_total_income_band": "hmrc.survey_of_personal_incomes", + "hmrc.spi.employment_income.count_by_self_employment_income_band": "hmrc.survey_of_personal_incomes", "hmrc.spi.employment_income.count_by_total_income_band": "hmrc.survey_of_personal_incomes", "hmrc.spi.other_investment_income.amount_by_total_income_band": "hmrc.survey_of_personal_incomes", "hmrc.spi.private_pension_income.amount_by_total_income_band": "hmrc.survey_of_personal_incomes", @@ -701,6 +703,7 @@ "dwp.hb.households_pension_age_social_rented": "Housing Benefit benefit units, pension age, social rented sector", "dwp.hb.households_private_rented": "Housing Benefit claimants, private rented", "dwp.hb.households_social_rented": "Housing Benefit claimants, social rented", + "dwp.jsa.amount_income_based": "Jobseeker's Allowance spending, income-based", "dwp.jsa_claimants": "Jobseeker\u2019s Allowance claimants", "dwp.pension_credit.benefit_units": "Pension Credit benefit units, Great Britain", "dwp.pension_credit.benefit_units_65_69": "Pension Credit benefit units, claimant aged 65 to 69", @@ -817,6 +820,7 @@ "hmrc.spi.dividend_income.amount_by_total_income_band": "Amount by total income band", "hmrc.spi.dividend_income.count_by_total_income_band": "Count by total income band", "hmrc.spi.employment_income.amount_by_total_income_band": "Amount by total income band", + "hmrc.spi.employment_income.count_by_self_employment_income_band": "Count with employment income by self-employment income band", "hmrc.spi.employment_income.count_by_total_income_band": "Count by total income band", "hmrc.spi.other_investment_income.amount_by_total_income_band": "Amount by total income band", "hmrc.spi.private_pension_income.amount_by_total_income_band": "Amount by total income band", @@ -3583,6 +3587,206 @@ "uprating_from_period": "2023", "uprating_to_period": 2025 }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1_500", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 1 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1_500", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1000_2000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 1000 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_1000_2000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_2000_3000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 2000 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_2000_3000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_3000_4000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 3000 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_3000_4000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_4000_5000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 4000 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_4000_5000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_500_1000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 500 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_500_1000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_5000_7500", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 5000 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_5000_7500", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, + { + "name": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_7500_10000", + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person", + "geography_level": "country", + "geography_id": "K02000001", + "dimension_values": { + "self_employment_income_lower_bound": 7500 + } + }, + "entity": "person", + "measure": "hmrc.spi.employment_income.count_by_self_employment_income_band.band_7500_10000", + "family": "hmrc_spi", + "period": 2025, + "metadata": { + "contract_target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "measure_kind": "prepared_column" + }, + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "uprating_from_period": "2023", + "uprating_to_period": 2025 + }, { "name": "hmrc/dividend_income_income_band_100_000_to_150_000", "ledger_selector": { @@ -48968,6 +49172,33 @@ "assertion_policy": "allow_source_projection", "value_operation": "calendar_year_window" }, + { + "name": "dwp.jsa.amount_income_based", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.benefit_expenditure_amount", + "entity_name": "government", + "dimension_values": { + "benefit_forecast_line": "unemployment_benefits__jobseekers_allowance__income_based" + }, + "source_measure_id_by_opening_year": { + "2024": "expenditure_2024", + "2025": "expenditure_2025" + }, + "geography_level": "country", + "geography_id": "K03000001" + }, + "entity": "benunit", + "measure": "dwp/jsa/amount_income_based", + "family": "dwp_benefit_expenditure", + "period": 2025, + "metadata": { + "contract_target_id": "dwp.jsa.amount_income_based", + "measure_kind": "prepared_column" + }, + "assertion_policy": "allow_source_projection", + "value_operation": "calendar_year_window" + }, { "name": "microcosm.high_value_homes.over_1m_by_region@E12000001", "ledger_selector": { 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 26e6397d7..386db982b 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 @@ -836,6 +836,7 @@ "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).", + "dwp.jsa.amount_income_based": "Post-freeze fact family (DWP benefit expenditure and caseload tables, income-based Jobseeker's Allowance spending), no uk-data metric (microcosm#1428).", "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).", @@ -895,7 +896,8 @@ "hmrc.itl.savings_tax_at_additional_rate": "Post-freeze fact family (HMRC Income Tax liabilities statistics Table 2.6, July 2026, in Chronicle: Income Tax on savings income at each rate, all payers, the calendar-2025 window of the 2024-25 and 2025-26 projections), no uk-data metric (microcosm#1418).", "hmrc.spi.other_investment_income.amount_by_total_income_band": "New declaration backed by SPI 2023-24 Table 3.7 other income in Chronicle (spi_income_bands_2023_24); no uk-data metric holds other investment income by band (microcosm#1418).", "hmrc.spi.self_employment_income.count_by_self_employment_income_band": "New declaration backed by SPI 2023-24 Table 3.10 in Chronicle (spi_income_bands_2023_24, PolicyEngine/chronicle#394); no uk-data metric holds self-employment income by self-employment band (microcosm#1418).", - "hmrc.spi.self_employment_income.amount_by_self_employment_income_band": "New declaration backed by SPI 2023-24 Table 3.10 in Chronicle (spi_income_bands_2023_24, PolicyEngine/chronicle#394); no uk-data metric holds self-employment income by self-employment band (microcosm#1418)." + "hmrc.spi.self_employment_income.amount_by_self_employment_income_band": "New declaration backed by SPI 2023-24 Table 3.10 in Chronicle (spi_income_bands_2023_24, PolicyEngine/chronicle#394); no uk-data metric holds self-employment income by self-employment band (microcosm#1418).", + "hmrc.spi.employment_income.count_by_self_employment_income_band": "New declaration backed by SPI 2023-24 Table 3.10 in Chronicle (spi_income_bands_2023_24, PolicyEngine/chronicle#394); no uk-data metric holds employment income by self-employment band (microcosm#1418)." }, "suppressed_ancestors": { "hmrc/salary_sacrifice_it_relief_basic_rate": { @@ -971,6 +973,7 @@ "hmrc.spi.self_employment_income.count_by_total_income_band", "hmrc.spi.self_employment_income.count_by_self_employment_income_band", "hmrc.spi.self_employment_income.amount_by_self_employment_income_band", + "hmrc.spi.employment_income.count_by_self_employment_income_band", "hmrc.spi.dividend_income.amount_by_total_income_band", "hmrc.spi.dividend_income.count_by_total_income_band", "hmrc.spi.property_income.amount_by_total_income_band", @@ -1320,6 +1323,7 @@ "dwp.hb.households_pension_age_social_rented", "dwp.hb.households_pension_age_private_rented", "dwp.hb.amount_pension_age", + "dwp.jsa.amount_income_based", "microcosm.high_value_homes.over_1m_by_region", "microcosm.high_value_homes.over_1_5m_by_region", "microcosm.high_value_homes.over_2m_by_region", @@ -4058,6 +4062,54 @@ "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands_times_obr_per_capita_mixed_income", "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period as recipients times the amount per recipient: the count rows' index (HMRC's projected growth of all taxpayers over all income ranges, Income Tax liabilities statistics Table 2.5) times gov.economic_assumptions.indices.obr.per_capita.mixed_income on the pinned engine, the index policyengine-uk grows self_employment_income by. Each band's mean therefore moves as the engine moves incomes; growing the counts and amounts on unrelated indices would have made calibration add people to each band and tilt them toward its bottom edge (the independent review of microcosm#1420, finding 2). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane)." }, + { + "target_id": "hmrc.spi.employment_income.count_by_self_employment_income_band", + "family": "hmrc_spi", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "person", + "map_to": "household", + "concept": "uk.person.count", + "groupby_dimension": "hmrc.self_employment_income_band" + }, + "bindings": { + "policyengine": { + "metric_name": "hmrc/employment_income_count_self_employment_income_band", + "value_variable": "person_count", + "from_entity": "person", + "map_to": "household", + "groupby_variable": "self_employment_income_after_loss_relief_annual", + "band_filter_dimension": "self_employment_income", + "band_upper_bound": 10000, + "band_upper_bound_inclusive": false, + "filters": [ + { + "variable": "employment_income_with_benefits_in_kind_annual", + "operator": "!=", + "value": 0 + } + ], + "notes": "SPI 2023-24 Table 3.10: the individuals with a self-employment source, taxpayer or not, who also have employment income, by range of self-employment income. Bound for the eight bands from GBP 1 to GBP 10,000 beside the self-employment counts (María's rulings of 2026-10-29 and 2026-10-30, microcosm#1418): the counts alone let the solve meet each band with traders who have no pay, where more than half of Table 3.10's traders below GBP 3,000 have employment income too, and the trading allowance measures (Table 6.1 line 15) reach the ones who pay Income Tax. The target fans out over every published band and target_reference_signed_exclusions.json signs out the others. Banded by the person's own self-employment income after loss relief, half-open, with the top bound band closed at GBP 10,000 (band_upper_bound). Employment income is the year's pay with its company car and car fuel benefits, employment_income_with_benefits_in_kind_annual, the term every HMRC employment income row reads (microcosm#1430)." + }, + "axiom": { + "metric_name": "hmrc/employment_income_count_self_employment_income_band", + "status": "pending", + "value_rule": "uk.person.count" + } + }, + "ledger_selector": { + "source_name": "hmrc", + "source_concept": "hmrc.spi_table_3_10_employment_income_count", + "groupby_dimension": "hmrc.self_employment_income_band", + "entity_name": "person" + }, + "category_id": "hmrc.survey_of_personal_incomes", + "label": "Count with employment income by self-employment income band", + "uprating_index": "hmrc.itl_2026.taxpayer_count_growth_all_bands", + "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by HMRC's projected growth of all taxpayers over all income ranges (Income Tax liabilities statistics Table 2.5), as the self-employment counts by self-employment band are. Table 3.10's employment income is income assessed under the Income Tax (Earnings and Pensions) Act 2003 and other miscellaneous income (its note 4), wider than Table 3.6's: the binding reads employment_income_with_benefits_in_kind_annual, as Table 3.6's employment rows do, so it leaves out miscellaneous income and Incapacity Benefit, contribution-based Employment and Support Allowance and Jobseeker's Allowance. Taxable benefits in kind count on SPI rows, whose pay holds the tape's pay, benefits and termination pay (PAY + EPB + TAXTERM), and the company car and car fuel benefits count on every row; FRS rows' other benefits in kind don't, their pay being the cash pay reported (the #1428 review, finding 13; microcosm#1430). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane)." + }, { "target_id": "hmrc.spi.dividend_income.amount_by_total_income_band", "family": "hmrc_spi", @@ -6581,7 +6633,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "household_conditions": [ { "variable": "region", @@ -6640,7 +6692,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_affected_child_count_proxy", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "household_conditions": [ { "variable": "region", @@ -6699,7 +6751,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "household_conditions": [ { "variable": "region", @@ -6780,7 +6832,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -6846,7 +6898,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -6912,7 +6964,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -6978,7 +7030,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7044,7 +7096,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7110,7 +7162,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7176,7 +7228,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7242,7 +7294,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7308,7 +7360,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7374,7 +7426,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7440,7 +7492,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "benunit_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -7506,7 +7558,7 @@ "affected_flag_variable": "uc_tcl_affected_benunit_proxy", "value_variable": "uc_tcl_qualifying_child_count", "map_to": "household", - "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims, and model birth_year >= 2017 for 6 April 2017. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", + "notes": "Count UC benefit units and their own dependent children/qualifying young people, excluding claimant/partner roles. Statistical affected children are third or subsequent own qualifying children born on/after the source cutoff, including exceptions; this is separate from losing a child element. The current measurement is explicitly a proxy: annual universal_credit > 0 for source open claims; births are dated by policyengine-uk's uc_is_child_born_before_child_limit (a date of birth before 6 April 2017), from whole ages placed at 6 October of the model year. Retain the source April observation date; annual assignment does not create a yearly average or an April claim-history replay. Claimant PIP counts only retained claimant/partner roles; disabled-child-element status counts only own qualifying children. GB residential-region mask excludes NI. Source: https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology", "from_entity": "benunit", "filters": [ { @@ -15031,7 +15083,7 @@ "value_expression": "savings_interest_income + tax_free_savings_income + other_investment_income", "from_entity": "person", "map_to": "household", - "notes": "uk-data holds the last actual flat for later years - a populace-side declaration (uk-data targets/sources/ons_savings.py:39-44 at 12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3). The row measures cash interest in and outside tax-free accounts. From policyengine-uk 2.162.2 savings_interest_income is the interest outside them, which the frame stores so under the additive ISA income contract, with ISA interest apart in individual_savings_account_interest_income; the ISA part, tax_free_savings_income, adds it back (microcosm#1095, microcosm#1360). D.41 also counts interest on gilts, National Savings bonds and other securities, which the frame holds in other_investment_income: the SPI tape's other investment income on SPI rows and, from microcosm#1418, the FRS NS&I and gilt accounts on FRS rows. The engine taxes it as savings income beside savings_interest_income." + "notes": "uk-data holds the last actual flat for later years - a populace-side declaration (uk-data targets/sources/ons_savings.py:39-44 at 12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3). The row measures cash interest in and outside tax-free accounts. From policyengine-uk 2.162.2 savings_interest_income is the interest outside them, which the frame stores so under the additive ISA income contract, with ISA interest apart in individual_savings_account_interest_income; the ISA part, tax_free_savings_income, adds it back (microcosm#1095, microcosm#1360). D.41 also counts interest on gilts, National Savings bonds and other securities, which the frame holds in other_investment_income: the SPI tape's other investment income on SPI rows and, from microcosm#1418, the FRS gilt accounts on FRS rows; FRS NS&I accounts are interest in savings_interest_income (microcosm#1428). The engine taxes it as savings income beside savings_interest_income." }, "axiom": { "metric_name": "ons/savings_interest_income", @@ -22351,12 +22403,12 @@ "bindings": { "policyengine": { "metric_name": "hmrc/other_investment_income_income_band", - "value_variable": "other_investment_income", + "value_variable": "other_investment_income_spi_concept", "from_entity": "person", "map_to": "household", "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", - "notes": "Other investment income of taxpayers by total-income band: SPI Table 3.7's 'Other income' [Note 6], the part of total investment income outside property, bank and building society interest and dividends (SPI's OTHERINV, which the SPI donors carry into other_investment_income). From microcosm#1418 the frame also holds the FRS NS&I and gilt accounts there, and the engine taxes the column as savings income beside savings_interest_income. Banded by person-level total income; band edges come from the fact dimension. No row held the column before, so the ITL Table 2.6 savings tax rows were met mostly through it: on PR A's national (microcosm#1419) it carried about GBP 9.5bn in 2024, where Table 3.7 publishes GBP 5.85bn for 2023-24 (María's ruling of 2026-10-29, microcosm#1418).", + "notes": "Other investment income of taxpayers by total-income band: SPI Table 3.7's 'Other income' [Note 6], the part of total investment income outside property, bank and building society interest and dividends (SPI's OTHERINV, which the SPI donors carry into other_investment_income). On FRS rows the column holds gilt interest, which the engine taxes as savings income beside savings_interest_income, and the measure (other_investment_income_spi_concept) adds the purchased-annuity and trust payments that FRS rows keep in private_pension_income, where the engine's means tests, household income and tax read them; the frame stores their part as annuity_and_trust_income (microcosm#1428). Banded by person-level total income; band edges come from the fact dimension. No row held the column before, so the ITL Table 2.6 savings tax rows were met mostly through it: on PR A's national (microcosm#1419) it carried about GBP 9.5bn in 2024, where Table 3.7 publishes GBP 5.85bn for 2023-24 (María's ruling of 2026-10-29, microcosm#1418).", "filters": [ { "variable": "income_tax_before_winter_fuel_payment_charge_annual", @@ -30526,6 +30578,79 @@ "label": "Housing Benefit spending, pension age", "value_operation": "calendar_year_window" }, + { + "target_id": "dwp.jsa.amount_income_based", + "family": "dwp_benefit_expenditure", + "geography_levels": [ + "country" + ], + "measurement": { + "entity": "benunit", + "concept": "uk.benefits.jsa_income.amount", + "filters": [ + { + "concept": "uk.geography.country", + "operator": "in", + "value": [ + "england", + "scotland", + "wales" + ] + } + ] + }, + "bindings": { + "policyengine": { + "metric_name": "dwp/jsa/amount_income_based", + "value_variable": "jsa_income", + "from_entity": "benunit", + "household_conditions": [ + { + "variable": "region", + "entity": "household", + "reduce": "any", + "operator": "in", + "value": [ + "NORTH_EAST", + "NORTH_WEST", + "YORKSHIRE", + "EAST_MIDLANDS", + "WEST_MIDLANDS", + "EAST_OF_ENGLAND", + "LONDON", + "SOUTH_EAST", + "SOUTH_WEST", + "WALES", + "SCOTLAND" + ], + "map_to": "benunit" + } + ], + "notes": "DWP benefit expenditure and caseload tables, Spring Forecast 2026: the Unemployment_benefits worksheet line 'Jobseeker's Allowance, income-based', nominal GBP, which DWP publishes for Great Britain plus residents overseas (Notes tab), so the model column sums Great Britain households only. Bound at the calendar-2025 window of the fiscal years (three twelfths of the FY2024-25 outturn, nine twelfths of the FY2025-26 forecast; calendar_year_window), as the pension-age Housing Benefit row is. Income-based JSA closes with the move to Universal Credit: GBP 144.7m in FY2023-24, GBP 91.0m in FY2024-25 and under GBP 1m forecast from FY2025-26. Without the split, the calibration met OBR's total JSA (obr.jobseekers_allowance) by scaling income-based reporters with contributory claimants, well above the window's level (microcosm#1428, María's ruling of 2026-10-31). The value is the engine's jsa_income, income-based JSA paid on the reported award." + }, + "axiom": { + "metric_name": "dwp/jsa/amount_income_based", + "status": "pending", + "value_rule": "uk.benefits.jsa_income.amount" + } + }, + "assertion_policy": "allow_source_projection", + "ledger_selector": { + "source_name": "dwp", + "source_concept": "dwp.benefit_expenditure_amount", + "entity_name": "government", + "dimension_values": { + "benefit_forecast_line": "unemployment_benefits__jobseekers_allowance__income_based" + }, + "source_measure_id_by_opening_year": { + "2024": "expenditure_2024", + "2025": "expenditure_2025" + } + }, + "category_id": "dwp.legacy_benefits", + "label": "Jobseeker's Allowance spending, income-based", + "value_operation": "calendar_year_window" + }, { "target_id": "microcosm.high_value_homes.over_1m_by_region", "family": "high_value_homes", 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 7a152ccfd..17d640a37 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 @@ -2089,6 +2089,7 @@ def _ledger_compile_parity_required_artifacts( "minimum_entitled_units", # microcosm#1334 uc_take_up stage-health check. "maximum_caseload_deviation", + "maximum_reachable_pool_deviation", # frs_annual_income_spells stage-health check. "pay_concept_ratio_bounds", "annual_over_current_earners_bounds", @@ -2220,6 +2221,9 @@ def _ledger_compile_parity_required_artifacts( "rate_key", "maximum_relative_realization_deviation", "maximum_implied_relief", + # uk-data#629 spi_hourly_pay check: the drawn employees' share + # below the minimum wage may exceed the FRS rows' by this much. + "maximum_share_above_frs", } ), artifact_keys=frozenset({"stage_evidence"}), diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py index 0a88fab22..885b48ed1 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/calibration_run.py @@ -121,6 +121,7 @@ "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", + "uk_stage_hmrc_spi_income_spine_hourly_pay", "uk_stage_spi_benefit_coherence", "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/carers_allowance_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/carers_allowance_take_up.py index f0e909b1e..8bba385ec 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/carers_allowance_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/carers_allowance_take_up.py @@ -9,11 +9,11 @@ that widens it, such as the April 2025 rise of the limit from £151 to £196 a week, reached a carer who would claim once entitled. -This stage runs after the SPI income chain and the other take-up redraws. It -opens the flag to the carers the earnings limit alone keeps from the -allowance at the survey year: a person whose ``care_hours`` reach the engine's -``gov.dwp.carers_allowance.min_hours``, who cares for a severely disabled -person, who reports no Carer's Allowance, and whom +This stage runs after the SPI income chain, the other take-up redraws and +``salary_sacrifice`` (below). It opens the flag to the carers the earnings +limit alone keeps from the allowance at the survey year: a person whose +``care_hours`` reach the engine's ``gov.dwp.carers_allowance.min_hours``, who +cares for a severely disabled person, who reports no Carer's Allowance, and whom ``is_gainfully_employed_for_carers_allowance`` holds gainfully employed at the survey year (the 2024-25 limit of £151 a week; the engine reads a year's parameters at 30 April). Such a person claims at the take-up contract's @@ -64,13 +64,15 @@ The receipt reports Scotland apart. The incoming flag must still follow reported receipt, since the stage rebuilds it from that anchor. -The earnings test is read where the stage runs, before ``salary_sacrifice`` -moves some contributors' pension amounts out of their pay, which lowers the -earnings the engine tests by about half the amount moved. A drawn carer whom -that conversion takes under the survey year's limit keeps the flag, although -the released file then shows them entitled and not reporting. Reading the -converted pay would need the stage after ``salary_sacrifice``, with a draw -keyed on lineage so that the CGT clones keep their source's draw. +The stage runs after ``salary_sacrifice``, which moves some contributors' +pension amounts out of their pay and so lowers the earnings the engine tests +by about half the amount moved: the earnings test reads the converted pay +(microcosm#1095 B5). That puts it after the CGT stages too, which copy +households, so each draw is keyed on lineage (``lineage_roots``): a CGT copy +draws as the member it copies, and the balanced placement takes a source and +its copies in one stratum as one record of their joint weight +(``lineage_balanced_positions``), so they claim together, as when the stage +ran before the CGT clones. The receipt is aggregate-only (UKDS disclosure control): support as bands of distinct source households, a weighted total only where three or more carry @@ -93,6 +95,7 @@ weight_balanced_uniforms, ) from microcosm.build.uk_runtime.frs_release import resolve_uk_year_rule +from microcosm.build.uk_runtime.household_lineage import uk_household_clone_paths from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, uk_national_frame, @@ -115,6 +118,7 @@ from microcosm.frame.rules import assert_rules_engine_country __all__ = [ + "CGT_LINEAGE_BRANCHES", "CARERS_ALLOWANCE_DISABILITY_TEST", "CARERS_ALLOWANCE_ENGINE_VARIABLES", "CARERS_ALLOWANCE_NEWLY_ENTITLED_RATE_KEY", @@ -129,6 +133,8 @@ "carers_allowance_eligible", "carers_allowance_pool", "carers_allowance_take_up_operation_parameters", + "lineage_balanced_positions", + "lineage_roots", "means_tested_benefit_unit", "other_member_severely_disabled", "redraw_carers_allowance_take_up", @@ -219,8 +225,24 @@ f"({CARERS_ALLOWANCE_STRATA}) and rotated by a start drawn per stratum, so " "that every stratum claims the rate of its eligible weight to within one " "record and every eligible carer claims with probability equal to the rate; " - "would_claim = reported OR (eligible " - "AND position < rate), so a reporter stays true and every other person false" + "a CGT copy draws as the member it copies, placed with it as one record of " + "their joint weight, so the two claim together; would_claim = reported OR " + "(eligible AND position < rate), so a reporter stays true and every other " + "person false" +) +#: The CGT stages' branches of a household's clone path: a support copy, an +#: incidence clone and a residential arm each copy their source household's +#: members in ``person_id`` order (microcosm#1095 B5). +CGT_LINEAGE_BRANCHES = frozenset( + {"cgt_support_split", "cgt_incidence_clone", "cgt_residential_split"} +) +#: The household columns that mark a frame the CGT stages have copied. +_CGT_LINEAGE_COLUMNS = ( + "household_is_cgt_support_copy", + "cgt_support_copy_index", + "household_is_capital_gains_clone", + "household_is_cgt_residential_clone", + "cgt_residential_clone_index", ) @@ -332,17 +354,12 @@ def carers_allowance_pool( # eligible part, so the balance is struck over them. drawable = other & ~np.asarray(reported, dtype=bool) draws = draws.copy() - drawable_strata = np.asarray(pool_strata)[drawable] - draws[drawable] = weight_balanced_uniforms( - drawable_strata, + draws[drawable] = lineage_balanced_positions( + np.asarray(pool_strata)[drawable], draws[drawable], weights[drawable], - starts=stratum_starts( - drawable_strata, - salt=CARERS_ALLOWANCE_POOL_SALT, - identities=np.asarray(pool_identities)[drawable], - draws=draws[drawable], - ), + identities=np.asarray(pool_identities)[drawable], + salt=CARERS_ALLOWANCE_POOL_SALT, ) drawn = draws < (share or 0.0) pool = (in_household & disabled_member) | (other & drawn) @@ -386,6 +403,112 @@ def stratum_starts( return starts[np.searchsorted(unique, codes)] +def lineage_roots(person: pd.DataFrame, household: pd.DataFrame) -> np.ndarray: + """Each person's lineage root: the ``person_id`` the draws key them on. + + A CGT support copy, incidence clone or residential arm copies its source + household's members in ``person_id`` order, so a member's root is the + member at the same rank of the household with the same source id and clone + path without the CGT branches (``CGT_LINEAGE_BRANCHES``). Every other + person, an SPI or high-value support copy's included, is their own root, + as is everyone in a frame the CGT stages have not copied. + """ + + ids = pd.to_numeric(person["person_id"], errors="raise").astype("int64") + if not any(column in household.columns for column in _CGT_LINEAGE_COLUMNS): + return ids.to_numpy() + source_ids, paths = uk_household_clone_paths(household) + stripped = [ + tuple(branch for branch in path if branch[0] not in CGT_LINEAGE_BRANCHES) + for path in paths + ] + household_ids = household["household_id"].to_numpy() + keys = pd.Series( + [ + str((source, path)) + for source, path in zip(source_ids, stripped, strict=True) + ], + index=household_ids, + ) + is_root = pd.Series( + [path == bare for path, bare in zip(paths, stripped, strict=True)], + index=household_ids, + ) + members = person["person_household_id"] + household_keys = members.map(keys) + if household_keys.isna().any(): + raise ValueError("Every Carer's Allowance person must map to a household.") + rank = ids.groupby(members.to_numpy()).rank(method="first").astype("int64") + lineage = household_keys.astype(str) + "#" + rank.astype(str) + root_rows = members.map(is_root).to_numpy(dtype=bool) + roots = pd.Series(ids.to_numpy()[root_rows], index=lineage.to_numpy()[root_rows]) + if roots.index.has_duplicates: + raise ValueError("Two source members share one Carer's Allowance lineage.") + mapped = lineage.map(roots) + if mapped.isna().any(): + raise ValueError( + "A CGT copy's member has no member at its rank in the household it copies." + ) + return mapped.astype("int64").to_numpy() + + +def lineage_balanced_positions( + strata: np.ndarray, + draws: np.ndarray, + weights: np.ndarray, + *, + identities: np.ndarray, + salt: str, +) -> np.ndarray: + """Weight-balanced positions, the rows of one identity in a stratum as one record. + + A source and its CGT copies share an identity (``lineage_roots``) and so a + draw. Placed as one record of their joint weight they take one position + and claim together, as when the stage ran before the CGT clones split the + source's weight (microcosm#1095 B5). Rows of distinct identities are placed + exactly as ``weight_balanced_uniforms`` places them. + + One lineage can sit in two strata, as a record of each: salary sacrifice + draws each copy's pay on its own id, so a copy's earnings band can differ + from its source's. Each stratum's start is keyed on its own members, so the + starts are drawn stratum by stratum, where an identity names one record. + """ + + strata = np.asarray(strata) + identities = np.asarray(identities) + draws = np.asarray(draws, dtype=np.float64) + weights = np.asarray(weights, dtype=np.float64) + if not strata.shape == identities.shape == draws.shape == weights.shape: + raise ValueError( + "Balanced positions need one stratum, identity and draw per row." + ) + if not len(strata): + return draws.copy() + record, unique = pd.factorize(pd.MultiIndex.from_arrays([strata, identities])) + low = pd.Series(draws).groupby(record).min().sort_index().to_numpy() + high = pd.Series(draws).groupby(record).max().sort_index().to_numpy() + if (low != high).any(): + raise ValueError("Rows of one lineage must share their draw.") + record_strata = unique.get_level_values(0).to_numpy() + record_identities = unique.get_level_values(1).to_numpy() + starts = np.empty(len(unique), dtype=np.float64) + for code in pd.unique(record_strata): + members = record_strata == code + starts[members] = stratum_starts( + record_strata[members], + salt=salt, + identities=record_identities[members], + draws=low[members], + ) + positions = weight_balanced_uniforms( + record_strata, + low, + np.bincount(record, weights=weights, minlength=len(unique)), + starts=starts, + ) + return np.asarray(positions, dtype=np.float64)[record] + + def assert_strata_within_one_record( members: np.ndarray, strata: np.ndarray, @@ -394,13 +517,15 @@ def assert_strata_within_one_record( share: float, *, label: str, + identities: np.ndarray | None = None, ) -> None: """Refuse a stratum whose taken weight misses ``share`` of its weight by more than its heaviest record. The rotated placement takes each stratum's share to within half a record at each end of the stratum's arc, one record in all, by construction. A - larger miss means the draw was not balanced. + larger miss means the draw was not balanced. With ``identities``, a record + is the rows of one lineage, as ``lineage_balanced_positions`` places them. """ weights = np.maximum(np.asarray(weights, dtype=np.float64), 0.0) @@ -408,11 +533,21 @@ def assert_strata_within_one_record( for code in np.unique(strata[members]): stratum = members & (strata == code) stratum_weights = weights[stratum] + heaviest = ( + float(stratum_weights.max()) + if identities is None + else float( + pd.Series(stratum_weights) + .groupby(np.asarray(identities)[stratum]) + .sum() + .max() + ) + ) miss = abs( float(weights[stratum & taken].sum()) - float(share) * float(stratum_weights.sum()) ) - if miss > float(stratum_weights.max()) * (1.0 + 1e-9) + 1e-9: + if miss > heaviest * (1.0 + 1e-9) + 1e-9: raise ValueError( f"Carer's Allowance {label} stratum {int(code)} misses its share by " "more than one record: the draw was not balanced by weight." @@ -662,6 +797,8 @@ def redraw_carers_allowance_take_up( .to_numpy(dtype=np.float64) ) weights = np.asarray(frame.resolve_weights("person").values, dtype=np.float64) + # Every draw is keyed on lineage: a CGT copy draws as the member it copies. + roots = lineage_roots(person, household) pool = carers_allowance_pool( care_hours=care_hours, in_household_hours=in_household_hours, @@ -671,13 +808,11 @@ def redraw_carers_allowance_take_up( reported=reported, weights=weights, pool_draws=stable_identity_uniforms( - person["person_id"].to_numpy(), - seed=CARERS_ALLOWANCE_TAKE_UP_SEED, - salt=CARERS_ALLOWANCE_POOL_SALT, + roots, seed=CARERS_ALLOWANCE_TAKE_UP_SEED, salt=CARERS_ALLOWANCE_POOL_SALT ), min_hours=resolved.min_hours, pool_strata=pool_strata, - pool_identities=person["person_id"].to_numpy(), + pool_identities=roots, ) eligible = carers_allowance_eligible( pool=pool.pool, @@ -685,31 +820,37 @@ def redraw_carers_allowance_take_up( gainfully_employed=gainfully_employed, ) draws = stable_identity_uniforms( - person["person_id"].to_numpy(), - seed=CARERS_ALLOWANCE_TAKE_UP_SEED, - salt=CARERS_ALLOWANCE_TAKE_UP_OUTPUT, + roots, seed=CARERS_ALLOWANCE_TAKE_UP_SEED, salt=CARERS_ALLOWANCE_TAKE_UP_OUTPUT ) # The take-up strata add the route to the pool's. take_up_strata = pool.other.astype(np.int64) * 2 * _BAND_COUNT + pool_strata positions = draws.copy() - positions[eligible] = weight_balanced_uniforms( + positions[eligible] = lineage_balanced_positions( take_up_strata[eligible], draws[eligible], weights[eligible], - starts=stratum_starts( - take_up_strata[eligible], - salt=CARERS_ALLOWANCE_TAKE_UP_OUTPUT, - identities=person["person_id"].to_numpy()[eligible], - draws=draws[eligible], - ), + identities=roots[eligible], + salt=CARERS_ALLOWANCE_TAKE_UP_OUTPUT, ) drawn = eligible & (positions < rate) drawable_other = pool.other & ~reported assert_strata_within_one_record( - eligible, take_up_strata, drawn, weights, rate, label="take-up" + eligible, + take_up_strata, + drawn, + weights, + rate, + label="take-up", + identities=roots, ) assert_strata_within_one_record( - drawable_other, pool_strata, pool.pool, weights, pool.share or 0.0, label="pool" + drawable_other, + pool_strata, + pool.pool, + weights, + pool.share or 0.0, + label="pool", + identities=roots, ) would_claim = reported | drawn person[CARERS_ALLOWANCE_TAKE_UP_OUTPUT] = would_claim 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 1c06a56c2..999b4bac6 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 @@ -5,7 +5,10 @@ pairwise ``R01``-``R14`` matrix — and derives four frame columns: the relationship to the head, each person's role inside an ONS family, the family index within the household, and the household's ONS Families and households -Table 7 type. The ten household-type values are Chronicle's own category +Table 7 type. From the same grid it marks two kinds of child for +policyengine-uk: those a local authority placed in the household, and those +in non-parental care, the two-child limit's kinship and friends care +exception. The ten household-type values are Chronicle's own category identifiers for the ``ons.household_type`` dimension, so the frame value, the manifest declaration, the contract row and the published fact share one string. @@ -58,6 +61,7 @@ "FRS_RELATIONSHIPS_STAGE_NAME", "FRS_RELATIONSHIPS_TABLES", "LOOKED_AFTER_CHILD_RULE", + "NON_PARENTAL_CARE_CHILD_RULE", "ONS_DEPENDENT_CHILD_RULE", "ONS_FAMILY_LINK_CODES", "ONS_FAMILY_ROLE_VALUES", @@ -81,6 +85,7 @@ "ons_family_role", "ons_family_index", "is_looked_after_by_local_authority", + "child_limit_non_parental_caring_exception", "ons_household_type", ) FRS_RELATIONSHIPS_PERSON_OUTPUT_COLUMNS = ( @@ -88,6 +93,7 @@ "ons_family_role", "ons_family_index", "is_looked_after_by_local_authority", + "child_limit_non_parental_caring_exception", ) FRS_RELATIONSHIPS_HOUSEHOLD_OUTPUT_COLUMNS = ("ons_household_type",) @@ -195,6 +201,21 @@ "foster_parent_code": 9, "below_age": 16, } +#: A child in non-parental care (policyengine-uk's +#: ``child_limit_non_parental_caring_exception``; UC Regs 2013 Sch 12 para 4, +#: CTC Regs 2002 reg 12): a child-table member whom no adult of their own +#: benefit unit claims as a natural, adopted or step child (the child's grid +#: code toward the adult is child or step child, 3 or 4, or the adult's toward +#: the child is parent or step parent, 7 or 8; either side's declaration +#: establishes the link) and whom no local authority placed there. Such a +#: child lives with kinship or friends carers, or with a claimant whose own +#: child is the child's parent. The multiple births, adoption and +#: non-consensual conception exceptions stay false: the FRS has no twin field, +#: and its birth dates are blank (María's ruling of 2026-11-02). +NON_PARENTAL_CARE_CHILD_RULE: Mapping[str, tuple[int, ...]] = { + "child_codes": (3, 4), + "parent_codes": (7, 8), +} FRS_RELATIONSHIPS_DOCUMENTATION: Mapping[str, str] = { "frs_codes": ( @@ -238,6 +259,9 @@ def frs_relationships_operation_parameters() -> dict[str, Any]: }, "dependent_child": dict(ONS_DEPENDENT_CHILD_RULE), "looked_after_child": dict(LOOKED_AFTER_CHILD_RULE), + "non_parental_care_child": { + key: list(codes) for key, codes in NON_PARENTAL_CARE_CHILD_RULE.items() + }, "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), @@ -469,6 +493,7 @@ 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) + non_parental_care = np.zeros(len(person), dtype=bool) household_type: dict[int, str] = {} counters = { @@ -516,6 +541,15 @@ def derive_frs_relationships( & (ages[rows] < LOOKED_AFTER_CHILD_RULE["below_age"]) & (foster_child_of & same_unit).any(axis=1) ) + parented_by = np.isin( + matrix, NON_PARENTAL_CARE_CHILD_RULE["child_codes"] + ) | np.isin(matrix.T, NON_PARENTAL_CARE_CHILD_RULE["parent_codes"]) + unit_adult = same_unit & ~from_child_tab[rows][None, :] + non_parental_care[rows] = ( + from_child_tab[rows] + & ~looked_after[rows] + & ~(parented_by & unit_adult).any(axis=1) + ) counters["grid_reciprocity_mismatches"] += int( np.triu(partner != partner.T, 1).sum() ) + int((child_of != parent_of.T).sum()) @@ -676,6 +710,7 @@ def derive_frs_relationships( "domain_violations": domain_violations, "dependency_counts": dependency_counts, "looked_after_children": int(looked_after.sum()), + "non_parental_care_children": int(non_parental_care.sum()), "household_type_counts": type_counts, "household_type_weighted": type_weighted, "partition_closes": sum(type_counts.values()) == int(len(household_table_ids)), @@ -689,6 +724,7 @@ def derive_frs_relationships( "ons_family_role": role, "ons_family_index": family_index, "is_looked_after_by_local_authority": looked_after, + "child_limit_non_parental_caring_exception": non_parental_care, }, 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 d031d8287..f0de6bdbe 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 @@ -221,6 +221,21 @@ "owned_company_carries_on_property_business", "owned_company_income_share", ) + +#: FRS PENSION.PENTYPE codes of an annuity not bought with pension funds (4) +#: and a trust or covenant (5), the ADULT ANYPEN4 and ANYPEN5 sources. Their +#: payments stay in private_pension_income, where policyengine-uk's means +#: tests, household income and tax read them: trust and covenant payments +#: are generally non-savings income (ITA 2007 s. 18), and the FRS records the +#: whole annuity payment, of which only the income element is savings income +#: (ITTOIA 2005 s. 717). SPI Table 3.7 counts both as other income (note 6), +#: so the part they make of private_pension_income is stored apart, in +#: ANNUITY_AND_TRUST_INCOME_COLUMN, a calibration-only descriptor that the +#: Table 3.7 other-income measure adds (microcosm#1428 review, finding 1). +FRS_ANNUITY_AND_TRUST_PENSION_TYPES = (4, 5) +#: The descriptor: each person's annual annuity and trust payments, already +#: inside private_pension_income. No engine input reads it. +ANNUITY_AND_TRUST_INCOME_COLUMN = "annuity_and_trust_income" #: Each person's company car from the FRS job tab, the four carriers the #: company_cars stage reads to set policyengine-uk's company car inputs #: (PolicyEngine/uk-autumn-budget-dashboard-2026#316); see @@ -232,6 +247,7 @@ "frs_company_car_private_fuel", "frs_company_car_contribution", ) + OUTPUT_COLUMNS = ( "person_id", "person_benunit_id", @@ -260,6 +276,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", + ANNUITY_AND_TRUST_INCOME_COLUMN, "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", @@ -625,7 +642,9 @@ def _assemble_frame( # tape's into both inputs (microcosm#1095). pe_person["loss_relief"] = 0.0 pe_person["ni_class_4_losses_brought_forward"] = 0.0 - _add_private_pension(pe_person, person, frs["pension"]) + pe_person[ANNUITY_AND_TRUST_INCOME_COLUMN] = _add_private_pension( + pe_person, person, frs["pension"] + ) _add_accounts(pe_person, person, frs["accounts"]) owner_managers = frs_owner_manager_inputs(person, frs["job"]) for column in OWNER_MANAGER_COLUMNS: @@ -729,30 +748,43 @@ def _assemble_frame( def _add_private_pension( pe_person: pd.DataFrame, person: pd.DataFrame, pension: pd.DataFrame -) -> None: - pension_payment = _sum_to_entity( - _number(pension, "penpay") * (_number(pension, "penpay") > 0), - pension.get("person_id", pd.Series(dtype="float64")), - person["person_id"], - ) - pension_tax_paid = _sum_to_entity( - _number(pension, "ptamt") - * ((_number(pension, "ptinc") == 2) & (_number(pension, "ptamt") > 0)), - pension.get("person_id", pd.Series(dtype="float64")), - person["person_id"], - ) - pension_deductions_removed = _sum_to_entity( - _number(pension, "poamt") +) -> pd.Series: + """Write private pension income; return its annuity and trust part. + + Each row's gross weekly amount is the payment, any tax paid at source and + any deductions taken out. Every row is private pension income; the rows + of FRS_ANNUITY_AND_TRUST_PENSION_TYPES are also returned, annual, by + person, as the descriptor of that part. + """ + + penpay = _number(pension, "penpay") + ptamt = _number(pension, "ptamt") + poamt = _number(pension, "poamt") + gross = ( + penpay * (penpay > 0) + + ptamt * ((_number(pension, "ptinc") == 2) & (ptamt > 0)) + + poamt * ( ((_number(pension, "poinc") == 2) | (_number(pension, "penoth") == 1)) - & (_number(pension, "poamt") > 0) - ), - pension.get("person_id", pd.Series(dtype="float64")), - person["person_id"], + & (poamt > 0) + ) + ) + if len(pension) and "pentype" not in pension.columns: + raise ValueError( + "FRS PENSION tab needs PENTYPE to tell annuity and trust payments " + "from pensions." + ) + annuity_or_trust = _number(pension, "pentype").isin( + FRS_ANNUITY_AND_TRUST_PENSION_TYPES ) + person_ids = pension.get("person_id", pd.Series(dtype="float64")) pe_person["private_pension_income"] = ( - pension_payment + pension_tax_paid + pension_deductions_removed - ) * WEEKS_IN_YEAR + _sum_to_entity(gross, person_ids, person["person_id"]) * WEEKS_IN_YEAR + ) + return ( + _sum_to_entity(gross * annuity_or_trust, person_ids, person["person_id"]) + * WEEKS_IN_YEAR + ) #: FRS ACCOUNTS.ACCOUNT codes (FRS 2024-25 variable listing, SN 9563) by the @@ -763,25 +795,29 @@ def _add_private_pension( #: ISA interest under the producer's additive ISA income contract #: (microcosm#1360); policyengine-uk's tax_free_savings_income adds it. ISA_INTEREST_COLUMN = "individual_savings_account_interest_income" -#: Deposit interest, savings_interest_income: current (1), NS&I investment -#: (3), savings and investment (5), fixed-rate, guaranteed income and -#: guaranteed growth bond (25; no interest recorded on FRS 2024-25), basic -#: (27) and credit union (28) accounts. -FRS_DEPOSIT_INTEREST_ACCOUNTS = (1, 3, 5, 25, 27, 28) +#: Deposit interest, savings_interest_income: current (1), NS&I Direct Saver +#: (2), NS&I investment (3), savings and investment (5), National Savings +#: capital (10), Pensioners Guaranteed (13), income (16) and deposit (17) +#: bonds, First Option (18), fixed-rate, guaranteed income and guaranteed +#: growth (25) and guaranteed equity (26) bonds, basic (27) and credit union +#: (28) accounts. SPI has counted all interest paid gross or net, NS&I's +#: taxable products included, as interest from banks and building societies +#: since 2016-17 (Table 3.7 note 4), so the NS&I products sit here, not in +#: other income (microcosm#1428; PR A routed them to other income). On FRS +#: 2024-25 codes 16, 25 and 26 record no interest, and 10, 13, 17 and 18 do +#: not occur. +FRS_DEPOSIT_INTEREST_ACCOUNTS = (1, 2, 3, 5, 10, 13, 16, 17, 18, 25, 26, 27, 28) #: Other interest, other_investment_income, which policyengine-uk taxes as #: savings income beside deposit interest (taxable_savings_interest_income): -#: NS&I Direct Saver (2), gilts (6, booked as dividends before), National -#: Savings capital (10), income (16) and deposit (17) bonds, Pensioners -#: Guaranteed (13), First Option (18) and guaranteed equity (26) bonds. The -#: tax-free products stay out: National Savings certificates (11, 12), SAYE -#: (14), Premium Bonds (15), the Yearly Plan (19) and friendly society -#: investments (31). On FRS 2024-25 only the NS&I Direct Saver and gilts carry -#: interest in this group: codes 16 and 26 record none, and 10, 13, 17 and 18 -#: do not occur. Of the codes no list maps, only a share club (24) records -#: interest on FRS 2024-25: the club's returns are on shares, so they are out -#: of scope as interest; profit sharing (22), company share option plans (23), -#: an endowment policy not linked (29) and informal assets (32) record none. -FRS_OTHER_INTEREST_ACCOUNTS = (2, 6, 10, 13, 16, 17, 18, 26) +#: gilts (6, booked as dividends before microcosm#1418), the interest on +#: securities SPI counts as other income (Table 3.7 note 6). The tax-free +#: products stay out: National Savings certificates (11, 12), SAYE (14), +#: Premium Bonds (15), the Yearly Plan (19) and friendly society investments +#: (31). Of the codes no list maps, only a share club (24) records interest on +#: FRS 2024-25: the club's returns are on shares, so they are out of scope as +#: interest; profit sharing (22), company share option plans (23), an +#: endowment policy not linked (29) and informal assets (32) record none. +FRS_OTHER_INTEREST_ACCOUNTS = (6,) #: Dividends: unit and investment trusts (7) and stocks, shares and bonds (8). FRS_DIVIDEND_ACCOUNTS = (7, 8) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py index 5ecf91722..8b3bc5f69 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/full_build_cli.py @@ -193,6 +193,8 @@ from .property_localisation import ( BAND_MIX_SOURCE, QUANTILES_SOURCE, + SCOTLAND_BAND_MIX_RESOURCE, + SCOTLAND_BAND_MIX_SOURCE, SUPPORT_STRATUM_LOWER, ) from .property_localisation import ( @@ -498,7 +500,9 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace: help=( "Dense role: price_paid (default) reads each owner clone's home value " "off its assigned constituency's uprated Land Registry sale distribution " - "and its council-tax band off its LSOA's VOA mix (microcosm#1240); none " + "and its council-tax band off its LSOA's VOA mix (microcosm#1240), or a " + "Scottish owner clone's off its council area's CTAXBASE mix " + "(microcosm#1095 B12); none " "keeps the spine's values and bands on every clone, for measurement " "builds only." ), @@ -936,15 +940,18 @@ def _input_record(path: Path, pin: dict, *, pinned: bool) -> dict: } -#: Graph source name to command-line label of the two property-localisation -#: resources (microcosm#1240). Both default to the committed package files. +#: Graph source name to command-line label of the property-localisation +#: resources (microcosm#1240; the Scottish band mix, microcosm#1095 B12). Each +#: defaults to the committed package file. _PROPERTY_RESOURCE_ARGUMENTS = { QUANTILES_SOURCE: "price-paid-quantiles", BAND_MIX_SOURCE: "voa-band-mix", + SCOTLAND_BAND_MIX_SOURCE: "scotgov-band-mix", } _PROPERTY_RESOURCE_FILES = { QUANTILES_SOURCE: "high_value_home_value_knots.json", BAND_MIX_SOURCE: "voa_council_tax_band_mix_lsoa.json", + SCOTLAND_BAND_MIX_SOURCE: SCOTLAND_BAND_MIX_RESOURCE, } 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 86bca0290..3bc1247cd 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -629,6 +629,12 @@ ("household", "stocks_and_shares_isa"), } ), + # The gross receipts are drawn on each person's final self-employment + # profit, and the receipt counts support by source household + # (microcosm#1428). + "trading_allowance_receipts": frozenset( + {("person", "self_employment_income"), ("household", "household_source_id")} + ), # The copies' childcare re-draw reads every household's clone path (the # lineage columns of each copy stage) and keeps the native units' cells: # an open surface, like the other take-up redraws. @@ -790,6 +796,7 @@ def _cells( _Cell("person", "ons_family_role", "string"), _Cell("person", "ons_family_index", "int64"), _Cell("person", "is_looked_after_by_local_authority", "bool"), + _Cell("person", "child_limit_non_parental_caring_exception", "bool"), _Cell("household", "ons_household_type", "string"), ), "frs_employment": ( @@ -1250,6 +1257,9 @@ def _cells( "lifetime_isa_payments", ), ), + "trading_allowance_receipts": ( + _Cell("person", "self_employment_gross_receipts", "float64"), + ), "company_cars": ( _Cell("person", "company_car_list_price", "float64"), _Cell("person", "company_car_co2_emissions", "float64"), @@ -1347,6 +1357,10 @@ def _cells( "self_employment_income_other_spells", "private_pension_income_annual_share", "contributory_benefits_annual_share", + # A drawn row's private pension is the tape's, which books annuity and + # trust payments as other income, so its annuity and trust descriptor is + # zero (microcosm#1428). + "annuity_and_trust_income", ) _HMRC_SPI_HIDDEN_STRING = ( "aa_category", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py index 2a44b177c..3d9bd23d8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_kernels.py @@ -138,6 +138,7 @@ "student_loans": "student_loans", "private_school_attendance": "private_school_attendance", "isa_subscriptions": "isa_subscriptions", + "trading_allowance_receipts": "trading_allowance_receipts", "company_cars": "company_cars", "childcare_copy_take_up": "childcare_copy_take_up", "age_tail": "age_tail", @@ -240,6 +241,9 @@ hmrc_isa_statistics, receipt_counts, ), + # The claimant draw is keyed on identity and its receipt rounds its + # support (microcosm#1428). + "trading_allowance_receipts": (receipt_counts, stochastic_assignment), # The company cars read their carriers' names and codes from frs_spine, # HMRC's company car tables through their typed loader, the engine's CPI # index and contribution cap, the SPI channel's EPB and tape-row flag, and @@ -610,6 +614,7 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: ) from .student_loans import UKStudentLoansStageTransform from .take_up_contract import load_uk_take_up_contract + from .trading_allowance_receipts import UKTradingAllowanceReceiptsStageTransform from .uc_capital_coherence import UKUCCapitalCoherenceStageTransform from .uc_deduction_attributes import UKUCDeductionAttributesStageTransform from .uc_reporter_redraw import UKUCReporterRedrawStageTransform @@ -875,6 +880,9 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: donor_household=was, donor_person=was_person, ), + "trading_allowance_receipts": UKTradingAllowanceReceiptsStageTransform( + stage=stages["trading_allowance_receipts"] + ), "company_cars": UKCompanyCarsStageTransform( stage=stages["company_cars"], engine=engine, diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_population.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_population.py index 31690fc58..6de89bcd1 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_population.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_population.py @@ -583,6 +583,11 @@ def run(self, context: KernelContext) -> KernelResult: mix = property_localisation.load_band_mix( Path(context.sources[property_localisation.BAND_MIX_SOURCE]).read_bytes() ) + scotland_mix = property_localisation.load_scotland_band_mix( + Path( + context.sources[property_localisation.SCOTLAND_BAND_MIX_SOURCE] + ).read_bytes() + ) band_dtype = household["council_tax_band"].dtype tenure = household["tenure_type"].to_numpy() new_band, receipt = property_localisation.localise_bands( @@ -591,13 +596,22 @@ def run(self, context: KernelContext) -> KernelResult: band=household["council_tax_band"].to_numpy(), tenure_type=tenure, mix=mix, + scotland_mix=scotland_mix, + local_authority_code=household["local_authority_code"].to_numpy(), ) lsoa = household["lsoa_code"].to_numpy().astype(str) - rewritten = ( - np.char.startswith(lsoa, "E") | np.char.startswith(lsoa, "W") - ) & property_localisation.owner_mask(tenure) + area = household["local_authority_code"].to_numpy().astype(str) + owner = property_localisation.owner_mask(tenure) + england_wales = np.char.startswith(lsoa, "E") | np.char.startswith(lsoa, "W") + scotland = np.char.startswith(area, "S12") & ~england_wales + # Each mix is monotone within its area: an LSOA in England and Wales, + # a council area in Scotland. violations = property_localisation.monotone_violations( - u[rewritten], new_band[rewritten], lsoa[rewritten] + u[england_wales & owner], + new_band[england_wales & owner], + lsoa[england_wales & owner], + ) + property_localisation.monotone_violations( + u[scotland & owner], new_band[scotland & owner], area[scotland & owner] ) if violations: raise ValueError("Property band draws are not monotone in the uniform.") @@ -612,6 +626,7 @@ def run(self, context: KernelContext) -> KernelResult: **receipt, "monotone_violations": violations, "band_mix_sha256": mix.source_sha256, + "scotland_band_mix_facts_sha256": scotland_mix.source_sha256, }, ) @@ -1402,7 +1417,7 @@ def _property_localisation_nodes( raise ValueError( f"Property localisation needs the household column {column!r} on the pool." ) - for column in ("constituency_code", "lsoa_code"): + for column in ("constituency_code", "lsoa_code", "local_authority_code"): if ("household", column) not in cells: raise ValueError( f"Property localisation runs after geography derives {column!r}." @@ -1475,7 +1490,16 @@ def _property_localisation_nodes( inputs=( Slice( "household", - tuple(sorted({*_PROPERTY_BAND_COLUMNS, "lsoa_code", "tenure_type"})), + tuple( + sorted( + { + *_PROPERTY_BAND_COLUMNS, + "local_authority_code", + "lsoa_code", + "tenure_type", + } + ) + ), ), ), outputs=( @@ -1486,12 +1510,19 @@ def _property_localisation_nodes( rewrite=True, ), ), - sources=(property_localisation.BAND_MIX_SOURCE,), + sources=( + property_localisation.BAND_MIX_SOURCE, + property_localisation.SCOTLAND_BAND_MIX_SOURCE, + ), params={**common, "method": property_localisation.METHOD_VERSION}, artifact_inputs=( ArtifactInput("quantiles", value.id, "quantiles", PROPERTY_QUANTILE_TYPE), ), - description="Draw each England-and-Wales clone's council-tax band from its LSOA's VOA mix at the same uniform.", + description=( + "Draw each England-and-Wales owner clone's council-tax band from its " + "LSOA's VOA mix, and each Scottish owner clone's from its council " + "area's CTAXBASE mix, at the same uniform." + ), ) sources = ( SourceRef( @@ -1504,6 +1535,12 @@ def _property_localisation_nodes( "raw-bytes-v1", "Per-LSOA cumulative VOA council-tax band shares.", ), + SourceRef( + property_localisation.SCOTLAND_BAND_MIX_SOURCE, + "raw-bytes-v1", + "Per-council-area chargeable dwellings by council-tax band " + "(Scottish Government CTAXBASE), vendored from Chronicle.", + ), ) return (value, band), sources diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_source_contract.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_source_contract.py index 707aab80a..c6933e044 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_source_contract.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_source_contract.py @@ -111,6 +111,7 @@ "draw_spi_donor_ages_by_population", "fit_weighted_qrf_stage1", "resample_band_donor_leaves", + "floor_spi_employment_pay_at_minimum_wage", "fit_weighted_qrf_stage2", "zero_pension_age_reports_below_state_pension_age", "materialize_hmrc_income_bands_fail_closed", 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 da17983b1..abd9e2120 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 @@ -1368,6 +1368,46 @@ def _savings_income_level( return value +def uk_obr_savings_income_level( + opening_year: int, + year: int, + *, + rows: list[Mapping[str, Any]] | None = None, +) -> tuple[float, dict[int, tuple[float, str]]]: + """Savings income in fiscal ``year`` over fiscal ``opening_year``. + + The OBR's growth rates chained between the two fiscal years, read from + the vendored resource the SPI savings rows grow by; returns the level and + each year's growth with its source record. The SPI income stage rebases + its donors' savings to the frame's year on it (microcosm#1418 savings + follow-up). A year the resource does not carry is refused. + """ + + if rows is None: + rows = list( + vendored_rows( + UK_OBR_SAVINGS_INCOME_GROWTH_RESOURCE, + concept=UK_OBR_SAVINGS_INCOME_GROWTH_CONCEPT, + period_type="fiscal_year", + ) + ) + if int(year) < int(opening_year): + raise ValueError( + f"The OBR savings income path runs forward only: fiscal year {year} " + f"is before {opening_year}." + ) + growth = _obr_savings_income_growth(rows) + used: dict[int, tuple[float, str]] = {} + level = _savings_income_level( + growth, + opening_year=int(opening_year), + year=int(year), + spec_name="SPI donor savings", + used=used, + ) + return level, used + + def align_hmrc_row_by_obr_savings_income_growth( reference: LedgerTargetReference, registry: TargetRegistry, @@ -1534,4 +1574,5 @@ def align_hmrc_count_row_by_taxpayer_growth( "installed_engine_version", "live_engine_parameter_value", "hmrc_uprating_appliers", + "uk_obr_savings_income_level", ] diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/isa_subscriptions.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/isa_subscriptions.py index 0f8eba40f..37dafd756 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/isa_subscriptions.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/isa_subscriptions.py @@ -60,7 +60,6 @@ import numpy as np import pandas as pd -from microcosm.build.gates import FitWeightRecord from microcosm.build.source_manifest import SourceStageSpec from microcosm.build.stochastic_assignment import stable_identity_uniforms from microcosm.build.uk_runtime.cgt_imputation import ( @@ -1643,10 +1642,6 @@ class UKISASubscriptionsStageTransform: statistics: HMRCISAStatistics | None = None last_receipt: Mapping[str, Any] | None = field(default=None, init=False) - @property - def fit_weight_records(self) -> tuple[FitWeightRecord, ...]: - return () - def _table( self, supplied: pd.DataFrame | None, 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 7cf4b64da..53299a165 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 @@ -41,6 +41,7 @@ SELF_EMPLOYMENT_INCOME_ANNUAL_SHARE, SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, ) +from microcosm.build.uk_runtime.frs_spine import ANNUITY_AND_TRUST_INCOME_COLUMN from microcosm.build.uk_runtime.high_value_homes import ( high_value_home_mask, uk_high_value_home_thresholds, @@ -283,6 +284,33 @@ def savings_interest_outside_isas(frame: Any) -> np.ndarray: return np.maximum(interest, 0.0) +#: SPI Table 3.7's other income (note 6): the engine's other_investment_income +#: plus the purchased-annuity and trust payments that FRS rows keep in +#: private_pension_income, where the engine's means tests, household income +#: and tax read them (microcosm#1428). The frame stores each person's annuity +#: and trust payments at the dataset's year +#: (frs_spine.ANNUITY_AND_TRUST_INCOME_COLUMN), and their share of the stored +#: private pension carries them to the measurement year on the engine's own +#: private pension path. A frame without the column (the enhanced FRS +#: reference) reads the engine's other income alone. A reserved name. +UK_SPI_OTHER_INVESTMENT_INCOME = "other_investment_income_spi_concept" + + +def annuity_and_trust_share(frame: Any) -> np.ndarray: + """Each person's annuity and trust share of their stored private pension.""" + + person = frame.table("person") + if ( + ANNUITY_AND_TRUST_INCOME_COLUMN not in person + or "private_pension_income" not in person + ): + return np.zeros(len(person)) + part = person[ANNUITY_AND_TRUST_INCOME_COLUMN].to_numpy(dtype=float) + pension = person["private_pension_income"].to_numpy(dtype=float) + share = np.divide(part, pension, out=np.zeros(len(person)), where=pension > 0) + return np.clip(share, 0.0, 1.0) + + #: The named terms the resolver computes: measures declared once as #: expressions of the binding interpreter's grammar, evaluated from their #: leaves wherever a binding names them (a value, a band or a filter, on the @@ -568,6 +596,21 @@ def compute_uk_measure_input( else "frame_columns:savings_interest_income", ) + if variable == UK_SPI_OTHER_INVESTMENT_INCOME: + if entity != "person": + raise KeyError(f"{variable} is person-only: {entity}") + other, route = compute_uk_measure_input( + frame, simulation, "person", "other_investment_income", year + ) + pension, _ = compute_uk_measure_input( + frame, simulation, "person", "private_pension_income", year + ) + return ( + other + annuity_and_trust_share(frame) * pension, + "engine_components:other_investment_income" + f"+annuity_and_trust_share*private_pension_income:{route}", + ) + if variable in DFE_SPRING_TERM_MEASURES: if entity != "person": raise KeyError(f"DfE spring-term measures are person-only: {entity}") @@ -1065,11 +1108,12 @@ def __init__( "High-value home measures must not be persisted as source inputs: " f"{persisted_high_value}" ) - if UK_SAVINGS_INTEREST_OUTSIDE_ISAS in set(map(str, frame.table("person"))): - raise ValueError( - f"{UK_SAVINGS_INTEREST_OUTSIDE_ISAS} must not be persisted as a " - "source input." - ) + for reserved in ( + UK_SAVINGS_INTEREST_OUTSIDE_ISAS, + UK_SPI_OTHER_INVESTMENT_INCOME, + ): + if reserved in set(map(str, frame.table("person"))): + raise ValueError(f"{reserved} must not be persisted as a source input.") persisted_spring_term = sorted( set(DFE_SPRING_TERM_MEASURES) & set(map(str, frame.table("person"))) ) @@ -1193,6 +1237,12 @@ def knows(self, entity: str, variable: str) -> bool: return entity == "person" and "savings_interest_income" in set( map(str, self.frame.table("person")) ) + if variable == UK_SPI_OTHER_INVESTMENT_INCOME: + return ( + entity == "person" + and self.knows("person", "other_investment_income") + and self.knows("person", "private_pension_income") + ) if variable in DFE_SPRING_TERM_MEASURES: return entity == "person" and all( self.knows("person", name) for name in DFE_SPRING_TERM_ENGINE_INPUTS @@ -1237,7 +1287,10 @@ def entity_for(self, variable: str) -> str | None: return "household" if variable == UK_CHILD_BENEFIT_CHILD_COUNT: return "benunit" - if variable == UK_SAVINGS_INTEREST_OUTSIDE_ISAS: + if variable in ( + UK_SAVINGS_INTEREST_OUTSIDE_ISAS, + UK_SPI_OTHER_INVESTMENT_INCOME, + ): return "person" if _cgt_model_measure(variable, self.year) is not None: return "person" diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/property_localisation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/property_localisation.py index f9528875f..17b61ca85 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/property_localisation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/property_localisation.py @@ -14,8 +14,11 @@ Non-owner rows and rows outside England and Wales keep their value. - ``uk.full.property.band`` draws the clone's band from its assigned LSOA's VOA band mix (``uk/voa_council_tax_band_mix_lsoa.json``) at the same - uniform, so value and band agree by construction. Rows outside England and - Wales keep their band. + uniform, so value and band agree by construction. A Scottish owner clone + draws from its council area's chargeable dwellings by band, A to H, at + September 2025 (``uk/scotgov_chargeable_dwellings_by_band.json``, vendored + from Chronicle) at the same uniform, its rank among Scotland's owners + (microcosm#1095 B12). Northern Ireland's rows keep their band. Everything here is pure: the kernels in ``graph_population`` hand these functions arrays and resources and write the results back. @@ -49,11 +52,15 @@ "PROPERTY_LOCALISATION_MODES", "PROPERTY_STREAM_EXPERIMENT", "RANK_BASIS_COLUMN", + "SCOTLAND_BAND_MIX_RESOURCE", + "SCOTLAND_BAND_MIX_SOURCE", + "SCOTLAND_BAND_PERIOD", "SUPPORT_STRATUM_LOWER", "QUANTILES_SOURCE", "BandMix", "ValueKnots", "load_band_mix", + "load_scotland_band_mix", "load_value_knots", "localise_bands", "localise_values", @@ -64,6 +71,13 @@ QUANTILES_SOURCE = "uk_price_paid_quantiles" BAND_MIX_SOURCE = "uk_voa_band_mix" +#: The Scottish Government's chargeable dwellings by council-tax band per +#: council area (CTAXBASE), vendored from Chronicle (microcosm#1095 B12). +SCOTLAND_BAND_MIX_SOURCE = "uk_scotgov_band_mix" +SCOTLAND_BAND_MIX_RESOURCE = "scotgov_chargeable_dwellings_by_band.json" +SCOTLAND_BAND_PERIOD = "2025-09" +_SCOTLAND_BAND_CONCEPT = "scotgov.chargeable_dwellings_band_" +_SCOTTISH_COUNCIL_AREA_PREFIX = "S12" PROPERTY_LOCALISATION_MODES: tuple[str, ...] = ("none", "price_paid") PROPERTY_STREAM_EXPERIMENT = "uk-post-clone-property-localisation-v1" METHOD_VERSION = "uk-property-localisation-v1" @@ -169,6 +183,67 @@ def load_band_mix(payload: bytes) -> BandMix: ) +def load_scotland_band_mix(payload: bytes) -> BandMix: + """Per-council-area cumulative band shares from the vendored CTAXBASE rows. + + Scotland's bands run A to H, so a council area's share of band I is zero + and its cumulative shares reach one at H. Every council area must carry + every band once, at ``SCOTLAND_BAND_PERIOD``. + """ + data = json.loads(payload.decode("utf-8")) + if ( + data.get("kind") != "uk_vendored_ledger_facts" + or data.get("resource") != SCOTLAND_BAND_MIX_RESOURCE + ): + raise ValueError( + "The Scottish band mix resource is not the vendored CTAXBASE facts." + ) + scottish_bands = COUNCIL_TAX_BANDS[:-1] + counts: dict[str, dict[str, float]] = {} + for row in data.get("rows") or (): + concept = str(row.get("concept") or "") + geography = row.get("geography") or {} + period = row.get("period") or {} + if ( + not concept.startswith(_SCOTLAND_BAND_CONCEPT) + or geography.get("level") != "local_authority" + or str(period.get("value")) != SCOTLAND_BAND_PERIOD + ): + continue + band = concept[len(_SCOTLAND_BAND_CONCEPT) :].upper() + if band not in scottish_bands: + raise ValueError(f"Unknown Scottish council-tax band {band!r}.") + cells = counts.setdefault(str(geography.get("id")), {}) + if band in cells: + raise ValueError(f"Council area {geography.get('id')} repeats band {band}.") + value = float(row["value"]) + if not np.isfinite(value) or value < 0: + raise ValueError( + f"Council area {geography.get('id')} has a bad band count." + ) + cells[band] = value + cumulative: dict[str, np.ndarray] = {} + for code, cells in sorted(counts.items()): + if set(cells) != set(scottish_bands): + raise ValueError( + f"Council area {code} lacks bands {sorted(set(scottish_bands) - set(cells))}." + ) + values = np.asarray([cells[band] for band in scottish_bands], dtype=float) + if not values.sum() > 0: + raise ValueError(f"Council area {code} has no chargeable dwellings.") + shares = np.cumsum(values) / values.sum() + shares[-1] = 1.0 + cumulative[code] = np.append(shares, 1.0) + if not cumulative: + raise ValueError("The Scottish band mix resource carries no council areas.") + feed = data.get("source_fact_feed") or {} + return BandMix( + cumulative=cumulative, + source_sha256=str(feed.get("facts_sha256") or ""), + as_at=SCOTLAND_BAND_PERIOD, + ) + + # ---------------------------------------------------------------------------- # Keys and uniforms @@ -418,12 +493,17 @@ def localise_bands( band: np.ndarray, tenure_type: np.ndarray, mix: BandMix, + scotland_mix: BandMix | None = None, + local_authority_code: np.ndarray | None = None, ) -> tuple[np.ndarray, dict[str, object]]: - """Draw each England-and-Wales owner's band from its LSOA's cumulative shares at ``u``. - - Renters keep the spine's band: the VOA mix counts every dwelling in the - LSOA, and drawing a social tenant from it would hand the owners' share of - the high bands to households the value node leaves untouched. + """Draw each England-and-Wales owner's band from its LSOA's cumulative shares at + ``u``, and with ``scotland_mix`` each Scottish owner's from its council area's + (microcosm#1095 B12). + + Renters keep the spine's band: the VOA and CTAXBASE mixes count every + dwelling, and drawing a social tenant from them would hand the owners' share + of the high bands to households the value node leaves untouched. Without + ``scotland_mix`` Scotland keeps its bands, as Northern Ireland always does. """ u = np.asarray(u, dtype=float) codes = np.asarray(lsoa_code).astype(str) @@ -435,18 +515,29 @@ def localise_bands( if missing: raise ValueError(f"No VOA band mix for LSOA {missing[:5]}.") new_band = current.copy() - for code in sorted(set(codes[target])): - rows = np.flatnonzero(target & (codes == code)) - cumulative = mix.cumulative[code] - index = np.searchsorted(cumulative, u[rows], side="right") - index = np.minimum(index, len(COUNCIL_TAX_BANDS) - 1) - new_band[rows] = np.asarray(COUNCIL_TAX_BANDS, dtype=object)[index] + _draw_bands(new_band, target, codes, mix, u) + scotland = np.zeros(len(current), dtype=bool) + areas = np.full(len(current), "", dtype=object) + if scotland_mix is not None: + if local_authority_code is None: + raise ValueError("A Scottish band mix needs the rows' council areas.") + areas = np.asarray(local_authority_code).astype(str) + scotland = ( + np.char.startswith(areas, _SCOTTISH_COUNCIL_AREA_PREFIX) & ~england_wales + ) + missing = sorted(set(areas[scotland & owner]) - set(scotland_mix.cumulative)) + if missing: + raise ValueError(f"No Scottish band mix for council area {missing[:5]}.") + _draw_bands(new_band, scotland & owner, areas, scotland_mix, u) + scottish_owner = scotland & owner changed = target & (new_band != current) receipt = { "rows": int(len(current)), "rows_rewritten": int(target.sum()), "rows_changed": int(changed.sum()), - "rows_untouched_outside_england_wales": int((~england_wales).sum()), + "rows_untouched_outside_england_wales": int( + (~england_wales & ~scottish_owner).sum() + ), "rows_untouched_renters_england_wales": int((england_wales & ~owner).sum()), "lsoas": int(len(set(codes[target]))), "band_mix_before": { @@ -458,9 +549,38 @@ def localise_bands( "voa_as_at": mix.as_at, "method": METHOD_VERSION, } + if scotland_mix is not None: + receipt["scotland"] = { + "rows_rewritten": int(scottish_owner.sum()), + "rows_changed": int((scottish_owner & (new_band != current)).sum()), + "rows_untouched_renters": int((scotland & ~owner).sum()), + "council_areas": int(len(set(areas[scottish_owner]))), + "band_mix_before": { + b: int((current[scottish_owner] == b).sum()) for b in COUNCIL_TAX_BANDS + }, + "band_mix_after": { + b: int((new_band[scottish_owner] == b).sum()) for b in COUNCIL_TAX_BANDS + }, + "as_at": scotland_mix.as_at, + } return new_band, receipt +def _draw_bands( + new_band: np.ndarray, + target: np.ndarray, + codes: np.ndarray, + mix: BandMix, + u: np.ndarray, +) -> None: + """Write each target row's band from its area's cumulative shares at ``u``.""" + for code in sorted(set(codes[target])): + rows = np.flatnonzero(target & (codes == code)) + index = np.searchsorted(mix.cumulative[code], u[rows], side="right") + index = np.minimum(index, len(COUNCIL_TAX_BANDS) - 1) + new_band[rows] = np.asarray(COUNCIL_TAX_BANDS, dtype=object)[index] + + def monotone_violations(u: np.ndarray, band: np.ndarray, lsoa_code: np.ndarray) -> int: """Count LSOAs where a higher uniform drew a lower band (expected zero).""" frame = pd.DataFrame( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/property_purchases.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/property_purchases.py index 0de65a844..895e6bb68 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/property_purchases.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/property_purchases.py @@ -33,20 +33,32 @@ vendored from the pinned Chronicle feed (``hmrc_stamp_tax_facts.json``): * additional dwellings: Table 6b's higher-rates (HRAD) transactions and value - for 2024-25, which cover every buyer, allocated to households by - individuals' shares of Table 8a's residential transactions and value (what - other buyers would take is companies'); the price bands are Table 6b's, with - the five above GBP 2m combined, as uk-data#573 does; + for 2024-25, which cover every buyer, allocated to households band by band. + At or below GBP 250,000, the residential nil-rate band for all of 2024-25, a + purchase owes standard-rate SDLT of nil, so other buyers' Table 8a receipts + there are surcharge: their HRAD transactions and value are those receipts + over Table 6b's gross HRAD receipts per transaction and per pound of value, + and households take the rest of the band. Table 8a does not split out the 2% + non-resident surcharge, which a non-resident buyer pays on top in the band; + read as HRAD surcharge, it raises other buyers' implied HRAD and lowers + households', so households' purchases there are, if anything, understated. + Above GBP 250,000, households take individuals' shares of all Table 8a + residential transactions and value (what other buyers would take is + companies'), as uk-data#573 does. The price bands are Table 6b's, with the + five above GBP 2m combined, as uk-data#573 does, at the households' counts + and values; * non-residential: Table 8b's individuals for 2024-25, and its six bands. -Households' HRAD is not published, so the allocation by individuals' shares -of all Table 8a residential transactions is an assumption, as uk-data#573 -says, not an observed household series. uk-data#573 gives the household -lower bounds Tables 6b and 8a imply, 87,200 transactions and GBP 33.28bn; -the allocation probably overstates households' purchases, since nearly all -receipts at or below the GBP 250,000 nil-rate band are surcharge, and other -buyers' receipts there imply more higher-rates transactions than their share -of all transactions gives them. +Households' HRAD is not published, so the allocation is an assumption, not an +observed household series. Allocating every band by individuals' shares of all +Table 8a transactions, as uk-data#573 does, left other buyers about 13,400 of +the nil-rate band's HRAD transactions, where their receipts imply about 48,000, +so it overstated households' purchases there (microcosm#1095). Above the band +the standard rates make up most receipts, and solving for other buyers' HRAD +from them gives negative values above GBP 1m, so the shares remain there and +probably still overstate households' purchases. uk-data#573 gives the +household lower bounds Tables 6b and 8a imply, 87,200 transactions and +GBP 33.28bn. The stage fits transactions and value only. Households' SDLT is fitted at build level through ``obr.sdlt``; uk-data#573 also holds its file's @@ -156,6 +168,9 @@ "over_gbp_10m", ) _TOTAL_BAND = "all_price_bands" +#: The band in which the 2024-25 residential nil-rate band (GBP 250,000 all +#: year) charges no standard-rate SDLT, so other buyers' receipts are surcharge. +_NIL_RATE_BAND = "gbp_250k_or_less" _BAND_ORDER = ( "gbp_250k_or_less", "gbp_250k_to_500k", @@ -440,13 +455,60 @@ def property_purchase_targets_from_rows( )[_TOTAL_BAND] count_share = individuals_count / (individuals_count + other_count) value_share = individuals_value / (individuals_value + other_value) - band_ids, band_counts, band_values = _bands( + # At or below the nil-rate band other buyers' receipts are surcharge, so + # Table 6b's gross receipts per transaction and per pound turn them into + # other buyers' HRAD there; households take the rest of the band. + hrad_receipts = _cells( + rows, record_set=_HRAD_RECORD_SET, concept="hmrc.sdlt.hrad_receipts_gross" + ) + other_nil_receipts = _cells( + rows, + record_set=_RESIDENTIAL_OTHER_BUYERS_RECORD_SET, + concept="hmrc.sdlt.receipts", + )[_NIL_RATE_BAND] + other_nil_count = _cells( + rows, + record_set=_RESIDENTIAL_OTHER_BUYERS_RECORD_SET, + concept="hmrc.sdlt.transactions", + )[_NIL_RATE_BAND] + nil_count = hrad_count[_NIL_RATE_BAND] + nil_value = hrad_value[_NIL_RATE_BAND] + nil_receipts = hrad_receipts[_NIL_RATE_BAND] + if not nil_receipts > 0: + raise ValueError("Table 6b carries no nil-rate band HRAD receipts.") + other_nil_hrad_count = other_nil_receipts * nil_count / nil_receipts + other_nil_hrad_value = other_nil_receipts * nil_value / nil_receipts + if not ( + 0.0 < other_nil_hrad_count <= min(nil_count, other_nil_count) + and 0.0 < other_nil_hrad_value < nil_value + ): + raise ValueError( + "Other buyers' nil-rate band receipts imply HRAD outside Table 6b's " + "band or their own transactions." + ) + band_ids, all_buyer_counts, all_buyer_values = _bands( hrad_count, hrad_value, combine=_HRAD_TOP_BANDS ) + band_counts = tuple( + nil_count - other_nil_hrad_count + if band == _NIL_RATE_BAND + else count * count_share + for band, count in zip(band_ids, all_buyer_counts, strict=True) + ) + band_values = tuple( + nil_value - other_nil_hrad_value + if band == _NIL_RATE_BAND + else value * value_share + for band, value in zip(band_ids, all_buyer_values, strict=True) + ) additional = UKPropertyPurchaseTarget( property_type="additional_residential", - transactions=hrad_count[_TOTAL_BAND] * count_share, - value=hrad_value[_TOTAL_BAND] * value_share, + transactions=(hrad_count[_TOTAL_BAND] - nil_count) * count_share + + nil_count + - other_nil_hrad_count, + value=(hrad_value[_TOTAL_BAND] - nil_value) * value_share + + nil_value + - other_nil_hrad_value, band_ids=band_ids, band_transactions=band_counts, band_values=band_values, @@ -482,6 +544,16 @@ def property_purchase_targets_from_rows( "hrad_property_value_all_buyers": hrad_value[_TOTAL_BAND], "residential_individuals_transaction_share": count_share, "residential_individuals_value_share": value_share, + "nil_band_hrad_receipts_gross": nil_receipts, + "nil_band_other_buyers_receipts": other_nil_receipts, + "nil_band_other_buyers_hrad_transactions": other_nil_hrad_count, + "nil_band_other_buyers_hrad_value": other_nil_hrad_value, + "nil_band_other_buyers_hrad_transaction_share": ( + other_nil_hrad_count / other_nil_count + ), + "nil_band_households_hrad_transaction_share": ( + 1.0 - other_nil_hrad_count / nil_count + ), }, ) @@ -773,9 +845,13 @@ def property_purchases_operation_parameters() -> tuple[ for kind in PROPERTY_PURCHASE_KINDS }, "additional_residential_targets": ( - "Table 6b HRAD transactions and value, 2024-25, times " - "individuals' shares of Table 8a residential transactions and " - "value; Table 6b bands with the five above GBP 2m combined" + "Table 6b HRAD transactions and value, 2024-25: at or below " + "GBP 250,000, the band less other buyers' HRAD, their Table 8a " + "receipts over Table 6b's gross HRAD receipts per transaction " + "and per pound; above it, times individuals' shares of Table " + "8a residential transactions and value; Table 6b bands with " + "the five above GBP 2m combined, at the households' counts " + "and values" ), "non_residential_targets": ( "Table 8b individuals' transactions, value and bands, 2024-25" 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 9f185ec42..bb4d8dd40 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 @@ -4,10 +4,10 @@ import hashlib import math -from collections.abc import Mapping, Sequence +from collections.abc import Collection, Mapping, Sequence from dataclasses import dataclass from functools import cache -from importlib import import_module +from importlib import import_module, metadata from pathlib import Path from typing import Any @@ -21,6 +21,7 @@ ) from microcosm.build.uk_runtime.frs_annual_income import ( ANNUAL_SHARE_COLUMNS, + EMPLOYMENT_INCOME_ANNUAL_SHARE, EMPLOYMENT_INCOME_OTHER_SPELLS, FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, SELF_EMPLOYMENT_INCOME_OTHER_SPELLS, @@ -37,8 +38,10 @@ FRS_HMRC_UBISJA_COLUMN, ) from microcosm.build.uk_runtime.frs_spine import ( + ANNUITY_AND_TRUST_INCOME_COLUMN, FRS_LANDLORD_CARRIER_COLUMN, ISA_INTEREST_COLUMN, + WEEKS_IN_YEAR, uc_gainful_self_employment, ) from microcosm.build.uk_runtime.hmrc_income import ( @@ -50,7 +53,15 @@ coherence_receipt, twin_rate_employer_contributions, ) -from microcosm.build.uk_runtime.receipt_counts import UK_DISCLOSURE_FLOOR, UK_WITHHELD +from microcosm.build.uk_runtime.receipt_counts import ( + UK_DISCLOSURE_FLOOR, + UK_WITHHELD, + distinct_count, + extreme_count, + rounded_count, + small_cell_share, + source_household_ids, +) from microcosm.build.uk_runtime.spi_rank_transport import ( SPI_STAGE1_ANALOGUE_COLUMNS, SPI_TRANSPORT_FIT_NAME, @@ -167,9 +178,18 @@ #: against the leaves both ways. SPI_MAIN_SOURCE_LEVELS = ("pay", "pension", "trade", "other") SPI_BRIDGE_PREDICTOR_COLUMNS = ("main_source", "has_pay", "has_trade", "at_or_over_spa") -SPI_STAGE1_CATEGORICAL_PREDICTORS = ("gender", "region", "main_source") +#: The stage-1 forests' age predictor: the SPI AGERANGE band, not an age +#: (uk-data#628). It is categorical: on an ordinal scale the composite records' +#: code (-1) would sit next to 16-24. See SPI_STAGE1_AGE_PREDICTOR_RULE. +SPI_STAGE1_AGE_BAND_COLUMN = "spi_age_band" +SPI_STAGE1_CATEGORICAL_PREDICTORS = ( + SPI_STAGE1_AGE_BAND_COLUMN, + "gender", + "region", + "main_source", +) SPI_STAGE1_PREDICTOR_COLUMNS = ( - "age", + SPI_STAGE1_AGE_BAND_COLUMN, "gender", "region", *SPI_BRIDGE_PREDICTOR_COLUMNS, @@ -210,8 +230,39 @@ #: microcosm#1095) until microcosm#1418 moved them to the OBR's savings income #: forecast, so the calibration closes the gap between the two. The engine #: lockstep test holds both indices to the engine's. +#: +#: Since the microcosm#1418 savings follow-up, interest and other income +#: reach the frame's year on SPI's own path instead +#: (``rebase_spi_donor_savings``): HMRC's published 2023-24 Table 3.7 amounts +#: by range of total income, then the OBR's savings income growth, the path +#: the band targets take. On their engine indices the 2022-23 tape reached +#: 2024 at about half of SPI's 2023-24 other income (GDP per head, x1.09, +#: against x1.65 in that one year) and short of its interest, and the solve +#: met the band targets by stretching the few households that carry them. +#: Both columns run on the projection's index from the frame's year on, named +#: here. SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION = { "savings_interest_income": "gov.economic_assumptions.indices.obr.per_capita.gdp", + "other_investment_income": "gov.economic_assumptions.indices.obr.per_capita.gdp", +} +#: The published rows the donor savings rebase reads (vendored from the +#: Chronicle feed pin), the year they describe, and each rebased column's +#: Table 3.7 concept. +SPI_SAVINGS_REBASE_RESOURCE = "hmrc_spi_table_3_7_savings_rows.json" +#: The prepared tape's taxpayer flag, the records the rebase factors read. +SPI_DONOR_TAXPAYER_COLUMN = "is_taxpayer" +#: The tape's TAXPAYER codes: 1, a taxpayer, and 2, not. +SPI_TAXPAYER_CODES = (1, 2) +#: The band factors assume the tape's FACT weights gross to the taxpayer +#: population. A column whose banded tape amount is under this share of the +#: published amount does not (a synthetic tape, whose factors would run to +#: millions), and is left as it is. The production tape holds about 0.37 of +#: the published interest and 0.6 of the other income. +SPI_SAVINGS_REBASE_MINIMUM_GROSSING = 0.1 +SPI_SAVINGS_REBASE_YEAR = 2023 +SPI_SAVINGS_REBASE_CONCEPTS = { + "savings_interest_income": "hmrc.spi_savings_interest_income_amount", + "other_investment_income": "hmrc.spi_other_income_amount", } # Use the pinned engine's variable-specific indices. None explicitly retains # nominal amounts where no same-scope indexed mapping has been reviewed; @@ -413,13 +464,20 @@ "Pension age, and the band's below-State-Pension-age population share is " "drawn from its non-recipients" ) -#: Donors carry a drawn fractional age and frame people a whole-year one, so -#: the stage-1 forest is queried at the middle of each person's year of age. -#: At the whole year a person sits on any split the forest places at that -#: birthday; at State Pension age, below which no donor draws State Pension, -#: about half of the frame's 66-year-olds drew a 65-year-old donor's zero -#: State Pension leaf (microcosm#1069, c4 measurement arm). -SPI_STAGE1_QUERY_AGE_RULE = "whole-year age plus half a year" +#: The tape gives age only as a band, so a donor's drawn age carries no income +#: pattern within its band: a forest trained on it learns none, and twins of +#: one band faced conditionals that differed only by that noise +#: (uk-data#628). The stage-1 tape and reference forests are trained and +#: queried on the band code instead: the tape's own AGERANGE code, composite +#: records (-1) as a band of their own, and the band of a frame person's +#: whole-year age. The State Pension split inside a band is at_or_over_spa's. +#: The drawn ages stay on the donor for their other readers (the band pools' +#: State Pension side). +SPI_STAGE1_AGE_PREDICTOR_RULE = ( + "the SPI 2022-23 AGERANGE band code, one-hot encoded: the tape's own code, " + "composite records (-1) as a band of their own, and the band of a frame " + "person's whole-year age" +) _SPI_REGION_MAP = { 1: "NORTH_EAST", 2: "NORTH_WEST", @@ -670,6 +728,368 @@ def _zero_below_state_pension_age( } +#: The employment pay floor (uk-data#629). Stage 1 draws a recipient's annual +#: tape pay on age, sex, region and the bridge predictors, not on hours, and +#: the tape holds part-year workers, so a twin who reports usual hours in an +#: employee job could draw a year's pay below what those hours earn at the +#: minimum wage, and the drawn rows of employees working 16 or more hours fell +#: under the 30 hours' minimum-income threshold more often than FRS rows. The +#: floor lifts the drawn PAY leaf to the twin's usual weekly hours times the +#: weeks below times the lower of the engine's minimum wage for the twin's age +#: and the twin's own hourly pay, before employment_income is derived from the +#: leaves and before stage 2 conditions on it, so every later reader sees the +#: floored pay as the row's current pay. The tape's pay stays the row's year of +#: pay: the row's employment_income_annual_share becomes its unfloored over its +#: floored employment income, so the annual measures, which the HMRC income +#: rows read, still read the tape (low tape pay is read as part of a year at +#: the floored rate). The twin's own rate caps the floor so that no copy is held +#: to a higher hourly rate than the FRS person it copies. +#: +#: The pay basis: tape PAY is taxable pay, net of net-pay pension +#: contributions and salary sacrifice, while the minimum wage binds gross pay. +#: The floor compares PAY alone with the gross minimum (benefits in kind and +#: termination pay are not minimum-wage pay), since PAY is the leaf the +#: engine's employment_income input carries and the engine reads that input +#: as gross pay. A twin at exactly the minimum wage who pays net-pay +#: contributions can therefore be lifted by up to those contributions; the +#: conservative choices elsewhere (52 paid weeks, the non-apprentice rate the +#: engine itself applies, no floor on a twin with a trade, whose hours cover +#: both jobs, or on a zero draw) keep the floor at or below what the twin's +#: own hours require. +SPI_EMPLOYMENT_PAY_FLOOR_COLUMN = SPI_HMRC_PAY_COLUMN +SPI_EMPLOYMENT_PAY_FLOOR_HOURS_COLUMN = "hours_worked" +#: The engine's minimum wage by age for a non-apprentice, the rate its own +#: ``minimum_wage`` variable pays a person whose ``is_apprentice`` is false, as +#: the spine leaves it. Apprentices cannot be told apart on the FRS twin. +SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER = "gov.hmrc.minimum_wage.non_apprentice" +#: Paid weeks a year. The spine's hours_worked annualises usual weekly hours at +#: frs_spine.WEEKS_IN_YEAR (52, uk-data#531); the floor reads them back to +#: weekly hours and pays 52 weeks. +SPI_EMPLOYMENT_PAY_FLOOR_WEEKS = 52 +#: employment_status values of an employee main job (FRS EMPSTATI 1 and 2). +SPI_EMPLOYMENT_PAY_FLOOR_EMPLOYEE_STATUSES = ("FT_EMPLOYED", "PT_EMPLOYED") +SPI_EMPLOYMENT_PAY_FLOOR_RATE_RULE = ( + "the lower of the minimum wage for the twin's age and the twin's own hourly " + "pay (its employment income before the draw over its annual hours)" +) +SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE = ( + "the floored pay is the row's current pay; its employment_income_annual_share " + "becomes its unfloored over its floored employment income, so the annual " + "measures read the tape's year of pay" +) + + +@dataclass(frozen=True) +class SPIEmploymentPayFloor: + """The minimum wage by age that floors drawn tape pay, read from the engine. + + ``age_thresholds`` and ``hourly_rates`` are the bracket scale's thresholds + and amounts: an age takes the rate of the highest threshold at or below it. + """ + + age_thresholds: tuple[float, ...] + hourly_rates: tuple[float, ...] + weeks_in_year: float + instant: str + source: str + + def __post_init__(self) -> None: + thresholds = np.asarray(self.age_thresholds, dtype=np.float64) + rates = np.asarray(self.hourly_rates, dtype=np.float64) + if ( + thresholds.size == 0 + or thresholds.size != rates.size + or thresholds[0] != 0.0 + or (np.diff(thresholds) <= 0.0).any() + ): + raise ValueError( + "The SPI pay floor needs increasing age thresholds from zero, " + "one hourly rate each." + ) + if not np.isfinite(rates).all() or (rates <= 0.0).any(): + raise ValueError("The SPI pay floor's hourly rates must be positive.") + if not math.isfinite(self.weeks_in_year) or self.weeks_in_year <= 0.0: + raise ValueError("The SPI pay floor's weeks must be positive.") + + def hourly_rate(self, ages: np.ndarray) -> np.ndarray: + """Each age's hourly rate.""" + + index = np.searchsorted( + np.asarray(self.age_thresholds, dtype=np.float64), + np.asarray(ages, dtype=np.float64), + side="right", + ) + return np.asarray(self.hourly_rates, dtype=np.float64)[ + np.clip(index - 1, 0, None) + ] + + def evidence(self) -> dict[str, object]: + return { + "parameter": SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER, + "instant": self.instant, + "source": self.source, + "hourly_rate_by_age_from": { + str(int(threshold)): rate + for threshold, rate in zip( + self.age_thresholds, self.hourly_rates, strict=True + ) + }, + "weeks_in_year": self.weeks_in_year, + } + + +def load_spi_employment_pay_floor(build_period: int | str) -> SPIEmploymentPayFloor: + """Read the engine's minimum wage by age as it reads ``build_period``. + + policyengine-uk reads a yearly parameter at 30 April of the year + (``convert_to_fiscal_year_parameters``), so the survey year 2024 reads + the 2024-25 rates: £11.44 from age 21, £8.60 from 18 and £6.40 below. + """ + + try: + import policyengine_uk + from policyengine_core.parameters import ParameterNode + except ImportError as exc: + raise ImportError( + "The SPI employment pay floor requires `uv sync --all-packages --extra uk`." + ) from exc + + parameters = ParameterNode( + directory_path=str(Path(policyengine_uk.__file__).parent / "parameters") + ) + instant = f"{int(build_period)}-04-30" + scale = parameters.gov.hmrc.minimum_wage.non_apprentice(instant) + return SPIEmploymentPayFloor( + age_thresholds=tuple(float(value) for value in scale.thresholds), + hourly_rates=tuple(float(value) for value in scale.amounts), + weeks_in_year=float(SPI_EMPLOYMENT_PAY_FLOOR_WEEKS), + instant=instant, + source=( + f"policyengine-uk {metadata.version('policyengine-uk')} " + f"{SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER} at {instant}" + ), + ) + + +def _floor_employment_pay_at_minimum_wage( + person: pd.DataFrame, + *, + rows: pd.Series, + floor: SPIEmploymentPayFloor, + household: pd.DataFrame, + person_weights: pd.Series, + twin_pay: pd.Series, + reference_rows: pd.Series, +) -> tuple[pd.DataFrame, dict[str, object], pd.Series]: + """Lift drawn PAY on ``rows`` to the twin's hours at its floor rate. + + The floor rate is the lower of the minimum wage for the twin's age and the + twin's own hourly pay (``twin_pay``, its employment income before the + draw, over its annual hours; a twin without pay caps nothing). ``rows`` are + the recipients the floor may touch (the caller leaves out band carriers + and twins with a trade). Of those, a row is floored only when its twin's + main job is as an employee, its twin reports usual hours and its drawn PAY + is positive: the floor lifts drawn pay, never creates it. Self-employment + and every other leaf keep their draws. + + The hourly-pay check compares the drawn rows with ``reference_rows``, the + FRS rows under the same employee rule on their own pay: the share of each + below the minimum wage for their age, before and after the floor. Returns + the frame, the receipt and each row's uplift (zero elsewhere), from which + the caller keeps the tape's pay as the row's year of pay. The receipt + counts support by distinct source household and gives no row's value. + """ + + _require_columns( + person, + ( + SPI_EMPLOYMENT_PAY_FLOOR_COLUMN, + SPI_EMPLOYMENT_PAY_FLOOR_HOURS_COLUMN, + "employment_status", + "self_employment_income", + "age", + "person_household_id", + ), + label="SPI employment pay floor", + ) + + def employee_rows( + mask: pd.Series, + ) -> tuple[pd.DataFrame, np.ndarray, np.ndarray, np.ndarray]: + frame = person.loc[mask.reindex(person.index).fillna(False).astype(bool)] + hours = pd.to_numeric( + frame[SPI_EMPLOYMENT_PAY_FLOOR_HOURS_COLUMN], errors="coerce" + ).to_numpy(dtype=np.float64) + pay = pd.to_numeric( + frame[SPI_EMPLOYMENT_PAY_FLOOR_COLUMN], errors="coerce" + ).to_numpy(dtype=np.float64) + if not np.isfinite(hours).all() or (hours < 0.0).any(): + raise ValueError("SPI pay floor hours must be finite and non-negative.") + if not np.isfinite(pay).all(): + raise ValueError("SPI pay floor drawn pay must be finite.") + employee = ( + frame["employment_status"] + .astype(str) + .isin(SPI_EMPLOYMENT_PAY_FLOOR_EMPLOYEE_STATUSES) + .to_numpy() + ) + return frame, hours, pay, employee & (hours > 0.0) & (pay > 0.0) + + candidates, hours, pay, eligible = employee_rows(rows) + paid_hours = hours / WEEKS_IN_YEAR * floor.weeks_in_year + minimum_wage = floor.hourly_rate(candidates["age"].to_numpy(dtype=np.float64)) + twin = pd.to_numeric(twin_pay.reindex(candidates.index), errors="coerce").to_numpy( + dtype=np.float64 + ) + if not np.isfinite(twin[eligible]).all(): + raise ValueError("SPI pay floor needs the FRS twin's pay of every drawn row.") + with np.errstate(divide="ignore", invalid="ignore"): + twin_rate = np.where( + (twin > 0.0) & (paid_hours > 0.0), twin / paid_hours, np.inf + ) + minimum = paid_hours * np.minimum(minimum_wage, twin_rate) + floored = eligible & (pay < minimum) + uplift = np.where(floored, minimum - pay, 0.0) + floored_pay = np.where(floored, minimum, pay) + result = person.copy() + result.loc[candidates.index[floored], SPI_EMPLOYMENT_PAY_FLOOR_COLUMN] = minimum[ + floored + ] + + household_sources = pd.Series( + source_household_ids(household, stage="The SPI employment pay floor"), + index=household["household_id"].to_numpy(), + ) + + def sources_of(frame: pd.DataFrame) -> np.ndarray: + return frame["person_household_id"].map(household_sources).to_numpy() + + weights = person_weights.loc[candidates.index].to_numpy(dtype=np.float64) + sources = sources_of(candidates) + eligible_sources = distinct_count(sources, eligible, weights) + floored_sources = distinct_count(sources, floored, weights) + unfloored_sources = distinct_count(sources, eligible & ~floored, weights) + eligible_weight = float(weights[eligible].sum()) + floored_weight = float(weights[floored].sum()) + added = float((uplift * weights).sum()) + # Weighted figures over one or two source households are withheld ("<3"); + # a count of support is rounded (receipt_counts), and no row's value is + # given. + # The floored weight beside the eligible weight gives back the unfloored + # side, and so do the share times the eligible weight and the amount added + # over the mean uplift, so the floored weight, share and amount are given + # only where neither side rests on one or two sources, as the Carer's + # Allowance take-up receipt gives its share. + either_side_small = ( + 0 < min(floored_sources, unfloored_sources) < UK_DISCLOSURE_FLOOR + ) + + def beside_eligible(value: float) -> float | str: + if either_side_small: + return UK_WITHHELD + return small_cell_share(value, floored_sources) + + def below_share( + below: np.ndarray, among: np.ndarray, frame_sources, frame_weights + ) -> float | str: + # A share is given only where neither the rows below nor the rest rest + # on one or two source households: a share of one or of nought over a + # few households describes each of them. + below_count = distinct_count(frame_sources, among & below, frame_weights) + rest_count = distinct_count(frame_sources, among & ~below, frame_weights) + if ( + 0 < below_count < UK_DISCLOSURE_FLOOR + or 0 < rest_count < UK_DISCLOSURE_FLOOR + ): + return UK_WITHHELD + total = float(frame_weights[among].sum()) + return float(frame_weights[among & below].sum()) / total if total else 0.0 + + below_before = pay < paid_hours * minimum_wage + below_after = floored_pay < paid_hours * minimum_wage + # The rows the floor lifts out from under the minimum wage are the + # difference of the two shares, so both are withheld beside a small one. + lifted_out = eligible & below_before & ~below_after + lifted_small = ( + 0 < distinct_count(sources, lifted_out, weights) < UK_DISCLOSURE_FLOOR + ) + reference, reference_hours, reference_pay, reference_eligible = employee_rows( + reference_rows + ) + reference_eligible &= ( + pd.to_numeric(reference["self_employment_income"], errors="coerce") + .fillna(0.0) + .to_numpy(dtype=np.float64) + == 0.0 + ) + reference_weights = person_weights.loc[reference.index].to_numpy(dtype=np.float64) + reference_below = reference_pay < ( + reference_hours + / WEEKS_IN_YEAR + * floor.weeks_in_year + * floor.hourly_rate(reference["age"].to_numpy(dtype=np.float64)) + ) + below_their_floor = int((eligible & (floored_pay < minimum * (1.0 - 1e-12))).sum()) + hourly_pay_check = { + "spi_drawn_employees": { + "source_households": rounded_count(eligible_sources), + "below_minimum_wage_share_before": ( + UK_WITHHELD + if lifted_small + else below_share(below_before, eligible, sources, weights) + ), + "below_minimum_wage_share_after": ( + UK_WITHHELD + if lifted_small + else below_share(below_after, eligible, sources, weights) + ), + }, + "frs_employees": { + "source_households": rounded_count( + distinct_count( + sources_of(reference), reference_eligible, reference_weights + ) + ), + "below_minimum_wage_share": below_share( + reference_below, + reference_eligible, + sources_of(reference), + reference_weights, + ), + }, + "eligible_rows_below_their_floor_after": extreme_count(below_their_floor), + } + + return ( + result, + { + "step": "spi_employment_pay_floor_before_stage2", + "column": SPI_EMPLOYMENT_PAY_FLOOR_COLUMN, + "rate": SPI_EMPLOYMENT_PAY_FLOOR_RATE_RULE, + **floor.evidence(), + "eligible": { + "source_households": rounded_count(eligible_sources), + "weighted_people": small_cell_share(eligible_weight, eligible_sources), + }, + "floored": { + "source_households": rounded_count(floored_sources), + "weighted_people": beside_eligible(floored_weight), + "weighted_share_of_eligible": beside_eligible( + floored_weight / eligible_weight if eligible_weight > 0.0 else 0.0 + ), + "weighted_mean_uplift_gbp": small_cell_share( + added / floored_weight if floored_weight > 0.0 else 0.0, + floored_sources, + ), + "weighted_amount_added_gbp": beside_eligible(added), + }, + "annual_income": SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE, + "hourly_pay_check": hourly_pay_check, + }, + pd.Series(uplift, index=candidates.index, dtype=np.float64), + ) + + @dataclass(frozen=True) class UKSPIIncomeImputationResult: """SPI-filled person table and auditable fit/source evidence.""" @@ -696,6 +1116,8 @@ class UKSPIIncomeImputationResult: state_pension_age: int | None = None gainful_self_employment_refresh: Mapping[str, object] | None = None pension_coherence: Mapping[str, object] | None = None + employment_pay_floor: Mapping[str, object] | None = None + gift_anchor: Mapping[str, object] | None = None @dataclass(frozen=True) @@ -803,6 +1225,7 @@ def impute_uk_spi_income_support( state_pension_age_guard: UKSPIStatePensionAgeGuard | None = None, state_pension_age: int | None = None, allow_in_sample_reference: bool = False, + employment_pay_floor: SPIEmploymentPayFloor | None = None, ) -> UKSPIIncomeImputationResult: """Run strict SPI-income and FRS-only QRFs on rebuilt positive support. @@ -816,6 +1239,10 @@ def impute_uk_spi_income_support( age with receipts (microcosm#1069). ``state_pension_age`` is the boundary the bridge predictor ``at_or_over_spa`` reads on the twin; it defaults to the donor age model's and is required without one (microcosm#840). + ``employment_pay_floor`` lifts drawn PAY to the twin's usual hours at the + minimum wage for its age, on drawn recipients other than band carriers + whose twin works as an employee with no trade, before employment_income + is derived and stage 2 reads it; ``None`` keeps every draw. """ if support.household_weight_kind is not WeightKind.IMPORTANCE: @@ -853,9 +1280,26 @@ def impute_uk_spi_income_support( raise TypeError("donor_table must be a pandas DataFrame.") raw_donor = donor_table.copy(deep=True) donor = _prepare_spi_donor(raw_donor, seed=seed, age_model=donor_age_model) + # The tape's interest and other income move to SPI's 2023-24 bands before + # any draw, so the forest and the band donors read the same rebased tape + # (microcosm#1418 savings follow-up). + savings_rebase = None + rebased_columns: frozenset[str] = frozenset() + if rebase_income_to_build_period and int(build_period) >= SPI_SAVINGS_REBASE_YEAR: + rebase_year_factors, _ = _spi_income_uprating_factors(SPI_SAVINGS_REBASE_YEAR) + donor, savings_rebase = rebase_spi_donor_savings( + donor, + spi_savings_rebase_published(), + income_factors=rebase_year_factors, + ) + rebased_columns = frozenset( + column + for column, entry in savings_rebase["columns"].items() + if "skipped" not in entry + ) donor_age_draw = { **donor.attrs.get("age_draw", {}), - "stage1_query_age": SPI_STAGE1_QUERY_AGE_RULE, + "stage1_age_predictor": SPI_STAGE1_AGE_PREDICTOR_RULE, } donor_full = donor donor_fit_weights = donor["FACT"].to_numpy(dtype=np.float64) @@ -942,6 +1386,7 @@ def impute_uk_spi_income_support( twin_employer = person.loc[spi_people, "employer_pension_contributions"].to_numpy( dtype=np.float64, copy=True ) + twin_pay_by_row = pd.Series(twin_pay, index=person.index[spi_people.to_numpy()]) recipient_domain = { "minimum_age": SPI_MINIMUM_RECIPIENT_AGE, "role_column": SPI_RECIPIENT_ROLE_COLUMN, @@ -968,6 +1413,13 @@ def impute_uk_spi_income_support( columns=initialize_frs_channel_columns, ) person = _seed_frs_hmrc_auxiliary_leaves(person, spi_people=spi_people) + # The stage-1 transport reads each twin's analogue. A stage-1 output that is + # its own analogue and that the frame does not carry (other income, on a + # frame whose FRS rows hold none) is the stage-time zero the fill below + # writes, on every row, so the twins locate as non-carriers (microcosm#1428). + for column in SPI_INCOME_QRF_OUTPUT_COLUMNS: + if SPI_STAGE1_ANALOGUE_COLUMNS.get(column) == column and column not in person: + person[column] = 0.0 base_household = household[household_channel] == BASE_FRS_SUPPORT_CHANNEL if "clone_index" in household: @@ -1105,8 +1557,10 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): uprating_factors = dict.fromkeys(SPI_INCOME_QRF_OUTPUT_COLUMNS, 1.0) if rebase_income_to_build_period: uprating_factors, income_uprating = _spi_income_uprating_factors( - int(build_period) + int(build_period), rebased_columns=rebased_columns ) + if savings_rebase is not None: + income_uprating = {**income_uprating, "savings_rebase": savings_rebase} stage1_draws = stage1_draws.mul(pd.Series(uprating_factors), axis="columns") nonnegative_stage1 = [ column @@ -1153,6 +1607,37 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): step="spi_state_pension_leaf_after_stage1", ) guard_receipts.append(receipt) + pay_floor_receipt = None + pay_floor_uplift = pd.Series(dtype=np.float64) + if employment_pay_floor is not None: + # The floor reads the twin's hours, status and trade, which stage 1 + # leaves alone, and lifts the drawn PAY leaf after every stage-1 + # writer and before the employment input, stage 2 and the HMRC + # aggregates read it. The transport receipt was taken on the + # unfloored draws' uniforms above. Band carriers keep their pool + # draws, whose total income sits in a published band; a twin with a + # trade reports hours that cover both jobs. + no_trade = recipient_predictors["has_trade"].to_numpy(dtype=np.float64) == 0.0 + band_carrier = ( + person.loc[spi_people, SPI_INCOME_BAND_CARRIER_COLUMN] + .astype(bool) + .to_numpy() + if SPI_INCOME_BAND_CARRIER_COLUMN in person.columns + else np.zeros(int(spi_people.sum()), dtype=bool) + ) + floor_rows = spi_people.copy() + floor_rows.loc[spi_people] = no_trade & ~band_carrier + person, pay_floor_receipt, pay_floor_uplift = ( + _floor_employment_pay_at_minimum_wage( + person, + rows=floor_rows, + floor=employment_pay_floor, + household=household, + person_weights=_person_household_weights(person, household), + twin_pay=twin_pay_by_row, + reference_rows=base_people, + ) + ) person = _derive_policyengine_employment_input(person, spi_people=spi_people) taxable_interest_draw = person.loc[spi_people, "savings_interest_income"].to_numpy( @@ -1302,6 +1787,9 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): ) guard_receipts.append(receipt) + person, gift_anchor = _anchor_spi_gifts_to_tape( + person, household, donor_full, spi_people=spi_people + ) # Non-ISA interest is the stage-1 taxable draw; ISA interest is the stage-2 # draw beside it (microcosm#1360). person.loc[spi_people, "savings_interest_income"] = taxable_interest_draw @@ -1317,6 +1805,8 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): ) person = _refresh_landlord_carrier(person, spi_people=spi_people) person = _refresh_annual_income_descriptors(person, spi_people=spi_people) + person = _keep_floored_rows_year_of_pay(person, uplift=pay_floor_uplift) + person = _clear_annuity_and_trust_descriptor(person, spi_people=spi_people) # The flag is the stage's last new column, where the graph's typed cells # place it (the legacy oracle and the graph must agree on column order). person[SPI_INCOME_RECIPIENT_COLUMN] = ( @@ -1357,6 +1847,8 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): state_pension_age=int(state_pension_age), gainful_self_employment_refresh=gainful_self_employment, pension_coherence=pension_coherence, + employment_pay_floor=pay_floor_receipt, + gift_anchor=gift_anchor, ) @@ -1675,10 +2167,23 @@ def _resample_band_donor_leaves( @cache -def _spi_income_uprating_factors(year: int): - """Rebase SPI money flows before conditioning on build-year FRS incomes.""" +def _spi_income_uprating_factors( + year: int, *, rebased_columns: Collection[str] | None = None +): + """Rebase SPI money flows before conditioning on build-year FRS incomes. + + ``rebased_columns`` names the savings columns rebase_spi_donor_savings + moved to 2023-24, which grow on the OBR's savings path from there; a + savings column it left as it was (a tape that does not gross) grows on + its engine index from 2022-23. None takes every rebased concept. + """ from policyengine_uk import CountryTaxBenefitSystem + from microcosm.build.uk_runtime.hmrc_uprating import ( + UK_OBR_SAVINGS_INCOME_GROWTH_INDEX_CONCEPT, + uk_obr_savings_income_level, + ) + if year < SPI_DONOR_INCOME_YEAR: raise ValueError("SPI income cannot be rebased before its source year.") if set(SPI_INCOME_UPRATING_VARIABLES) != set(SPI_INCOME_QRF_OUTPUT_COLUMNS): @@ -1689,6 +2194,31 @@ def _spi_income_uprating_factors(year: int): factors = {} columns = {} for column, variable_name in SPI_INCOME_UPRATING_VARIABLES.items(): + rebased = ( + column in SPI_SAVINGS_REBASE_CONCEPTS + if rebased_columns is None + else column in rebased_columns + ) + if rebased and year >= SPI_SAVINGS_REBASE_YEAR: + # rebase_spi_donor_savings has moved the column to SPI's 2023-24 + # bands; the OBR's savings income growth carries it on. + factor, used = uk_obr_savings_income_level(SPI_SAVINGS_REBASE_YEAR, year) + factors[column] = factor + columns[column] = { + "variable": variable_name, + "index": UK_OBR_SAVINGS_INCOME_GROWTH_INDEX_CONCEPT, + "factor": factor, + "basis": "spi_published_bands_then_obr_savings_income", + "from_period": SPI_SAVINGS_REBASE_YEAR, + "growth_by_year": { + str(fiscal_year): growth + for fiscal_year, (growth, _) in used.items() + }, + "dataset_projection_index": ( + SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION[variable_name] + ), + } + continue path = None factor = 1.0 if variable_name is not None: @@ -1772,6 +2302,229 @@ def prepare_spi_donor_table( return _prepare_spi_donor(raw, seed=seed, age_model=age_model) +def spi_savings_rebase_published( + rows: Sequence[Mapping[str, Any]] | None = None, +) -> dict[str, dict[int, float]]: + """HMRC's 2023-24 Table 3.7 amounts by band, for each rebased column. + + Read from the vendored resource (``SPI_SAVINGS_REBASE_RESOURCE``): the + United Kingdom interest from banks and building societies and other + income of taxpayers, keyed by each band's lower bound of total income. + Both columns must name the same bands, once each. + """ + + if rows is None: + from microcosm.build.uk_runtime.ledger_fact_vendoring import vendored_rows + + rows = vendored_rows(SPI_SAVINGS_REBASE_RESOURCE) + columns = { + concept: column for column, concept in SPI_SAVINGS_REBASE_CONCEPTS.items() + } + published: dict[str, dict[int, float]] = { + column: {} for column in SPI_SAVINGS_REBASE_CONCEPTS + } + for row in rows: + column = columns.get(str(row.get("concept"))) + if column is None: + continue + period = int((row.get("period") or {}).get("value", -1)) + if period != SPI_SAVINGS_REBASE_YEAR: + raise ValueError( + f"{SPI_SAVINGS_REBASE_RESOURCE}: a {column} row describes {period}, " + f"not {SPI_SAVINGS_REBASE_YEAR}." + ) + bound = int((row.get("dimensions") or {})["total_income_lower_bound"]) + if bound in published[column]: + raise ValueError( + f"{SPI_SAVINGS_REBASE_RESOURCE}: {column} has two rows from {bound}." + ) + value = float(row["value"]) + if not np.isfinite(value) or value < 0.0: + raise ValueError( + f"{SPI_SAVINGS_REBASE_RESOURCE}: {column} from {bound} is {value!r}." + ) + published[column][bound] = value + bands = {column: tuple(sorted(by_bound)) for column, by_bound in published.items()} + if not all(bands.values()) or len(set(bands.values())) != 1: + raise ValueError( + f"{SPI_SAVINGS_REBASE_RESOURCE} must give every rebased column the " + f"same total-income bands: {bands}." + ) + return published + + +#: The rebase bands the tape once on its income moved to 2023-24, the year the +#: published bands describe: each income leaf at its 2022-23 to 2023-24 factor, +#: each rebased column at its factor over all bands, and the rest of the +#: published total held nominal, as the band donors' realised income is built. +#: Its consumers band again on income uprated to the frame's year, so banding +#: on 2022-23 income counted the band-edge migration twice (microcosm#1428 +#: review, finding 6). Moving each record's savings by its own band's factor +#: has no fixed point: the band sets the factor and the factor the band, and on +#: the production tape records kept changing band from pass to pass. + + +def _tape_income_in_rebase_year( + donor: pd.DataFrame, + rebased: pd.DataFrame, + income_factors: Mapping[str, float], +) -> np.ndarray: + """The tape's total income moved to SPI_SAVINGS_REBASE_YEAR.""" + + columns = [ + column + for column in SPI_INCOME_QRF_OUTPUT_COLUMNS + if column in donor + and column not in (SPI_PROPERTY_FINANCE_COSTS_COLUMN, *SPI_LOSS_DRAW_COLUMNS) + ] + leaves = donor[columns].to_numpy(dtype=np.float64) + rest = np.maximum( + donor["total_income"].to_numpy(dtype=np.float64) - leaves.sum(axis=1), 0.0 + ) + moved = rebased[columns].to_numpy(dtype=np.float64) @ np.asarray( + [float(income_factors.get(column, 1.0)) for column in columns] + ) + return moved + rest + + +def rebase_spi_donor_savings( + donor: pd.DataFrame, + published: Mapping[str, Mapping[int, float]], + *, + income_factors: Mapping[str, float] | None = None, +) -> tuple[pd.DataFrame, dict[str, object]]: + """Move the tape's interest and other income to SPI's 2023-24 bands. + + Each taxpayer donor's column is scaled by its band's factor: HMRC's + published 2023-24 amount over the tape's FACT-weighted amount for the + taxpayers in the same range of total income, since Table 3.7 counts + taxpayers only. The tape is banded once on its income moved to 2023-24: + each income leaf at its 2022-23 to 2023-24 factor (``income_factors``; + none holds the leaves nominal) and each rebased column at its factor over + all bands, on the tape's 2022-23 bands. A taxpayer is a record the tape + flags (TAXPAYER 1) or one whose income crosses the first band's lower + bound, the Personal Allowance, by 2023-24, so a saver who becomes a + taxpayer is counted once. A flagged taxpayer below the first band sits in + it, as the table's lowest band holds those taxpayers (note 8); a tape + without the flag counts every record at or above the first band. Any + other donor takes the factor of all bands together. Banded so, the rebased + tape reproduces the published amounts band by band; a record whose band's + factor differs from the factor of all bands can sit across an edge when + banded on its rebased income. The OBR's savings income growth then + carries both columns to the frame's year (``_spi_income_uprating_factors``). + A band the tape carries none of has nothing to scale, and a band whose tape + amount is under SPI_SAVINGS_REBASE_MINIMUM_GROSSING of its published amount + takes the factor of all bands; both are named in the receipt. A column + whose tape does not gross to the population is left as it is, and the + receipt says so. The receipt carries the factors only. + """ + + bounds = sorted(next(iter(published.values()))) + edges = np.asarray(bounds, dtype=np.float64) + weights = donor["FACT"].to_numpy(dtype=np.float64) + flagged = SPI_DONOR_TAXPAYER_COLUMN in donor + tape_taxpayer = ( + donor[SPI_DONOR_TAXPAYER_COLUMN].to_numpy(dtype=bool) + if flagged + else np.zeros(len(donor), dtype=bool) + ) + band_2022 = ( + np.searchsorted( + edges, donor["total_income"].to_numpy(dtype=np.float64), side="right" + ) + - 1 + ) + factors_in = dict(income_factors or {}) + rebased = donor.copy() + receipt: dict[str, object] = { + "resource": SPI_SAVINGS_REBASE_RESOURCE, + "from_period": SPI_DONOR_INCOME_YEAR, + "to_period": SPI_SAVINGS_REBASE_YEAR, + "band_by": "total_income_moved_to_2023_24", + "factors_over": ( + "taxpayers: TAXPAYER 1, or income crossing the first band by 2023-24" + if flagged + else "all_records_at_or_above_the_first_band" + ), + "band_lower_bounds": [int(bound) for bound in bounds], + "columns": {}, + } + original: dict[str, np.ndarray] = {} + targets: dict[str, np.ndarray] = {} + banding_factors: dict[str, float] = {} + for column, by_bound in published.items(): + values = donor[column].to_numpy(dtype=np.float64) + target = np.asarray([float(by_bound[bound]) for bound in bounds]) + taxpayer = tape_taxpayer | (band_2022 >= 0) + tape = np.bincount( + np.clip(band_2022, 0, None)[taxpayer], + weights=(weights * values)[taxpayer], + minlength=len(bounds), + ) + carried = tape > 0.0 + # Over the bands the tape carries; an empty band is left as it is. + if ( + tape[carried].sum() + < SPI_SAVINGS_REBASE_MINIMUM_GROSSING * target[carried].sum() + ): + receipt["columns"][column] = { + "concept": SPI_SAVINGS_REBASE_CONCEPTS[column], + "skipped": "the tape's weights do not gross to the published amounts", + } + continue + original[column] = values + targets[column] = target + banding_factors[column] = ( + float(target[carried].sum() / tape[carried].sum()) if carried.any() else 1.0 + ) + if not original: + return rebased, receipt + + income = _tape_income_in_rebase_year( + donor, donor, {**factors_in, **banding_factors} + ) + band = np.searchsorted(edges, income, side="right") - 1 + crossed = ~tape_taxpayer & (band_2022 < 0) & (band >= 0) + taxpayer = tape_taxpayer | crossed if flagged else (band >= 0) + band = np.where(taxpayer, np.clip(band, 0, None), band) + for column, values in original.items(): + target = targets[column] + tape = np.bincount( + band[taxpayer], + weights=(weights * values)[taxpayer], + minlength=len(bounds), + ) + carried = tape > 0.0 + thin = carried & (tape < SPI_SAVINGS_REBASE_MINIMUM_GROSSING * target) + usable = carried & ~thin + all_bands = ( + float(target[usable].sum() / tape[usable].sum()) if usable.any() else 1.0 + ) + factors = np.where(usable, target / np.where(usable, tape, 1.0), all_bands) + rebased[column] = values * np.where( + taxpayer, factors[np.clip(band, 0, None)], all_bands + ) + receipt["columns"][column] = { + "concept": SPI_SAVINGS_REBASE_CONCEPTS[column], + "banding_factor": banding_factors[column], + "band_factors": { + str(bound): float(factor) + for bound, factor, kept in zip(bounds, factors, usable, strict=True) + if kept + }, + "below_first_band_factor": all_bands, + "bands_without_tape_amounts": [ + int(bound) + for bound, kept in zip(bounds, carried, strict=True) + if not kept + ], + "bands_too_thin_to_gross": [ + int(bound) for bound, kept in zip(bounds, thin, strict=True) if kept + ], + } + return rebased, receipt + + def _prepare_spi_donor( raw: pd.DataFrame, *, @@ -1849,10 +2602,26 @@ def _prepare_spi_donor( # on the prepared tape for the band donors' propensity and band pools; # they are never QRF inputs or outputs. donor["total_income"] = numeric["TI"].to_numpy(dtype=float) + # Table 3.7 counts taxpayers only, so the savings rebase reads the tape's + # TAXPAYER flag (SPI_TAXPAYER_CODES: 1, a taxpayer; 2, not). A tape without + # it carries no flag, and the rebase's receipt says it counted every record + # at or above the first band (microcosm#1428). + if "TAXPAYER" in raw.columns: + taxpayer = pd.to_numeric(raw["TAXPAYER"], errors="coerce") + unknown = sorted(set(taxpayer.dropna().unique()) - set(SPI_TAXPAYER_CODES)) + if taxpayer.isna().any() or unknown: + raise ValueError( + "SPI TAXPAYER must be 1 (a taxpayer) or 2 (not) on every record; " + f"found {unknown or 'blanks'}." + ) + donor[SPI_DONOR_TAXPAYER_COLUMN] = taxpayer.eq(1).to_numpy() donor["is_composite"] = numeric["AGERANGE"].astype(int).eq(-1).to_numpy() # The published age band travels with the record so band pools can match # a carrier's age band; composites (-1) have none and never age-match. donor["agerange"] = age_codes.to_numpy(dtype=np.int64) + # The stage-1 forests read the band itself, a composite's -1 included + # (uk-data#628). + donor[SPI_STAGE1_AGE_BAND_COLUMN] = age_codes.to_numpy(dtype=np.float64) # The bridge predictors (microcosm#840): main_source and has_pay from the # leaves, as on the twin; has_trade and at_or_over_spa from the published # indicator codes, the leaves serving a composite record without codes; @@ -2213,11 +2982,13 @@ def _stage1_query_predictors( *, state_pension_age: int, ) -> pd.DataFrame: - """Stage-1 query predictors: age at the middle of the year of age, and the + """Stage-1 query predictors: the SPI band of the whole-year age, and the bridge predictors derived from the row's own (the twin's) leaves.""" predictors = _person_predictors(person, household, income_predictors=()) - predictors["age"] = np.floor(predictors["age"].to_numpy(dtype=np.float64)) + 0.5 + predictors[SPI_STAGE1_AGE_BAND_COLUMN] = _spi_agerange_codes( + np.floor(predictors["age"].to_numpy(dtype=np.float64)) + ).astype(np.float64) bridge = _person_bridge(person, state_pension_age=state_pension_age) for column in SPI_BRIDGE_PREDICTOR_COLUMNS: predictors[column] = bridge[column].to_numpy() @@ -2590,6 +3361,56 @@ def _spi_pension_coherence( return person, receipt +#: Gift Aid and charitable investment gifts are sparse leaves the stage-1 +#: chain draws after, and conditioned on, the income leaves, so a change to +#: the donor savings rebase reshuffles which few donors' gifts the copies +#: draw: rebasing on the tape's taxpayers moved the copies' charitable +#: investment gifts far from the tape's own total (microcosm#1428). Their +#: totals over the drawn copies, at the stage's weights, are anchored to the +#: tape's own FACT-weighted totals (María's ruling of 2026-10-31). Both +#: columns are nominal, as their uprating mapping (None) keeps them. +SPI_GIFT_ANCHOR_COLUMNS = ("gift_aid", "charitable_investment_gifts") +SPI_GIFT_ANCHOR_BASIS = ( + "each column's total over the drawn copies at the stage's household " + "weights equals the prepared tape's FACT-weighted total; a column the " + "tape or the copies carry none of is left as drawn" +) + + +def _anchor_spi_gifts_to_tape( + person: pd.DataFrame, + household: pd.DataFrame, + donor: pd.DataFrame, + *, + spi_people: pd.Series, +) -> tuple[pd.DataFrame, dict[str, object]]: + """Scale the copies' gift leaves to the tape's FACT-weighted totals. + + The receipt records each column's factor only, never a total or a count. + """ + + weights = _person_household_weights(person, household).to_numpy(dtype=np.float64) + rows = spi_people.reindex(person.index).fillna(False).to_numpy(dtype=bool) + fact = donor["FACT"].to_numpy(dtype=np.float64) + factors: dict[str, float] = {} + for column in SPI_GIFT_ANCHOR_COLUMNS: + tape = float((fact * donor[column].to_numpy(dtype=np.float64)).sum()) + values = person[column].to_numpy(dtype=np.float64, copy=True) + drawn = float((weights[rows] * values[rows]).sum()) + if tape <= 0.0 or drawn <= 0.0: + factors[column] = 1.0 + continue + factor = tape / drawn + values[rows] = values[rows] * factor + person[column] = values + factors[column] = factor + return person, { + "columns": list(SPI_GIFT_ANCHOR_COLUMNS), + "basis": SPI_GIFT_ANCHOR_BASIS, + "factors": factors, + } + + def _person_household_weights( person: pd.DataFrame, household: pd.DataFrame, @@ -2967,6 +3788,36 @@ def _refresh_uc_start_up_period( } +def _keep_floored_rows_year_of_pay( + person: pd.DataFrame, *, uplift: pd.Series +) -> pd.DataFrame: + """Keep a floored row's year of pay at its tape pay (uk-data#629). + + The pay floor lifts current pay only. The tape's pay is the year's, so a + floored row's annual share becomes its unfloored over its floored + employment income, and the annual measures (current times share plus the + other spells, which a row that drew pay has none of) read the tape's pay. + """ + + if EMPLOYMENT_INCOME_ANNUAL_SHARE not in person.columns: + return person + lifted = uplift.reindex(person.index).fillna(0.0).to_numpy(dtype=np.float64) + rows = lifted > 0.0 + if not rows.any(): + return person + current = pd.to_numeric( + person.loc[rows, "employment_income"], errors="coerce" + ).to_numpy(dtype=np.float64) + tape = current - lifted[rows] + if not (np.isfinite(current).all() and (tape > 0.0).all()): + raise ValueError( + "A floored row's employment income must exceed its uplift, as its " + "drawn pay is positive." + ) + person.loc[rows, EMPLOYMENT_INCOME_ANNUAL_SHARE] = tape / current + return person + + def _refresh_annual_income_descriptors( person: pd.DataFrame, *, spi_people: pd.Series ) -> pd.DataFrame: @@ -3012,6 +3863,29 @@ def _refresh_annual_income_descriptors( return person +def _clear_annuity_and_trust_descriptor( + person: pd.DataFrame, *, spi_people: pd.Series +) -> pd.DataFrame: + """Zero a drawn row's annuity and trust descriptor. + + A drawn row's private pension is the tape's, which excludes purchased + annuity and trust income: SPI books those as other income, in the drawn + other_investment_income (microcosm#1428). An undrawn copy keeps its twin's + descriptor with its twin's pension. + """ + + if ANNUITY_AND_TRUST_INCOME_COLUMN not in person: + return person + _assign_spi_values( + person, + spi_people, + ANNUITY_AND_TRUST_INCOME_COLUMN, + np.zeros(int(spi_people.sum())), + default=0.0, + ) + return person + + def _refresh_uc_gainful_self_employment( person: pd.DataFrame, *, spi_people: pd.Series ) -> tuple[pd.DataFrame, dict[str, object] | None]: @@ -3113,6 +3987,12 @@ def _assign_spi_values( "SPI_DONOR_UKDS_STUDY", "SPI_DONOR_VINTAGE", "SPI_DONOR_REQUIRED_COLUMNS", + "SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE", + "SPI_EMPLOYMENT_PAY_FLOOR_EMPLOYEE_STATUSES", + "SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER", + "SPI_EMPLOYMENT_PAY_FLOOR_RATE_RULE", + "SPI_EMPLOYMENT_PAY_FLOOR_WEEKS", + "SPIEmploymentPayFloor", "SPI_INCOME_RECIPIENT_COLUMN", "SPI_INCOME_SOURCE_COLUMNS", "SPI_DERIVED_POLICYENGINE_SOURCE_COLUMNS", @@ -3127,6 +4007,7 @@ def _assign_spi_values( "SPI_INCOME_BAND_CARRIER_KEY_COLUMN", "SPI_INCOME_BAND_DONOR_LEAF_DRAW_SALT", "SPI_INCOME_BAND_LOWER_BOUND_COLUMN", + "load_spi_employment_pay_floor", "prepare_spi_donor_table", "SPI_SOURCE_LEAF_RECONCILIATION_ABS_TOLERANCE_GBP", "SPI_SOURCE_TEI_FORMULA", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py index 10ba7f27a..596ec88f3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_rank_transport.py @@ -32,7 +32,7 @@ conditional's mass, so its located quantile was mostly the random placement inside that atom and the draw kept under half of the twin's rank within a cell. The rows are therefore split into folds by a stable hash of their -predictor cell (gender, region, whole-year age and the four bridge +predictor cell (gender, region, SPI age band and the four bridge predictors), each fold's recipients are located by a forest fitted on the other folds, and because a copy shares its twin's cell, the forest that locates it never saw the twin. Keying the folds on the cell rather than the @@ -98,11 +98,12 @@ SPI_TRANSPORT_FOLDS = 5 #: The fold draw's salt; the fold of a cell is ``floor(uniform * folds)``. SPI_TRANSPORT_FOLD_SALT = "spi_stage1:reference_fold" -#: The columns whose values name a predictor cell, age at the whole year. +#: The columns whose values name a predictor cell: the stage-1 predictors, +#: age as its SPI band (uk-data#628). SPI_TRANSPORT_CELL_COLUMNS = ( "gender", "region", - "age", + "spi_age_band", "main_source", "has_pay", "has_trade", @@ -119,7 +120,12 @@ "dividend_income": "dividend_income", "private_pension_income": "private_pension_income", "property_income": "property_income", - "other_investment_income": None, + # The twin's own other income locates the draw (microcosm#1428): its gilt + # interest, so a copy's OTHERINV draw ranks with its twin's holdings + # instead of an identity uniform that ignores them. The twin's purchased + # annuity and trust payments stay in private_pension_income, whose + # analogue they join. + "other_investment_income": "other_investment_income", "gift_aid": None, "charitable_investment_gifts": None, SPI_HMRC_PAY_COLUMN: FRS_HMRC_PAY_COLUMN, @@ -239,7 +245,7 @@ def cell_fold_ids( """Assign every row to a fold by a stable hash of its predictor cell. ``predictors`` holds the raw (unencoded) stage-1 predictors; a cell is the - tuple of :data:`SPI_TRANSPORT_CELL_COLUMNS` with age at the whole year. + tuple of :data:`SPI_TRANSPORT_CELL_COLUMNS`. The fold is ``floor(uniform * used)`` of the cell's identity-keyed uniform, so two frames whose rows share a cell share its fold. ``used`` is ``folds`` capped at the number of distinct cells in ``predictors``, or @@ -252,9 +258,6 @@ def cell_fold_ids( if missing: raise ValueError(f"fold cells need predictor column(s) {missing}.") cells = predictors[list(SPI_TRANSPORT_CELL_COLUMNS)].copy() - cells["age"] = np.floor( - pd.to_numeric(cells["age"], errors="coerce").to_numpy(dtype=np.float64) - ) keys = cells.astype(str).agg("|".join, axis=1).to_numpy() if used is None: used = min(folds, len(set(keys.tolist()))) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py index 0fcbd5724..96a507038 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_spine.py @@ -29,6 +29,7 @@ _raw_source_person_ids, _read_raw_frs_table, ) +from microcosm.build.uk_runtime.frs_spine import ANNUITY_AND_TRUST_INCOME_COLUMN from microcosm.build.uk_runtime.frs_take_up import uk_take_up_population_policy from microcosm.build.uk_runtime.hmrc_income import ( HMRCIncomeTargetSet, @@ -62,6 +63,10 @@ SPI_DONOR_AGE_POPULATION_PERIOD, SPI_DONOR_AGE_POPULATION_RESOURCE, SPI_DONOR_INCOME_YEAR, + SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE, + SPI_EMPLOYMENT_PAY_FLOOR_EMPLOYEE_STATUSES, + SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER, + SPI_EMPLOYMENT_PAY_FLOOR_WEEKS, SPI_INCOME_BAND_CARRIER_KEY_COLUMN, SPI_INCOME_BAND_DONOR_LEAF_DRAW_SALT, SPI_INCOME_RECIPIENT_COLUMN, @@ -78,6 +83,7 @@ assert_frs_hmrc_auxiliary_crosswalk_available, impute_uk_spi_income_support, load_spi_donor_age_model, + load_spi_employment_pay_floor, verify_spi_donor_identity, ) from microcosm.build.uk_runtime.spi_rank_transport import ( @@ -162,6 +168,55 @@ "state_pension_age": SPI_SPINE_STATE_PENSION_AGE_SOURCE, "receipt": "rows, weighted people and amounts removed per column and step", } +#: Reviewed declaration of the employment pay floor (spi_income's +#: SPI_EMPLOYMENT_PAY_FLOOR_* notes give the reasons). +SPI_SPINE_EMPLOYMENT_PAY_FLOOR: Mapping[str, object] = { + "column": "hmrc_spi_pay", + "hours_column": "hours_worked", + "hours_basis": ( + "annual usual hours: the FRS adult's usual weekly hours in all jobs " + "(TOTHOURS) times 52, read back to weekly hours" + ), + "weeks_in_year": SPI_EMPLOYMENT_PAY_FLOOR_WEEKS, + "hourly_rate": ( + f"the lower of policyengine-uk {SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER} by the " + "twin's age, read as the engine reads the build year (30 April), and the " + "twin's own hourly pay (its employment income before the draw over its " + "annual hours)" + ), + "employee_statuses": list(SPI_EMPLOYMENT_PAY_FLOOR_EMPLOYEE_STATUSES), + "rows": ( + "drawn recipients other than reserved band carriers, whose twin has no " + "trade (has_trade 0), an employee main job and usual hours above zero, " + "and whose drawn PAY is positive; FRS rows, undrawn copies and every " + "other leaf keep their values" + ), + "pay_basis": ( + "tape PAY is taxable pay, net of net-pay pension contributions and " + "salary sacrifice; the minimum wage binds gross pay, and the engine " + "reads employment_income (PAY + EPB + TAXTERM) as gross pay, so PAY " + "alone is compared with the gross minimum and a twin at the minimum " + "wage who pays net-pay contributions can be lifted by up to them" + ), + "reason": ( + "stage 1 draws annual tape pay without hours and the tape holds " + "part-year workers, so a twin reporting usual employee hours could " + "draw a year's pay below what those hours earn at the minimum wage " + "(uk-data#629)" + ), + "annual_income": SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE, + "receipt": ( + "eligible and floored source households (rounded), weighted people, " + "the weighted share floored, the weighted mean uplift and the amount " + "added; figures on one or two source households are <3, and the " + "floored weight, share and amount are given only where neither the " + "floored nor the unfloored side rests on one or two; the hourly-pay " + "check gives the weighted shares of the drawn employees below the " + "minimum wage for their age before and after the floor, beside the FRS " + "employees' share, each withheld beside a side on one or two, and the " + "eligible rows still below their floor (banded, zero exact)" + ), +} UK_FRS_HMRC_SPINE_LEAF_OUTPUT_COLUMNS = ( *FRS_HMRC_RETAINED_LEAF_COLUMNS, @@ -215,8 +270,8 @@ "self_employment_income", "savings_interest_income", "dividend_income", - # The tape's OTHERINV on SPI rows; FRS rows carry their own NS&I and gilt - # interest from frs_spine (microcosm#1418). + # The tape's OTHERINV on SPI rows; FRS rows carry their own gilt interest + # from frs_spine (microcosm#1418), NS&I being interest (microcosm#1428). "other_investment_income", "private_pension_income", "property_income", @@ -270,6 +325,10 @@ # tape's income is the tax year's, so each share is 1, and the twin's # other spells stay only where the tape drew none of that income. *FRS_ANNUAL_INCOME_OUTPUT_COLUMNS, + # A drawn row's private pension is the tape's, which books annuity and + # trust payments as other income, so its descriptor of them is zero + # (microcosm#1428). + ANNUITY_AND_TRUST_INCOME_COLUMN, ) # Reviewed constants for every load-bearing manifest parameter the spine @@ -279,8 +338,8 @@ # applies to every declared parameter: it needs (1) a drift assert and (2) an # executed-effect receipt, or an explicit absence statement. Seeded draws use # twin-build determinism as their executed-effect receipt. -#: Age, gender and region, then the four bridge predictors the tape and the -#: twin derive from their own leaves (microcosm#840). +#: The SPI age band (uk-data#628), gender and region, then the four bridge +#: predictors the tape and the twin derive from their own leaves (microcosm#840). SPI_SPINE_STAGE1_PREDICTORS = SPI_STAGE1_PREDICTOR_COLUMNS SPI_SPINE_STAGE1_CATEGORICAL_PREDICTORS = SPI_STAGE1_CATEGORICAL_PREDICTORS #: The stage-1 draw: each drawn recipient's twin is located on the FRS @@ -315,6 +374,7 @@ ) # Held to the drawn incomes by rule, not drawn by stage 2. and column not in FRS_ANNUAL_INCOME_OUTPUT_COLUMNS + and column != ANNUITY_AND_TRUST_INCOME_COLUMN ) #: Both forests are queried over every SPI-channel person aged 16 and over, #: and the dependants' rows are discarded (uk-data#504, microcosm#1095). @@ -405,7 +465,9 @@ def evidence(self) -> dict[str, object]: self.imputation.gainful_self_employment_refresh ), "pension_coherence": self.imputation.pension_coherence, + "gift_anchor": self.imputation.gift_anchor, "state_pension_age_guard": list(self.imputation.state_pension_age_guard), + "employment_pay_floor": self.imputation.employment_pay_floor, "targets": { "count": len(self.source_targets.targets), "classification": dict(self.replay_report.summary), @@ -775,6 +837,14 @@ def __call__(self, frame: Frame) -> Frame: if age_op is not None or guard_op is not None else None ) + floor_op = _optional_operation( + self.stage, "floor_spi_employment_pay_at_minimum_wage" + ) + employment_pay_floor = ( + None + if floor_op is None + else load_spi_employment_pay_floor(uk_time_period(frame)) + ) state_pension_age_guard = ( None if guard_op is None or donor_age_model is None @@ -814,6 +884,7 @@ def __call__(self, frame: Frame) -> Frame: state_pension_age=( None if donor_age_model is None else donor_age_model.state_pension_age ), + employment_pay_floor=employment_pay_floor, ) result_frame = uk_national_frame( person=imputation.person, @@ -1330,6 +1401,10 @@ def _assert_income_stage_parameters( "zero_pension_age_reports_below_state_pension_age", SPI_SPINE_STATE_PENSION_AGE_GUARD, ), + ( + "floor_spi_employment_pay_at_minimum_wage", + SPI_SPINE_EMPLOYMENT_PAY_FLOOR, + ), ): operation = _optional_operation(stage, kind) if operation is None: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py index 230b776fa..8c8d7ed86 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_support.py @@ -138,9 +138,9 @@ # The enhanced-FRS FRS-only stage uses these six income predictors. OTHERINV # is a first-stage SPI draw and HMRC accounting component. The FRS base -# carries `other_investment_income` only from its NS&I and gilt accounts -# (microcosm#1418), not OTHERINV's concept, so it stays out of second-stage -# training. +# carries `other_investment_income` only from its gilt accounts (microcosm#1418, +# NS&I being interest since microcosm#1428), not OTHERINV's concept, so it stays +# out of second-stage training. FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS = ( "employment_income", "self_employment_income", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py index d1e6b0f46..4be272932 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spine_build.py @@ -206,6 +206,9 @@ from microcosm.build.uk_runtime.staging import UK_STAGING_REPOSITORY from microcosm.build.uk_runtime.student_loans import UKStudentLoansStageTransform from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract +from microcosm.build.uk_runtime.trading_allowance_receipts import ( + UKTradingAllowanceReceiptsStageTransform, +) from microcosm.build.uk_runtime.uc_capital_coherence import ( UKUCCapitalCoherenceStageTransform, ) @@ -1924,6 +1927,12 @@ def prepare_uk_spine_execution( contract=stochastic_contract, stage=stages_by_name["childcare_copy_take_up"], ) + if "trading_allowance_receipts" in stage_names: + implementations["trading_allowance_receipts"] = ( + UKTradingAllowanceReceiptsStageTransform( + stage=stages_by_name["trading_allowance_receipts"] + ) + ) if "age_tail" in stage_names: implementations["age_tail"] = UKAgeTailStageTransform( stage=stages_by_name["age_tail"] 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 d36cda489..1d8830c72 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 @@ -108,6 +108,8 @@ def uk_stage_health_gate( return _property_components_gate(stage, evidence, parameters) if check == "property_purchases": return _property_purchases_gate(stage, evidence, parameters) + if check == "spi_hourly_pay": + return _spi_hourly_pay_gate(stage, evidence, parameters) if check == "regional_home_values": return _regional_home_values_gate(stage, evidence, parameters) if check == "legacy_closure_take_up": @@ -1503,9 +1505,12 @@ def _uc_take_up_gate( Every reporter claims and no unit outside the draw changes. Over a family type's pools whose target the draw can reach, the realized Great Britain - caseload is within ``maximum_caseload_deviation`` of DWP's count, and the - family type's whole caseload is no further from DWP's than before the - redraw, give or take that share of DWP's count. A family type whose + caseload is within ``maximum_reachable_pool_deviation`` of DWP's count, and + the family type's whole caseload is no further from DWP's than before the + redraw, give or take ``maximum_caseload_deviation`` of DWP's count. The two + are separate, so that widening the second for what a stage before the + redraw does to the caseload leaves the first as tight as the draw achieves. + A family type whose caseload rests on fewer than three sources, which the receipt prints "<3", is flagged unchecked, never failed: a small-cell rule never stops a build (María's ruling of 2026-10-26). @@ -1516,6 +1521,10 @@ def _uc_take_up_gate( parameters["maximum_caseload_deviation"], label=f"{stage}.maximum_caseload_deviation", ) + pool_tolerance = _finite_number( + parameters["maximum_reachable_pool_deviation"], + label=f"{stage}.maximum_reachable_pool_deviation", + ) failures: list[str] = [] details: dict[str, object] = {} for key in ("reporters_not_claiming", "changed_outside_draw"): @@ -1556,11 +1565,11 @@ def _uc_take_up_gate( realized = _finite_number( values["reachable_pools_caseload_after"], label=f"{stage}.{name}" ) - if target > 0.0 and abs(realized - target) > tolerance * target: + if target > 0.0 and abs(realized - target) > pool_tolerance * target: failures.append( f"{stage}: family type {name} realizes {realized:,.0f} against " f"DWP's {target:,.0f} over its reachable pools (tolerance " - f"{tolerance})." + f"{pool_tolerance})." ) if abs(after - dwp) > abs(before - dwp) + tolerance * dwp: failures.append( @@ -1616,6 +1625,80 @@ def _spi_income_spine_gate( ) +def _spi_hourly_pay_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """Drawn SPI employees are paid at least their floor (uk-data#629). + + The employment pay floor lifts each eligible drawn employee's pay to its + twin's hours at the lower of the minimum wage for its age and the twin's + own hourly pay, so none may stay below its floor; and after the floor the + drawn employees' weighted share below the minimum wage for their age may + exceed the FRS employees' own share by at most the declared margin. A + share the receipt withholds (one or two source households on a side) + is not judged, and the details give only the verdicts, not the shares. + """ + + check = "spi_hourly_pay" + floor = evidence.get("employment_pay_floor") + if not isinstance(floor, Mapping): + return _fail( + stage, check, [f"{stage}: the receipt carries no employment pay floor."], {} + ) + hourly = _mapping( + floor.get("hourly_pay_check"), + label=f"{stage}.employment_pay_floor.hourly_pay_check", + ) + margin = _finite_number( + parameters["maximum_share_above_frs"], label=f"{stage}.maximum_share_above_frs" + ) + failures: list[str] = [] + below_floor = hourly.get("eligible_rows_below_their_floor_after") + if below_floor != 0: + failures.append( + f"{stage}: eligible drawn employees remain below their pay floor " + f"({below_floor})." + ) + drawn = _mapping( + hourly.get("spi_drawn_employees"), + label=f"{stage}.hourly_pay_check.spi_drawn_employees", + ) + reference = _mapping( + hourly.get("frs_employees"), label=f"{stage}.hourly_pay_check.frs_employees" + ) + after = drawn.get("below_minimum_wage_share_after") + frs_share = reference.get("below_minimum_wage_share") + judged = all( + isinstance(value, (int, float)) and not isinstance(value, bool) + for value in (after, frs_share) + ) + within = None + if judged: + after_share = _finite_number( + after, label=f"{stage}.below_minimum_wage_share_after" + ) + reference_share = _finite_number( + frs_share, label=f"{stage}.frs_employees.below_minimum_wage_share" + ) + # A share at exactly the margin passes, whatever the float rounding. + within = after_share - reference_share <= margin + 1e-12 + if not within: + failures.append( + f"{stage}: after the floor, drawn employees sit below the minimum " + f"wage more often than FRS employees, beyond the margin {margin}." + ) + details = { + "eligible_rows_below_their_floor_after": below_floor, + "share_judged": judged, + "share_within_margin": within, + } + if failures: + return _fail(stage, check, failures, details) + return _pass(stage, check, details) + + def _source_signal_gate( stage: str, evidence: Mapping[str, object], diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py index e2fdcf03c..487f7df23 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 @@ -315,6 +315,12 @@ def __post_init__(self) -> None: "person.private_pension_income_annual_share", "person.self_employment_income_annual_share", "person.self_employment_income_other_spells", + # The self-employed's gross receipts for the trading allowance, which the + # enhanced FRS leaves at the engine's zero (microcosm#1428). + "person.self_employment_gross_receipts", + # The annuity and trust part of private_pension_income, a calibration-only + # descriptor no engine input reads (microcosm#1428). + "person.annuity_and_trust_income", "person.esa_health_condition_proxy", "person.esa_includes_support_component", "person.esa_support_group_proxy", @@ -381,6 +387,7 @@ def __post_init__(self) -> None: "person.uc_is_in_startup_period", "person.uc_is_in_gainful_self_employment", "person.is_looked_after_by_local_authority", + "person.child_limit_non_parental_caring_exception", "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/trading_allowance_receipts.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/trading_allowance_receipts.py new file mode 100644 index 000000000..d6e1cecda --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/trading_allowance_receipts.py @@ -0,0 +1,376 @@ +"""Self-employed gross receipts for the trading allowance (microcosm#1428). + +policyengine-uk applies the trading allowance (ITTOIA 2005 Part 7A) against a +trader's gross receipts, ``self_employment_gross_receipts``. Neither the FRS +nor the SPI tape records turnover. Without receipts the engine treats a profit +within the allowance as receipts within it (full relief) and a larger profit +as already reflecting the better of expenses and the allowance (no relief). A +rise in the allowance then relieves only profits up to the new allowance, in +full, and misses its main base: traders who already claim the GBP 1,000 +allowance against larger receipts and gain the larger deduction. + +Under current law a trader who claims the allowance deducts GBP 1,000 from +receipts in place of expenses, so their receipts are their profit plus +GBP 1,000 exactly; a trader who deducts actual expenses has expenses above +GBP 1,000. This stage, the last of the spine, flags each trader with a +positive profit as a claimant with a probability by profit band, drawn on +their own ``person_id``, and stores their receipts: + +* a claimant's are their profit plus GBP 1,000; +* any other trader's are their profit plus GBP 3,000, expenses at the raised + allowance, so that the raise does not relieve them (the costing books the + traders who would switch to the allowance as behaviour); +* a person with no positive profit keeps zero, which the engine reads as + unknown. + +The engine then gives a claimant nothing more at GBP 1,000 and up to +GBP 2,000 more at GBP 3,000, and any other trader nothing at either. Every +trader with a positive profit is treated as filing, as SPI Table 3.10's +counts, which bind the frame's traders, are of filers. So a profit of GBP 1,000 +or less is now taxed where the engine used to relieve it in full. + +Until HMRC publishes claimant counts, the rates are calibrated to HMRC's +policy paper on the Budget 2026 raise (about 400,000 taxpayers with a lower +bill, by about GBP 380 on average), measured on policyengine-uk for the +#1428 national from 9f70c71e7 at 2028-29 prices (María's choices of +2026-10-31 and 2026-11-01). The rates drift with each national, as its +small traders and their other incomes move, so microcosm#1439 re-measures +them after each build. + +Two limits of encoding the claim in receipts (#1428 review, findings 2 and 8; +María's ruling of 2026-11-01 keeps them, documented, until policyengine-uk#2765 +lets the stage store the claim status itself, microcosm#1439): + +* The engine uprates receipts with profit, so a claimant's GBP 1,000 grows with + the mixed income index while the allowance is nominal: the raise relieves + GBP 1,932 more in 2027-28, falling to GBP 1,758 by 2031-32, 3.4% to 12.1% + below GBP 2,000. +* The other traders' stored GBP 3,000 grows the same way, so an allowance above + GBP 3,000 times that index relieves them from the stored expense, not from + data. Allowances above GBP 3,000 are mis-scored on this encoding. +""" + +from __future__ import annotations + +from collections.abc import Mapping +from dataclasses import dataclass, field + +import numpy as np +import pandas as pd + +from microcosm.build.source_manifest import SourceStageSpec +from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.national_frame import ( + uk_household_weight_kind, + uk_national_frame, + uk_time_period, + validate_uk_national_frame, +) +from microcosm.build.uk_runtime.receipt_counts import ( + distinct_count, + rounded_count, + small_cell_share, + source_household_ids, + support_band, +) +from microcosm.frame import Frame, MassChangeRecord + +__all__ = [ + "TRADING_ALLOWANCE_CLAIMANT_BANDS", + "TRADING_ALLOWANCE_RECEIPTS_OUTPUT", + "TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME", + "UKTradingAllowanceReceiptsStageTransform", + "assign_trading_allowance_receipts", + "claimant_rates", + "store_trading_allowance_receipts", +] + +TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME = "trading_allowance_receipts" +TRADING_ALLOWANCE_RECEIPTS_OUTPUT = "self_employment_gross_receipts" +TRADING_ALLOWANCE_PROFIT = "self_employment_income" +TRADING_ALLOWANCE_CLAIMANT_DRAW = "trading_allowance_claimant" +TRADING_ALLOWANCE_CLAIMANT_SEED = 0 +#: The allowance a claimant deducts in place of expenses (2024-25). +TRADING_ALLOWANCE_CLAIMANT_EXPENSES = 1_000.0 +#: Any other trader's expenses: at the raised allowance, which then gives them +#: nothing. +TRADING_ALLOWANCE_OTHER_EXPENSES = 3_000.0 +#: The claimant probability by the person's profit, upper bounds inclusive, +#: over profits above zero; the last band has no upper bound. +TRADING_ALLOWANCE_CLAIMANT_BANDS: tuple[tuple[float | None, float], ...] = ( + (4_500.0, 0.72), + (9_000.0, 0.14), + (None, 0.0), +) +TRADING_ALLOWANCE_CALIBRATION = ( + "HMRC's policy paper on the Budget 2026 trading allowance raise: about " + "400,000 taxpayers with a lower bill, by about GBP 380 on average, measured " + "on policyengine-uk for the microcosm#1428 national from 9f70c71e7 at " + "2028-29 prices; the rates drift with each national (microcosm#1439), until " + "HMRC publishes claimant counts (María's choices of 2026-10-31 and 2026-11-01)" +) +TRADING_ALLOWANCE_RECEIPTS_MASS_CHANGE_REASON = ( + "Self-employed gross receipts stored on the source spine: household weights " + "pass through unchanged and total household mass is conserved." +) +_MASS_GATE_COUPLING = ( + "The terminal family gate requires a valid mass-conserving MassChangeRecord " + "carrying exactly this stage-specific reason." +) +TRADING_ALLOWANCE_RECEIPTS_RULE = ( + "a person with a positive self_employment_income claims the allowance " + "where their uniform falls below their profit band's rate; a claimant's " + "receipts are their profit plus claimant_expenses, any other trader's their " + "profit plus other_expenses, and a person with no positive profit keeps zero" +) + + +def _expected_operations() -> tuple[tuple[str, Mapping[str, object]], ...]: + bands: list[dict[str, object]] = [] + for upper, rate in TRADING_ALLOWANCE_CLAIMANT_BANDS: + band: dict[str, object] = {"rate": rate} + if upper is not None: + band = {"upper_inclusive": upper, **band} + bands.append(band) + return ( + ( + "assign_trading_allowance_receipts", + { + "output": TRADING_ALLOWANCE_RECEIPTS_OUTPUT, + "profit": TRADING_ALLOWANCE_PROFIT, + "draw": TRADING_ALLOWANCE_CLAIMANT_DRAW, + "seed": TRADING_ALLOWANCE_CLAIMANT_SEED, + "claimant_bands": bands, + "claimant_expenses": TRADING_ALLOWANCE_CLAIMANT_EXPENSES, + "other_expenses": TRADING_ALLOWANCE_OTHER_EXPENSES, + "calibration": TRADING_ALLOWANCE_CALIBRATION, + "rule": TRADING_ALLOWANCE_RECEIPTS_RULE, + }, + ), + ( + "record_mass_conservation_receipt", + { + "entity": "household", + "reason": TRADING_ALLOWANCE_RECEIPTS_MASS_CHANGE_REASON, + "declared_factor": 1.0, + "gate_coupling": _MASS_GATE_COUPLING, + }, + ), + ) + + +def claimant_rates(profit: np.ndarray) -> np.ndarray: + """Each person's claimant probability by their profit band (zero at none).""" + + profit = np.asarray(profit, dtype=np.float64) + rates = np.zeros(profit.shape, dtype=np.float64) + lower = 0.0 + for upper, rate in TRADING_ALLOWANCE_CLAIMANT_BANDS: + in_band = profit > lower + if upper is not None: + in_band &= profit <= upper + rates[in_band] = rate + if upper is None: + break + lower = upper + return rates + + +def assign_trading_allowance_receipts( + person_ids: np.ndarray, + profit: np.ndarray, + *, + seed: int = TRADING_ALLOWANCE_CLAIMANT_SEED, + salt: str = TRADING_ALLOWANCE_CLAIMANT_DRAW, +) -> tuple[np.ndarray, np.ndarray]: + """Each person's gross receipts and claimant flag.""" + + profit = np.asarray(profit, dtype=np.float64) + ids = np.asarray(person_ids) + if ids.shape != profit.shape: + raise ValueError("Trading allowance person ids must align to the profits.") + if not np.isfinite(profit).all(): + raise ValueError("Trading allowance profits must be finite.") + trader = profit > 0.0 + claimant = np.zeros(profit.shape, dtype=bool) + if trader.any(): + uniforms = stable_identity_uniforms(ids[trader], seed=seed, salt=salt) + claimant[trader] = uniforms < claimant_rates(profit[trader]) + receipts = np.where( + claimant, + profit + TRADING_ALLOWANCE_CLAIMANT_EXPENSES, + np.where(trader, profit + TRADING_ALLOWANCE_OTHER_EXPENSES, 0.0), + ) + return receipts, claimant + + +@dataclass(frozen=True) +class UKTradingAllowanceReceiptsResult: + """The frame with the stored receipts, and the stage's receipt.""" + + frame: Frame + traders: int + claimants: int + bands: tuple[Mapping[str, object], ...] + + def evidence(self) -> dict[str, object]: + return { + "stage": TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME, + "seed": TRADING_ALLOWANCE_CLAIMANT_SEED, + "draw": TRADING_ALLOWANCE_CLAIMANT_DRAW, + # Record counts are rounded, never exact (UKDS CD137 cl. 8). + "traders": rounded_count(self.traders), + "claimants": rounded_count(self.claimants), + "bands": [dict(band) for band in self.bands], + } + + +@dataclass(frozen=True) +class UKTradingAllowanceReceiptsStageTransform: + """Whole-stage callable storing the self-employed's gross receipts.""" + + stage: SourceStageSpec + last_result: UKTradingAllowanceReceiptsResult | None = field( + default=None, init=False + ) + + def __call__(self, frame: Frame) -> Frame: + _assert_stage_parameters(self.stage) + result = store_trading_allowance_receipts(frame) + object.__setattr__(self, "last_result", result) + return result.frame + + @staticmethod + def output_columns() -> tuple[str, ...]: + return (TRADING_ALLOWANCE_RECEIPTS_OUTPUT,) + + def checkpoint_metadata(self) -> dict[str, object]: + if self.last_result is None: + raise RuntimeError("checkpoint metadata requires a completed stage run.") + return {"evidence": self.last_result.evidence()} + + +def store_trading_allowance_receipts(frame: Frame) -> UKTradingAllowanceReceiptsResult: + """Store each person's gross receipts; nothing else moves.""" + + validate_uk_national_frame(frame) + person = frame.table("person").copy() + household = frame.table("household").copy() + if TRADING_ALLOWANCE_RECEIPTS_OUTPUT in person.columns: + raise ValueError( + f"{TRADING_ALLOWANCE_RECEIPTS_OUTPUT!r} is already stored: this stage " + "is its only writer." + ) + if TRADING_ALLOWANCE_PROFIT not in person.columns: + raise KeyError( + f"person.{TRADING_ALLOWANCE_PROFIT} is missing; the receipts are " + "drawn on the final self-employment profit." + ) + if pd.Series(person["person_id"]).duplicated().any(): + raise ValueError( + "person_id must be unique: the trading allowance draw is keyed on it." + ) + profit = pd.to_numeric(person[TRADING_ALLOWANCE_PROFIT], errors="raise") + receipts, claimant = assign_trading_allowance_receipts( + person["person_id"].to_numpy(), profit.to_numpy(dtype=np.float64) + ) + person[TRADING_ALLOWANCE_RECEIPTS_OUTPUT] = receipts + + household_weights = frame.weights_for("household") + weight_by_household = dict( + zip( + household["household_id"].to_numpy(), + np.asarray(household_weights.values, dtype=float), + strict=True, + ) + ) + source_by_household = dict( + zip( + household["household_id"].to_numpy(), + source_household_ids( + household, stage=TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME + ), + strict=True, + ) + ) + weights = person["person_household_id"].map(weight_by_household).to_numpy(float) + sources = person["person_household_id"].map(source_by_household).to_numpy() + rates = claimant_rates(profit.to_numpy(dtype=np.float64)) + trader = profit.to_numpy(dtype=np.float64) > 0.0 + bands: list[dict[str, object]] = [] + for rate in sorted(set(rates[trader].tolist()), reverse=True): + in_band = trader & (rates == rate) + supported = distinct_count(sources, in_band, weights) + weighted = float(weights[in_band].sum()) + bands.append( + { + "rate": rate, + "support": support_band(supported), + # Weighted totals over the band's traders at the spine's weights. + "weighted_traders": small_cell_share(weighted, supported), + "weighted_claimant_share": small_cell_share( + float(weights[in_band & claimant].sum()) / weighted + if weighted > 0.0 + else 0.0, + supported, + ), + } + ) + mass = MassChangeRecord( + entity="household", + old_total=household_weights.total, + new_total=household_weights.total, + declared_factor=1.0, + reason=TRADING_ALLOWANCE_RECEIPTS_MASS_CHANGE_REASON, + ) + result = uk_national_frame( + person=person, + benunit=frame.table("benunit").copy(), + household=household, + time_period=uk_time_period(frame), + weight_kind=uk_household_weight_kind(frame), + household_weights=household_weights.values, + mass_log=(*frame.mass_log, mass), + ) + validate_uk_national_frame(result) + return UKTradingAllowanceReceiptsResult( + frame=result, + traders=int(trader.sum()), + claimants=int(claimant.sum()), + bands=tuple(bands), + ) + + +def _assert_stage_parameters(stage: SourceStageSpec) -> None: + """Bind the stage's operation closed-world.""" + + if stage.stage != TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME: + raise ValueError( + f"Trading allowance receipts received stage {stage.stage!r}, expected " + f"{TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME!r}." + ) + expected = _expected_operations() + operations = tuple(stage.operations) + if tuple(operation.kind for operation in operations) != tuple( + kind for kind, _ in expected + ): + raise ValueError( + "trading_allowance_receipts operation order drifted: " + f"{[operation.kind for operation in operations]!r}." + ) + for operation, (kind, parameters) in zip(operations, expected, strict=True): + if dict(operation.parameters) != dict(parameters): + raise ValueError( + f"trading_allowance_receipts {kind} parameters drifted: " + f"{dict(operation.parameters)!r} != {dict(parameters)!r}." + ) + if tuple(stage.outputs) != (TRADING_ALLOWANCE_RECEIPTS_OUTPUT,): + raise ValueError( + f"{TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME} must declare exactly the " + f"{TRADING_ALLOWANCE_RECEIPTS_OUTPUT} output." + ) + if stage.grain != "person": + raise ValueError( + f"{TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME} is person grain, got " + f"{stage.grain!r}." + ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py index 38160cb24..1b1086fe8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_take_up.py @@ -121,6 +121,7 @@ "UC_TAKE_UP_ENTITLEMENT_VARIABLES", "UC_TAKE_UP_MINIMUM_POOL_SOURCES", "UC_TAKE_UP_OPERATION_KIND", + "UC_TAKE_UP_POOLING", "UC_TAKE_UP_RECORD_SETS", "UC_TAKE_UP_RESOURCE", "UC_TAKE_UP_STAGE_NAME", @@ -189,9 +190,20 @@ f"£{100 * _BOUND_BANDS}.01 or over", ) UC_TAKE_UP_MINIMUM_POOL_SOURCES = 30 +UC_TAKE_UP_POOLING = ( + "adjacent bands within a family type, lowest first, until the drawn units " + "rest on minimum_pool_sources distinct household_source_id values; a short " + "last pool joins the one before it. Then, lowest first, a pool that cannot " + "meet DWP's count (its undrawn entitled units' expected claims exceed the " + "count, or the count exceeds them plus the weight of every drawn unit) is " + "held at the nearer of those ends and passes its gap to the other pools, " + "nearest first, the one with more room first at equal distance and the " + "lower on a tie, each taking what its room allows; a gap no pool can take " + "leaves the family type unreachable" +) UC_TAKE_UP_RESIDUAL_RATE = ( "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the " - "pool's bands, D the weight of the pool's drawn units and F the expected " + "pool's bands after any gaps passed to or from it, D the weight of the pool's drawn units and F the expected " "claims of its other entitled units, over Great Britain at the stage's " "household weights; realized by the drawn units claiming in the order of " "their uniform until their weight is nearest T - F, and applied at that " @@ -334,6 +346,78 @@ def _pools( return pools +def _pass_unmet_gaps( + sums: Sequence[tuple[float, float, float]], +) -> tuple[list[float], list[float]]: + """Each pool's count after the unmet pools pass their gaps on. + + ``sums`` gives each pool's DWP count ``T``, the expected claims ``F`` of + its undrawn entitled units and the weight ``D`` of its drawn units, lowest + band first. A pool realizes between ``F`` and ``F + D`` claims; one whose + count lies outside is held at the nearer end, and its gap is the claims it + cannot add (a shortfall) or give up (an excess). A shortfall and an excess + offset each other in the family type's total, so they cancel first, nearest + pools first. What remains goes to the other pools, nearest first, the one + with more room first at equal distance and the lower on a tie, each taking + what its room allows (``F + D - T`` for a shortfall, ``T - F`` for an + excess). Joining an unmet pool to its neighbour applied one rate to both, + so the drawn units of an overshooting band started claiming and those of + a short band stopped; held at its clip, each band realizes as near its own + count as it can, and the family type's total is unchanged wherever its + entitled units allow (microcosm#1428 review, finding 7). Returns each + pool's count and the part of its gap no pool could take. + """ + + targets = [float(target) for target, _, _ in sums] + lows = [float(undrawn) for _, undrawn, _ in sums] + highs = [float(undrawn) + float(drawn) for _, undrawn, drawn in sums] + counts = [ + min(max(t, lo), hi) for t, lo, hi in zip(targets, lows, highs, strict=True) + ] + gaps = [t - count for t, count in zip(targets, counts, strict=True)] + for index, target in enumerate(targets): + if abs(gaps[index]) <= 1e-9 * max(1.0, abs(target)): + gaps[index] = 0.0 + + def nearest(index: int) -> list[int]: + return sorted( + (other for other in range(len(sums)) if other != index), + key=lambda other: (abs(other - index), other), + ) + + for index in range(len(sums)): + for other in nearest(index): + if gaps[index] == 0.0: + break + if gaps[index] * gaps[other] < 0.0: + offset = min(abs(gaps[index]), abs(gaps[other])) + gaps[index] -= np.sign(gaps[index]) * offset + gaps[other] -= np.sign(gaps[other]) * offset + for index, gap in enumerate(gaps): + if gap == 0.0: + continue + short = gap > 0.0 + + def room(other: int, *, short: bool = short) -> float: + return max( + highs[other] - counts[other] if short else counts[other] - lows[other], + 0.0, + ) + + remaining = abs(gap) + for other in sorted( + nearest(index), + key=lambda other: (abs(other - index), -room(other), other), + ): + take = min(remaining, room(other)) + counts[other] += take if short else -take + remaining -= take + if remaining <= 0.0: + break + gaps[index] = remaining if short else -remaining + return counts, gaps + + def _realized_rate( residual: float, *, need: float, draws: np.ndarray, weights: np.ndarray ) -> float: @@ -454,39 +538,50 @@ def assign_award_band_take_up( def sources(mask: np.ndarray) -> int: return distinct_count(source_ids, mask, weights) + def balance( + name: str, in_family: np.ndarray, bands: list[int] + ) -> tuple[float, float, float]: + solve = in_family & np.isin(band, bands) & gb + return ( + float(sum(targets[(name, index)] for index in bands)), + float((weights * fixed)[solve & ~drawable].sum()), + float(weights[solve & drawable].sum()), + ) + pools: list[dict[str, object]] = [] for name, _ in UC_FAMILY_TYPES: in_family = family == name - for bands in _pools( + family_pools = _pools( band=band, in_family=in_family, drawable_gb=drawable & gb, source_ids=source_ids, weights=weights, minimum_sources=minimum_pool_sources, + ) + sums = [balance(name, in_family, bands) for bands in family_pools] + counts, unplaced = _pass_unmet_gaps(sums) + feasible = not any(unplaced) + for bands, (target, undrawn, drawn), count in zip( + family_pools, sums, counts, strict=True ): in_pool = in_family & np.isin(band, bands) solve = in_pool & gb - target = float(sum(targets[(name, index)] for index in bands)) - undrawn = float((weights * fixed)[solve & ~drawable].sum()) - drawn = float(weights[solve & drawable].sum()) - # Where the undrawn claims reach DWP's count, or the count exceeds - # every entitled unit, the clipped rate cannot meet it. if drawn > 0.0: - residual = (target - undrawn) / drawn rates[in_pool & drawable] = _realized_rate( - residual, - need=target - undrawn, + (count - undrawn) / drawn, + need=count - undrawn, draws=draws[solve & drawable], weights=weights[solve & drawable], ) - else: - residual = 0.0 if undrawn >= target else 1.0 + # Where the undrawn claims reach DWP's count, or the count exceeds + # every entitled unit, the pool cannot meet it alone: it is held + # at its clip and its gap passed on. unreachable = ( "undrawn_claims_reach_target" - if residual <= 0.0 + if target <= undrawn else "target_exceeds_entitled" - if residual >= 1.0 + if target >= undrawn + drawn else None ) pools.append( @@ -496,6 +591,7 @@ def sources(mask: np.ndarray) -> int: "mask": solve, "target": target, "unreachable": unreachable, + "feasible_family": feasible, } ) would_claim = np.where(drawable, draws < rates, previous | reporter) @@ -530,7 +626,10 @@ def sources(mask: np.ndarray) -> int: ), } ) - if pool["unreachable"] is None: + # A family type whose gaps every pool could take realizes its + # count over all its pools; one whose count exceeds what its + # entitled units can realize has no reachable pool. + if pool["feasible_family"]: reachable |= solve reachable_target += float(pool["target"]) supported = sources(basis) @@ -950,12 +1049,7 @@ def uc_take_up_operation_parameters() -> tuple[tuple[str, dict[str, object]], .. "DWP Stat-Xplore Households on Universal Credit by family type " "and monthly award band, mean of April to December 2025" ), - "pooling": ( - "adjacent bands within a family type, lowest first, until the " - "drawn units rest on minimum_pool_sources distinct " - "household_source_id values; a short last pool joins the one " - "before it" - ), + "pooling": UC_TAKE_UP_POOLING, "minimum_pool_sources": UC_TAKE_UP_MINIMUM_POOL_SOURCES, "drawn_units": ( "entitled, non-reporting units of the population that the " diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py index 423fd2477..309186d1f 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_target_measurements.py @@ -6,9 +6,10 @@ https://www.gov.uk/government/statistics/universal-credit-claimants-statistics-on-the-two-child-limit-policy-april-2025/background-information-and-methodology The retained frame does not identify April open claims or exact birth dates. -The affected measures therefore explicitly remain positive-award/birth-year -proxies. Their receipts record those gaps; improved fit is not evidence that -the administrative population has been reconstructed. +The affected measures therefore explicitly remain positive-award proxies, and +they date births by the engine's own cutoff test, whose dates of birth come +from whole ages. Their receipts record those gaps; improved fit is not +evidence that the administrative population has been reconstructed. """ from __future__ import annotations @@ -162,10 +163,14 @@ def uc_tcl_comparison_contract(year: int) -> dict[str, Any]: "source_claim_state": "open claim within source observation month", "model_claim_state_proxy": "universal_credit > 0", "source_birth_cutoff": "2017-04-06", - "model_birth_cutoff_proxy": "model birth_year >= 2017", + "model_birth_cutoff_proxy": ( + "policyengine-uk uc_is_child_born_before_child_limit: date of birth " + "before 2017-04-06" + ), "birth_precision": ( - "model year minus retained age input; source-age observation " - "date/advancement and exact birth date unavailable" + "whole-year interview age and the stored months since the last " + "birthday, placed at 6 October of the model year; policyengine-uk " + "holds ages fixed across years; exact birth date unavailable" ), "source_period": "April snapshot; retain matched fact's observation year", "model_period": str(year), @@ -225,12 +230,14 @@ def count(mask: np.ndarray) -> np.ndarray: > 0 ) - birth_year = calculate("birth_year") - if not np.equal(birth_year, np.floor(birth_year)).all(): - raise ValueError("UC birth-year proxy requires integer model birth years.") + # The engine's own cutoff test, a date of birth before 6 April 2017, so the + # measure dates births as the child element does: a calendar year would + # count a child born from January to 5 April 2017 as affected + # (microcosm#1095 AC3). + born_before = calculate("uc_is_child_born_before_child_limit").astype(bool) # The first two children are the oldest. This count is invariant to person # row order and ties, and deliberately includes excepted later children. - post_cutoff_count = count(children & (birth_year >= 2017)) + post_cutoff_count = count(children & ~born_before) affected_count = np.minimum(post_cutoff_count, np.maximum(child_count - 2, 0)) if variable == "uc_tcl_affected_child_count_proxy": return affected_count 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 809202601..b98f75645 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 @@ -35,10 +35,11 @@ def test_spi_band_targets_move_by_the_engines_dataset_projection() -> None: (microcosm#1418); Table 3.10's amounts move by their count index times the engine's index for self-employment income (the independent review of microcosm#1420); and a measure derived from an input moves by the input's - index. The SPI donors still reach the dataset's year by savings interest's - own history index and run on the projection's from there - (``spi_income.SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION``), so both are - held to the engine's.""" + index. The SPI donors reach the dataset's year with interest and other + income on SPI's 2023-24 bands and the OBR's savings income growth (the + microcosm#1418 savings follow-up) and run on the projection's index from + there (``spi_income.SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION``), so the + projection's index is held to the engine's.""" import json from importlib.resources import files @@ -74,7 +75,8 @@ def test_spi_band_targets_move_by_the_engines_dataset_projection() -> None: } variables = policyengine_uk.CountryTaxBenefitSystem().variables assert SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION == { - "savings_interest_income": UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER + "savings_interest_income": UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER, + "other_investment_income": projection["other_investment_income"], } assert ( UK_SPI_SAVINGS_INTEREST_PROJECTION_PARAMETER @@ -120,11 +122,13 @@ def test_spi_band_targets_move_by_the_engines_dataset_projection() -> None: assert len(checked) > 1 -def test_savings_interest_targets_take_the_donors_path() -> None: - """The SPI donors reach 2025 by the history index to the frame's year, - then the engine's projection. The band targets took the same path until - they moved to the OBR's savings income forecast (microcosm#1418), so the - gap between the two is now the calibration's to close.""" +def test_the_two_leg_savings_interest_factor() -> None: + """Savings interest outside SPI Table 3.7's rows reaches 2025 by the history + index to the frame's year, then the engine's projection (microcosm#1095). + The SPI donors and Table 3.7's rows took the same path until + microcosm#1418, when they moved to the published 2023-24 bands and the + OBR's savings income growth; the earlier start below pins only the + history index's 2022-23 step (the #1428 review, finding 13).""" history = "gov.economic_assumptions.indices.ons.household_interest_income" projection = "gov.economic_assumptions.indices.obr.per_capita.gdp" @@ -135,13 +139,13 @@ def ratio(path: str, start: int, end: int) -> float: ) target_factor = ratio(history, 2023, 2024) * ratio(projection, 2024, 2025) - donor_path = ratio(history, 2022, 2024) * ratio(projection, 2024, 2025) + earlier_start = ratio(history, 2022, 2024) * ratio(projection, 2024, 2025) # 1.1920 until policyengine-uk 2.155.0 took population from the OBR's # March 2026 long-term determinants (PolicyEngine/policyengine-uk#2611), # and 1.1961 until 2.164.1's October 2026 outlook (microcosm#1415). assert target_factor == pytest.approx(1.1932, abs=1e-4) - # The donors start a year earlier and so carry one more year of history. - assert donor_path / target_factor == pytest.approx( + # A path starting a year earlier carries one more year of history. + assert earlier_start / target_factor == pytest.approx( ratio(history, 2022, 2023), rel=1e-12 ) diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py b/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py index 0960bc3dd..b6ab8bf7c 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_release_input_coverage.py @@ -474,6 +474,7 @@ def test_shipped_manifest_is_current(self) -> None: "was_wealth", "was_lisa", "isa_subscriptions", + "trading_allowance_receipts", "company_cars", "regional_property_uprating", "high_value_home_support", 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 174e03e7a..e0022e4e4 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 @@ -1,6 +1,8 @@ """Tests split from packages/microcosm-build/tests/test_uk_spi_income.py.""" # ruff: noqa: F403, F405 +from microcosm.build.uk_runtime.hmrc_uprating import uk_obr_savings_income_level +from microcosm.build.uk_runtime.spi_income import SPI_SAVINGS_REBASE_CONCEPTS from test_support.microcosm_build.uk_spi_income import * @@ -45,7 +47,8 @@ def test_spi_preserves_observed_children_outside_the_donor_age_domain( ) assert result.spi_prediction_rows == int(person[channel].eq("spi").sum()) - 1 adults = person[channel].eq("spi") & person.age.ge(16) - assert result.person.loc[adults, "gift_aid"].eq(10).all() + factor = result.gift_anchor["factors"]["gift_aid"] + assert result.person.loc[adults, "gift_aid"].eq(10.0 * factor).all() def test_spi_rebases_income_before_frs_fill_and_preserves_child_inputs( @@ -66,9 +69,23 @@ def test_spi_rebases_income_before_frs_fill_and_preserves_child_inputs( monkeypatch.setattr( spi_income, "_spi_income_uprating_factors", - lambda year: (factors, {"from_period": 2022, "to_period": year}), + lambda year, *, rebased_columns=None: ( + factors, + {"from_period": 2022, "to_period": year}, + ), raising=False, ) + # The donor savings rebase runs on the prepared tape when the stage rebases + # to the build year, and only then (microcosm#1418 savings follow-up), + # banding the tape on its income moved to 2023-24 (microcosm#1428). + rebased = [] + + def rebase(donor, published, *, income_factors=None): + rebased.append(sorted(published)) + assert income_factors == factors + return donor, {"rebased": True, "columns": {}} + + monkeypatch.setattr(spi_income, "rebase_spi_donor_savings", rebase) options = dict( seed=9, n_estimators=3, @@ -77,6 +94,7 @@ def test_spi_rebases_income_before_frs_fill_and_preserves_child_inputs( allow_in_sample_reference=True, ) reference = impute_uk_spi_income_support(support, donor_path, **options) + assert rebased == [] result = impute_uk_spi_income_support( support, donor_path, @@ -102,7 +120,12 @@ def test_spi_rebases_income_before_frs_fill_and_preserves_child_inputs( result.person.loc[base, "dividend_income"], person.loc[base, "dividend_income"], ) - assert result.income_uprating == {"from_period": 2022, "to_period": 2024} + assert rebased == [sorted(SPI_SAVINGS_REBASE_CONCEPTS)] + assert result.income_uprating == { + "from_period": 2022, + "to_period": 2024, + "savings_rebase": {"rebased": True, "columns": {}}, + } def test_child_exclusion_keeps_the_other_recipients_draws( @@ -351,7 +374,13 @@ def test_spi_qrf_stages_use_typed_weights_and_keep_isa_interest_apart( allow_in_sample_reference=True, ) - assert _FakeQRF.fit_weight_kinds == ["design", "importance", "importance"] + # The reference forest is fitted once per cross-fit fold in use (the + # transport receipt's ``folds``), all under its one fit record. + assert _FakeQRF.fit_weight_kinds == [ + "design", + *["importance"] * result.stage1_transport["folds"], + "importance", + ] assert [ (record.fit_name, record.weight_kind) for record in result.fit_weight_records ] == [ @@ -394,8 +423,19 @@ def test_spi_qrf_stages_use_typed_weights_and_keep_isa_interest_apart( .all() ) assert result.person.loc[spi_people, "other_investment_income"].eq(25.0).all() - assert result.person.loc[spi_people, "gift_aid"].eq(10.0).all() - assert result.person.loc[spi_people, "charitable_investment_gifts"].eq(2.0).all() + # The gift leaves are the forest's draws scaled to the tape's FACT-weighted + # totals, 300 of Gift Aid and 30 of investment gifts (microcosm#1428). + factors = result.gift_anchor["factors"] + weights = result.person.loc[spi_people, "person_household_id"].map( + support.household.set_index("household_id")["household_weight"] + ) + for column, draw, tape in ( + ("gift_aid", 10.0, 300.0), + ("charitable_investment_gifts", 2.0, 30.0), + ): + gifts = result.person.loc[spi_people, column] + assert gifts.eq(draw * factors[column]).all() + assert (weights * gifts).sum() == pytest.approx(tape) assert ( result.person.loc[ spi_people, @@ -712,6 +752,10 @@ def test_spi_uprating_uses_actual_engine_indices_and_explicit_nominal_holdouts() for column, row in receipt["columns"].items() if row["basis"] == "held_nominal" } == nominal + # Interest and other income are rebased to SPI's 2023-24 bands first and + # grow on the OBR's savings income path from there (microcosm#1418 savings + # follow-up). + obr_level, _ = uk_obr_savings_income_level(2023, 2024) for column, row in receipt["columns"].items(): if column in nominal: assert row == { @@ -720,6 +764,10 @@ def test_spi_uprating_uses_actual_engine_indices_and_explicit_nominal_holdouts() "factor": 1.0, "basis": "held_nominal", } + elif column in SPI_SAVINGS_REBASE_CONCEPTS: + assert row["basis"] == "spi_published_bands_then_obr_savings_income" + assert row["from_period"] == 2023 + assert factors[column] == pytest.approx(obr_level) else: path, ratio = expected[row["variable"]] assert row["index"] == "gov.economic_assumptions.indices." + path @@ -779,13 +827,13 @@ def test_spi_rebasing_index_is_the_projections_except_where_named() -> None: 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" - ) - # 2.2692 until policyengine-uk 2.164.1's October 2026 outlook revised the - # ONS interest history (microcosm#1415). - assert savings["factor"] == pytest.approx(2.2686, abs=1e-4) + # Interest and other income reach the frame's year on SPI's own path, the + # 2023-24 bands then the OBR's savings income growth (+35.0% in 2024-25), + # and run on the projection's GDP per head from there. + for column in SPI_SAVINGS_REBASE_CONCEPTS: + savings = receipt["columns"][column] + assert savings["index"] == "obr.efo_2026_10.savings_income_growth" + assert savings["factor"] == pytest.approx(1.3496108, abs=1e-7) def _zero_taxable_income(person: pd.DataFrame, rows) -> None: @@ -847,7 +895,8 @@ def test_undrawn_twins_keep_their_values_and_are_receipted( result.person.loc[copy[0], kept], person.loc[copy[0], kept], check_names=False ) others = person.index[(person[channel] == "spi") & (person["age"] >= 16)].drop(copy) - assert result.person.loc[others, "gift_aid"].eq(10.0).all() + factor = result.gift_anchor["factors"]["gift_aid"] + assert result.person.loc[others, "gift_aid"].eq(10.0 * factor).all() # The stage flags the rows it drew, and only those, for the later stages # that tell tape-concept rows from FRS-concept ones. flag = result.person["person_is_spi_income_recipient"] @@ -940,9 +989,19 @@ def predict_from_uniforms(self, predictors, *, quantiles, sign_uniforms): ) stage1 = captured["stage1"] assert stage1["rows"] == result.spi_prediction_rows == 3 - assert {"has_pay", "has_trade", "at_or_over_spa", "main_source_pay", "age"} <= set( - stage1["columns"] - ) + assert { + "has_pay", + "has_trade", + "at_or_over_spa", + "main_source_pay", + } <= set(stage1["columns"]) + # The forest reads the band, one-hot so that composites are a category of + # their own, never the age drawn inside it (uk-data#628; Vahid's review of + # #1426). + band = spi_income.SPI_STAGE1_AGE_BAND_COLUMN + assert band not in stage1["columns"] + assert any(column.startswith(f"{band}_") for column in stage1["columns"]) + assert "age" not in stage1["columns"] # The no-analogue leaf's sign uniform is the identity-keyed one for the # drawn recipients, in their order. age_domain = (result.person[channel] == "spi") & result.person["age"].ge(16) @@ -1023,3 +1082,127 @@ def test_the_stage_resets_the_esa_group_and_clears_a_loss_beside_a_profit( profit = person.loc[drawn, "self_employment_income"].to_numpy() > 0.0 assert profit.any() assert (person.loc[drawn, "trading_loss"].to_numpy()[profit] == 0.0).all() + + +def test_spi_pay_floor_reads_the_engines_minimum_wage_for_the_build_year() -> None: + floor = spi_income.load_spi_employment_pay_floor(2024) + assert floor.instant == "2024-04-30" + assert floor.weeks_in_year == 52.0 + assert spi_income.SPI_EMPLOYMENT_PAY_FLOOR_PARAMETER in floor.source + # 2024-25: the national living wage from 21 (S.I. 2024/432), £8.60 from 18 + # and £6.40 below. + np.testing.assert_allclose( + floor.hourly_rate(np.array([16, 17, 18, 20, 21, 30, 70])), + [6.40, 6.40, 8.60, 8.60, 11.44, 11.44, 11.44], + ) + + +def test_spi_pay_floor_lifts_drawn_pay_before_employment_income_and_stage2( + monkeypatch, tmp_path +) -> None: + """The floor moves only drawn PAY, before employment_income is derived. + + FRS rows and every other leaf keep their values, the transport receipt is + the unfloored draw's, and a floored row's year of pay stays the tape's. + """ + + support = _dead_support() + person = support.person.copy() + # The spine annualises usual weekly hours at frs_spine.WEEKS_IN_YEAR (52). + hours = 35 * 52 + person["hours_worked"] = hours + # A twin without a trade, so its hours are all employee hours. + person["self_employment_income"] = 0.0 + # A copy carries its twin's age and pay. The floor is the hours at the + # lower of the minimum wage and the twin's own hourly pay (uk-data#629): + # these twins earn £20 an hour, above every age's rate, but lineage 104's + # earns £5, which caps its copy's floor. + lineage = person["person_source_id"] + person.loc[lineage.eq(101), "age"] = 19 + person["employment_income"] = hours * 20.0 + person.loc[lineage.eq(104), "employment_income"] = hours * 5.0 + for column in spi_income.FRS_ANNUAL_INCOME_OUTPUT_COLUMNS: + person[column] = 1.0 if column in spi_income.ANNUAL_SHARE_COLUMNS else 0.0 + support = replace(support, person=person) + donor_path = tmp_path / SPI_DONOR_FILENAME + _write_donor(donor_path) + monkeypatch.setattr(spi_income, "QRF", _FakeQRF) + _bypass_reviewed_donor_identity(monkeypatch) + options = dict( + seed=9, + n_estimators=3, + donor_sample_size=None, + state_pension_age=66, + allow_in_sample_reference=True, + ) + unfloored = impute_uk_spi_income_support(support, donor_path, **options) + floor = spi_income.load_spi_employment_pay_floor(2024) + floored = impute_uk_spi_income_support( + support, donor_path, employment_pay_floor=floor, **options + ) + + drawn = floored.person[spi_income.SPI_INCOME_RECIPIENT_COLUMN].to_numpy(bool) + assert drawn.any() + ages = floored.person.loc[drawn, "age"].to_numpy(dtype=float) + twin_rate = ( + support.person.loc[floored.person.index[drawn], "employment_income"].to_numpy( + dtype=float + ) + / hours + ) + expected = hours * np.minimum(floor.hourly_rate(ages), twin_rate) + assert (twin_rate < floor.hourly_rate(ages)).any() + np.testing.assert_allclose( + floored.person.loc[drawn, SPI_HMRC_PAY_COLUMN].to_numpy(), expected + ) + assert ( + unfloored.person.loc[drawn, SPI_HMRC_PAY_COLUMN].to_numpy() < expected + ).all() + # The annual share puts the year's pay back at the tape's draw. + np.testing.assert_allclose( + floored.person.loc[drawn, "employment_income"].to_numpy() + * floored.person.loc[drawn, spi_income.EMPLOYMENT_INCOME_ANNUAL_SHARE], + unfloored.person.loc[drawn, "employment_income"].to_numpy(), + ) + np.testing.assert_allclose( + floored.person.loc[drawn, "employment_income"].to_numpy(), + floored.person.loc[ + drawn, + [ + SPI_HMRC_PAY_COLUMN, + SPI_HMRC_EMPLOYMENT_BENEFITS_COLUMN, + SPI_HMRC_TAXABLE_TERMINATION_PAY_COLUMN, + ], + ] + .sum(axis=1) + .to_numpy(), + ) + untouched = [ + column + for column in floored.person.columns + if column + not in { + SPI_HMRC_PAY_COLUMN, + "employment_income", + # Pension coherence prices a row's employer contribution at its + # twin's rate on the row's own pay. + "employer_pension_contributions", + spi_income.EMPLOYMENT_INCOME_ANNUAL_SHARE, + SPI_HMRC_EMPLOYED_INCOME_COLUMN, + SPI_HMRC_TOTAL_EARNED_INCOME_COLUMN, + HMRC_SPI_ASSESSABLE_INCOME_COLUMN, + } + and not column.startswith("hmrc_spi_total") + ] + pd.testing.assert_frame_equal( + floored.person.loc[~drawn], unfloored.person.loc[~drawn] + ) + pd.testing.assert_frame_equal( + floored.person.loc[drawn, untouched], unfloored.person.loc[drawn, untouched] + ) + assert floored.stage1_transport == unfloored.stage1_transport + assert unfloored.employment_pay_floor is None + receipt = floored.employment_pay_floor + assert receipt["step"] == "spi_employment_pay_floor_before_stage2" + # The floored rows rest on three to nine source households: a banded count. + assert receipt["floored"]["source_households"] == "3-9" 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 079c9da96..f00ddb3be 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 @@ -1013,6 +1013,8 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "hmrc_stamp_tax_facts.json", "hmrc_isa_statistics_facts.json", "hmrc_benefits_in_kind_facts.json", + # microcosm#1095: dense builds only, uk_runtime.property_localisation. + "scotgov_chargeable_dwellings_by_band.json", "dwp_move_to_uc_facts.json", "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", @@ -1023,6 +1025,7 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "bonus_sacrifice_anchor.json", "obr_salary_sacrifice_costing_facts.json", "obr_savings_income_growth_facts.json", + "hmrc_spi_table_3_7_savings_rows.json", "slc_liable_stocks.json", "hmrc_income_release_gate_report.json", "hmrc_income_replay_report.json", @@ -1128,13 +1131,13 @@ def test_uk_source_manifest_contains_only_canonical_spine_stages(self) -> None: # microcosm#1240 high_value_home_support between them, microcosm#1112 # regional_home_values after it, and microcosm#1095 property_purchases # after property_components and uc_legacy_closure_take_up after - # uc_capital_coherence; microcosm#1319 carers_allowance_take_up after - # child_benefit_take_up; childcare_copy_take_up last, after every copy + # uc_capital_coherence; microcosm#1319 carers_allowance_take_up, after + # salary_sacrifice since microcosm#1095 B5; childcare_copy_take_up last, after every copy # stage; microcosm#1334 uc_take_up after uc_capital_coherence and - # frs_annual_income_spells after frs_hmrc_spine_leaves; microcosm#1415 - # isa_subscriptions last; company_cars right after + # frs_annual_income_spells after frs_hmrc_spine_leaves; microcosm#1428 + # trading_allowance_receipts last; company_cars right after # hmrc_spi_income_spine (PolicyEngine/uk-autumn-budget-dashboard-2026#316). - assert len(spec.sources.stages) == 51 + assert len(spec.sources.stages) == 52 assert not {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.intersection( stage.stage for stage in spec.sources.stages ) @@ -1179,6 +1182,8 @@ def test_uk_package_loads(self) -> None: "hmrc_stamp_tax_facts.json", "hmrc_isa_statistics_facts.json", "hmrc_benefits_in_kind_facts.json", + # microcosm#1095: dense builds only, uk_runtime.property_localisation. + "scotgov_chargeable_dwellings_by_band.json", "dwp_move_to_uc_facts.json", "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", @@ -1189,6 +1194,7 @@ def test_uk_package_loads(self) -> None: "bonus_sacrifice_anchor.json", "obr_salary_sacrifice_costing_facts.json", "obr_savings_income_growth_facts.json", + "hmrc_spi_table_3_7_savings_rows.json", "slc_liable_stocks.json", "hmrc_income_release_gate_report.json", "hmrc_income_replay_report.json", @@ -1545,6 +1551,7 @@ def test_declares_the_full_june_battery(self, manifest) -> None: "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", + "uk_stage_hmrc_spi_income_spine_hourly_pay", "uk_stage_spi_benefit_coherence", "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", @@ -1684,6 +1691,7 @@ def test_strict_absent_evidence_entries_are_declared(self, manifest) -> None: "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", + "uk_stage_hmrc_spi_income_spine_hourly_pay", "uk_stage_spi_benefit_coherence", "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_carers_allowance_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_carers_allowance_take_up.py index c56e3b001..6cb4ba618 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_carers_allowance_take_up.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_carers_allowance_take_up.py @@ -941,12 +941,13 @@ def test_the_packaged_stage_carries_the_reviewed_operations_in_order() -> None: assert assign.parameters["seed"] == 0 order = [stage.stage for stage in load_country_spec("uk").sources.stages] position = order.index(ca.CARERS_ALLOWANCE_TAKE_UP_STAGE_NAME) - # After the SPI income chain and the other take-up redraws, before the - # deduction attributes and every CGT clone. - assert order[position - 1] == "child_benefit_take_up" - assert order[position + 1] == "uc_deduction_attributes" - assert position > order.index("hmrc_spi_income_spine") - assert position < order.index("cgt_support_split") + # After the SPI income chain, the other take-up redraws and + # salary_sacrifice, whose converted pay the earnings test reads, so after + # every CGT copy too, which draws as the member it copies (microcosm#1095 + # B5). + assert order[position - 1] == "salary_sacrifice" + assert position > order.index("child_benefit_take_up") + assert position > order.index("cgt_residential_split") def test_a_drifted_manifest_is_refused() -> None: @@ -1138,3 +1139,207 @@ def test_the_stage_draws_the_same_carers_whatever_the_row_order() -> None: _run(shuffled, rate=0.6).frame.table("person").set_index("person_id")[_OUTPUT] ) pd.testing.assert_series_equal(again.loc[first.index], first) + + +def _cgt_lineage(household: pd.DataFrame) -> pd.DataFrame: + """``household`` with every CGT lineage column, each row an original.""" + + household = household.copy() + n = len(household) + household["source_household_id"] = household["household_id"].astype("int64") + household["household_support_channel"] = "frs" + household["household_support_clone_index"] = np.zeros(n, dtype="int64") + household["household_is_cgt_support_copy"] = np.zeros(n, dtype=bool) + household["cgt_support_copy_index"] = np.zeros(n, dtype="int64") + household["household_is_capital_gains_clone"] = np.zeros(n, dtype=bool) + household["household_is_cgt_residential_clone"] = np.zeros(n, dtype=bool) + household["cgt_residential_clone_index"] = np.zeros(n, dtype="int64") + return household + + +def test_a_cgt_copy_draws_as_the_member_it_copies() -> None: + """Since the stage runs after salary_sacrifice it sees the CGT stages' + copies; a member of a copy is keyed on the member at the same rank of the + household it copies, and an SPI support copy on itself (microcosm#1095 + B5).""" + + household = _cgt_lineage(pd.DataFrame({"household_id": [1, 2, 3, 4]})) + household["source_household_id"] = np.array([7, 7, 7, 7], dtype="int64") + # 2 is the incidence clone of 1, 3 its support copy, 4 an SPI copy. + household.loc[1, "household_is_capital_gains_clone"] = True + household.loc[2, "household_is_cgt_support_copy"] = True + household.loc[2, "cgt_support_copy_index"] = 1 + household.loc[3, "household_support_channel"] = "spi" + household.loc[3, "household_support_clone_index"] = 1 + person = pd.DataFrame( + { + "person_id": [12, 11, 1011, 1012, 2012, 2011, 41], + "person_household_id": [1, 1, 2, 2, 3, 3, 4], + } + ) + roots = ca.lineage_roots(person, household) + np.testing.assert_array_equal(roots, [12, 11, 11, 12, 12, 11, 41]) + # Without CGT lineage everyone is their own root. + np.testing.assert_array_equal( + ca.lineage_roots(person, household[["household_id"]]), person["person_id"] + ) + # A copy whose source is not in the frame cannot be keyed. + with pytest.raises(ValueError, match="no member at its rank"): + ca.lineage_roots(person.iloc[2:], household.iloc[1:]) + + +def test_a_source_and_its_copies_take_one_position() -> None: + """A source and its copies, sharing a draw, are placed as one record of + their joint weight, so they claim together, and every other record keeps + the position it took before the copy split the source's weight.""" + + strata = np.array([1, 1, 1, 1, 2]) + identities = np.array([11, 11, 21, 31, 41], dtype="int64") + draws = stable_identity_uniforms(identities, seed=0, salt="test") + split = ca.lineage_balanced_positions( + strata, + draws, + np.array([0.5, 0.5, 1.0, 1.0, 1.0]), + identities=identities, + salt="test", + ) + whole = ca.lineage_balanced_positions( + strata[1:], + draws[1:], + np.array([1.0, 1.0, 1.0, 1.0]), + identities=identities[1:], + salt="test", + ) + assert split[0] == split[1] + np.testing.assert_array_equal(split[1:], whole) + # Distinct identities are placed as the balanced uniforms place them. + np.testing.assert_array_equal( + whole, + weight_balanced_uniforms( + strata[1:], + draws[1:], + np.ones(4), + starts=ca.stratum_starts( + strata[1:], salt="test", identities=identities[1:], draws=draws[1:] + ), + ), + ) + with pytest.raises(ValueError, match="share their draw"): + ca.lineage_balanced_positions( + strata[:2], + np.array([0.1, 0.2]), + np.ones(2), + identities=identities[:2], + salt="test", + ) + + +def test_the_stage_draws_a_cgt_clone_with_the_carer_it_copies() -> None: + """Every carer and their CGT clone, each at half the carer's weight, + claim as the carer alone did at the whole weight (microcosm#1095 B5): a + clone drawn on its own identity would match its carer only by chance.""" + + n = 12 + weights = np.random.default_rng(5).uniform(0.5, 2.0, n) + base = _frame( + care_hours=[40.0] * n, + reported=[0.0] * n, + weekly_pay=[200.0] * n, + weights=weights, + ) + before = _run(base).frame.table("person").set_index("person_id")[_OUTPUT] + person = base.table("person") + benunit = base.table("benunit") + household = _cgt_lineage(base.table("household")) + for source in range(1, n + 1): + clone = household[household["household_id"] == source].copy() + clone["household_id"] = source + 100 + clone["household_is_capital_gains_clone"] = True + members = person[person["person_household_id"] == source].copy() + members["person_id"] += 10_000 + members["person_household_id"] = source + 100 + members["person_benunit_id"] += 1_000 + person = pd.concat([person, members], ignore_index=True) + benunit = pd.concat( + [benunit, pd.DataFrame({"benunit_id": members["person_benunit_id"]})], + ignore_index=True, + ) + household = pd.concat([household, clone], ignore_index=True) + halved = np.r_[weights, weights] / 2.0 + cloned = uk_national_frame( + person=person, + benunit=benunit.sort_values("benunit_id", ignore_index=True), + household=household, + time_period="2024", + weight_kind=WeightKind.DESIGN, + household_weights=halved, + ) + after = _run(cloned).frame.table("person").set_index("person_id")[_OUTPUT] + pd.testing.assert_series_equal(after.loc[before.index], before) + clones = after.loc[before.index + 10_000] + np.testing.assert_array_equal(clones.to_numpy(), before.to_numpy()) + assert 0 < before.sum() < n + + +def test_a_lineage_in_two_strata_is_placed_in_each() -> None: + """Salary sacrifice draws a copy's pay on its own id, so a copy's earnings + band, and so its stratum, can differ from its source's. The lineage is then + a record of each stratum, each placed with its own stratum's start, as if + each stratum were placed alone (B-1's first spine, microcosm#1095 B5).""" + + strata = np.array([1, 2, 1, 2]) + identities = np.array([11, 11, 21, 31], dtype="int64") + draws = stable_identity_uniforms(identities, seed=0, salt="test") + weights = np.array([0.5, 0.5, 1.0, 1.0]) + positions = ca.lineage_balanced_positions( + strata, draws, weights, identities=identities, salt="test" + ) + for code in (1, 2): + rows = strata == code + np.testing.assert_array_equal( + positions[rows], + ca.lineage_balanced_positions( + strata[rows], + draws[rows], + weights[rows], + identities=identities[rows], + salt="test", + ), + ) + + +def test_the_stage_draws_a_clone_whose_pay_left_its_source_s_band() -> None: + """A CGT clone whose pay salary sacrifice moved into another earnings band + draws in that band's stratum, keyed on its source, and every stratum still + claims its rate to within one record (microcosm#1095 B5).""" + + n = 12 + base = _frame(care_hours=[40.0] * n, reported=[0.0] * n, weekly_pay=[200.0] * n) + person = base.table("person") + household = _cgt_lineage(base.table("household")) + clone = household[household["household_id"] == 1].copy() + clone["household_id"] = 101 + clone["household_is_capital_gains_clone"] = True + members = person[person["person_household_id"] == 1].copy() + members["person_id"] += 10_000 + members["person_household_id"] = 101 + members["person_benunit_id"] += 1_000 + # The clone's pay sits three weekly rates above the limit. + members.loc[members["care_hours"] > 0, "employment_income"] = 600.0 * 52.0 + benunit = pd.concat( + [ + base.table("benunit"), + pd.DataFrame({"benunit_id": members["person_benunit_id"]}), + ], + ignore_index=True, + ).sort_values("benunit_id", ignore_index=True) + cloned = uk_national_frame( + person=pd.concat([person, members], ignore_index=True), + benunit=benunit, + household=pd.concat([household, clone], ignore_index=True), + time_period="2024", + weight_kind=WeightKind.DESIGN, + household_weights=np.r_[0.5, np.ones(n - 1), 0.5], + ) + result = _run(cloned).frame.table("person").set_index("person_id")[_OUTPUT] + assert result.loc[[11, 10_011]].notna().all() diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_fit_weight_records_roster.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_fit_weight_records_roster.py new file mode 100644 index 000000000..2fc979ec5 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_fit_weight_records_roster.py @@ -0,0 +1,69 @@ +"""Which UK stage transforms declare fit-weight records (microcosm#1428). + +The release-cut weights audit reads an empty list of fit-weight records as a +fitting stage that recorded nothing, and fails (``uk_weights_audit``). The ISA +subscriptions stage declared an empty tuple although it fits no model, and +every national certified after #1421 failed. This roster pins the stage +transforms that declare the hook, read from the source so it runs without +the engine: a new one must be reviewed, and either record each fit it makes +or declare nothing (the #1428 review, finding 3). +""" + +from __future__ import annotations + +import ast +from pathlib import Path + +from microcosm.build import uk_runtime + +#: The stage transforms that fit a model and record its weights. +FITTING_STAGE_TRANSFORMS = frozenset( + { + "etb_services.UKETBServicesStageTransform", + "etb_vat.UKETBVATStageTransform", + "lcfs_consumption.UKLCFSConsumptionStageTransform", + "nts_bus_travel.UKNTSBusTravelStageTransform", + "property_components.UKPropertyComponentsStageTransform", + "spi_spine.UKSPIIncomeSpineStageTransform", + "was_lisa.UKWASLISAStageTransform", + "was_wealth.UKWASWealthStageTransform", + } +) + + +def _declares_hook(node: ast.ClassDef) -> bool: + for item in node.body: + if ( + isinstance(item, ast.FunctionDef | ast.AsyncFunctionDef) + and item.name == "fit_weight_records" + ): + return True + if isinstance(item, ast.AnnAssign | ast.Assign) and "fit_weight_records" in { + target.id + for target in ( + [item.target] if isinstance(item, ast.AnnAssign) else item.targets + ) + if isinstance(target, ast.Name) + }: + return True + return False + + +def test_only_the_reviewed_stage_transforms_declare_fit_weight_records() -> None: + declared = set() + for path in sorted(Path(uk_runtime.__file__).parent.glob("*.py")): + tree = ast.parse(path.read_text(encoding="utf-8")) + for node in ast.walk(tree): + if ( + isinstance(node, ast.ClassDef) + and node.name.endswith("StageTransform") + and _declares_hook(node) + ): + declared.add(f"{path.stem}.{node.name}") + assert declared == FITTING_STAGE_TRANSFORMS + + +def test_the_isa_subscriptions_stage_declares_nothing() -> None: + assert "isa_subscriptions.UKISASubscriptionsStageTransform" not in ( + FITTING_STAGE_TRANSFORMS + ) 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 373b7a9a7..15736cf7b 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 @@ -445,6 +445,64 @@ def test_older_foster_children_and_other_units_are_not_marked() -> None: assert result.evidence["looked_after_children"] == 0 +def _non_parental(result) -> list[bool]: + return result.person_values["child_limit_non_parental_caring_exception"].tolist() + + +def test_a_child_whose_unit_holds_no_parent_is_in_non_parental_care() -> None: + # A grandparent carer; a lone teenage mother's baby in her parents' unit, + # whose own parent is a child of that unit (UC Regs 2013 Sch 12 para 4). + result = _derive( + [ + _person(1, age=60, hrp=True, rel={2: GRANDPARENT}), + _person(2, age=7, table="child", rel={1: GRANDCHILD}), + ], + [ + _person(1, age=45, hrp=True, rel={2: PARENT, 3: GRANDPARENT}), + _person(2, age=15, table="child", rel={1: CHILD, 3: PARENT}), + _person(3, age=0, table="child", rel={1: GRANDCHILD, 2: CHILD}), + ], + ) + + assert _non_parental(result) == [False, True, False, False, True] + assert result.evidence["non_parental_care_children"] == 2 + + +def test_a_parent_or_step_parent_in_the_unit_leaves_the_child_unmarked() -> None: + # Natural, adopted and step children of either partner, the link read + # from either side of the grid (the child's own entry here reads + # non-relative). + one, two = _couple(code=COHABITEE) + result = _derive( + [ + _person(1, age=40, hrp=True, rel={**one, 3: PARENT, 4: STEP_PARENT}), + _person(2, age=38, rel={**two, 3: STEP_PARENT}), + _person(3, age=12, table="child", rel={1: CHILD, 2: STEP_CHILD}), + _person(4, age=9, table="child", rel={1: NON_RELATIVE, 2: NON_RELATIVE}), + ] + ) + + assert _non_parental(result) == [False, False, False, False] + assert result.evidence["non_parental_care_children"] == 0 + + +def test_placed_children_adults_and_parents_elsewhere_follow_the_unit() -> None: + result = _derive( + [ + # A local authority placement is not a caring arrangement of the + # claimant's own. + _person(1, age=45, hrp=True, rel={2: FOSTER_PARENT, 3: GRANDPARENT}), + _person(2, age=8, table="child", rel={1: FOSTER_CHILD}), + # The parent lives in the household in a benefit unit of their + # own, so the grandparent's unit holds no parent of the child. + _person(3, age=4, table="child", rel={1: GRANDCHILD, 4: CHILD}), + _person(4, age=20, rel={1: CHILD, 3: PARENT}, benunit=2), + ] + ) + + assert _non_parental(result) == [False, False, True, False] + + 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 c2d144014..1d6d27af1 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 @@ -400,13 +400,29 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "SERNUM": 1, "BENUNIT": 1, "PERSON": 1, + "PENTYPE": 1, "PENPAY": 10.0, "PTAMT": 2.0, "PTINC": 2, "POAMT": 3.0, "POINC": 2, "PENOTH": 0, - } + }, + # Income from a trust (PENTYPE 5) stays private pension income, + # where the engine's means tests read it; its part is stored for + # SPI Table 3.7's other income (microcosm#1428). + { + "SERNUM": 2, + "BENUNIT": 1, + "PERSON": 1, + "PENTYPE": 5, + "PENPAY": 6.0, + "PTAMT": 0.0, + "PTINC": 0, + "POAMT": 0.0, + "POINC": 0, + "PENOTH": 0, + }, ], "oddjob": [{"SERNUM": 1, "BENUNIT": 1, "PERSON": 1, "OJAMT": 4.0, "OJNOW": 1}], "accounts": [ @@ -437,8 +453,9 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]: "ACCTAX": 0, "INVTAX": 0, }, - # Other interest (microcosm#1418): an NS&I Direct Saver paid after - # tax and a gilt paid after tax, both grossed up at the basic rate. + # An NS&I Direct Saver paid after tax, deposit interest since + # microcosm#1428 (SPI Table 3.7 note 4), and a gilt paid after tax, + # other interest (microcosm#1418), both grossed up at the basic rate. { "SERNUM": 1, "BENUNIT": 1, @@ -1262,12 +1279,20 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["individual_savings_account_interest_income"] == pytest.approx( 1 * WEEKS_IN_YEAR ) - assert adult["savings_interest_income"] == pytest.approx(2.5 * WEEKS_IN_YEAR) + # The current account (2 x 1.25) and the NS&I Direct Saver (2 x 1.25), + # deposit interest since microcosm#1428 (SPI Table 3.7 note 4). + assert adult["savings_interest_income"] == pytest.approx(5.0 * WEEKS_IN_YEAR) assert "tax_free_savings_income" not in adult # The gilt is other interest, no longer a dividend (microcosm#1418). assert adult["dividend_income"] == pytest.approx(3 * WEEKS_IN_YEAR) - assert adult["other_investment_income"] == pytest.approx(7.5 * WEEKS_IN_YEAR) + assert adult["other_investment_income"] == pytest.approx(5.0 * WEEKS_IN_YEAR) + # Household 2's trust income stays private pension, where the engine's + # means tests and household income read it, and its part is stored as + # the descriptor SPI Table 3.7's other-income measure adds (microcosm#1428). assert other["other_investment_income"] == 0.0 + assert other["private_pension_income"] == pytest.approx(6.0 * WEEKS_IN_YEAR) + assert other["annuity_and_trust_income"] == pytest.approx(6.0 * WEEKS_IN_YEAR) + assert adult["annuity_and_trust_income"] == 0.0 # ROYYR1 2 only: CVPAY (1 a week) is rent the adult pays as a lodger # (rent_paid_as_lodger, pe-uk#2006), not their own income, and household 1 # does not sub-let. @@ -1579,6 +1604,21 @@ def test_frs_spine_refuses_an_adult_tab_without_alimony( build_uk_frs_spine_frame(tmp_path, stage=stage) +def test_frs_spine_refuses_a_pension_tab_without_pentype(tmp_path: Path) -> None: + """A vintage without PENTYPE refuses; it never reads every annuity and + trust payment as a pension (microcosm#1428).""" + + tables = _fixture_tables() + tables["pension"] = [ + {key: value for key, value in row.items() if key != "PENTYPE"} + for row in tables["pension"] + ] + stage = _write_fixture(tmp_path, tables) + + with pytest.raises(ValueError, match="PENTYPE"): + build_uk_frs_spine_frame(tmp_path, stage=stage) + + def test_frs_spine_refuses_an_adult_tab_without_sppadj(tmp_path: Path) -> None: """A vintage without SPPADJ refuses; it never sets nobody's paternity pay.""" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py index 23320b84e..a7aa7dbab 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_graph.py @@ -59,8 +59,10 @@ def test_uk_spine_graph_contains_every_manifest_stage() -> None: # adds property_purchases and uc_legacy_closure_take_up, microcosm#1112 # regional_home_values, microcosm#1319 carers_allowance_take_up, the # copies' childcare re-draw childcare_copy_take_up, and microcosm#1334 - # uc_take_up and frs_annual_income_spells. - assert len(expected) == 51 + # uc_take_up and frs_annual_income_spells; microcosm#1428 the last stage, + # trading_allowance_receipts; and company_cars after hmrc_spi_income_spine + # (PolicyEngine/uk-autumn-budget-dashboard-2026#316). + assert len(expected) == 52 assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(expected) assert set(expected) <= ids assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(ids) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_subscriptions.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_subscriptions.py index 1bed74c56..b11dc5592 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_subscriptions.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_subscriptions.py @@ -9,6 +9,7 @@ import pytest from microcosm.build.source_manifest import SourceOperationSpec +from microcosm.build.stage_evidence import snapshot_stage_evidence from microcosm.build.uk_runtime import isa_subscriptions as stage from microcosm.build.uk_runtime.cgt_imputation import ( UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS, @@ -392,6 +393,17 @@ def test_the_whole_stage_writes_its_columns_within_every_invariant(statistics): assert gate.passed, (gate_id, gate.failures) +def test_the_stage_declares_no_fit_weight_records(statistics): + """The stage fits no model. A declared empty list read as a fitting stage + that recorded nothing, which failed the release-cut weights audit + (microcosm#1428).""" + + transform = _transform(statistics) + transform(_stage_frame()) + evidence = snapshot_stage_evidence("isa_subscriptions", transform) + assert "fit_weight_records" not in evidence + + def test_reordering_the_rows_moves_no_draw(statistics): """Every draw is keyed on identity, so row order moves none. @@ -550,8 +562,8 @@ def test_the_committed_declaration_carries_the_tested_operations(): lisa = {artifact["role"]: artifact for artifact in stages["was_lisa"].artifacts} for role in ("was_qrf_donor", "was_person_tab"): assert roles[role] == lisa[role] - # Nothing runs after it. - assert list(stages)[-1] == "isa_subscriptions" + # Only the self-employed's gross receipts run after it (microcosm#1428). + assert list(stages)[-2:] == ["isa_subscriptions", "trading_allowance_receipts"] def test_no_calibration_target_reads_an_isa_fact(): 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 d0909249a..e856f0b7a 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 @@ -13,6 +13,7 @@ from microcosm.build.uk_runtime.income_tax_rate_classes import UK_RATE_CLASS_ALLOCATION from microcosm.build.uk_runtime.measure_simulation import ( UK_SAVINGS_INTEREST_OUTSIDE_ISAS, + UK_SPI_OTHER_INVESTMENT_INCOME, UKMeasureResolver, apply_uk_calibration_measure_exclusions, compute_uk_measure_input, @@ -608,6 +609,9 @@ def test_exclusion_applier_warns_within_week_of_expiry(): # the devolved repayments by nation and holds Scotland's and Northern # Ireland's (ruling R4: Wales only binds). "_1_000_000_to_inf": 10, + # The SPI Table 3.7 £1m-and-over interest amount row, held on the savings + # follow-up's measured distortion (microcosm#1428, 2026-10-30). + "hmrc.spi.savings_interest_income.amount_by_total_income_band.band_1000000": 1, "slc.": 5, # microcosm#1095 (2026-10-07): two bands whose few supporting awards # policyengine-uk 2.122.2 moves out join the uk-data#452 class. @@ -739,8 +743,10 @@ def test_packaged_exclusions_load(): # 2024 duty (microcosm#1420). microcosm#1095 holds the three OBR council # tax nation rows, pinned to the UK as measured diagnostics (ruling D14e), # and SLC's Scottish and Northern Irish repayment rows (ruling R4: Wales - # only binds). - assert len(names) == len(set(names)) == 44 + # only binds). The savings follow-up holds the SPI £1m-and-over interest + # amount row on its measured distortion (microcosm#1428, María's ruling of + # 2026-10-30). + assert len(names) == len(set(names)) == 45 (gas,) = [ entry for entry in exclusions @@ -2075,6 +2081,62 @@ def test_the_resolver_routes_interest_outside_isas_to_persons( ) +def _annuity_frame() -> FrameStub: + """A pensioner with a purchased annuity inside the stored pension, a + gilt holder, and a person with no stored pension (microcosm#1428).""" + + frame = FrameStub() + frame._tables["person"] = frame._tables["person"].assign( + private_pension_income=[4_000.0, 0.0, 0.0], + annuity_and_trust_income=[1_000.0, 0.0, 0.0], + ) + return frame + + +def test_spi_other_income_adds_the_annuity_and_trust_part_of_pensions(): + """SPI Table 3.7's other income counts purchased-annuity and trust + payments, which FRS rows keep in private pension for the engine's means + tests: the measure adds their share of the engine's pension, so they grow + on its private pension path (microcosm#1428).""" + + simulation = SimulationStub( + { + "other_investment_income": ("person", np.array([0.0, 500.0, 0.0])), + "private_pension_income": ("person", np.array([4_400.0, 0.0, 0.0])), + } + ) + values, route = compute_uk_measure_input( + _annuity_frame(), simulation, "person", UK_SPI_OTHER_INVESTMENT_INCOME, 2025 + ) + # A quarter of the stored pension, grown by the engine's 10%. + np.testing.assert_allclose(values, [1_100.0, 500.0, 0.0]) + assert route.startswith("engine_components:other_investment_income") + # A frame without the descriptor (the enhanced FRS reference) reads the + # engine's other income alone. + np.testing.assert_allclose( + compute_uk_measure_input( + FrameStub(), simulation, "person", UK_SPI_OTHER_INVESTMENT_INCOME, 2025 + )[0], + [0.0, 500.0, 0.0], + ) + with pytest.raises(KeyError, match="person-only"): + compute_uk_measure_input( + _annuity_frame(), + simulation, + "household", + UK_SPI_OTHER_INVESTMENT_INCOME, + 2025, + ) + + +def test_the_spi_other_income_rows_read_the_annuity_measure(): + targets = measure_simulation._uk_contract_targets() + binding = targets["hmrc.spi.other_investment_income.amount_by_total_income_band"][ + "bindings" + ]["policyengine"] + assert binding["value_variable"] == UK_SPI_OTHER_INVESTMENT_INCOME + + def _stub_engine_term_calendar(monkeypatch) -> None: """policyengine-uk's year of terms, without the engine installed.""" diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py index d02836f8c..71b929b6d 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py @@ -160,3 +160,56 @@ def test_winter_fuel_diagnostic_carries_the_published_count() -> None: assert metadata[f"{prefix}_status"] == "available" assert float(metadata[f"{prefix}_value_count"]) == 11_003_237 assert metadata[f"{prefix}_ledger_geography_id"] == "K04000001" + + +def test_income_based_jsa_spending_reads_the_dwp_calendar_window() -> None: + # DWP's income-based JSA line beside OBR's total JSA, so the solve meets + # OBR's total on contributory claimants (microcosm#1428; its review's + # finding 12 asked for this pin, as the Housing Benefit row has). + target = _targets()["dwp.jsa.amount_income_based"] + selector = target["ledger_selector"] + assert selector["source_concept"] == "dwp.benefit_expenditure_amount" + assert selector["dimension_values"] == { + "benefit_forecast_line": ( + "unemployment_benefits__jobseekers_allowance__income_based" + ) + } + assert target["value_operation"] == "calendar_year_window" + assert selector["source_measure_id_by_opening_year"] == { + "2024": "expenditure_2024", + "2025": "expenditure_2025", + } + assert target["assertion_policy"] == "allow_source_projection" + assert target["family"] == "dwp_benefit_expenditure" + binding = target["bindings"]["policyengine"] + assert binding["value_variable"] == "jsa_income" + assert binding["from_entity"] == "benunit" + assert "filters" not in binding + # Great Britain, the scope of DWP's line. + (condition,) = binding["household_conditions"] + assert condition["variable"] == "region" + assert "NORTHERN_IRELAND" not in condition["value"] + + +def test_the_table_3_10_employment_counts_read_pay_with_benefits_by_profit_band() -> ( + None +): + # Table 3.10's individuals with employment income over the self-employment + # bands from GBP 1 to GBP 10,000 (microcosm#1428; review finding 12). + target = _targets()[ + "hmrc.spi.employment_income.count_by_self_employment_income_band" + ] + binding = target["bindings"]["policyengine"] + assert binding["value_variable"] == "person_count" + assert binding["groupby_variable"] == ( + "self_employment_income_after_loss_relief_annual" + ) + assert binding["filters"] == [ + { + "variable": "employment_income_with_benefits_in_kind_annual", + "operator": "!=", + "value": 0, + } + ] + assert binding["band_upper_bound"] == 10_000 + assert binding["band_upper_bound_inclusive"] is False 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 1da010eb0..6118f3f07 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 @@ -263,9 +263,11 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: # rates, three national rows, SPI pension income and the taxpayers with # it by constituency and local authority, SPI Table 3.7's other income by # total-income band, and SPI Table 3.10's self-employment counts and - # amounts by self-employment band; microcosm#1095 R4 splits SLC's - # devolved repayments into one row per nation, three rows for one. - assert len(resource["targets"]) == 458 + # amounts by self-employment band and the counts with employment income + # among them, and DWP's income-based Jobseeker's Allowance spending; + # microcosm#1095 R4 splits SLC's devolved repayments into one row per + # nation, three rows for one. + assert len(resource["targets"]) == 460 providers = resource["hierarchy"]["providers"] categories = resource["hierarchy"]["categories"] @@ -279,10 +281,10 @@ 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) == 396 + assert len(registry_scope) == 398 assert len(profile_scope) == 62 - assert target_ids[:396] == registry_scope - assert target_ids[396:] == profile_scope + assert target_ids[:398] == registry_scope + assert target_ids[398:] == profile_scope parity = resource["registry_parity"] assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3" @@ -309,11 +311,12 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None: # OBR excise rows have no uk-data metric (uk-data never measured excise # duty), nor microcosm#1069's two Income Tax payer by age rows, nor # microcosm#1418's three Income Tax on savings rows, its SPI other income - # row or its two Table 3.10 rows. microcosm#1095 R4 maps the incumbent's - # three devolved SLC repayment metrics one to one, to three nation rows in - # place of one devolved row. + # row or its three Table 3.10 rows, nor microcosm#1428's income-based JSA + # spending row. microcosm#1095 R4 maps the incumbent's three devolved SLC + # repayment metrics one to one, to three nation rows in place of one + # devolved row. assert len(mapped_target_ids) == 185 - assert len(unmapped_declarations) == 211 + assert len(unmapped_declarations) == 213 suppressed_ancestors = parity["suppressed_ancestors"] assert len(suppressed_ancestors) == 5 assert set(suppressed_ancestors).isdisjoint(parity["mapped"]) @@ -825,7 +828,7 @@ 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) == 458 + assert len(target_ids) == 460 assert len(target_ids) == len(set(target_ids)) @@ -1328,7 +1331,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() - def test_paid_joint_diagnostics_do_not_add_active_targets() -> None: targets = _load()["targets"] - assert len(targets) == 458 + assert len(targets) == 460 assert not any( f.get("variable") == "uc_calibration_child_entitlement" for target in targets @@ -1578,7 +1581,7 @@ def test_household_composition_cells_do_not_share_the_household_total_signature( 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 31 banded bindings keep the half-open + upper-closed bands; the other 32 banded bindings keep the half-open reading.""" banded = { @@ -1591,7 +1594,7 @@ def test_only_the_uc_award_bands_read_whole_pence() -> None: for target_id, binding in banded.items() if binding.get("band_semantics") == "whole_pence_upper_closed" } - assert len(banded) == 35 + assert len(banded) == 36 assert whole_pence == { f"dwp.uc.payment_distribution_{family}" for family in ( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py index 7c371c0e1..a163fbb49 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_private_school_attendance.py @@ -107,16 +107,19 @@ def test_the_stage_needs_the_stored_draw_and_writes_attendance_once(): store_private_school_attendance(stored, engine=engine, calibration_year=2025) -def test_only_the_isa_subscriptions_follow_the_stage(): +def test_only_the_isa_subscriptions_and_receipts_follow_the_stage(): """No later stage copies or re-keys a person, so the stored flags reach - the release as the engine set them. Only the ISA subscriptions - (microcosm#1415) follow it, writing person columns on the rows they are - given.""" + the release as the engine set them. Only the ISA subscriptions and the + self-employed's gross receipts follow it, each writing person columns on + the rows they are given (microcosm#1415, microcosm#1428).""" stages = load_country_spec("uk").sources.stages names = [stage.stage for stage in stages] later = stages[names.index("private_school_attendance") + 1 :] - assert [stage.stage for stage in later] == ["isa_subscriptions"] + assert [stage.stage for stage in later] == [ + "isa_subscriptions", + "trading_allowance_receipts", + ] for stage in later: assert PRIVATE_SCHOOL_ATTENDANCE_OUTPUT not in { *stage.outputs, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_property_localisation.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_property_localisation.py index dc08f0134..568111495 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_property_localisation.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_property_localisation.py @@ -286,6 +286,92 @@ def test_localise_bands_is_monotone_in_the_uniform_and_skips_scotland() -> None: assert receipt["band_mix_after"]["I"] == 0 +def _scotland_mix() -> pl.BandMix: + return pl.load_scotland_band_mix( + json.dumps(toy_property_resources()["scotland_band_mix"]).encode() + ) + + +def test_scotland_band_mix_needs_every_band_of_every_council_area() -> None: + resource = toy_property_resources()["scotland_band_mix"] + mix = pl.load_scotland_band_mix(json.dumps(resource).encode()) + # Scotland's bands run A to H, so band I adds nothing. + assert all(values[-2] == values[-1] == 1.0 for values in mix.cumulative.values()) + assert mix.as_at == pl.SCOTLAND_BAND_PERIOD + short = {**resource, "rows": resource["rows"][1:]} + with pytest.raises(ValueError, match="lacks bands"): + pl.load_scotland_band_mix(json.dumps(short).encode()) + with pytest.raises(ValueError, match="not the vendored CTAXBASE facts"): + pl.load_scotland_band_mix(json.dumps({**resource, "kind": "other"}).encode()) + # Only the September 2025 rows are read. + stale = { + **resource, + "rows": [ + {**row, "period": {"type": "month", "value": "2024-09"}} + for row in resource["rows"] + ], + } + with pytest.raises(ValueError, match="no council areas"): + pl.load_scotland_band_mix(json.dumps(stale).encode()) + + +def test_localise_bands_draws_scottish_owners_from_their_council_area() -> None: + """With the CTAXBASE mix a Scottish owner draws its band from its council + area's shares at its uniform; a Scottish renter and Northern Ireland keep + theirs (microcosm#1095 B12).""" + mix, scotland = _mix(), _scotland_mix() + lsoa = next(iter(mix.cumulative)) + area = next(iter(scotland.cumulative)) + u = np.array([0.0, 0.124, 0.126, 0.999, 0.6, 0.6, 0.3]) + codes = np.array(["S01000001"] * 5 + ["N00000001", lsoa]) + areas = np.array([area] * 5 + ["N09000001", "E06000001"]) + band = np.array(["D"] * 7) + tenure = np.array( + ["OWNED_OUTRIGHT"] * 4 + ["RENT_PRIVATELY"] + ["OWNED_OUTRIGHT"] * 2 + ) + new_band, receipt = pl.localise_bands( + u=u, + lsoa_code=codes, + band=band, + tenure_type=tenure, + mix=mix, + scotland_mix=scotland, + local_authority_code=areas, + ) + # The toy council area splits A to H in eighths, so no owner reads I. + assert list(new_band[:4]) == ["A", "A", "B", "H"] + assert new_band[4] == "D" and new_band[5] == "D" + # England and Wales draw as before. + assert new_band[6] == "C" + scottish = receipt["scotland"] + assert scottish["rows_rewritten"] == 4 and scottish["rows_untouched_renters"] == 1 + assert scottish["council_areas"] == 1 and scottish["band_mix_after"]["I"] == 0 + assert scottish["as_at"] == "2025-09" + # Scotland's owners leave the untouched count; the renter and + # Northern Ireland stay in it. + assert receipt["rows_untouched_outside_england_wales"] == 2 + assert pl.monotone_violations(u[:4], new_band[:4], areas[:4]) == 0 + with pytest.raises(ValueError, match="No Scottish band mix"): + pl.localise_bands( + u=u, + lsoa_code=codes, + band=band, + tenure_type=tenure, + mix=mix, + scotland_mix=scotland, + local_authority_code=np.where(areas == area, "S12999999", areas), + ) + with pytest.raises(ValueError, match="council areas"): + pl.localise_bands( + u=u, + lsoa_code=codes, + band=band, + tenure_type=tenure, + mix=mix, + scotland_mix=scotland, + ) + + # ---------------------------------------------------------------------------- # Lineage diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_property_purchases.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_property_purchases.py index b0346a2dd..9065c158d 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_property_purchases.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_property_purchases.py @@ -53,6 +53,9 @@ def _rows() -> list[dict]: rows.append(_row(_HRAD, "hmrc.sdlt.hrad_property_value", band, value * 1e6)) rows.append(_row(_HRAD, "hmrc.sdlt.hrad_transactions", "all_price_bands", 1_800.0)) rows.append(_row(_HRAD, "hmrc.sdlt.hrad_property_value", "all_price_bands", 625e6)) + # Gross HRAD receipts: GBP 6,000 a transaction and 4p a pound in the nil band. + rows.append(_row(_HRAD, "hmrc.sdlt.hrad_receipts_gross", "gbp_250k_or_less", 6e6)) + rows.append(_row(_HRAD, "hmrc.sdlt.hrad_receipts_gross", "all_price_bands", 25e6)) for record_set, count, value in ( (_RES_IND, 9_000.0, 3_000e6), (_RES_OTHER, 1_000.0, 1_000e6), @@ -63,6 +66,11 @@ def _rows() -> list[dict]: rows.append( _row(record_set, "hmrc.sdlt.property_value", "all_price_bands", value) ) + # Other buyers' nil-band receipts, GBP 1.2m, are 200 HRAD transactions and + # GBP 30m at the band's gross receipts per transaction and per pound. + rows.append(_row(_RES_OTHER, "hmrc.sdlt.transactions", "gbp_250k_or_less", 400.0)) + rows.append(_row(_RES_OTHER, "hmrc.sdlt.receipts", "gbp_250k_or_less", 1.2e6)) + rows.append(_row(_RES_OTHER, "hmrc.sdlt.receipts", "all_price_bands", 5e6)) for band, (count, value) in _NONRES_BANDS.items(): rows.append(_row(_NONRES_IND, "hmrc.sdlt.transactions", band, count)) rows.append(_row(_NONRES_IND, "hmrc.sdlt.property_value", band, value * 1e6)) @@ -71,16 +79,39 @@ def _rows() -> list[dict]: return rows -def test_targets_allocate_hrad_by_individuals_shares_and_combine_top_bands() -> None: +def test_targets_allocate_the_nil_rate_band_from_receipts_and_combine_top_bands() -> ( + None +): targets = pp.property_purchase_targets_from_rows(_rows()) additional = targets.additional_residential - # uk-data#573: Table 6b's all-buyer HRAD times individuals' Table 8a shares. - assert additional.transactions == pytest.approx(1_800.0 * 9_000 / 10_000) - assert additional.value == pytest.approx(625e6 * 3_000 / 4_000) + # At or below GBP 250,000 households take Table 6b's band less the 200 + # transactions and GBP 30m other buyers' receipts imply; above it, + # individuals' Table 8a shares of Table 6b (uk-data#573). + assert additional.transactions == pytest.approx( + (1_800.0 - 1_000.0) * 9_000 / 10_000 + 1_000.0 - 200.0 + ) + assert additional.value == pytest.approx( + (625e6 - 150e6) * 3_000 / 4_000 + 150e6 - 30e6 + ) assert additional.band_ids == pp.PROPERTY_PURCHASE_BAND_IDS + assert additional.band_transactions[0] == pytest.approx(800.0) + assert additional.band_values[0] == pytest.approx(120e6) + assert additional.band_transactions[1] == pytest.approx(500.0 * 0.9) # The five bands above GBP 2m become one: 8+4+2+2+1 transactions. - assert additional.band_transactions[-1] == 17.0 - assert additional.band_values[-1] == pytest.approx(72e6) + assert additional.band_transactions[-1] == pytest.approx(17.0 * 0.9) + assert additional.band_values[-1] == pytest.approx(72e6 * 0.75) + assert targets.derivation["nil_band_other_buyers_hrad_transactions"] == ( + pytest.approx(200.0) + ) + assert targets.derivation["nil_band_other_buyers_hrad_value"] == ( + pytest.approx(30e6) + ) + assert targets.derivation["nil_band_other_buyers_hrad_transaction_share"] == ( + pytest.approx(0.5) + ) + assert targets.derivation["nil_band_households_hrad_transaction_share"] == ( + pytest.approx(0.8) + ) assert additional.minimum_price == pp.HMRC_SDLT_MINIMUM_CONSIDERATION non_residential = targets.non_residential assert non_residential.transactions == 400.0 @@ -92,6 +123,20 @@ def test_targets_allocate_hrad_by_individuals_shares_and_combine_top_bands() -> assert targets.derivation["residential_individuals_value_share"] == 0.75 +def test_targets_refuse_nil_band_receipts_beyond_other_buyers_transactions() -> None: + rows = [ + _row(_RES_OTHER, "hmrc.sdlt.receipts", "gbp_250k_or_less", 3e6) + if row["layout"]["record_set_id"] == _RES_OTHER + and row["concept"] == "hmrc.sdlt.receipts" + and row["layout"]["groupby_value_id"] == "gbp_250k_or_less" + else row + for row in _rows() + ] + # GBP 3m implies 500 HRAD transactions, more than other buyers' 400. + with pytest.raises(ValueError, match="imply HRAD outside"): + pp.property_purchase_targets_from_rows(rows) + + def test_targets_round_trip_through_the_payload() -> None: targets = pp.property_purchase_targets_from_rows(_rows()) assert pp.UKPropertyPurchaseTargets.from_payload(targets.payload()) == targets @@ -484,12 +529,25 @@ def test_the_stage_fits_one_kind_at_its_stock_beside_one_it_scales() -> None: ) -def test_the_packaged_targets_reproduce_uk_data_573() -> None: - # The vendored HMRC Stamp Tax rows give uk-data#573's household figures. +def test_the_packaged_targets_allocate_the_nil_rate_band_from_receipts() -> None: + # The vendored HMRC Stamp Tax rows (2024-25, corrected): Table 6b's + # 211,700 HRAD transactions and GBP 77.97bn, 113,500 and GBP 16.635bn of + # them at or below GBP 250,000 with GBP 625m gross receipts; Table 8a's + # individuals' shares 925,100 / 1,049,600 and GBP 335.515bn / 380.205bn; and + # other buyers' GBP 265m of receipts in that band. targets = pp.load_property_purchase_targets() additional = targets.additional_residential - assert additional.transactions == pytest.approx(186_588.8624, rel=1e-9) - assert additional.value == pytest.approx(68_805_261_766.68, rel=1e-9) + other_count = 265e6 * 113_500 / 625e6 + other_value = 265e6 * 16.635e9 / 625e6 + assert additional.transactions == pytest.approx( + (211_700 - 113_500) * 925_100 / 1_049_600 + 113_500 - other_count, rel=1e-9 + ) + assert additional.value == pytest.approx( + (77.97e9 - 16.635e9) * 335.515e9 / 380.205e9 + 16.635e9 - other_value, + rel=1e-9, + ) + # Both stay above uk-data#573's household lower bounds. + assert additional.transactions > 87_200 and additional.value > 33.28e9 non_residential = targets.non_residential assert non_residential.transactions == pytest.approx(25_000.0) assert non_residential.value == pytest.approx(7.64e9) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py index 23fa2e06d..e39e6c39b 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_source_stages.py @@ -101,11 +101,6 @@ CHILD_BENEFIT_TAKE_UP_STAGE_NAMES = [ "child_benefit_take_up", ] -# microcosm#1319: the Carer's Allowance draw reads the post-SPI earnings and -# the anchor the SPI income chain refreshes. -CARERS_ALLOWANCE_TAKE_UP_STAGE_NAMES = [ - "carers_allowance_take_up", -] E9_STAGE_NAMES = [ "uc_deduction_attributes", ] @@ -119,6 +114,10 @@ "cgt_residential_split", "hmrc_cgt_asset_type_spine", "salary_sacrifice", + # microcosm#1095 B5: the Carer's Allowance draw reads the earnings test on + # the pay salary sacrifice converts, each draw keyed on lineage so that a + # CGT copy claims with the member it copies (microcosm#1319). + "carers_allowance_take_up", "student_loans", # The copies' childcare re-draw runs after every copy stage, before the # private-school attendance ranks households on net income (which counts @@ -129,6 +128,9 @@ # microcosm#1415: the ISA subscriptions run on final incomes; nothing # reads their outputs. "isa_subscriptions", + # microcosm#1428: the self-employed's gross receipts run last, on final + # profits; only the engine's trading allowance reads them. + "trading_allowance_receipts", ] UK_SOURCE_STAGE_NAMES = [ "frs_spine", @@ -148,7 +150,6 @@ *UC_LEGACY_CLOSURE_TAKE_UP_STAGE_NAMES, *PENSION_CREDIT_TAKE_UP_STAGE_NAMES, *CHILD_BENEFIT_TAKE_UP_STAGE_NAMES, - *CARERS_ALLOWANCE_TAKE_UP_STAGE_NAMES, *E9_STAGE_NAMES, *E8_STAGE_NAMES, ] @@ -222,7 +223,6 @@ def test_e5_and_e6_follow_e7_and_precede_the_uc_rewrites(self) -> None: *UC_LEGACY_CLOSURE_TAKE_UP_STAGE_NAMES, *PENSION_CREDIT_TAKE_UP_STAGE_NAMES, *CHILD_BENEFIT_TAKE_UP_STAGE_NAMES, - *CARERS_ALLOWANCE_TAKE_UP_STAGE_NAMES, *E9_STAGE_NAMES, ] @@ -316,6 +316,7 @@ def test_country_stage_plan_assembles_spine_plan(self) -> None: "student_loans": _identity, "private_school_attendance": _identity, "isa_subscriptions": _identity, + "trading_allowance_receipts": _identity, "company_cars": _identity, "childcare_copy_take_up": _identity, "age_tail": _identity, @@ -731,6 +732,7 @@ def test_e3_operation_kinds_are_declared_in_order(self) -> None: "draw_spi_donor_ages_by_population", "fit_weighted_qrf_stage1", "resample_band_donor_leaves", + "floor_spi_employment_pay_at_minimum_wage", "fit_weighted_qrf_stage2", "zero_pension_age_reports_below_state_pension_age", "materialize_hmrc_income_bands_fail_closed", @@ -1094,12 +1096,13 @@ def test_e7_declared_seed_lockstep(self) -> None: assert stages["hmrc_spi_income_spine"].operations[3].parameters["seed"] == 42 # The reserved carriers' resample draws at stage seed + 2 (PolicyEngine/chronicle#280 lane). assert stages["hmrc_spi_income_spine"].operations[4].parameters["seed"] == 44 - assert stages["hmrc_spi_income_spine"].operations[5].parameters["seed"] == 43 - # The donor age draw and the State Pension age guard take no seed of - # their own: ages draw on the stage-1 seed, the guard is deterministic - # (microcosm#1069). + assert stages["hmrc_spi_income_spine"].operations[6].parameters["seed"] == 43 + # The donor age draw, the employment pay floor and the State Pension + # age guard take no seed of their own: ages draw on the stage-1 seed, + # the floor and the guard are deterministic (microcosm#1069). assert "seed" not in stages["hmrc_spi_income_spine"].operations[2].parameters - assert "seed" not in stages["hmrc_spi_income_spine"].operations[6].parameters + assert "seed" not in stages["hmrc_spi_income_spine"].operations[5].parameters + assert "seed" not in stages["hmrc_spi_income_spine"].operations[7].parameters assert stages["spi_income_band_donors"].operations[0].parameters["seed"] == 3 assert stages["uc_reporter_redraw"].operations[3].parameters["seed"] == 44 assert stages["uc_capital_coherence"].operations[1].parameters["seed"] == 0 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 06afdf957..ea0fdfa48 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 @@ -1063,3 +1063,300 @@ def predict_from_uniforms(self, predictors, *, quantiles, sign_uniforms): assert seen.get("reached") is True if result is not None: assert result.stage1_transport["sign_class_mismatches"] == mismatch + + +_PAY_FLOOR_2024_25 = spi_income.SPIEmploymentPayFloor( + age_thresholds=(0.0, 18.0, 21.0, 23.0, 25.0), + hourly_rates=(6.40, 8.60, 11.44, 11.44, 11.44), + weeks_in_year=52.0, + instant="2024-04-30", + source="fixture", +) + + +def _pay_floor_frames( + rows: list[dict[str, object]], +) -> tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series, pd.Series]: + """One person per household; ``source`` names its source household. + + ``twin_pay`` is a drawn row's FRS twin's employment income before the + draw (well paid unless given), and an FRS row (``drawn`` false) is the + floor's reference for the hourly-pay check. + """ + + person = pd.DataFrame( + { + "person_household_id": np.arange(1, len(rows) + 1), + "age": [row["age"] for row in rows], + "employment_status": [row.get("status", "FT_EMPLOYED") for row in rows], + # The spine annualises usual weekly hours at 52 (uk-data#531). + "hours_worked": [row["weekly_hours"] * 52.0 for row in rows], + SPI_HMRC_PAY_COLUMN: [row["pay"] for row in rows], + "self_employment_income": [row.get("trade", 0.0) for row in rows], + }, + index=np.arange(10, 10 + len(rows)), + ) + household = pd.DataFrame( + { + "household_id": np.arange(1, len(rows) + 1), + "household_source_id": [row.get("source", i) for i, row in enumerate(rows)], + } + ) + rows_mask = pd.Series([row.get("drawn", True) for row in rows], index=person.index) + weights = pd.Series( + [float(row.get("weight", 100.0)) for row in rows], index=person.index + ) + twin_pay = pd.Series( + [float(row.get("twin_pay", 60_000.0)) for row in rows], index=person.index + )[rows_mask] + return person, household, rows_mask, weights, twin_pay + + +def _floor(person, household, rows, weights, twin_pay): + return spi_income._floor_employment_pay_at_minimum_wage( + person, + rows=rows, + floor=_PAY_FLOOR_2024_25, + household=household, + person_weights=weights, + twin_pay=twin_pay, + reference_rows=~rows, + ) + + +def test_spi_pay_floor_rate_follows_the_age_bands() -> None: + rates = _PAY_FLOOR_2024_25.hourly_rate(np.array([16, 17.9, 18, 20.5, 21, 64])) + np.testing.assert_allclose(rates, [6.40, 6.40, 8.60, 8.60, 11.44, 11.44]) + with pytest.raises(ValueError, match="increasing age thresholds from zero"): + spi_income.SPIEmploymentPayFloor( + age_thresholds=(16.0, 21.0), + hourly_rates=(6.40, 11.44), + weeks_in_year=52.0, + instant="2024-04-30", + source="fixture", + ) + with pytest.raises(ValueError, match="hourly rates must be positive"): + spi_income.SPIEmploymentPayFloor( + age_thresholds=(0.0,), + hourly_rates=(0.0,), + weeks_in_year=52.0, + instant="2024-04-30", + source="fixture", + ) + + +def test_spi_pay_floor_lifts_only_drawn_employee_pay_below_its_floor() -> None: + person, household, rows, weights, twin_pay = _pay_floor_frames( + [ + {"age": 30, "weekly_hours": 30, "pay": 5_000.0}, # floored, 21+ + {"age": 19, "weekly_hours": 20, "pay": 2_000.0}, # floored, 18-20 + {"age": 17, "weekly_hours": 10, "pay": 1_000.0}, # floored, under 18 + # The twin's own rate, under the minimum wage, caps the floor. + {"age": 30, "weekly_hours": 30, "pay": 5_000.0, "twin_pay": 9_000.0}, + # A twin without pay caps nothing. + {"age": 30, "weekly_hours": 30, "pay": 5_000.0, "twin_pay": 0.0}, + {"age": 30, "weekly_hours": 30, "pay": 50_000.0}, # above the floor + {"age": 30, "weekly_hours": 0, "pay": 1_000.0}, # no hours + {"age": 30, "weekly_hours": 30, "pay": 0.0}, # no drawn pay + { + "age": 30, + "weekly_hours": 40, + "pay": 1_000.0, + "status": "FT_SELF_EMPLOYED", + }, + {"age": 30, "weekly_hours": 40, "pay": 1_000.0, "drawn": False}, # FRS row + ] + ) + before = person.copy() + floored, receipt, uplift = _floor(person, household, rows, weights, twin_pay) + + pay = floored[SPI_HMRC_PAY_COLUMN].to_numpy() + lifted = [ + 30 * 52 * 11.44, + 20 * 52 * 8.60, + 10 * 52 * 6.40, + 9_000.0, + 30 * 52 * 11.44, + ] + np.testing.assert_allclose(pay[:5], lifted) + np.testing.assert_array_equal(pay[5:], before[SPI_HMRC_PAY_COLUMN].to_numpy()[5:]) + np.testing.assert_allclose( + uplift.to_numpy(), + [ + *(np.asarray(lifted) - [5_000.0, 2_000.0, 1_000.0, 5_000.0, 5_000.0]), + 0, + 0, + 0, + 0, + ], + ) + assert list(uplift.index) == list(person.index[rows.to_numpy()]) + # Only the PAY leaf moves; the input frame is not modified. + pd.testing.assert_frame_equal( + floored.drop(columns=[SPI_HMRC_PAY_COLUMN]), + before.drop(columns=[SPI_HMRC_PAY_COLUMN]), + ) + pd.testing.assert_frame_equal(person, before) + # Five floored rows on five source households beside one unfloored: the + # floored weight, share and amount beside the eligible weight would give + # back that one household's, so they are withheld; the mean uplift rests + # on the five. + assert receipt["floored"] == { + "source_households": "3-9", + "weighted_people": "<3", + "weighted_share_of_eligible": "<3", + "weighted_mean_uplift_gbp": pytest.approx(float(uplift.to_numpy()[:5].mean())), + "weighted_amount_added_gbp": "<3", + } + assert receipt["eligible"]["source_households"] == "3-9" + assert receipt["rate"] == spi_income.SPI_EMPLOYMENT_PAY_FLOOR_RATE_RULE + assert receipt["annual_income"] == spi_income.SPI_EMPLOYMENT_PAY_FLOOR_ANNUAL_RULE + assert receipt["hourly_rate_by_age_from"] == { + "0": 6.40, + "18": 8.60, + "21": 11.44, + "23": 11.44, + "25": 11.44, + } + # Each share has a side on one source household (the unfloored row, the + # twin-capped row still under the minimum wage, the one FRS employee), so + # none is given; and no row stays below its floor. + assert receipt["hourly_pay_check"] == { + "spi_drawn_employees": { + "source_households": "3-9", + "below_minimum_wage_share_before": "<3", + "below_minimum_wage_share_after": "<3", + }, + "frs_employees": { + "source_households": "<3", + "below_minimum_wage_share": "<3", + }, + "eligible_rows_below_their_floor_after": 0, + } + # Deterministic: the same inputs floor the same rows to the same pay. + again, again_receipt, _ = _floor(person, household, rows, weights, twin_pay) + pd.testing.assert_frame_equal(again, floored) + assert again_receipt == receipt + + +def test_spi_pay_floor_receipt_counts_source_households_not_copies() -> None: + # Twelve floored copies of two source households, then twelve floored + # rows of twelve households beside ten, then two, unfloored ones. + copies = [ + {"age": 40, "weekly_hours": 35, "pay": 1_000.0, "source": index % 2} + for index in range(12) + ] + _, receipt, _ = _floor(*_pay_floor_frames(copies)) + assert receipt["floored"]["source_households"] == "<3" + assert receipt["floored"]["weighted_mean_uplift_gbp"] == "<3" + + def receipt_of(unfloored: int) -> dict[str, object]: + rows_ = [ + {"age": 40, "weekly_hours": 35, "pay": 1_000.0, "source": 100 + index} + for index in range(12) + ] + [ + {"age": 40, "weekly_hours": 35, "pay": 90_000.0, "source": 200 + index} + for index in range(unfloored) + ] + return _floor(*_pay_floor_frames(rows_))[1] + + receipt = receipt_of(10) + minimum = 35 * 52 * 11.44 + assert receipt["eligible"] == {"source_households": 20, "weighted_people": 2_200.0} + assert receipt["floored"]["source_households"] == 10 + assert receipt["floored"]["weighted_people"] == pytest.approx(1_200.0) + assert receipt["floored"]["weighted_share_of_eligible"] == pytest.approx( + 1_200.0 / 2_200.0 + ) + assert receipt["floored"]["weighted_mean_uplift_gbp"] == pytest.approx( + minimum - 1_000.0 + ) + assert receipt["floored"]["weighted_amount_added_gbp"] == pytest.approx( + 1_200.0 * (minimum - 1_000.0) + ) + # Before the floor twelve of the twenty-two weighted rows were below the + # minimum wage, after it none; the reference has no FRS rows here. + check = receipt["hourly_pay_check"] + assert check["spi_drawn_employees"]["below_minimum_wage_share_before"] == ( + pytest.approx(1_200.0 / 2_200.0) + ) + assert check["spi_drawn_employees"]["below_minimum_wage_share_after"] == 0.0 + assert check["eligible_rows_below_their_floor_after"] == 0 + + # Two unfloored sources: the floored weight, share or amount beside the + # eligible weight would give back their weight, so none is given; nor are + # the hourly-pay shares, whose rest is the same two. + receipt = receipt_of(2) + assert receipt["eligible"] == {"source_households": 10, "weighted_people": 1_400.0} + assert receipt["floored"] == { + "source_households": 10, + "weighted_people": "<3", + "weighted_share_of_eligible": "<3", + "weighted_mean_uplift_gbp": pytest.approx(minimum - 1_000.0), + "weighted_amount_added_gbp": "<3", + } + assert ( + receipt["hourly_pay_check"]["spi_drawn_employees"][ + "below_minimum_wage_share_before" + ] + == "<3" + ) + + +def test_spi_pay_floor_compares_the_drawn_rows_with_frs_employees() -> None: + # Ten FRS employees on the minimum wage and ten below it beside twelve + # drawn rows the floor lifts: half the FRS rows are below the minimum wage. + frs = [ + { + "age": 40, + "weekly_hours": 35, + "pay": 35 * 52 * (11.44 if index < 10 else 9.0), + "source": 300 + index, + "drawn": False, + } + for index in range(20) + ] + drawn = [ + {"age": 40, "weekly_hours": 35, "pay": 1_000.0, "source": 100 + index} + for index in range(12) + ] + _, receipt, _ = _floor(*_pay_floor_frames(frs + drawn)) + assert receipt["hourly_pay_check"]["frs_employees"] == { + "source_households": 20, + "below_minimum_wage_share": pytest.approx(0.5), + } + + +def test_spi_pay_floor_refuses_a_frame_without_hours() -> None: + person, household, rows, weights, twin_pay = _pay_floor_frames( + [{"age": 30, "weekly_hours": 30, "pay": 5_000.0}] + ) + with pytest.raises(ValueError, match="hours_worked"): + _floor( + person.drop(columns=["hours_worked"]), household, rows, weights, twin_pay + ) + + +def test_floored_rows_keep_the_tape_pay_as_their_year_of_pay() -> None: + person = pd.DataFrame( + { + "employment_income": [20_000.0, 30_000.0, 50_000.0], + "employment_income_annual_share": [1.0, 1.0, 0.4], + }, + index=[10, 11, 12], + ) + uplift = pd.Series([8_000.0, 0.0], index=[10, 11]) + kept = spi_income._keep_floored_rows_year_of_pay(person.copy(), uplift=uplift) + # Tape pay 12,000 of a floored 20,000: a 0.6 share; the others keep theirs. + np.testing.assert_allclose( + kept["employment_income_annual_share"].to_numpy(), [0.6, 1.0, 0.4] + ) + # Without the descriptors (a frame built before #1344) nothing moves. + bare = person.drop(columns=["employment_income_annual_share"]) + pd.testing.assert_frame_equal( + spi_income._keep_floored_rows_year_of_pay(bare.copy(), uplift=uplift), bare + ) + with pytest.raises(ValueError, match="must exceed its uplift"): + spi_income._keep_floored_rows_year_of_pay( + person.copy(), uplift=pd.Series([20_000.0], index=[10]) + ) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_pension_age.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_pension_age.py index cc16e1631..88b10ac05 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_pension_age.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_pension_age.py @@ -271,11 +271,13 @@ def test_band_resample_without_age_pooling_keeps_the_legacy_pools() -> None: assert receipt["bands"][0]["age_matched_carriers"] == 0 -def test_stage1_queries_sit_at_the_middle_of_the_year_of_age() -> None: +def test_stage1_queries_carry_the_spi_age_band() -> None: + # The forests read the SPI AGERANGE band of the whole-year age, not an + # age (uk-data#628): 24 is in 16-24 (1), 66 in 65-74 (6), 75 in 75+ (7). person = pd.DataFrame( { "person_household_id": [1, 1, 2], - "age": [65, 66, 74], + "age": [24, 66, 75], "gender": ["MALE", "FEMALE", "MALE"], # The bridge predictors read the row's own leaves (microcosm#840). "employment_income": [20_000.0, 0.0, 0.0], @@ -291,34 +293,39 @@ def test_stage1_queries_sit_at_the_middle_of_the_year_of_age() -> None: person, household, state_pension_age=66 ) - assert predictors["age"].tolist() == [65.5, 66.5, 74.5] + assert predictors[spi_income.SPI_STAGE1_AGE_BAND_COLUMN].tolist() == [1.0, 6.0, 7.0] + assert "age" not in predictors.columns assert predictors["region"].tolist() == ["LONDON", "LONDON", "WALES"] assert list(predictors.columns) == list(spi_income.SPI_STAGE1_PREDICTOR_COLUMNS) assert predictors["main_source"].tolist() == ["pay", "pension", "pension"] assert predictors["at_or_over_spa"].tolist() == [0.0, 1.0, 1.0] -def test_whole_year_frame_ages_draw_state_pension_from_their_own_year() -> None: - # Donors carry a fractional age and State Pension from 66.0. A frame - # person aged 66 queried at the whole year sits on the forest's split at - # that birthday; at the middle of the year every 66-year-old draws from - # the donors who have reached State Pension age (microcosm#1069). +def test_the_band_and_state_pension_flag_split_state_pension_inside_a_band() -> None: + # The forests no longer see an age inside the 65-74 band (uk-data#628), + # so the State Pension split there is at_or_over_spa's: a 65-year-old + # draws from the band's donors below State Pension age, and 66- and + # 67-year-olds from those who have reached it (microcosm#1069). from microcosm.frame import WeightKind rng = np.random.default_rng(0) count = 4_000 ages = rng.integers(60, 75, size=count) + rng.random(count) + at_or_over_spa = (ages >= 66.0).astype(float) donor = pd.DataFrame( { - "age": ages, + spi_income.SPI_STAGE1_AGE_BAND_COLUMN: spi_income._spi_agerange_codes( + np.floor(ages) + ).astype(float), "gender": np.where(rng.random(count) < 0.5, "MALE", "FEMALE"), "region": "LONDON", + "at_or_over_spa": at_or_over_spa, } ) targets = pd.DataFrame( { spi_income.SPI_HMRC_STATE_PENSION_INCOME_COLUMN: np.where( - ages >= 66.0, 9_000.0 + 1_500.0 * rng.random(count), 0.0 + at_or_over_spa > 0, 9_000.0 + 1_500.0 * rng.random(count), 0.0 ), "employment_income": np.where(ages < 66.0, 25_000.0, 4_000.0) * rng.random(count), @@ -332,10 +339,13 @@ def test_whole_year_frame_ages_draw_state_pension_from_their_own_year() -> None: } ) household = pd.DataFrame({"household_id": [1], "region": ["LONDON"]}) - # The age rule alone is under test, so the query carries the donor's three - # predictors at the middle of the year of age, without the bridge. query = spi_income._person_predictors(person, household, income_predictors=()) - query["age"] = np.floor(query["age"].to_numpy(dtype=float)) + 0.5 + query[spi_income.SPI_STAGE1_AGE_BAND_COLUMN] = spi_income._spi_agerange_codes( + query["age"].to_numpy(dtype=float) + ).astype(float) + query["at_or_over_spa"] = (query["age"].to_numpy(dtype=float) >= 66.0).astype(float) + query = query[list(donor.columns)] + assert set(query[spi_income.SPI_STAGE1_AGE_BAND_COLUMN]) == {6.0} encoded_donor, encoded_query = spi_income._encode_predictor_pair(donor, query) frame = spi_income._person_fit_frame( predictors=encoded_donor, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_rank_transport.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_rank_transport.py index dc03d0c9d..db8f7878a 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_rank_transport.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_rank_transport.py @@ -267,7 +267,7 @@ def test_cell_folds_share_a_cell_between_twins_and_copies() -> None: { "gender": ["MALE", "MALE", "FEMALE", "FEMALE", "MALE"], "region": ["LONDON"] * 5, - "age": [30.5, 30.5, 40.5, 40.5, 50.5], + "spi_age_band": [2.0, 2.0, 3.0, 3.0, 4.0], "main_source": ["pay", "pay", "pension", "pension", "trade"], "has_pay": [1.0, 1.0, 0.0, 0.0, 0.0], "has_trade": [0.0, 0.0, 0.0, 0.0, 1.0], @@ -304,11 +304,15 @@ def test_cross_fit_locates_a_copy_on_a_forest_that_never_saw_its_twin() -> None: rng = np.random.default_rng(21) n = 600 ages = rng.integers(25, 60, n).astype(float) + 0.5 + # The stage's age predictor is the SPI band (uk-data#628): codes 2 to 5. + bands = np.floor((ages - 5.0) / 10.0) + # Band cells are coarse, so the frame spans regions to fill all five folds. + regions = np.array(["LONDON", "WALES", "SCOTLAND", "NORTH_EAST", "SOUTH_WEST"]) raw = pd.DataFrame( { "gender": np.where(rng.random(n) < 0.5, "MALE", "FEMALE"), - "region": "LONDON", - "age": ages, + "region": regions[rng.integers(0, len(regions), n)], + "spi_age_band": bands, "main_source": "pay", "has_pay": 1.0, "has_trade": 0.0, @@ -317,7 +321,7 @@ def test_cross_fit_locates_a_copy_on_a_forest_that_never_saw_its_twin() -> None: ) encoded = pd.DataFrame( { - "age": ages, + "spi_age_band": bands, "gender_MALE": (raw.gender == "MALE").astype(float), "gender_FEMALE": (raw.gender == "FEMALE").astype(float), } @@ -373,7 +377,7 @@ def fit_reference(encoded_train, observed_train): located = copies[folds[copies] == fold] assert not (set(located.tolist()) & rows_seen) assert len(rows_seen) == int((folds != fold).sum()) - # Within a cell (same whole-year age and gender, and here the same + # Within a cell (same SPI age band, gender and region, and here the same # earlier leaves) the located pay quantiles are monotone in the twin's # pay: one forest locates the whole cell and holds no twin's own value. pay = observed["hmrc_spi_pay"].to_numpy()[copies] @@ -381,8 +385,9 @@ def fit_reference(encoded_train, observed_train): cells = pd.DataFrame( { "cell": ( - np.floor(ages[copies]).astype(int).astype(str) + bands[copies].astype(int).astype(str) + raw.gender.to_numpy()[copies] + + raw.region.to_numpy()[copies] ), "pay": pay, "u": located, @@ -409,7 +414,7 @@ def fit_reference(encoded_train, observed_train): # A copy whose fold-key cell moved away from its twin's is refused: the # forest that located it could have seen the twin. moved = raw.iloc[copies].reset_index(drop=True) - moved["age"] = moved["age"] + 7.0 + moved["spi_age_band"] = moved["spi_age_band"] + 1.0 with pytest.raises(ValueError, match="differs from its twin's"): cross_fit_transport( fit_reference, @@ -429,8 +434,10 @@ def fit_reference(encoded_train, observed_train): ) # A frame of one cell cannot hold anything out: refused unless the caller # is a sampled rung or a synthetic fixture, and then the receipt says so. - one_cell = raw.iloc[:10].assign(age=30.5, gender="MALE") - one_encoded = encoded.iloc[:10].assign(age=30.5, gender_MALE=1.0, gender_FEMALE=0.0) + one_cell = raw.iloc[:10].assign(spi_age_band=2.0, gender="MALE", region="LONDON") + one_encoded = encoded.iloc[:10].assign( + spi_age_band=2.0, gender_MALE=1.0, gender_FEMALE=0.0 + ) folds_one, used_one = cell_fold_ids(one_cell, folds=5, seed=42) assert used_one == 1 one_cell_call = dict( @@ -495,11 +502,13 @@ def test_cross_fit_refuses_folds_that_disagree_on_sign_classes() -> None: rng = np.random.default_rng(5) n = 400 ages = rng.integers(25, 60, n).astype(float) + 0.5 + # The stage's age predictor is the SPI band (uk-data#628): codes 2 to 5. + bands = np.floor((ages - 5.0) / 10.0) raw = pd.DataFrame( { "gender": np.where(rng.random(n) < 0.5, "MALE", "FEMALE"), "region": "LONDON", - "age": ages, + "spi_age_band": bands, "main_source": "pay", "has_pay": 1.0, "has_trade": 0.0, @@ -508,7 +517,7 @@ def test_cross_fit_refuses_folds_that_disagree_on_sign_classes() -> None: ) encoded = pd.DataFrame( { - "age": ages, + "spi_age_band": bands, "gender_MALE": (raw.gender == "MALE").astype(float), "gender_FEMALE": (raw.gender == "FEMALE").astype(float), } diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_savings_rebase.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_savings_rebase.py new file mode 100644 index 000000000..30ed7a6ef --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_savings_rebase.py @@ -0,0 +1,468 @@ +"""The SPI donors' savings rebase (microcosm#1418 savings follow-up). + +The 2022-23 tape's interest and other income move to HMRC's published +2023-24 Table 3.7 amounts band by band, then on the OBR's savings income +growth to the frame's year, in place of each column's engine index. +""" + +from __future__ import annotations + +import sys +from types import SimpleNamespace + +import pandas as pd +import pytest + +from microcosm.build.uk_runtime import spi_income +from microcosm.build.uk_runtime.hmrc_uprating import uk_obr_savings_income_level +from microcosm.build.uk_runtime.spi_income import ( + SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION, + SPI_SAVINGS_REBASE_CONCEPTS, + SPI_SAVINGS_REBASE_YEAR, + rebase_spi_donor_savings, + spi_savings_rebase_published, +) + +PUBLISHED = { + "savings_interest_income": {12_570: 1_500.0, 50_000: 2_000.0, 1_000_000: 3_000.0}, + "other_investment_income": {12_570: 400.0, 50_000: 400.0, 1_000_000: 18_000.0}, +} + + +def _rows(amounts): + return [ + { + "concept": SPI_SAVINGS_REBASE_CONCEPTS[column], + "period": {"type": "tax_year", "value": SPI_SAVINGS_REBASE_YEAR}, + "dimensions": {"total_income_lower_bound": bound}, + "value": value, + } + for column, by_bound in amounts.items() + for bound, value in by_bound.items() + ] + + +def _donor() -> pd.DataFrame: + return pd.DataFrame( + { + "total_income": [5_000.0, 15_000.0, 15_000.0, 50_000.0, 2_000_000.0], + "FACT": [10.0, 5.0, 5.0, 2.0, 1.0], + "savings_interest_income": [100.0, 40.0, 60.0, 500.0, 1_000.0], + "other_investment_income": [0.0, 10.0, 30.0, 100.0, 9_000.0], + "dividend_income": [1.0, 2.0, 3.0, 4.0, 5.0], + } + ) + + +def test_the_rebased_tape_reproduces_the_published_bands() -> None: + donor = _donor() + rebased, receipt = rebase_spi_donor_savings(donor, PUBLISHED) + + # A donor at a band's lower bound sits in that band. + rows_by_band = {12_570: [1, 2], 50_000: [3], 1_000_000: [4]} + for column, by_bound in PUBLISHED.items(): + for bound, rows in rows_by_band.items(): + weighted = rebased.loc[rows, column] * rebased.loc[rows, "FACT"] + assert weighted.sum() == pytest.approx(by_bound[bound]) + # Interest: the tape's 500, 1,000 and 1,000 against 1,500, 2,000 and + # 3,000; a donor below the first band takes all bands' 6,500 over 2,500. + assert receipt["columns"]["savings_interest_income"]["band_factors"] == { + "12570": 3.0, + "50000": 2.0, + "1000000": 3.0, + } + assert rebased.loc[0, "savings_interest_income"] == pytest.approx(100.0 * 2.6) + assert receipt["columns"]["savings_interest_income"][ + "below_first_band_factor" + ] == pytest.approx(2.6) + # Other columns, and the tape passed in, are left as they were. + pd.testing.assert_series_equal(rebased["dividend_income"], donor["dividend_income"]) + assert donor.loc[0, "savings_interest_income"] == 100.0 + assert receipt["from_period"] == 2022 and receipt["to_period"] == 2023 + # The receipt carries factors, never a count of tape records. + assert set(receipt["columns"]["other_investment_income"]) == { + "concept", + "banding_factor", + "band_factors", + "below_first_band_factor", + "bands_without_tape_amounts", + "bands_too_thin_to_gross", + } + assert ( + receipt["columns"]["other_investment_income"]["bands_without_tape_amounts"] + == [] + ) + + +def test_a_band_the_tape_does_not_carry_is_left_as_it_is() -> None: + # A synthetic tape need not carry every band; there is nothing to scale + # there, and the receipt names the band. + donor = _donor() + donor.loc[4, "other_investment_income"] = 0.0 + rebased, receipt = rebase_spi_donor_savings(donor, PUBLISHED) + other = receipt["columns"]["other_investment_income"] + assert other["bands_without_tape_amounts"] == [1_000_000] + assert "1000000" not in other["band_factors"] + assert rebased.loc[4, "other_investment_income"] == 0.0 + # The other bands, and below the first, still rebase: 800 over 400. + assert other["below_first_band_factor"] == pytest.approx(2.0) + assert rebased.loc[3, "other_investment_income"] == pytest.approx(200.0) + + +def test_the_published_rows_are_checked() -> None: + rows = _rows(PUBLISHED) + assert spi_savings_rebase_published(rows) == PUBLISHED + stale = [dict(rows[0], period={"type": "tax_year", "value": 2022}), *rows[1:]] + with pytest.raises(ValueError, match="describes 2022"): + spi_savings_rebase_published(stale) + with pytest.raises(ValueError, match="two rows from"): + spi_savings_rebase_published([*rows, rows[0]]) + uneven = [ + row + for row in rows + if not ( + row["concept"] == SPI_SAVINGS_REBASE_CONCEPTS["other_investment_income"] + and row["dimensions"]["total_income_lower_bound"] == 50_000 + ) + ] + with pytest.raises(ValueError, match="same total-income bands"): + spi_savings_rebase_published(uneven) + + +def test_the_committed_rows_give_both_columns_table_3_7s_thirteen_bands() -> None: + published = spi_savings_rebase_published() + assert {column: len(bands) for column, bands in published.items()} == { + "savings_interest_income": 13, + "other_investment_income": 13, + } + assert min(published["savings_interest_income"]) == 12_570 + # SPI 2023-24 Table 3.7, all bands: GBP 18.34bn of interest and GBP + # 5.85bn of other income. + assert sum(published["savings_interest_income"].values()) == pytest.approx( + 18.343e9, rel=1e-3 + ) + assert sum(published["other_investment_income"].values()) == pytest.approx( + 5.846e9, rel=1e-3 + ) + + +def test_the_savings_columns_grow_on_the_obr_path_from_2023_24(monkeypatch) -> None: + variables = { + name: SimpleNamespace(uprating="index") + for name in spi_income.SPI_INCOME_UPRATING_VARIABLES.values() + if name is not None + } + + def parameters(period): + return SimpleNamespace(index=100.0 if period.startswith("2022") else 120.0) + + system = SimpleNamespace(variables=variables, parameters=parameters) + monkeypatch.setitem( + sys.modules, + "policyengine_uk", + SimpleNamespace(CountryTaxBenefitSystem=lambda: system), + ) + factors, receipt = spi_income._spi_income_uprating_factors.__wrapped__(2024) + + level, used = uk_obr_savings_income_level(2023, 2024) + # The OBR's +35.0% for 2024-25 (October 2026 EFO, Table 4.5). + assert level == pytest.approx(1.3496108, abs=1e-7) + assert sorted(used) == [2024] + for column in SPI_SAVINGS_REBASE_CONCEPTS: + row = receipt["columns"][column] + assert row["basis"] == "spi_published_bands_then_obr_savings_income" + assert row["from_period"] == SPI_SAVINGS_REBASE_YEAR + assert factors[column] == pytest.approx(level) + assert ( + row["dataset_projection_index"] + == (SPI_HISTORY_INDEX_DIFFERS_FROM_PROJECTION[column]) + ) + # Every other indexed column keeps its engine index from the tape's year. + assert factors["dividend_income"] == pytest.approx(1.2) + # Before 2023-24 there is nothing to rebase to: the source year is the + # identity, and the path refuses to run backwards. + unchanged, _ = spi_income._spi_income_uprating_factors.__wrapped__(2022) + assert unchanged["other_investment_income"] == pytest.approx(1.0) + with pytest.raises(ValueError, match="runs forward only"): + uk_obr_savings_income_level(2024, 2023) + + +def test_the_factors_read_the_tapes_taxpayers() -> None: + # Table 3.7 counts taxpayers only, so a band's factor is its published + # amount over the tape's taxpayers' amount there; a donor who pays no tax + # takes the factor of all bands together (option F of the microcosm#1428 + # other-income investigation). + donor = _donor() + donor[spi_income.SPI_DONOR_TAXPAYER_COLUMN] = [False, True, False, True, True] + rebased, receipt = rebase_spi_donor_savings(donor, PUBLISHED) + interest = receipt["columns"]["savings_interest_income"] + assert receipt["factors_over"].startswith("taxpayers") + # The £12,570 band's taxpayers hold 5 x 40 = 200 of the tape's interest + # there: 1,500 over 200. All bands: 6,500 over 2,200. + assert interest["band_factors"]["12570"] == pytest.approx(7.5) + assert rebased.loc[1, "savings_interest_income"] == pytest.approx(40.0 * 7.5) + all_bands = 6_500.0 / 2_200.0 + assert interest["below_first_band_factor"] == pytest.approx(all_bands) + assert rebased.loc[2, "savings_interest_income"] == pytest.approx(60.0 * all_bands) + # The band's taxpayers reproduce its published amount. + weighted = rebased.loc[[1], "savings_interest_income"] * rebased.loc[[1], "FACT"] + assert weighted.sum() == pytest.approx(1_500.0) + # Without the flag every record counts, as before. + _, plain = rebase_spi_donor_savings(_donor(), PUBLISHED) + assert plain["factors_over"] == "all_records_at_or_above_the_first_band" + + +def test_a_tape_that_does_not_gross_to_the_population_is_left_as_it_is() -> None: + # A synthetic tape's weights do not gross to the published amounts, and its + # factors would run to millions; the rebase leaves such a column as it is. + donor = _donor() + donor["FACT"] = donor["FACT"] / 1e6 + rebased, receipt = rebase_spi_donor_savings(donor, PUBLISHED) + for column in PUBLISHED: + assert "skipped" in receipt["columns"][column] + pd.testing.assert_series_equal(rebased[column], donor[column]) + + +def test_gift_leaves_are_anchored_to_the_tapes_weighted_totals() -> None: + # Two copies drawn, one FRS row and one undrawn copy; the tape's + # FACT-weighted totals are 3,000 of Gift Aid and 600 of investment gifts. + person = pd.DataFrame( + { + "person_id": [1, 2, 3, 4], + "person_household_id": [10, 20, 30, 40], + "gift_aid": [100.0, 300.0, 50.0, 70.0], + "charitable_investment_gifts": [0.0, 400.0, 0.0, 9.0], + } + ) + household = pd.DataFrame( + {"household_id": [10, 20, 30, 40], "household_weight": [2.0, 3.0, 5.0, 7.0]} + ) + donor = pd.DataFrame( + { + "FACT": [10.0, 20.0], + "gift_aid": [100.0, 100.0], + "charitable_investment_gifts": [0.0, 30.0], + } + ) + drawn = pd.Series([True, True, False, False]) + anchored, receipt = spi_income._anchor_spi_gifts_to_tape( + person.copy(), household, donor, spi_people=drawn + ) + weights = pd.Series([2.0, 3.0]) + # The drawn copies carried 1,100 of Gift Aid and 1,200 of investment gifts. + assert (weights * anchored.loc[:1, "gift_aid"]).sum() == pytest.approx(3_000.0) + assert (weights * anchored.loc[:1, "charitable_investment_gifts"]).sum() == ( + pytest.approx(600.0) + ) + assert receipt["factors"] == pytest.approx( + {"gift_aid": 3_000.0 / 1_100.0, "charitable_investment_gifts": 0.5} + ) + # Rows the stage did not draw keep their values. + assert anchored.loc[2:, "gift_aid"].tolist() == [50.0, 70.0] + assert anchored.loc[2:, "charitable_investment_gifts"].tolist() == [0.0, 9.0] + # A column the tape carries none of is left as drawn. + _, receipt = spi_income._anchor_spi_gifts_to_tape( + person.copy(), + household, + donor.assign(charitable_investment_gifts=0.0), + spi_people=drawn, + ) + assert receipt["factors"]["charitable_investment_gifts"] == 1.0 + + +def test_drawn_rows_drop_their_twins_annuity_and_trust_descriptor() -> None: + # A drawn row's private pension is the tape's, which books annuity and + # trust payments as other income; an undrawn copy keeps its twin's part + # (microcosm#1428). + person = pd.DataFrame({"annuity_and_trust_income": [500.0, 700.0, 0.0]}) + drawn = pd.Series([True, False, False]) + cleared = spi_income._clear_annuity_and_trust_descriptor( + person.copy(), spi_people=drawn + ) + assert cleared["annuity_and_trust_income"].tolist() == [0.0, 700.0, 0.0] + # A frame without the descriptor is left as it is. + bare = pd.DataFrame({"private_pension_income": [1.0]}) + assert spi_income._clear_annuity_and_trust_descriptor( + bare.copy(), spi_people=pd.Series([True]) + ).equals(bare) + + +INTEREST_ONLY = {"savings_interest_income": {12_570: 100.0, 50_000: 400.0}} + + +def _edge_donor() -> pd.DataFrame: + # A just below the £50,000 edge in 2022-23, B well above it. + return pd.DataFrame( + { + "total_income": [49_950.0, 60_000.0], + "FACT": [100.0, 100.0], + "savings_interest_income": [1.0, 1.0], + "dividend_income": [5_000.0, 0.0], + } + ) + + +def test_the_tape_is_banded_on_its_income_moved_to_2023_24() -> None: + # Banded on its 2022-23 income, A sits in the £12,570 band. Moved to + # 2023-24 (dividends x1.1, interest at all bands' 500 over 200), it crosses + # the £50,000 edge, the band the consumers will find it in after uprating, + # so its migration is counted once (microcosm#1428 review, finding 6). + held, held_receipt = rebase_spi_donor_savings(_edge_donor(), INTEREST_ONLY) + assert held["savings_interest_income"].tolist() == pytest.approx([1.0, 4.0]) + moved, receipt = rebase_spi_donor_savings( + _edge_donor(), INTEREST_ONLY, income_factors={"dividend_income": 1.1} + ) + assert moved["savings_interest_income"].tolist() == pytest.approx([2.0, 2.0]) + assert receipt["columns"]["savings_interest_income"]["band_factors"] == { + "50000": pytest.approx(2.0) + } + assert receipt["band_by"] == "total_income_moved_to_2023_24" + assert receipt["columns"]["savings_interest_income"][ + "banding_factor" + ] == pytest.approx(2.5) + + +def test_savings_move_by_their_factor_over_all_bands_for_the_banding() -> None: + # A's interest is a large part of its income. All bands' factor (3,000 + # over the tape's 600) moves it from 500 to 2,500 for the banding, which + # takes A across the £50,000 edge; there the band's factor is 2,000 over + # 600 for both records, and A's rebased income stays above the edge. One + # pass: a record's own band's factor would set the band it is banded in + # (microcosm#1428 review, finding 6). + donor = pd.DataFrame( + { + "total_income": [49_000.0, 60_000.0], + "FACT": [1.0, 1.0], + "savings_interest_income": [500.0, 100.0], + } + ) + rebased, receipt = rebase_spi_donor_savings( + donor, {"savings_interest_income": {12_570: 1_000.0, 50_000: 2_000.0}} + ) + interest = receipt["columns"]["savings_interest_income"] + assert interest["banding_factor"] == pytest.approx(5.0) + assert interest["band_factors"] == {"50000": pytest.approx(2_000.0 / 600.0)} + assert interest["bands_without_tape_amounts"] == [12_570] + assert rebased["savings_interest_income"].tolist() == pytest.approx( + [500.0 * 2_000.0 / 600.0, 100.0 * 2_000.0 / 600.0] + ) + assert 48_500.0 + rebased.loc[0, "savings_interest_income"] >= 50_000.0 + + +def test_a_saver_who_becomes_a_taxpayer_counts_once() -> None: + # C pays no tax in 2022-23 and crosses the Personal Allowance by 2023-24; + # D is a taxpayer throughout. Both are 2023-24 taxpayers in the £12,570 + # band, so their 2,000 meets its 4,000 at a factor of 2. Counting only D + # (factor 4) and giving C the all-bands factor would count C twice + # (microcosm#1428 review, finding 10). + donor = pd.DataFrame( + { + "total_income": [12_000.0, 20_000.0], + "FACT": [10.0, 10.0], + "savings_interest_income": [100.0, 100.0], + "dividend_income": [1_000.0, 0.0], + spi_income.SPI_DONOR_TAXPAYER_COLUMN: [False, True], + } + ) + rebased, receipt = rebase_spi_donor_savings( + donor, + {"savings_interest_income": {12_570: 4_000.0, 50_000: 1.0}}, + income_factors={"dividend_income": 1.6}, + ) + assert receipt["columns"]["savings_interest_income"]["band_factors"][ + "12570" + ] == pytest.approx(2.0) + weighted = rebased["savings_interest_income"] * rebased["FACT"] + assert weighted.sum() == pytest.approx(4_000.0) + + +def test_a_flagged_taxpayer_below_the_first_band_sits_in_it() -> None: + # Table 3.7's lowest band holds taxpayers below the Personal Allowance + # (note 8): a flagged taxpayer there shares the band's factor. + donor = pd.DataFrame( + { + "total_income": [8_000.0, 20_000.0], + "FACT": [10.0, 10.0], + "savings_interest_income": [100.0, 100.0], + spi_income.SPI_DONOR_TAXPAYER_COLUMN: [True, True], + } + ) + rebased, _ = rebase_spi_donor_savings( + donor, {"savings_interest_income": {12_570: 6_000.0, 50_000: 1.0}} + ) + assert rebased["savings_interest_income"].tolist() == pytest.approx([300.0, 300.0]) + + +def test_a_band_too_thin_to_gross_takes_the_all_bands_factor() -> None: + # The £50,000 band's tape holds 10 against a published 1,000, under a + # tenth: its factor would be 100, so it takes the all-bands factor and is + # named (microcosm#1428 review, finding 10). + donor = pd.DataFrame( + { + "total_income": [20_000.0, 60_000.0], + "FACT": [10.0, 1.0], + "savings_interest_income": [100.0, 10.0], + } + ) + rebased, receipt = rebase_spi_donor_savings( + donor, {"savings_interest_income": {12_570: 2_000.0, 50_000: 1_000.0}} + ) + interest = receipt["columns"]["savings_interest_income"] + assert interest["bands_too_thin_to_gross"] == [50_000] + assert "50000" not in interest["band_factors"] + assert rebased.loc[1, "savings_interest_income"] == pytest.approx( + 10.0 * interest["below_first_band_factor"] + ) + + +def test_a_skipped_column_keeps_its_engine_index_from_2022_23(monkeypatch) -> None: + variables = { + name: SimpleNamespace(uprating="index") + for name in spi_income.SPI_INCOME_UPRATING_VARIABLES.values() + if name is not None + } + + def parameters(period): + return SimpleNamespace(index=100.0 if period.startswith("2022") else 120.0) + + monkeypatch.setitem( + sys.modules, + "policyengine_uk", + SimpleNamespace( + CountryTaxBenefitSystem=lambda: SimpleNamespace( + variables=variables, parameters=parameters + ) + ), + ) + factors, receipt = spi_income._spi_income_uprating_factors.__wrapped__( + 2024, rebased_columns=frozenset({"other_investment_income"}) + ) + # Interest the rebase left as it was grows on its engine index from + # 2022-23, not on the OBR's path from 2023-24 (microcosm#1428 review). + assert receipt["columns"]["savings_interest_income"]["basis"] == "model_index" + assert factors["savings_interest_income"] == pytest.approx(1.2) + assert receipt["columns"]["other_investment_income"]["basis"] == ( + "spi_published_bands_then_obr_savings_income" + ) + + +def test_the_tape_taxpayer_flag_is_validated(tmp_path) -> None: + from test_support.microcosm_build.uk_spi_income import _write_donor + + path = tmp_path / "donor.tab" + _write_donor(path) + raw = pd.read_csv(path, delimiter="\t") + # Without the flag the prepared tape carries none. + assert spi_income.SPI_DONOR_TAXPAYER_COLUMN not in ( + spi_income.prepare_spi_donor_table(raw, seed=0) + ) + flagged = raw.assign(TAXPAYER=[1, 2, 1, 2]) + prepared = spi_income.prepare_spi_donor_table(flagged, seed=0) + assert prepared[spi_income.SPI_DONOR_TAXPAYER_COLUMN].tolist() == [ + True, + False, + True, + False, + ] + with pytest.raises(ValueError, match="TAXPAYER"): + spi_income.prepare_spi_donor_table(raw.assign(TAXPAYER=[1, 0, 1, 2]), seed=0) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py index c137c57ef..53e439b25 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_spine.py @@ -24,6 +24,7 @@ SPI_DONOR_REQUIRED_COLUMNS, SPI_INCOME_QRF_OUTPUT_COLUMNS, SPIDonorAgeModel, + SPIEmploymentPayFloor, impute_uk_spi_income_support, ) from microcosm.build.uk_runtime.spi_spine import ( @@ -62,6 +63,8 @@ def _base_frame(*, time_period: str = "2023") -> object: "dividend_income": [20.0, 10.0], "private_pension_income": [0.0, 0.0], "property_income": [0.0, 0.0], + # The twin's other income locates the copy's draw (microcosm#1428). + "other_investment_income": [0.0, 0.0], "employee_pension_contributions": [3.0, 2.0], "employer_pension_contributions": [9.0, 6.0], "hmrc_spi_pay": [0.0, 0.0], @@ -70,6 +73,8 @@ def _base_frame(*, time_period: str = "2023") -> object: "ossben_identifiable_subset": [0.0, 0.0], "srp_regular_code5": [0.0, 0.0], "employment_status": ["FT_EMPLOYED", "FT_EMPLOYED"], + # Annual usual hours, as frs_spine writes them. + "hours_worked": [30 * 365.25 / 7, 0.0], "miscellaneous_income": [0.0, 0.0], **{ column: [0.0, 0.0] @@ -609,7 +614,7 @@ def test_spi_spine_parsed_inputs_match_the_path_resolution( # This test checks parsed/path source resolution, independently of the engine. # Real index coverage lives in test_uk_spi_income.py. - def uprating_factors(year): + def uprating_factors(year, *, rebased_columns=None): return ( {column: 1.25 for column in SPI_INCOME_QRF_OUTPUT_COLUMNS}, {"from_period": 2022, "to_period": year, "basis": "synthetic_test"}, @@ -629,6 +634,19 @@ def uprating_factors(year): source="engine-free fixture", ), ) + # The minimum wage is an engine read too; tests/engine/uk/ + # test_uk_spi_income.py reads the real parameter. + monkeypatch.setattr( + spi_spine, + "load_spi_employment_pay_floor", + lambda _build_period: SPIEmploymentPayFloor( + age_thresholds=(0.0, 18.0, 21.0), + hourly_rates=(6.40, 8.60, 11.44), + weeks_in_year=52.0, + instant="2024-04-30", + source="engine-free fixture", + ), + ) support_frame = UKSPISupportChannelStageTransform( stage=_committed_stage("spi_support_channel"), sample_fraction=0.0002, @@ -717,6 +735,9 @@ def unexpected_loader(*_args, **_kwargs): assert uk_frame_content_identity(from_path) == uk_frame_content_identity(from_seam) assert path_transform.checkpoint_metadata() == seam_transform.checkpoint_metadata() + pay_floor = path_transform.checkpoint_metadata()["evidence"]["employment_pay_floor"] + assert pay_floor["step"] == "spi_employment_pay_floor_before_stage2" + assert pay_floor["source"] == "engine-free fixture" def test_income_stage_parameters_accept_the_committed_manifest() -> None: @@ -760,6 +781,12 @@ def test_income_stage_parameters_refuse_a_base_channel_redraw() -> None: "spi_synthetic_support_channel", "target population", ), + ( + "floor_spi_employment_pay_at_minimum_wage", + "weeks_in_year", + 365.25 / 7, + "floor_spi_employment_pay_at_minimum_wage declaration drifted", + ), ], ) def test_income_stage_parameters_refuse_a_recipient_domain_drift( diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py index d0208c0b8..b419b9069 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spine_acceptance_receipt.py @@ -148,9 +148,10 @@ def _apply_pending_roster_transformations( assert "child_benefit_take_up" not in roster roster.insert(roster.index("pension_credit_take_up") + 1, "child_benefit_take_up") # microcosm#1319 re-mint pending: the Carer's Allowance draw reads the - # post-SPI earnings, right after the Child Benefit redraw. + # post-SPI earnings, right after salary_sacrifice, whose converted pay + # its earnings test reads (microcosm#1095 B5). assert "carers_allowance_take_up" not in roster - roster.insert(roster.index("child_benefit_take_up") + 1, "carers_allowance_take_up") + roster.insert(roster.index("salary_sacrifice") + 1, "carers_allowance_take_up") # microcosm#1095 re-mint pending: the SPI benefit pass runs after the last # UC report writer and before every UC, PC and CB consumer. assert "spi_benefit_coherence" not in roster @@ -172,13 +173,17 @@ def _apply_pending_roster_transformations( # follows the copies' childcare re-draw. assert "private_school_attendance" not in roster roster.append("private_school_attendance") - # microcosm#1415 re-mint pending: the ISA subscriptions run last. + # microcosm#1415 re-mint pending: the ISA subscriptions follow it. assert "isa_subscriptions" not in roster roster.append("isa_subscriptions") # PolicyEngine/uk-autumn-budget-dashboard-2026#316 re-mint pending: the # company cars follow the SPI income stage, whose pay they carve. assert "company_cars" not in roster roster.insert(roster.index("hmrc_spi_income_spine") + 1, "company_cars") + # microcosm#1428 re-mint pending: the self-employed's gross receipts run + # last. + assert "trading_allowance_receipts" not in roster + roster.append("trading_allowance_receipts") return tuple(roster) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_health.py index 23d0c02e8..d4529eed0 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 @@ -2322,14 +2322,20 @@ def family(dwp, before, after, reachable, realized): "reachable_pools_caseload_after": realized, } - # 0.05 until microcosm#1418. + # 0.05 until microcosm#1418, and 0.055 after it: the savings follow-up + # (microcosm#1428) joins unmet pools instead of widening it. The caseload's + # was 0.12 for the SPI pay floor (uk-data#629) from María's ruling of + # 2026-10-29 until the pool joining brought every family type closer to + # DWP's count (her ruling of 2026-11-02). The reachable pools have a + # tolerance of their own (Vahid's review of #1426). parameters = { "stage": "uc_take_up", "check": "uc_take_up", "maximum_caseload_deviation": 0.055, + "maximum_reachable_pool_deviation": 0.055, } - def gate(families, **counts): + def gate(families, parameters=parameters, **counts): return uk_stage_health_gate( evidence={ "stage": "uc_take_up", @@ -2353,12 +2359,26 @@ def gate(families, **counts): assert not gate(good, outside="<3").passed # The reachable pools miss DWP's count by more than the tolerance. assert not gate( - {**good, "SINGLE": family(3_000.0, 3_300.0, 3_020.0, 2_500.0, 2_700.0)} + {**good, "SINGLE": family(3_000.0, 3_300.0, 3_020.0, 2_500.0, 2_900.0)} + ).passed + # The pools keep their own tolerance: 8% off fails even where the caseload's + # allows more. + assert not gate( + {**good, "SINGLE": family(3_000.0, 3_300.0, 3_020.0, 2_500.0, 2_700.0)}, + parameters={**parameters, "maximum_caseload_deviation": 0.12}, ).passed # The family type moves further from DWP than before. assert not gate( {**good, "LONE_PARENT": family(2_100.0, 1_400.0, 1_100.0, 0.0, 0.0)} ).passed + # A caseload at DWP's before the redraw may end up to 0.055 of DWP's count + # from it after, not 0.1. + assert _passed( + gate({**good, "SINGLE": family(3_000.0, 3_000.0, 2_850.0, 2_500.0, 2_510.0)}) + ) + assert not gate( + {**good, "SINGLE": family(3_000.0, 3_000.0, 2_700.0, 2_500.0, 2_510.0)} + ).passed # A caseload resting on fewer than three sources cannot be checked: it is # flagged, never failed (María's ruling of 2026-10-26); one missing fails. thin = gate({**good, "SINGLE": family(3_000.0, "<3", 1.0, 0.0, 0.0)}) @@ -2779,6 +2799,50 @@ def test_property_purchases_gate_flags_a_kind_on_fewer_than_three_buyers() -> No assert clean.details["kinds_unchecked_small_cells"] == [] +def _hourly_pay_receipt(after=0.10, frs=0.15, below_floor=0) -> dict: + return { + "stage": "hmrc_spi_income_spine", + "employment_pay_floor": { + "hourly_pay_check": { + "spi_drawn_employees": { + "source_households": "30+", + "below_minimum_wage_share_before": 0.37, + "below_minimum_wage_share_after": after, + }, + "frs_employees": { + "source_households": "30+", + "below_minimum_wage_share": frs, + }, + "eligible_rows_below_their_floor_after": below_floor, + } + }, + } + + +def test_spi_hourly_pay_gate_holds_drawn_employees_to_their_floor() -> None: + gate_id = "uk_stage_hmrc_spi_income_spine_hourly_pay" + passed = _gate(gate_id, _hourly_pay_receipt()) + assert _passed(passed) + # The details give the verdicts, never the shares. + assert passed.details == { + "stage": "hmrc_spi_income_spine", + "check": "spi_hourly_pay", + "eligible_rows_below_their_floor_after": 0, + "share_judged": True, + "share_within_margin": True, + } + assert _passed(_gate(gate_id, _hourly_pay_receipt(after=0.17, frs=0.15))) + above = _gate(gate_id, _hourly_pay_receipt(after=0.18, frs=0.15)) + assert "more often than FRS employees" in above.failures[0] + below_floor = _gate(gate_id, _hourly_pay_receipt(below_floor="<3")) + assert "below their pay floor" in below_floor.failures[0] + # A withheld share is not judged; the floor invariant still is. + withheld = _gate(gate_id, _hourly_pay_receipt(after="<3")) + assert _passed(withheld) and withheld.details["share_judged"] is False + missing = _gate(gate_id, {"stage": "hmrc_spi_income_spine"}) + assert "no employment pay floor" in missing.failures[0] + + def _home_values_receipt() -> dict: regions = [f"E1200000{index}" for index in range(1, 10)] + ["W92000004"] return { diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py index 734f040c4..4209f87b3 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_support_floor.py @@ -360,10 +360,12 @@ def test_the_packaged_floor_drops_only_unsupported_bands_and_binds_thin_ones(): }, # Extended on María's ruling of 2026-10-22 (the next policyengine-uk # bump), then to the SPI families microcosm#1418 binds: Table 3.7's - # other income and Table 3.10's self-employment bands. + # other income and Table 3.10's self-employment bands, and to the + # employment counts over those bands microcosm#1428 binds. "hmrc_spi_income_band_counts": { *(f"hmrc.spi.{family}.count_by_total_income_band" for family in spi), "hmrc.spi.self_employment_income.count_by_self_employment_income_band", + "hmrc.spi.employment_income.count_by_self_employment_income_band", }, "hmrc_spi_income_band_amounts": { *(f"hmrc.spi.{family}.amount_by_total_income_band" for family in spi), 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 46a82d305..a137a728e 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 @@ -77,7 +77,10 @@ # national calibration tests read it from the committed membership, or not at # all, so a change to the target set moves this pin alone. How it is made up: # microcosm#1418 PR C: + 13, SPI Table 3.7's other income by band, and + 16, -# SPI Table 3.10's self-employment bands from GBP 1 to GBP 10,000; +# SPI Table 3.10's self-employment bands from GBP 1 to GBP 10,000; its +# savings follow-up (microcosm#1428): + 8, Table 3.10's individuals with +# employment income over the same bands, and + 1, DWP's income-based +# Jobseeker's Allowance spending; # microcosm#1418: + 3, HMRC's Income Tax on savings at each rate; # microcosm#1069: + 24, HMRC's Income Tax payers of State Pension age and aged # 65 and over in the twelve region-tier areas; microcosm#1095 (the bump after @@ -125,7 +128,7 @@ # below the small-cell floor; microcosm#1095 (#1422): the three OBR council # tax nation rows, pinned to the UK, and SLC's three nation repayment rows in # place of the devolved total -ACTIVE_REFERENCE_COUNT = 1409 +ACTIVE_REFERENCE_COUNT = 1418 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER} @@ -1134,7 +1137,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: # repayment rows (R4), so no row is left without a fact. assert membership["status_counts"] == { "active": ACTIVE_REFERENCE_COUNT, - "signed_excluded": 30, + "signed_excluded": 37, } assert membership["genuine_sum_residue"] assert membership["uprating_holds"] @@ -1150,7 +1153,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None: { "family": "hmrc_spi", "status": "active_strict_band_pins", - "active_reference_count": 211, + "active_reference_count": 219, "signed_rationale": ( "SPI income-band targets fan out by strict total-income-band " "dimension pins, and SPI Table 3.10's by self-employment band, " diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_trading_allowance_receipts.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_trading_allowance_receipts.py new file mode 100644 index 000000000..0e15252f5 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_trading_allowance_receipts.py @@ -0,0 +1,147 @@ +"""The self-employed's gross receipts for the trading allowance (microcosm#1428).""" + +from __future__ import annotations + +from dataclasses import replace + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.country_spec import load_country_spec +from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.receipt_counts import rounded_count, small_cell_share +from microcosm.build.uk_runtime.trading_allowance_receipts import ( + TRADING_ALLOWANCE_RECEIPTS_OUTPUT, + TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME, + UKTradingAllowanceReceiptsStageTransform, + assign_trading_allowance_receipts, + claimant_rates, +) + + +def _stage(): + return load_country_spec("uk").sources.stage_map()[ + TRADING_ALLOWANCE_RECEIPTS_STAGE_NAME + ] + + +def _frame(profit: np.ndarray): + ids = np.arange(1, len(profit) + 1, dtype=np.int64) + household = pd.DataFrame( + { + "household_id": ids, + "household_source_id": ids, + "household_weight": 2.0, + "region": "LONDON", + } + ) + person = pd.DataFrame( + { + "person_id": ids, + "person_benunit_id": ids, + "person_household_id": ids, + "age": 40, + "self_employment_income": np.asarray(profit, dtype=np.float64), + } + ) + benunit = pd.DataFrame({"benunit_id": ids}) + return uk_national_frame( + person=person, benunit=benunit, household=household, time_period="2024" + ) + + +def test_claimant_rates_follow_the_profit_bands_with_inclusive_upper_bounds() -> None: + profit = np.asarray([-500.0, 0.0, 0.01, 4_500.0, 4_500.01, 9_000.0, 9_000.01]) + assert claimant_rates(profit).tolist() == [0.0, 0.0, 0.72, 0.72, 0.14, 0.14, 0.0] + + +def test_claimants_add_the_allowance_and_other_traders_the_raised_one() -> None: + profit = np.asarray([0.0, -1_200.0, 800.0, 6_000.0, 25_000.0] * 200) + ids = np.arange(len(profit)) + receipts, claimant = assign_trading_allowance_receipts(ids, profit) + trader = profit > 0.0 + # No positive profit: no receipts, which the engine reads as unknown. + assert (receipts[~trader] == 0.0).all() + assert not claimant[~trader].any() + np.testing.assert_allclose(receipts[claimant], profit[claimant] + 1_000.0) + np.testing.assert_allclose( + receipts[trader & ~claimant], profit[trader & ~claimant] + 3_000.0 + ) + # Nobody above GBP 9,000 claims; the draw is the person's own uniform. + assert not claimant[profit > 9_000.0].any() + uniforms = stable_identity_uniforms( + ids[trader], seed=0, salt="trading_allowance_claimant" + ) + assert ( + claimant[trader].tolist() + == (uniforms < claimant_rates(profit[trader])).tolist() + ) + # Two hundred draws per band land near the band's rate. + assert claimant[profit == 800.0].mean() == pytest.approx(0.72, abs=0.1) + assert claimant[profit == 6_000.0].mean() == pytest.approx(0.14, abs=0.1) + + +def test_the_stage_stores_receipts_deterministically_and_moves_nothing_else() -> None: + profit = np.tile([0.0, 900.0, 3_000.0, 7_000.0, 40_000.0], 40) + frame = _frame(profit) + first = UKTradingAllowanceReceiptsStageTransform(stage=_stage())(frame) + second = UKTradingAllowanceReceiptsStageTransform(stage=_stage())(frame) + person = first.table("person") + pd.testing.assert_series_equal( + person[TRADING_ALLOWANCE_RECEIPTS_OUTPUT], + second.table("person")[TRADING_ALLOWANCE_RECEIPTS_OUTPUT], + ) + expenses = ( + person[TRADING_ALLOWANCE_RECEIPTS_OUTPUT] - person["self_employment_income"] + ) + assert set(np.round(expenses[profit > 0.0], 6)) <= {1_000.0, 3_000.0} + assert (person.loc[profit <= 0.0, TRADING_ALLOWANCE_RECEIPTS_OUTPUT] == 0.0).all() + before = frame.table("person") + pd.testing.assert_frame_equal( + person.drop(columns=[TRADING_ALLOWANCE_RECEIPTS_OUTPUT]), before + ) + np.testing.assert_array_equal( + first.weights_for("household").values, frame.weights_for("household").values + ) + (record,) = first.mass_log[len(frame.mass_log) :] + assert record.declared_factor == 1.0 + assert record.old_total == record.new_total + + +def test_the_receipt_rounds_counts_and_bands_shares() -> None: + transform = UKTradingAllowanceReceiptsStageTransform(stage=_stage()) + transform(_frame(np.tile([500.0, 6_000.0, 20_000.0], 50))) + evidence = transform.checkpoint_metadata()["evidence"] + assert evidence["traders"] == 150 + assert isinstance(evidence["claimants"], int) + assert [band["rate"] for band in evidence["bands"]] == [0.72, 0.14, 0.0] + assert all(band["support"] == "30+" for band in evidence["bands"]) + assert evidence["bands"][0]["weighted_traders"] == pytest.approx(100.0) + # Fewer than ten source households behind a band print the small-cell + # label, whatever the rounding rules make it (microcosm#1428 review). + transform(_frame(np.asarray([500.0] * 5))) + small = transform.checkpoint_metadata()["evidence"] + assert small["traders"] == rounded_count(5) + assert small["bands"][0]["weighted_traders"] == small_cell_share(10.0, 5) + + +def test_the_stage_refuses_drifted_parameters_and_a_second_writer() -> None: + stage = _stage() + operation, receipt = stage.operations + drifted = replace( + stage, + operations=( + replace( + operation, + parameters={**dict(operation.parameters), "claimant_expenses": 900.0}, + ), + receipt, + ), + ) + with pytest.raises(ValueError, match="parameters drifted"): + UKTradingAllowanceReceiptsStageTransform(stage=drifted)(_frame([500.0])) + frame = UKTradingAllowanceReceiptsStageTransform(stage=stage)(_frame([500.0])) + with pytest.raises(ValueError, match="only writer"): + UKTradingAllowanceReceiptsStageTransform(stage=stage)(frame) 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 a822fecec..b10a09e7e 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 @@ -64,18 +64,16 @@ def test_stage_orders_after_every_universal_credit_report_writer() -> None: assert reporter_writers assert all(index < coherence_index for index, _ in reporter_writers) # The Universal Credit redraw (microcosm#1334), the Move to UC claim draw - # (microcosm#1095), the Pension Credit take-up redraw (microcosm#1069 c7), - # the Child Benefit redraw (microcosm#1063 c8) and the Carer's Allowance - # draw (microcosm#1319) sit between the coherence stage and the deduction - # attributes; none writes a UC report. + # (microcosm#1095), the Pension Credit take-up redraw (microcosm#1069 c7) + # and the Child Benefit redraw (microcosm#1063 c8) sit between the + # coherence stage and the deduction attributes; none writes a UC report. assert [ - stage.stage for stage in stages[coherence_index + 1 : coherence_index + 7] + stage.stage for stage in stages[coherence_index + 1 : coherence_index + 6] ] == [ "uc_take_up", "uc_legacy_closure_take_up", "pension_credit_take_up", "child_benefit_take_up", - "carers_allowance_take_up", "uc_deduction_attributes", ] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py index 047be4831..c0f48ccca 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_take_up.py @@ -11,6 +11,7 @@ from microcosm.build.country_spec import load_country_spec from microcosm.build.source_manifest import SourceStageSpec +from microcosm.build.uk_runtime import uc_take_up from microcosm.build.uk_runtime.frs_take_up import ( UK_TAKE_UP_SIGNAL_OUTPUTS, UKTakeUpPopulationPolicy, @@ -233,9 +234,11 @@ def test_a_pool_nearer_zero_than_its_first_unit_draws_no_claimant() -> None: assert would_claim.tolist() == [True, False] -def test_clipped_pools_are_flagged_unreachable() -> None: - # Band 0: reporters alone exceed DWP's count, so no drawn unit claims. - # Band 2: DWP's count exceeds every entitled unit, so all of them claim. +def test_a_family_type_that_cannot_meet_its_count_is_flagged_unreachable() -> None: + # Band 0: reporters alone exceed DWP's count; band 2: the count exceeds + # every entitled unit. Each band keeps its own pool: the two gaps offset, + # the remaining shortfall takes band 0's one drawn unit, and the family + # type still falls short, so every drawn unit claims (microcosm#1428). award = np.asarray([100.0] * 4 + [3_000.0] * 4) reporter = np.asarray([True, True, True, False, False, False, False, False]) would_claim, receipt = _assign( @@ -247,39 +250,102 @@ def test_clipped_pools_are_flagged_unreachable() -> None: source_ids=np.asarray([0, 0, 1, 1, 4, 5, 6, 7]), minimum_pool_sources=1, ) - assert would_claim.tolist() == [True, True, True, False, True, True, True, True] + assert would_claim.all() flags = { - tuple(pool["bands"]): pool["target_unreachable"] + pool["bands"][0]: pool["target_unreachable"] for pool in receipt["pools"] if pool["family_type"] == "SINGLE" } - # A pool on fewer than three sources withholds its flag. - assert flags[(UC_AWARD_BAND_LABELS[0],)] == "<3" - pools = receipt["pools"] - assert all("rate" not in pool for pool in pools) - # With three or more sources behind a pool the flag says which side it is on. - many = np.asarray([100.0] * 12 + [3_000.0] * 12) + # A pool on fewer than three sources withholds its flag; band 2's pool, + # with the empty band 1 and those above, rests on four. + assert flags == { + UC_AWARD_BAND_LABELS[0]: "<3", + UC_AWARD_BAND_LABELS[1]: "target_exceeds_entitled", + } + assert all("rate" not in pool for pool in receipt["pools"]) + # With three or more sources behind a pool the flag says which side it is + # on: the singles' reporters exceed their count, and the couples' count + # exceeds every entitled unit. + family = np.asarray(["SINGLE"] * 12 + ["COUPLE_NO_CHILDREN"] * 12, dtype=object) _, receipt = _assign( - award=many, - reporter=np.asarray([True] * 10 + [False] * 14), - targets=_targets(SINGLE_0=2.0, SINGLE_2=50.0), + award=np.asarray([100.0] * 12 + [3_000.0] * 12), + family=family, + reporter=np.asarray([True] * 12 + [False] * 12), + targets=_targets(SINGLE_0=2.0, COUPLE_NO_CHILDREN_2=50.0), minimum_pool_sources=1, ) + flags = { + pool["family_type"]: pool["target_unreachable"] + for pool in receipt["pools"] + if pool["family_type"] in {"SINGLE", "COUPLE_NO_CHILDREN"} + } + assert flags == { + "SINGLE": "undrawn_claims_reach_target", + "COUPLE_NO_CHILDREN": "target_exceeds_entitled", + } + # No pool is reachable, so the gate's reachable totals are empty. + assert receipt["families"]["SINGLE"]["reachable_pools_dwp_caseload"] == 0.0 - # Band 1 holds no unit, so it pools with band 2, and the empty bands above - # join that pool. - def flag(label): - return next( - pool["target_unreachable"] - for pool in receipt["pools"] - if pool["family_type"] == "SINGLE" and label in pool["bands"] - ) - assert flag(UC_AWARD_BAND_LABELS[0]) == "undrawn_claims_reach_target" - assert flag(UC_AWARD_BAND_LABELS[2]) == "target_exceeds_entitled" +def test_an_unmet_pool_passes_its_gap_to_the_neighbour_with_more_room() -> None: + # Band 0: four drawn units and a count of 3 (one to spare); band 2: two + # drawn units and a count of 5 (three short); band 4: ten drawn units and a + # count of 5 (five to spare). Band 2 is held at its two claims and passes + # its three to band 4, which has more room; band 0 keeps its own count, and + # the singles land on DWP's 13 (microcosm#1428 review, finding 7). + award = np.asarray([600.0] * 4 + [3_000.0] * 2 + [5_500.0] * 10) + would_claim, receipt = _assign( + award=award, targets=_targets(SINGLE_0=3.0, SINGLE_2=5.0, SINGLE_4=5.0) + ) + assert int(would_claim[:4].sum()) == 3 + assert would_claim[4:6].all() + assert int(would_claim[6:].sum()) == 8 single = receipt["families"]["SINGLE"] - # No pool is reachable, so the gate's reachable totals are empty. - assert single["reachable_pools_dwp_caseload"] == 0.0 + assert single["caseload_after"] == pytest.approx(13.0) + assert single["reachable_pools_dwp_caseload"] == pytest.approx(13.0) + # On equal room the lower neighbour takes the gap. + award = np.asarray([600.0] * 3 + [3_000.0] + [5_500.0] * 3) + would_claim, _ = _assign( + award=award, targets=_targets(SINGLE_0=1.0, SINGLE_2=2.0, SINGLE_4=1.0) + ) + assert [ + int(would_claim[:3].sum()), + int(would_claim[3]), + int(would_claim[4:].sum()), + ] == [2, 1, 1] + + +def test_an_overshooting_pool_passes_its_excess_to_one_with_drawn_claims() -> None: + # Band 0: three reporters, one drawn unit and a count of 1 (two over); + # band 2: six drawn units and a count of 4. Band 0 is held at its + # reporters and its drawn unit does not claim; its two over come off band + # 2, whose drawn units carry the two claims DWP's 5 leaves. A joined pool + # had let band 0's drawn unit claim (microcosm#1428 review, finding 7). + award = np.asarray([600.0] * 4 + [3_000.0] * 6) + reporter = np.asarray([True] * 3 + [False] * 7) + would_claim, receipt = _assign( + award=award, reporter=reporter, targets=_targets(SINGLE_0=1.0, SINGLE_2=4.0) + ) + assert would_claim[:3].all() + assert not would_claim[3] + assert int(would_claim[4:].sum()) == 2 + assert receipt["families"]["SINGLE"]["caseload_after"] == pytest.approx(5.0) + + +def test_gaps_offset_before_they_pass() -> None: + # One pool overshoots by 1 and another falls short by 6: the excess + # offsets the shortfall, and only the remaining 5 look for room + # (microcosm#1428 review, finding 7). + counts, unplaced = uc_take_up._pass_unmet_gaps([(2.0, 3.0, 1.0), (10.0, 0.0, 4.0)]) + assert counts == [4.0, 4.0] + assert unplaced == [0.0, 4.0] + # A feasible family type keeps its total: 3 + 5 + 5 over pools that hold + # 0-4, 0-2 and 0-10. + counts, unplaced = uc_take_up._pass_unmet_gaps( + [(3.0, 0.0, 4.0), (5.0, 0.0, 2.0), (5.0, 0.0, 10.0)] + ) + assert counts == [3.0, 2.0, 8.0] + assert not any(unplaced) def test_undrawn_units_keep_their_flag_and_count_at_their_expected_claim() -> None: diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_target_measurements.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_target_measurements.py index 36213c3ad..3b8b47893 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_target_measurements.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_target_measurements.py @@ -23,9 +23,11 @@ from microcosm.frame import EntitySchema, Frame, WeightKind, Weights -def _fixture(): +def _fixture(*, third_child_born_early_2017=False): # Own QYP ages, award, dwelling. Units 1, 3, 4 share a dwelling: two - # affected claims and an unrelated claimant with a baby and PIP. + # affected claims and an unrelated claimant with a baby and PIP. A child + # of 9 or over was born before 6 April 2017; the 8-year-old third child of + # unit 5 after it, or with the keyword from January to 5 April 2017. scenarios = [ ([19, 17, 15, 13, 11], 100, 0), # five older qualifying children ([19, 10, 3], 100, 1), # includes an older QYP; child gets PIP @@ -47,7 +49,8 @@ def _fixture(): person_benunit_id=bu, person_household_id=hh, age=age, - birth_year=2025 - age, + born_before=i > 0 + and (age >= 9 or (bu == 5 and third_child_born_early_2017)), is_benunit_head=i == 0, is_parent=i == 0, qyp=i > 0 or bu == 3, # young claimant also QYP: exclude @@ -70,6 +73,7 @@ def _fixture(): "is_child_or_qualifying_young_person_for_universal_credit": "qyp", "uc_individual_disabled_child_element": "disabled_element", "uc_is_child_limit_affected": "denied_element", + "uc_is_child_born_before_child_limit": "born_before", } def calculate(variable, year): @@ -116,6 +120,26 @@ def test_tcl_counts_include_exceptions_and_do_not_use_denied_element_flags(): assert before[6] and not before[0] +def test_tcl_dates_births_by_the_engine_cutoff_not_the_calendar_year(): + """A third child born from January to 5 April 2017 is not affected: the + measure reads the engine's own cutoff test, not the birth year + (microcosm#1095 AC3).""" + for variable in ( + "uc_tcl_affected_child_count_proxy", + "uc_tcl_affected_benunit_proxy", + ): + frame, sim = _fixture() + after_cutoff, _ = compute_uk_measure_input( + frame, sim, "benunit", variable, 2025 + ) + frame, sim = _fixture(third_child_born_early_2017=True) + early_2017, _ = compute_uk_measure_input(frame, sim, "benunit", variable, 2025) + assert after_cutoff[5] == 1 and early_2017[5] == 0 + np.testing.assert_array_equal( + np.delete(after_cutoff, 5), np.delete(early_2017, 5) + ) + + def test_tcl_receipt_explicitly_records_remaining_claim_date_and_period_proxies(): frame, sim = _fixture() resolver = UKMeasureResolver.__new__(UKMeasureResolver) @@ -130,7 +154,10 @@ def test_tcl_receipt_explicitly_records_remaining_claim_date_and_period_proxies( resolver.compute("benunit", variable) contract = resolver.receipt()["uc_tcl_comparison_contract"] assert contract["source_birth_cutoff"] == "2017-04-06" - assert contract["model_birth_cutoff_proxy"] == "model birth_year >= 2017" + assert contract["model_birth_cutoff_proxy"] == ( + "policyengine-uk uc_is_child_born_before_child_limit: date of birth " + "before 2017-04-06" + ) assert contract["model_claim_state_proxy"] == "universal_credit > 0" assert contract["status"] == "partial_alignment_with_explicit_proxies" assert contract["model_period"] == "2025" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index ec54058b5..04c42bacb 100644 --- a/packages/microcosm-data/src/microcosm/data/contract.py +++ b/packages/microcosm-data/src/microcosm/data/contract.py @@ -451,13 +451,13 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: # fingerprint derives from the manifest digest. Editing the spec moves all # three here in the same reviewed change. _UK_GATE_BATTERY_POLICY_SHA256 = ( - "cac5b2786970a1a1b0a6684be0322841a5383e675c095fad7f9a6b37e344313b" + "a122451b95dceef568d5010f9bb10e28bdbd541b018130a49de2dca28918a245" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "66c83b391237abf8df13abfe551936cc008a805980b73e87eb11b96b13a2ef2b" + "54715004a5c750dab827297401073948b30105aa390e01afd76e3e01394ea702" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "426c45bc1413477f66cea2766a977042c10a769f7ac3ef285c307dea57703577" + "d5f9b680538cf546f1151f77b1c7caf42db5e8ba4bacc4ad12d8a52b8d364764" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -529,6 +529,7 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "stage_health", "transferred", ), + "uk_stage_hmrc_spi_income_spine_hourly_pay": ("stage_health", "transferred"), "uk_stage_spi_benefit_coherence": ("stage_health", "transferred"), "uk_stage_uc_take_up": ("stage_health", "transferred"), "uk_stage_pension_credit_take_up": ("stage_health", "transferred"), @@ -655,6 +656,7 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "uk_stage_frs_annual_income_spells", "uk_stage_spi_support_channel_mass", "uk_stage_hmrc_spi_income_spine_identity", + "uk_stage_hmrc_spi_income_spine_hourly_pay", "uk_stage_spi_benefit_coherence", "uk_stage_uc_take_up", "uk_stage_pension_credit_take_up", @@ -909,6 +911,7 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: "uk_stage_high_value_home_support_mass", "uk_stage_hmrc_cgt_asset_type_spine_summary", "uk_stage_hmrc_cgt_gains_spine_summary", + "uk_stage_hmrc_spi_income_spine_hourly_pay", "uk_stage_hmrc_spi_income_spine_identity", "uk_stage_isa_subscriptions", "uk_stage_isa_subscriptions_invariants", @@ -979,10 +982,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: _UK_CERTIFICATION_PART_DIGESTS: Mapping[str, Mapping[str, str]] = { "spine": { "gates_manifest_sha256": ( - "64139dd980805155547ad72c72425877572090edf45f58cc42375b634a8a2741" + "7cced0cc3d07c899a095b42655c6771a514bc3262c10a06efa68d8c1888403c7" ), "policy_sha256": ( - "33e851f78f1dc1fde7969c6d875ea2d85b9ea41a39551680ad5a59b369b60f44" + "aef498aef8a00c156c196bd4823f34c8ac0a36ab02f640c272836f6d3d714778" ), }, "calibration_seam": { @@ -995,10 +998,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: }, "release_cut": { "gates_manifest_sha256": ( - "9d238b45f13afc31ed27b512855d324cdfd890bd34c93ecc5a1a16d9c81bfd9f" + "a982dd3b327468726be216501a162baa0c35ae4973979fc8ad5c50addf825a26" ), "policy_sha256": ( - "5cb11fbce2f0de97f7db6f3c90536f86e1e65795cce59eb3472d8a42a2654023" + "cd33838ccee5bc26ddf086c5b655cf1a8f6785858eaf68b0e9f89b60136d79d8" ), }, } 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 f9d099253..f0d85bcb9 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -220,13 +220,13 @@ 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 = ( - "cac5b2786970a1a1b0a6684be0322841a5383e675c095fad7f9a6b37e344313b" + "a122451b95dceef568d5010f9bb10e28bdbd541b018130a49de2dca28918a245" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "66c83b391237abf8df13abfe551936cc008a805980b73e87eb11b96b13a2ef2b" + "54715004a5c750dab827297401073948b30105aa390e01afd76e3e01394ea702" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "426c45bc1413477f66cea2766a977042c10a769f7ac3ef285c307dea57703577" + "d5f9b680538cf546f1151f77b1c7caf42db5e8ba4bacc4ad12d8a52b8d364764" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "f246388de42654dd68dbbb40c42282001b5b0a8bf48976162bcb1199085cb1d3" @@ -307,6 +307,7 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: "transferred", None, ), + "uk_stage_hmrc_spi_income_spine_hourly_pay": ("stage_health", "transferred", None), "uk_stage_spi_benefit_coherence": ("stage_health", "transferred", None), "uk_stage_uc_take_up": ("stage_health", "transferred", None), "uk_stage_pension_credit_take_up": ("stage_health", "transferred", None), @@ -1445,6 +1446,7 @@ def _gate_battery_payload( "uk_stage_frs_annual_income_spells": "frs_annual_income_spells", "uk_stage_spi_support_channel_mass": "spi_support_channel", "uk_stage_hmrc_spi_income_spine_identity": "hmrc_spi_income_spine", + "uk_stage_hmrc_spi_income_spine_hourly_pay": "hmrc_spi_income_spine", "uk_stage_spi_benefit_coherence": "spi_benefit_coherence", "uk_stage_uc_take_up": "uc_take_up", "uk_stage_pension_credit_take_up": "pension_credit_take_up", diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py index 1040ab8df..6497ce0bd 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py @@ -231,33 +231,23 @@ ) _UK_CHILD_LIMIT_EXCEPTIONS_AT_DEFAULT = ( "The exceptions to the Universal Credit and Child Tax Credit two-child limit " - "(UC Regs 2013 Sch 12; CTC Regs 2002 regs 9 to 14; policyengine-uk 2.161.0): " - "multiple births, adoption, non-parental caring arrangements, non-consensual " - "conception and the continuation of an earlier exception. The FRS records " - "none of them as such, so each stays false, and a third or later child who " - "qualifies for one gets no child element: Universal Credit is understated " - "for those families until the limit ends on 6 April 2026. Multiple births " - "(siblings of the same age) and non-parental care (a child whose benefit " - "unit holds no parent) could be read from the FRS household grid after this " - "lock (microcosm#1095; for María's signature)." + "(UC Regs 2013 Sch 12; CTC Regs 2002 regs 9 to 14; policyengine-uk 2.161.0) " + "for multiple births, adoption, non-consensual conception and the " + "continuation of an earlier exception. The FRS records none of them as such, " + "so each stays false, and a third or later child who qualifies for one gets " + "no child element: Universal Credit is understated for those families until " + "the limit ends on 6 April 2026. Multiple births stay false although " + "siblings of the same age could stand for them: the FRS has no twin field and " + "its birth dates are blank, so same-age siblings would also take in siblings " + "born under a year apart (María's ruling of 2 November 2026). The " + "non-parental caring exception is read from the household grid by " + "frs_relationships (microcosm#1095; for María's signature)." ) #: Other engine inputs the build leaves at the engine's default, each signed as #: an approximation with the error it can make (microcosm#1095). UK_ENGINE_DEFAULT_INPUTS: dict[str, str] = { - "self_employment_gross_receipts": ( - "No source of the self-employed's turnover is read yet (the FRS job " - "record and the SPI tape are the candidates, as #1145 found landlords' " - "receipts). Since policyengine-uk 2.108.0 the trading allowance replaces " - "expenses and does not stack on them, so without receipts " - "trading_allowance_deduction relieves a profit of GBP 1,000 or less in " - "full and gives a larger profit no allowance. Taxable profit is off by at " - "most GBP 1,000 a person: too low where receipts above GBP 1,000 leave a " - "small profit, too high just above GBP 1,000 where the allowance would " - "beat the expenses. Signed as an approximation with the plan for the " - "next policyengine-uk bump, approved on 2026-10-23." - ), "housing_benefit_residential_capital_exception": ( "The working-age residential capital tariff exception (Housing Benefit " "Regulations 2006 reg. 52(4)-(5), (8)-(9); NI reg. 49) covers residents " @@ -340,7 +330,6 @@ "child_limit_continuation_exception", "child_limit_multiple_birth_exception", "child_limit_non_consensual_conception_exception", - "child_limit_non_parental_caring_exception", ), _UK_CHILD_LIMIT_EXCEPTIONS_AT_DEFAULT, ), diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json index 40bf20145..e2e93890d 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/cgt_asset_type_facts.json @@ -2,70 +2,70 @@ "resource": "synthetic-cgt-asset-type.json", "resource_sha256": "synthetic", "source_commit": "synthetic", - "table4_all_gains": 6472476.363235764, + "table4_all_gains": 6432140.442900506, "table4_all_tax": 0.0, - "table4_all_taxpayers": 15.64584768489452, + "table4_all_taxpayers": 15.620692070399844, "table4_bands": [ { - "gains": 6343.7876538201135, + "gains": 5904.4347908435375, "lower_bound": 0, "tax": null, - "taxpayers": 1.4508598323557498, + "taxpayers": 1.3744469955188574, "upper_bound": 10000 }, { - "gains": 31485.760340906432, + "gains": 32034.75504552742, "lower_bound": 10000, "tax": null, - "taxpayers": 1.9244306390596129, + "taxpayers": 1.9314676701362754, "upper_bound": 25000 }, { - "gains": 63324.14855305699, + "gains": 54864.57734075372, "lower_bound": 25000, "tax": null, - "taxpayers": 1.7239151594793525, + "taxpayers": 1.5032132743952833, "upper_bound": 50000 }, { - "gains": 140800.40954871886, + "gains": 152528.4560198875, "lower_bound": 50000, "tax": null, - "taxpayers": 2.0175072572478596, + "taxpayers": 2.202207105013423, "upper_bound": 100000 }, { - "gains": 155344.30651445186, + "gains": 243549.7014216287, "lower_bound": 100000, "tax": null, - "taxpayers": 0.9869230277893157, + "taxpayers": 1.441606580784334, "upper_bound": 250000 }, { - "gains": 630694.061491824, + "gains": 452970.1208343457, "lower_bound": 250000, "tax": null, - "taxpayers": 1.7500749317552278, + "taxpayers": 1.3231397337347075, "upper_bound": 500000 }, { - "gains": 790243.0554791468, + "gains": 822724.5070800455, "lower_bound": 500000, "tax": null, - "taxpayers": 1.1378960035535624, + "taxpayers": 1.1770468204494895, "upper_bound": 1000000 }, { - "gains": 4654240.833653839, + "gains": 4667563.8903674735, "lower_bound": 1000000, "tax": null, - "taxpayers": 4.654240833653839, + "taxpayers": 4.667563890367473, "upper_bound": null } ], - "table4_individuals_gains": 6472476.363235764, + "table4_individuals_gains": 6432140.442900506, "table4_individuals_tax": 0.0, - "table4_individuals_taxpayers": 15.64584768489452, + "table4_individuals_taxpayers": 15.620692070399844, "table4_trusts_gains": 0.0, "table4_trusts_tax": 0.0, "table7_total_disposals": 2743095.2380952383, @@ -114,12 +114,12 @@ "proceeds": 9000000000.0 } ], - "table8a_disposals_total": 29.50359849151538, - "table8a_gains_total": 36909134.810149446, - "table8a_tax_total": 7381826.962029889, - "table8a_taxpayers_total": 26.82145317410489, - "table8b_all_gains": 36909134.810149446, - "table8b_all_taxpayers": 26.82145317410489, - "table8b_individuals_gains": 36909134.810149446, - "table8b_individuals_taxpayers": 26.82145317410489 + "table8a_disposals_total": 29.456162189896844, + "table8a_gains_total": 46158292.702458374, + "table8a_tax_total": 9231658.540491676, + "table8a_taxpayers_total": 26.778329263542584, + "table8b_all_gains": 46158292.702458374, + "table8b_all_taxpayers": 26.778329263542584, + "table8b_individuals_gains": 46158292.702458374, + "table8b_individuals_taxpayers": 26.778329263542584 } 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 ed88f8626..784d330f8 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 @@ -98,7 +98,7 @@ "artifacts": [], "grain": "person", "nonnegative_outputs": [], - "notes": "Runs after the SPI income chain, UC capital coherence and the Pension Credit and Child Benefit redraws, before CGT cloning, so every clone carries its source's draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from \u00a3151 to \u00a3196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer's Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame's own values, gives each person's is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year's parameters at 30 April, so the limit is 2024-25's \u00a3151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (Mar\u00eda's decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine's gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract's carers_allowance_newly_entitled rate (0.6, Mar\u00eda's ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person's identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk's stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (Mar\u00eda's ruling of 2026-10-23): there the flag is Carer Support Payment's, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year's limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum's realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony's reviews of microcosm#1420). The stage has no health gate.", + "notes": "Runs after the SPI income chain, UC capital coherence, the Pension Credit and Child Benefit redraws and salary_sacrifice, so the earnings test reads the pay salary sacrifice converts (microcosm#1095 B5); a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so every clone carries its source's draw (microcosm#1319). frs_spine sets would_claim_carers_allowance from reported receipt and the SPI income chain re-derives it from the refilled receipt on the rows it redraws, so that care_hours qualifies a carer for the Universal Credit carer element without paying the allowance to every carer (#882). A carer above the earnings limit cannot report the allowance, so no reform that widens it reached a carer who would claim once entitled: the April 2025 rise of the limit from \u00a3151 to \u00a3196 a week brought an estimated 61,000 more people into eligibility (Independent Review of Carer's Allowance Overpayments, July 2025, chapter 7). One temporary engine materialization at the survey year, on the frame's own values, gives each person's is_gainfully_employed_for_carers_allowance, is_severely_disabled_for_carers_allowance and carers_allowance_earnings (consumed only); the engine reads a year's parameters at 30 April, so the limit is 2024-25's \u00a3151. The allowance is paid only for caring for a severely disabled person (SSCBA 1992 s. 70(2)), which the care hours do not record (microcosm#1418): a carer whose largest in-household band alone reaches the hours (frs_care_hours_in_household) qualifies when another member of the household is severely disabled, and a carer whose largest in-household band does not reach them (care outside the household, or hours spread over bands; the allowance needs the hours for one person) joins on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member (Mar\u00eda's decision (a) of 2026-10-28). A person in that pool whose care_hours reach the engine's gov.dwp.carers_allowance.min_hours (35), who reports no allowance and whom the earnings test holds gainfully employed claims at the take-up contract's carers_allowance_newly_entitled rate (0.6, Mar\u00eda's ruling of 2026-10-20, sensitivity 0.45 to 0.75) on the person's identity-keyed uniform (seed 0, salt would_claim_carers_allowance), placed by weight within strata and rotated by a start drawn per stratum, so that each stratum claims the rate of its eligible weight to within one record and each eligible carer with probability equal to the rate (weight_balanced_uniforms, policyengine-uk's stratified_uniform with a random start; microcosm#1418 and the independent review of microcosm#1420): the route, whether the benefit unit reports a means-tested benefit at the survey year, and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three. would_claim = reported OR (eligible AND position < rate). A reporter stays true; a carer under the limit who reports no allowance stays false, as the survey records them entitled and not claiming. Scotland is drawn too (Mar\u00eda's ruling of 2026-10-23): there the flag is Carer Support Payment's, which policyengine-uk pays only under its own earnings limit from 2.146.1 (PolicyEngine/policyengine-uk#2473), and the receipt reports Scotland apart. The earnings test is read before salary_sacrifice moves some contributors' pension amounts out of their pay, so a drawn carer whom that conversion takes under the survey year's limit keeps the flag although the file then shows them entitled and not reporting. The stage rebuilds the flag from reported receipt and refuses a frame whose flag no longer follows it. Its receipt bands support on distinct source households (household_source_id), and gives a weighted total only where three or more carry it and a share only where neither of its sides rests on one or two; it lists each stratum's realised share against its target, and the stage refuses a take-up or pool stratum whose drawn weight misses its share by more than its heaviest record, the most the rotated placement allows, so the balance holds on every build (Anthony's reviews of microcosm#1420). The stage has no health gate.", "operations": [ { "consumed_only": true, @@ -116,7 +116,7 @@ "eligibility": "persons whose care_hours reach policyengine-uk's gov.dwp.carers_allowance.min_hours, who care for a severely disabled person, who report no Carer's Allowance, and whom is_gainfully_employed_for_carers_allowance holds gainfully employed at the survey year: the carers the earnings limit alone keeps from the allowance (a carer whose frs_care_hours_in_household reach min_hours cares for a severely disabled person when another member of the household is is_severely_disabled_for_carers_allowance at the survey year; a carer whose largest in-household band does not reach the hours does on a second identity-keyed uniform (salt carers_allowance_outside_household_pool), placed by weight within strata of survey-year means-tested receipt and earnings band, below the household-weighted share of the in-household carers reporting no allowance who have such a member). In Scotland the flag is Carer Support Payment's, which policyengine-uk pays under its own earnings limit from 2.146.1", "kind": "assign_binary_from_rate", "output": "would_claim_carers_allowance", - "rate_derivation": "the take-up contract's carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool's strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false", + "rate_derivation": "the take-up contract's carers_allowance_newly_entitled rate, with the identity-keyed uniforms placed by weight within strata (the route (in-household or not), whether the benefit unit reports a means-tested benefit at the survey year (universal credit, housing benefit, income support, income-based JSA, income-related ESA, tax credits or pension credit), and the survey-year weekly earnings band, one weekly rate wide above the limit and open above three; the pool's strata are the last two) and rotated by a start drawn per stratum, so that every stratum claims the rate of its eligible weight to within one record and every eligible carer claims with probability equal to the rate; a CGT copy draws as the member it copies, placed with it as one record of their joint weight, so the two claim together; would_claim = reported OR (eligible AND position < rate), so a reporter stays true and every other person false", "rate_key": "carers_allowance_newly_entitled", "reported_true_anchor": "carers_allowance_reported > 0", "seed": 0 @@ -2125,7 +2125,7 @@ "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. 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.", + "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. It also sets child_limit_non_parental_caring_exception, policyengine-uk's two-child limit exception for non-parental caring arrangements (UC Regs 2013 Sch 12 para 4; CTC Regs 2002 reg 12), for a child-table member whom no adult of their own benefit unit claims as a natural, adopted or step child (the child's grid code toward the adult is child or step child, 3 or 4, or the adult's toward the child is parent or step parent, 7 or 8) and whom no local authority placed there: a child living with kinship or friends carers, or with a claimant whose own child is the child's parent. Nothing set it before, so the engine paid no child element for such a third or later child born from 6 April 2017. The multiple births, adoption and non-consensual conception exceptions stay at the engine's default: the FRS has no twin field and its birth dates are blank (Mar\u00eda's ruling of 2 November 2026; microcosm#1095).", "operations": [ { "delimiter": "\t", @@ -2264,6 +2264,16 @@ "foster_child_code": 5, "foster_parent_code": 9 }, + "non_parental_care_child": { + "child_codes": [ + 3, + 4 + ], + "parent_codes": [ + 7, + 8 + ] + }, "one_person_age_split": 65, "reciprocity_mismatch_tolerance": 3, "source_columns": { @@ -2298,6 +2308,7 @@ "ons_family_role", "ons_family_index", "is_looked_after_by_local_authority", + "child_limit_non_parental_caring_exception", "ons_household_type" ], "rewrites": [], @@ -2456,8 +2467,8 @@ "locator": "pension.tab", "role": "frs_table", "runtime_sha256_required": true, - "sha256": "7090be19cac198c6fc4545266764d267f9e39073b83c5411d0615eda2ac47e9c", - "size_bytes": 3597, + "sha256": "79ba6767e1300e1bb5d3de808706cb4ee1d86e4694a26e9fd94958e821858f33", + "size_bytes": 3875, "table": "pension", "vintage": "synthetic-h2" } @@ -2475,6 +2486,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", + "annuity_and_trust_income", "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", @@ -2575,6 +2587,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", + "annuity_and_trust_income", "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", @@ -3275,7 +3288,7 @@ "hmrc_spi_total_investment_income", "hmrc_spi_assessable_income" ], - "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes.", + "notes": "Runs the SPI-trained income QRFs on the raw-spine support channel, drawing for its claimants and partners aged 16 and over so dependants aged 16 to 19 keep their FRS twin's values (uk-data#504, microcosm#1095), initializes FRS charity columns to zero, trains FRS-only stage 2 on the FRS rows' reported values, flags the rows it drew (person_is_spi_income_recipient) so later stages tell tape-concept rows from FRS-concept ones without re-deriving the recipient rule (microcosm#840), leaves every FRS-channel income as reported (no base-channel redraw of dividends; uk-data#498, microcosm#1095), and emits a sidecar-only 208-fact replay report for the spine path. Stage 2 also draws trading_loss, the year's trading loss the tape does not carry, from the FRS rows (uk-data#528), and a drawn row whose self_employment_income is positive keeps no loss, so profit and loss never both hold an amount, as on the FRS rows (microcosm#1095). The chain draws self_employment_income net of the tape's losses brought forward (LOSSBF), as the tape holds profits, and last of all, so no earlier draw moves, the year's profit before them and the losses. After the FRS-only fill and the HMRC aggregates the stage moves the losses out of the profit into loss_relief and ni_class_4_losses_brought_forward, the inputs policyengine-uk relieves them against (income tax under ITA 2007 s.83, Class 4 under SSCBA 1992 Sch 2 para 3), so Universal Credit, HBAI and market income read the year's profit; where the drawn net profit is positive the losses are its gap to the year's profit and where it is zero they are at least that profit, so the profit after the relief is the drawn net profit on every row, which the HMRC aggregates and the HMRC self-employment rows read (uk-data#528, microcosm#1095). The engine carries both losses into each projected year at their nominal value while the profit grows with its index. Stage 2 draws the six owner-manager inputs last, the five flags as 0 or 1 and then the income share, so each follows the drawn incomes; the root stage's rules then hold on the drawn rows (an owner works in the company, its other inputs need an owner, and a company carries on a trade or a property business, never both), since the tape carries no director answers (microcosm#1095). A drawn row's annual income descriptors follow its drawn incomes. The tape records the tax year's income, so its pay, profit and private pension shares are 1, and its contributory benefits, stage-2 draws on the current concept with no spell of their own, take share 1 too. It keeps its twin's other-spell pay only where it drew no pay, and its twin's ceased self-employment only where it drew no profit, since the bridge draws a record without pay for a twin who left a job in the year. An undrawn copy keeps its twin's descriptors with its twin's incomes. A drawn row's annuity and trust descriptor is zero, since the tape's private pension excludes those payments and books them as other income (microcosm#1428).", "operations": [ { "artifact_roles": [ @@ -3366,6 +3379,7 @@ } }, "categorical_predictors": [ + "spi_age_band", "gender", "region", "main_source" @@ -3447,7 +3461,7 @@ ], "post_sample_fit_weight": "uniform", "predictors": [ - "age", + "spi_age_band", "gender", "region", "main_source", @@ -3478,7 +3492,7 @@ "hmrc_spi_state_pension_income": "srp_regular_code5", "hmrc_spi_taxable_termination_pay": null, "hmrc_spi_unemployment_benefit_income": "hmrc_spi_unemployment_benefit_income", - "other_investment_income": null, + "other_investment_income": "other_investment_income", "private_pension_income": "private_pension_income", "property_finance_costs": null, "property_income": "property_income", @@ -3488,7 +3502,7 @@ "spi_profit_before_losses_draw": null }, "fit_weight_kind": "importance", - "fold_key": "predictor cell (gender, region, whole-year age, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell's stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold's recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest", + "fold_key": "predictor cell (gender, region, spi_age_band, main_source, has_pay, has_trade, at_or_over_spa), the fold being floor(uniform x folds) of the cell's stable_identity_uniforms draw at the stage seed and salt spi_stage1:reference_fold; each fold's recipients are located by a forest fitted on the other folds, so the forest that locates a copy never saw its twin, and every row of a cell is located by one forest", "folds": 5, "method": "Rosenblatt transport (microcosm#840): each drawn recipient's twin values are located on the reference forest, the sign uniform in the gate's cumulative class probabilities and the quantile on the sign's magnitude forest, each leaf chained on the twin's observed earlier leaves, and the stage-1 forest draws at those uniforms, so the recipient receives the SPI income of someone in the same position among SPI people like it as the twin holds among FRS people like it. taxable_savings_interest is savings_interest_income, the interest outside ISAs, floored at zero. A leaf without an analogue, or whose analogue is degenerate on the FRS rows, draws at identity-keyed uniforms, as do the placements inside a class or a flat stretch of a conditional; no model random stream is consumed. The reference locates a leaf given the twin's observed earlier analogue leaves, while the stage-1 forest draws it given every earlier output, the identity-uniform leaves included, so the located quantile is read off a conditional the identity draws also shape (microcosm#840 follow-up: the chain order). A full build refuses a reference the predictor cells cannot cross-fit (only a sampled rung or a synthetic fixture may locate in-sample, and its receipt says so), a recipient whose fold differs from its twin's, and a reference whose sign classes differ from the stage-1 forest's on any located target.", "n_estimators": 4, @@ -3633,6 +3647,23 @@ "weighting": "FACT", "with_replacement": true }, + { + "annual_income": "the floored pay is the row's current pay; its employment_income_annual_share becomes its unfloored over its floored employment income, so the annual measures read the tape's year of pay", + "column": "hmrc_spi_pay", + "employee_statuses": [ + "FT_EMPLOYED", + "PT_EMPLOYED" + ], + "hourly_rate": "the lower of policyengine-uk gov.hmrc.minimum_wage.non_apprentice by the twin's age, read as the engine reads the build year (30 April), and the twin's own hourly pay (its employment income before the draw over its annual hours)", + "hours_basis": "annual usual hours: the FRS adult's usual weekly hours in all jobs (TOTHOURS) times 52, read back to weekly hours", + "hours_column": "hours_worked", + "kind": "floor_spi_employment_pay_at_minimum_wage", + "pay_basis": "tape PAY is taxable pay, net of net-pay pension contributions and salary sacrifice; the minimum wage binds gross pay, and the engine reads employment_income (PAY + EPB + TAXTERM) as gross pay, so PAY alone is compared with the gross minimum and a twin at the minimum wage who pays net-pay contributions can be lifted by up to them", + "reason": "stage 1 draws annual tape pay without hours and the tape holds part-year workers, so a twin reporting usual employee hours could draw a year's pay below what those hours earn at the minimum wage (uk-data#629)", + "receipt": "eligible and floored source households (rounded), weighted people, the weighted share floored, the weighted mean uplift and the amount added; figures on one or two source households are <3, and the floored weight, share and amount are given only where neither the floored nor the unfloored side rests on one or two; the hourly-pay check gives the weighted shares of the drawn employees below the minimum wage for their age before and after the floor, beside the FRS employees' share, each withheld beside a side on one or two, and the eligible rows still below their floor (banded, zero exact)", + "rows": "drawn recipients other than reserved band carriers, whose twin has no trade (has_trade 0), an employee main job and usual hours above zero, and whose drawn PAY is positive; FRS rows, undrawn copies and every other leaf keep their values", + "weeks_in_year": 52 + }, { "categorical_predictors": [ "gender", @@ -4076,7 +4107,8 @@ "self_employment_income_annual_share", "self_employment_income_other_spells", "private_pension_income_annual_share", - "contributory_benefits_annual_share" + "contributory_benefits_annual_share", + "annuity_and_trust_income" ], "source": "https://assets.publishing.service.gov.uk/media/69f1f12d2fae53a03709682f/Collated_Tables_3_1_to_3_11_2324.ods", "stage": "hmrc_spi_income_spine", @@ -6244,7 +6276,7 @@ "source_vintage": "2024-25" }, { - "additional_residential_targets": "Table 6b HRAD transactions and value, 2024-25, times individuals' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined", + "additional_residential_targets": "Table 6b HRAD transactions and value, 2024-25: at or below GBP 250,000, the band less other buyers' HRAD, their Table 8a receipts over Table 6b's gross HRAD receipts per transaction and per pound; above it, times individuals' shares of Table 8a residential transactions and value; Table 6b bands with the five above GBP 2m combined, at the households' counts and values", "eligibility": "stock at least minimum_price, the chargeable consideration HMRC's Stamp Tax tables cover, and positive weight; at most one purchase of each kind a year", "event_score": "-log1p(-u) / hazard", "fit_regions": [ @@ -7002,6 +7034,54 @@ "stage": "student_loans", "survey": "Family Resources Survey 2024-25 and Student Loans Company borrower forecasts for England" }, + "trading_allowance_receipts": { + "artifacts": [], + "grain": "person", + "nonnegative_outputs": [ + "self_employment_gross_receipts" + ], + "notes": "The last spine stage (microcosm#1428). policyengine-uk applies the trading allowance against self_employment_gross_receipts, which neither the FRS nor the SPI tape records; without receipts a rise in the allowance relieves only profits up to the new allowance, in full, and misses its main base, the traders who already claim the GBP 1,000 allowance against larger receipts. Under current law a claimant's receipts are their profit plus GBP 1,000 exactly. Each trader with a positive profit is flagged a claimant at their profit band's rate, drawn on their own person_id: a claimant's receipts are their profit plus GBP 1,000, any other trader's their profit plus GBP 3,000 (expenses at the raised allowance, so the raise does not relieve them; the costing books switchers as behaviour), and a person with no positive profit keeps zero, which the engine reads as unknown. Every trader with a positive profit is treated as filing, as SPI Table 3.10's counts, which bind the frame's traders, are of filers, so a profit of GBP 1,000 or less is now taxed where the engine used to relieve it in full. The rates are calibrated to HMRC's policy paper until HMRC publishes claimant counts. The engine uprates receipts with profit, so a claimant's GBP 1,000 grows with the mixed income index while the allowance is nominal: the raise relieves GBP 1,932 more in 2027-28, falling to GBP 1,758 by 2031-32, 3.4% to 12.1% below GBP 2,000. The other traders' stored GBP 3,000 grows the same way, so an allowance above GBP 3,000 times that index relieves them from the stored expense, not from data: allowances above GBP 3,000 are mis-scored until policyengine-uk#2765 lets the stage store the claim status (microcosm#1439; Mar\u00eda's ruling of 2026-11-01).", + "operations": [ + { + "calibration": "HMRC's policy paper on the Budget 2026 trading allowance raise: about 400,000 taxpayers with a lower bill, by about GBP 380 on average, measured on policyengine-uk for the microcosm#1428 national from 9f70c71e7 at 2028-29 prices; the rates drift with each national (microcosm#1439), until HMRC publishes claimant counts (Mar\u00eda's choices of 2026-10-31 and 2026-11-01)", + "claimant_bands": [ + { + "rate": 0.72, + "upper_inclusive": 4500 + }, + { + "rate": 0.14, + "upper_inclusive": 9000 + }, + { + "rate": 0 + } + ], + "claimant_expenses": 1000, + "draw": "trading_allowance_claimant", + "kind": "assign_trading_allowance_receipts", + "other_expenses": 3000, + "output": "self_employment_gross_receipts", + "profit": "self_employment_income", + "rule": "a person with a positive self_employment_income claims the allowance where their uniform falls below their profit band's rate; a claimant's receipts are their profit plus claimant_expenses, any other trader's their profit plus other_expenses, and a person with no positive profit keeps zero", + "seed": 0 + }, + { + "declared_factor": 1, + "entity": "household", + "gate_coupling": "The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason.", + "kind": "record_mass_conservation_receipt", + "reason": "Self-employed gross receipts stored on the source spine: household weights pass through unchanged and total household mass is conserved." + } + ], + "outputs": [ + "self_employment_gross_receipts" + ], + "rewrites": [], + "source": "self_employment_income after the SPI income stage; HMRC's published gainers of the raise to GBP 3,000, about 400,000 taxpayers with a lower bill by about GBP 380 on average", + "stage": "trading_allowance_receipts", + "survey": "The self-employed's own profit on the final spine, and HMRC's policy paper on the Budget 2026 trading allowance raise (calibration)" + }, "uc_capital_coherence": { "artifacts": [], "grain": "benunit", @@ -7349,9 +7429,9 @@ "minimum_pool_sources": 30, "not_drawn": "keeps its stored flag, except a unit keeping income-related ESA, which claims none", "output": "would_claim_uc", - "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it", + "pooling": "adjacent bands within a family type, lowest first, until the drawn units rest on minimum_pool_sources distinct household_source_id values; a short last pool joins the one before it. Then, lowest first, a pool that cannot meet DWP's count (its undrawn entitled units' expected claims exceed the count, or the count exceeds them plus the weight of every drawn unit) is held at the nearer of those ends and passes its gap to the other pools, nearest first, the one with more room first at equal distance and the lower on a tie, each taking what its room allows; a gap no pool can take leaves the family type unreachable", "population": "uc_age_eligible", - "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", + "residual_rate": "(T - F) / D, clipped to [0, 1]; T DWP's calendar-2025 mean count of the pool's bands after any gaps passed to or from it, D the weight of the pool's drawn units and F the expected claims of its other entitled units, over Great Britain at the stage's household weights; realized by the drawn units claiming in the order of their uniform until their weight is nearest T - F, and applied at that uniform to Northern Ireland's drawn units too", "seed": 0, "solve_scope": "great_britain", "targets": "DWP Stat-Xplore Households on Universal Credit by family type and monthly award band, mean of April to December 2025", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/pension.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/pension.tab index 7b8df7453..da8ce3e7c 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/pension.tab +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/pension.tab @@ -1,136 +1,136 @@ -SERNUM BENUNIT PERSON PENPAY PTAMT PTINC POAMT POINC PENOTH -1 1 1 10.0 2.0 2 3.0 2 0 -2 1 1 10.0 2.0 2 3.0 2 0 -3 1 1 10.0 2.0 2 3.0 2 0 -4 1 1 10.0 2.0 2 3.0 2 0 -5 1 1 10.0 2.0 2 3.0 2 0 -6 1 1 10.0 2.0 2 3.0 2 0 -7 1 1 10.0 2.0 2 3.0 2 0 -8 1 1 10.0 2.0 2 3.0 2 0 -9 1 1 10.0 2.0 2 3.0 2 0 -10 1 1 10.0 2.0 2 3.0 2 0 -11 1 1 10.0 2.0 2 3.0 2 0 -12 1 1 10.0 2.0 2 3.0 2 0 -13 1 1 10.0 2.0 2 3.0 2 0 -14 1 1 10.0 2.0 2 3.0 2 0 -15 1 1 10.0 2.0 2 3.0 2 0 -16 1 1 10.0 2.0 2 3.0 2 0 -17 1 1 10.0 2.0 2 3.0 2 0 -18 1 1 10.0 2.0 2 3.0 2 0 -19 1 1 10.0 2.0 2 3.0 2 0 -20 1 1 10.0 2.0 2 3.0 2 0 -21 1 1 10.0 2.0 2 3.0 2 0 -22 1 1 10.0 2.0 2 3.0 2 0 -23 1 1 10.0 2.0 2 3.0 2 0 -24 1 1 10.0 2.0 2 3.0 2 0 -25 1 1 10.0 2.0 2 3.0 2 0 -26 1 1 10.0 2.0 2 3.0 2 0 -27 1 1 10.0 2.0 2 3.0 2 0 -28 1 1 10.0 2.0 2 3.0 2 0 -29 1 1 10.0 2.0 2 3.0 2 0 -30 1 1 10.0 2.0 2 3.0 2 0 -31 1 1 10.0 2.0 2 3.0 2 0 -32 1 1 10.0 2.0 2 3.0 2 0 -33 1 1 10.0 2.0 2 3.0 2 0 -34 1 1 10.0 2.0 2 3.0 2 0 -35 1 1 10.0 2.0 2 3.0 2 0 -36 1 1 10.0 2.0 2 3.0 2 0 -37 1 1 10.0 2.0 2 3.0 2 0 -38 1 1 10.0 2.0 2 3.0 2 0 -39 1 1 10.0 2.0 2 3.0 2 0 -40 1 1 10.0 2.0 2 3.0 2 0 -41 1 1 10.0 2.0 2 3.0 2 0 -42 1 1 10.0 2.0 2 3.0 2 0 -43 1 1 10.0 2.0 2 3.0 2 0 -44 1 1 10.0 2.0 2 3.0 2 0 -45 1 1 10.0 2.0 2 3.0 2 0 -46 1 1 10.0 2.0 2 3.0 2 0 -47 1 1 10.0 2.0 2 3.0 2 0 -48 1 1 10.0 2.0 2 3.0 2 0 -49 1 1 10.0 2.0 2 3.0 2 0 -50 1 1 10.0 2.0 2 3.0 2 0 -51 1 1 10.0 2.0 2 3.0 2 0 -52 1 1 10.0 2.0 2 3.0 2 0 -53 1 1 10.0 2.0 2 3.0 2 0 -54 1 1 10.0 2.0 2 3.0 2 0 -55 1 1 10.0 2.0 2 3.0 2 0 -56 1 1 10.0 2.0 2 3.0 2 0 -57 1 1 10.0 2.0 2 3.0 2 0 -58 1 1 10.0 2.0 2 3.0 2 0 -59 1 1 10.0 2.0 2 3.0 2 0 -60 1 1 10.0 2.0 2 3.0 2 0 -61 1 1 10.0 2.0 2 3.0 2 0 -62 1 1 10.0 2.0 2 3.0 2 0 -63 1 1 10.0 2.0 2 3.0 2 0 -64 1 1 10.0 2.0 2 3.0 2 0 -65 1 1 10.0 2.0 2 3.0 2 0 -66 1 1 10.0 2.0 2 3.0 2 0 -67 1 1 10.0 2.0 2 3.0 2 0 -68 1 1 10.0 2.0 2 3.0 2 0 -69 1 1 10.0 2.0 2 3.0 2 0 -70 1 1 10.0 2.0 2 3.0 2 0 -71 1 1 10.0 2.0 2 3.0 2 0 -72 1 1 10.0 2.0 2 3.0 2 0 -73 1 1 10.0 2.0 2 3.0 2 0 -74 1 1 10.0 2.0 2 3.0 2 0 -75 1 1 10.0 2.0 2 3.0 2 0 -76 1 1 10.0 2.0 2 3.0 2 0 -77 1 1 10.0 2.0 2 3.0 2 0 -78 1 1 10.0 2.0 2 3.0 2 0 -79 1 1 10.0 2.0 2 3.0 2 0 -80 1 1 10.0 2.0 2 3.0 2 0 -81 1 1 10.0 2.0 2 3.0 2 0 -82 1 1 10.0 2.0 2 3.0 2 0 -83 1 1 10.0 2.0 2 3.0 2 0 -84 1 1 10.0 2.0 2 3.0 2 0 -85 1 1 10.0 2.0 2 3.0 2 0 -86 1 1 10.0 2.0 2 3.0 2 0 -87 1 1 10.0 2.0 2 3.0 2 0 -88 1 1 10.0 2.0 2 3.0 2 0 -89 1 1 10.0 2.0 2 3.0 2 0 -90 1 1 10.0 2.0 2 3.0 2 0 -91 1 1 10.0 2.0 2 3.0 2 0 -92 1 1 10.0 2.0 2 3.0 2 0 -93 1 1 10.0 2.0 2 3.0 2 0 -94 1 1 10.0 2.0 2 3.0 2 0 -95 1 1 10.0 2.0 2 3.0 2 0 -96 1 1 10.0 2.0 2 3.0 2 0 -97 1 1 10.0 2.0 2 3.0 2 0 -98 1 1 10.0 2.0 2 3.0 2 0 -99 1 1 10.0 2.0 2 3.0 2 0 -100 1 1 10.0 2.0 2 3.0 2 0 -101 1 1 10.0 2.0 2 3.0 2 0 -102 1 1 10.0 2.0 2 3.0 2 0 -103 1 1 10.0 2.0 2 3.0 2 0 -104 1 1 10.0 2.0 2 3.0 2 0 -105 1 1 10.0 2.0 2 3.0 2 0 -106 1 1 10.0 2.0 2 3.0 2 0 -107 1 1 10.0 2.0 2 3.0 2 0 -108 1 1 10.0 2.0 2 3.0 2 0 -109 1 1 10.0 2.0 2 3.0 2 0 -110 1 1 10.0 2.0 2 3.0 2 0 -111 1 1 10.0 2.0 2 3.0 2 0 -112 1 1 10.0 2.0 2 3.0 2 0 -113 1 1 10.0 2.0 2 3.0 2 0 -114 1 1 10.0 2.0 2 3.0 2 0 -115 1 1 10.0 2.0 2 3.0 2 0 -116 1 1 10.0 2.0 2 3.0 2 0 -117 1 1 10.0 2.0 2 3.0 2 0 -118 1 1 10.0 2.0 2 3.0 2 0 -119 1 1 10.0 2.0 2 3.0 2 0 -120 1 1 10.0 2.0 2 3.0 2 0 -121 1 1 10.0 2.0 2 3.0 2 0 -122 1 1 10.0 2.0 2 3.0 2 0 -123 1 1 10.0 2.0 2 3.0 2 0 -124 1 1 10.0 2.0 2 3.0 2 0 -125 1 1 10.0 2.0 2 3.0 2 0 -126 1 1 10.0 2.0 2 3.0 2 0 -127 1 1 10.0 2.0 2 3.0 2 0 -128 1 1 10.0 2.0 2 3.0 2 0 -129 1 1 10.0 2.0 2 3.0 2 0 -130 1 1 10.0 2.0 2 3.0 2 0 -131 1 1 10.0 2.0 2 3.0 2 0 -132 1 1 10.0 2.0 2 3.0 2 0 -133 1 1 10.0 2.0 2 3.0 2 0 -134 1 1 10.0 2.0 2 3.0 2 0 -135 1 1 10.0 2.0 2 3.0 2 0 +SERNUM BENUNIT PERSON PENTYPE PENPAY PTAMT PTINC POAMT POINC PENOTH +1 1 1 1 10.0 2.0 2 3.0 2 0 +2 1 1 1 10.0 2.0 2 3.0 2 0 +3 1 1 1 10.0 2.0 2 3.0 2 0 +4 1 1 1 10.0 2.0 2 3.0 2 0 +5 1 1 5 10.0 2.0 2 3.0 2 0 +6 1 1 1 10.0 2.0 2 3.0 2 0 +7 1 1 1 10.0 2.0 2 3.0 2 0 +8 1 1 1 10.0 2.0 2 3.0 2 0 +9 1 1 1 10.0 2.0 2 3.0 2 0 +10 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b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ 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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_purchase_targets.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_purchase_targets.json index 626cc85c3..80a98038f 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_purchase_targets.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_purchase_targets.json @@ -27,7 +27,7 @@ "minimum_price": 1000.0, "property_type": "additional_residential", "transactions": 27.097631578947365, - "value": 179110.4255908138 + "value": 183301.81586439192 }, "derivation": {}, "non_residential": { @@ -57,7 +57,7 @@ ], "minimum_price": 1000.0, "property_type": "non_residential", - "transactions": 9.064159936519806, - "value": 111830.66958141114 + "transactions": 9.176436238560177, + "value": 112796.95808421359 } } diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_rental_facts.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_rental_facts.json index 34ab284d8..44b3e6808 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_rental_facts.json +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/property_rental_facts.json @@ -1,90 +1,90 @@ { "expense_landlords_by_type": { - "legal_management_and_professional_fees": 68.27759643273899, - "non_residential_finance_costs": 68.27759643273899, - "other_allowable_expenses": 68.27759643273899, - "rent_rates_and_insurance": 68.27759643273899, - "repairs_and_maintenance": 68.27759643273899, - "residential_finance_costs": 68.27759643273899, - "services_including_wages": 68.27759643273899 + "legal_management_and_professional_fees": 68.73493528177121, + "non_residential_finance_costs": 68.73493528177121, + "other_allowable_expenses": 68.73493528177121, + "rent_rates_and_insurance": 68.73493528177121, + "repairs_and_maintenance": 68.73493528177121, + "residential_finance_costs": 68.73493528177121, + "services_including_wages": 68.73493528177121 }, "expenses": { - "all": 32716.81002019269, - "individual": 32389.64191999076, - "partnership": 327.1681002019286 + "all": 32249.572175972167, + "individual": 31927.076454212445, + "partnership": 322.4957217597221 }, "expenses_by_type": { - "legal_management_and_professional_fees": 3916.602293064794, - "non_residential_finance_costs": 1252.1829446577347, - "other_allowable_expenses": 4312.028486114605, - "rent_rates_and_insurance": 3587.080465523285, - "repairs_and_maintenance": 6034.956898688782, - "residential_finance_costs": 12069.913797377563, - "services_including_wages": 1544.0451347659284 + "legal_management_and_professional_fees": 3860.66820869192, + "non_residential_finance_costs": 1234.3001724904457, + "other_allowable_expenses": 4250.447210531009, + "rent_rates_and_insurance": 3535.8523738260133, + "repairs_and_maintenance": 5948.770004258463, + "residential_finance_costs": 11897.540008516926, + "services_including_wages": 1521.9941976573916 }, "landlords": { - "all": 136.55519286547798, - "individual": 135.1896409368232, - "partnership": 1.3655519286547815 + "all": 137.46987056354243, + "individual": 136.095171857907, + "partnership": 1.3746987056354385 }, "receipts": { - "all": 52346.8960323083, - "individual": 51823.42707198522, - "partnership": 523.46896032308 + "all": 51599.31548155547, + "individual": 51083.322326739915, + "partnership": 515.9931548155582 }, "receipts_bands": [ { - "landlords": 61.63949677955603, + "landlords": 62.052372129376785, "lower_bound": 0, "upper_bound": 10000 }, { - "landlords": 40.77689786955245, + "landlords": 41.05003079328003, "lower_bound": 10000, "upper_bound": 20000 }, { - "landlords": 15.646949182502686, + "landlords": 15.75175600207257, "lower_bound": 20000, "upper_bound": 30000 }, { - "landlords": 7.112249628410311, + "landlords": 7.159889091851168, "lower_bound": 30000, "upper_bound": 40000 }, { - "landlords": 3.793199801818832, + "landlords": 3.818607515653956, "lower_bound": 40000, "upper_bound": 50000 }, { - "landlords": 2.3707498761367707, + "landlords": 2.386629697283723, "lower_bound": 50000, "upper_bound": 60000 }, { - "landlords": 1.4224499256820622, + "landlords": 1.4319778183702336, "lower_bound": 60000, "upper_bound": 70000 }, { - "landlords": 0.948299950454708, + "landlords": 0.954651878913489, "lower_bound": 70000, "upper_bound": 80000 }, { - "landlords": 0.474149975227354, + "landlords": 0.4773259394567445, "lower_bound": 80000, "upper_bound": 90000 }, { - "landlords": 0.474149975227354, + "landlords": 0.4773259394567445, "lower_bound": 90000, "upper_bound": 100000 }, { - "landlords": 1.896599900909416, + "landlords": 1.909303757826978, "lower_bound": 100000, "upper_bound": null } 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 a5635a61a..304aee05a 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 @@ -32,6 +32,7 @@ "access_fund": "float64", "afcs_reported": "float64", "age": "int64", + "annuity_and_trust_income": "float64", "attendance_allowance_reported": "float64", "bsp_reported": "float64", "care_hours": "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 2c8641c8d..f1875dcc4 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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UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/uk_atomic_support_fixtures.py b/test_support/microcosm_build/uk_atomic_support_fixtures.py index 51051b581..0d6761f00 100644 --- a/test_support/microcosm_build/uk_atomic_support_fixtures.py +++ b/test_support/microcosm_build/uk_atomic_support_fixtures.py @@ -148,6 +148,7 @@ def toy_property_resources() -> dict[str, dict]: grid = [0.0, 0.25, 0.5, 0.75, 1.0] seats = {} lsoas = {} + council_areas = set() for rows in toy_support_frames().values(): for row in rows: seat = str(row["constituency_code"]) @@ -157,6 +158,8 @@ def toy_property_resources() -> dict[str, dict]: "weight": 100.0, "quantiles": [100_000.0 + 1_900_000.0 * p for p in grid], } + if str(row["local_authority_code"]).startswith("S12"): + council_areas.add(str(row["local_authority_code"])) lsoa = str(row["lsoa_code"]) if lsoa[0] in "EW": cumulative = [(i + 1) / 8 for i in range(8)] + [1.0] @@ -188,14 +191,42 @@ def toy_property_resources() -> dict[str, dict]: "receipts": {}, "lsoa": lsoas, } - return {"knots": knots, "band_mix": band_mix} + # Each toy council area splits bands A to H equally, in the vendored + # CTAXBASE rows the Scottish draw reads (microcosm#1095 B12). + scotland_rows = [ + { + "concept": f"scotgov.chargeable_dwellings_band_{band.lower()}", + "measure_id": f"band_{band.lower()}", + "geography": {"id": code, "level": "local_authority"}, + "period": {"type": "month", "value": "2025-09"}, + "value": 100.0, + } + for code in sorted(council_areas) + for band in COUNCIL_TAX_BANDS[:-1] + ] + scotland_band_mix = { + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "resource": "scotgov_chargeable_dwellings_by_band.json", + "source_fact_feed": {"facts_sha256": "toy"}, + "selections": [], + "row_count": len(scotland_rows), + "rows": scotland_rows, + } + return { + "knots": knots, + "band_mix": band_mix, + "scotland_band_mix": scotland_band_mix, + } def write_toy_property_resources(directory: Path) -> dict[str, Path]: - """Write the toy knots and band mix as graph sources named by the nodes.""" + """Write the toy knots and band mixes as graph sources named by the nodes.""" from microcosm.build.uk_runtime.property_localisation import ( BAND_MIX_SOURCE, QUANTILES_SOURCE, + SCOTLAND_BAND_MIX_SOURCE, ) directory.mkdir(parents=True, exist_ok=True) @@ -203,7 +234,11 @@ def write_toy_property_resources(directory: Path) -> dict[str, Path]: paths = { QUANTILES_SOURCE: directory / "toy-knots.json", BAND_MIX_SOURCE: directory / "toy-band-mix.json", + SCOTLAND_BAND_MIX_SOURCE: directory / "toy-scotland-band-mix.json", } + paths[SCOTLAND_BAND_MIX_SOURCE].write_text( + json.dumps(resources["scotland_band_mix"]), encoding="utf-8" + ) paths[QUANTILES_SOURCE].write_text(json.dumps(resources["knots"]), encoding="utf-8") paths[BAND_MIX_SOURCE].write_text( json.dumps(resources["band_mix"]), encoding="utf-8" diff --git a/tools/build_uk_release_input_coverage_manifest.py b/tools/build_uk_release_input_coverage_manifest.py index 598a2d84e..6d21b13ea 100644 --- a/tools/build_uk_release_input_coverage_manifest.py +++ b/tools/build_uk_release_input_coverage_manifest.py @@ -932,6 +932,10 @@ def build_manifest( stage_name="isa_subscriptions", candidate_source=candidate_source, ), + "trading_allowance_receipts": _source_stage_family_coverage_contract( + stage_name="trading_allowance_receipts", + candidate_source=candidate_source, + ), # PolicyEngine/uk-autumn-budget-dashboard-2026#316: the company cars # declare their household-mass receipt like every other stage. "company_cars": _source_stage_family_coverage_contract( diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index b93d92050..4762595c5 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -177,6 +177,9 @@ ) from microcosm.build.uk_runtime.student_loans import UKStudentLoansStageTransform from microcosm.build.uk_runtime.take_up_contract import load_uk_take_up_contract +from microcosm.build.uk_runtime.trading_allowance_receipts import ( + UKTradingAllowanceReceiptsStageTransform, +) from microcosm.build.uk_runtime.uc_capital_coherence import ( UKUCCapitalCoherenceStageTransform, ) @@ -209,7 +212,7 @@ _SPI_DONOR_SAMPLE_SIZE = 64 #: The packaged FRS spine roster the fixture exercises (manifest minus the #: certified-pair exclusions); moves whenever a spine stage is added. -UK_FIXTURE_STAGE_COUNT = 51 +UK_FIXTURE_STAGE_COUNT = 52 _QRF_ESTIMATORS = 4 # These are the complete object-string surface observed in the unchanged @@ -754,6 +757,10 @@ def _frs_tables() -> dict[str, pd.DataFrame]: "SERNUM": household_id, "BENUNIT": 1, "PERSON": 1, + # Every fifth household's payment is trust income (PENTYPE 5), + # which stays private pension with its part stored as the + # annuity and trust descriptor (microcosm#1428). + "PENTYPE": 5 if household_id % 5 == 0 else 1, "PENPAY": 10.0, "PTAMT": 2.0, "PTINC": 2, @@ -2201,6 +2208,9 @@ def capture_root(frame: Frame) -> Frame: donor_household=was, donor_person=was_person, ), + "trading_allowance_receipts": UKTradingAllowanceReceiptsStageTransform( + stage=stages["trading_allowance_receipts"] + ), "company_cars": UKCompanyCarsStageTransform( stage=stages["company_cars"], engine=engine,