diff --git a/changelog.d/1360-uk-additive-isa-income.changed.md b/changelog.d/1360-uk-additive-isa-income.changed.md new file mode 100644 index 0000000000..fcab888152 --- /dev/null +++ b/changelog.d/1360-uk-additive-isa-income.changed.md @@ -0,0 +1 @@ +The UK producer now stores interest outside ISAs in `savings_interest_income` and ISA interest apart in `individual_savings_account_interest_income`, and declares policyengine-uk's additive ISA income contract (`{"version": 1, "representation": "additive", "projection": "additive"}`) on every frame and H5 it writes; `tax_free_savings_income` is left to the engine, which adds the ISA parts. Frames carry the contract they were loaded with, so the enhanced-FRS reference and older Microcosm files keep their legacy gross reading, and a build refuses them as inputs. Base-year cash and tax are unchanged: predictors that read gross interest read the two parts together and the SPI stage-2 draws keep their salts. From the base year the engine grows each part by its own index instead of growing gross interest and taking off an unchanged ISA part, which removes a small overstatement of taxable interest in projected years (microcosm#1360). diff --git a/changelog.d/1415-uk-isa-statistics-facts.added.md b/changelog.d/1415-uk-isa-statistics-facts.added.md new file mode 100644 index 0000000000..f52f2e1f23 --- /dev/null +++ b/changelog.d/1415-uk-isa-statistics-facts.added.md @@ -0,0 +1 @@ +A new vendored resource, `hmrc_isa_statistics_facts.json`, carries the 1,074 rows of HMRC's 2026 annual savings statistics the ISA subscriptions stage and the Lifetime ISA anchor read (microcosm#1415): Table 9.7 2023-24 subscribers and holders by income and size of subscription for each type paid into, Tables 9.8 and 9.9 by age, sex and region, Table 9.4 accounts and amounts for 2023-24 and 2024-25, Table 9.6 market values, the cash ISA limit reduction impact note's shares and Lifetime ISA Table 1a. They come from the pinned `53815b2` feed, which carries PolicyEngine/chronicle#392's ISA statistics; the pin and the other vendored resources are unchanged. None is a calibration target. diff --git a/changelog.d/1415-uk-isa-subscriptions.added.md b/changelog.d/1415-uk-isa-subscriptions.added.md new file mode 100644 index 0000000000..b0d7066eaf --- /dev/null +++ b/changelog.d/1415-uk-isa-subscriptions.added.md @@ -0,0 +1 @@ +The UK spine imputes ISA subscriptions per adult (microcosm#1415). A new last stage, `isa_subscriptions`, splits each household's cash and stocks-and-shares ISA balances across its adults (`cash_isa_balance`, `stocks_and_shares_isa_balance`) and draws who paid into which type in 2024-25 and how much (`cash_isa_subscriptions`, `stocks_and_shares_isa_subscriptions`): subscribers and their amounts follow HMRC's 2023-24 Table 9.7 by income, size of subscription and type paid into, raked to Tables 9.8 and 9.9, and grow to 2024-25: people by Table 9.4's account growth, and amounts to its 2024-25 amounts, holding subscribers already at the £20,000 overall limit and tapering amounts above £18,000 up to it. Every Lifetime ISA holder aged 18 to 47, the ages that can hold one in 2024-25, with room under the limit pays in (`lifetime_isa_payments`), drawn to Table 9.4's 2024-25 Lifetime ISA amount, so policyengine-uk's Lifetime ISA bonus is no longer zero. policyengine-uk 2.164.2 reports the subscriptions an ISA limit leaves out but taxes none of them yet, so a limit reform costs nothing until it does. No ISA fact is a calibration target; the national build reports them as checks. Two gates check the stage: `uk_stage_isa_subscriptions_invariants` holds the rules any population keeps (limits, ages, holdings, finite and non-negative amounts, household sums), and the synthetic smoke build blocks on it; `uk_stage_isa_subscriptions` holds Table 9.4's totals. See `docs/uk-isa-subscriptions-1415.md`. diff --git a/changelog.d/1415-uk-lisa-anchor.changed.md b/changelog.d/1415-uk-lisa-anchor.changed.md new file mode 100644 index 0000000000..b441c12600 --- /dev/null +++ b/changelog.d/1415-uk-lisa-anchor.changed.md @@ -0,0 +1 @@ +The `was_lisa` stage anchors Lifetime ISA holders to HMRC's 2024-25 payers (microcosm#1415). The WAS round-8 model implied about 660,000 holders on the spine against HMRC's 1.14 million Lifetime ISA accounts subscribed to in 2024-25; every payer holds an account and pays into one a year, so the ownership logit now shifts up on ages 18 to 47, the ages that can hold and pay into one in 2024-25, until the holders at those ages who survive the financial-wealth cap equal that count. The same keyed uniforms decide ownership, so the anchor only adds holders. A new gate, `uk_stage_was_lisa_anchor`, checks the count. diff --git a/changelog.d/uk-policyengine-uk-2-164-2.changed.md b/changelog.d/uk-policyengine-uk-2-164-2.changed.md new file mode 100644 index 0000000000..69c789fe98 --- /dev/null +++ b/changelog.d/uk-policyengine-uk-2-164-2.changed.md @@ -0,0 +1 @@ +The UK lock moves from policyengine-uk 2.162.14 to 2.164.2 (microcosm#1415). 2.164.0 (PolicyEngine/policyengine-uk#2712) adds the ISA annual subscription limit, a cash ISA limit that current law leaves unset, per-person cash and stocks-and-shares ISA subscription and balance inputs, and the 20% Lifetime ISA withdrawal charge on a first home above the £450,000 cap; its household `cash_isa` and `stocks_and_shares_isa` sum the person balances. The release therefore stops storing those household totals, and the engine reads the `isa_subscriptions` stage's person columns. 2.164.1 moves the economic assumptions to the OBR's October 2026 Economic and fiscal outlook, and the vendored engine pins move with it: average earnings at 2025 fall 1.1% and household interest income 6.1%, while GDP per head grows by the same 8.8% from 2023 to 2025. On the national surface the eight SPI Table 3.10 self-employment bands, which mixed income per head takes to 2025, compile 0.55% lower, and the dividend bands move by under 0.001% (the compile-parity receipt is re-signed); the savings interest bands grow by the OBR's October savings income growth since microcosm#1420, so the engine pins no longer move them. The annual-income earnings index, the weight-year population path and the bonus-sacrifice anchor's earnings and population growth (factor 1.1149 to 1.1123) move too, and the CGT projection gate's pinned growth path is re-pinned to the October path (its horizon stays 2030, though the path now runs to 2031). 2.162.15 to 2.164.2 also change Housing Benefit, Income Support and Pension Credit rules, project the Scottish Carer Supplement and the Carer's Allowance earnings limit, and run the property allowance election on only the households with a choice. Concept coverage drops the two household ISA totals as inputs and gains the four person ISA columns and Housing Benefit's pre-assessed permitted-work disregard, which the build leaves at its default. diff --git a/docs/concept-coverage/policyengine-uk.md b/docs/concept-coverage/policyengine-uk.md index cc98f94f35..d02baaf1ab 100644 --- a/docs/concept-coverage/policyengine-uk.md +++ b/docs/concept-coverage/policyengine-uk.md @@ -1,11 +1,11 @@ # Concept coverage: policyengine-uk -Engine version `2.162.14`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. +Engine version `2.164.2`; concept schema `5a55669d0447`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand. -- Engine inputs: 357 +- Engine inputs: 360 - Fed by a concept: 41 - Structural: 11 -- Uncovered: 305 +- Uncovered: 308 - Mapped but missing from the engine: 0 ## Inputs each concept feeds @@ -44,20 +44,20 @@ Engine version `2.162.14`; concept schema `5a55669d0447`. Generated by `tools/re | `fact:person.legal_marital_status` | policyengine-uk derives marital status from benefit-unit composition; marital_status and is_married are formula-owned. | | `fact:person.partner_person_id` | Partnerships reach policyengine-uk only through benefit-unit membership, a structure the bundle must already carry (the adapter builds no units); is_benunit_head and is_married are formula-owned. | | `fact:household.reference_person_id` | is_household_head is formula-owned (the oldest member). The UK build overrides it with the FRS household reference person (frs_spine.py), an override the loader accepts, but it is not an input, so the mapping does not target it. Every housing input is household-level, so nothing is allocated to the reference person. | -| `fact:person.liquid_financial_assets` | policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. savings and share_like_capital are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources); since policyengine-uk 2.147.0 share_like_capital is corporate_wealth unless its components, the household inputs directly_held_shares and unit_and_investment_trusts, add up to more; the WAS stage writes both on its corporate_wealth less the stocks-and-shares ISA, so they and the ISA sum to it (microcosm#1095). Universal Credit's one person-level source is lifetime_isa_countable_capital, the Lifetime ISA at its surrender value (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either would be a modelling choice for a UK build, not a mapping of this concept. | +| `fact:person.liquid_financial_assets` | policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. Since policyengine-uk 2.164.0 cash_isa_balance and stocks_and_shares_isa_balance hold the person's ISAs, and the household cash_isa and stocks_and_shares_isa sum them; the isa_subscriptions stage splits the WAS household totals into them (microcosm#1415), a split of one wrapper's balance, not of liquid financial assets. savings and share_like_capital are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources); since policyengine-uk 2.147.0 share_like_capital is corporate_wealth unless its components, the household inputs directly_held_shares and unit_and_investment_trusts, add up to more; the WAS stage writes both on its corporate_wealth less the stocks-and-shares ISA, so they and the ISA sum to it (microcosm#1095). Universal Credit's one person-level source is lifetime_isa_countable_capital, the Lifetime ISA at its surrender value (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either would be a modelling choice for a UK build, not a mapping of this concept. | | `fact:person.public_pension_income` | The State Pension is computed from state_pension_reported, which is formula-owned: the loader accepts it as an override, but it is not an input. | | `fact:household.source_household_id` | Original donor household lineage is build provenance and weight diagnostic support, not a policyengine-uk rules input. | ## Inputs no concept covers -### benunit (17) +### benunit (18) -`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `benunit_reported_capital`, `housing_benefit_board_and_attendance_determination`, `housing_benefit_family_premium_category`, `housing_benefit_has_main_phase_esa`, `housing_benefit_residential_capital_exception`, `in_specified_or_temporary_accommodation`, `liable_for_share_of_household_rent`, `maximum_extended_childcare_hours_usage`, `meals_included_in_rent`, `severe_disability_premium_joint_occupier_exclusion_applies`, `tax_free_childcare_meets_uk_connection`, `uc_receives_new_claimant_health_element`, `working_tax_credit_run_on`, `would_claim_wales_childcare_offer` +`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `benunit_reported_capital`, `housing_benefit_board_and_attendance_determination`, `housing_benefit_family_premium_category`, `housing_benefit_has_main_phase_esa`, `housing_benefit_permitted_work_disregard`, `housing_benefit_residential_capital_exception`, `in_specified_or_temporary_accommodation`, `liable_for_share_of_household_rent`, `maximum_extended_childcare_hours_usage`, `meals_included_in_rent`, `severe_disability_premium_joint_occupier_exclusion_applies`, `tax_free_childcare_meets_uk_connection`, `uc_receives_new_claimant_health_element`, `working_tax_credit_run_on`, `would_claim_wales_childcare_offer` -### household (107) +### household (105) -`accommodation_type`, `additional_residential_property_purchased`, `alcohol_and_tobacco_consumption`, `beer_draught_share`, `beer_litres`, `betting_exchange_commission`, `bingo_net_receipts`, `bus_fare_spending_reported`, `bus_subsidy_spending`, `cash_isa`, `cider_draught_share`, `cider_litres`, `cigarettes_per_week`, `cigars_grams_per_week`, `clothing_and_footwear_consumption`, `communication_consumption`, `consumer_debt`, `corporate_wealth`, `council_tax_band`, `cumulative_non_residential_rent`, `cumulative_residential_rent`, `dfe_education_spending`, `diesel_spending`, `directly_held_shares`, `domestic_energy_consumption`, `domestic_rates`, `ebr_energy_bills_credit_eligible`, `education_consumption`, `electricity_consumption`, `electricity_consumption_kwh`, `external_child_payments`, `financial_spread_betting_net_losses`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gas_consumption_kwh`, `gross_financial_wealth`, `hand_rolling_tobacco_grams_per_week`, `health_consumption`, `heated_tobacco_grams_per_week`, `horse_and_dog_pool_betting_net_losses`, `household_furnishings_consumption`, `household_owns_tv`, `housing_service_charges`, `housing_water_and_electricity_consumption`, `in_original_frs`, `in_rural_fuel_duty_relief_area`, `is_shared_accommodation`, `land_based_casino_net_losses`, `land_based_fixed_odds_betting_net_losses`, `liquid_fuel_consumption`, `local_authority`, `lpg_kg`, `machine_games_higher_rate_net_takings`, `machine_games_lower_rate_net_takings`, `machine_games_standard_rate_net_takings`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `main_residential_property_purchased_is_in_uk`, `main_residential_property_purchased_with_mortgage`, `miscellaneous_consumption`, `mortgage_debt`, `national_lottery_ticket_spending`, `natural_gas_kg`, `net_financial_wealth`, `ni_epg_full_rate_applies`, `non_residential_property_purchased`, `non_residential_property_secured_debt`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_secured_debt`, `other_residential_property_value`, `other_spread_betting_net_losses`, `other_tobacco_grams_per_week`, `owned_land`, `petrol_spending`, `pool_betting_net_receipts`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `remote_gaming_net_losses`, `remote_other_betting_net_losses`, `remote_uk_horseracing_betting_net_losses`, `restaurants_and_hotels_consumption`, `savings`, `solid_fuel_consumption`, `sparkling_cider_draught_share`, `sparkling_cider_litres`, `spi_imputed`, `spirits_draught_share`, `spirits_litres`, `stocks_and_shares_isa`, `structural_insurance_payments`, `transport_consumption`, `uc_migrated`, `unit_and_investment_trusts`, `uses_energy_prepayment_meter`, `water_and_sewerage_charges`, `wine_draught_share`, `wine_litres`, `would_evade_tv_licence_fee` +`accommodation_type`, `additional_residential_property_purchased`, `alcohol_and_tobacco_consumption`, `beer_draught_share`, `beer_litres`, `betting_exchange_commission`, `bingo_net_receipts`, `bus_fare_spending_reported`, `bus_subsidy_spending`, `cider_draught_share`, `cider_litres`, `cigarettes_per_week`, `cigars_grams_per_week`, `clothing_and_footwear_consumption`, `communication_consumption`, `consumer_debt`, `corporate_wealth`, `council_tax_band`, `cumulative_non_residential_rent`, `cumulative_residential_rent`, `dfe_education_spending`, `diesel_spending`, `directly_held_shares`, `domestic_energy_consumption`, `domestic_rates`, `ebr_energy_bills_credit_eligible`, `education_consumption`, `electricity_consumption`, `electricity_consumption_kwh`, `external_child_payments`, `financial_spread_betting_net_losses`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gas_consumption_kwh`, `gross_financial_wealth`, `hand_rolling_tobacco_grams_per_week`, `health_consumption`, `heated_tobacco_grams_per_week`, `horse_and_dog_pool_betting_net_losses`, `household_furnishings_consumption`, `household_owns_tv`, `housing_service_charges`, `housing_water_and_electricity_consumption`, `in_original_frs`, `in_rural_fuel_duty_relief_area`, `is_shared_accommodation`, `land_based_casino_net_losses`, `land_based_fixed_odds_betting_net_losses`, `liquid_fuel_consumption`, `local_authority`, `lpg_kg`, `machine_games_higher_rate_net_takings`, `machine_games_lower_rate_net_takings`, `machine_games_standard_rate_net_takings`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `main_residential_property_purchased_is_in_uk`, `main_residential_property_purchased_with_mortgage`, `miscellaneous_consumption`, `mortgage_debt`, `national_lottery_ticket_spending`, `natural_gas_kg`, `net_financial_wealth`, `ni_epg_full_rate_applies`, `non_residential_property_purchased`, `non_residential_property_secured_debt`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_secured_debt`, `other_residential_property_value`, `other_spread_betting_net_losses`, `other_tobacco_grams_per_week`, `owned_land`, `petrol_spending`, `pool_betting_net_receipts`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `remote_gaming_net_losses`, `remote_other_betting_net_losses`, `remote_uk_horseracing_betting_net_losses`, `restaurants_and_hotels_consumption`, `savings`, `solid_fuel_consumption`, `sparkling_cider_draught_share`, `sparkling_cider_litres`, `spi_imputed`, `spirits_draught_share`, `spirits_litres`, `structural_insurance_payments`, `transport_consumption`, `uc_migrated`, `unit_and_investment_trusts`, `uses_energy_prepayment_meter`, `water_and_sewerage_charges`, `wine_draught_share`, `wine_litres`, `would_evade_tv_licence_fee` -### person (181) +### person (185) -`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_limit_adoption_exception`, `child_limit_continuation_exception`, `child_limit_multiple_birth_exception`, `child_limit_non_consensual_conception_exception`, `child_limit_non_parental_caring_exception`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `defined_benefit_pension_share`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `early_years_school_start_deferred`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `individual_savings_account_dividend_income`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_engaged_in_owned_company_trade`, `is_full_year_category_a_director`, `is_in_approved_training`, `is_in_first_year_of_apprenticeship`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `lifetime_isa_first_time_buyer`, `lifetime_isa_months_since_first_payment`, `lifetime_isa_payments`, `lifetime_isa_terminally_ill`, `lifetime_isa_withdrawals`, `lifetime_isa_withdrawals_towards_first_home`, `local_bus_uncapped_single_fare`, `loss_relief`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `non_cash_isa_cash_interest`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `owned_company_capital`, `owned_company_carries_on_property_business`, `owned_company_carries_on_trade`, `owned_company_holding_value`, `owned_company_income_share`, `owned_company_intermediary_earnings_chapter`, `owned_company_intermediary_earnings_from_main_employment`, `owned_company_is_main_employment`, `owned_company_trade_assets`, `pension_annual_allowance_taxable_lump_sum_death_benefits`, `pension_contributions_via_bonus_sacrifice`, `pension_contributions_via_salary_sacrifice`, `pension_contributions_via_salary_sacrifice_pre_2015`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_points`, `pip_m_points`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `salary_sacrifice_returned_to_income`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `spousal_maintenance_income`, `ssmg_reported`, `stands_as_sole_owner_or_partner_of_company`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `war_widows_pension_reported`, `widowed_parents_allowance_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` +`a_and_e_visits`, `aa_category`, `access_fund`, `admitted_patient_visits`, `adult_dependants_grant_has_other_adult_dependant`, `adult_dependants_grant_other_adult_income`, `adult_dependants_grant_receives_postgraduate_loan`, `adult_ema`, `afcs_reported`, `age_started_or_accepted_current_education_or_training`, `armed_forces_independence_payment`, `attends_private_school_random_draw`, `base_net_income`, `blind_persons_allowance`, `bsp_reported`, `bursary_fund_16_to_19_in_care_or_care_leaver`, `bursary_fund_16_to_19_participation_costs`, `bursary_fund_16_to_19_self_supporting`, `bus_in_london_trips`, `bus_pass_eligible`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `car_co2_emissions`, `car_engine_size`, `car_first_registration_date`, `car_fuel_type`, `car_is_ved_exempt`, `car_list_price`, `care_hours`, `carers_allowance_reported`, `cash_isa_balance`, `cash_isa_subscriptions`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `child_limit_adoption_exception`, `child_limit_continuation_exception`, `child_limit_multiple_birth_exception`, `child_limit_non_consensual_conception_exception`, `child_limit_non_parental_caring_exception`, `child_tax_credit_reported`, `childcare_expenses`, `childcare_grant_child_has_special_educational_needs`, `childcare_grant_provider_is_nanny`, `childcare_grant_receives_nhs_childcare_support`, `childcare_grant_receives_postgraduate_loan`, `council_tax_benefit_reported`, `covenanted_payments`, `deficiency_relief`, `defined_benefit_pension_share`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `early_years_school_start_deferred`, `education_grants`, `employee_pension_contributions_reported`, `employer_pension_contributions`, `employment_expenses`, `employment_sector`, `employment_status`, `esa_contrib_reported`, `esa_income_reported`, `free_school_fruit_veg`, `free_school_meals`, `free_school_milk`, `gift_aid`, `healthy_start_vouchers`, `housing_benefit_reported`, `iidb_reported`, `in_FE`, `incapacity_benefit_reported`, `income_support_reported`, `individual_savings_account_dividend_income`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_engaged_in_owned_company_trade`, `is_full_year_category_a_director`, `is_in_approved_training`, `is_in_first_year_of_apprenticeship`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `lifetime_isa_first_time_buyer`, `lifetime_isa_months_since_first_payment`, `lifetime_isa_payments`, `lifetime_isa_terminally_ill`, `lifetime_isa_withdrawals`, `lifetime_isa_withdrawals_towards_first_home`, `local_bus_uncapped_single_fare`, `loss_relief`, `lump_sum_income`, `maintenance_expenses`, `maintenance_income`, `married_couples_allowance`, `maternity_allowance_reported`, `max_free_entitlement_hours_used`, `miscellaneous_income`, `nhs_a_and_e_spending`, `nhs_admitted_patient_spending`, `nhs_outpatient_spending`, `ni_class_3`, `non_cash_isa_cash_interest`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `owned_company_capital`, `owned_company_carries_on_property_business`, `owned_company_carries_on_trade`, `owned_company_holding_value`, `owned_company_income_share`, `owned_company_intermediary_earnings_chapter`, `owned_company_intermediary_earnings_from_main_employment`, `owned_company_is_main_employment`, `owned_company_trade_assets`, `pension_annual_allowance_taxable_lump_sum_death_benefits`, `pension_contributions_via_bonus_sacrifice`, `pension_contributions_via_salary_sacrifice`, `pension_contributions_via_salary_sacrifice_pre_2015`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_points`, `pip_m_points`, `private_transfer_income`, `property_finance_costs`, `property_finance_costs_brought_forward`, `property_rental_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `salary_sacrifice_returned_to_income`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `spousal_maintenance_income`, `ssmg_reported`, `stands_as_sole_owner_or_partner_of_company`, `statutory_maternity_pay`, `statutory_paternity_pay`, `statutory_sick_pay`, `stocks_and_shares_isa_balance`, `stocks_and_shares_isa_subscriptions`, `student_loan_balance`, `student_loan_plan`, `student_loans`, `sublet_income`, `tax_free_childcare_on_adoption_leave`, `tax_free_childcare_on_carers_leave`, `tax_free_childcare_on_qualifying_leave`, `tax_free_childcare_on_shared_parental_leave`, `tax_free_childcare_self_employment_start_up_period`, `tax_free_childcare_spend_routed_share`, `tax_free_childcare_uses_qualifying_provider`, `tax_reported`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period`, `uc_is_adopter_in_first_year`, `uc_is_foster_parent_or_new_friend_or_family_carer`, `uc_is_in_pregnancy_or_post_confinement_period`, `uc_is_in_startup_period`, `uc_is_responsible_foster_parent_of_child_under_one`, `uc_is_student_with_no_work_related_requirements`, `uc_is_temporarily_absent_from_claimant_household`, `universal_credit_reported`, `war_widows_pension_reported`, `widowed_parents_allowance_reported`, `winter_fuel_allowance_reported`, `working_tax_credit_reported` diff --git a/docs/uk-chronicle-feed-repin.md b/docs/uk-chronicle-feed-repin.md index 1d2d7dc675..a66100e413 100644 --- a/docs/uk-chronicle-feed-repin.md +++ b/docs/uk-chronicle-feed-repin.md @@ -345,3 +345,9 @@ The national register version moves with the new references, so the uk-candidate register is re-frozen before the next national solve. The high-value home stock still names `735820c`: its inputs are the same on `53815b2` (the feed-gated fingerprint check passes), so it was not rebuilt. + +The ISA subscriptions (microcosm#1415, PolicyEngine/microcosm#1421) then vendored a new resource +from the same `53815b2` artifact: `hmrc_isa_statistics_facts.json`, the 1,074 rows of #392's ISA +statistics the ISA subscriptions stage and the Lifetime ISA anchor read. None is a calibration +target, so no reference moves for it. That pull request first pinned #392's head (`e56af9a`) and +then its merge (`0b721a4`), and took `53815b2` when PR C merged first. diff --git a/docs/uk-isa-subscriptions-1415.md b/docs/uk-isa-subscriptions-1415.md new file mode 100644 index 0000000000..c1e9585a61 --- /dev/null +++ b/docs/uk-isa-subscriptions-1415.md @@ -0,0 +1,206 @@ +# ISA subscriptions per adult (microcosm#1415) + +The `isa_subscriptions` stage gives every adult of the UK spine what they paid +into ISAs in the year, by type, and the per-person ISA balances behind them. It +runs last, on final incomes, and nothing in the spine reads its outputs. + +| Column | Meaning | +|---|---| +| `person.cash_isa_balance` | The person's share of the household's cash ISA balance (`was_wealth`), in pounds. | +| `person.stocks_and_shares_isa_balance` | The person's share of the household's stocks-and-shares ISA balance. | +| `person.cash_isa_subscriptions` | Paid into cash ISAs in 2024-25. | +| `person.stocks_and_shares_isa_subscriptions` | Paid into stocks-and-shares ISAs in 2024-25. | +| `person.lifetime_isa_payments` | Paid into the person's Lifetime ISA in 2024-25 (at most £4,000). | + +The `was_lisa` stage also anchors Lifetime ISA holders to HMRC's payers (below, +and [the LISA notes](uk-lisa-1003.md)). + +The pinned policyengine-uk 2.164.2 reads all five: + +- `lifetime_isa_payments`, whose 25% bonus is `lifetime_isa_bonus`; +- the subscriptions, against the limits of policyengine-uk#2712 + (`isa_subscriptions_above_limit` and `cash_isa_subscriptions_above_limit` are + what a limit leaves out); +- the person balances, which its household `cash_isa` and + `stocks_and_shares_isa` sum. + +The release does not store those household totals. A stored total would +override the sum, so the means tests, which count the household `cash_isa`, +read the sum of the person balances. + +## Sources + +- HMRC, [Annual savings statistics 2026](https://www.gov.uk/government/statistics/annual-savings-statistics-2026) + (September 2026), through the pinned Chronicle feed (`53815b2`, which carries + PolicyEngine/chronicle#392's ISA statistics): + - Table 9.7: individuals subscribing in 2023-24 by total income and size of + subscription, for each type paid into (cash only, stocks and shares only, + both). The tables head their columns "(thousands)" but count people. + - Table 9.8 by age and sex, and Table 9.9 by region. + - Table 9.4: accounts and amounts subscribed by type to 2024-25 (the latest + year provisional), including Lifetime ISAs. + - Table 9.6: market values, and Tables 9.10 to 9.12: holders by market value, + both checks only. +- HMRC, the cash ISA limit reduction tax information and impact note: 22% of + cash subscribers under 65 paid in more than £12,000 in 2022-23 (a check). +- HMRC, Lifetime ISA statistics, Table 1a: withdrawals (a check). +- ONS Wealth and Assets Survey round 8 (UKDS SN 7215), the person tab's ISA + values, for the split of household balances across adults. + +How the tables relate, on HMRC's own figures: + +- Table 9.7's type blocks are the types paid into: cash only plus both + reproduces Table 9.4's 2023-24 cash accounts, and stocks and shares only plus + both its stocks-and-shares accounts. Lifetime ISAs, Innovative Finance ISAs + and Junior ISAs are outside Table 9.7; Help to Buy ISAs are inside cash. +- Table 9.7's bands imply more money than Table 9.4 records. Valued at their + floors they give at least £114.3bn (cash only alone £78.1bn), against Table + 9.4's £100.6bn of cash plus stocks and shares, so the two cannot both bind. +- From 6 April 2024 (SI 2024/350) a person may pay into more than one ISA of a + type in a year, but still only one Lifetime ISA, so Table 9.4's 2024-25 + accounts are an upper bound on people. + +## Method + +1. **Balances per adult.** Each household's 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 (up to four), drawn at + household-keyed uniforms and conditioned so every subscriber holds the type + they pay into. Person values sum exactly to the household totals, which are + unchanged. +2. **Who subscribed in 2023-24.** Adults aged 18 or over in households holding + the type are eligible: cash only needs a cash balance, stocks and shares only + a stocks-and-shares balance, and both needs both. For each class, a small + iterative proportional fit rakes the eligible weighted mass to Table 9.7's + income bands (on the CGT stage's income proxy), Table 9.8's age and sex and + Table 9.9's regions (overseas dropped, each margin on Table 9.7's total). + Keyed walks select subscribers cell by cell, both first, then stocks and + shares only, then cash only. A cell without enough eligible mass is reported + (`shortfall_weight_unmet`); no holder is invented. +3. **How much.** Within each income band and class, amounts follow Table 9.7's + subscription bands: uniform inside a band, exactly £20,000 at the top. The + rank blends the person's balance rank with a keyed uniform. +4. **The both class.** Each person's amount is split by their balance shares, + shifted on the logit scale so the class's stocks-and-shares total equals + Table 9.4's 2023-24 stocks-and-shares amount less the stocks-and-shares-only + payments (about 27% stocks and shares). A shift the floor stops is named. +5. **Growth to 2024-25.** Each class's people grow by Table 9.4's account + growth (cash accounts for cash only, stocks-and-shares accounts for stocks and + shares only, both together for both), added from eligible holders not yet + subscribing. Amounts then scale, one factor per type, to Table 9.4's 2024-25 + amounts (£95.6bn of cash and £37.2bn of stocks and shares). Subscribers + already at the £20,000 overall limit stay there. A person whose combined + amount lies between £18,000 (Table 9.7's last band floor below the limit) and + the limit moves on a line from their scaled £18,000 up to it, so the range just + below the limit stays filled; the rest are capped at it. +6. **Lifetime ISA payments.** Every holder aged 18 to 47 with room under the + overall limit pays in; a holder with none pays nothing. 18 to 47 are the ages + that can hold one in 2024-25, since accounts opened at 18 to 39 from April + 2017. Amounts lie in (0, £4,000], drawn by a truncated-exponential tilt so + the total equals Table 9.4's 2024-25 Lifetime ISA amount (£2,818m from + 1,136,000 accounts), so the baseline leaves nothing above the overall limit. + The rank blends the balance and income ranks with a keyed uniform. + +## Decisions (28 October 2026) + +- **Measure, bind none.** No ISA fact enters the weight solve: binding the cells + the stage draws to would be circular. A reviewed fence declares every vendored + ISA fact measured only; the checks below are reported and never block a build. +- **Table 9.7's people, the stocks-and-shares residual.** Table 9.7 keeps its + people in every band; the both class's split takes its stocks-and-shares + total from Table 9.4's residual. +- **People by accounts.** Growth uses Table 9.4's account growth, the upper + bound on people; the flat-people bound is reported beside it. +- **Lifetime ISAs here.** Holders are re-anchored to HMRC's payers and payments + drawn in this change. The evidence is the next pair build. + +## Decisions (29 October 2026, on the review of microcosm#1421) + +- **Cash grows to Table 9.4's level, holding and tapering to the limit.** + Growing by Table 9.4's ratio carried Table 9.7's cash excess into 2024-25 and, + with the subscribers at £20,000 unable to grow, scaled everyone else by about + 2.05. On populations built from HMRC's own tables that gave £127bn of cash + against Table 9.4's £95.6bn, 51% of cash subscribers above £12,000 and 40% of + all subscribers at the limit. Scaling to Table 9.4's 2024-25 amounts holds the + subscribers at the limit, since a plain factor below one would have moved every + one of them to about £19,100. One factor of about 0.89 below the limit still + left nobody between about £17,750 and £20,000, so amounts above £18,000 now + taper up to the limit. That gives a cash factor of about 0.878, 36.9% of cash + subscribers above £12,000, 28.8% at the limit (28.8% in 2023-24) and amounts + up to about £19,950 below it. +- **The Lifetime ISA anchor's reach is counted on the pair.** Three later stages + read the whole frame (`uc_reporter_redraw`, `uc_take_up` and + `pension_credit_take_up`), and a Lifetime ISA counts as Universal Credit and + Pension Credit capital, so the added holders can move their draws. The pair + build's national counts, at its final weights, the benefit units whose + entitlement changes without the added holders' balances, and the units that + move in or out of the take-up pools. Where the financial-wealth cap binds, an + added holder also scales the household's existing balances down pro rata. + +## What the tables leave open + +- **The cash excess.** Table 9.7 at band midpoints implies about £92.5bn of + cash (£84.5bn cash only and £8.1bn from the both class) against Table 9.4's + £69.5bn, about £23bn above it. The stage keeps Table 9.7's people and bands, + so its 2023-24 cash total sits above Table 9.4's, and the receipt reports by + how much; the 2024-25 total meets Table 9.4. +- **The impact note's year.** On HMRC's tables 36.9% of 2024-25 cash + subscribers pay in more than £12,000, against the impact note's 22% of under-65 + cash subscribers in 2022-23. Age explains little of that: Table 9.8 puts about a + quarter of cash-only subscribers at 66 or over, who would need about 80% above + £12,000 to bring the under-65s to 22%. Most of it is the year: Table 9.4's + cash subscriptions were £41.6bn in 2022-23, £69.5bn in 2023-24 and £95.6bn in + 2024-25, so the 22% does not validate 2024-25 either way. Amounts follow Table + 9.7's bands within income band and class, not age. +- **The both class's residual share** depends on where the stocks-and-shares-only + payments sit inside their bands: about 27% stocks and shares at band + midpoints, 50% at band floors and 7% at band tops. +- **One year of growth.** The individual tables are 2023-24, grown one year + across the April 2024 rule change. +- **Balances.** WAS balances are April 2020 to March 2022 pounds from a Great + Britain donor, not uprated; Northern Ireland adults take the Great Britain + patterns. + +## Gates + +`uk_stage_isa_subscriptions_invariants` holds the rules any population keeps: +the limits, ages, holding what is paid into, finite and non-negative amounts and +person balances that sum to the household totals. It is no population fact +check, so the synthetic smoke build blocks on it as every licensed build does. +`uk_stage_isa_subscriptions` holds the 2024-25 totals, the both class's residual +and the Lifetime ISA amount to Table 9.4. A synthetic population cannot reach +HMRC's national totals, so it is a population fact check. + +## Checks (reported on the pair build's national file) + +- Subscription cells before and after calibration, by class and band, and above + £12,000. +- Holders against Tables 9.10 to 9.12, and market values against Table 9.6. +- The impact note's 22% of under-65 cash subscribers above £12,000. +- Lifetime ISA payers and amounts against Table 9.4, and `lifetime_isa_bonus` + against 25% of payments; the 2025-26 house-purchase withdrawals (99,750) as a + loose check on holders. +- The flat-people growth bound. +- The Lifetime ISA anchor's reach: benefit units whose Universal Credit or + Pension Credit entitlement changes without the added holders' balances, at + the final weights, and the units that move in or out of the take-up pools. + +## What the fields support + +The fields support the subscriptions a limit leaves out: the overall £20,000 +limit and a separate cash limit (the £12,000 under-65 setting is a reform in +policyengine-uk), through `isa_subscriptions_above_limit` and +`cash_isa_subscriptions_above_limit`. policyengine-uk 2.164.2 reads neither in +any tax or spending variable, so a limit reform changes those amounts but costs +nothing until the engine models where the excess goes. The subscriptions are +flows the engine does not uprate, so a later year reads 2024-25 amounts. In +particular the cash subscriptions above £12,000 in 2027-28, which a £12,000 cash +limit from April 2027 would leave out, are 2024-25 levels here, not a 2027-28 +base: the dashboard's cash ISA card should not read them as one (Vahid's review +of microcosm#1421). policyengine-uk's own docstring notes a producer could grow +them and cap each person at the limit in force; this stage does not, since +HMRC's tables stop at 2024-25. The +Lifetime ISA bonus does reach government spending, so its rate and payment +limit score. The fields also support analyses of who subscribes how much. They +do not support withdrawals or first-home purchase rules, Innovative Finance or +Junior ISAs, or behavioural responses to a limit. diff --git a/docs/uk-lisa-1003.md b/docs/uk-lisa-1003.md index 45151e07bc..c7787aa207 100644 --- a/docs/uk-lisa-1003.md +++ b/docs/uk-lisa-1003.md @@ -139,10 +139,14 @@ financial wealth. LISAs are not inside `savings` (savings accounts), `cash_isa`, `stocks_and_shares_isa` or `corporate_wealth`. The LISA columns are a detail of gross financial wealth: they must never be added to it, to `savings`, or to the ISA columns. The engine's `total_wealth` does not read -`gross_financial_wealth`, and no engine variable reads the LISA columns today; -the engine's means-test capital reads `savings` and `corporate_wealth`, so -neither cash ISAs nor LISAs reach it. Innovative-finance ISAs and ISAs of -unknown type remain inside gross financial wealth only. +`gross_financial_wealth`. On policyengine-uk 2.164.2 the means tests of +Universal Credit, Housing Benefit, Pension Credit, income-based ESA and JSA and +Income Support count a LISA at its surrender value +(`lifetime_isa_countable_capital`: the balance less the withdrawal charge below +the charge-free age) as person capital, and count the household `cash_isa`, +the sum of its members' `cash_isa_balance` (microcosm#1415), beside `savings`. +Innovative-finance ISAs and ISAs of unknown type remain inside gross financial +wealth only. ## Coverage and vintage @@ -157,6 +161,23 @@ unknown type remain inside gross financial wealth only. candidate spine's 660,000 holders ([Individual Savings Account statistics, September 2026](https://www.gov.uk/government/statistics/annual-savings-statistics-2026), Table 9.4; receipts, Part F). +- Since microcosm#1415 the stage anchors ownership to those payers. The + ownership logit shifts up on ages 18 to 47, the ages that can hold and pay + into a LISA in 2024-25 (opened at 18 to 39 from April 2017), until the + weighted holders at those ages that survive the financial-wealth cap equal + Table 9.4's 2024-25 accounts subscribed to. Every payer holds a LISA and pays + into one a year, so that is the fewest holders consistent with HMRC. Older + holders the model draws are not counted. The same keyed uniforms decide + ownership, so the anchor only adds holders and keeps every drawn one; a + target the largest shift cannot reach, and unshifted holders above it, are + named in the receipt, and `uk_stage_was_lisa_anchor` checks the rest. +- Where the cap binds, an added holder scales the household's balances down + pro rata, an existing holder's included. +- Three later stages read the whole frame (`uc_reporter_redraw`, `uc_take_up` + and `pension_credit_take_up`), and the means tests count a LISA as capital, + so the added holders can move their draws. The pair build counts the benefit + units whose entitlement changes without them (María's ruling of 29 October + 2026). - Northern Ireland adults are predicted from the Great Britain model. - Conditional ownership odds from 2020-22 are applied to the 2024-25 spine. A holder in 2024-25 can be up to about 47; the donor, interviewed earlier, @@ -168,7 +189,8 @@ unknown type remain inside gross financial wealth only. ## On the candidate spine -Measured on the licensed candidate spine (receipts, Parts C to F): +Measured on the licensed candidate spine before the anchor of microcosm#1415 +(receipts, Parts C to F): - 1.25% of weighted adults hold a LISA, against the donor's 0.89%. Every age group under 45 is above the donor's share, the youngest most: 4.1% of the @@ -190,20 +212,24 @@ Measured on the licensed candidate spine (receipts, Parts C to F): The fields support analysis of who holds LISAs and how much they hold, for example the distribution of LISA wealth, or a means-test or wealth measure that -counts LISA balances once the engine reads them. They do not support reliable -scoring of reforms to the annual payment limit or the bonus rate, which depend -on contributions; reforms to withdrawals or to first-home purchases, which need -withdrawal and purchase flows and eligibility; or a cash-versus-stocks-and- -shares split, which WAS does not record. +counts LISA balances. Payments now come from the `isa_subscriptions` stage +([microcosm#1415](uk-isa-subscriptions-1415.md)), so reforms to the annual +payment limit or the bonus rate can be scored with its limits. The fields do not +support reforms to withdrawals or to first-home purchases, which need withdrawal +and purchase flows and eligibility, or a cash-versus-stocks-and-shares split, +which WAS does not record. ## The annual-contribution gap WAS measures holdings and values at interview. It has no LISA-specific amount paid in during a tax year. A balance accumulates payments, bonuses, returns and withdrawals, so it does not identify the payments of a particular year, and -holding a LISA does not show whether the person paid in that year. The stage +holding a LISA does not show whether the person paid in that year. This stage therefore creates no contribution column and derives no contribution or bonus -from a balance. +from a balance. Since microcosm#1415 the `isa_subscriptions` stage draws +`lifetime_isa_payments` for every holder aged 18 to 47 with room under the +overall limit, to Table 9.4's 2024-25 Lifetime ISA amount; a holder's balance +only ranks who pays more. References for a future contribution extension, carried from the issue as references only (not requests to obtain data or to contact providers or HMRC): diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml index 92bafb9f7b..15360f21fc 100644 --- a/packages/microcosm-build/pyproject.toml +++ b/packages/microcosm-build/pyproject.toml @@ -46,7 +46,7 @@ us = [ # The UK extra adds the rules engine for local metric generation from a # Microcosm UK H5. Target tables remain explicit inputs, and the base package # still does not import policyengine-uk at import time. -uk = ["policyengine-uk>=2.162.14", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] +uk = ["policyengine-uk>=2.164.2", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"] [project.scripts] microcosm-build-uk = "microcosm.build.uk_runtime.full_build_cli:main" diff --git a/packages/microcosm-build/src/microcosm/build/frame_serializer_registry.py b/packages/microcosm-build/src/microcosm/build/frame_serializer_registry.py index 87fffeddbc..51f50d2eb0 100644 --- a/packages/microcosm-build/src/microcosm/build/frame_serializer_registry.py +++ b/packages/microcosm-build/src/microcosm/build/frame_serializer_registry.py @@ -236,8 +236,8 @@ class HdfWriteExclusion: "_write_isa_income_contract", ), reason=( - "Adds the ISA income contract root attribute only " - "(policyengine-uk 2.162.2; microcosm#1095)." + "Adds the frame's ISA income contract root attribute only " + "(policyengine-uk 2.162.2; microcosm#1095, microcosm#1360)." ), ), HdfWriteExclusion( diff --git a/packages/microcosm-build/src/microcosm/build/source_manifest.py b/packages/microcosm-build/src/microcosm/build/source_manifest.py index 631d7b254d..00d13a4cf5 100644 --- a/packages/microcosm-build/src/microcosm/build/source_manifest.py +++ b/packages/microcosm-build/src/microcosm/build/source_manifest.py @@ -76,6 +76,16 @@ "clone_records", "clean_nts_travel_tables", "clean_was_lisa_donor", + # microcosm#1415: ISA subscriptions per adult and the Lifetime ISA anchor. + "allocate_isa_subscribers", + "anchor_lifetime_isa_ownership", + "classify_isa_facts_with_reviewed_fence", + "clean_was_isa_person_donor", + "draw_isa_subscription_amounts", + "draw_lifetime_isa_payments", + "grow_isa_subscriptions", + "split_both_type_subscriptions", + "split_household_isa_balances", "convert_bonuses_to_bonus_sacrifice", "convert_donors_to_target_stock", "convert_interest_to_structural_mortgage_inputs", 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 2702284494..51b7208ffa 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 @@ -3559,7 +3559,7 @@ "type": "object", "additionalProperties": false, "properties": { - "gross_savings_interest_income": { + "savings_interest_income": { "type": "string" }, "refresh_disability_categories": { @@ -3576,7 +3576,7 @@ } }, "required": [ - "gross_savings_interest_income", + "savings_interest_income", "refresh_disability_categories", "refresh_disability_flags" ] @@ -8277,7 +8277,7 @@ "type": "object", "additionalProperties": false, "properties": { - "gross_savings_interest_income": { + "savings_interest_income": { "type": "string" }, "refresh_disability_categories": { @@ -13267,6 +13267,385 @@ } } } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "household_key", + "household_weight", + "person_columns", + "household_aggregates", + "aggregate_tolerance_gbp", + "non_dependent_code" + ], + "properties": { + "kind": { + "const": "clean_was_isa_person_donor" + }, + "household_key": { + "type": "string" + }, + "household_weight": { + "type": "string" + }, + "person_columns": { + "type": "object", + "additionalProperties": false, + "required": [ + "person_number", + "is_dependent_child", + "age_band", + "cash_isa_value", + "stocks_and_shares_isa_value" + ], + "properties": { + "person_number": { + "type": "string" + }, + "is_dependent_child": { + "type": "string" + }, + "age_band": { + "type": "string" + }, + "cash_isa_value": { + "type": "string" + }, + "stocks_and_shares_isa_value": { + "type": "string" + } + } + }, + "household_aggregates": { + "type": "object", + "additionalProperties": false, + "required": [ + "cash_isa", + "stocks_and_shares_isa" + ], + "properties": { + "cash_isa": { + "type": "string" + }, + "stocks_and_shares_isa": { + "type": "string" + } + } + }, + "aggregate_tolerance_gbp": { + "type": "number" + }, + "non_dependent_code": { + "type": "integer" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "outputs", + "seed", + "salt", + "maximum_donor_adults" + ], + "properties": { + "kind": { + "const": "split_household_isa_balances" + }, + "outputs": { + "type": "array", + "minItems": 1, + "items": { + "type": "string" + } + }, + "seed": { + "type": "integer" + }, + "salt": { + "type": "string" + }, + "maximum_donor_adults": { + "type": "integer" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "seed", + "salt", + "minimum_age", + "selection_order" + ], + "properties": { + "kind": { + "const": "allocate_isa_subscribers" + }, + "seed": { + "type": "integer" + }, + "salt": { + "type": "string" + }, + "minimum_age": { + "type": "integer" + }, + "selection_order": { + "type": "array", + "minItems": 3, + "maxItems": 3, + "items": { + "enum": [ + "cash_and_stocks_and_shares", + "stocks_and_shares_only", + "cash_only" + ] + } + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "outputs", + "seed", + "salt", + "within_band", + "balance_rank_weight" + ], + "properties": { + "kind": { + "const": "draw_isa_subscription_amounts" + }, + "outputs": { + "type": "array", + "minItems": 1, + "items": { + "type": "string" + } + }, + "seed": { + "type": "integer" + }, + "salt": { + "type": "string" + }, + "within_band": { + "enum": [ + "uniform" + ] + }, + "balance_rank_weight": { + "type": "number" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "anchor", + "share_floor" + ], + "properties": { + "kind": { + "const": "split_both_type_subscriptions" + }, + "anchor": { + "enum": [ + "table_9_4_stocks_and_shares_residual" + ] + }, + "share_floor": { + "type": "number" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "people", + "amounts", + "from_tax_year", + "to_tax_year", + "overall_limit_gbp", + "taper_from_gbp" + ], + "properties": { + "kind": { + "const": "grow_isa_subscriptions" + }, + "people": { + "enum": [ + "table_9_4_account_growth" + ] + }, + "amounts": { + "enum": [ + "table_9_4_amount_level_tapered_to_limit" + ] + }, + "from_tax_year": { + "type": "integer" + }, + "to_tax_year": { + "type": "integer" + }, + "overall_limit_gbp": { + "type": "number" + }, + "taper_from_gbp": { + "type": "number" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "outputs", + "seed", + "salt", + "minimum_age", + "maximum_age", + "annual_limit_gbp" + ], + "properties": { + "kind": { + "const": "draw_lifetime_isa_payments" + }, + "outputs": { + "type": "array", + "minItems": 1, + "items": { + "type": "string" + } + }, + "seed": { + "type": "integer" + }, + "salt": { + "type": "string" + }, + "minimum_age": { + "type": "integer" + }, + "maximum_age": { + "type": "integer" + }, + "annual_limit_gbp": { + "type": "number" + } + } + }, + { + "type": "object", + "additionalProperties": false, + "required": [ + "kind", + "output", + "target", + "tax_year", + "ages", + "counted_ages" + ], + "properties": { + "kind": { + "const": "anchor_lifetime_isa_ownership" + }, + "output": { + "type": "string" + }, + "target": { + "enum": [ + "table_9_4_lifetime_isa_accounts_subscribed" + ] + }, + "tax_year": { + "type": "integer" + }, + "ages": { + "type": "object", + "additionalProperties": false, + "required": [ + "minimum", + "maximum" + ], + "properties": { + "minimum": { + "type": "integer" + }, + "maximum": { + "type": "integer" + } + } + }, + "counted_ages": { + "type": "object", + "additionalProperties": false, + "required": [ + "minimum", + "maximum" + ], + "properties": { + "minimum": { + "type": "integer" + }, + "maximum": { + "type": "integer" + } + } + } + } + }, + { + "type": "object", + "additionalProperties": false, + "properties": { + "adjudication": { + "type": "string" + }, + "calibration_permitted": { + "const": false + }, + "classification_rationale": { + "type": "string" + }, + "fact_fence_id": { + "type": "string" + }, + "fenced_fact_composition": { + "type": "string" + }, + "fenced_fact_count": { + "type": "number" + }, + "kind": { + "const": "classify_isa_facts_with_reviewed_fence" + } + }, + "required": [ + "adjudication", + "calibration_permitted", + "classification_rationale", + "fact_fence_id", + "fenced_fact_composition", + "fenced_fact_count", + "kind" + ] } ], "description": "Closed kind-discriminated source artifact and operation records, covering the packaged US, UK, and BE source vocabularies." diff --git a/packages/microcosm-build/src/microcosm/build/uk/bonus_sacrifice_anchor.json b/packages/microcosm-build/src/microcosm/build/uk/bonus_sacrifice_anchor.json index d73e08b638..fa4b00fdf7 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/bonus_sacrifice_anchor.json +++ b/packages/microcosm-build/src/microcosm/build/uk/bonus_sacrifice_anchor.json @@ -2,7 +2,7 @@ "schema_version": 1, "engine": { "package": "policyengine-uk", - "version": "2.162.14" + "version": "2.164.2" }, "written_by": "tools/pin_uk_bonus_sacrifice_anchor.py", "source": { @@ -22,11 +22,11 @@ 2027 ], "rates": [ - 0.053, - 0.034, - 0.024 + 0.0412, + 0.0373, + 0.0299 ], - "factor": 1.114933248 + "factor": 1.112329859124 }, "weight_growth": { "parameter": "gov.economic_assumptions.yoy_growth.ons.population", @@ -36,11 +36,11 @@ 2027 ], "rates": [ - 0.003, - 0.0035, - 0.0029 + 0.0033, + 0.002, + 0.0013 ], - "factor": 1.0094293804499999 + "factor": 1.0066134985800002 }, "upper_earnings_limit": { "parameter": "gov.hmrc.national_insurance.class_1.thresholds.upper_earnings_limit", 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 beeab50447..8d76a051ef 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/country_package.json +++ b/packages/microcosm-build/src/microcosm/build/uk/country_package.json @@ -102,6 +102,11 @@ "kind": "legacy_json", "schema_id": "legacy_json" }, + { + "path": "hmrc_isa_statistics_facts.json", + "kind": "legacy_json", + "schema_id": "legacy_json" + }, { "path": "dwp_move_to_uc_facts.json", "kind": "legacy_json", diff --git a/packages/microcosm-build/src/microcosm/build/uk/efrs_parity_known_gaps.json b/packages/microcosm-build/src/microcosm/build/uk/efrs_parity_known_gaps.json index f3994ddc6a..96faaca49c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/efrs_parity_known_gaps.json +++ b/packages/microcosm-build/src/microcosm/build/uk/efrs_parity_known_gaps.json @@ -637,6 +637,12 @@ "tracking_note": "microcosm#1106 (the uk-data#543 defect): the release and calibration boundaries drop the WAS total, and policyengine-uk sums main_residence_value, other_residential_property_value and non_residential_property_value, which stay required.", "approved_by": "juaristi22", "approved_on": "2026-10-11" + }, + "tax_free_savings_income": { + "reason": "formula-owned in policyengine-uk and derived from persisted components; a persisted copy overrides the formula and is not uprated", + "tracking_note": "microcosm#1360: under the additive ISA income contract the producer stores ISA interest in individual_savings_account_interest_income, which policyengine-uk adds into tax_free_savings_income; savings_interest_income, now interest outside ISAs, stays required.", + "approved_by": "juaristi22", + "approved_on": "2026-10-28" } }, "exclusion_policy": { diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json index 31c672e511..e4ac4fa3e3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/gates.json +++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json @@ -166,6 +166,20 @@ }, "notes": "Stage-time support-clip health gate over the WAS Lifetime ISA receipt (microcosm#1003): the balance is drawn from the QRF fitted on the credible donor holders, so every draw lies inside their realised range and non-holders are zero; the zero allowances make any clip a signal." }, + { + "id": "uk_stage_was_lisa_anchor", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "population_fact_check": true, + "parameters": { + "stage": "was_lisa", + "check": "was_lisa_anchor", + "maximum_relative_deviation": 0.01 + }, + "notes": "Stage-time check on the was_lisa ownership anchor (microcosm#1415): WAS round 8 implies about half the Lifetime ISA holders HMRC's 2024-25 subscriptions need, so the stage shifts its ownership logit on ages 18 to 47, the ages that can hold and pay into one in 2024-25, until the weighted holders at those ages the financial-wealth cap leaves equal Table 9.4's Lifetime ISA accounts subscribed to (one subscription a year, so payers). The receipt's realised post-cap holders must sit within maximum_relative_deviation of that target, and a positive shift must remove no holder (the same person-keyed uniforms decide ownership). A target above the largest shift's reach and unshifted holders above the target are named, not failed: the anchor only adds holders. A population fact check: the synthetic smoke records it without blocking." + }, { "id": "uk_stage_property_components", "gate": "stage_health", @@ -921,6 +935,33 @@ }, "notes": "Stage-time gate over the is_apprentice receipt (microcosm#1373). The draw's target is the contract's apprentice_stock_19_to_24 for the build year (DfE's England apprentices in learning at the start of the academic year, participation less starts, per head aged 19 to 24) times the weighted population aged 21 to 24 at the stage's weights, and eligible employees aged 21 to 24 are flagged in the order of their identity-keyed uniform until their weight is nearest it, so the stage misses it by at most half one person's weight. The gate checks that the receipt carries the contract's rate under the declared key, that its target is that rate times its population, that its support is printed in bands, and, unless the eligible employees weigh no more than the target (cap-bound, every one of them flagged), that the flagged weight lies within maximum_relative_realization_deviation (5 percent) of the target. That tolerance is a declared rule set above the half-weight bound, not a fact. A figure resting on fewer than ten source households, which the receipt withholds under the UKDS disclosure rule and prints \"<10\", is named unchecked, never failed: a small-cell rule never stops a build (María's ruling of 2026-10-26). The receipt also records the relief the flags imply at the installed engine's own Class 1 schedule for the build year (on a 2024 frame, 13.8 percent between GBP 9,100 and GBP 50,270), with each flagged employee's pay standing in for NIC earnings, and the gate holds it at or below maximum_implied_relief: GBP 370m, HMRC's NIC - NS1 for 2024-25 (tax relief statistics, January 2026, Table 2), which covers every apprentice under 25, under-21s included, since the apprentice relief takes precedence (Vahid's review of microcosm#1373, S1). It is a ceiling, not a target. The stage refuses an installed engine without the s.9B threshold or without policyengine-uk#2683's split of the apprentice minimum wage from the flag. The entry is a population_fact_check, so the synthetic smoke build over the H2 fixture records its outcome without blocking; every licensed build blocks on it." }, + { + "id": "uk_stage_isa_subscriptions_invariants", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "parameters": { + "stage": "isa_subscriptions", + "check": "isa_subscriptions_invariants", + "maximum_household_sum_error_gbp": 0.01 + }, + "notes": "Stage-time rules of the isa_subscriptions receipt that hold for any population (microcosm#1415, review of microcosm#1421). It fails on any record the receipt counts against them (a non-finite value; cash, stocks and shares and Lifetime ISA payments over GBP 20,000; a Lifetime ISA payment over GBP 4,000; a subscriber under 18; a payment into a type the person does not hold; a both-type subscriber missing a part; a negative amount) and on person balances that miss the household was_wealth draws by more than maximum_household_sum_error_gbp. It is no population fact check, so the synthetic smoke build blocks on it as every licensed build does; the comparisons with HMRC's tables are uk_stage_isa_subscriptions." + }, + { + "id": "uk_stage_isa_subscriptions", + "gate": "stage_health", + "phase": "transferred", + "criticality": "release_blocking", + "evidence_absent_blocks": true, + "population_fact_check": true, + "parameters": { + "stage": "isa_subscriptions", + "check": "isa_subscriptions", + "maximum_relative_total_deviation": 1e-06 + }, + "notes": "Stage-time check of the isa_subscriptions receipt against HMRC's tables (microcosm#1415). It fails on the 2024-25 cash and stocks-and-shares totals off Table 9.4's 2024-25 amounts (María's ruling of 29 October 2026), the both-type split's stocks-and-shares residual or the Lifetime ISA total off its Table 9.4 amount by more than maximum_relative_total_deviation; a reported saturation is named, not failed. Cells short of eligible holders are named, never failed: the stage invents no holders, and the distance from HMRC's tables after calibration is measured, not gated (María's ruling of 28 October 2026). A population fact check: the synthetic smoke records it without blocking, since a synthetic population cannot reach HMRC's national totals; the rules any population must keep are uk_stage_isa_subscriptions_invariants." + }, { "id": "uk_release_input_coverage", "gate": "release_input_coverage", @@ -1226,6 +1267,12 @@ "person.is_uc_claimant", "person.legacy_jobseeker_proxy", "person.lifetime_isa_balance", + "person.lifetime_isa_payments", + "person.cash_isa_balance", + "person.stocks_and_shares_isa_balance", + "person.cash_isa_subscriptions", + "person.stocks_and_shares_isa_subscriptions", + "person.individual_savings_account_interest_income", "person.local_bus_single_fare_share", "person.local_bus_trips", "person.local_bus_use_band", @@ -1329,10 +1376,11 @@ ], "reviewed_exclusions": { "person.incapacity_benefit_reported": "The enhanced FRS stores this legacy reported-benefit input as an all-zero layer; the candidate must drop dead zero layers.", - "household.property_wealth": "policyengine-uk derives property_wealth from main_residence_value, other_residential_property_value and non_residential_property_value; a persisted copy overrides that sum and has no uprating index, so the candidate must drop it (microcosm#1106, the uk-data#543 defect)." + "household.property_wealth": "policyengine-uk derives property_wealth from main_residence_value, other_residential_property_value and non_residential_property_value; a persisted copy overrides that sum and has no uprating index, so the candidate must drop it (microcosm#1106, the uk-data#543 defect).", + "person.tax_free_savings_income": "policyengine-uk derives tax_free_savings_income as the sum of the ISA interest it adds (individual_savings_account_interest_income); a persisted copy overrides that sum and is never uprated, so the candidate stores ISA interest apart from non-ISA interest under the additive ISA income contract and drops it (microcosm#1360)." } }, - "notes": "Reviewed candidate-only fields from the June UK prototype (source provenance or genuine additional model inputs, not incumbent-surface losses), with household.uses_energy_prepayment_meter added for microcosm#1110 (the LCFS prepayment-meter flag policyengine-uk reads from 2.123.3 for the Energy Price Guarantee prepayment discount; an older engine ignores it, and the incumbent does not carry it until uk-data#539), the LCFS excise step's 24 household inputs added for microcosm#1359 (alcohol litres and the beer and cider draught shares, tobacco a week and the gambling duty bases; policyengine-uk reads the alcohol and tobacco ones from 2.145.0 and the gambling ones from 2.151.0, an older engine ignores them, and the incumbent carries none) plus the two reviewed reference-side exclusions (the all-zero incapacity benefit layer and the formula-owned property_wealth total, microcosm#1106). The two hard-required columns that may never be waived stay a code-level guard, per the microcosm#327 rule that a parity comparison's registers are declared inputs. During the migration window the runtime merges the exclusion register with the legacy constant and the spec-pin tests hold the two equal; the register becomes solely load-bearing when the constant retires." + "notes": "Reviewed candidate-only fields from the June UK prototype (source provenance or genuine additional model inputs, not incumbent-surface losses), with household.uses_energy_prepayment_meter added for microcosm#1110 (the LCFS prepayment-meter flag policyengine-uk reads from 2.123.3 for the Energy Price Guarantee prepayment discount; an older engine ignores it, and the incumbent does not carry it until uk-data#539), the LCFS excise step's 24 household inputs added for microcosm#1359 (alcohol litres and the beer and cider draught shares, tobacco a week and the gambling duty bases; policyengine-uk reads the alcohol and tobacco ones from 2.145.0 and the gambling ones from 2.151.0, an older engine ignores them, and the incumbent carries none) plus the reviewed reference-side exclusions (the all-zero incapacity benefit layer and the formula-owned property_wealth total, microcosm#1106; the formula-owned tax_free_savings_income total, whose ISA interest the candidate stores apart under the additive ISA income contract, microcosm#1360). The two hard-required columns that may never be waived stay a code-level guard, per the microcosm#327 rule that a parity comparison's registers are declared inputs. During the migration window the runtime merges the exclusion register with the legacy constant and the spec-pin tests hold the two equal; the register becomes solely load-bearing when the constant retires." }, { "id": "uk_take_up_signal", @@ -1541,14 +1589,14 @@ "gains_growth_parameter": "gov.economic_assumptions.yoy_growth.obr.per_capita.gdp", "exempt_amount_parameter": "gov.hmrc.cgt.annual_exempt_amount", "expected_yoy_growth_by_year": { - "2023": 0.0524, - "2024": 0.0389, - "2025": 0.0474, - "2026": 0.0294, - "2027": 0.0326, - "2028": 0.0317, - "2029": 0.0306, - "2030": 0.0315 + "2023": 0.0522, + "2024": 0.0401, + "2025": 0.0462, + "2026": 0.04, + "2027": 0.031, + "2028": 0.0344, + "2029": 0.036, + "2030": 0.0343 }, "expected_exempt_amount_by_year": { "2023": 6000.0, @@ -1564,7 +1612,7 @@ "bound_resource": "hmrc_cgt_conditioning_facts.json", "bound_size_band_lower_bound": 3000 }, - "notes": "Projection fence over the calibrated frame's sub-exempt gainers (microcosm#970). The engine freezes the annual exempt amount at its nominal value and uprates capital_gains by OBR per-capita GDP growth, so a gainer at or just below the exempt amount in the build period becomes a CGT taxpayer in a later projected year without any change in behaviour. For every year to the horizon the gate counts the cumulative stock of build-period sub-exempt gainers (gains in (0, exempt amount]) whose gains, uprated by the cumulative growth factor to that year, exceed that year's exempt amount; the stock is non-decreasing in the year, so the horizon year carries the largest count. The bound is the vendored HMRC Table 2.1a count of 2024-25 taxpayers with gains from 3,000 to 5,999 (73,000): people already above the exempt amount, not entrants, so the bound is a plausibility ceiling (the population of the thinnest liable band), not a published entrant count. The horizon 2030 is the last year of the OBR per-capita growth path in the engine's parameter tree; later years repeat the 2030 rate, so a longer horizon would only compound a constant. The growth path and the exempt amount by year the seam reads from the installed engine are pinned here and drift-checked (the exempt amount was 6,000 in 2023-24 and 3,000 from 2024-25, so a 2023-period frame's base year reads 6,000), so an engine bump cannot move the verdict silently and re-pinning is a reviewed change (re-pinned at the policyengine-uk 2.161.0 lock: from 2.155.0 the engine takes population growth from the OBR's March 2026 determinants, microcosm#1095); without an installed engine the seam builds the projection from these pins and the receipt says so. The margin is thin by construction: on the first rebuilt candidate the stock ran from 12.7k in 2025 to 67.6k in 2030, about 13k a year, and 61k of the 67.6k were band donors the redraw had demoted below the exempt amount, a separate decision; the anchored clones contributed 6.4k. The v20 candidate would have failed with 10.8 million in the first projected year. The bound comes from the same vendored facts the amounts stage is pinned to, never from a literal." + "notes": "Projection fence over the calibrated frame's sub-exempt gainers (microcosm#970). The engine freezes the annual exempt amount at its nominal value and uprates capital_gains by OBR per-capita GDP growth, so a gainer at or just below the exempt amount in the build period becomes a CGT taxpayer in a later projected year without any change in behaviour. For every year to the horizon the gate counts the cumulative stock of build-period sub-exempt gainers (gains in (0, exempt amount]) whose gains, uprated by the cumulative growth factor to that year, exceed that year's exempt amount; the stock is non-decreasing in the year, so the horizon year carries the largest count. The bound is the vendored HMRC Table 2.1a count of 2024-25 taxpayers with gains from 3,000 to 5,999 (73,000): people already above the exempt amount, not entrants, so the bound is a plausibility ceiling (the population of the thinnest liable band), not a published entrant count. The horizon 2030 was the last year of the OBR per-capita growth path in the engine's parameter tree, so a longer horizon would only have compounded a constant; since policyengine-uk 2.164.1 the October 2026 path runs to 2031 and later years repeat the 2031 rate, so a 2031 horizon would add one more year of growth to the count. The growth path and the exempt amount by year the seam reads from the installed engine are pinned here and drift-checked (the exempt amount was 6,000 in 2023-24 and 3,000 from 2024-25, so a 2023-period frame's base year reads 6,000), so an engine bump cannot move the verdict silently and re-pinning is a reviewed change (re-pinned at the policyengine-uk 2.161.0 lock: from 2.155.0 the engine takes population growth from the OBR's March 2026 determinants, microcosm#1095; and at the 2.164.2 lock for the OBR's October 2026 outlook, microcosm#1415); without an installed engine the seam builds the projection from these pins and the receipt says so. The margin is thin by construction: on the first rebuilt candidate the stock ran from 12.7k in 2025 to 67.6k in 2030, about 13k a year, and 61k of the 67.6k were band donors the redraw had demoted below the exempt amount, a separate decision; the anchored clones contributed 6.4k. The v20 candidate would have failed with 10.8 million in the first projected year. The bound comes from the same vendored facts the amounts stage is pinned to, never from a literal." }, { "id": "uk_input_mass_parity", diff --git a/packages/microcosm-build/src/microcosm/build/uk/hmrc_isa_statistics_facts.json b/packages/microcosm-build/src/microcosm/build/uk/hmrc_isa_statistics_facts.json new file mode 100644 index 0000000000..f67cc85ed0 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk/hmrc_isa_statistics_facts.json @@ -0,0 +1,50441 @@ +{ + "schema_version": 1, + "country": "uk", + "kind": "uk_vendored_ledger_facts", + "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.", + "consumers": [ + "uk_runtime.hmrc_isa_statistics" + ], + "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": "53815b24844cb63c103209f197f9d3d9e2e424a9", + "build": "build-bundle --suite uk -> build-consumer-artifact", + "artifact_schema_version": "policyengine_ledger.consumer_artifact.v2", + "fact_row_count": 1511063, + "facts_sha256": "4b0e5c1ad057946407807428a1c79c70d9b14e2f8539ea8fb93524573c026227", + "manifest_sha256": "708231c77432d2838bf1ee7d8f3080e49ca0e59edccdd45c36b3405db2cb45fb" + }, + "selections": [ + { + "label": "table9_7_ty2023_subscribers_and_holders_by_income_and_subscription", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.all.subscribers", + "hmrc.isa_statistics_2026.table9_7.ty2023.all.holders", + 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"tools/pin_uk_uprating_engine_values.py", @@ -24,28 +24,28 @@ "2022-01-01": 1.38414, "2023-01-01": 1.47023, "2024-01-01": 1.54521, - "2025-01-01": 1.62711, - "2026-01-01": 1.68243 + "2025-01-01": 1.60887, + "2026-01-01": 1.66888 }, "gov.economic_assumptions.indices.obr.per_capita.mixed_income": { "2019-01-01": 1.0, "2020-01-01": 1.0, "2021-01-01": 1.0, - "2022-01-01": 1.0288, - "2023-01-01": 1.02263, - "2024-01-01": 1.05014, - "2025-01-01": 1.0619, - "2026-01-01": 1.09471 + "2022-01-01": 1.0281, + "2023-01-01": 1.02173, + "2024-01-01": 1.05034, + "2025-01-01": 1.05517, + "2026-01-01": 1.08651 }, "gov.economic_assumptions.indices.obr.per_capita.gdp": { "2019-01-01": 1.0, "2020-01-01": 1.0, "2021-01-01": 1.0, - "2022-01-01": 1.1004, - "2023-01-01": 1.15806, - "2024-01-01": 1.20311, - "2025-01-01": 1.26014, - "2026-01-01": 1.29719 + "2022-01-01": 1.0997, + "2023-01-01": 1.1571, + "2024-01-01": 1.2035, + "2025-01-01": 1.2591, + "2026-01-01": 1.30946 }, "gov.economic_assumptions.indices.obr.private_pension_index": { "2019-01-01": 1.0, @@ -55,17 +55,17 @@ "2023-01-01": 1.10581, "2024-01-01": 1.1611, "2025-01-01": 1.20278, - "2026-01-01": 1.25209 + "2026-01-01": 1.25245 }, "gov.economic_assumptions.indices.ons.household_interest_income": { "2019-01-01": 1.0, "2020-01-01": 1.0, - "2021-01-01": 1.225, - "2022-01-01": 2.70725, - "2023-01-01": 5.37931, - "2024-01-01": 6.14317, - "2025-01-01": 6.35757, - "2026-01-01": 6.60997 + "2021-01-01": 1.2238, + "2022-01-01": 2.70717, + "2023-01-01": 5.38483, + "2024-01-01": 6.1414, + "2025-01-01": 5.9719, + "2026-01-01": 6.15225 }, "gov.dwp.state_pension.new_state_pension.amount": { "2019-01-01": 168.6, @@ -95,11 +95,11 @@ "2019-01-01": 0.0036, "2020-01-01": 0.0036, "2021-01-01": 0.0036, - "2022-01-01": 0.0098, - "2023-01-01": 0.0132, - "2024-01-01": 0.011, - "2025-01-01": 0.003, - "2026-01-01": 0.0035 + "2022-01-01": 0.0105, + "2023-01-01": 0.0134, + "2024-01-01": 0.0097, + "2025-01-01": 0.0033, + "2026-01-01": 0.002 } } } diff --git a/packages/microcosm-build/src/microcosm/build/uk/input_mass_reviewed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/input_mass_reviewed_exclusions.json index 9a7584decd..b1511d68ed 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/input_mass_reviewed_exclusions.json +++ b/packages/microcosm-build/src/microcosm/build/uk/input_mass_reviewed_exclusions.json @@ -16,6 +16,13 @@ "adjudication": "microcosm#1106 (#1145): part B measured on the A9 candidate at 2025 and on the final-head arm c7 of 2026-10-11; ruling 2026-10-11 in session", "approved_on": "2026-10-11", "expires_on": "2027-04-11" + }, + "tax_free_savings_income": { + "reason": "The candidate no longer ships person.tax_free_savings_income: policyengine-uk derives it as the sum of individual_savings_account_interest_income, which the candidate stores apart from non-ISA interest under the additive ISA income contract, and a persisted copy overrides that sum and is never uprated (microcosm#1360). The enhanced-FRS reference stores it in the legacy gross layout, so the column is present in the reference and absent from the candidate, which this gate otherwise fails whatever the tolerance. Non-ISA savings_interest_income stays compared; it falls below the reference's gross total by the ISA part.", + "approved_by": "juaristi22", + "adjudication": "microcosm#1360 folded into microcosm#1418's PR B (ruling 2026-10-28 in session)", + "approved_on": "2026-10-28", + "expires_on": "2027-04-28" } } } 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 fca065da3e..dc1c625441 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 @@ -2802,56 +2802,56 @@ }, { "kind": "ledger_only", - "ledger_value": 378874888.2542839, + "ledger_value": 376805314.3875684, "name": "hmrc.spi.self_employment_income.amount_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": 29058251.016552746, + "ledger_value": 28899522.63739463, "name": "hmrc.spi.self_employment_income.amount_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": 558812519.5490913, + "ledger_value": 555760050.7191275, "name": "hmrc.spi.self_employment_income.amount_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": 723103400.2965242, + "ledger_value": 719153505.630551, "name": "hmrc.spi.self_employment_income.amount_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": 871747530.4965824, + "ledger_value": 866985679.1218389, "name": "hmrc.spi.self_employment_income.amount_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": 2838767599.309384, + "ledger_value": 2823261057.6531677, "name": "hmrc.spi.self_employment_income.amount_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": 91645253.20605098, + "ledger_value": 91144648.31793691, "name": "hmrc.spi.self_employment_income.amount_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": 3878158885.6706934, + "ledger_value": 3856974751.9907446, "name": "hmrc.spi.self_employment_income.amount_by_self_employment_income_band.band_7500_10000", "period": 2025, "reason": "Ledger-compiled row has no fixture counterpart at this comparison period." @@ -7244,7 +7244,7 @@ { "fixture_value": 6870000000.0, "kind": "calibration_drift", - "ledger_value": 7475572768.250349, + "ledger_value": 7475600207.41509, "name": "hmrc/dividend_income_income_band_100_000_to_150_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7252,7 +7252,7 @@ { "fixture_value": 175000000.0, "kind": "calibration_drift", - "ledger_value": 190425798.31787643, + "ledger_value": 190426497.27767697, "name": "hmrc/dividend_income_income_band_12_570_to_15_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7260,7 +7260,7 @@ { "fixture_value": 3450000000.0, "kind": "calibration_drift", - "ledger_value": 3754108595.409564, + "ledger_value": 3754122374.9027743, "name": "hmrc/dividend_income_income_band_150_000_to_200_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7268,7 +7268,7 @@ { "fixture_value": 855000000.0, "kind": "calibration_drift", - "ledger_value": 930366043.2101963, + "ledger_value": 930369458.1280789, "name": "hmrc/dividend_income_income_band_15_000_to_20_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7276,7 +7276,7 @@ { "fixture_value": 11100000000.0, "kind": "calibration_drift", - "ledger_value": 12078436350.448162, + "ledger_value": 12078480684.469795, "name": "hmrc/dividend_income_income_band_1_000_000_to_inf", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7284,7 +7284,7 @@ { "fixture_value": 3700000000.0, "kind": "calibration_drift", - "ledger_value": 4026145450.1493874, + "ledger_value": 4026160228.1565986, "name": "hmrc/dividend_income_income_band_200_000_to_300_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7292,7 +7292,7 @@ { "fixture_value": 3330000000.0, "kind": "calibration_drift", - "ledger_value": 3623530905.1344485, + "ledger_value": 3623544205.340939, "name": "hmrc/dividend_income_income_band_20_000_to_30_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7300,7 +7300,7 @@ { "fixture_value": 3430000000.0, "kind": "calibration_drift", - "ledger_value": 3732345647.030378, + "ledger_value": 3732359346.6424685, "name": "hmrc/dividend_income_income_band_300_000_to_500_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7308,7 +7308,7 @@ { "fixture_value": 4700000000.0, "kind": "calibration_drift", - "ledger_value": 5114292869.108682, + "ledger_value": 5114311641.171896, "name": "hmrc/dividend_income_income_band_30_000_to_40_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7316,7 +7316,7 @@ { "fixture_value": 8400000000.0, "kind": "calibration_drift", - "ledger_value": 9140438319.258068, + "ledger_value": 9140471869.328495, "name": "hmrc/dividend_income_income_band_40_000_to_50_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7324,7 +7324,7 @@ { "fixture_value": 3820000000.0, "kind": "calibration_drift", - "ledger_value": 4156723140.424503, + "ledger_value": 4156738397.7184343, "name": "hmrc/dividend_income_income_band_500_000_to_1_000_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7332,7 +7332,7 @@ { "fixture_value": 12500000000.0, "kind": "calibration_drift", - "ledger_value": 13601842736.991173, + "ledger_value": 13601892662.691212, "name": "hmrc/dividend_income_income_band_50_000_to_70_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." @@ -7340,7 +7340,7 @@ { "fixture_value": 9440000000.0, "kind": "calibration_drift", - "ledger_value": 10272111634.975735, + "ledger_value": 10272149338.864403, "name": "hmrc/dividend_income_income_band_70_000_to_100_000", "period": 2025, "reason": "Declared uprating (PolicyEngine/chronicle#280 lane, María's ruling of 2026-09-22): ours moves the SPI 2023-24 band value to the 2025 calibration year by the index the reference declares (amounts by the pinned engine's per-variable OBR and ONS indices between 1 January 2023 and 1 January 2025, counts by HMRC's projected taxpayer growth in the containing Table 2.5 band as the calendar-2025 window); the frozen incumbent fixture holds the 2023-24 values it projects one year forward with its own income-projection ratios." 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 f84307961b..f427d46074 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 @@ -1726,6 +1726,99 @@ "expected_row_count": 8 } ] + }, + { + "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.", + "consumers": [ + "uk_runtime.hmrc_isa_statistics" + ], + "planned_consumers": [], + "selections": [ + { + "label": "table9_7_ty2023_subscribers_and_holders_by_income_and_subscription", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.all.subscribers", + "hmrc.isa_statistics_2026.table9_7.ty2023.all.holders", + "hmrc.isa_statistics_2026.table9_7.ty2023.cash_only.subscribers", + "hmrc.isa_statistics_2026.table9_7.ty2023.cash_only.holders", + "hmrc.isa_statistics_2026.table9_7.ty2023.stocks_and_shares_only.subscribers", + "hmrc.isa_statistics_2026.table9_7.ty2023.stocks_and_shares_only.holders", + "hmrc.isa_statistics_2026.table9_7.ty2023.cash_and_stocks_and_shares.subscribers", + "hmrc.isa_statistics_2026.table9_7.ty2023.cash_and_stocks_and_shares.holders" + ] + }, + "expected_row_count": 624 + }, + { + "label": "table9_8_ty2023_subscribers_and_holders_by_age_and_sex", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.isa_statistics_2026.table9_8.ty2023.male.subscribers", + "hmrc.isa_statistics_2026.table9_8.ty2023.male.holders", + "hmrc.isa_statistics_2026.table9_8.ty2023.female.subscribers", + "hmrc.isa_statistics_2026.table9_8.ty2023.female.holders", + "hmrc.isa_statistics_2026.table9_8.ty2023.all.subscribers", + "hmrc.isa_statistics_2026.table9_8.ty2023.all.holders" + ] + }, + "expected_row_count": 325 + }, + { + "label": "table9_9_ty2023_subscribers_and_holders_by_region", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.isa_statistics_2026.table9_9.ty2023.subscribers", + "hmrc.isa_statistics_2026.table9_9.ty2023.holders" + ] + }, + "expected_row_count": 70 + }, + { + "label": "table9_4_ty2023_ty2024_accounts_and_amounts_subscribed", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.isa_statistics_2026.table9_4.accounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2024", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2024" + ] + }, + "expected_row_count": 24 + }, + { + "label": "table9_6_ty2024_market_value", + "selector": { + "source_name": "hmrc", + "record_set_id": "hmrc.isa_statistics_2026.table9_6.ty2024" + }, + "expected_row_count": 19 + }, + { + "label": "impact_note_ty2022_cash_subscribers_under_65_by_band", + "selector": { + "source_name": "hmrc", + "record_set_id": "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022" + }, + "expected_row_count": 2 + }, + { + "label": "lisa_table1a_ty2024_ty2025_withdrawals", + "selector": { + "source_name": "hmrc", + "record_set_id": [ + "hmrc.lisa_statistics_2026.table1a.ty2024", + "hmrc.lisa_statistics_2026.table1a.ty2025" + ] + }, + "expected_row_count": 10 + } + ] } ] } 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 387a9091f3..a95a52ea06 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 @@ -395,7 +395,9 @@ "status": "required" }, "tax_free_savings_income": { - "status": "required" + "reason": "formula-owned in policyengine-uk and derived from persisted components; a persisted copy overrides the formula and is not uprated", + "status": "reviewed_exclusion", + "tracking_note": "microcosm#1360: under the additive ISA income contract the producer stores ISA interest in individual_savings_account_interest_income, which policyengine-uk adds into tax_free_savings_income; savings_interest_income, now interest outside ISAs, stays required." }, "tenure_type": { "status": "required" @@ -447,8 +449,8 @@ } }, "counts": { - "required": 144, - "reviewed_exclusion": 1, + "required": 143, + "reviewed_exclusion": 2, "total": 145 }, "derivation": "Surface = efrs_parity_reference.json populated effective loader inputs. status='required' when the sha-pinned candidate evidence records non-default signal on at least the reviewed owning-entity effective-mass share OR the column is pinned in restored_required_columns after a source-family restoration; all remaining surface columns are reviewed_exclusion with reason 'not yet ported from enhanced FRS pipeline — pending review' and a UK_COVERAGE_PROGRESS.md tracking note, except the engine_derived_exclusions, which are reviewed_exclusion with their own reason because the engine computes them from persisted inputs. The final release gate applies the same effective-mass floor, and distributional restorations must pass it on their required source channel.", @@ -470,7 +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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, the HMRC Table 3 redrawn gains on the spine and Advani-Summers capital-gains incidence" @@ -493,7 +495,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "Advani and Summers (2020), Capital Gains and UK Inequality, CAGE Working Paper 465", "survey": "Family Resources Survey 2024-25, SPI synthetic support, and Advani-Summers capital-gains incidence" @@ -516,7 +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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative) on the amounts the Table 3 redraw placed" @@ -538,7 +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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "HMRC Capital Gains Tax statistics, July 2026 release, Table 3 for 2024-25, via the vendored conditioning facts", "survey": "Family Resources Survey 2024-25, SPI synthetic support and HMRC Capital Gains Tax statistics Table 3 (2024-25 individuals by size of gain and taxable income)" @@ -567,7 +569,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab, DfT rail fare index, and public NHS activity/cost table.", "survey": "Effects of Taxes and Benefits 1977-2024 and NHS age-gender public table" @@ -587,7 +589,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "UK Data Service SN 8856 Effects of Taxes and Benefits household tab and cited VAT anchor resource.", "survey": "Effects of Taxes and Benefits 1977-2024" @@ -609,7 +611,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "Microcosm structural operator (microcosm#1240 fix 1); no publisher input", "survey": "Family Resources Survey 2024-25 and SPI synthetic support, after the WAS wealth draw and the regional property uprating" @@ -630,7 +632,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "https://www.gov.uk/government/statistics/capital-gains-tax-statistics", "survey": "HMRC Capital Gains Tax statistics 2026 release, Table 8 (UK residential property disposals, 2024-25, administrative), Table 4.1 (individuals claiming Business Asset Disposal Relief or Investors' Relief by band of qualifying gain, 2024-25, administrative) and Table 7 (disposals, proceeds and gains by asset type, 2023-24, sample-based), vendored from the pinned Chronicle feed" @@ -654,7 +656,7 @@ "capital_gains" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "conditioning_resource": "hmrc_cgt_conditioning_facts.json", "conditioning_resource_sha256": "f7425784231e1f6aa657d0f26fc02591d9523600eb12fce5d35becf5dabf7569", @@ -744,7 +746,7 @@ "full_frs_tei_band_unavailable" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "hmrc_surface": "2023-24", "mapped_build_period": "2024", @@ -755,6 +757,30 @@ "stage": "hmrc_spi_income_spine", "status": "required_at_build" }, + "isa_subscriptions": { + "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", + "base_candidate_tier": "frs", + "effective_mass_requirements": {}, + "mass_change_semantics": "mass_conserving", + "output_weight_kind": "importance", + "outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions", + "lifetime_isa_payments" + ], + "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": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", + "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" + }, + "stage": "isa_subscriptions", + "status": "required_at_build" + }, "lcfs_consumption": { "base_candidate_sha256": "f17306ccb2aad7ff0130be3589b560afb2e2a12a943570911cd0c77f07934833", "base_candidate_tier": "frs", @@ -812,7 +838,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -838,7 +864,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -858,7 +884,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -881,7 +907,7 @@ "property_finance_costs" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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)" @@ -902,7 +928,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -925,7 +951,7 @@ "property_wealth" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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)" @@ -949,7 +975,7 @@ "non_residential_property_value" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -977,7 +1003,7 @@ "employment_income" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -1010,7 +1036,7 @@ "household_source_id" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -1032,7 +1058,7 @@ "student_loan_plan" ], "source_manifest": "source_stages.json", - "source_manifest_sha256": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -1054,7 +1080,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "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" @@ -1093,7 +1119,7 @@ "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": "97b6209310001fb63d04a8bc601a0235d85f0deaec098fc18b6746ed65e26a81", + "source_manifest_sha256": "03692a33949bf12bd223a409c1ff1c1954839ad49ca53c013cf666185a5ffdb1", "source_vintages": { "source": "Office for National Statistics Wealth and Assets Survey, UK Data Service SN 7215, DOI 10.5255/UKDA-SN-7215-20; local licensed 2006-22 household tab.", "survey": "Wealth and Assets Survey round 8" diff --git a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json index 6153d9dfa7..2b105e1505 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json +++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json @@ -277,7 +277,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", "other_investment_income", @@ -379,7 +379,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", "other_investment_income", @@ -1133,7 +1133,7 @@ "maximum_extended_childcare_hours_usage", "max_free_entitlement_hours_used" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.162.14's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30." + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.164.2's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30." }, { "stage": "frs_person_draws", @@ -1939,7 +1939,7 @@ } }, "recipient_income_rule": { - "drawn": "the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income less tax_free_savings_income), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves", + "drawn": "the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income, the interest outside ISAs), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves", "undrawn": "the twin has no tax record for the tape to condition on; the copy keeps the twin's values at stage 1 and stage 2 (a declared modelling boundary like the under-16 one, microcosm#840)", "unearned_income_floor_gbp": 1000 }, @@ -1978,7 +1978,7 @@ "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", - "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 less tax_free_savings_income, 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.", + "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 positive-row count under 10 is withheld; the counts in the stage evidence stay with the build and are not published" }, "identity_uniforms": { @@ -2194,7 +2194,7 @@ "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", @@ -2234,7 +2234,6 @@ "maternity_allowance_reported": "Absent from the pinned enhanced-FRS export and certified Microcosm UK base; no training source can be materialized for this stage." }, "postprocess": { - "gross_savings_interest_income": "stage1 INCBBS draw + stage2 tax_free_savings_income", "refresh_disability_categories": [ "aa_category", "dla_sc_category", @@ -2246,7 +2245,8 @@ "is_disabled_for_benefits", "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits" - ] + ], + "savings_interest_income": "the stage-1 INCBBS draw, interest outside ISAs; individual_savings_account_interest_income is the stage-2 draw beside it (microcosm#1360)" }, "joint_draw": true, "require_all_predictors": true, @@ -2578,7 +2578,7 @@ "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", @@ -3029,9 +3029,41 @@ "access": "private_local_input", "locator": "caller-supplied local input", "runtime_sha256_required": true + }, + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "hmrc_isa_statistics_facts.json", + "role": "isa_statistics_facts", + "runtime_sha256_required": true } ], "operations": [ + { + "artifact_role": "isa_statistics_facts", + "fail_on_mismatch": true, + "feed_pin": "chronicle_feed.json", + "kind": "verify_vendored_fact_resource", + "mapped_build_period": 2024, + "period_mapping": "individual_tables_grown_to_the_table_9_4_build_year", + "record_sets": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.", + "hmrc.isa_statistics_2026.table9_8.ty2023.", + "hmrc.isa_statistics_2026.table9_9.ty2023.", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2024", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2024", + "hmrc.isa_statistics_2026.table9_6.ty2024", + "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022", + "hmrc.lisa_statistics_2026.table1a.ty2024", + "hmrc.lisa_statistics_2026.table1a.ty2025" + ], + "require_before_source_read": true, + "resource": "hmrc_isa_statistics_facts.json", + "runtime_sha256_required": true, + "source_vintage": "2023-24 individuals; Table 9.4 for 2023-24 and 2024-25" + }, { "kind": "clean_was_lisa_donor", "scope": "WAS round-8 person tab joined many-to-one to the cleaned household tab on the anonymised case number; exact lower-case column matching; the released person LISA value must sum to the household aggregate", @@ -3146,6 +3178,20 @@ "minimum_age": 18 } }, + { + "ages": { + "maximum": 47, + "minimum": 18 + }, + "counted_ages": { + "maximum": 47, + "minimum": 18 + }, + "kind": "anchor_lifetime_isa_ownership", + "output": "has_lifetime_isa", + "target": "table_9_4_lifetime_isa_accounts_subscribed", + "tax_year": 2024 + }, { "kind": "impute_lifetime_isa_balance", "output": "lifetime_isa_balance", @@ -3203,7 +3249,7 @@ "lifetime_isa_balance", "household_lifetime_isa_balance" ], - "notes": "Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor's weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner's keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus." + "notes": "Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor's weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner's keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus. HMRC's 2024-25 Lifetime ISA accounts subscribed to (Table 9.4) anchor the holders (microcosm#1415): the ownership logit shifts up on ages 18 to 47, the ages that can hold and pay into one in 2024-25 (opened at 18 to 39 since April 2017), until the holders at those ages who survive the financial-wealth cap equal HMRC's payers, the fewest holders consistent with them; the same keyed uniforms decide ownership, so the anchor only adds holders, and a target it cannot reach is named, not failed. Where the cap binds, an added holder scales the household's balances down pro rata, existing holders' included. Three later stages read the whole frame (uc_reporter_redraw, uc_take_up, pension_credit_take_up), and the Lifetime ISA counts as Universal Credit and Pension Credit capital, so the added holders can move their draws; the pair build counts the benefit units whose entitlement moves (María's ruling of 29 October 2026)." }, { "stage": "regional_property_uprating", @@ -6017,7 +6063,7 @@ "rows": "spi_channel", "donor_rows": "base_frs_with_available_capital", "reporter_status": "universal_credit_reported_anchor", - "investment_income": "savings_interest_income + dividend_income + other_investment_income, summed over benefit-unit members", + "investment_income": "savings_interest_income + individual_savings_account_interest_income + dividend_income + other_investment_income, summed over benefit-unit members", "investment_income_band_edges": [ 0.0, 240.0, @@ -6064,7 +6110,7 @@ "frs_benunit_capital", "would_claim_uc" ], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.162.14 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base." + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.164.2 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base." }, { "stage": "uc_take_up", @@ -6515,6 +6561,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -6589,6 +6636,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -6684,10 +6732,11 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], - "components_semantics": "Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted.", + "components_semantics": "Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted. ISA interest is exempt but stays beside non-ISA interest, as gross interest held it before microcosm#1360, so the proxy is unchanged.", "allowance": "tapered Personal Allowance from the policy_parameters artifact", "fail_on_missing_component": true }, @@ -6865,6 +6914,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -6916,7 +6966,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.162.14 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.164.2 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount" ], @@ -6997,7 +7047,7 @@ { "role": "policy_parameters", "kind": "versioned_parameter_tree", - "dependency": "policyengine-uk>=2.162.14 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.164.2 via microcosm-build[uk]", "parameters": [ "gov.hmrc.cgt.annual_exempt_amount", "gov.hmrc.cgt.badr.lifetime_limit", @@ -7487,7 +7537,181 @@ "outputs": [ "attends_private_school" ], - "notes": "The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine." + "notes": "The last spine stage before the ISA subscriptions (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine." + }, + { + "artifacts": [ + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "hmrc_isa_statistics_facts.json", + "role": "isa_statistics_facts", + "runtime_sha256_required": true + }, + { + "access": "private_local_input", + "doi": "10.5255/UKDA-SN-7215-20", + "filename": "was_round_8_hhold_eul_may_2025_230525.tab", + "kind": "private_microdata", + "locator": "caller-supplied local input", + "role": "was_qrf_donor", + "runtime_sha256_required": true, + "sha256": "18b3eb980c02c99f3d8a3254af859bee31682b2bdc11703877677292b3ce9374", + "size_bytes": 39073613, + "ukds_study_number": 7215 + }, + { + "access": "private_local_input", + "doi": "10.5255/UKDA-SN-7215-20", + "filename": "was_round_8_person_eul_may_2025_230525.tab", + "format": "tab", + "kind": "private_microdata", + "locator": "caller-supplied local input", + "role": "was_person_tab", + "runtime_sha256_required": true, + "sha256": "1ca6fd37d9c677242112e0d7839df8b406eb1611db8322b20771f054e329d71a", + "size_bytes": 356930989, + "ukds_study_number": 7215, + "vintage": "round_8" + } + ], + "grain": "person", + "nonnegative_outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "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.", + "operations": [ + { + "artifact_role": "isa_statistics_facts", + "fail_on_mismatch": true, + "feed_pin": "chronicle_feed.json", + "kind": "verify_vendored_fact_resource", + "mapped_build_period": 2024, + "period_mapping": "individual_tables_grown_to_the_table_9_4_build_year", + "record_sets": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.", + "hmrc.isa_statistics_2026.table9_8.ty2023.", + "hmrc.isa_statistics_2026.table9_9.ty2023.", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2024", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2024", + "hmrc.isa_statistics_2026.table9_6.ty2024", + "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022", + "hmrc.lisa_statistics_2026.table1a.ty2024", + "hmrc.lisa_statistics_2026.table1a.ty2025" + ], + "require_before_source_read": true, + "resource": "hmrc_isa_statistics_facts.json", + "runtime_sha256_required": true, + "source_vintage": "2023-24 individuals; Table 9.4 for 2023-24 and 2024-25" + }, + { + "aggregate_tolerance_gbp": 1, + "household_aggregates": { + "cash_isa": "DVCISAVR8_aggr", + "stocks_and_shares_isa": "DVIISAVR8_aggr" + }, + "household_key": "CASER8", + "household_weight": "R8xshhwgt", + "kind": "clean_was_isa_person_donor", + "non_dependent_code": 2, + "person_columns": { + "age_band": "DVAge17R8", + "cash_isa_value": "DVCISAvR8", + "is_dependent_child": "IsDepR8", + "person_number": "PersonR8", + "stocks_and_shares_isa_value": "DVIISAvR8" + } + }, + { + "kind": "split_household_isa_balances", + "maximum_donor_adults": 4, + "outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance" + ], + "salt": "isa_subscriptions:balance_split", + "seed": 0 + }, + { + "kind": "allocate_isa_subscribers", + "minimum_age": 18, + "salt": "isa_subscriptions:subscribers", + "seed": 0, + "selection_order": [ + "cash_and_stocks_and_shares", + "stocks_and_shares_only", + "cash_only" + ] + }, + { + "balance_rank_weight": 0.5, + "kind": "draw_isa_subscription_amounts", + "outputs": [ + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions" + ], + "salt": "isa_subscriptions:amounts", + "seed": 0, + "within_band": "uniform" + }, + { + "anchor": "table_9_4_stocks_and_shares_residual", + "kind": "split_both_type_subscriptions", + "share_floor": 0.02 + }, + { + "amounts": "table_9_4_amount_level_tapered_to_limit", + "from_tax_year": 2023, + "kind": "grow_isa_subscriptions", + "overall_limit_gbp": 20000, + "people": "table_9_4_account_growth", + "to_tax_year": 2024, + "taper_from_gbp": 18000 + }, + { + "annual_limit_gbp": 4000, + "kind": "draw_lifetime_isa_payments", + "maximum_age": 47, + "minimum_age": 18, + "outputs": [ + "lifetime_isa_payments" + ], + "salt": "isa_subscriptions:lifetime_isa_payments", + "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": "ISA subscriptions per adult from HMRC's ISA tables on the source spine: household weights pass through unchanged and total household mass is conserved." + }, + { + "adjudication": "https://github.com/PolicyEngine/microcosm/issues/1415", + "calibration_permitted": false, + "classification_rationale": "The stage draws subscribers and amounts to these cells, so binding them in the weight solve would be circular. The national build reports the subscription cells after calibration, with the holders (Tables 9.10 to 9.12) and market values (Table 9.6) as independent checks; none blocks a build.", + "fact_fence_id": "isa_facts_imputation_seeds_measured_only", + "fenced_fact_composition": "Table 9.7 2023-24 subscribers by income and subscription band and holders making no further subscription, for all subscribers and each type paid into (624); Table 9.8 by age and sex (325); Table 9.9 by region (70); Table 9.4 accounts and amounts for 2023-24 and 2024-25 (24); Table 9.6 2024-25 market values (19); the impact note's shares of under-65 cash subscribers either side of GBP 12,000 (2); Lifetime ISA Table 1a for 2024-25 and 2025-26 (10)", + "fenced_fact_count": 1074, + "kind": "classify_isa_facts_with_reviewed_fence" + } + ], + "outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions", + "lifetime_isa_payments" + ], + "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" } ] } 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 3598d6afa0..f106b2d763 100644 --- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml +++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml @@ -408,7 +408,7 @@ stages: - loss_relief - ni_class_4_losses_brought_forward - private_pension_income - - tax_free_savings_income + - individual_savings_account_interest_income - savings_interest_income - dividend_income - other_investment_income @@ -509,7 +509,7 @@ stages: - loss_relief - ni_class_4_losses_brought_forward - private_pension_income - - tax_free_savings_income + - individual_savings_account_interest_income - savings_interest_income - dividend_income - other_investment_income @@ -1033,7 +1033,7 @@ stages: nonnegative_outputs: - maximum_extended_childcare_hours_usage - max_free_entitlement_hours_used - notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.162.14's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30. + notes: Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.164.2's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30. - stage: frs_person_draws survey: Family Resources Survey 2024-25 source: Sourced UK take-up contract rates and identity-keyed deterministic draws. @@ -1676,7 +1676,7 @@ stages: twin: age at or over policyengine-uk gov.dwp.state_pension.age at the build instant validation: SRP > 0 share among SPA 1 and SPA 1 share among SRP > 0 recipient_income_rule: - drawn: the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income less tax_free_savings_income), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves + drawn: the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income, the interest outside ISAs), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves undrawn: the twin has no tax record for the tape to condition on; the copy keeps the twin's values at stage 1 and stage 2 (a declared modelling boundary like the under-16 one, microcosm#840) unearned_income_floor_gbp: 1000 draw: twin_conditional_quantiles @@ -1713,7 +1713,7 @@ stages: 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' - 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 less tax_free_savings_income, 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.' + 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 positive-row count under 10 is withheld; the counts in the stage evidence stay with the build and are not published identity_uniforms: key: person_source_id @@ -1885,7 +1885,7 @@ stages: - employer_pension_contributions - personal_pension_contributions - pension_contributions_via_salary_sacrifice - - tax_free_savings_income + - individual_savings_account_interest_income - universal_credit_reported - pension_credit_reported - child_benefit_reported @@ -1923,7 +1923,7 @@ stages: incapacity_benefit_reported: Absent/all-default on the pinned enhanced-FRS export and certified Microcosm UK base; not a populated loader layer. maternity_allowance_reported: Absent from the pinned enhanced-FRS export and certified Microcosm UK base; no training source can be materialized for this stage. postprocess: - gross_savings_interest_income: stage1 INCBBS draw + stage2 tax_free_savings_income + savings_interest_income: the stage-1 INCBBS draw, interest outside ISAs; individual_savings_account_interest_income is the stage-2 draw beside it (microcosm#1360) refresh_disability_categories: - aa_category - dla_sc_category @@ -2203,7 +2203,7 @@ stages: - employer_pension_contributions - personal_pension_contributions - pension_contributions_via_salary_sacrifice - - tax_free_savings_income + - individual_savings_account_interest_income - universal_credit_reported - pension_credit_reported - child_benefit_reported @@ -2589,7 +2589,34 @@ stages: access: private_local_input locator: caller-supplied local input runtime_sha256_required: true + - role: isa_statistics_facts + kind: public_aggregate_reference + resource: hmrc_isa_statistics_facts.json + format: json + runtime_sha256_required: true operations: + - kind: verify_vendored_fact_resource + artifact_role: isa_statistics_facts + resource: hmrc_isa_statistics_facts.json + feed_pin: chronicle_feed.json + record_sets: + - hmrc.isa_statistics_2026.table9_7.ty2023. + - hmrc.isa_statistics_2026.table9_8.ty2023. + - hmrc.isa_statistics_2026.table9_9.ty2023. + - hmrc.isa_statistics_2026.table9_4.accounts.ty2023 + - hmrc.isa_statistics_2026.table9_4.amounts.ty2023 + - hmrc.isa_statistics_2026.table9_4.accounts.ty2024 + - hmrc.isa_statistics_2026.table9_4.amounts.ty2024 + - hmrc.isa_statistics_2026.table9_6.ty2024 + - hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022 + - hmrc.lisa_statistics_2026.table1a.ty2024 + - hmrc.lisa_statistics_2026.table1a.ty2025 + source_vintage: 2023-24 individuals; Table 9.4 for 2023-24 and 2024-25 + mapped_build_period: 2024 + period_mapping: individual_tables_grown_to_the_table_9_4_build_year + require_before_source_read: true + runtime_sha256_required: true + fail_on_mismatch: true - kind: clean_was_lisa_donor scope: WAS round-8 person tab joined many-to-one to the cleaned household tab on the anonymised case number; exact lower-case column matching; the released person LISA value must sum to the household aggregate household_key: CASER8 @@ -2660,6 +2687,16 @@ stages: - num_children population: minimum_age: 18 + - kind: anchor_lifetime_isa_ownership + output: has_lifetime_isa + target: table_9_4_lifetime_isa_accounts_subscribed + tax_year: 2024 + ages: + minimum: 18 + maximum: 47 + counted_ages: + minimum: 18 + maximum: 47 - kind: impute_lifetime_isa_balance output: lifetime_isa_balance seed: 0 @@ -2702,7 +2739,7 @@ stages: nonnegative_outputs: - lifetime_isa_balance - household_lifetime_isa_balance - notes: 'Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor''s weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner''s keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus.' + notes: 'Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor''s weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner''s keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus. HMRC''s 2024-25 Lifetime ISA accounts subscribed to (Table 9.4) anchor the holders (microcosm#1415): the ownership logit shifts up on ages 18 to 47, the ages that can hold and pay into one in 2024-25 (opened at 18 to 39 since April 2017), until the holders at those ages who survive the financial-wealth cap equal HMRC''s payers, the fewest holders consistent with them; the same keyed uniforms decide ownership, so the anchor only adds holders, and a target it cannot reach is named, not failed. Where the cap binds, an added holder scales the household''s balances down pro rata, existing holders'' included. Three later stages read the whole frame (uc_reporter_redraw, uc_take_up, pension_credit_take_up), and the Lifetime ISA counts as Universal Credit and Pension Credit capital, so the added holders can move their draws; the pair build counts the benefit units whose entitlement moves (María''s ruling of 29 October 2026).' - stage: regional_property_uprating survey: Public regional property reference 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). @@ -4859,7 +4896,7 @@ stages: rows: spi_channel donor_rows: base_frs_with_available_capital reporter_status: universal_credit_reported_anchor - investment_income: savings_interest_income + dividend_income + other_investment_income, summed over benefit-unit members + investment_income: savings_interest_income + individual_savings_account_interest_income + dividend_income + other_investment_income, summed over benefit-unit members investment_income_band_edges: - 0.0 - 240.0 @@ -4896,7 +4933,7 @@ stages: rewrites: - frs_benunit_capital - would_claim_uc - notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.162.14 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base. + notes: Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.164.2 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (María's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base. - stage: uc_take_up survey: Family Resources Survey 2024-25 source: 'DWP Stat-Xplore, Households on Universal Credit by family type and monthly award amount band, April to December 2025, applied to PolicyEngine-UK pre-take-up Universal Credit awards after the SPI income chain and UC capital coherence.' @@ -5236,6 +5273,7 @@ stages: - private_pension_income - property_income - savings_interest_income + - individual_savings_account_interest_income - dividend_income - miscellaneous_income allowance_subtraction: true @@ -5297,6 +5335,7 @@ stages: - private_pension_income - property_income - savings_interest_income + - individual_savings_account_interest_income - dividend_income - miscellaneous_income allowance_subtraction: false @@ -5372,9 +5411,10 @@ stages: - private_pension_income - property_income - savings_interest_income + - individual_savings_account_interest_income - dividend_income - miscellaneous_income - components_semantics: Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted. + components_semantics: Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted. ISA interest is exempt but stays beside non-ISA interest, as gross interest held it before microcosm#1360, so the proxy is unchanged. allowance: tapered Personal Allowance from the policy_parameters artifact fail_on_missing_component: true - kind: rake_allocation_targets @@ -5520,6 +5560,7 @@ stages: - private_pension_income - property_income - savings_interest_income + - individual_savings_account_interest_income - dividend_income - miscellaneous_income allowance_subtraction: false @@ -5562,7 +5603,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.162.14 via microcosm-build[uk] + dependency: policyengine-uk>=2.164.2 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount instant_rule: raw dated parameter files evaluated at 1 June of the build period's tax year @@ -5627,7 +5668,7 @@ stages: artifacts: - role: policy_parameters kind: versioned_parameter_tree - dependency: policyengine-uk>=2.162.14 via microcosm-build[uk] + dependency: policyengine-uk>=2.164.2 via microcosm-build[uk] parameters: - gov.hmrc.cgt.annual_exempt_amount - gov.hmrc.cgt.badr.lifetime_limit @@ -6020,4 +6061,139 @@ stages: gate_coupling: The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason. outputs: - attends_private_school - notes: 'The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine''s attendance once, at the calibration year and the spine''s design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.' + notes: 'The last spine stage before the ISA subscriptions (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine''s attendance once, at the calibration year and the spine''s design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.' +- stage: isa_subscriptions + survey: HMRC Annual savings statistics (September 2026 release); Wealth and Assets Survey round 8 + 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). + grain: person + artifacts: + - role: isa_statistics_facts + kind: public_aggregate_reference + resource: hmrc_isa_statistics_facts.json + format: json + runtime_sha256_required: true + - role: was_qrf_donor + kind: private_microdata + ukds_study_number: 7215 + doi: 10.5255/UKDA-SN-7215-20 + filename: was_round_8_hhold_eul_may_2025_230525.tab + sha256: 18b3eb980c02c99f3d8a3254af859bee31682b2bdc11703877677292b3ce9374 + size_bytes: 39073613 + access: private_local_input + locator: caller-supplied local input + runtime_sha256_required: true + - role: was_person_tab + kind: private_microdata + ukds_study_number: 7215 + doi: 10.5255/UKDA-SN-7215-20 + filename: was_round_8_person_eul_may_2025_230525.tab + format: tab + vintage: round_8 + sha256: 1ca6fd37d9c677242112e0d7839df8b406eb1611db8322b20771f054e329d71a + size_bytes: 356930989 + access: private_local_input + locator: caller-supplied local input + runtime_sha256_required: true + operations: + - kind: verify_vendored_fact_resource + artifact_role: isa_statistics_facts + resource: hmrc_isa_statistics_facts.json + feed_pin: chronicle_feed.json + record_sets: + - hmrc.isa_statistics_2026.table9_7.ty2023. + - hmrc.isa_statistics_2026.table9_8.ty2023. + - hmrc.isa_statistics_2026.table9_9.ty2023. + - hmrc.isa_statistics_2026.table9_4.accounts.ty2023 + - hmrc.isa_statistics_2026.table9_4.amounts.ty2023 + - hmrc.isa_statistics_2026.table9_4.accounts.ty2024 + - hmrc.isa_statistics_2026.table9_4.amounts.ty2024 + - hmrc.isa_statistics_2026.table9_6.ty2024 + - hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022 + - hmrc.lisa_statistics_2026.table1a.ty2024 + - hmrc.lisa_statistics_2026.table1a.ty2025 + source_vintage: 2023-24 individuals; Table 9.4 for 2023-24 and 2024-25 + mapped_build_period: 2024 + period_mapping: individual_tables_grown_to_the_table_9_4_build_year + require_before_source_read: true + runtime_sha256_required: true + fail_on_mismatch: true + - kind: clean_was_isa_person_donor + household_key: CASER8 + household_weight: R8xshhwgt + person_columns: + person_number: PersonR8 + is_dependent_child: IsDepR8 + age_band: DVAge17R8 + cash_isa_value: DVCISAvR8 + stocks_and_shares_isa_value: DVIISAvR8 + household_aggregates: + cash_isa: DVCISAVR8_aggr + stocks_and_shares_isa: DVIISAVR8_aggr + aggregate_tolerance_gbp: 1.0 + non_dependent_code: 2 + - kind: split_household_isa_balances + outputs: + - cash_isa_balance + - stocks_and_shares_isa_balance + seed: 0 + salt: isa_subscriptions:balance_split + maximum_donor_adults: 4 + - kind: allocate_isa_subscribers + seed: 0 + salt: isa_subscriptions:subscribers + minimum_age: 18 + selection_order: + - cash_and_stocks_and_shares + - stocks_and_shares_only + - cash_only + - kind: draw_isa_subscription_amounts + outputs: + - cash_isa_subscriptions + - stocks_and_shares_isa_subscriptions + seed: 0 + salt: isa_subscriptions:amounts + within_band: uniform + balance_rank_weight: 0.5 + - kind: split_both_type_subscriptions + anchor: table_9_4_stocks_and_shares_residual + share_floor: 0.02 + - kind: grow_isa_subscriptions + people: table_9_4_account_growth + amounts: table_9_4_amount_level_tapered_to_limit + from_tax_year: 2023 + to_tax_year: 2024 + overall_limit_gbp: 20000.0 + taper_from_gbp: 18000.0 + - kind: draw_lifetime_isa_payments + outputs: + - lifetime_isa_payments + seed: 0 + salt: isa_subscriptions:lifetime_isa_payments + minimum_age: 18 + maximum_age: 47 + annual_limit_gbp: 4000.0 + - kind: record_mass_conservation_receipt + entity: household + 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.' + declared_factor: 1.0 + gate_coupling: The terminal family gate requires a valid mass-conserving MassChangeRecord carrying exactly this stage-specific reason. + - kind: classify_isa_facts_with_reviewed_fence + calibration_permitted: false + fact_fence_id: isa_facts_imputation_seeds_measured_only + fenced_fact_count: 1074 + fenced_fact_composition: Table 9.7 2023-24 subscribers by income and subscription band and holders making no further subscription, for all subscribers and each type paid into (624); Table 9.8 by age and sex (325); Table 9.9 by region (70); Table 9.4 accounts and amounts for 2023-24 and 2024-25 (24); Table 9.6 2024-25 market values (19); the impact note's shares of under-65 cash subscribers either side of GBP 12,000 (2); Lifetime ISA Table 1a for 2024-25 and 2025-26 (10) + classification_rationale: The stage draws subscribers and amounts to these cells, so binding them in the weight solve would be circular. The national build reports the subscription cells after calibration, with the holders (Tables 9.10 to 9.12) and market values (Table 9.6) as independent checks; none blocks a build. + adjudication: https://github.com/PolicyEngine/microcosm/issues/1415 + outputs: + - cash_isa_balance + - stocks_and_shares_isa_balance + - cash_isa_subscriptions + - stocks_and_shares_isa_subscriptions + - lifetime_isa_payments + nonnegative_outputs: + - cash_isa_balance + - stocks_and_shares_isa_balance + - cash_isa_subscriptions + - 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.' 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 02991f8bec..c66fb0d282 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 @@ -14820,7 +14820,7 @@ "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: the engine takes the frame's tax_free_savings_income out of the frame's gross interest when it loads the frame. The ISA part, tax_free_savings_income, is added back (microcosm#1095). 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 NS&I and gilt accounts on FRS rows. The engine taxes it as savings income beside savings_interest_income." }, "axiom": { "metric_name": "ons/savings_interest_income", @@ -21901,7 +21901,7 @@ "map_to": "household", "groupby_variable": "total_income_after_loss_relief_annual", "band_filter_dimension": "total_income", - "notes": "Taxable interest from banks and building societies of taxpayers by total-income band (SPI Table 3.7; ISA interest excluded), the taxpayer-concept anchor microcosm#866 asked for in place of the ONS D.41 row. Banded by person-level total income; band edges come from the fact dimension. From policyengine-uk 2.162.2 the engine's savings_interest_income leaves ISA interest out, as Table 3.7 does: the engine takes the frame's tax_free_savings_income out of the frame's gross interest when it loads the frame. Through 2.161.0 the measure was gross of ISA interest (microcosm#1095). The frame declares the legacy-gross contract, so the engine grows the gross interest to the calibration year and takes off the stored ISA part unchanged; the measure then overstates the interest outside ISAs a little, by about the ISA share times the year's interest growth. Writing ordinary interest under the additive contract would remove that (microcosm#1095).", + "notes": "Taxable interest from banks and building societies of taxpayers by total-income band (SPI Table 3.7; ISA interest excluded), the taxpayer-concept anchor microcosm#866 asked for in place of the ONS D.41 row. Banded by person-level total income; band edges come from the fact dimension. From policyengine-uk 2.162.2 the engine's savings_interest_income leaves ISA interest out, as Table 3.7 does. Through 2.161.0 the measure was gross of ISA interest (microcosm#1095). Under the legacy-gross contract the engine grew the gross interest to the calibration year and took off the stored ISA part unchanged, overstating the interest outside ISAs by about the ISA share times the year's interest growth; since microcosm#1360 the frame stores interest outside ISAs under the additive contract, which grows it by its own index.", "filters": [ { "variable": "income_tax_before_winter_fuel_payment_charge_annual", @@ -21971,7 +21971,7 @@ "value_variable": "person_count", "from_entity": "person", "map_to": "household", - "notes": "Counts the recipients of interest outside ISAs, as SPI Table 3.7 counts the recipients of taxable interest: the filter reads savings_interest_income_outside_isas, the frame's stored gross savings_interest_income less its tax_free_savings_income, so a person whose only interest is ISA interest is not counted (Vahid's review of #1400, round 2)." + "notes": "Counts the recipients of interest outside ISAs, as SPI Table 3.7 counts the recipients of taxable interest: the filter reads savings_interest_income_outside_isas, the frame's stored interest outside ISAs (a legacy gross reference's savings_interest_income less its tax_free_savings_income), so a person whose only interest is ISA interest is not counted (Vahid's review of #1400, round 2; microcosm#1360)." }, "axiom": { "metric_name": "hmrc/savings_interest_income_count_income_band", 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 d59375ec4e..d59e24a452 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 @@ -2178,6 +2178,11 @@ def _ledger_compile_parity_required_artifacts( # bounds on the scale that anchors the added stakes. "maximum_stratum_shortfall", "amount_scale_bounds", + # #1415 isa_subscriptions check: how far a total may sit from + # its target, and how far person balances may miss the + # household draws they split. + "maximum_relative_total_deviation", + "maximum_household_sum_error_gbp", # #1113 road_fuel_incidence check: the ceiling on the chain's # zero share among flagged fuel-car households. "maximum_flagged_zero_share", 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 6298dbc81e..4261f87b60 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 @@ -99,6 +99,7 @@ "uk_stage_was_wealth_support", "uk_stage_was_wealth_coherence", "uk_stage_was_lisa_support", + "uk_stage_was_lisa_anchor", "uk_stage_high_value_home_support_mass", "uk_stage_property_components", "uk_stage_nts_bus_travel_support", @@ -139,6 +140,8 @@ "uk_stage_property_purchases_fit", "uk_stage_uc_legacy_closure_take_up_contract", "uk_stage_frs_person_draws_apprentices", + "uk_stage_isa_subscriptions_invariants", + "uk_stage_isa_subscriptions", # Weight-independent like the BRMA enum below, and its column exists from # frs_relationships onward (#791). "uk_ons_household_type_enum_domain", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py index 8832719670..360c4d6fb3 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/cgt_imputation.py @@ -191,7 +191,10 @@ #: Persisted components of the model's ``total_income`` concept (ITA 2007 #: s.23: taxable income after tax reliefs and before allowances). #: ``state_pension_reported`` stands in for ``social_security_income``; the -#: other taxable benefits inside that concept are not persisted inputs. +#: other taxable benefits inside that concept are not persisted inputs. ISA +#: interest is exempt, but gross interest held it before the additive +#: contract (microcosm#1360), so it stays beside non-ISA interest and the +#: proxy, and every draw on it, are unchanged. UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS: tuple[str, ...] = ( "employment_income", "self_employment_income", @@ -199,6 +202,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income", ) @@ -2340,7 +2344,9 @@ def _assert_cgt_spine_stage_parameters(stage: SourceStageSpec) -> None: "2007 s.23); state_pension_reported stands in for " "social_security_income, whose other taxable benefits are " "not persisted; reliefs such as pension contributions and " - "Gift Aid are not deducted." + "Gift Aid are not deducted. ISA interest is exempt but stays " + "beside non-ISA interest, as gross interest held it before " + "microcosm#1360, so the proxy is unchanged." ), "allowance": ( "tapered Personal Allowance from the policy_parameters artifact" 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 1af0349e8f..67333a5c63 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 @@ -248,7 +248,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", "other_investment_income", @@ -740,9 +740,13 @@ def _add_private_pension( #: FRS ACCOUNTS.ACCOUNT codes (FRS 2024-25 variable listing, SN 9563) by the -#: input their weekly interest (ACCINT) lands in (microcosm#1418). ISAs (21) -#: are tax-free, kept apart in tax_free_savings_income inside gross interest. +#: input their weekly interest (ACCINT) lands in (microcosm#1418). ISA (21) +#: interest is tax-free and stored apart from non-ISA interest, in +#: individual_savings_account_interest_income (microcosm#1360). FRS_ISA_ACCOUNT = 21 +#: 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 @@ -794,8 +798,13 @@ def annual(codes: tuple[int, ...], after_tax: object) -> pd.Series: # A savings product answers ACCTAX and a gilt INVTAX. other = annual(FRS_OTHER_INTEREST_ACCOUNTS, (acctax == 1) | (invtax == 1)) dividends = annual(FRS_DIVIDEND_ACCOUNTS, invtax == 1) - pe_person["tax_free_savings_income"] = np.maximum(0, tax_free) - pe_person["savings_interest_income"] = np.maximum(0, taxable + tax_free) + isa_interest = np.maximum(0, tax_free) + pe_person[ISA_INTEREST_COLUMN] = isa_interest + # Non-ISA interest is gross interest less the ISA part, as policyengine-uk + # normalises the legacy gross layout, so base-year amounts match it exactly. + pe_person["savings_interest_income"] = ( + np.maximum(0, taxable + tax_free) - isa_interest + ) pe_person["dividend_income"] = np.maximum(0, dividends) pe_person["other_investment_income"] = np.maximum(0, other) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py index f1f8d226c8..f8b4ae749d 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_take_up.py @@ -80,7 +80,7 @@ for output in (*FRS_TAKE_UP_OUTPUT_COLUMNS, *FRS_TAKE_UP_PERSON_OUTPUT_COLUMNS) } # Universal Credit is claimable only by a benefit unit whose claimant or -# partner meets its age conditions: policyengine-uk 2.162.14's ``is_uc_eligible`` +# partner meets its age conditions: policyengine-uk 2.164.2's ``is_uc_eligible`` # (capital aside) needs a single or joint claimant, taken from # ``is_uc_claimant``, who meets UC's minimum age and has not reached Pension # Credit qualifying age. The draw's population is therefore the units with a 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 0e92fbdce7..5ecf917222 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 @@ -181,6 +181,7 @@ from .national_doctrine import uk_doctrine_with_overrides from .national_frame import ( load_uk_national_frame, + require_uk_producer_isa_income_contract, uk_release_export_frame, write_uk_national_frame, ) @@ -1295,6 +1296,7 @@ def prepare_full_build( args.input_h5, pins["dataset"], pinned=args.input_sha256 is not None ) frame, _ = load_uk_national_frame(args.input_h5) + require_uk_producer_isa_income_contract(frame, label="--input-h5") sidecar_path = args.input_sidecar or args.input_h5.with_suffix(".build.json") gates_path = args.input_spine_gates or args.input_h5.with_suffix( ".spine_gates.json" @@ -2827,6 +2829,7 @@ def prepare_national_build( pins = {"dataset": _pin(input_h5, args.input_sha256)} inputs = {"dataset": _input_record(input_h5, pins["dataset"], pinned=True)} frame, _ = load_uk_national_frame(input_h5) + require_uk_producer_isa_income_contract(frame, label="--input-h5") sidecar_path = args.input_sidecar or input_h5.with_suffix(".build.json") gates_path = args.input_spine_gates or input_h5.with_suffix(".spine_gates.json") load_bound_spine_checkpoint( 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 21d63ff3d4..02351c6a56 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py @@ -159,6 +159,9 @@ "frs_legacy_proxies": ("frs",), "was_wealth": ("was",), "was_lisa": ("was", "was_person"), + # The ISA subscriptions split household ISA balances by a WAS donor + # household's holding pattern (microcosm#1415). + "isa_subscriptions": ("was", "was_person"), # The finance-cost model trains on the SPI tape (microcosm#1106). "property_components": ("spi",), "nts_bus_travel": ( @@ -356,6 +359,7 @@ ("person", "self_employment_income"), ("person", "private_pension_income"), ("person", "savings_interest_income"), + ("person", "individual_savings_account_interest_income"), ("person", "dividend_income"), ("person", "property_income"), ("person", "other_investment_income"), @@ -436,6 +440,7 @@ ("person", "universal_credit_reported"), # The capital cells' financial investment income (microcosm#1095). ("person", "savings_interest_income"), + ("person", "individual_savings_account_interest_income"), ("person", "dividend_income"), ("person", "other_investment_income"), # The recorded capitals' property: the household reference @@ -501,7 +506,7 @@ "private_pension_income", "property_income", "state_pension_reported", - "tax_free_savings_income", + "individual_savings_account_interest_income", "age", ) ), @@ -559,6 +564,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income", ) @@ -570,6 +576,35 @@ # One engine materialization of private-school attendance over the whole # frame at its weights: an open surface (microcosm#1095). "private_school_attendance": None, + # Subscribers are cells of age, sex, region and the CGT income proxy; the + # balance split reads the household ISA totals and the payments the LISA + # holding (microcosm#1415). + "isa_subscriptions": frozenset( + { + *( + ("person", column) + for column in ( + "age", + "gender", + "has_lifetime_isa", + "lifetime_isa_balance", + "employment_income", + "self_employment_income", + "state_pension_reported", + "private_pension_income", + "property_income", + "savings_interest_income", + "individual_savings_account_interest_income", + "dividend_income", + "miscellaneous_income", + "loss_relief", + ) + ), + ("household", "region"), + ("household", "cash_isa"), + ("household", "stocks_and_shares_isa"), + } + ), # 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. @@ -1174,6 +1209,16 @@ def _cells( ), "student_loans": (_Cell("person", "student_loan_plan", "string"),), "private_school_attendance": (_Cell("person", "attends_private_school", "bool"),), + "isa_subscriptions": _cells( + "person", + ( + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions", + "lifetime_isa_payments", + ), + ), "childcare_copy_take_up": ( *_cells( "benunit", @@ -1218,7 +1263,7 @@ def _cells( "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_build.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_build.py index ddc2b92294..54152fec59 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_build.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_build.py @@ -373,7 +373,9 @@ def uk_full_graph( def _load_spine_h5(path: Path) -> Frame: - return national_frame.load_uk_national_frame(path)[0] + frame = national_frame.load_uk_national_frame(path)[0] + national_frame.require_uk_producer_isa_income_contract(frame, label=str(path)) + return frame SOURCE_CODECS.register("uk-spine-h5-v1", _load_spine_h5) 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 c0e445b901..d89bd216fe 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 @@ -47,11 +47,14 @@ from .. import stage_evidence, stochastic_assignment from . import ( bus_use_incidence, + cgt_imputation, energy_pricing, frs_disability, frs_education, frs_hmrc_source, frs_take_up, + hmrc_income, + hmrc_isa_statistics, hmrc_property_rental, household_lineage, ledger_fact_vendoring, @@ -131,6 +134,7 @@ "salary_sacrifice": "salary_sacrifice", "student_loans": "student_loans", "private_school_attendance": "private_school_attendance", + "isa_subscriptions": "isa_subscriptions", "childcare_copy_take_up": "childcare_copy_take_up", "age_tail": "age_tail", } @@ -151,8 +155,9 @@ "frs_brma": (uk_engine_adapter,), "was_wealth": (uk_engine_adapter,), # The LISA stage reads its household donor through the WAS cleaning and its - # household predictors through the WAS recipient surface. - "was_lisa": (uk_engine_adapter, was_wealth), + # household predictors through the WAS recipient surface; the anchor counts + # HMRC's payers through the typed ISA facts (microcosm#1415). + "was_lisa": (uk_engine_adapter, was_wealth, hmrc_isa_statistics), # Landlords' receipts read the vendored PRIS rows through their typed # loader, the tape through the income stage's preparation and uprating, # and the support channel's names (microcosm#1106). @@ -216,6 +221,15 @@ ), # The stored attendance is one engine materialization (microcosm#1095). "private_school_attendance": (uk_engine_adapter,), + # The subscriptions walk the CGT bands on its income proxy (with the + # proxy's loss relief), read HMRC's ISA tables through their typed loader + # and round their receipts' support (microcosm#1415). + "isa_subscriptions": ( + cgt_imputation, + hmrc_income, + hmrc_isa_statistics, + receipt_counts, + ), # The copies' childcare re-draw draws through frs_take_up's childcare # helper and reads each household's clone path. "childcare_copy_take_up": (frs_take_up, household_lineage), @@ -544,6 +558,7 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: from .frs_relationships import UKFRSRelationshipsStageTransform from .frs_take_up import UKFRSTakeUpStageTransform from .high_value_home_support import UKHighValueHomeSupportStageTransform + from .isa_subscriptions import UKISASubscriptionsStageTransform from .lcfs_consumption import UKLCFSConsumptionStageTransform from .legacy_closure_take_up import UKLegacyClosureTakeUpStageTransform from .nts_bus_travel import UKNTSBusTravelStageTransform @@ -826,6 +841,12 @@ def _fixture_implementations(source: Path) -> Mapping[str, object]: engine=engine, calibration_year=calibration_year, ), + "isa_subscriptions": UKISASubscriptionsStageTransform( + stage=stages["isa_subscriptions"], + engine=engine, + donor_household=was, + donor_person=was_person, + ), "childcare_copy_take_up": UKChildcareCopyTakeUpStageTransform( contract=contract, stage=stages["childcare_copy_take_up"] ), diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py index 25fe8c8bbb..0d6bc5534b 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph_terminal.py @@ -25,6 +25,7 @@ from microcosm.calibrate import TargetRegistry from microcosm.frame import Frame, MassChangeRecord, WeightKind, engine_tables +from microcosm.frame.adapters.policyengine_uk import UK_ISA_INCOME_CONTRACT from microcosm.graph import ( ArtifactInput, ArtifactOutput, @@ -68,6 +69,7 @@ _write_uk_single_year_tables, uk_household_weight_kind, uk_identifier_range_failures, + uk_isa_income_contract, uk_release_identifier_tables, uk_time_period, validate_uk_national_frame, @@ -297,6 +299,7 @@ def materialize_uk_export( time_period=uk_time_period(frame), weight_kind=uk_household_weight_kind(frame), mass_log=frame.mass_log, + isa_income_contract=uk_isa_income_contract(frame), path=destination, ) return file_artifact(destination) @@ -323,6 +326,8 @@ def validate_uk_export( for key in ("time_period", "weight_kind", "mass_log", "hash_environment"): if actual[key] != descriptor[key]: failures.append(f"Exported {key} differs from its graph descriptor.") + if payload["isa_income_contract"] != dict(UK_ISA_INCOME_CONTRACT): + failures.append("Exported ISA income contract is not the producer's.") failures.extend(_area_code_failures(payload["household"], descriptor)) failures.extend(uk_identifier_range_failures(payload)) if file_artifact(path) != dataset: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py index a860d9365d..185dfbf0cf 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_calibration.py @@ -23,6 +23,7 @@ ) from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, + uk_isa_income_contract, uk_time_period, ) from microcosm.build.uk_runtime.spi_income import derive_hmrc_income_auxiliaries @@ -110,7 +111,7 @@ def materialize_uk_hmrc_calibration_frame( """ _validate_materialization_inputs(frame, source_targets) - person = frame.table("person").copy() + person = _person_with_interest_outside_isas(frame) household = frame.table("household").copy() simulation_input = _uk_single_year_dataset(frame) @@ -135,7 +136,6 @@ def materialize_uk_hmrc_calibration_frame( person = derive_hmrc_income_auxiliaries(person) component_values: dict[str, np.ndarray] = {} - tax_free_values: np.ndarray | None = None for component in HMRC_SPI_INCOME_COMPONENTS: if component == "employment_income": source_column = SPI_HMRC_EMPLOYED_INCOME_COLUMN @@ -165,36 +165,17 @@ def materialize_uk_hmrc_calibration_frame( .to_numpy(dtype=float), ) if component == "savings_interest_income": - if "tax_free_savings_income" not in person: - raise ValueError( - "HMRC savings-interest materialization requires " - "tax_free_savings_income so Table 3.7 excludes tax-exempt " - "interest." - ) - tax_free = pd.to_numeric( - person["tax_free_savings_income"], errors="coerce" - ).to_numpy(dtype=float, na_value=np.nan) - if not np.isfinite(tax_free).all() or (tax_free < 0.0).any(): - raise ValueError( - "HMRC tax-free savings interest must be finite and non-negative." - ) - if (values < tax_free).any(): + # HMRC Table 3.7 and its total-income bands count interest outside + # ISAs, the column after _person_with_interest_outside_isas; it + # feeds both the measure and the band assignment. + if (values < 0.0).any(): raise ValueError( - "HMRC taxable-savings bridge requires PolicyEngine gross " - "savings_interest_income to be at least " - "tax_free_savings_income on every person row." + "HMRC taxable-savings bridge requires non-negative savings " + "interest outside ISAs on every person row." ) - # PolicyEngine's persisted input is gross savings interest, while - # HMRC Table 3.7 and its total-income bands exclude tax-exempt - # interest. Keep the PE input intact and derive the taxable source - # component explicitly for both the measure and band assignment. - values = values - tax_free - tax_free_values = tax_free person[HMRC_TAXABLE_SAVINGS_INTEREST_COLUMN] = values component_values[component] = values - if tax_free_values is None: # pragma: no cover - fixed component contract - raise RuntimeError("HMRC savings-interest bridge was not materialized.") auxiliary_values: dict[str, np.ndarray] = {} for column in ( SPI_HMRC_OTHER_INCOME_COLUMN, @@ -439,6 +420,34 @@ def _validate_materialization_inputs( ) +def _person_with_interest_outside_isas(frame: Frame) -> pd.DataFrame: + """The person table with ``savings_interest_income`` outside ISAs. + + The producer's additive layout stores it so. A legacy gross frame stores + gross interest with the exempt part in ``tax_free_savings_income``, taken + off here once (microcosm#1360); the engine reads either from the frame's + declared contract. + """ + + person = frame.table("person").copy() + if uk_isa_income_contract(frame)["representation"] != "legacy_gross": + return person + if "tax_free_savings_income" not in person: + raise ValueError( + "A legacy gross frame must carry tax_free_savings_income so Table " + "3.7 excludes tax-exempt interest." + ) + tax_free = pd.to_numeric(person["tax_free_savings_income"], errors="coerce") + tax_free = tax_free.to_numpy(dtype=float, na_value=np.nan) + if not np.isfinite(tax_free).all() or (tax_free < 0.0).any(): + raise ValueError( + "HMRC tax-free savings interest must be finite and non-negative." + ) + gross = pd.to_numeric(person["savings_interest_income"], errors="coerce") + person["savings_interest_income"] = gross.to_numpy(dtype=float) - tax_free + return person + + def _uk_single_year_dataset(frame: Frame) -> Any: try: from policyengine_uk.data import UKSingleYearDataset @@ -446,7 +455,6 @@ def _uk_single_year_dataset(frame: Frame) -> Any: raise ImportError( "HMRC target materialization requires policyengine-uk." ) from exc - from microcosm.frame.adapters.policyengine_uk import UK_ISA_INCOME_CONTRACT tables = engine_tables(frame) return UKSingleYearDataset( @@ -454,7 +462,7 @@ def _uk_single_year_dataset(frame: Frame) -> Any: benunit=tables["benunit"], household=tables["household"], fiscal_year=int(uk_time_period(frame)), - isa_income_contract=dict(UK_ISA_INCOME_CONTRACT), + isa_income_contract=uk_isa_income_contract(frame), ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_isa_statistics.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_isa_statistics.py new file mode 100644 index 0000000000..cf7c376739 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/hmrc_isa_statistics.py @@ -0,0 +1,516 @@ +"""HMRC's annual savings statistics, typed from the pinned Chronicle feed (microcosm#1415). + +The FRS and WAS record ISA balances and interest, not what people pay in during a +year, so a UK build that scores an ISA limit has to impute subscriptions. HMRC's +ISA tables (September 2026 release) are the only per-person evidence: Table 9.7 +counts the individuals subscribing in 2023-24 by total income, size of +subscription and the types they paid into, Table 9.8 by age and sex and Table 9.9 +by region. Table 9.4 gives the accounts and amounts subscribed by type to +2024-25, including the Lifetime ISA rows, which Tables 9.7 to 9.9 leave out. + +Every row here is copied verbatim from the pinned Chronicle consumer feed into one +vendored resource (``hmrc_isa_statistics_facts.json``, regenerated by +``tools/vendor_uk_ledger_facts.py``). The loader types those rows, refuses a +resource that was not vendored from the committed feed pin, and refuses a roster +that has drifted from the module constants: the bands must stay contiguous, keep +their GBP 12,000 edge and their exact GBP 20,000 band. + +Read the published basis before using these facts: + +* Tables 9.7 to 9.12 head their columns "(thousands)" but count individuals; the + feed carries them unscaled. Table 9.4 counts accounts, not individuals. +* Table 9.7's type blocks are the types paid into: the cash-only block plus the + "both" block reproduce Table 9.4's 2023-24 cash accounts, and the stocks-and- + shares-only block plus "both" its stocks-and-shares accounts. In the "both" + block the band is the combined amount. +* Innovative Finance ISAs are excluded from Tables 9.7 to 9.12, and so are + Lifetime ISAs (their counts are not in the cash or stocks-and-shares accounts). +* Table 9.7 has no amounts, and its bands do not reconcile with Table 9.4's + amounts subscribed: valued at band floors, cash-only subscribers alone come to + more than Table 9.4's cash amount. The stage keeps Table 9.7's people and uses + Table 9.4 for ratios only (microcosm#1415, María's ruling of 28 October 2026). +""" + +from __future__ import annotations + +import hashlib +import re +from collections.abc import Mapping +from dataclasses import dataclass +from importlib.resources import files +from typing import Any + +from microcosm.build.uk_runtime.ledger_fact_vendoring import ( + load_vendored_resource, + verify_vendored_resource_feed_identity, +) + +__all__ = [ + "HMRC_ISA_AGE_BAND_IDS", + "HMRC_ISA_BUILD_TAX_YEAR", + "HMRC_ISA_INCOME_BAND_IDS", + "HMRC_ISA_REGION_IDS", + "HMRC_ISA_STATISTICS_ARTIFACT_ROLE", + "HMRC_ISA_STATISTICS_RECORD_SETS", + "HMRC_ISA_STATISTICS_RESOURCE", + "HMRC_ISA_SUBSCRIPTION_BAND_IDS", + "HMRC_ISA_TYPES", + "HMRCISABand", + "HMRCISAStatistics", + "HMRCISAStatisticsError", + "check_isa_statistics_declaration", + "isa_band_bounds", + "lifetime_isa_paying_ages", + "load_hmrc_isa_statistics", +] + +#: The per-concern vendored resource; regenerated from the pinned Chronicle feed by +#: ``tools/vendor_uk_ledger_facts.py``, never edited by hand. +HMRC_ISA_STATISTICS_RESOURCE = "hmrc_isa_statistics_facts.json" + +#: Tax year (opening year) of the individual tables (9.7 to 9.12). +HMRC_ISA_INDIVIDUAL_TAX_YEAR = 2023 +#: Tax years of Table 9.4 the stages read: the individual tables' year and the +#: latest (provisional) year, the build's. +HMRC_ISA_ACCOUNT_TAX_YEARS: tuple[int, ...] = (2023, 2024) + +#: The build period the stages map the facts to: Table 9.4's latest year, to which +#: the subscriptions are grown and at which the Lifetime ISA holders are anchored. +HMRC_ISA_BUILD_TAX_YEAR = HMRC_ISA_ACCOUNT_TAX_YEARS[-1] +#: The artifact role under which a stage declares the vendored resource. +HMRC_ISA_STATISTICS_ARTIFACT_ROLE = "isa_statistics_facts" + +#: The Lifetime ISA opened on 6 April 2017 to people aged 18 to 39 and takes +#: payments until the holder turns 50. +LIFETIME_ISA_FIRST_TAX_YEAR = 2017 +LIFETIME_ISA_OPENING_AGES: tuple[int, int] = (18, 39) +LIFETIME_ISA_LAST_PAYING_AGE = 49 + + +def lifetime_isa_paying_ages(tax_year: int) -> tuple[int, int]: + """The ages at which someone can hold and pay into a Lifetime ISA in a tax year. + + An account opened at 39 on 6 April 2017 belongs to someone who turned 40 within + the year, so in ``tax_year`` its holder is at most 40 + (``tax_year`` - 2017), + 47 in 2024-25, until payments stop at 50 (review of microcosm#1421). + """ + + if tax_year < LIFETIME_ISA_FIRST_TAX_YEAR: + raise ValueError(f"no Lifetime ISA existed in tax year {tax_year}.") + oldest = LIFETIME_ISA_OPENING_AGES[1] + 1 + tax_year - LIFETIME_ISA_FIRST_TAX_YEAR + return LIFETIME_ISA_OPENING_AGES[0], min(oldest, LIFETIME_ISA_LAST_PAYING_AGE) + + +_PREFIX = "hmrc.isa_statistics_2026." +_NOTE_RECORD_SET = "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022" +_LISA_PREFIX = "hmrc.lisa_statistics_2026.table1a." + +#: Every Chronicle record set the stages read from the vendored resource, as the +#: manifests' ``verify_vendored_fact_resource`` operation declares them (prefixes end +#: in a dot; whole record sets do not). +HMRC_ISA_STATISTICS_RECORD_SETS: tuple[str, ...] = ( + f"{_PREFIX}table9_7.ty{HMRC_ISA_INDIVIDUAL_TAX_YEAR}.", + f"{_PREFIX}table9_8.ty{HMRC_ISA_INDIVIDUAL_TAX_YEAR}.", + f"{_PREFIX}table9_9.ty{HMRC_ISA_INDIVIDUAL_TAX_YEAR}.", + *( + f"{_PREFIX}table9_4.{measure}.ty{year}" + for year in HMRC_ISA_ACCOUNT_TAX_YEARS + for measure in ("accounts", "amounts") + ), + f"{_PREFIX}table9_6.ty2024", + _NOTE_RECORD_SET, + f"{_LISA_PREFIX}ty2024", + f"{_LISA_PREFIX}ty2025", +) + +#: Table 9.7's type blocks other than 'all': the types an individual paid into. +HMRC_ISA_TYPES: tuple[str, ...] = ( + "cash_only", + "stocks_and_shares_only", + "cash_and_stocks_and_shares", +) + +#: Table 9.7's total-income bands (GBP, SPI total income), ascending. +HMRC_ISA_INCOME_BAND_IDS: tuple[str, ...] = ( + "income_0_to_12570", + "income_12571_to_20000", + "income_20001_to_30000", + "income_30001_to_40000", + "income_40001_to_50270", + "income_50271_to_60000", + "income_60001_to_80000", + "income_80001_to_100000", + "income_100001_to_125140", + "income_125141_to_150000", + "income_150001_plus", +) + +#: Table 9.7's subscription bands (GBP subscribed in the year), ascending. The band +#: edge at GBP 12,000 is the one a cash limit of that size turns on; the top band is +#: exactly the GBP 20,000 annual limit. +HMRC_ISA_SUBSCRIPTION_BAND_IDS: tuple[str, ...] = ( + "subscription_1_to_2000", + "subscription_2001_to_4000", + "subscription_4001_to_6000", + "subscription_6001_to_8000", + "subscription_8001_to_10000", + "subscription_10001_to_12000", + "subscription_12001_to_14000", + "subscription_14001_to_16000", + "subscription_16001_to_18000", + "subscription_18001_to_19999", + "subscription_20000_to_20000", +) + +#: Table 9.8's age bands, ascending (age from the SPI). +HMRC_ISA_AGE_BAND_IDS: tuple[str, ...] = ( + "age_under_24", + "age_24_to_26", + "age_27_to_29", + "age_30_to_32", + "age_33_to_35", + "age_36_to_38", + "age_39_to_41", + "age_42_to_44", + "age_45_to_47", + "age_48_to_50", + "age_51_to_53", + "age_54_to_56", + "age_57_to_59", + "age_60_to_62", + "age_63_to_65", + "age_66_to_68", + "age_69_to_71", + "age_72_to_74", + "age_75_to_77", + "age_78_to_80", + "age_81_plus", +) + +#: Table 9.9's detail rows (row value ids): the English regions, the three other +#: nations and HMRC's overseas-and-unknown row. England and the UK are totals. +HMRC_ISA_REGION_IDS: tuple[str, ...] = ( + "E12000001", + "E12000002", + "E12000003", + "E12000004", + "E12000005", + "E12000006", + "E12000007", + "E12000008", + "E12000009", + "W92000004", + "S92000003", + "N92000002", + "overseas_and_unknown", +) + +_SEXES = ("male", "female", "all") +_COMPONENTS = ("cash", "stocks_and_shares", "innovative_finance", "lifetime", "all") +_TYPE_MEASURES = { + "cash_only": "cash_only_subscribers", + "stocks_and_shares_only": "stocks_and_shares_only_subscribers", + "cash_and_stocks_and_shares": "cash_and_stocks_and_shares_subscribers", + "all": "all_subscribers", +} + + +class HMRCISAStatisticsError(ValueError): + """Raised when the vendored ISA facts do not carry the published roster.""" + + +@dataclass(frozen=True) +class HMRCISABand: + """One published band: ``lower < x <= upper`` (``upper is None`` is open). + + The exact GBP 20,000 band is ``lower == upper == 20000``; a lower bound is + exclusive, so the first income band (GBP 0 to 12,570) carries ``lower = None``. + """ + + value_id: str + lower: float | None + upper: float | None + + @property + def is_point(self) -> bool: + return self.lower is not None and self.lower == self.upper + + +def isa_band_bounds(value_id: str) -> tuple[int, int | None]: + """Parse ``__to_`` or ``__plus`` into published edges.""" + + match = re.fullmatch(r"[a-z_]+?_(\d+)_to_(\d+)", value_id) + if match: + return int(match.group(1)), int(match.group(2)) + match = re.fullmatch(r"[a-z_]+?_(\d+)_plus", value_id) + if match: + return int(match.group(1)), None + raise HMRCISAStatisticsError(f"Unparseable ISA band id {value_id!r}.") + + +def _bands(value_ids: tuple[str, ...]) -> tuple[HMRCISABand, ...]: + """Contiguous bands from published integer edges (``lo`` to ``hi`` inclusive).""" + + bands = [] + previous_high: int | None = None + for value_id in value_ids: + low, high = isa_band_bounds(value_id) + if previous_high is not None and low not in (previous_high + 1, previous_high): + raise HMRCISAStatisticsError( + f"ISA band {value_id!r} does not continue from {previous_high}." + ) + if low == high: + bands.append(HMRCISABand(value_id, float(low), float(high))) + else: + if previous_high is not None: + lower: float | None = float(previous_high) + elif low == 0: + lower = None + else: + lower = float(low - 1) + upper = None if high is None else float(high) + bands.append(HMRCISABand(value_id, lower, upper)) + previous_high = high + return tuple(bands) + + +@dataclass(frozen=True) +class HMRCISAStatistics: + """The ISA facts the subscription and Lifetime ISA stages read. + + Keys use the published value ids. Counts are individuals (Tables 9.7 to 9.9), + accounts (Table 9.4) or pounds; nothing is aged, scaled or derived. + """ + + income_bands: tuple[HMRCISABand, ...] + subscription_bands: tuple[HMRCISABand, ...] + #: Table 9.7: (type, income band, subscription band) -> individuals; type in + #: HMRC_ISA_TYPES or 'all', band ids or 'all'. + subscribers_by_income: Mapping[tuple[str, str, str], float] + #: Table 9.7's holders who made no further subscription: (type, income band). + #: The all-holders column restates Table 9.10's, which the feed ports once. + non_subscribing_holders_by_income: Mapping[tuple[str, str], float] + #: Table 9.8: (type, sex, age band) -> individuals subscribing; age bands only, + #: as the feed ports the all-ages totals once, in Table 9.7. + subscribers_by_age_sex: Mapping[tuple[str, str, str], float] + #: Table 9.9: (type, region row id) -> individuals subscribing; the regions, + #: overseas and England (the UK total is Table 9.7's). + subscribers_by_region: Mapping[tuple[str, str], float] + #: Table 9.4: (component, tax year) -> accounts subscribed to / GBP subscribed. + accounts_subscribed: Mapping[tuple[str, int], float] + amount_subscribed: Mapping[tuple[str, int], float] + #: Table 9.6, 2024-25: component -> market value (GBP) at 5 April 2025. + market_value_2024: Mapping[str, float] + #: The impact note's 2022-23 shares of under-65 cash ISA subscribers (percent). + under_65_cash_subscriber_shares: Mapping[str, float] + #: Lifetime ISA Table 1a: tax year -> individuals withdrawing for a house purchase. + lisa_house_purchase_withdrawers: Mapping[int, float] + resource_sha256: str + source_fact_feed: Mapping[str, Any] + + def subscribers(self, isa_type: str, income: str, band: str) -> float: + return self.subscribers_by_income[(isa_type, income, band)] + + def evidence(self) -> dict[str, object]: + return { + "resource": HMRC_ISA_STATISTICS_RESOURCE, + "resource_sha256": self.resource_sha256, + "source_commit": self.source_fact_feed.get("source_commit"), + "individual_tax_year": HMRC_ISA_INDIVIDUAL_TAX_YEAR, + "account_tax_years": list(HMRC_ISA_ACCOUNT_TAX_YEARS), + } + + +def _load_payload(verify_feed_identity: bool) -> tuple[dict[str, Any], str]: + payload = load_vendored_resource(HMRC_ISA_STATISTICS_RESOURCE) + if verify_feed_identity: + verify_vendored_resource_feed_identity(payload) + digest = hashlib.sha256( + files("microcosm.build.uk").joinpath(HMRC_ISA_STATISTICS_RESOURCE).read_bytes() + ).hexdigest() + return payload, digest + + +def _row_values(rows: list[Mapping[str, Any]]) -> dict[str, float]: + values: dict[str, float] = {} + for row in rows: + record_id = str(row["source_record_id"]) + if record_id in values: + raise HMRCISAStatisticsError(f"Duplicate vendored ISA fact {record_id!r}.") + value = row.get("value") + if not isinstance(value, (int, float)) or isinstance(value, bool): + raise HMRCISAStatisticsError( + f"Vendored ISA fact {record_id!r} is not numeric." + ) + values[record_id] = float(value) + return values + + +def _take(values: Mapping[str, float], record_id: str) -> float: + try: + return values[record_id] + except KeyError: + raise HMRCISAStatisticsError( + f"The vendored ISA facts lack {record_id!r}; re-vendor from the pinned feed." + ) from None + + +def check_isa_statistics_declaration( + parameters: Mapping[str, Any], *, stage: str, period: int +) -> None: + """The runtime arm of a stage's ``verify_vendored_fact_resource`` operation. + + The declaration names this module's resource, artifact role and record sets, and + maps the facts to the frame's build period. + """ + + expected = { + "artifact_role": HMRC_ISA_STATISTICS_ARTIFACT_ROLE, + "resource": HMRC_ISA_STATISTICS_RESOURCE, + "record_sets": list(HMRC_ISA_STATISTICS_RECORD_SETS), + "mapped_build_period": HMRC_ISA_BUILD_TAX_YEAR, + } + declared = {key: parameters.get(key) for key in expected} + if declared != expected: + raise HMRCISAStatisticsError( + f"{stage}'s verify_vendored_fact_resource must declare {expected}, " + f"not {declared}." + ) + if int(period) != HMRC_ISA_BUILD_TAX_YEAR: + raise HMRCISAStatisticsError( + f"{stage} maps HMRC's ISA facts to build period {HMRC_ISA_BUILD_TAX_YEAR}, " + f"but the frame's period is {period}." + ) + + +def load_hmrc_isa_statistics( + *, + payload: Mapping[str, Any] | None = None, + verify_feed_identity: bool = True, +) -> HMRCISAStatistics: + """Type the vendored ISA facts, refusing any drift from the published roster. + + ``payload`` replaces the packaged resource (tests and the synthetic fixture); + the packaged resource is checked against the committed feed pin first. + """ + + if payload is None: + payload, digest = _load_payload(verify_feed_identity) + else: + if verify_feed_identity: + verify_vendored_resource_feed_identity(payload) + digest = "supplied" + values = _row_values(list(payload.get("rows", ()))) + income_bands = _bands(HMRC_ISA_INCOME_BAND_IDS) + subscription_bands = _bands(HMRC_ISA_SUBSCRIPTION_BAND_IDS) + if not any(band.upper == 12000 for band in subscription_bands): + raise HMRCISAStatisticsError( + "The subscription bands lost their GBP 12,000 edge." + ) + if not subscription_bands[-1].is_point or subscription_bands[-1].upper != 20000: + raise HMRCISAStatisticsError( + "The top subscription band must be exactly GBP 20,000." + ) + tax_year = f"ty{HMRC_ISA_INDIVIDUAL_TAX_YEAR}" + + subscribers_by_income: dict[tuple[str, str, str], float] = {} + non_subscribing: dict[tuple[str, str], float] = {} + for isa_type in ("all", *HMRC_ISA_TYPES): + record_set = f"{_PREFIX}table9_7.{tax_year}.{isa_type}" + for income in (*HMRC_ISA_INCOME_BAND_IDS, "all"): + for band in HMRC_ISA_SUBSCRIPTION_BAND_IDS: + subscribers_by_income[(isa_type, income, band)] = _take( + values, f"{record_set}.subscribers.{income}.{band}" + ) + subscribers_by_income[(isa_type, income, "all")] = _take( + values, f"{record_set}.subscribers.{income}.all_subscriptions" + ) + non_subscribing[(isa_type, income)] = _take( + values, f"{record_set}.holders.{income}.no_further_subscription" + ) + + subscribers_by_age_sex: dict[tuple[str, str, str], float] = {} + for sex in _SEXES: + record_set = f"{_PREFIX}table9_8.{tax_year}.{sex}" + for age in HMRC_ISA_AGE_BAND_IDS: + for isa_type, measure in _TYPE_MEASURES.items(): + subscribers_by_age_sex[(isa_type, sex, age)] = _take( + values, f"{record_set}.subscribers.{age}.{measure}" + ) + + subscribers_by_region: dict[tuple[str, str], float] = {} + for region in (*HMRC_ISA_REGION_IDS, "E92000001"): + for isa_type, measure in _TYPE_MEASURES.items(): + subscribers_by_region[(isa_type, region)] = _take( + values, f"{_PREFIX}table9_9.{tax_year}.subscribers.{region}.{measure}" + ) + + accounts: dict[tuple[str, int], float] = {} + amounts: dict[tuple[str, int], float] = {} + for year in HMRC_ISA_ACCOUNT_TAX_YEARS: + for component in _COMPONENTS: + accounts[(component, year)] = _take( + values, f"{_PREFIX}table9_4.accounts.ty{year}.{component}.accounts" + ) + amounts[(component, year)] = _take( + values, f"{_PREFIX}table9_4.amounts.ty{year}.{component}.amounts" + ) + + market_value = { + component: _take( + values, f"{_PREFIX}table9_6.ty2024.{component}.all.market_value" + ) + for component in ("stocks_and_shares", "cash", "innovative_finance", "all") + } + market_value["stocks_and_shares_cash_on_deposit"] = _take( + values, + f"{_PREFIX}table9_6.ty2024.stocks_and_shares.cash_on_deposit.market_value", + ) + shares = { + band: _take(values, f"{_NOTE_RECORD_SET}.{band}.share_of_cash_subscribers") + for band in ("under_12000", "over_12000") + } + lisa_withdrawers = { + year: _take( + values, + f"{_LISA_PREFIX}ty{year}.tax_year_{year}.house_purchase_withdrawers", + ) + for year in (2024, 2025) + } + statistics = HMRCISAStatistics( + income_bands=income_bands, + subscription_bands=subscription_bands, + subscribers_by_income=subscribers_by_income, + non_subscribing_holders_by_income=non_subscribing, + subscribers_by_age_sex=subscribers_by_age_sex, + subscribers_by_region=subscribers_by_region, + accounts_subscribed=accounts, + amount_subscribed=amounts, + market_value_2024=market_value, + under_65_cash_subscriber_shares=shares, + lisa_house_purchase_withdrawers=lisa_withdrawers, + resource_sha256=digest, + source_fact_feed=dict(payload.get("source_fact_feed", {})), + ) + _check_published_identities(statistics) + return statistics + + +def _check_published_identities(statistics: HMRCISAStatistics) -> None: + """Refuse a resource whose own tables disagree beyond publication rounding. + + These are HMRC's identities, not reconciliations: the 'all' block is the sum of + the type blocks, band by band and in the all-subscriptions column. The totals + Tables 9.8 and 9.9 restate from Table 9.7 are not in the feed: Chronicle checks + each restated copy against Table 9.7 and ports it once. + """ + + tolerance = 25.0 + cells = statistics.subscribers_by_income + for income in (*HMRC_ISA_INCOME_BAND_IDS, "all"): + for band in (*HMRC_ISA_SUBSCRIPTION_BAND_IDS, "all"): + parts = sum(cells[(t, income, band)] for t in HMRC_ISA_TYPES) + if abs(cells[("all", income, band)] - parts) > tolerance: + raise HMRCISAStatisticsError( + f"Table 9.7's all block differs from its type blocks at {income}/{band}." + ) 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 94ce046cf6..ab0dff656e 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 @@ -533,8 +533,8 @@ def assert_installed_engine_is_pinned() -> str: jobs and the H2 parity run them on the lock. Three things must hold. The distribution's version is the pinned one, - exactly, so a post-release or a local build of it (``2.162.14.post1``, - ``2.162.14+local``) is refused. ``policyengine_uk`` imports from that + exactly, so a post-release or a local build of it (``2.164.2.post1``, + ``2.164.2+local``) is refused. ``policyengine_uk`` imports from that distribution's own files, so a copy earlier on ``sys.path`` or an editable install is refused. And its ``__init__.py`` matches the distribution's RECORD, so a stale record beside other files is refused where the files 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 new file mode 100644 index 0000000000..c7145e9d17 --- /dev/null +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/isa_subscriptions.py @@ -0,0 +1,1777 @@ +"""ISA subscriptions per adult, imputed from HMRC's ISA tables (microcosm#1415). + +The FRS and the Wealth and Assets Survey record ISA balances, not what people pay +in during a year, so no survey donor exists for subscriptions. This stage, the +last in the UK roster, draws them from HMRC's published tables and writes five +person inputs policyengine-uk reads: ``cash_isa_balance``, +``stocks_and_shares_isa_balance``, ``cash_isa_subscriptions``, +``stocks_and_shares_isa_subscriptions`` and ``lifetime_isa_payments``. It rewrites +no household column, so every earlier reader of the ``was_wealth`` household +totals and every means test is unchanged. + +The steps, every draw keyed on identity, so reordering rows moves none. Adding +rows can move others' amounts: the rakes, the band walks and the growth factors +read the whole population. + +1. **Per-person balances.** Each household's ``cash_isa`` and + ``stocks_and_shares_isa`` (the ``was_wealth`` draws) are split across its adults + by the pattern of a WAS round-8 donor household with the same number of adults + and the same types held. Who in a donor household holds what is read jointly + from the person tab (``DVCISAvR8``, ``DVIISAvR8``), whose values must sum to the + household aggregates; person values sum exactly to the household draw. +2. **Who subscribes (2023-24).** For the types paid into (both, then stocks and + shares only, then cash only), eligible adults (18 or over, holding what they + pay into) are raked over income band x age band x sex x region to Tables 9.7, + 9.8 and 9.9, and selected inside each cell by a keyed systematic walk. A cell + with fewer eligible adults than its target is reported, and its excess moves to + cells of the same income band with room; nobody is made a holder. +3. **How much (2023-24).** Within each (type, income band) the subscribers walk + Table 9.7's band masses from the top, ranked by a key that blends their balance + rank with a keyed uniform, so larger holders tend to pay in more. Amounts are + spread evenly inside each band on weight-proportional strata; GBP 20,000 is a + point mass. +4. **The split for people paying into both** (María's ruling, 28 October 2026). + Each person's cash share follows their own balances; one common logit shift sets + the group's stocks-and-shares total to Table 9.4's stocks-and-shares amount less + the stocks-and-shares-only payments. Table 9.7's cash bands imply more cash than + Table 9.4 records, so the cash residual cannot anchor it; Table 9.7's people + are kept and the cash excess over Table 9.4 is reported. +5. **Growth to 2024-25.** Subscriber counts grow by Table 9.4's account growth by + type, an upper bound on people because from 6 April 2024 one person may pay into + several ISAs of one type. Amounts then scale by one factor per type to Table + 9.4's 2024-25 amounts; subscribers already at GBP 20,000 stay there, amounts + between GBP 18,000 and the limit move on a line up to it so no range below it + empties, and the rest are capped at it (María's rulings of 29 October 2026). +6. **Lifetime ISA payments (2024-25).** Holders aged 18 to 47, the ages that can + hold one in 2024-25, pay in amounts on (0, GBP 4,000], tilted so the total + equals Table 9.4's Lifetime ISA amount and ranked like step 3, within each + person's room under GBP 20,000; a holder with no room pays nothing. + +Receipts carry weighted aggregates and rounded counts only (microcosm#1164). +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +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 ( + _truncated_exponential_quantile, + _walk_bands, + uk_cgt_component_sum_income, +) +from microcosm.build.uk_runtime.frs_spine import read_pinned_tab +from microcosm.build.uk_runtime.hmrc_isa_statistics import ( + HMRC_ISA_AGE_BAND_IDS, + HMRC_ISA_BUILD_TAX_YEAR, + HMRC_ISA_INCOME_BAND_IDS, + HMRC_ISA_INDIVIDUAL_TAX_YEAR, + HMRC_ISA_REGION_IDS, + HMRC_ISA_SUBSCRIPTION_BAND_IDS, + HMRC_ISA_TYPES, + HMRCISAStatistics, + check_isa_statistics_declaration, + isa_band_bounds, + lifetime_isa_paying_ages, + load_hmrc_isa_statistics, +) +from microcosm.build.uk_runtime.national_frame import ( + uk_household_mass_conservation_receipt, + uk_household_weight_kind, + uk_national_frame, + uk_time_period, + validate_uk_national_frame, +) +from microcosm.build.uk_runtime.receipt_counts import rounded_count +from microcosm.calibrate.geography_constants import UK_REGION_TIER_ENUM +from microcosm.frame import Frame +from microcosm.frame.rules import assert_rules_engine_country + +__all__ = [ + "UK_ISA_SUBSCRIPTIONS_MASS_CONSERVATION_REASON", + "UK_ISA_SUBSCRIPTIONS_NONNEGATIVE_OUTPUT_COLUMNS", + "UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS", + "UK_ISA_SUBSCRIPTIONS_STAGE_NAME", + "ISASubscriptionsError", + "ISASubscriptionsSpec", + "UKISASubscriptionsStageTransform", + "WASISADonor", + "clean_was_isa_person_donor", + "impute_isa_subscriptions", + "split_household_isa_balances", +] + +UK_ISA_SUBSCRIPTIONS_STAGE_NAME = "isa_subscriptions" +#: The household-mass receipt this stage records (the manifest's +#: ``record_mass_conservation_receipt`` operation repeats it). +UK_ISA_SUBSCRIPTIONS_MASS_CONSERVATION_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." +) +UK_WAS_HOUSEHOLD_TAB_ROLE = "was_qrf_donor" +UK_WAS_PERSON_TAB_ROLE = "was_person_tab" + +CLEAN_WAS_ISA_PERSON_DONOR_KIND = "clean_was_isa_person_donor" +SPLIT_HOUSEHOLD_ISA_BALANCES_KIND = "split_household_isa_balances" +ALLOCATE_ISA_SUBSCRIBERS_KIND = "allocate_isa_subscribers" +DRAW_ISA_SUBSCRIPTION_AMOUNTS_KIND = "draw_isa_subscription_amounts" +SPLIT_BOTH_TYPE_SUBSCRIPTIONS_KIND = "split_both_type_subscriptions" +GROW_ISA_SUBSCRIPTIONS_KIND = "grow_isa_subscriptions" +DRAW_LIFETIME_ISA_PAYMENTS_KIND = "draw_lifetime_isa_payments" +VERIFY_VENDORED_FACT_RESOURCE_KIND = "verify_vendored_fact_resource" +CLASSIFY_ISA_FACTS_KIND = "classify_isa_facts_with_reviewed_fence" + +#: The reviewed fence over every vendored ISA fact (the manifest's +#: ``classify_isa_facts_with_reviewed_fence`` operation repeats it exactly). +UK_ISA_FACT_FENCE: dict[str, Any] = { + "calibration_permitted": False, + "fact_fence_id": "isa_facts_imputation_seeds_measured_only", + "fenced_fact_count": 1074, + "fenced_fact_composition": ( + "Table 9.7 2023-24 subscribers by income and subscription band and holders " + "making no further subscription, for all subscribers and each type paid " + "into (624); Table 9.8 by age and sex (325); Table 9.9 by region (70); " + "Table 9.4 accounts and amounts for 2023-24 and 2024-25 (24); Table 9.6 " + "2024-25 market values (19); the impact note's shares of under-65 cash " + "subscribers either side of GBP 12,000 (2); Lifetime ISA Table 1a for " + "2024-25 and 2025-26 (10)" + ), + "classification_rationale": ( + "The stage draws subscribers and amounts to these cells, so binding them in " + "the weight solve would be circular. The national build reports the " + "subscription cells after calibration, with the holders (Tables 9.10 to " + "9.12) and market values (Table 9.6) as independent checks; none blocks a " + "build." + ), + "adjudication": "https://github.com/PolicyEngine/microcosm/issues/1415", +} + +CASH_BALANCE = "cash_isa_balance" +STOCKS_AND_SHARES_BALANCE = "stocks_and_shares_isa_balance" +CASH_SUBSCRIPTIONS = "cash_isa_subscriptions" +STOCKS_AND_SHARES_SUBSCRIPTIONS = "stocks_and_shares_isa_subscriptions" +LIFETIME_ISA_PAYMENTS = "lifetime_isa_payments" +UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS: tuple[str, ...] = ( + CASH_BALANCE, + STOCKS_AND_SHARES_BALANCE, + CASH_SUBSCRIPTIONS, + STOCKS_AND_SHARES_SUBSCRIPTIONS, + LIFETIME_ISA_PAYMENTS, +) +UK_ISA_SUBSCRIPTIONS_NONNEGATIVE_OUTPUT_COLUMNS = UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS + +HOUSEHOLD_CASH = "cash_isa" +HOUSEHOLD_STOCKS_AND_SHARES = "stocks_and_shares_isa" +LIFETIME_ISA_HOLDER = "has_lifetime_isa" +LIFETIME_ISA_BALANCE = "lifetime_isa_balance" + +BOTH = "cash_and_stocks_and_shares" +STOCKS_AND_SHARES_ONLY = "stocks_and_shares_only" +CASH_ONLY = "cash_only" +#: Where 2024-25 amounts start to taper to the overall limit: the floor of Table +#: 9.7's last subscription band below it (GBP 18,000, María's ruling of 29 +#: October 2026). +TAPER_FROM_GBP = float(isa_band_bounds(HMRC_ISA_SUBSCRIPTION_BAND_IDS[-2])[0] - 1) + +#: Selection order: the scarcest holding pattern first, so a person who holds +#: both types is offered the 'both' class before a single-type one. +CLASS_ORDER: tuple[str, ...] = (BOTH, STOCKS_AND_SHARES_ONLY, CASH_ONLY) +if set(CLASS_ORDER) != set(HMRC_ISA_TYPES): + raise RuntimeError("CLASS_ORDER must cover exactly HMRC_ISA_TYPES.") + +#: Spine region enum name -> Table 9.9's row id. +_REGION_ROW: Mapping[str, str] = { + name: code for code, name in UK_REGION_TIER_ENUM.items() +} +_OVERSEAS = "overseas_and_unknown" +if set(_REGION_ROW.values()) | {_OVERSEAS} != set(HMRC_ISA_REGION_IDS): + raise RuntimeError("Table 9.9's rows must be the region tier plus overseas.") + + +class ISASubscriptionsError(ValueError): + """Raised when the inputs or the declaration do not carry what the stage needs.""" + + +class _KeyedOffset: + """One identity-keyed offset in the shape ``_walk_bands`` reads (``random()``).""" + + def __init__(self, value: float) -> None: + self._value = float(value) + + def random(self) -> float: + return self._value + + +def _keyed_offset(seed: int, salt: str, key: str) -> _KeyedOffset: + return _KeyedOffset(stable_identity_uniforms([key], seed=seed, salt=salt)[0]) + + +# -------------------------------------------------------------------------- +# Declaration +# -------------------------------------------------------------------------- + + +def _operation(stage: SourceStageSpec, kind: str) -> Mapping[str, Any]: + for operation in stage.operations: + if operation.kind == kind: + return dict(operation.parameters) + raise ISASubscriptionsError(f"{stage.stage} declares no {kind!r} operation.") + + +def _artifact(stage: SourceStageSpec, role: str) -> Mapping[str, Any]: + for artifact in stage.artifacts: + if artifact.get("role") == role: + return artifact + raise ISASubscriptionsError(f"{stage.stage} declares no {role!r} artifact.") + + +@dataclass(frozen=True) +class WASISAPersonColumns: + """The declared WAS person-tab names and codes the balance split reads.""" + + household_key: str + household_weight: str + person_number: str + is_dependent_child: str + age_band: str + cash_isa_value: str + stocks_and_shares_isa_value: str + cash_isa_aggregate: str + stocks_and_shares_isa_aggregate: str + aggregate_tolerance_gbp: float + non_dependent_code: int + + @classmethod + def from_parameters(cls, parameters: Mapping[str, Any]) -> WASISAPersonColumns: + person = parameters.get("person_columns") + aggregates = parameters.get("household_aggregates") + if not isinstance(person, Mapping) or not isinstance(aggregates, Mapping): + raise ISASubscriptionsError( + "clean_was_isa_person_donor needs person_columns and household_aggregates." + ) + try: + return cls( + household_key=str(parameters["household_key"]), + household_weight=str(parameters["household_weight"]), + person_number=str(person["person_number"]), + is_dependent_child=str(person["is_dependent_child"]), + age_band=str(person["age_band"]), + cash_isa_value=str(person["cash_isa_value"]), + stocks_and_shares_isa_value=str(person["stocks_and_shares_isa_value"]), + cash_isa_aggregate=str(aggregates["cash_isa"]), + stocks_and_shares_isa_aggregate=str( + aggregates["stocks_and_shares_isa"] + ), + aggregate_tolerance_gbp=float(parameters["aggregate_tolerance_gbp"]), + non_dependent_code=int(parameters["non_dependent_code"]), + ) + except KeyError as error: + raise ISASubscriptionsError( + f"clean_was_isa_person_donor lacks {error.args[0]!r}." + ) from None + + def person_raw_columns(self) -> tuple[str, ...]: + return ( + self.household_key, + self.person_number, + self.is_dependent_child, + self.age_band, + self.cash_isa_value, + self.stocks_and_shares_isa_value, + ) + + +@dataclass(frozen=True) +class ISASubscriptionsSpec: + """Every declared parameter of the stage, read once from the manifest.""" + + columns: WASISAPersonColumns + split_seed: int + split_salt: str + maximum_donor_adults: int + minimum_age: int + select_seed: int + select_salt: str + amount_seed: int + amount_salt: str + balance_rank_weight: float + share_floor: float + base_tax_year: int + build_tax_year: int + overall_limit_gbp: float + taper_from_gbp: float + lifetime_seed: int + lifetime_salt: str + lifetime_minimum_age: int + lifetime_maximum_age: int + lifetime_limit_gbp: float + + @classmethod + def from_stage(cls, stage: SourceStageSpec) -> ISASubscriptionsSpec: + clean = _operation(stage, CLEAN_WAS_ISA_PERSON_DONOR_KIND) + split = _operation(stage, SPLIT_HOUSEHOLD_ISA_BALANCES_KIND) + select = _operation(stage, ALLOCATE_ISA_SUBSCRIBERS_KIND) + amounts = _operation(stage, DRAW_ISA_SUBSCRIPTION_AMOUNTS_KIND) + both = _operation(stage, SPLIT_BOTH_TYPE_SUBSCRIPTIONS_KIND) + grow = _operation(stage, GROW_ISA_SUBSCRIPTIONS_KIND) + lifetime = _operation(stage, DRAW_LIFETIME_ISA_PAYMENTS_KIND) + if _operation(stage, CLASSIFY_ISA_FACTS_KIND) != UK_ISA_FACT_FENCE: + raise ISASubscriptionsError( + "the declared ISA fact fence differs from the reviewed one: no ISA " + "fact is a calibration target." + ) + for operation, outputs in ( + (split, [CASH_BALANCE, STOCKS_AND_SHARES_BALANCE]), + (amounts, [CASH_SUBSCRIPTIONS, STOCKS_AND_SHARES_SUBSCRIPTIONS]), + (lifetime, [LIFETIME_ISA_PAYMENTS]), + ): + if list(operation.get("outputs", ())) != outputs: + raise ISASubscriptionsError( + f"the declaration writes {operation.get('outputs')!r}, the stage " + f"writes {outputs!r}." + ) + if select.get("selection_order") != list(CLASS_ORDER): + raise ISASubscriptionsError( + f"allocate_isa_subscribers must select in the order {list(CLASS_ORDER)}." + ) + if both.get("anchor") != "table_9_4_stocks_and_shares_residual": + raise ISASubscriptionsError( + "the both-type split is anchored on Table 9.4's stocks-and-shares residual." + ) + if amounts.get("within_band") != "uniform": + raise ISASubscriptionsError("amounts are spread evenly inside each band.") + if ( + grow.get("people") != "table_9_4_account_growth" + or grow.get("amounts") != "table_9_4_amount_level_tapered_to_limit" + ): + raise ISASubscriptionsError( + "growth is people by Table 9.4's account growth and amounts to its " + "build-year amounts, holding and tapering to the overall limit." + ) + taper_from = float(grow["taper_from_gbp"]) + if taper_from != TAPER_FROM_GBP: + raise ISASubscriptionsError( + f"the taper starts at Table 9.7's last band floor below the limit, " + f"GBP {TAPER_FROM_GBP:,.0f}, not GBP {taper_from:,.0f}." + ) + years = (int(grow["from_tax_year"]), int(grow["to_tax_year"])) + if years != (HMRC_ISA_INDIVIDUAL_TAX_YEAR, HMRC_ISA_BUILD_TAX_YEAR): + raise ISASubscriptionsError( + f"growth runs from the individual tables' {HMRC_ISA_INDIVIDUAL_TAX_YEAR} " + f"to Table 9.4's {HMRC_ISA_BUILD_TAX_YEAR}, not {years}." + ) + paying_ages = lifetime_isa_paying_ages(HMRC_ISA_BUILD_TAX_YEAR) + declared_ages = (int(lifetime["minimum_age"]), int(lifetime["maximum_age"])) + if declared_ages != paying_ages: + raise ISASubscriptionsError( + f"Lifetime ISA payments in {HMRC_ISA_BUILD_TAX_YEAR}-" + f"{(HMRC_ISA_BUILD_TAX_YEAR + 1) % 100:02d} come from holders aged " + f"{paying_ages[0]} to {paying_ages[1]}, not {declared_ages}." + ) + weight = float(amounts["balance_rank_weight"]) + if not 0.0 <= weight <= 1.0: + raise ISASubscriptionsError("balance_rank_weight must lie in [0, 1].") + floor = float(both["share_floor"]) + if not 0.0 < floor < 0.5: + raise ISASubscriptionsError("share_floor must lie in (0, 0.5).") + return cls( + columns=WASISAPersonColumns.from_parameters(clean), + split_seed=int(split["seed"]), + split_salt=str(split["salt"]), + maximum_donor_adults=int(split["maximum_donor_adults"]), + minimum_age=int(select["minimum_age"]), + select_seed=int(select["seed"]), + select_salt=str(select["salt"]), + amount_seed=int(amounts["seed"]), + amount_salt=str(amounts["salt"]), + balance_rank_weight=weight, + share_floor=floor, + base_tax_year=int(grow["from_tax_year"]), + build_tax_year=int(grow["to_tax_year"]), + overall_limit_gbp=float(grow["overall_limit_gbp"]), + taper_from_gbp=taper_from, + lifetime_seed=int(lifetime["seed"]), + lifetime_salt=str(lifetime["salt"]), + lifetime_minimum_age=int(lifetime["minimum_age"]), + lifetime_maximum_age=int(lifetime["maximum_age"]), + lifetime_limit_gbp=float(lifetime["annual_limit_gbp"]), + ) + + +# -------------------------------------------------------------------------- +# Donor: who in a WAS household holds what +# -------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class WASISADonor: + """Donor households: adults in age order, their shares and the household weight.""" + + adult_count: np.ndarray + holds_cash: np.ndarray + holds_stocks_and_shares: np.ndarray + weight: np.ndarray + cash_shares: tuple[np.ndarray, ...] + stocks_and_shares_shares: tuple[np.ndarray, ...] + receipt: Mapping[str, Any] + + +def _lowered(table: pd.DataFrame, label: str) -> dict[str, str]: + lowered: dict[str, str] = {} + for column in table.columns: + key = str(column).lower() + if key in lowered: + raise ISASubscriptionsError(f"the WAS {label} tab repeats column {key!r}.") + lowered[key] = column + return lowered + + +def _numeric( + table: pd.DataFrame, lowered: Mapping[str, str], name: str, label: str +) -> pd.Series: + actual = lowered.get(name.lower()) + if actual is None: + raise ISASubscriptionsError( + f"the WAS {label} tab lacks the declared column {name!r}." + ) + return pd.to_numeric(table[actual], errors="coerce") + + +def clean_was_isa_person_donor( + person_raw: pd.DataFrame, + household_raw: pd.DataFrame, + *, + columns: WASISAPersonColumns, + maximum_adults: int, +) -> WASISADonor: + """Read each WAS household's adults and their cash and stocks-and-shares shares. + + The person values must sum to the household aggregates on every household or + the tab is refused, and a missing or negative value is refused rather than read + as zero. Adults are ordered oldest first (age band, then person number), the + order recipients are matched in. + """ + + person_lower = _lowered(person_raw, "person") + household_lower = _lowered(household_raw, "household") + keys = _numeric(household_raw, household_lower, columns.household_key, "household") + if keys.isna().any() or keys.duplicated().any(): + raise ISASubscriptionsError( + "the WAS household tab has a missing or repeated case." + ) + aggregates = pd.DataFrame( + { + "household_key": keys.to_numpy(), + "weight": _numeric( + household_raw, household_lower, columns.household_weight, "household" + ).to_numpy(float), + "cash": _numeric( + household_raw, household_lower, columns.cash_isa_aggregate, "household" + ) + .fillna(0.0) + .to_numpy(float), + "stocks_and_shares": _numeric( + household_raw, + household_lower, + columns.stocks_and_shares_isa_aggregate, + "household", + ) + .fillna(0.0) + .to_numpy(float), + } + ).set_index("household_key") + person = pd.DataFrame( + { + "household_key": _numeric( + person_raw, person_lower, columns.household_key, "person" + ), + "person_number": _numeric( + person_raw, person_lower, columns.person_number, "person" + ), + "dependent": _numeric( + person_raw, person_lower, columns.is_dependent_child, "person" + ), + "age_band": _numeric(person_raw, person_lower, columns.age_band, "person"), + "cash": _numeric( + person_raw, person_lower, columns.cash_isa_value, "person" + ), + "stocks_and_shares": _numeric( + person_raw, person_lower, columns.stocks_and_shares_isa_value, "person" + ), + } + ) + if person["household_key"].isna().any(): + raise ISASubscriptionsError("the WAS person tab has a missing case number.") + if person.duplicated(["household_key", "person_number"]).any(): + raise ISASubscriptionsError("the WAS person tab repeats a (case, person) pair.") + if not person["household_key"].isin(aggregates.index).all(): + raise ISASubscriptionsError("every WAS person must map to a household row.") + for column in ("cash", "stocks_and_shares"): + values = person[column] + if values.isna().any() or (values < 0).any(): + raise ISASubscriptionsError( + f"the WAS person {column} ISA values must be present and non-negative." + ) + totals = values.groupby(person["household_key"]).sum() + published = aggregates[column].reindex(totals.index).fillna(0.0) + if float((totals - published).abs().max()) > columns.aggregate_tolerance_gbp: + raise ISASubscriptionsError( + f"the WAS person {column} ISA values do not sum to the household aggregate." + ) + adults = person.loc[person["dependent"] == columns.non_dependent_code].copy() + adults = adults.sort_values( + ["household_key", "age_band", "person_number"], + ascending=[True, False, True], + kind="stable", + ) + counts, cash_flags, stock_flags, weights, cash_shares, stock_shares = ( + [], + [], + [], + [], + [], + [], + ) + for key, group in adults.groupby("household_key", sort=True): + household_total_cash = float(group["cash"].sum()) + household_total_stocks = float(group["stocks_and_shares"].sum()) + if household_total_cash <= 0 and household_total_stocks <= 0: + continue + group = group.head(maximum_adults) + counts.append(len(group)) + cash_flags.append(household_total_cash > 0) + stock_flags.append(household_total_stocks > 0) + weights.append(float(aggregates.at[key, "weight"])) + cash_shares.append(_shares(group["cash"].to_numpy(float))) + stock_shares.append(_shares(group["stocks_and_shares"].to_numpy(float))) + weight_array = np.asarray(weights, dtype=float) + if (weight_array <= 0).any() or not np.isfinite(weight_array).all(): + raise ISASubscriptionsError( + "every WAS donor household needs a positive weight." + ) + count_array = np.asarray(counts, dtype=np.int64) + cash_array = np.asarray(cash_flags, dtype=bool) + stock_array = np.asarray(stock_flags, dtype=bool) + receipt = { + "donor_households": rounded_count(len(count_array)), + "by_pattern": { + pattern: rounded_count(int(mask.sum())) + for pattern, mask in ( + ("cash_only", cash_array & ~stock_array), + ("stocks_and_shares_only", ~cash_array & stock_array), + ("both", cash_array & stock_array), + ) + }, + "by_adult_count": { + str(count): rounded_count(int((count_array == count).sum())) + for count in range(1, maximum_adults + 1) + }, + "aggregate_pin_tolerance_gbp": columns.aggregate_tolerance_gbp, + } + return WASISADonor( + adult_count=count_array, + holds_cash=cash_array, + holds_stocks_and_shares=stock_array, + weight=weight_array, + cash_shares=tuple(cash_shares), + stocks_and_shares_shares=tuple(stock_shares), + receipt=receipt, + ) + + +def _shares(values: np.ndarray) -> np.ndarray: + total = float(values.sum()) + if total <= 0: + return np.zeros(len(values), dtype=float) + return values / total + + +# -------------------------------------------------------------------------- +# Step 1: per-person balances +# -------------------------------------------------------------------------- + + +def split_household_isa_balances( + *, + person: pd.DataFrame, + household: pd.DataFrame, + donor: WASISADonor, + seed: int, + salt: str, + minimum_age: int, + maximum_adults: int, +) -> tuple[np.ndarray, np.ndarray, dict[str, Any]]: + """Split each household's two ISA totals over its adults, exactly. + + A household holding a type takes a donor household with the same number of + adults (capped) and the same types held, chosen by its keyed uniform in + proportion to donor weight; with none, the adult count steps down. The donor's + shares go to the recipient's adults oldest first; any remainder lands on the + adult with the largest share, so person values sum to the household value. + """ + + person_ids = person["person_id"].to_numpy() + person_households = person["person_household_id"].to_numpy() + ages = pd.to_numeric(person["age"], errors="coerce").fillna(0.0).to_numpy(float) + cash_total = pd.to_numeric(household[HOUSEHOLD_CASH], errors="coerce").fillna(0.0) + stock_total = pd.to_numeric( + household[HOUSEHOLD_STOCKS_AND_SHARES], errors="coerce" + ).fillna(0.0) + if (cash_total < 0).any() or (stock_total < 0).any(): + raise ISASubscriptionsError("household ISA totals must be non-negative.") + household_ids = household["household_id"].to_numpy() + uniforms = stable_identity_uniforms(household_ids, seed=seed, salt=salt) + cash = np.zeros(len(person), dtype=float) + stocks = np.zeros(len(person), dtype=float) + positions = pd.Series(np.arange(len(person)), index=person.index) + members = pd.Series(positions.to_numpy()).groupby(person_households).apply(list) + pools: dict[tuple[int, bool, bool], np.ndarray] = {} + for count in range(1, maximum_adults + 1): + for holds_cash in (False, True): + for holds_stocks in (False, True): + mask = ( + (donor.adult_count == count) + & (donor.holds_cash == holds_cash) + & (donor.holds_stocks_and_shares == holds_stocks) + ) + pools[(count, holds_cash, holds_stocks)] = np.flatnonzero(mask) + stepped_down = 0 + without_adult = 0 + without_donor = 0 + split = 0 + for index, household_id in enumerate(household_ids): + cash_value = float(cash_total.iloc[index]) + stock_value = float(stock_total.iloc[index]) + if cash_value <= 0 and stock_value <= 0: + continue + rows = np.asarray(members.get(household_id, []), dtype=np.int64) + if rows.size == 0: + raise ISASubscriptionsError("a household holding ISAs has no persons.") + adults = rows[ages[rows] >= minimum_age] + if adults.size == 0: + without_adult += 1 + adults = rows + order = np.lexsort((person_ids[adults], -ages[adults])) + adults = adults[order] + key = (min(len(adults), maximum_adults), cash_value > 0, stock_value > 0) + pool = pools.get(key, np.empty(0, dtype=np.int64)) + while pool.size == 0 and key[0] > 1: + key = (key[0] - 1, key[1], key[2]) + pool = pools[key] + stepped_down += 1 + if pool.size == 0: + without_donor += 1 + cash_shares = np.zeros(len(adults)) + stock_shares = np.zeros(len(adults)) + cash_shares[0] = stock_shares[0] = 1.0 + else: + cumulative = np.cumsum(donor.weight[pool]) + chosen = pool[ + min( + int(np.searchsorted(cumulative, uniforms[index] * cumulative[-1])), + pool.size - 1, + ) + ] + cash_shares = _fit_shares(donor.cash_shares[chosen], len(adults)) + stock_shares = _fit_shares( + donor.stocks_and_shares_shares[chosen], len(adults) + ) + cash[adults] = _exact_split(cash_value, cash_shares) + stocks[adults] = _exact_split(stock_value, stock_shares) + split += 1 + receipt = { + "households_split": rounded_count(split), + "households_without_adult_split_over_all_persons": rounded_count(without_adult), + "households_matched_on_fewer_adults": rounded_count(stepped_down), + "households_without_donor_given_to_eldest": rounded_count(without_donor), + "maximum_donor_adults": maximum_adults, + } + return cash, stocks, receipt + + +def _fit_shares(shares: np.ndarray, adults: int) -> np.ndarray: + fitted = np.zeros(adults, dtype=float) + take = min(adults, len(shares)) + fitted[:take] = shares[:take] + total = float(fitted.sum()) + if total <= 0: + fitted[:] = 0.0 + fitted[0] = 1.0 + return fitted + return fitted / total + + +def _exact_split(total: float, shares: np.ndarray) -> np.ndarray: + values = total * shares + if total > 0: + largest = int(np.argmax(shares)) + values[largest] = total - (values.sum() - values[largest]) + return values + + +# -------------------------------------------------------------------------- +# Recipients +# -------------------------------------------------------------------------- + + +def _income_band_ids(total_income: np.ndarray) -> np.ndarray: + edges = [isa_band_bounds(band)[1] for band in HMRC_ISA_INCOME_BAND_IDS[:-1]] + index = np.searchsorted(np.asarray(edges, dtype=float), total_income, side="left") + return np.asarray(HMRC_ISA_INCOME_BAND_IDS, dtype=object)[index] + + +def _age_band_ids(ages: np.ndarray) -> np.ndarray: + uppers = [] + for band in HMRC_ISA_AGE_BAND_IDS[:-1]: + uppers.append(23 if band == "age_under_24" else isa_band_bounds(band)[1]) + index = np.searchsorted( + np.asarray(uppers, dtype=float), np.floor(ages), side="left" + ) + return np.asarray(HMRC_ISA_AGE_BAND_IDS, dtype=object)[index] + + +def _enum_name(value: object) -> str: + name = getattr(value, "name", value) + return str(name) + + +def recipient_table(frame: Frame) -> pd.DataFrame: + """One row per person: identity, weight, the cell attributes and LISA holding.""" + + person = frame.table("person") + household = frame.table("household") + for column in ("person_id", "person_household_id", "age", "gender"): + if column not in person: + raise ISASubscriptionsError(f"the person table lacks {column!r}.") + for column in ( + "household_id", + "region", + HOUSEHOLD_CASH, + HOUSEHOLD_STOCKS_AND_SHARES, + ): + if column not in household: + raise ISASubscriptionsError(f"the household table lacks {column!r}.") + for column in (LIFETIME_ISA_HOLDER, LIFETIME_ISA_BALANCE): + if column not in person: + raise ISASubscriptionsError( + f"the person table lacks {column!r} (was_lisa runs first)." + ) + weights = pd.Series( + np.asarray(frame.weights_for("household").values, dtype=float), + index=household["household_id"].to_numpy(), + ) + regions = household.set_index("household_id")["region"].map(_enum_name) + unknown = sorted(set(regions) - set(_REGION_ROW)) + if unknown: + raise ISASubscriptionsError(f"unrecognised household regions {unknown}.") + household_ids = person["person_household_id"].to_numpy() + ages = pd.to_numeric(person["age"], errors="coerce").fillna(0.0).to_numpy(float) + total_income = uk_cgt_component_sum_income(person) + sex = person["gender"].map(_enum_name).str.upper() + if not sex.isin(("MALE", "FEMALE")).all(): + raise ISASubscriptionsError("person gender must be MALE or FEMALE.") + return pd.DataFrame( + { + "person_id": person["person_id"].to_numpy(), + "household_id": household_ids, + "weight": weights.reindex(household_ids).to_numpy(float), + "age": ages, + "sex": sex.str.lower().to_numpy(), + "total_income": total_income, + "income_band": _income_band_ids(total_income), + "age_band": _age_band_ids(ages), + "region": regions.reindex(household_ids).map(_REGION_ROW).to_numpy(), + "holds_lifetime_isa": person[LIFETIME_ISA_HOLDER].astype(bool).to_numpy(), + "lifetime_isa_balance": pd.to_numeric( + person[LIFETIME_ISA_BALANCE], errors="coerce" + ) + .fillna(0.0) + .to_numpy(float), + }, + index=person.index, + ) + + +# -------------------------------------------------------------------------- +# Step 2: who subscribes +# -------------------------------------------------------------------------- + + +def _margins( + statistics: HMRCISAStatistics, isa_type: str, scale: float = 1.0 +) -> tuple[dict[str, float], dict[tuple[str, str], float], dict[str, float]]: + """Table 9.7's income, 9.8's age-by-sex and 9.9's region margins, on 9.7's total. + + Table 9.9's overseas-and-unknown row has no spine cell and is dropped; 9.8 and + 9.9 are scaled to Table 9.7's total, so the three margins agree. + """ + + income = { + band: statistics.subscribers(isa_type, band, "all") * scale + for band in HMRC_ISA_INCOME_BAND_IDS + } + total = sum(income.values()) + age_sex = { + (age, sex): statistics.subscribers_by_age_sex[(isa_type, sex, age)] + for age in HMRC_ISA_AGE_BAND_IDS + for sex in ("male", "female") + } + age_total = sum(age_sex.values()) + region = { + row: statistics.subscribers_by_region[(isa_type, row)] + for row in HMRC_ISA_REGION_IDS + if row != _OVERSEAS + } + region_total = sum(region.values()) + return ( + income, + {key: value * total / age_total for key, value in age_sex.items()}, + {key: value * total / region_total for key, value in region.items()}, + ) + + +def _rake( + seed_mass: np.ndarray, + labels: tuple[np.ndarray, np.ndarray, np.ndarray], + margins: tuple[Mapping[Any, float], Mapping[Any, float], Mapping[Any, float]], + *, + iterations: int = 500, + tolerance: float = 1e-10, +) -> np.ndarray: + """Iterative proportional fitting of cell masses to three margins.""" + + mass = seed_mass.astype(float).copy() + for _ in range(iterations): + worst = 0.0 + for label, margin in zip(labels, margins, strict=True): + current = pd.Series(mass).groupby(label).sum() + for key, target in margin.items(): + achieved = float(current.get(key, 0.0)) + if achieved <= 0: + continue + factor = target / achieved + worst = max(worst, abs(factor - 1.0)) + mass[label == key] *= factor + if worst < tolerance: + break + return mass + + +def select_subscribers( + recipients: pd.DataFrame, + eligible: np.ndarray, + margins: tuple[Mapping[Any, float], Mapping[Any, float], Mapping[Any, float]], + *, + seed: int, + salt: str, +) -> tuple[np.ndarray, dict[str, Any]]: + """Rake eligible mass to the margins and walk each cell to its share.""" + + selected = np.zeros(len(recipients), dtype=bool) + if not eligible.any(): + return selected, { + "selected_weight": 0.0, + "shortfall_weight": sum(margins[0].values()), + } + frame = recipients.loc[eligible] + weights = frame["weight"].to_numpy(float) + cells = frame.groupby(["income_band", "age_band", "sex", "region"], sort=True)[ + "weight" + ].sum() + index = cells.index + seed_mass = cells.to_numpy(float) + income = np.asarray(index.get_level_values(0), dtype=object) + age_sex = np.asarray( + list(zip(index.get_level_values(1), index.get_level_values(2), strict=True)), + dtype=object, + ) + age_sex_keys = np.asarray([f"{age}|{sex}" for age, sex in age_sex], dtype=object) + region = np.asarray(index.get_level_values(3), dtype=object) + age_sex_margin = {f"{age}|{sex}": value for (age, sex), value in margins[1].items()} + raked = _rake( + seed_mass, + (income, age_sex_keys, region), + (margins[0], age_sex_margin, margins[2]), + ) + target = np.minimum(raked, seed_mass) + shortfall = raked - target + # Move each income band's excess onto its cells with room, in proportion to room. + moved = 0.0 + for band in np.unique(income): + in_band = income == band + excess = float(shortfall[in_band].sum()) + room = seed_mass[in_band] - target[in_band] + if excess <= 0 or float(room.sum()) <= 0: + continue + take = min(excess, float(room.sum())) + target[in_band] += room / float(room.sum()) * take + moved += take + unmet = float(shortfall.sum()) - moved + uniforms = stable_identity_uniforms( + frame["person_id"].to_numpy(), seed=seed, salt=salt + ) + positions = np.flatnonzero(eligible) + cell_of = pd.MultiIndex.from_frame( + frame[["income_band", "age_band", "sex", "region"]] + ) + codes = index.get_indexer(cell_of) + person_ids = frame["person_id"].to_numpy() + # Each cell's rows in ascending position, as np.flatnonzero(codes == cell) would + # give them, from one stable sort instead of a scan per cell. + by_cell = np.argsort(codes, kind="stable") + cell_ids = np.arange(len(index)) + starts = np.searchsorted(codes[by_cell], cell_ids, side="left") + ends = np.searchsorted(codes[by_cell], cell_ids, side="right") + for cell in range(len(index)): + if target[cell] <= 0: + continue + rows = by_cell[starts[cell] : ends[cell]] + ranked = rows[np.lexsort((person_ids[rows], uniforms[rows]))] + members: dict[int, list[np.ndarray]] = {} + assigned, _ = _walk_bands( + ranked=ranked, + weights=weights[ranked], + boundaries=[(0, float(target[cell]))], + rng=_keyed_offset(seed, f"{salt}:cell", "|".join(map(str, index[cell]))), + members=members, + ) + selected[positions[ranked[assigned]]] = True + achieved_income = recipients.loc[selected].groupby("income_band")["weight"].sum() + receipt = { + "target_weight": float(sum(margins[0].values())), + "selected_weight": float(recipients.loc[selected, "weight"].sum()), + "eligible_weight": float(weights.sum()), + "cells": int(len(index)), + "cells_short_of_eligible_adults": int((shortfall > 1e-9).sum()), + "shortfall_weight_moved_within_income_band": moved, + "shortfall_weight_unmet": max(unmet, 0.0), + "income_margin_max_abs_relative_error": _max_relative_error( + margins[0], achieved_income + ), + "selected_records": rounded_count(int(selected.sum())), + } + return selected, receipt + + +def _max_relative_error( + targets: Mapping[str, float], achieved: pd.Series +) -> float | None: + errors = [ + abs(float(achieved.get(key, 0.0)) - value) / value + for key, value in targets.items() + if value > 0 + ] + return max(errors) if errors else None + + +# -------------------------------------------------------------------------- +# Step 3: how much +# -------------------------------------------------------------------------- + + +def _subscription_bands() -> list[tuple[float, float]]: + """(exclusive lower, upper) of each band; the top band is the point 20,000.""" + + bands = [] + previous = 0.0 + for band in HMRC_ISA_SUBSCRIPTION_BAND_IDS: + low, high = isa_band_bounds(band) + if low == high: + bands.append((float(low), float(high))) + else: + upper = float(high) + 1.0 if high == 19999 else float(high) + bands.append((previous, upper)) + previous = float(high) + return bands + + +SUBSCRIPTION_BANDS = _subscription_bands() + + +def _weighted_rank(values: np.ndarray, weights: np.ndarray) -> np.ndarray: + """Each value's weighted mid-rank in [0, 1] (ties share a rank).""" + + order = np.argsort(values, kind="stable") + cumulative = np.cumsum(weights[order]) + total = float(cumulative[-1]) if len(cumulative) else 0.0 + ranks = np.empty(len(values), dtype=float) + if total <= 0: + ranks[:] = 0.5 + return ranks + ranks[order] = (cumulative - weights[order] / 2.0) / total + return ranks + + +def draw_band_amounts( + rows: np.ndarray, + *, + weights: np.ndarray, + balance: np.ndarray, + person_ids: np.ndarray, + band_shares: np.ndarray, + balance_rank_weight: float, + seed: int, + salt: str, + key: str, +) -> np.ndarray: + """Amounts for one (type, income band): Table 9.7's band masses, evenly within bands.""" + + amounts = np.zeros(len(rows), dtype=float) + if len(rows) == 0: + return amounts + row_weights = weights[rows] + total = float(row_weights.sum()) + shares = np.asarray(band_shares, dtype=float) + if total <= 0 or shares.sum() <= 0: + return amounts + masses = shares / shares.sum() * total + uniforms = stable_identity_uniforms( + person_ids[rows], seed=seed, salt=f"{salt}:rank" + ) + rank_key = ( + balance_rank_weight * _weighted_rank(balance[rows], row_weights) + + (1.0 - balance_rank_weight) * uniforms + ) + ranked = np.lexsort((person_ids[rows], -rank_key)) + members: dict[int, list[np.ndarray]] = {} + boundaries = [ + (band, float(masses[band])) for band in range(len(masses) - 1, -1, -1) + ] + assigned, _ = _walk_bands( + ranked=ranked, + weights=row_weights[ranked], + boundaries=boundaries, + rng=_keyed_offset(seed, f"{salt}:walk", key), + members=members, + ) + leftover = ranked[~assigned] + if leftover.size: + members.setdefault(0, []).append(leftover) + for band, groups in members.items(): + positions = np.concatenate(groups) + lower, upper = SUBSCRIPTION_BANDS[band] + if lower == upper: + amounts[positions] = upper + continue + ordered = positions[np.argsort(rank_key[positions], kind="stable")] + cumulative = np.cumsum(row_weights[ordered]) + tops = cumulative / cumulative[-1] + bottoms = np.concatenate(([0.0], tops[:-1])) + jitter = stable_identity_uniforms( + [f"{key}:{band}"], seed=seed, salt=f"{salt}:jitter" + )[0] + quantiles = bottoms + (0.05 + 0.9 * jitter) * (tops - bottoms) + amounts[ordered] = lower + quantiles * (upper - lower) + return amounts + + +# -------------------------------------------------------------------------- +# Step 4: the split for people paying into both +# -------------------------------------------------------------------------- + + +def _share_logit(balance_share: np.ndarray, share_floor: float) -> np.ndarray: + base = np.clip(balance_share, share_floor, 1.0 - share_floor) + return np.log(base / (1.0 - base)) + + +def _shifted_share(logit: np.ndarray, shift: float, share_floor: float) -> np.ndarray: + """The shifted cash share, kept inside [floor, 1 - floor] so both parts stay positive.""" + + return np.clip( + 1.0 / (1.0 + np.exp(-(logit + shift))), share_floor, 1.0 - share_floor + ) + + +def split_both_type_amounts( + combined: np.ndarray, + cash_share_of_balance: np.ndarray, + weights: np.ndarray, + *, + stocks_and_shares_target: float, + share_floor: float, +) -> tuple[np.ndarray, dict[str, Any]]: + """Each person's cash share: their balance share plus one common logit shift.""" + + logit = _share_logit(cash_share_of_balance, share_floor) + weighted = combined * weights + + def stocks_total(shift: float) -> float: + share = _shifted_share(logit, shift, share_floor) + return float((weighted * (1.0 - share)).sum()) + + low, high = -40.0, 40.0 + lowest, highest = stocks_total(high), stocks_total(low) + saturated = None + if stocks_and_shares_target <= lowest: + shift, saturated = high, "stocks_and_shares_residual_below_reach" + elif stocks_and_shares_target >= highest: + shift, saturated = low, "stocks_and_shares_residual_above_reach" + else: + for _ in range(200): + mid = (low + high) / 2.0 + if stocks_total(mid) > stocks_and_shares_target: + low = mid + else: + high = mid + shift = (low + high) / 2.0 + share = _shifted_share(logit, shift, share_floor) + total = float(weighted.sum()) + receipt = { + "logit_shift": shift, + "saturated": saturated, + "stocks_and_shares_target_gbp": stocks_and_shares_target, + "stocks_and_shares_achieved_gbp": stocks_total(shift), + "stocks_and_shares_share_of_group": ( + stocks_total(shift) / total if total > 0 else None + ), + } + return share, receipt + + +# -------------------------------------------------------------------------- +# Step 5: growth and step 6: Lifetime ISA payments +# -------------------------------------------------------------------------- + + +def _capped( + cash: np.ndarray, stocks: np.ndarray, limit: float +) -> tuple[np.ndarray, np.ndarray]: + combined = cash + stocks + over = combined > limit + if over.any(): + factor = np.ones(len(cash)) + factor[over] = limit / combined[over] + return cash * factor, stocks * factor + return cash, stocks + + +def scale_to_build_year_amounts( + cash: np.ndarray, + stocks: np.ndarray, + weights: np.ndarray, + *, + cash_target: float, + stocks_target: float, + limit: float, + taper_from: float, +) -> tuple[np.ndarray, np.ndarray, dict[str, Any]]: + """One factor per type, solved to both totals, holding and tapering to the limit. + + Subscribers already paying the overall limit keep their amounts, so a factor + below one moves nobody off it (María's ruling of 29 October 2026). Everyone + else scales by the type's factor, except that a person whose combined amount + lies between ``taper_from`` and the limit moves on a line from their scaled + ``taper_from`` to the limit (María's ruling of 29 October 2026). One factor + up to the limit would empty the range just below it whenever it fell under + one (review of microcosm#1421). The scaled amounts are capped at the limit, + pro rata across the two types. + + The line rises only while a person's factor is below ``limit / taper_from`` + (about 1.11 from GBP 18,000). Above it the scaled ``taper_from`` already + passes the limit, so the cap puts everyone above ``limit / factor`` at the + limit, exactly as the plain factor would, and the taper does nothing. The + totals still rise with each factor, so the bisection holds either way + (Vahid's review of microcosm#1421). 2024-25's cash factor is about 0.88. + """ + + if not 0 < taper_from < limit: + raise ISASubscriptionsError("the taper starts inside (0, limit).") + combined = cash + stocks + at_limit = combined >= limit - 1e-6 + tapered = (combined > taper_from) & ~at_limit + + def scaled(cash_factor: float, stocks_factor: float) -> tuple[np.ndarray, ...]: + moved_cash, moved_stocks = cash * cash_factor, stocks * stocks_factor + if tapered.any(): + moved = moved_cash + moved_stocks + factor = moved[tapered] / combined[tapered] + start = factor * taper_from + target = start + (combined[tapered] - taper_from) * (limit - start) / ( + limit - taper_from + ) + ratio = np.ones(len(cash)) + ratio[tapered] = np.divide( + target, + moved[tapered], + out=np.ones(int(tapered.sum())), + where=moved[tapered] > 0, + ) + moved_cash, moved_stocks = moved_cash * ratio, moved_stocks * ratio + moved_cash, moved_stocks = _capped(moved_cash, moved_stocks, limit) + return ( + np.where(at_limit, cash, moved_cash), + np.where(at_limit, stocks, moved_stocks), + ) + + def totals(cash_factor: float, stocks_factor: float) -> tuple[float, float]: + new_cash, new_stocks = scaled(cash_factor, stocks_factor) + return float((new_cash * weights).sum()), float((new_stocks * weights).sum()) + + def solve(target: float, other: float, which: int) -> float: + low, high = 0.0, 50.0 + for _ in range(100): + mid = (low + high) / 2.0 + achieved = totals(mid, other)[0] if which == 0 else totals(other, mid)[1] + if achieved < target: + low = mid + else: + high = mid + return (low + high) / 2.0 + + cash_factor, stocks_factor = 1.0, 1.0 + for _ in range(50): + new_cash_factor = solve(cash_target, stocks_factor, 0) + new_stocks_factor = solve(stocks_target, new_cash_factor, 1) + converged = ( + abs(new_cash_factor - cash_factor) < 1e-10 + and abs(new_stocks_factor - stocks_factor) < 1e-10 + ) + cash_factor, stocks_factor = new_cash_factor, new_stocks_factor + if converged: + break + scaled_cash, scaled_stocks = scaled(cash_factor, stocks_factor) + achieved = totals(cash_factor, stocks_factor) + receipt = { + "cash_factor": cash_factor, + "stocks_and_shares_factor": stocks_factor, + "cash_target_gbp": cash_target, + "cash_achieved_gbp": achieved[0], + "stocks_and_shares_target_gbp": stocks_target, + "stocks_and_shares_achieved_gbp": achieved[1], + "held_at_overall_limit_records": rounded_count(int(at_limit.sum())), + "taper_from_gbp": taper_from, + # Above this factor the taper is inactive: the cap alone binds. + "taper_bound_factor": limit / taper_from, + "tapered_records": rounded_count(int(tapered.sum())), + "at_overall_limit_records": rounded_count( + int(((scaled_cash + scaled_stocks) >= limit - 1e-6).sum()) + ), + } + return scaled_cash, scaled_stocks, receipt + + +def draw_lifetime_isa_payments( + recipients: pd.DataFrame, + *, + headroom: np.ndarray, + target_gbp: float, + spec: ISASubscriptionsSpec, +) -> tuple[np.ndarray, dict[str, Any]]: + """Lifetime ISA payments on (0, limit], tilted to Table 9.4's amount, in headroom.""" + + payments = np.zeros(len(recipients), dtype=float) + ages = recipients["age"].to_numpy(float) + payers = ( + recipients["holds_lifetime_isa"].to_numpy(bool) + & (ages >= spec.lifetime_minimum_age) + & (ages <= spec.lifetime_maximum_age) + ) + rows = np.flatnonzero(payers) + weights = recipients["weight"].to_numpy(float) + receipt: dict[str, Any] = { + # Holders at the paying ages; those without room under the overall + # limit pay nothing, so the paying weight below is the payer count. + "eligible_holder_weight": float(weights[rows].sum()), + "eligible_holder_records": rounded_count(int(rows.size)), + "target_gbp": target_gbp, + } + if rows.size == 0: + receipt.update({"achieved_gbp": 0.0, "paying_weight": 0.0, "paying_records": 0}) + return payments, receipt + payer_weights = weights[rows] + person_ids = recipients["person_id"].to_numpy()[rows] + uniforms = stable_identity_uniforms( + person_ids, seed=spec.lifetime_seed, salt=f"{spec.lifetime_salt}:rank" + ) + key = ( + spec.balance_rank_weight + * _weighted_rank( + recipients["lifetime_isa_balance"].to_numpy(float)[rows], payer_weights + ) + + (1.0 - spec.balance_rank_weight) * uniforms + ) + order = np.lexsort((person_ids, key)) + cumulative = np.cumsum(payer_weights[order]) + tops = cumulative / cumulative[-1] + bottoms = np.concatenate(([0.0], tops[:-1])) + quantiles = np.empty(rows.size) + quantiles[order] = (bottoms + tops) / 2.0 + room = np.clip(headroom[rows], 0.0, spec.lifetime_limit_gbp) + limit = spec.lifetime_limit_gbp + + def drawn(mean: float) -> np.ndarray: + return np.minimum( + _truncated_exponential_quantile(quantiles, 0.0, limit, mean), room + ) + + low, high = limit * 1e-6, limit * (1 - 1e-6) + if float((drawn(high) * payer_weights).sum()) <= target_gbp: + mean, receipt["saturated"] = high, "target_above_reach" + else: + for _ in range(100): + mid = (low + high) / 2.0 + if float((drawn(mid) * payer_weights).sum()) < target_gbp: + low = mid + else: + high = mid + mean = (low + high) / 2.0 + receipt["saturated"] = None + payments[rows] = drawn(mean) + paying = payments[rows] > 0 + receipt.update( + { + "uncapped_mean_gbp": mean, + "achieved_gbp": float((payments[rows] * payer_weights).sum()), + "paying_weight": float(payer_weights[paying].sum()), + "paying_records": rounded_count(int(paying.sum())), + "trimmed_to_overall_limit_records": rounded_count( + int( + ( + room + < _truncated_exponential_quantile(quantiles, 0.0, limit, mean) + ).sum() + ) + ), + } + ) + return payments, receipt + + +# -------------------------------------------------------------------------- +# The whole imputation +# -------------------------------------------------------------------------- + + +@dataclass(frozen=True) +class ISASubscriptionsResult: + cash_balance: np.ndarray + stocks_and_shares_balance: np.ndarray + cash_subscriptions: np.ndarray + stocks_and_shares_subscriptions: np.ndarray + lifetime_isa_payments: np.ndarray + receipt: Mapping[str, Any] + + +def _class_balance(isa_type: str, cash: np.ndarray, stocks: np.ndarray) -> np.ndarray: + if isa_type == CASH_ONLY: + return cash + if isa_type == STOCKS_AND_SHARES_ONLY: + return stocks + return cash + stocks + + +def _eligible(isa_type: str, cash: np.ndarray, stocks: np.ndarray) -> np.ndarray: + if isa_type == CASH_ONLY: + return cash > 0 + if isa_type == STOCKS_AND_SHARES_ONLY: + return stocks > 0 + return (cash > 0) & (stocks > 0) + + +def impute_isa_subscriptions( + recipients: pd.DataFrame, + *, + cash_balance: np.ndarray, + stocks_balance: np.ndarray, + statistics: HMRCISAStatistics, + spec: ISASubscriptionsSpec, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, dict[str, Any]]: + """Steps 2 to 6 on recipients that already carry per-person balances.""" + + weights = recipients["weight"].to_numpy(float) + person_ids = recipients["person_id"].to_numpy() + adult = recipients["age"].to_numpy(float) >= spec.minimum_age + taken = np.zeros(len(recipients), dtype=bool) + class_of = np.full(len(recipients), "", dtype=object) + extra = np.zeros(len(recipients), dtype=bool) + base_year, build_year = spec.base_tax_year, spec.build_tax_year + growth = { + CASH_ONLY: statistics.accounts_subscribed[("cash", build_year)] + / statistics.accounts_subscribed[("cash", base_year)], + STOCKS_AND_SHARES_ONLY: statistics.accounts_subscribed[ + ("stocks_and_shares", build_year) + ] + / statistics.accounts_subscribed[("stocks_and_shares", base_year)], + } + growth[BOTH] = ( + statistics.accounts_subscribed[("cash", build_year)] + + statistics.accounts_subscribed[("stocks_and_shares", build_year)] + ) / ( + statistics.accounts_subscribed[("cash", base_year)] + + statistics.accounts_subscribed[("stocks_and_shares", base_year)] + ) + selection: dict[str, Any] = {} + for isa_type in CLASS_ORDER: + eligible = adult & ~taken & _eligible(isa_type, cash_balance, stocks_balance) + chosen, receipt = select_subscribers( + recipients, + eligible, + _margins(statistics, isa_type), + seed=spec.select_seed, + salt=f"{spec.select_salt}:{isa_type}", + ) + taken |= chosen + class_of[chosen] = isa_type + # Step 5's people: the account growth, drawn from those still eligible. + more = adult & ~taken & _eligible(isa_type, cash_balance, stocks_balance) + added, growth_receipt = select_subscribers( + recipients, + more, + _margins(statistics, isa_type, scale=growth[isa_type] - 1.0), + seed=spec.select_seed, + salt=f"{spec.select_salt}:{isa_type}:growth", + ) + taken |= added + class_of[added] = isa_type + extra |= added + selection[isa_type] = { + "base": receipt, + "growth": {**growth_receipt, "account_growth": growth[isa_type]}, + } + + combined = np.zeros(len(recipients), dtype=float) + for isa_type in CLASS_ORDER: + balance = _class_balance(isa_type, cash_balance, stocks_balance) + for income in HMRC_ISA_INCOME_BAND_IDS: + shares = np.asarray( + [ + statistics.subscribers(isa_type, income, band) + for band in HMRC_ISA_SUBSCRIPTION_BAND_IDS + ] + ) + for cohort, mask in (("base", ~extra), ("growth", extra)): + rows = np.flatnonzero( + (class_of == isa_type) + & mask + & (recipients["income_band"].to_numpy() == income) + ) + combined[rows] = draw_band_amounts( + rows, + weights=weights, + balance=balance, + person_ids=person_ids, + band_shares=shares, + balance_rank_weight=spec.balance_rank_weight, + seed=spec.amount_seed, + salt=f"{spec.amount_salt}:{isa_type}", + key=f"{isa_type}|{income}|{cohort}", + ) + + cash = np.where(class_of == CASH_ONLY, combined, 0.0) + stocks = np.where(class_of == STOCKS_AND_SHARES_ONLY, combined, 0.0) + both = class_of == BOTH + base = ~extra + stocks_only_base = float((stocks[base] * weights[base]).sum()) + residual = ( + statistics.amount_subscribed[("stocks_and_shares", base_year)] + - stocks_only_base + ) + holding = cash_balance + stocks_balance + balance_share = np.divide( + cash_balance, holding, out=np.full(len(holding), 0.5), where=holding > 0 + ) + share = np.zeros(len(recipients)) + share[both & base], split_receipt = split_both_type_amounts( + combined[both & base], + balance_share[both & base], + weights[both & base], + stocks_and_shares_target=residual, + share_floor=spec.share_floor, + ) + shift = split_receipt["logit_shift"] + if (both & extra).any(): + share[both & extra] = _shifted_share( + _share_logit(balance_share[both & extra], spec.share_floor), + shift, + spec.share_floor, + ) + cash[both] = combined[both] * share[both] + stocks[both] = combined[both] * (1.0 - share[both]) + + base_cash = float((cash[base] * weights[base]).sum()) + base_stocks = float((stocks[base] * weights[base]).sum()) + # Table 9.4's build-year amounts are the 2024-25 targets, not its growth on + # the stage's own 2023-24 totals. Table 9.7's cash bands sit about a third + # above Table 9.4, and growing that excess by Table 9.4's ratio, with the + # limit holding the top, scaled everyone below the limit by about 2.05 on + # populations built from HMRC's own tables (María's ruling of 29 October + # 2026, review of microcosm#1421). Stocks and shares meet Table 9.4 in + # 2023-24 through the both class's residual, so the two rules agree for them. + cash, stocks, scale_receipt = scale_to_build_year_amounts( + cash, + stocks, + weights, + cash_target=statistics.amount_subscribed[("cash", build_year)], + stocks_target=statistics.amount_subscribed[("stocks_and_shares", build_year)], + limit=spec.overall_limit_gbp, + taper_from=spec.taper_from_gbp, + ) + headroom = spec.overall_limit_gbp - cash - stocks + lifetime, lifetime_receipt = draw_lifetime_isa_payments( + recipients, + headroom=headroom, + target_gbp=statistics.amount_subscribed[("lifetime", build_year)], + spec=spec, + ) + lifetime_receipt["hmrc_accounts_subscribed"] = statistics.accounts_subscribed[ + ("lifetime", build_year) + ] + receipt = { + "selection": selection, + "both_type_split": split_receipt, + "base_year": { + "tax_year": base_year, + "cash_gbp": base_cash, + "stocks_and_shares_gbp": base_stocks, + "table_9_4_cash_gbp": statistics.amount_subscribed[("cash", base_year)], + "table_9_4_stocks_and_shares_gbp": statistics.amount_subscribed[ + ("stocks_and_shares", base_year) + ], + "cash_excess_over_table_9_4_gbp": base_cash + - statistics.amount_subscribed[("cash", base_year)], + }, + "growth": { + "to_tax_year": build_year, + "people": "table_9_4_account_growth (upper bound on people)", + "account_growth": growth, + "amounts": "table_9_4_amount_level_tapered_to_limit", + **scale_receipt, + }, + "lifetime_isa": lifetime_receipt, + "checks": _checks( + recipients, + cash, + stocks, + lifetime, + class_of, + cash_balance, + stocks_balance, + spec, + ), + } + return cash, stocks, lifetime, receipt + + +def _checks( + recipients: pd.DataFrame, + cash: np.ndarray, + stocks: np.ndarray, + lifetime: np.ndarray, + class_of: np.ndarray, + cash_balance: np.ndarray, + stocks_balance: np.ndarray, + spec: ISASubscriptionsSpec, +) -> dict[str, Any]: + weights = recipients["weight"].to_numpy(float) + ages = recipients["age"].to_numpy(float) + under_65_cash = (cash > 0) & (ages < 65) + cash_only_under_65 = (class_of == CASH_ONLY) & (ages < 65) + # NaN compares false, so every comparison below would pass it: count it first. + finite = np.ones(len(cash), dtype=bool) + for values in (cash, stocks, lifetime, cash_balance, stocks_balance): + finite &= np.isfinite(values) + violations = { + "non_finite_values": int((~finite).sum()), + "over_overall_limit": int( + (cash + stocks + lifetime > spec.overall_limit_gbp + 1e-6).sum() + ), + "lifetime_over_annual_limit": int( + (lifetime > spec.lifetime_limit_gbp + 1e-6).sum() + ), + "subscriber_under_minimum_age": int( + (((cash + stocks) > 0) & (ages < spec.minimum_age)).sum() + ), + "cash_without_cash_holding": int(((cash > 0) & (cash_balance <= 0)).sum()), + "stocks_without_stocks_holding": int( + ((stocks > 0) & (stocks_balance <= 0)).sum() + ), + "both_class_missing_a_part": int( + ((class_of == BOTH) & ((cash <= 0) | (stocks <= 0))).sum() + ), + "negative_amounts": int(((cash < 0) | (stocks < 0) | (lifetime < 0)).sum()), + } + return { + "violations": violations, + "impact_note_under_65_cash_share_over_12000": { + "cash_part": _share(cash[under_65_cash] > 12000, weights[under_65_cash]), + "cash_only_subscribers": _share( + cash[cash_only_under_65] > 12000, weights[cash_only_under_65] + ), + "published_2022_23_percent": 22, + }, + "subscribers_above_their_balance_records": rounded_count( + int( + ( + ((cash > cash_balance) & (cash > 0)) + | ((stocks > stocks_balance) & (stocks > 0)) + ).sum() + ) + ), + } + + +def _share(mask: np.ndarray, weights: np.ndarray) -> float | None: + total = float(weights.sum()) + return float(weights[mask].sum() / total) if total > 0 else None + + +# -------------------------------------------------------------------------- +# Stage transform +# -------------------------------------------------------------------------- + + +@dataclass +class UKISASubscriptionsStageTransform: + """Whole-stage callable for the ISA subscriptions imputation.""" + + stage: SourceStageSpec + engine: object + was_tab_path: str | Path | None = None + was_person_tab_path: str | Path | None = None + donor_household: pd.DataFrame | None = None + donor_person: pd.DataFrame | None = None + #: Supplied facts (tests and the synthetic fixture); else the vendored resource. + 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, + path: str | Path | None, + role: str, + *, + columns: Sequence[str] | None = None, + ) -> pd.DataFrame: + if supplied is not None: + return supplied + if path is None: + raise ISASubscriptionsError( + f"{UK_ISA_SUBSCRIPTIONS_STAGE_NAME} requires the caller-supplied {role} tab." + ) + return read_pinned_tab( + Path(path).expanduser().resolve(), + _artifact(self.stage, role), + columns=columns, + ) + + def __call__(self, frame: Frame) -> Frame: + assert_rules_engine_country(self.engine, "uk") + spec = ISASubscriptionsSpec.from_stage(self.stage) + check_isa_statistics_declaration( + _operation(self.stage, VERIFY_VENDORED_FACT_RESOURCE_KIND), + stage=UK_ISA_SUBSCRIPTIONS_STAGE_NAME, + period=int(uk_time_period(frame)), + ) + statistics = self.statistics or load_hmrc_isa_statistics() + donor = clean_was_isa_person_donor( + self._table( + self.donor_person, + self.was_person_tab_path, + UK_WAS_PERSON_TAB_ROLE, + columns=spec.columns.person_raw_columns(), + ), + self._table( + self.donor_household, self.was_tab_path, UK_WAS_HOUSEHOLD_TAB_ROLE + ), + columns=spec.columns, + maximum_adults=spec.maximum_donor_adults, + ) + person = frame.table("person").copy() + household = frame.table("household") + cash_balance, stocks_balance, split_receipt = split_household_isa_balances( + person=person, + household=household, + donor=donor, + seed=spec.split_seed, + salt=spec.split_salt, + minimum_age=spec.minimum_age, + maximum_adults=spec.maximum_donor_adults, + ) + recipients = recipient_table(frame) + cash, stocks, lifetime, receipt = impute_isa_subscriptions( + recipients, + cash_balance=cash_balance, + stocks_balance=stocks_balance, + statistics=statistics, + spec=spec, + ) + person[CASH_BALANCE] = cash_balance + person[STOCKS_AND_SHARES_BALANCE] = stocks_balance + person[CASH_SUBSCRIPTIONS] = cash + person[STOCKS_AND_SHARES_SUBSCRIPTIONS] = stocks + person[LIFETIME_ISA_PAYMENTS] = lifetime + household_weights = np.asarray( + frame.weights_for("household").values, dtype=float + ) + sums = ( + pd.DataFrame( + { + "cash": cash_balance, + "stocks": stocks_balance, + "household": person["person_household_id"].to_numpy(), + } + ) + .groupby("household")[["cash", "stocks"]] + .sum() + ) + published = household.set_index("household_id")[ + [HOUSEHOLD_CASH, HOUSEHOLD_STOCKS_AND_SHARES] + ] + published = published.apply(pd.to_numeric, errors="coerce").fillna(0.0) + aligned = sums.reindex(published.index).fillna(0.0) + exact = float( + max( + (aligned["cash"] - published[HOUSEHOLD_CASH]).abs().max(), + (aligned["stocks"] - published[HOUSEHOLD_STOCKS_AND_SHARES]) + .abs() + .max(), + ) + ) + result = uk_national_frame( + person=person, + benunit=frame.table("benunit").copy(), + household=household.copy(), + time_period=uk_time_period(frame), + weight_kind=uk_household_weight_kind(frame), + household_weights=household_weights, + mass_log=( + *frame.mass_log, + uk_household_mass_conservation_receipt( + frame, UK_ISA_SUBSCRIPTIONS_MASS_CONSERVATION_REASON + ), + ), + ) + validate_uk_national_frame(result) + self.last_receipt = { + "stage": UK_ISA_SUBSCRIPTIONS_STAGE_NAME, + "facts": statistics.evidence(), + "donor": donor.receipt, + "balance_split": { + **split_receipt, + "max_abs_household_sum_error_gbp": exact, + }, + **receipt, + } + return result + + @staticmethod + def output_columns() -> tuple[str, ...]: + return UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS + + def checkpoint_metadata(self) -> dict[str, object]: + if self.last_receipt is None: + return {} + return {"evidence": dict(self.last_receipt)} 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 3022a06db3..9496cfee74 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 @@ -80,7 +80,10 @@ ) from microcosm.calibrate import TargetRegistry from microcosm.frame import Frame, MassChangeRecord, Weights -from microcosm.frame.adapters.policyengine_uk import validate_uc_claimant_input +from microcosm.frame.adapters.policyengine_uk import ( + frame_isa_income_contract, + validate_uc_claimant_input, +) _ENTITY_LINK = {"benunit": "person_benunit_id", "household": "person_household_id"} _ENTITY_ID = { @@ -250,17 +253,22 @@ def household_high_value_home(frame: Any, variable: str) -> np.ndarray: ).astype(float) -#: Savings interest outside ISAs, from the frame's stored columns: the frame -#: stores savings_interest_income gross, ISA interest included, with the ISA -#: part in tax_free_savings_income. SPI Table 3.7 counts the recipients of -#: taxable interest, so its count rows filter on this, read at the frame as a -#: filter's stored columns are (microcosm#1095). A reserved name: a persisted -#: column would bypass the difference. +#: Savings interest outside ISAs, from the frame's stored columns. SPI Table +#: 3.7 counts the recipients of taxable interest, so its count rows filter on +#: this, read at the frame as a filter's stored columns are (microcosm#1095). +#: The producer's additive layout stores it as savings_interest_income; a +#: legacy gross frame (the enhanced FRS reference) stores gross interest with +#: the ISA part in tax_free_savings_income (microcosm#1360). A reserved name: a +#: persisted column would bypass the frame's contract. UK_SAVINGS_INTEREST_OUTSIDE_ISAS = "savings_interest_income_outside_isas" +def _legacy_gross_savings(frame: Any) -> bool: + return frame_isa_income_contract(frame)["representation"] == "legacy_gross" + + def savings_interest_outside_isas(frame: Any) -> np.ndarray: - """Each person's stored gross savings interest less its ISA part, not below zero.""" + """Each person's stored savings interest outside ISAs, not below zero.""" person = frame.table("person") if "savings_interest_income" not in person: @@ -268,13 +276,10 @@ def savings_interest_outside_isas(frame: Any) -> np.ndarray: f"{UK_SAVINGS_INTEREST_OUTSIDE_ISAS} needs the stored person column " "savings_interest_income." ) - gross = person["savings_interest_income"].to_numpy(dtype=float) - tax_free = ( - person["tax_free_savings_income"].to_numpy(dtype=float) - if "tax_free_savings_income" in person - else np.zeros(len(person)) - ) - return np.maximum(gross - tax_free, 0.0) + interest = person["savings_interest_income"].to_numpy(dtype=float) + if _legacy_gross_savings(frame) and "tax_free_savings_income" in person: + interest = interest - person["tax_free_savings_income"].to_numpy(dtype=float) + return np.maximum(interest, 0.0) #: The named terms the resolver computes: measures declared once as @@ -533,7 +538,9 @@ def compute_uk_measure_input( raise KeyError(f"{variable} is person-only: {entity}") return ( savings_interest_outside_isas(frame), - "frame_columns:savings_interest_income_less_tax_free_savings_income", + "frame_columns:savings_interest_income_less_tax_free_savings_income" + if _legacy_gross_savings(frame) + else "frame_columns:savings_interest_income", ) if variable in DFE_SPRING_TERM_MEASURES: diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py index 9b9ca065ee..b4977766cf 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/national_frame.py @@ -64,8 +64,10 @@ "uk_build_identifier_frame", "uk_build_identifier_tables", "uk_build_identifiers", + "require_uk_producer_isa_income_contract", "uk_household_weight_kind", "uk_identifier_range_failures", + "uk_isa_income_contract", "uk_national_frame", "uk_release_identifier_tables", "uk_household_mass_conservation_receipt", @@ -225,18 +227,18 @@ def _write_energy_price_basis(path: Path, household: pd.DataFrame) -> None: ) -def _write_isa_income_contract(path: Path) -> None: +def _write_isa_income_contract(path: Path, contract: Mapping[str, object]) -> None: """Declare how the H5's savings interest holds ISA interest. - policyengine-uk 2.162.2 reads the contract from this root attribute and - takes the ISA part off gross interest when it loads a ``legacy_gross`` - dataset, the frame's representation. Written as the engine writes its own - declaration, keys sorted (microcosm#1095). + policyengine-uk 2.162.2 reads the contract from this root attribute: the + frame's own declaration, so a file never claims a layout its columns do not + hold. Written as the engine writes its own declaration, keys sorted + (microcosm#1095, microcosm#1360). """ from microcosm.frame.adapters.policyengine_uk import ( - UK_ISA_INCOME_CONTRACT, UK_ISA_INCOME_CONTRACT_ATTR, + uk_isa_income_contract_from, ) try: @@ -245,10 +247,30 @@ def _write_isa_income_contract(path: Path) -> None: raise RuntimeError("h5py is required to write UK national metadata.") from exc with h5py.File(path, mode="r+") as file: file.attrs[UK_ISA_INCOME_CONTRACT_ATTR] = json.dumps( - dict(UK_ISA_INCOME_CONTRACT), sort_keys=True + uk_isa_income_contract_from(contract), sort_keys=True ) +def _read_isa_income_contract(path: Path) -> dict[str, object]: + """The H5's declared contract; a file declaring none reads as legacy gross.""" + + from microcosm.frame.adapters.policyengine_uk import ( + UK_ISA_INCOME_CONTRACT_ATTR, + UK_LEGACY_ISA_INCOME_CONTRACT, + uk_isa_income_contract_from, + ) + + try: + import h5py + except ImportError as exc: # pragma: no cover - UK H5 runtime dependency + raise RuntimeError("h5py is required to read UK national metadata.") from exc + with h5py.File(path, mode="r") as file: + stored = file.attrs.get(UK_ISA_INCOME_CONTRACT_ATTR) + if stored is None: + return dict(UK_LEGACY_ISA_INCOME_CONTRACT) + return uk_isa_income_contract_from(stored) + + def _mass_log_from_stored(value: object) -> tuple[MassChangeRecord, ...]: if isinstance(value, bytes): value = value.decode("utf-8") @@ -346,6 +368,7 @@ def _read_uk_national_tables( fingerprint_before = _uk_source_file_fingerprint(input_path) stored_kind, stored_mass_log = _read_weight_metadata(input_path) + isa_income_contract = _read_isa_income_contract(input_path) with pd.HDFStore(input_path, mode="r") as store: keys = {key.lstrip("/") for key in store.keys()} missing = sorted(set(UK_NATIONAL_H5_TABLES) - keys) @@ -363,6 +386,7 @@ def _read_uk_national_tables( "time_period": str(raw_period.iloc[0]), "household_weight_kind": _weight_kind_from_stored(stored_kind), "mass_log": _mass_log_from_stored(stored_mass_log), + "isa_income_contract": isa_income_contract, } fingerprint_after = _uk_source_file_fingerprint(input_path) if fingerprint_after != fingerprint_before: @@ -386,6 +410,7 @@ def load_uk_national_frame( time_period=payload["time_period"], weight_kind=payload["household_weight_kind"], mass_log=payload["mass_log"], + isa_income_contract=payload["isa_income_contract"], ) validate_uk_national_frame(frame) return frame, UKStagingProvenance(source_h5=input_path, fingerprint=fingerprint) @@ -400,9 +425,14 @@ def uk_national_frame( weight_kind: WeightKind = WeightKind.DESIGN, household_weights: Sequence[float] | np.ndarray | pd.Series | None = None, mass_log: tuple[MassChangeRecord, ...] = (), + isa_income_contract: Mapping[str, object] | None = None, ) -> Frame: """Assemble the UK national tables into a validated Frame. + ``isa_income_contract`` is how the savings columns hold ISA interest; the + producer's own contract unless the tables came from a file declaring + another (``load_uk_national_frame``). + The typed household weights are built from ``household_weights`` when supplied, otherwise from a legacy input ``household_weight`` column. The stored carrier table never keeps that export column; materialization @@ -432,15 +462,55 @@ def uk_national_frame( ) _assert_uk_benunit_nesting(person) carrier_household = household.drop(columns=["household_weight"], errors="ignore") + from microcosm.frame.adapters.policyengine_uk import ( + UK_ISA_INCOME_CONTRACT, + UK_ISA_INCOME_CONTRACT_ATTR, + uk_isa_income_contract_from, + ) + + contract = uk_isa_income_contract_from( + UK_ISA_INCOME_CONTRACT if isa_income_contract is None else isa_income_contract + ) return Frame( tables={"person": person, "benunit": benunit, "household": carrier_household}, schema=UK_NATIONAL_SCHEMA, weights={"household": weights}, mass_log=mass_log, - metadata={UK_TIME_PERIOD_METADATA_KEY: period}, + # The contract travels as the H5 attribute's JSON, which the graph's + # frame-metadata store serialises as it is. + metadata={ + UK_TIME_PERIOD_METADATA_KEY: period, + UK_ISA_INCOME_CONTRACT_ATTR: json.dumps(contract, sort_keys=True), + }, ) +def uk_isa_income_contract(frame: Frame) -> dict[str, object]: + """How the frame's savings columns hold ISA interest (microcosm#1360).""" + + from microcosm.frame.adapters.policyengine_uk import frame_isa_income_contract + + return frame_isa_income_contract(frame) + + +def require_uk_producer_isa_income_contract(frame: Frame, *, label: str) -> None: + """Refuse a build input whose savings columns are not the producer's. + + A stage writes the producer's columns under the producer's declaration, so + an input holding another layout (an older gross-interest file) would come + out mislabelled; it is rebuilt from source instead. + """ + + from microcosm.frame.adapters.policyengine_uk import UK_ISA_INCOME_CONTRACT + + declared = uk_isa_income_contract(frame) + if declared != dict(UK_ISA_INCOME_CONTRACT): + raise ValueError( + f"{label} declares the ISA income contract {declared}, not the " + f"producer's {dict(UK_ISA_INCOME_CONTRACT)}; rebuild it from source." + ) + + def uk_household_mass_conservation_receipt( frame: Frame, reason: str ) -> MassChangeRecord: @@ -485,6 +555,7 @@ def _write_uk_single_year_tables( time_period: str, weight_kind: WeightKind, mass_log: tuple[MassChangeRecord, ...], + isa_income_contract: Mapping[str, object], path: Path, ) -> Path: """The one physical writer for every UK single-year H5.""" @@ -524,7 +595,7 @@ def _write_uk_single_year_tables( temporary_path, weight_kind=weight_kind, mass_log=mass_log ) _write_energy_price_basis(temporary_path, household) - _write_isa_income_contract(temporary_path) + _write_isa_income_contract(temporary_path, isa_income_contract) temporary_path.replace(path) finally: temporary_path.unlink(missing_ok=True) @@ -576,10 +647,26 @@ def _write_uk_single_year_tables( # (frs_spine.FRS_IN_HOUSEHOLD_CARE_HOURS_COLUMN) the Carer's Allowance # draw reads (microcosm#1418); it is no engine input. "frs_care_hours_in_household", + # policyengine-uk's sum of the ISA interest parts (microcosm#1360). The + # producer stores individual_savings_account_interest_income instead, + # so no stage writes it; a persisted copy would override the sum and + # never be uprated, so the boundary drops one should it appear. + "tax_free_savings_income", + ), + "household": ( + "property_wealth", + # The value the spine's regional home values replaced (microcosm#1112): + # the dense node ranks on it, and it is no engine input. + "main_residence_value_rank_basis", + # Since policyengine-uk 2.164.0 these add the person cash_isa_balance + # and stocks_and_shares_isa_balance, which the isa_subscriptions stage + # splits them into (microcosm#1415). A stored total would override the + # sum, so a reform or situation that edits a person's balance would + # leave it unchanged. The spine keeps both (the WAS stage writes them + # and the split reads them); the engine sums the person balances. + "cash_isa", + "stocks_and_shares_isa", ), - # The value the spine's regional home values replaced (microcosm#1112): - # the dense node ranks on it, and it is no engine input. - "household": ("property_wealth", "main_residence_value_rank_basis"), } @@ -856,6 +943,7 @@ def uk_release_export_frame(frame: Frame) -> Frame: weight_kind=uk_household_weight_kind(frame), household_weights=frame.weights_for("household").values, mass_log=frame.mass_log, + isa_income_contract=uk_isa_income_contract(frame), ) @@ -874,6 +962,7 @@ def write_uk_national_frame(frame: Frame, path: str | Path) -> Path: time_period=uk_time_period(frame), weight_kind=uk_household_weight_kind(frame), mass_log=frame.mass_log, + isa_income_contract=uk_isa_income_contract(frame), path=output_path, ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py index 1de31166ac..72483559b8 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/private_school_attendance.py @@ -120,7 +120,7 @@ def store_private_school_attendance( if PRIVATE_SCHOOL_ATTENDANCE_OUTPUT in person.columns: raise ValueError( f"{PRIVATE_SCHOOL_ATTENDANCE_OUTPUT!r} is already stored: this stage " - "is its only writer and runs last." + "is its only writer." ) period = uk_time_period(frame) year = ( diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/rowwise_dataset.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/rowwise_dataset.py index 7713bdc764..f0c23d4356 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/rowwise_dataset.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/rowwise_dataset.py @@ -8,6 +8,7 @@ from __future__ import annotations import importlib.util +from collections.abc import Mapping from dataclasses import dataclass, replace from pathlib import Path from typing import Any @@ -27,10 +28,12 @@ ) from microcosm.build.uk_runtime.national_frame import ( _mass_log_from_stored, + _read_isa_income_contract, _read_weight_metadata, _weight_kind_from_stored, _write_uk_single_year_tables, uk_household_weight_kind, + uk_isa_income_contract, uk_national_frame, uk_time_period, ) @@ -137,6 +140,8 @@ class UKRowwiseDatasetResult: output_path: Path | None = None household_weight_kind: WeightKind = WeightKind.DESIGN mass_log: tuple[MassChangeRecord, ...] = () + #: The source's ISA income contract; ``None`` is the producer's. + isa_income_contract: Mapping[str, object] | None = None @property def id_multiplier(self) -> int: @@ -171,6 +176,7 @@ def clone_uk_dataset_tables_with_rowwise_geography( household_weight_kind: WeightKind = WeightKind.DESIGN, mass_log: tuple[MassChangeRecord, ...] = (), source_lineage_modulus: int | None = None, + isa_income_contract: Mapping[str, object] | None = None, ) -> UKRowwiseDatasetResult: """Clone UK person/benunit/household tables and assign row-wise geography.""" @@ -273,6 +279,7 @@ def clone_uk_dataset_tables_with_rowwise_geography( time_period=_normalise_time_period(time_period, source_year=source_year), household_weight_kind=household_weight_kind, mass_log=(*mass_log, clone_record), + isa_income_contract=isa_income_contract, ) validate_uk_rowwise_dataset_tables(result.person, result.benunit, result.household) return result @@ -329,6 +336,7 @@ def expand_uk_geographic_pool( household_weight_kind: WeightKind = WeightKind.DESIGN, mass_log: tuple[MassChangeRecord, ...] = (), source_lineage_modulus: int | None = None, + isa_income_contract: Mapping[str, object] | None = None, ) -> UKGeographicPool: """Prepare lineage and expand linked entities using shared clone operations. @@ -451,6 +459,7 @@ def expand_uk_geographic_pool( time_period=_normalise_time_period(time_period, source_year=source_year), weight_kind=household_weight_kind, mass_log=(*mass_log, clone_record), + isa_income_contract=isa_income_contract, ), n_clones=n_clones, id_multiplier=id_multiplier, @@ -483,6 +492,7 @@ def assign_uk_geographic_pool( weight_kind=frame.weights_for("household").kind, household_weights=frame.weights_for("household").values, mass_log=frame.mass_log, + isa_income_contract=uk_isa_income_contract(frame), ) @@ -533,6 +543,7 @@ def clone_uk_dataset_tables_with_ladder_geography( household_weight_kind: WeightKind = WeightKind.DESIGN, mass_log: tuple[MassChangeRecord, ...] = (), source_lineage_modulus: int | None = None, + isa_income_contract: Mapping[str, object] | None = None, ) -> UKLadderRowwiseDatasetResult: """Clone UK tables and assign geography through the OA ladder. @@ -551,6 +562,7 @@ def clone_uk_dataset_tables_with_ladder_geography( household_weight_kind=household_weight_kind, mass_log=mass_log, source_lineage_modulus=source_lineage_modulus, + isa_income_contract=isa_income_contract, ) frame = assign_uk_geographic_pool( pool, @@ -613,6 +625,7 @@ def clone_uk_dataset_with_ladder_geography( household_weight_kind=tables["household_weight_kind"], mass_log=tables["mass_log"], source_lineage_modulus=source_lineage_modulus, + isa_income_contract=tables["isa_income_contract"], ) if output_path is None: return result @@ -763,6 +776,7 @@ def clone_uk_dataset_with_rowwise_geography( household_weight_kind=tables["household_weight_kind"], mass_log=tables["mass_log"], source_lineage_modulus=source_lineage_modulus, + isa_income_contract=tables["isa_income_contract"], ) if output_path is None: return result @@ -776,6 +790,7 @@ def clone_uk_dataset_with_rowwise_geography( output_path=path, household_weight_kind=result.household_weight_kind, mass_log=result.mass_log, + isa_income_contract=result.isa_income_contract, ) @@ -814,6 +829,7 @@ def load_uk_rowwise_dataset(path: str | Path): time_period=payload["time_period"], weight_kind=payload["household_weight_kind"], mass_log=payload["mass_log"], + isa_income_contract=payload["isa_income_contract"], ) validate_uk_national_frame(frame) return frame, UKStagingProvenance(source_h5=input_path, fingerprint=fingerprint) @@ -846,10 +862,19 @@ def write_uk_rowwise_dataset( time_period=result.time_period, weight_kind=result.household_weight_kind, mass_log=result.mass_log, + isa_income_contract=_result_isa_income_contract(result), path=Path(output_path), ) +def _result_isa_income_contract(result: UKRowwiseDatasetResult) -> Mapping[str, object]: + from microcosm.frame.adapters.policyengine_uk import UK_ISA_INCOME_CONTRACT + + if result.isa_income_contract is None: + return UK_ISA_INCOME_CONTRACT + return result.isa_income_contract + + def _write_uk_ladder_rowwise_dataset( result: UKLadderRowwiseDatasetResult, output_path: str | Path, @@ -900,6 +925,7 @@ def _write_uk_ladder_rowwise_dataset( time_period=uk_time_period(frame), weight_kind=uk_household_weight_kind(frame), mass_log=frame.mass_log, + isa_income_contract=uk_isa_income_contract(frame), path=Path(output_path), ) @@ -976,6 +1002,7 @@ def _dataset_tables( ), "household_weight_kind": uk_household_weight_kind(dataset), "mass_log": dataset.mass_log, + "isa_income_contract": uk_isa_income_contract(dataset), } raise TypeError( "UK dataset table extraction requires a microcosm Frame or a UK " @@ -991,6 +1018,7 @@ def _read_uk_single_year_h5(path: str | Path) -> dict[str, Any]: if not dataset_path.exists(): raise FileNotFoundError(f"UK single-year dataset not found: {dataset_path}.") weight_kind, mass_log = read_uk_single_year_weight_metadata(dataset_path) + isa_income_contract = _read_isa_income_contract(dataset_path) with pd.HDFStore(dataset_path, mode="r") as store: keys = {key.lstrip("/") for key in store.keys()} missing = sorted(set(UK_SINGLE_YEAR_TABLES) - keys) @@ -1004,6 +1032,7 @@ def _read_uk_single_year_h5(path: str | Path) -> dict[str, Any]: "time_period": time_period, "household_weight_kind": weight_kind, "mass_log": mass_log, + "isa_income_contract": isa_income_contract, } diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_housing_shell.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_housing_shell.py index 3899457365..a20a2a592c 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_housing_shell.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/spi_housing_shell.py @@ -102,8 +102,11 @@ "investment_income", "state_pension_reported", ) +#: Interest is ISA interest included, as gross interest held it before the +#: additive contract (microcosm#1360). _INVESTMENT_COLUMNS = ( "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "property_income", "other_investment_income", 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 fb6b52cdfd..b4213132cd 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 @@ -38,6 +38,7 @@ ) from microcosm.build.uk_runtime.frs_spine import ( FRS_LANDLORD_CARRIER_COLUMN, + ISA_INTEREST_COLUMN, uc_gainful_self_employment, ) from microcosm.build.uk_runtime.hmrc_income import ( @@ -105,6 +106,9 @@ ) SPI_DONOR_FIT_NAME = "uk_spi_2022_23_income" FRS_ONLY_FIT_NAME = "uk_frs_only_spi_fill" +#: The stage-2 draw salts keep the name each target had under the legacy gross +#: layout, so moving to the additive contract redraws nothing (microcosm#1360). +_STAGE2_SALT_NAMES = {ISA_INTEREST_COLUMN: "tax_free_savings_income"} DEFAULT_SPI_DONOR_SAMPLE_SIZE = 100_000 # Recipient guard matching the existing constructed SPI age support below. # SN 9422 Annex A labels the youngest band "Under 25"; it does not establish @@ -184,7 +188,7 @@ SPI_RECIPIENT_INCOME_RULE = ( "drawn when the twin reports pay, a trade (self-employment income or a " "self-employed main job) or a pension, or more than the floor of taxable " - "interest (savings interest less the tax-free part), dividends, property " + "interest (savings interest outside ISAs), dividends, property " "and miscellaneous income together, and always for a reserved band " "carrier; otherwise the twin has no tax record for the tape to condition " "on and the copy keeps the twin's values at stage 1 and stage 2; a twin " @@ -1170,8 +1174,15 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): guard_receipts.append(receipt) income_predictors = FRS_ONLY_SPI_FILL_INCOME_PREDICTOR_COLUMNS + # Training rows condition on gross interest, ISA interest included, as + # under the legacy layout, so the fit is unchanged by the additive + # contract (microcosm#1360); recipients condition on the taxable draw. + training_person = person.loc[training_people].copy() + training_person["savings_interest_income"] = pd.to_numeric( + training_person["savings_interest_income"], errors="coerce" + ) + pd.to_numeric(training_person[ISA_INTEREST_COLUMN], errors="coerce") train_predictors = _person_predictors( - person.loc[training_people], + training_person, household, income_predictors=income_predictors, ) @@ -1241,13 +1252,19 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): encoded_target, quantiles={ target: stable_identity_uniforms( - stage2_keys, seed=seed + 1, salt=f"{FRS_ONLY_FIT_NAME}:{target}" + stage2_keys, + seed=seed + 1, + salt=f"{FRS_ONLY_FIT_NAME}:{_STAGE2_SALT_NAMES.get(target, target)}", ) for target in stage2_outputs }, sign_uniforms={ target: stable_identity_uniforms( - stage2_keys, seed=seed + 1, salt=f"{FRS_ONLY_FIT_NAME}:{target}:sign" + stage2_keys, + seed=seed + 1, + salt=( + f"{FRS_ONLY_FIT_NAME}:{_STAGE2_SALT_NAMES.get(target, target)}:sign" + ), ) for target in stage2_outputs }, @@ -1284,10 +1301,9 @@ def fit_reference(encoded: pd.DataFrame, observed: pd.DataFrame): ) guard_receipts.append(receipt) - tax_free = person.loc[spi_people, "tax_free_savings_income"].to_numpy( - dtype=np.float64 - ) - person.loc[spi_people, "savings_interest_income"] = taxable_interest_draw + tax_free + # 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 person = derive_hmrc_income_auxiliaries(person, row_mask=spi_people) person = _move_losses_brought_forward_to_loss_relief(person, spi_people=spi_people) person = _refresh_disability_derived_inputs(person, spi_people=spi_people) @@ -2055,7 +2071,6 @@ def derive_hmrc_income_auxiliaries( "self_employment_income", "private_pension_income", "savings_interest_income", - "tax_free_savings_income", "dividend_income", "property_income", "other_investment_income", @@ -2082,14 +2097,9 @@ def derive_hmrc_income_auxiliaries( numeric["self_employment_income"].to_numpy(dtype=float), relief.fillna(0.0).to_numpy(dtype=float), ) - taxable_interest = ( - numeric["savings_interest_income"] - numeric["tax_free_savings_income"] - ) - if (taxable_interest < 0.0).any(): - raise ValueError( - "HMRC accounting identity requires gross savings_interest_income " - "to be at least tax_free_savings_income on every person row." - ) + # Taxable interest is the non-ISA interest the producer stores + # (microcosm#1360), held non-negative with the other leaves above. + taxable_interest = numeric["savings_interest_income"] employed_income = ( np.maximum( numeric[SPI_HMRC_PAY_COLUMN] @@ -2235,7 +2245,6 @@ def _main_source(pay: np.ndarray, pension: np.ndarray, trade: np.ndarray) -> np. ) _UNEARNED_INCOME_COLUMNS = ( "savings_interest_income", - "tax_free_savings_income", "dividend_income", "property_income", "miscellaneous_income", @@ -2293,9 +2302,7 @@ def _taxable_income_mask(person: pd.DataFrame) -> np.ndarray: | (values["private_pension_income"] + values["state_pension_reported"] > 0.0) ) unearned = ( - np.maximum( - values["savings_interest_income"] - values["tax_free_savings_income"], 0.0 - ) + np.maximum(values["savings_interest_income"], 0.0) + values["dividend_income"] + values["property_income"] + values["miscellaneous_income"] 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 d11eb52133..b8f1316c9f 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 @@ -85,8 +85,8 @@ SPI_TRANSPORT_SALT = "spi_stage1" #: The identity a recipient's uniforms are keyed on: the FRS person it copies. SPI_TRANSPORT_KEY_COLUMN = "person_source_id" -#: Taxable savings interest on the twin: the FRS gross interest less the -#: tax-free part, the concept the tape's INCBBS leaf holds. +#: Taxable savings interest on the twin: the FRS interest outside ISAs +#: (microcosm#1360), the concept the tape's INCBBS leaf holds. TAXABLE_SAVINGS_INTEREST_ANALOGUE = "taxable_savings_interest" #: Per-group uniformity is reported only over at least this many rows. SPI_TRANSPORT_GROUP_MINIMUM_ROWS = 10 @@ -186,7 +186,7 @@ def analogue_source_columns() -> tuple[str, ...]: for column in SPI_STAGE1_ANALOGUE_COLUMNS.values() if column and column != TAXABLE_SAVINGS_INTEREST_ANALOGUE } - columns |= {"savings_interest_income", "tax_free_savings_income"} + columns |= {"savings_interest_income"} return tuple(sorted(columns)) @@ -197,8 +197,8 @@ def observed_analogues( """The twin's observed values of every analogue target, named by target. Rows are FRS persons (the twins, or the SPI copies before the draw, which - still carry the twin's values). Taxable savings interest is derived as the - gross FRS interest less the tax-free part, floored at zero. + still carry the twin's values). Taxable savings interest is the FRS + interest outside ISAs, floored at zero. """ missing = sorted(set(analogue_source_columns()) - set(person.columns)) @@ -207,9 +207,6 @@ def observed_analogues( taxable_interest = np.maximum( pd.to_numeric(person["savings_interest_income"], errors="coerce").to_numpy( dtype=np.float64, na_value=np.nan - ) - - pd.to_numeric(person["tax_free_savings_income"], errors="coerce").to_numpy( - dtype=np.float64, na_value=np.nan ), 0.0, ) 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 f3e54ee5d9..0fcbd5724b 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 @@ -224,7 +224,7 @@ EMPLOYER_PENSION_CONTRIBUTIONS_COLUMN, "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", 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 1892257ac5..230b776fa8 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 @@ -165,7 +165,7 @@ "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", 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 a2947aae7c..add5eaa2d4 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 @@ -140,6 +140,9 @@ from microcosm.build.uk_runtime.high_value_home_support import ( UKHighValueHomeSupportStageTransform, ) +from microcosm.build.uk_runtime.isa_subscriptions import ( + UKISASubscriptionsStageTransform, +) from microcosm.build.uk_runtime.lcfs_consumption import ( UKLCFSConsumptionStageTransform, ) @@ -358,8 +361,8 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: "--was-person-tab", type=Path, help=( - "Caller-supplied private WAS round-8 person tab for was_lisa (with " - "--was-tab)." + "Caller-supplied private WAS round-8 person tab for was_lisa and " + "isa_subscriptions (with --was-tab)." ), ) parser.add_argument( @@ -845,6 +848,15 @@ def _declared_seeds(stages) -> dict[str, dict[str, int]]: stage_seeds[str(operation.parameters["salt"])] = seed elif operation.kind == "top_up_to_stock": stage_seeds[str(operation.parameters["salt"])] = seed + elif operation.kind in ( + "split_household_isa_balances", + "allocate_isa_subscribers", + "draw_isa_subscription_amounts", + "draw_lifetime_isa_payments", + ): + # Identity-keyed draws: the seed is declared under the salt + # it is hashed with (microcosm#1415). + stage_seeds[str(operation.parameters["salt"])] = seed if stage_seeds: declared[stage.stage] = stage_seeds return declared @@ -1546,7 +1558,12 @@ def prepare_uk_spine_execution( raise ValueError( "--was-tab is required when the was_wealth stage is scheduled." ) - if args.synthetic_fixture_dir is None and "was_lisa" in stage_names: + for was_person_stage in ("was_lisa", "isa_subscriptions"): + if ( + args.synthetic_fixture_dir is not None + or was_person_stage not in stage_names + ): + continue missing_was = [ flag for flag, value in ( @@ -1557,7 +1574,7 @@ def prepare_uk_spine_execution( ] if missing_was: raise ValueError( - "was_lisa requires caller-supplied private inputs: " + f"{was_person_stage} requires caller-supplied private inputs: " f"{', '.join(missing_was)}." ) if args.synthetic_fixture_dir is None and "nts_bus_travel" in stage_names: @@ -1693,6 +1710,15 @@ def prepare_uk_spine_execution( was_person_tab_path=sources["was_person"], ) ) + if "isa_subscriptions" in stage_names: + implementations["isa_subscriptions"] = _GraphSourceTransform( + lambda sources: UKISASubscriptionsStageTransform( + stage=stages_by_name["isa_subscriptions"], + engine=engine, + was_tab_path=sources["was"], + was_person_tab_path=sources["was_person"], + ) + ) if "nts_bus_travel" in stage_names: implementations["nts_bus_travel"] = _GraphSourceTransform( lambda sources: UKNTSBusTravelStageTransform( @@ -1930,9 +1956,10 @@ def prepare_uk_spine_execution( graph_sources = _synthetic_graph_sources(args.synthetic_fixture_dir) else: graph_sources = {"frs": args.frs_raw_dir} - if "was_wealth" in stage_names or "was_lisa" in stage_names: + was_person_stages = {"was_lisa", "isa_subscriptions"} & set(stage_names) + if "was_wealth" in stage_names or was_person_stages: graph_sources["was"] = args.was_tab - if "was_lisa" in stage_names: + if was_person_stages: graph_sources["was_person"] = args.was_person_tab if "nts_bus_travel" in stage_names: graph_sources["nts_household"] = args.nts_household_tab 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 45f6097c7a..4abfe79d10 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 @@ -107,6 +107,12 @@ def uk_stage_health_gate( return _legacy_closure_take_up_gate(stage, evidence, parameters) if check == "apprentice_draw": return _apprentice_draw_gate(stage, evidence, parameters) + if check == "isa_subscriptions": + return _isa_subscriptions_gate(stage, evidence, parameters) + if check == "isa_subscriptions_invariants": + return _isa_subscriptions_invariants_gate(stage, evidence, parameters) + if check == "was_lisa_anchor": + return _was_lisa_anchor_gate(stage, evidence, parameters) return GateResult( name="stage_health", passed=False, @@ -4717,3 +4723,206 @@ def _figure(key: str) -> float | None: if failures: return _fail(stage, check, failures, details) return _pass(stage, check, details) + + +def _isa_subscriptions_invariants_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """ISA subscriptions keep the rules any population must (microcosm#1415). + + Fails on any record the receipt counts against them (non-finite values, the + overall and Lifetime ISA limits, ages, holding what is paid into, both parts of + a both-type subscription, negative amounts) and on person balances that do not + sum to the household draws within ``maximum_household_sum_error_gbp``. None of + these depends on the population, so the entry is no population fact check and + the synthetic smoke build blocks on it (review of microcosm#1421). + """ + + check = "isa_subscriptions_invariants" + sum_tolerance = _finite_number( + parameters["maximum_household_sum_error_gbp"], + label=f"{stage}.maximum_household_sum_error_gbp", + ) + failures: list[str] = [] + checks = _mapping(evidence.get("checks"), label=f"{stage}.checks") + violations = _mapping(checks.get("violations"), label=f"{stage}.violations") + for name, count in violations.items(): + if count != 0: + failures.append(f"{stage}: {count} record(s) break {name}.") + split = _mapping(evidence.get("balance_split"), label=f"{stage}.balance_split") + error = _finite_number( + split.get("max_abs_household_sum_error_gbp"), + label=f"{stage}.max_abs_household_sum_error_gbp", + ) + if error > sum_tolerance: + failures.append( + f"{stage}: person balances miss the household draws by up to {error} GBP." + ) + return GateResult( + name="stage_health", + passed=not failures, + failures=tuple(failures), + details={"stage": stage, "check": check, "violations": dict(violations)}, + ) + + +def _isa_subscriptions_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """ISA subscriptions meet HMRC's Table 9.4 totals (microcosm#1415). + + Fails on the 2024-25 cash and stocks-and-shares totals off Table 9.4's 2024-25 + amounts, the Lifetime ISA total off Table 9.4's Lifetime ISA amount or the + both-type split's stocks-and-shares total off its residual by more than + ``maximum_relative_total_deviation`` (a reported saturation is named, not + failed). Cells short of eligible adults are named, never failed: the stage + invents no holders, and how far the result sits from HMRC's tables after + calibration is a measurement, not a gate (María's ruling of 28 October 2026). + These are population facts, so a synthetic smoke build records them without + blocking; the rules any population must keep are + ``_isa_subscriptions_invariants_gate``. + """ + + check = "isa_subscriptions" + tolerance = _finite_number( + parameters["maximum_relative_total_deviation"], + label=f"{stage}.maximum_relative_total_deviation", + ) + failures: list[str] = [] + growth = _mapping(evidence.get("growth"), label=f"{stage}.growth") + named: list[str] = [] + for kind in ("cash", "stocks_and_shares"): + target = _finite_number( + growth.get(f"{kind}_target_gbp"), label=f"{stage}.{kind}_target_gbp" + ) + achieved = _finite_number( + growth.get(f"{kind}_achieved_gbp"), label=f"{stage}.{kind}_achieved_gbp" + ) + if target > 0 and abs(achieved - target) / target > tolerance: + failures.append( + f"{stage}: 2024-25 {kind} subscriptions miss Table 9.4's 2024-25 " + "amount." + ) + both = _mapping(evidence.get("both_type_split"), label=f"{stage}.both_type_split") + if both.get("saturated"): + named.append(f"both_type_split:{both['saturated']}") + else: + target = _finite_number( + both.get("stocks_and_shares_target_gbp"), label=f"{stage}.both_target" + ) + achieved = _finite_number( + both.get("stocks_and_shares_achieved_gbp"), label=f"{stage}.both_achieved" + ) + if target > 0 and abs(achieved - target) / target > tolerance: + failures.append(f"{stage}: the both-type split misses its residual.") + lifetime = _mapping(evidence.get("lifetime_isa"), label=f"{stage}.lifetime_isa") + if lifetime.get("saturated"): + named.append(f"lifetime_isa:{lifetime['saturated']}") + else: + target = _finite_number( + lifetime.get("target_gbp"), label=f"{stage}.lisa_target" + ) + achieved = _finite_number( + lifetime.get("achieved_gbp"), label=f"{stage}.lisa_achieved" + ) + if target > 0 and abs(achieved - target) / target > tolerance: + failures.append(f"{stage}: Lifetime ISA payments miss Table 9.4's amount.") + selection = _mapping(evidence.get("selection"), label=f"{stage}.selection") + # Subscribers the eligible holders could not supply: the stage invents none. + for isa_type, steps in selection.items(): + for step, receipt in _mapping(steps, label=f"{stage}.{isa_type}").items(): + if not isinstance(receipt, Mapping): + continue + unmet = float(receipt.get("shortfall_weight_unmet") or 0.0) + if unmet > 0: + named.append(f"selection:{isa_type}:{step}:shortfall_weight_unmet") + return GateResult( + name="stage_health", + passed=not failures, + failures=tuple(failures), + details={ + "stage": stage, + "check": check, + "saturations_named": named, + "selection": dict(selection), + # People who pay in (a positive payment), not every eligible holder: + # was_lisa already anchors the holders to HMRC's accounts. + "lifetime_isa_paying_weight": lifetime.get("paying_weight"), + "lifetime_isa_hmrc_accounts_subscribed": lifetime.get( + "hmrc_accounts_subscribed" + ), + }, + ) + + +def _was_lisa_anchor_gate( + stage: str, + evidence: Mapping[str, object], + parameters: Mapping[str, object], +) -> GateResult: + """The anchored Lifetime ISA holders reach HMRC's payers (microcosm#1415). + + After the financial-wealth cap the weighted holders at the counted ages lie + within ``maximum_relative_deviation`` of the anchor's target, or at or above it + when the drawn holders already were (no shift), and a positive shift removes no + holder (the same keyed uniforms decide ownership). A target above what the + largest shift reaches is named, not failed, and so are unshifted holders above + the target: the anchor only adds holders, so it cannot correct the base + model's overshoot. + """ + + check = "was_lisa_anchor" + tolerance = _finite_number( + parameters["maximum_relative_deviation"], + label=f"{stage}.maximum_relative_deviation", + ) + model = _mapping(evidence.get("ownership_model"), label=f"{stage}.ownership_model") + anchor = _mapping(model.get("anchor"), label=f"{stage}.anchor") + target = _finite_number( + anchor.get("target_holders"), label=f"{stage}.target_holders" + ) + achieved = _finite_number( + evidence.get("anchored_holders_after_cap"), + label=f"{stage}.anchored_holders_after_cap", + ) + shift = _finite_number(anchor.get("logit_shift"), label=f"{stage}.logit_shift") + saturated = anchor.get("saturated") + failures: list[str] = [] + named: list[str] = [] if saturated is None else [f"anchor:{saturated}"] + if target <= 0: + failures.append(f"{stage}: the anchor's target must be positive.") + elif shift < 0: + failures.append(f"{stage}: the anchor's shift must not be negative.") + elif saturated is not None: + pass + elif shift == 0: + if achieved < target * (1.0 - tolerance): + failures.append( + f"{stage}: unshifted holders after the cap fall below the target." + ) + elif achieved > target * (1.0 + tolerance): + # The anchor only adds holders, so it cannot bring an overshoot of the + # base model down: named for the build report, not failed. + named.append("anchor:unshifted_holders_above_target") + elif abs(achieved - target) / target > tolerance: + failures.append( + f"{stage}: anchored holders after the cap miss the target by more than " + f"{tolerance:.2%}." + ) + if anchor.get("removed_holders") != 0: + failures.append(f"{stage}: the anchor removed holders.") + return GateResult( + name="stage_health", + passed=not failures, + failures=tuple(failures), + details={ + "stage": stage, + "check": check, + "anchor": dict(anchor), + "saturations_named": named, + }, + ) 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 9303123f7c..075ef0c8b4 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 @@ -322,6 +322,12 @@ def __post_init__(self) -> None: "person.is_uc_claimant", "person.legacy_jobseeker_proxy", "person.lifetime_isa_balance", + "person.lifetime_isa_payments", + "person.cash_isa_balance", + "person.stocks_and_shares_isa_balance", + "person.cash_isa_subscriptions", + "person.stocks_and_shares_isa_subscriptions", + "person.individual_savings_account_interest_income", "person.local_bus_single_fare_share", "person.local_bus_trips", "person.local_bus_use_band", @@ -473,6 +479,13 @@ def __post_init__(self) -> None: "a persisted copy overrides that sum and has no uprating index, so the " "candidate must drop it (microcosm#1106, the uk-data#543 defect)." ), + "person.tax_free_savings_income": ( + "policyengine-uk derives tax_free_savings_income as the sum of the ISA " + "interest it adds (individual_savings_account_interest_income); a " + "persisted copy overrides that sum and is never uprated, so the " + "candidate stores ISA interest apart from non-ISA interest under the " + "additive ISA income contract and drops it (microcosm#1360)." + ), } _STRUCTURAL_COLUMNS: Mapping[str, frozenset[str]] = { diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py index 03b7d03b5e..cc841c75c4 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py @@ -76,7 +76,7 @@ _UC_CAPITAL_LEVELS = ("exact_cell", *UC_CAPITAL_COARSENING) #: The UC capital limits the receipt measures the redraw against. _UC_CAPITAL_RECEIPT_LIMITS = (6_000.0, 16_000.0) -#: policyengine-uk 2.162.14 counts these household property values as Universal +#: policyengine-uk 2.164.2 counts these household property values as Universal #: Credit capital (``property_sources`` under #: ``gov.dwp.universal_credit.means_test.capital``, at 90% of value less any #: secured debt since 2.136.1) and as Pension Credit capital (the ``sources`` diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py index 16ce77e1f8..e4e2bfdc45 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_relationships.py @@ -83,12 +83,14 @@ def frs_uc_couple_mask(person: pd.DataFrame, benunit: pd.DataFrame) -> np.ndarra return counts.eq(2).to_numpy(dtype=bool) -#: Benefit-unit financial investment income: interest (tax-free included), -#: dividends and other investment income. TOTCAPB4 counts accounts and assets -#: and has no property code (SN 9563), so let-property income is not part of -#: the capital it predicts (microcosm#1095). +#: Benefit-unit financial investment income: interest (ISA interest included, +#: stored beside non-ISA interest since microcosm#1360), dividends and other +#: investment income. TOTCAPB4 counts accounts and assets and has no property +#: code (SN 9563), so let-property income is not part of the capital it +#: predicts (microcosm#1095). UC_FINANCIAL_INVESTMENT_INCOME_COLUMNS = ( "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "other_investment_income", ) diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py index c376de20fe..e8c595f700 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_reporter_redraw.py @@ -115,10 +115,13 @@ UC_REPORTER_REWRITE_ROWS = "spi_channel" UC_REPORTER_LANDING = "eldest_working_age_adult_lowest_person_id" _HOUSEHOLD_CGT_CLONE_COLUMN = "household_is_capital_gains_clone" +#: Interest is ISA interest included, as gross interest held it before the +#: additive contract (microcosm#1360). _PERSON_INCOME_COLUMNS = ( "employment_income", "self_employment_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "property_income", "other_investment_income", @@ -553,6 +556,7 @@ def _benefit_unit_predictors( grouped[ [ "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "property_income", "other_investment_income", diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/was_lisa.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/was_lisa.py index 8b1871e5cf..aa9c7d63f9 100644 --- a/packages/microcosm-build/src/microcosm/build/uk_runtime/was_lisa.py +++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/was_lisa.py @@ -44,6 +44,14 @@ persons' balances scaled down pro rata, with a receipt. WAS records holdings, not annual contributions; nothing here derives a contribution or a bonus from a balance. + +When the stage declares ``anchor_lifetime_isa_ownership`` (microcosm#1415), the +ownership logit is shifted on the ages that can hold a LISA in the build year until +the weighted holders who survive the financial-wealth cap, at the ages that may pay +in, equal HMRC's Table 9.4 Lifetime ISA accounts subscribed to that year. A person +pays into one LISA a year and every payer holds one, so that count is the fewest +holders consistent with HMRC. The same keyed uniforms decide ownership, so the +anchor only adds holders; one it cannot reach is named in the receipt. """ from __future__ import annotations @@ -60,6 +68,13 @@ from microcosm.build.source_manifest import SourceStageSpec from microcosm.build.stochastic_assignment import stable_identity_uniforms from microcosm.build.uk_runtime.frs_spine import read_pinned_tab +from microcosm.build.uk_runtime.hmrc_isa_statistics import ( + HMRC_ISA_BUILD_TAX_YEAR, + HMRCISAStatistics, + check_isa_statistics_declaration, + lifetime_isa_paying_ages, + load_hmrc_isa_statistics, +) from microcosm.build.uk_runtime.national_frame import ( uk_household_mass_conservation_receipt, uk_household_weight_kind, @@ -85,10 +100,12 @@ "UK_WAS_LISA_OUTPUT_COLUMNS", "UK_WAS_LISA_STAGE_NAME", "BalanceModelSpec", + "OwnershipAnchorSpec", "OwnershipModelSpec", "UKWASLISAStageTransform", "WASLISAColumns", "WASLISAError", + "anchor_lifetime_isa_ownership", "cap_lifetime_isa_to_financial_wealth", "clean_was_lisa_donor", "fit_ownership_model", @@ -114,8 +131,10 @@ CLEAN_WAS_LISA_DONOR_KIND = "clean_was_lisa_donor" IMPUTE_LIFETIME_ISA_OWNERSHIP_KIND = "impute_lifetime_isa_ownership" +ANCHOR_LIFETIME_ISA_OWNERSHIP_KIND = "anchor_lifetime_isa_ownership" IMPUTE_LIFETIME_ISA_BALANCE_KIND = "impute_lifetime_isa_balance" CAP_LIFETIME_ISA_TO_FINANCIAL_WEALTH_KIND = "cap_lifetime_isa_to_financial_wealth" +VERIFY_VENDORED_FACT_RESOURCE_KIND = "verify_vendored_fact_resource" OWNERSHIP_COLUMN = "has_lifetime_isa" BALANCE_COLUMN = "lifetime_isa_balance" @@ -192,10 +211,17 @@ def _artifact(stage: SourceStageSpec, role: str) -> Mapping[str, Any]: def _operation(stage: SourceStageSpec, kind: str) -> Mapping[str, Any]: + declared = _declared_operation(stage, kind) + if declared is None: + raise WASLISAError(f"{stage.stage} declares no {kind!r} operation.") + return declared + + +def _declared_operation(stage: SourceStageSpec, kind: str) -> Mapping[str, Any] | None: for operation in stage.operations: if operation.kind == kind: return dict(operation.parameters) - raise WASLISAError(f"{stage.stage} declares no {kind!r} operation.") + return None def _mapping(value: object, label: str) -> Mapping[str, Any]: @@ -1034,6 +1060,168 @@ def impute_lifetime_isa_ownership( ) +@dataclass(frozen=True) +class OwnershipAnchorSpec: + """The declared anchor of ownership to HMRC's count (``anchor_lifetime_isa_ownership``). + + WAS round 8 (2020-22) implies about half the Lifetime ISA holders HMRC's 2024-25 + subscriptions need (microcosm#1415): every payer holds an account and a person + can pay into one Lifetime ISA a year, so Table 9.4's accounts subscribed to are + payers. Without a published holder count the anchor makes the weighted holders + after the financial-wealth cap equal that payer count, the fewest holders + consistent with HMRC. ``ages`` are the ages at which holders may be added and + ``counted_ages`` those whose holders are counted against the payers. Both are + the ages that can hold and pay into a LISA in the tax year + (``lifetime_isa_paying_ages``: opened at 18 to 39 since April 2017, so 18 to 47 + in 2024-25): an older holder the base model draws cannot be one of HMRC's + payers (review of microcosm#1421). + """ + + target: str + tax_year: int + minimum_age: int + maximum_age: int + counted_minimum_age: int + counted_maximum_age: int + + @classmethod + def from_parameters(cls, parameters: Mapping[str, Any]) -> OwnershipAnchorSpec: + if parameters.get("output") != OWNERSHIP_COLUMN: + raise WASLISAError( + f"anchor_lifetime_isa_ownership must rewrite {OWNERSHIP_COLUMN!r}." + ) + target = str(parameters.get("target")) + if target != "table_9_4_lifetime_isa_accounts_subscribed": + raise WASLISAError( + "the ownership anchor is Table 9.4's Lifetime ISA accounts subscribed to." + ) + tax_year = int(parameters["tax_year"]) + if tax_year != HMRC_ISA_BUILD_TAX_YEAR: + raise WASLISAError( + f"the ownership anchor counts Table 9.4's {HMRC_ISA_BUILD_TAX_YEAR} " + f"payers, not {tax_year}'s." + ) + ages = _mapping(parameters.get("ages"), "ages") + minimum, maximum = int(ages["minimum"]), int(ages["maximum"]) + counted = _mapping(parameters.get("counted_ages"), "counted_ages") + counted_minimum, counted_maximum = ( + int(counted["minimum"]), + int(counted["maximum"]), + ) + if not 0 < minimum <= maximum: + raise WASLISAError("the anchor's ages must be a non-empty range.") + if not counted_minimum <= minimum <= maximum <= counted_maximum: + raise WASLISAError( + "the anchor adds holders only at ages whose holders it counts." + ) + paying = lifetime_isa_paying_ages(tax_year) + if (counted_minimum, counted_maximum) != paying: + raise WASLISAError( + f"the anchor counts holders aged {paying[0]} to {paying[1]}, the ages " + f"that can pay into a Lifetime ISA in {tax_year}, not " + f"{counted_minimum} to {counted_maximum}." + ) + return cls( + target=target, + tax_year=tax_year, + minimum_age=minimum, + maximum_age=maximum, + counted_minimum_age=counted_minimum, + counted_maximum_age=counted_maximum, + ) + + def counted(self, ages: np.ndarray) -> np.ndarray: + """Persons whose holding counts against the payers.""" + + ages = np.asarray(ages, dtype=float) + return (ages >= self.counted_minimum_age) & (ages <= self.counted_maximum_age) + + +def anchor_lifetime_isa_ownership( + ownership: OwnershipImputation, + recipient: pd.DataFrame, + *, + spec: OwnershipModelSpec, + anchor: OwnershipAnchorSpec, + target_holders: float, + person_weights: np.ndarray, + survives_cap: np.ndarray, +) -> OwnershipImputation: + """Shift the ownership logit on the anchor ages up to the target holder count. + + The same person-keyed uniforms decide ownership, so the shift, never negative, + only adds holders. Holders are counted at the anchor's counted ages as the + financial-wealth cap leaves them (``survives_cap``: the household's gross + financial wealth is positive), on the spine's weights; the shift is solved by + bisection to within one person's weight of the target. Holders already at or + above the target are left as drawn; a target above what the largest shift + reaches is named ``target_above_reach``. + """ + + ages = recipient["age"].to_numpy(dtype=float) + adult = ages >= spec.minimum_age + in_ages = adult & (ages >= anchor.minimum_age) & (ages <= anchor.maximum_age) + uniforms = stable_identity_uniforms( + recipient["person_id"].to_numpy(), seed=spec.seed, salt=spec.salt + ) + probability = np.clip(ownership.probability, 1e-12, 1.0 - 1e-12) + logit = np.log(probability / (1.0 - probability)) + weights = np.asarray(person_weights, dtype=float) + counted = np.asarray(survives_cap, dtype=bool) & anchor.counted(ages) + + def draw(shift: float) -> tuple[np.ndarray, np.ndarray, float]: + shifted = np.where( + in_ages, 1.0 / (1.0 + np.exp(-(logit + shift))), ownership.probability + ) + shifted = np.where(adult, shifted, 0.0) + holds = adult & (uniforms < shifted) + return holds, shifted, float(weights[holds & counted].sum()) + + _, _, before = draw(0.0) + maximum_shift = 30.0 + saturated = None + if before >= target_holders: + shift = 0.0 + elif draw(maximum_shift)[2] < target_holders: + shift = maximum_shift + saturated = "target_above_reach" + else: + low, high = 0.0, maximum_shift + for _ in range(200): + mid = (low + high) / 2.0 + if draw(mid)[2] < target_holders: + low = mid + else: + high = mid + shift = high + holds, shifted, after = draw(shift) + if shift == 0.0: + holds, shifted = ownership.holds, ownership.probability + added = holds & ~ownership.holds + receipt = { + **ownership.receipt, + "anchor": { + "target": anchor.target, + "tax_year": anchor.tax_year, + "ages": [anchor.minimum_age, anchor.maximum_age], + "counted_ages": [anchor.counted_minimum_age, anchor.counted_maximum_age], + "target_holders": float(target_holders), + "holders_before": before, + "holders_after": after, + "logit_shift": shift, + "saturated": saturated, + "holders_added_weight": float(weights[added].sum()), + "removed_holders": int((ownership.holds & ~holds).sum()), + }, + } + return OwnershipImputation( + holds=holds, + probability=shifted, + receipt=receipt, + fit_weight_records=ownership.fit_weight_records, + ) + + # -------------------------------------------------------------------------- # Balance: holders-only QRF at the owner's keyed quantile # -------------------------------------------------------------------------- @@ -1270,9 +1458,11 @@ class UKWASLISAResult: realised: Mapping[str, Any] financial_wealth_coherence: Mapping[str, Any] population: Mapping[str, Any] + #: Weighted holders at the anchor's counted ages after the cap (anchored only). + anchored_holders_after_cap: float | None = None def evidence(self) -> dict[str, object]: - return { + evidence: dict[str, object] = { "stage": UK_WAS_LISA_STAGE_NAME, "support_clip": self.support_clip.evidence(), "donor": dict(self.donor), @@ -1282,6 +1472,9 @@ def evidence(self) -> dict[str, object]: "financial_wealth_coherence": dict(self.financial_wealth_coherence), "population": dict(self.population), } + if self.anchored_holders_after_cap is not None: + evidence["anchored_holders_after_cap"] = self.anchored_holders_after_cap + return evidence # -------------------------------------------------------------------------- @@ -1301,6 +1494,9 @@ class UKWASLISAStageTransform: #: case-insensitive either way. donor_household: pd.DataFrame | None = None donor_person: pd.DataFrame | None = None + #: Supplied ISA facts (tests and the synthetic fixture); else the vendored + #: resource, read only when the stage declares the ownership anchor. + statistics: HMRCISAStatistics | None = None last_result: UKWASLISAResult | None = field(default=None, init=False) last_fit_weight_records: tuple[FitWeightRecord, ...] | None = field( default=None, init=False, repr=False @@ -1355,6 +1551,22 @@ def __call__(self, frame: Frame) -> Frame: f"{BALANCE_COLUMN!r} at {FINANCIAL_WEALTH_COLUMN!r} " f"({CAP_METHOD}) and re-derive {OWNERSHIP_COLUMN!r}." ) + anchor_parameters = _declared_operation( + self.stage, ANCHOR_LIFETIME_ISA_OWNERSHIP_KIND + ) + anchor = ( + None + if anchor_parameters is None + else OwnershipAnchorSpec.from_parameters(anchor_parameters) + ) + statistics = None + if anchor is not None: + check_isa_statistics_declaration( + _operation(self.stage, VERIFY_VENDORED_FACT_RESOURCE_KIND), + stage=UK_WAS_LISA_STAGE_NAME, + period=int(uk_time_period(frame)), + ) + statistics = self.statistics or load_hmrc_isa_statistics() donor = clean_was_lisa_donor( self._table( self.donor_person, @@ -1373,6 +1585,37 @@ def __call__(self, frame: Frame) -> Frame: ownership = impute_lifetime_isa_ownership( donor.person, recipient, spec=ownership_spec ) + household_table = frame.table("household") + household_weights = np.asarray( + frame.weights_for("household").values, dtype=float + ) + household_ids = household_table["household_id"].to_numpy() + person_household_ids = recipient["person_household_id"].to_numpy() + person_weights = ( + pd.Series(household_weights, index=household_ids) + .reindex(person_household_ids) + .to_numpy(dtype=float) + ) + financial_wealth = ( + pd.to_numeric(household_table[FINANCIAL_WEALTH_COLUMN], errors="coerce") + .fillna(0.0) + .to_numpy(dtype=float) + ) + if anchor is not None and statistics is not None: + ownership = anchor_lifetime_isa_ownership( + ownership, + recipient, + spec=ownership_spec, + anchor=anchor, + target_holders=statistics.accounts_subscribed[ + ("lifetime", anchor.tax_year) + ], + person_weights=person_weights, + survives_cap=pd.Series(financial_wealth > 0.0, index=household_ids) + .reindex(person_household_ids) + .fillna(False) + .to_numpy(dtype=bool), + ) balance = impute_lifetime_isa_balance( donor.person, recipient, holds=ownership.holds, spec=balance_spec ) @@ -1382,20 +1625,11 @@ def __call__(self, frame: Frame) -> Frame: columns=UK_WAS_LISA_SUPPORT_CLIP_COLUMNS, stage=UK_WAS_LISA_STAGE_NAME, ) - household_table = frame.table("household") - household_weights = np.asarray( - frame.weights_for("household").values, dtype=float - ) - household_ids = household_table["household_id"].to_numpy() capped, coherence = cap_lifetime_isa_to_financial_wealth( - person_household_ids=recipient["person_household_id"].to_numpy(), + person_household_ids=person_household_ids, balances=clip.clipped[BALANCE_COLUMN].to_numpy(dtype=float), household_ids=household_ids, - household_financial_wealth=pd.to_numeric( - household_table[FINANCIAL_WEALTH_COLUMN], errors="coerce" - ) - .fillna(0.0) - .to_numpy(dtype=float), + household_financial_wealth=financial_wealth, household_weights=household_weights, ) holds = capped > 0.0 @@ -1408,10 +1642,6 @@ def __call__(self, frame: Frame) -> Frame: recipient["person_household_id"].to_numpy(), capped, household_ids ) # Evidence. - weight_by_household = pd.Series(household_weights, index=household_ids) - person_weights = weight_by_household.reindex( - recipient["person_household_id"].to_numpy() - ).to_numpy(dtype=float) adult = recipient["age"].to_numpy(dtype=float) >= ownership_spec.minimum_age channel = None if "household_support_channel" in household_table: @@ -1497,6 +1727,15 @@ def __call__(self, frame: Frame) -> Frame: realised=realised, financial_wealth_coherence=coherence, population=population, + anchored_holders_after_cap=( + None + if anchor is None + else float( + person_weights[ + holds & anchor.counted(recipient["age"].to_numpy(dtype=float)) + ].sum() + ) + ), ) return result diff --git a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py index 0fa04de9d3..0598be3f5b 100644 --- a/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py +++ b/packages/microcosm-build/src/microcosm/build/us_runtime/worker_identity.py @@ -28,7 +28,7 @@ PRIMARY_QRF_WORKER_MODULE = "microcosm.build.us_runtime.puf_qrf_worker" PRIMARY_QRF_INTERPRETER_PLACEHOLDER = "{python_interpreter}" APPROVED_UV_LOCK_SHA256 = ( - "1a5e0ec898a5f35fa6538c460d46748a0cc2c9295e73012cc370f706b002c3af" + "a6e91aa722c5cf43a0a1d724b6fc3e0556eb0b89692d410f0adfa6f669426839" ) LEGACY_CAMPAIGN_UV_LOCK_SHA256 = ( "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee" diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_cgt_projection.py b/packages/microcosm-build/tests/engine/uk/test_uk_cgt_projection.py index f806c4e168..548b6c0ca1 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_cgt_projection.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_cgt_projection.py @@ -20,11 +20,12 @@ def test_engine_projection_matches_the_published_growth_path() -> None: assert projection.engine.startswith("policyengine-uk==") assert uk_cgt_projection_read_from_engine(projection.engine) assert set(projection.exempt_amount_by_year.values()) == {3_000.0} + # The OBR's October 2026 path (policyengine-uk 2.164.1, microcosm#1415). assert list(projection.yoy_growth_by_year.values()) == pytest.approx( - [0.0474, 0.0294, 0.0326, 0.0317, 0.0306, 0.0315], abs=5e-4 + [0.0462, 0.04, 0.031, 0.0344, 0.036, 0.0343], abs=5e-4 ) assert projection.cumulative_gains_factor_by_year["2030"] == pytest.approx( - 1.2211, abs=5e-4 + 1.2434, abs=5e-4 ) pinned = manifest_entry().parameters for year, rate in projection.yoy_growth_by_year.items(): @@ -32,6 +33,8 @@ def test_engine_projection_matches_the_published_growth_path() -> None: float(pinned["expected_yoy_growth_by_year"][year]), abs=float(pinned["maximum_growth_drift"]), ) - # Beyond the pinned horizon the engine repeats the 2030 rate. + # The path runs one year past the pinned horizon, to 2031, and later years + # repeat the 2031 rate. beyond = uk_cgt_projection(2024, 2033) - assert beyond.yoy_growth_by_year["2033"] == projection.yoy_growth_by_year["2030"] + assert beyond.yoy_growth_by_year["2031"] != projection.yoy_growth_by_year["2030"] + assert beyond.yoy_growth_by_year["2033"] == beyond.yoy_growth_by_year["2031"] diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_dfe_spring_term.py b/packages/microcosm-build/tests/engine/uk/test_uk_dfe_spring_term.py index 4b5c5a2c1c..2461abe46f 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_dfe_spring_term.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_dfe_spring_term.py @@ -1,4 +1,4 @@ -"""The DfE spring-term measures on hand-built households (policyengine-uk 2.162.14).""" +"""The DfE spring-term measures on hand-built households (policyengine-uk 2.164.2).""" from __future__ import annotations diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_calibration.py b/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_calibration.py index eeca0133e7..aa363365fe 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_calibration.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_hmrc_calibration.py @@ -1,6 +1,9 @@ """Tests split from packages/microcosm-build/tests/test_uk_hmrc_calibration.py.""" # ruff: noqa: F403, F405 +from microcosm.build.uk_runtime.hmrc_calibration import ( + HMRC_TAXABLE_SAVINGS_INTEREST_COLUMN, +) from test_support.microcosm_build.uk_hmrc_calibration import * @@ -107,15 +110,69 @@ def test_materialization_fails_closed_when_one_component_has_no_band_support() - ) -def test_materialization_rejects_tax_free_interest_above_gross() -> None: +def test_materialization_rejects_negative_interest_outside_isas() -> None: dataset, targets = _feasible_dataset_and_targets() person = dataset.person.copy() - person.loc[0, "tax_free_savings_income"] = 1.0 + person.loc[0, "savings_interest_income"] = -1.0 broken = _with(dataset, person=person) - with pytest.raises(ValueError, match="gross savings_interest_income"): + with pytest.raises(ValueError, match="negative"): materialize_uk_hmrc_calibration_frame( broken, targets, simulation_factory=_simulation_factory, ) + + +def test_materialization_counts_interest_outside_isas_once_in_either_layout() -> None: + """Table 3.7 and its bands read non-ISA interest once (microcosm#1360). + + The producer stores it; a legacy gross frame stores gross interest with the + ISA part in tax_free_savings_income, taken off once. + """ + + from microcosm.frame.adapters.policyengine_uk import ( + UK_LEGACY_ISA_INCOME_CONTRACT, + ) + + dataset, targets = _feasible_dataset_and_targets() + person = dataset.person.copy() + person["individual_savings_account_interest_income"] = 25.0 + additive = _with(dataset, person=person) + legacy_person = person.drop(columns="individual_savings_account_interest_income") + legacy_person["savings_interest_income"] = person["savings_interest_income"] + 25.0 + legacy_person["tax_free_savings_income"] = 25.0 + legacy = _with( + dataset, + person=legacy_person, + isa_income_contract=UK_LEGACY_ISA_INCOME_CONTRACT, + ) + + readings = [ + materialize_uk_hmrc_calibration_frame( + frame, targets, simulation_factory=_simulation_factory + ) + for frame in (additive, legacy) + ] + columns = (HMRC_TAXABLE_SAVINGS_INTEREST_COLUMN, "hmrc_spi_assessable_income") + tables = [reading.frame.table("person") for reading in readings] + for column in columns: + np.testing.assert_allclose( + tables[0][column].to_numpy(dtype=float), + tables[1][column].to_numpy(dtype=float), + ) + assert readings[0].taxpayer_rows == readings[1].taxpayer_rows + + broken_person = legacy_person.copy() + broken_person.loc[0, "tax_free_savings_income"] = ( + broken_person.loc[0, "savings_interest_income"] + 1.0 + ) + broken = _with( + dataset, + person=broken_person, + isa_income_contract=UK_LEGACY_ISA_INCOME_CONTRACT, + ) + with pytest.raises(ValueError, match="negative"): + materialize_uk_hmrc_calibration_frame( + broken, targets, simulation_factory=_simulation_factory + ) 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 df556390c6..8092026019 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 @@ -10,14 +10,17 @@ def factor(path: str) -> float: path, "2023-01-01" ) + # 1.1067 until policyengine-uk 2.164.1 moved to the OBR's October 2026 + # outlook, which revises 2025 earnings down (microcosm#1415). assert factor( "gov.economic_assumptions.indices.obr.average_earnings" - ) == pytest.approx(1.1067, abs=2e-3) + ) == pytest.approx(1.0943, abs=2e-3) # 1.0344 until policyengine-uk 2.155.0 took population from the OBR's - # March 2026 long-term determinants (PolicyEngine/policyengine-uk#2611). + # March 2026 long-term determinants (PolicyEngine/policyengine-uk#2611), + # and 1.0384 until 2.164.1's October 2026 outlook. assert factor( "gov.economic_assumptions.indices.obr.per_capita.mixed_income" - ) == pytest.approx(1.0384, abs=2e-3) + ) == pytest.approx(1.0327, abs=2e-3) assert factor("gov.dwp.state_pension.new_state_pension.amount") == pytest.approx( 230.25 / 203.85, abs=1e-6 ) @@ -135,8 +138,9 @@ def ratio(path: str, start: int, end: int) -> float: donor_path = 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). - assert target_factor == pytest.approx(1.1961, abs=1e-4) + # 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( ratio(history, 2022, 2023), rel=1e-12 diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_isa_income_contract.py b/packages/microcosm-build/tests/engine/uk/test_uk_isa_income_contract.py index 48f7dbf9b3..e4ae3d834e 100644 --- a/packages/microcosm-build/tests/engine/uk/test_uk_isa_income_contract.py +++ b/packages/microcosm-build/tests/engine/uk/test_uk_isa_income_contract.py @@ -1,11 +1,12 @@ -"""The frame's savings interest reaches policyengine-uk as interest outside ISAs. - -The frame stores ``savings_interest_income`` gross, ISA interest included, -with the ISA part in ``tax_free_savings_income``. From policyengine-uk 2.162.2 -the engine's ``savings_interest_income`` is the interest outside tax-free -accounts: the build declares the ``legacy_gross`` ISA income contract, so the -engine takes the ISA part off when it loads the frame, whatever its default -for a dataset that declares nothing (microcosm#1095). +"""The frame's ISA income contract reaches policyengine-uk (microcosm#1360). + +The producer stores non-ISA interest in ``savings_interest_income`` and ISA +interest in ``individual_savings_account_interest_income`` and declares the +``additive`` contract; ``tax_free_savings_income`` is the engine's sum of the +ISA parts and is never stored. A legacy gross file (the enhanced FRS, Microcosm +before #1360) stores gross interest with the ISA part in +``tax_free_savings_income``; it keeps that reading through every load, and the +build refuses it as an input. Both layouts give the same base-year cash and tax. """ from __future__ import annotations @@ -18,6 +19,9 @@ import pytest from microcosm.build.uk_runtime.national_frame import ( + load_uk_national_frame, + require_uk_producer_isa_income_contract, + uk_isa_income_contract, uk_national_frame, write_uk_national_frame, ) @@ -25,24 +29,42 @@ from microcosm.frame.adapters.policyengine_uk import ( UK_ISA_INCOME_CONTRACT, UK_ISA_INCOME_CONTRACT_ATTR, + UK_LEGACY_ISA_INCOME_CONTRACT, PolicyEngineUKEngine, + uk_isa_income_contract_from, ) - -def _frame(time_period: str = "2025"): - # One saver with 1,000 of gross interest, 300 of it in an ISA, and one - # whose only interest is 300 in an ISA. +#: One saver with 1,000 of interest, 300 of it in an ISA, and one whose only +#: interest is 300 in an ISA, in each layout. +_LAYOUTS = { + "additive": { + "savings_interest_income": [700.0, 0.0], + "individual_savings_account_interest_income": [300.0, 300.0], + }, + "legacy_gross": { + "savings_interest_income": [1_000.0, 300.0], + "tax_free_savings_income": [300.0, 300.0], + }, +} +_CONTRACTS = { + "additive": UK_ISA_INCOME_CONTRACT, + "legacy_gross": UK_LEGACY_ISA_INCOME_CONTRACT, +} + + +def _frame(layout: str = "additive", time_period: str = "2025"): person = pd.DataFrame( { "person_id": [11, 21], "person_benunit_id": [1, 2], "person_household_id": [1, 2], "age": [40.0, 40.0], - "savings_interest_income": [1_000.0, 300.0], - "tax_free_savings_income": [300.0, 300.0], + # The second earns little enough for Universal Credit. + "employment_income": [30_000.0, 8_000.0], + **_LAYOUTS[layout], } ) - benunit = pd.DataFrame({"benunit_id": [1, 2], "would_claim_uc": [False, False]}) + benunit = pd.DataFrame({"benunit_id": [1, 2], "would_claim_uc": [True, True]}) household = pd.DataFrame( { "household_id": [1, 2], @@ -59,47 +81,117 @@ def _frame(time_period: str = "2025"): household_weights=np.ones(2), weight_kind=WeightKind.DESIGN, time_period=time_period, + isa_income_contract=_CONTRACTS[layout], ) -def test_the_adapter_dataset_reads_interest_outside_isas_at_the_frames_year() -> None: - values = PolicyEngineUKEngine().materialize( - _frame(), ["savings_interest_income", "tax_free_savings_income"], 2025 - ) +_BASE_YEAR_MEASURES = ( + "savings_interest_income", + "tax_free_savings_income", + "capital_income", + "taxable_savings_interest_income", + "income_tax", + "universal_credit", + "household_net_income", +) - assert np.asarray(values["savings_interest_income"]) == pytest.approx([700.0, 0.0]) - assert np.asarray(values["tax_free_savings_income"]) == pytest.approx( + +def test_both_layouts_give_the_same_base_year_cash_and_tax() -> None: + engine = PolicyEngineUKEngine() + additive = engine.materialize(_frame("additive"), list(_BASE_YEAR_MEASURES), 2025) + legacy = engine.materialize(_frame("legacy_gross"), list(_BASE_YEAR_MEASURES), 2025) + + assert np.asarray(additive["savings_interest_income"]) == pytest.approx( + [700.0, 0.0] + ) + assert np.asarray(additive["tax_free_savings_income"]) == pytest.approx( [300.0, 300.0] ) + for measure in _BASE_YEAR_MEASURES: + assert np.asarray(additive[measure]) == pytest.approx( + np.asarray(legacy[measure]), abs=1e-6 + ), measure -def test_a_written_frame_declares_the_contract_and_reads_the_same(tmp_path) -> None: +def test_a_written_frame_declares_its_contract_and_loads_with_it(tmp_path) -> None: from policyengine_uk import Microsimulation path = write_uk_national_frame(_frame(), tmp_path / "frame.h5") with h5py.File(path, mode="r") as file: declared = json.loads(file.attrs[UK_ISA_INCOME_CONTRACT_ATTR]) assert declared == dict(UK_ISA_INCOME_CONTRACT) + loaded, _ = load_uk_national_frame(path) + assert uk_isa_income_contract(loaded) == dict(UK_ISA_INCOME_CONTRACT) + require_uk_producer_isa_income_contract(loaded, label="frame.h5") simulation = Microsimulation(dataset=str(path)) interest = simulation.calculate("savings_interest_income", 2025).values assert interest == pytest.approx([700.0, 0.0]) -def test_a_2024_frame_read_at_2025_grows_gross_interest_less_a_flat_isa_part( +def test_a_legacy_file_keeps_its_reading_and_is_refused_as_a_build_input( tmp_path, ) -> None: - """The build's frame is FRS 2024-25, read at 2025. Under legacy_gross the - engine grows the gross interest (by per-capita GDP, 1.0474 to 2025) and takes - off the stored ISA part unchanged, so the saver reads 1,000 x 1.0474 - 300 and - the ISA-only saver keeps 300 x 0.0474 as interest outside ISAs. The count - rows exclude that saver, as they read the frame; the amount rows do not. - Moving the frame to the additive contract (microcosm#1095) changes both - values (Vahid's review of #1400, round 3).""" + path = write_uk_national_frame(_frame("legacy_gross"), tmp_path / "legacy.h5") + loaded, _ = load_uk_national_frame(path) + assert uk_isa_income_contract(loaded) == dict(UK_LEGACY_ISA_INCOME_CONTRACT) + values = PolicyEngineUKEngine().materialize( + loaded, ["savings_interest_income"], 2025 + ) + assert np.asarray(values["savings_interest_income"]) == pytest.approx([700.0, 0.0]) + with pytest.raises(ValueError, match="rebuild it from source"): + require_uk_producer_isa_income_contract(loaded, label="legacy.h5") + + # A file declaring nothing reads as legacy gross, as policyengine-uk reads it. + with h5py.File(path, mode="r+") as file: + del file.attrs[UK_ISA_INCOME_CONTRACT_ATTR] + unmarked, _ = load_uk_national_frame(path) + assert uk_isa_income_contract(unmarked) == dict(UK_LEGACY_ISA_INCOME_CONTRACT) + + +def test_a_malformed_contract_fails_closed(tmp_path) -> None: + for declared in ( + {"version": 2, "representation": "additive", "projection": "additive"}, + {"version": 1, "representation": "legacy_gross", "projection": "additive"}, + {"version": True, "representation": "additive", "projection": "additive"}, + "not json", + ): + with pytest.raises(ValueError, match="ISA income contract"): + uk_isa_income_contract_from(declared) + path = write_uk_national_frame(_frame(), tmp_path / "frame.h5") + with h5py.File(path, mode="r+") as file: + file.attrs[UK_ISA_INCOME_CONTRACT_ATTR] = json.dumps({"version": 1}) + with pytest.raises(ValueError, match="ISA income contract"): + load_uk_national_frame(path) + + +def test_a_2024_frame_read_at_2025_projects_each_component(tmp_path) -> None: + """The build's frame is FRS 2024-25, read at 2025. + + Under legacy_gross the engine grows gross interest (by per-capita GDP, + 1.0462 to 2025 on policyengine-uk 2.164.2) and takes off the stored ISA part + unchanged: 1,000 x 1.0462 - 300 and 300 x 0.0462 outside ISAs. The additive + contract grows non-ISA and ISA interest each by its own index, so the + ISA-only saver has no interest outside ISAs and both savers' ISA interest + grows (microcosm#1360). + """ from policyengine_uk import Microsimulation - path = write_uk_national_frame(_frame("2024"), tmp_path / "frame.h5") - simulation = Microsimulation(dataset=str(path)) - interest = simulation.calculate("savings_interest_income", 2025).values - assert interest == pytest.approx([747.4, 14.22], abs=0.01) + readings = {} + for layout in ("legacy_gross", "additive"): + path = write_uk_national_frame( + _frame(layout, "2024"), tmp_path / f"{layout}.h5" + ) + simulation = Microsimulation(dataset=str(path)) + readings[layout] = { + name: simulation.calculate(name, 2025).values + for name in ("savings_interest_income", "tax_free_savings_income") + } + legacy, additive = readings["legacy_gross"], readings["additive"] + assert legacy["savings_interest_income"] == pytest.approx([746.2, 13.86], abs=0.01) + assert legacy["tax_free_savings_income"] == pytest.approx([300.0, 300.0]) + assert additive["savings_interest_income"] == pytest.approx([732.34, 0.0], abs=0.01) + assert additive["tax_free_savings_income"] == pytest.approx( + [313.86, 313.86], abs=0.01 + ) 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 5326688d92..40eea7a7b7 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 @@ -439,15 +439,21 @@ def test_shipped_manifest_is_current(self) -> None: ) # microcosm#1106: property_wealth is formula-owned in policyengine-uk, # so the release leaves it to the engine; it is an engine-derived - # exclusion, not parity debt, and its components stay required. + # exclusion, not parity debt, and its components stay required. So is + # tax_free_savings_income under the additive ISA income contract + # (microcosm#1360). assert manifest.required_columns == ( frozenset(load_efrs_parity_reference().populated_layers) - - {"property_wealth"} + - {"property_wealth", "tax_free_savings_income"} ) - assert set(manifest.reviewed_exclusions) == {"property_wealth"} + assert set(manifest.reviewed_exclusions) == { + "property_wealth", + "tax_free_savings_income", + } assert load_efrs_parity_known_gaps() == () assert [gap.variable for gap in load_efrs_engine_derived_exclusions()] == [ - "property_wealth" + "property_wealth", + "tax_free_savings_income", ] assert manifest.required_build_stages == frozenset( { @@ -467,6 +473,7 @@ def test_shipped_manifest_is_current(self) -> None: "private_school_attendance", "was_wealth", "was_lisa", + "isa_subscriptions", "regional_property_uprating", "high_value_home_support", "regional_home_values", @@ -783,12 +790,14 @@ def test_formula_owned_persisted_overrides_are_hard_covered(self) -> None: reference["engine"]["formula_owned_persisted_overrides_included"] ) assert len(overrides) == 13 - # The one exception is declared: property_wealth, whose persisted copy + # The exceptions are declared: property_wealth, whose persisted copy # overrides the engine's component sum and is never uprated, so the - # release leaves it to the formula (microcosm#1106). Every other + # release leaves it to the formula (microcosm#1106), and + # tax_free_savings_income, the engine's sum of the ISA interest the + # additive contract stores apart (microcosm#1360). Every other # formula-owned override stays required. engine_derived = {gap.variable for gap in load_efrs_engine_derived_exclusions()} - assert engine_derived == {"property_wealth"} + assert engine_derived == {"property_wealth", "tax_free_savings_income"} assert engine_derived <= overrides assert overrides - engine_derived <= set(manifest.required_columns) assert engine_derived.isdisjoint(manifest.required_columns) 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 9008564d2c..174e03e7ac 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 @@ -20,7 +20,7 @@ def test_spi_preserves_observed_children_outside_the_donor_age_domain( "state_pension_reported", "universal_credit_reported", "savings_interest_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "dla_sc_reported", ] person.loc[child, preserved] = [0, 0, 0, 0, 0, 12, 3, 42] @@ -89,9 +89,12 @@ def test_spi_rebases_income_before_frs_fill_and_preserves_child_inputs( result.person.loc[spi, "employment_income"], 2 * reference.person.loc[spi, "employment_income"], ) - # Only taxable SPI interest is rebased; the FRS fill's £5 tax-free draw - # is already in the build-year basis and must not be doubled. - assert result.person.loc[spi, "savings_interest_income"].eq(205).all() + # Only taxable SPI interest is rebased; the FRS fill's £5 ISA interest draw + # is already in the build-year basis and stays beside it (microcosm#1360). + assert result.person.loc[spi, "savings_interest_income"].eq(200).all() + assert ( + result.person.loc[spi, "individual_savings_account_interest_income"].eq(5).all() + ) # FRS rows keep their own reported dividends, children and adults alike: # the SPI forest never redraws them (uk-data#498, microcosm#1095). base = person[channel].eq("frs") @@ -325,7 +328,7 @@ def fit(self, frame, predictors, targets, *, weights): ) -def test_spi_qrf_stages_use_typed_weights_and_restore_gross_savings( +def test_spi_qrf_stages_use_typed_weights_and_keep_isa_interest_apart( monkeypatch, tmp_path, ) -> None: @@ -382,7 +385,14 @@ def test_spi_qrf_stages_use_typed_weights_and_restore_gross_savings( channel = support_channel_column("person") spi_people = result.person[channel] == "spi" base_people = ~spi_people - assert result.person.loc[spi_people, "savings_interest_income"].eq(105.0).all() + # Interest outside ISAs is the stage-1 draw; ISA interest is the stage-2 + # draw beside it (microcosm#1360). + assert result.person.loc[spi_people, "savings_interest_income"].eq(100.0).all() + assert ( + result.person.loc[spi_people, "individual_savings_account_interest_income"] + .eq(5.0) + .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() @@ -467,9 +477,9 @@ def test_spi_qrf_stages_use_typed_weights_and_restore_gross_savings( + profit_after_relief + result.person.loc[spi_people, "private_pension_income"] ) + # Interest outside ISAs is the taxable part (microcosm#1360). expected_total_investment = ( result.person.loc[spi_people, "savings_interest_income"] - - result.person.loc[spi_people, "tax_free_savings_income"] + result.person.loc[spi_people, "dividend_income"] + result.person.loc[spi_people, "property_income"] + result.person.loc[spi_people, "other_investment_income"] @@ -773,7 +783,9 @@ def test_spi_rebasing_index_is_the_projections_except_where_named() -> None: assert savings["index"] == ( "gov.economic_assumptions.indices.ons.household_interest_income" ) - assert savings["factor"] == pytest.approx(2.2692, abs=1e-4) + # 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) def _zero_taxable_income(person: pd.DataFrame, rows) -> None: @@ -785,7 +797,7 @@ def _zero_taxable_income(person: pd.DataFrame, rows) -> None: "private_pension_income", "state_pension_reported", "savings_interest_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "dividend_income", "property_income", "miscellaneous_income", @@ -953,9 +965,16 @@ def predict_from_uniforms(self, predictors, *, quantiles, sign_uniforms): stage2 = captured["stage2"] assert stage2["rows"] == len(domain_ids) for target in stage2["quantiles"]: + # ISA interest keeps the salt it had as tax_free_savings_income, so the + # additive contract redraws nothing (microcosm#1360). + name = ( + "tax_free_savings_income" + if target == "individual_savings_account_interest_income" + else target + ) for part, salt in ( - ("quantiles", f"{FRS_ONLY_FIT_NAME}:{target}"), - ("sign", f"{FRS_ONLY_FIT_NAME}:{target}:sign"), + ("quantiles", f"{FRS_ONLY_FIT_NAME}:{name}"), + ("sign", f"{FRS_ONLY_FIT_NAME}:{name}:sign"), ): np.testing.assert_array_equal( stage2[part][target], 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 4ef91ac803..dc7648f379 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_country_spec.py @@ -934,6 +934,7 @@ def test_spi_spine_adds_no_country_package_resources(self) -> None: "hmrc_cgt_asset_type_facts.json", "hmrc_property_rental_income_facts.json", "hmrc_stamp_tax_facts.json", + "hmrc_isa_statistics_facts.json", "dwp_move_to_uc_facts.json", "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", @@ -1053,7 +1054,7 @@ def test_uk_source_manifest_contains_only_canonical_spine_stages(self) -> None: # child_benefit_take_up; 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. - assert len(spec.sources.stages) == 49 + assert len(spec.sources.stages) == 50 assert not {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.intersection( stage.stage for stage in spec.sources.stages ) @@ -1096,6 +1097,7 @@ def test_uk_package_loads(self) -> None: "hmrc_cgt_asset_type_facts.json", "hmrc_property_rental_income_facts.json", "hmrc_stamp_tax_facts.json", + "hmrc_isa_statistics_facts.json", "dwp_move_to_uc_facts.json", "dwp_uc_award_band_facts.json", "obr_hvcts_costing_facts.json", @@ -1441,6 +1443,7 @@ def test_declares_the_full_june_battery(self, manifest) -> None: "uk_stage_was_wealth_support", "uk_stage_was_wealth_coherence", "uk_stage_was_lisa_support", + "uk_stage_was_lisa_anchor", "uk_stage_property_components", "uk_stage_nts_bus_travel_support", "uk_stage_nts_bus_travel_facts", @@ -1481,6 +1484,8 @@ def test_declares_the_full_june_battery(self, manifest) -> None: "uk_stage_property_purchases_fit", "uk_stage_uc_legacy_closure_take_up_contract", "uk_stage_frs_person_draws_apprentices", + "uk_stage_isa_subscriptions_invariants", + "uk_stage_isa_subscriptions", "uk_release_input_coverage", "uk_degenerate_release_surface", "uk_zero_weight_strata", @@ -1572,6 +1577,7 @@ def test_strict_absent_evidence_entries_are_declared(self, manifest) -> None: "uk_stage_was_wealth_support", "uk_stage_was_wealth_coherence", "uk_stage_was_lisa_support", + "uk_stage_was_lisa_anchor", "uk_stage_property_components", "uk_stage_nts_bus_travel_support", "uk_stage_nts_bus_travel_facts", @@ -1612,6 +1618,8 @@ def test_strict_absent_evidence_entries_are_declared(self, manifest) -> None: "uk_stage_property_purchases_fit", "uk_stage_uc_legacy_closure_take_up_contract", "uk_stage_frs_person_draws_apprentices", + "uk_stage_isa_subscriptions_invariants", + "uk_stage_isa_subscriptions", "uk_weights_audit", # The #970 projection fence: a seam that cannot project cannot # certify the candidate's sub-exempt gainers. diff --git a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py index 8146bdbacd..e05cfe827e 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_engine_core_pins.py @@ -33,7 +33,7 @@ #: policyengine-core its own floor brings. UK_ENGINE = { "policyengine-core": "3.33.3", - "policyengine-uk": "2.162.14", + "policyengine-uk": "2.164.2", } ENGINE_PACKAGES = ("microcosm-build", "microcosm-data", "microcosm-frame") diff --git a/packages/microcosm-build/tests/engine_free/shared/test_spec_source_citations.py b/packages/microcosm-build/tests/engine_free/shared/test_spec_source_citations.py index 9e1a37565f..636ca2b74b 100644 --- a/packages/microcosm-build/tests/engine_free/shared/test_spec_source_citations.py +++ b/packages/microcosm-build/tests/engine_free/shared/test_spec_source_citations.py @@ -64,7 +64,6 @@ import json import re import string -import time from collections.abc import Callable from html.entities import html5 from pathlib import Path, PurePosixPath @@ -3250,135 +3249,6 @@ def test_a_line_link_of_this_repository_is_refused_however_markup_escapes_it( } -def test_a_long_string_is_scanned_in_linear_time() -> None: - # Each of these once took time quadratic in its length: a run of path - # characters was rescanned from each of its characters, and a user was - # looked for before a GitHub host from each character of a run that can - # be in one. At these lengths the quadratic scans took seconds each. - started = time.process_time() - for text in ( - ":" * 60_000, - "//" * 30_000, - "a" * 60_000, - "github.com/" * 6_000, - "https://a/" + "%74" * 20_000, - ): - line_citations(text) - unrecognised_line_locators(text) - # So did a string holding thousands of citations: each one's clause, - # commits and commit-like tokens were found again from the whole string. - for text in ( - "x.py:1 " * 8_000, - "x.py:1 at a1b2c3d " * 4_000, - "a1b2c3d; " + "x.py:1; " * 8_000, - "x.py:1 y.py foo L1 " * 4_000, - ): - misspelled_commit_pins(text) - unpinned_line_citations(text) - unrecognised_line_locators(text) - assert time.process_time() - started < 2 - # Each of these took 4 to 28 seconds: a list of line numbers that failed - # was retried once for each way to share its whitespace out (exponential - # in its length), a failed locator once for each way to share a run of - # spaces or quotes, a failed range once for each way to share the spaces - # after its dash, a reverse locator was tried from each number of a - # failed list, and a long host label was encoded in Punycode (quadratic). - # The exponential ones come first, so a regression fails before the - # slower ones run. - for text in ( - "L1 and " * 24, - "lines " + "1 and " * 8 + "in x.py", - "lines " + "1 and " * 10 + "in x.py", - "lines " + "1, " * 24 + "in x.py", - "x.py" + " " * 1_024 + "x", - "x.py" + "'" * 16_000 + "L1x", - "line " + " " * 32_768 + "1x in x.py", - "lines L1-" + " " * 32_768 + "2x in x.py", - "x.py line" + " " * 65_536 + "1x", - "lines L1 to" + " " * 32_768 + "2x in x.py", - "L1 and " * 8_000, - "https://" + "".join(map(chr, range(0x4000, 0x4000 + 8_000))) + "/a.py#L12", - ): - line_citations(text) - unrecognised_line_locators(text) - assert time.process_time() - started < 2, text[:24] - # Since round 9: ranges that each end where the next starts, a list read - # again from its last number, and runs of what a markup escape starts with. - for text in ( - "L1-" * 16_000, - "L1-L2-L3" + ", 4" * 8_000 + " x", - "&#" + "0" * 100_000 + "35;", - "&#x" * 30_000, - "\\" * 100_000, - "&a" * 50_000, - ): - line_citations(text) - unrecognised_line_locators(text) - assert time.process_time() - started < 2, text[:24] - # Since round 10, in 2 s of their own: runs of what an attribute's value, - # a destination in angle brackets, a long legacy-looking name and a URL - # read through Unicode spaces start with. - started = time.process_time() - for text in ( - '="https:' * 20_000, - "='" + "x" * 100_000, - "]( None: "horizon_year": 2030, "gains_growth_parameter": UK_CGT_GAINS_GROWTH_PARAMETER, "exempt_amount_parameter": UK_CGT_EXEMPT_AMOUNT_PARAMETER, - # The OBR per-capita path as the engine carries it; 2030 is its last - # published year and later years repeat the 2030 rate. + # The OBR per-capita path as the engine carries it, from the October + # 2026 outlook since policyengine-uk 2.164.1 (microcosm#1415); the + # path runs to 2031 and later years repeat the 2031 rate. "expected_yoy_growth_by_year": { - "2023": 0.0524, - "2024": 0.0389, - "2025": 0.0474, - "2026": 0.0294, - "2027": 0.0326, - "2028": 0.0317, - "2029": 0.0306, - "2030": 0.0315, + "2023": 0.0522, + "2024": 0.0401, + "2025": 0.0462, + "2026": 0.04, + "2027": 0.031, + "2028": 0.0344, + "2029": 0.036, + "2030": 0.0343, }, # 6,000 in 2023-24, 3,000 from 2024-25: a 2023-period frame's base # year reads the older amount. @@ -366,7 +367,7 @@ def test_projection_from_pins_is_the_engine_free_statement_of_the_path() -> None assert pinned.engine == UK_CGT_PROJECTION_PINS_ENGINE assert pinned.exempt_amount_by_year["2023"] == 6_000.0 assert pinned.exempt_amount_by_year["2024"] == 3_000.0 - assert pinned.yoy_growth_by_year["2024"] == pytest.approx(0.0389) + assert pinned.yoy_growth_by_year["2024"] == pytest.approx(0.0401) with pytest.raises(ValueError, match="no .* value for 2031"): uk_cgt_projection_from_pins( 2024, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_council_tax_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_council_tax_targets.py index 33b051506d..1471c5a2be 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_council_tax_targets.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_council_tax_targets.py @@ -613,10 +613,13 @@ def test_composed_english_region_cells_sum_to_the_publisher_england_row() -> Non facts = _pinned_feed_facts() spec = load_country_spec("uk") + # England's single adult discount row (microcosm#953) is national, not a + # composed region cell. composed = { reference.name: reference for reference in spec.target_references if reference.name.startswith("mhclg.council_tax_stock.") + and reference.name != _SINGLE_ADULT_DISCOUNT } assert len(composed) == 81 compiled = compile_uk_target_registry(facts, target_period=2025).registry @@ -626,6 +629,7 @@ def test_composed_english_region_cells_sum_to_the_publisher_england_row() -> Non else target.name: (target.value) for target in compiled.specs if target.name.startswith("mhclg.council_tax_stock.") + and target.name.removesuffix("@2025") != _SINGLE_ADULT_DISCOUNT } assert len(values) == 81, sorted(values)[:3] 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 96658862f4..1b863ab4b9 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 @@ -1243,8 +1243,12 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["self_employment_income"] == pytest.approx(3 * WEEKS_IN_YEAR) assert adult["trading_loss"] == 0.0 assert adult["private_pension_income"] == pytest.approx(15 * WEEKS_IN_YEAR) - assert adult["tax_free_savings_income"] == pytest.approx(1 * WEEKS_IN_YEAR) - assert adult["savings_interest_income"] == pytest.approx(3.5 * WEEKS_IN_YEAR) + # ISA interest apart from non-ISA interest (microcosm#1360). + 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) + 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) @@ -1287,14 +1291,15 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None: assert adult["salary_sacrifice_asked"] == 1 -#: The spine's person income columns (#1320, uk-data#627). -#: tax_free_savings_income is not one: it is the ISA part of -#: savings_interest_income. +#: The spine's person income columns (#1320, uk-data#627). ISA interest is one +#: since the additive contract stores it apart from non-ISA interest +#: (microcosm#1360). _PERSON_INCOME_COLUMNS = ( "employment_income", "self_employment_income", "private_pension_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "other_investment_income", "property_income", diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py index 0b68f5f2e9..493f29e623 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_stochastic.py @@ -221,7 +221,7 @@ def test_uc_take_up_population_excludes_units_without_a_working_age_adult() -> N def test_uc_take_up_population_needs_a_claimant_or_partner_of_working_age() -> None: """A working-age dependant does not bring a unit into the UC population. - policyengine-uk 2.162.14's is_uc_eligible reads the age of the unit's UC + policyengine-uk 2.164.2's is_uc_eligible reads the age of the unit's UC claimant or partner (is_uc_claimant), not of any adult: a pensioner unit with an 18-year-old dependant cannot claim Universal Credit. """ 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 a1f47ebe7f..1e126d1d19 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 @@ -60,7 +60,7 @@ def test_uk_spine_graph_contains_every_manifest_stage() -> None: # regional_home_values, microcosm#1319 carers_allowance_take_up, the # copies' childcare re-draw childcare_copy_take_up, and microcosm#1334 # uc_take_up and frs_annual_income_spells. - assert len(expected) == 49 + assert len(expected) == 50 assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(expected) assert set(expected) <= ids assert {"frs_hmrc_retained_leaves", "hmrc_spi_income"}.isdisjoint(ids) @@ -220,6 +220,34 @@ def changed_helper_bytes(path: Path) -> bytes: assert registry.implementation_hash(control) == before[control] +def test_isa_statistics_source_changes_invalidate_the_stages_that_read_them( + monkeypatch, +): + # The ISA subscriptions and the Lifetime ISA anchor read HMRC's ISA tables + # through the typed loader, so it is bound as both stages' helper + # (microcosm#1415). + from microcosm.build.uk_runtime import hmrc_isa_statistics + + graph = uk_spine_graph() + registry = uk_registry(graph=graph) + affected = [graph.node(stage).kernel for stage in ("isa_subscriptions", "was_lisa")] + control = graph.node("frs_council_tax").kernel + before = {ref: registry.implementation_hash(ref) for ref in (*affected, control)} + helper_path = Path(hmrc_isa_statistics.__file__).resolve() + original = Path.read_bytes + + def changed_helper_bytes(path: Path) -> bytes: + content = original(path) + if path.resolve() == helper_path: + return content + b"\n# A helper-only source change.\n" + return content + + monkeypatch.setattr(Path, "read_bytes", changed_helper_bytes) + for ref in affected: + assert registry.implementation_hash(ref) != before[ref] + assert registry.implementation_hash(control) == before[control] + + def test_uk_adapter_source_changes_invalidate_all_consuming_stages(monkeypatch): from microcosm.frame.adapters import policyengine_uk diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_stage_health.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_stage_health.py new file mode 100644 index 0000000000..2fcb4e8602 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_stage_health.py @@ -0,0 +1,177 @@ +"""Stage-health checks of the ISA subscriptions and Lifetime ISA anchor (microcosm#1415).""" + +from __future__ import annotations + +import copy + +import pytest + +from microcosm.build.uk_runtime.stage_health import uk_stage_health_gate + +PARAMETERS = { + "maximum_relative_total_deviation": 1e-6, + "maximum_household_sum_error_gbp": 0.01, +} +PASSING = { + "stage": "isa_subscriptions", + "balance_split": {"max_abs_household_sum_error_gbp": 1e-9}, + "checks": { + "violations": { + "non_finite_values": 0, + "over_overall_limit": 0, + "lifetime_over_annual_limit": 0, + "subscriber_under_minimum_age": 0, + "cash_without_cash_holding": 0, + "stocks_without_stocks_holding": 0, + "both_class_missing_a_part": 0, + "negative_amounts": 0, + } + }, + "growth": { + "cash_target_gbp": 100.0, + "cash_achieved_gbp": 100.0, + "stocks_and_shares_target_gbp": 50.0, + "stocks_and_shares_achieved_gbp": 50.0, + }, + "both_type_split": { + "saturated": None, + "stocks_and_shares_target_gbp": 10.0, + "stocks_and_shares_achieved_gbp": 10.0, + }, + "lifetime_isa": { + "saturated": None, + "target_gbp": 5.0, + "achieved_gbp": 5.0, + "paying_weight": 3.0, + "hmrc_accounts_subscribed": 3.0, + }, + "selection": {"cash_only": {"base": {"shortfall_weight_unmet": 12.0}}}, +} + + +def _gate(evidence, check="isa_subscriptions", parameters=PARAMETERS): + return uk_stage_health_gate( + evidence=evidence, stage=evidence["stage"], check=check, parameters=parameters + ) + + +def test_a_coherent_receipt_passes_and_names_shortfalls(): + assert _gate(PASSING, "isa_subscriptions_invariants").passed + result = _gate(PASSING) + assert result.passed, result.failures + assert ( + result.details["selection"]["cash_only"]["base"]["shortfall_weight_unmet"] + == 12.0 + ) + # Subscribers the eligible holders could not supply are named, not failed. + assert result.details["saturations_named"] == [ + "selection:cash_only:base:shortfall_weight_unmet" + ] + assert result.details["lifetime_isa_paying_weight"] == 3.0 + assert result.details["lifetime_isa_hmrc_accounts_subscribed"] == 3.0 + + +@pytest.mark.parametrize( + ("path", "value", "check"), + [ + # Rules any population keeps: their own entry, which the synthetic + # smoke build blocks on (review of microcosm#1421). + (("checks", "violations", "over_overall_limit"), 1, "invariants"), + (("checks", "violations", "non_finite_values"), 1, "invariants"), + (("balance_split", "max_abs_household_sum_error_gbp"), 5.0, "invariants"), + # HMRC's totals: the population fact check. + (("growth", "cash_achieved_gbp"), 90.0, "totals"), + (("both_type_split", "stocks_and_shares_achieved_gbp"), 9.0, "totals"), + (("lifetime_isa", "achieved_gbp"), 4.0, "totals"), + ], +) +def test_each_entry_fails_on_its_own_rules(path, value, check): + evidence = copy.deepcopy(PASSING) + target = evidence + for key in path[:-1]: + target = target[key] + target[path[-1]] = value + invariants = _gate(evidence, "isa_subscriptions_invariants") + totals = _gate(evidence) + assert invariants.passed is (check != "invariants") + assert totals.passed is (check != "totals") + + +def test_saturation_is_named_not_failed(): + evidence = copy.deepcopy(PASSING) + evidence["lifetime_isa"] = { + "saturated": "target_above_reach", + "target_gbp": 5.0, + "achieved_gbp": 1.0, + } + result = _gate(evidence) + assert result.passed + assert result.details["saturations_named"] == [ + "lifetime_isa:target_above_reach", + "selection:cash_only:base:shortfall_weight_unmet", + ] + + +ANCHOR_PARAMETERS = {"maximum_relative_deviation": 0.01} + + +def _anchor_evidence(achieved=1004.0, **anchor): + return { + "stage": "was_lisa", + "ownership_model": { + "anchor": { + "target_holders": 1000.0, + "logit_shift": 1.2, + "saturated": None, + "removed_holders": 0, + **anchor, + } + }, + "anchored_holders_after_cap": achieved, + } + + +@pytest.mark.parametrize( + ("evidence", "passes"), + [ + (_anchor_evidence(), True), + (_anchor_evidence(900.0), False), + (_anchor_evidence(1000.0, removed_holders=3), False), + # Holders drawn at or above the payers are left as drawn. + (_anchor_evidence(1300.0, logit_shift=0.0), True), + (_anchor_evidence(900.0, logit_shift=0.0), False), + (_anchor_evidence(1000.0, logit_shift=-0.5), False), + ( + _anchor_evidence(400.0, logit_shift=30.0, saturated="target_above_reach"), + True, + ), + ], +) +def test_the_lifetime_isa_anchor_check(evidence, passes): + result = _gate(evidence, "was_lisa_anchor", ANCHOR_PARAMETERS) + assert result.passed is passes, result.failures + + +def test_an_unreachable_anchor_is_named(): + result = _gate( + _anchor_evidence(400.0, logit_shift=30.0, saturated="target_above_reach"), + "was_lisa_anchor", + ANCHOR_PARAMETERS, + ) + assert result.details["saturations_named"] == ["anchor:target_above_reach"] + + +def test_unshifted_holders_above_the_target_are_named(): + # The anchor only adds holders, so it cannot bring the base model's + # overshoot down: the build report names it. + result = _gate( + _anchor_evidence(1300.0, logit_shift=0.0), "was_lisa_anchor", ANCHOR_PARAMETERS + ) + assert result.passed + assert result.details["saturations_named"] == [ + "anchor:unshifted_holders_above_target" + ] + within = _gate( + _anchor_evidence(1005.0, logit_shift=0.0), "was_lisa_anchor", ANCHOR_PARAMETERS + ) + assert within.passed and within.details["saturations_named"] == [] 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 new file mode 100644 index 0000000000..9b0697df75 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_isa_subscriptions.py @@ -0,0 +1,590 @@ +"""The ISA subscriptions stage's pieces on synthetic data (microcosm#1415).""" + +from __future__ import annotations + +import dataclasses + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.source_manifest import SourceOperationSpec +from microcosm.build.uk_runtime import isa_subscriptions as stage +from microcosm.build.uk_runtime.cgt_imputation import ( + UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS, +) +from microcosm.build.uk_runtime.hmrc_isa_statistics import ( + HMRC_ISA_INCOME_BAND_IDS, + HMRCISAStatisticsError, + isa_band_bounds, + lifetime_isa_paying_ages, + load_hmrc_isa_statistics, +) +from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.frame import WeightKind +from test_support.microcosm_build.uk_isa_statistics import ( + isa_stage_spec, + synthetic_isa_statistics_payload, + synthetic_was_isa_tabs, +) + + +@pytest.fixture(scope="module") +def statistics(): + return load_hmrc_isa_statistics( + payload=synthetic_isa_statistics_payload(), verify_feed_identity=False + ) + + +@pytest.fixture(scope="module") +def spec(): + return stage.ISASubscriptionsSpec.from_stage(isa_stage_spec()) + + +@pytest.fixture(scope="module") +def donor(spec): + household, person = synthetic_was_isa_tabs() + return stage.clean_was_isa_person_donor( + person, + household, + columns=spec.columns, + maximum_adults=spec.maximum_donor_adults, + ) + + +def _population(seed: int = 1, households: int = 800): + rng = np.random.default_rng(seed) + household = pd.DataFrame( + { + "household_id": np.arange(households) + 1000, + "cash_isa": np.where( + rng.random(households) < 0.75, rng.integers(100, 80_000, households), 0 + ).astype(float), + "stocks_and_shares_isa": np.where( + rng.random(households) < 0.4, rng.integers(100, 150_000, households), 0 + ).astype(float), + "region": rng.choice(sorted(stage._REGION_ROW), households), + } + ) + rows = [] + for household_id in household["household_id"]: + for _ in range(int(rng.integers(1, 4))): + rows.append( + { + "person_id": 10_000 + len(rows), + "person_household_id": household_id, + "age": float(rng.integers(16, 90)), + "gender": rng.choice(["MALE", "FEMALE"]), + } + ) + person = pd.DataFrame(rows) + weights = pd.Series(rng.uniform(5, 30, households), index=household["household_id"]) + income = rng.lognormal(10.3, 0.9, len(person)) + ages = person["age"].to_numpy(float) + recipients = pd.DataFrame( + { + "person_id": person["person_id"].to_numpy(), + "household_id": person["person_household_id"].to_numpy(), + "weight": weights.reindex(person["person_household_id"]).to_numpy(), + "age": ages, + "sex": person["gender"].str.lower().to_numpy(), + "total_income": income, + "income_band": stage._income_band_ids(income), + "age_band": stage._age_band_ids(ages), + "region": household.set_index("household_id")["region"] + .reindex(person["person_household_id"]) + .map(stage._REGION_ROW) + .to_numpy(), + # Enough holders that the payments reach the synthetic Table 9.4 + # amount without saturating. + "holds_lifetime_isa": (rng.random(len(person)) < 0.10) + & (ages < 45) + & (ages >= 18), + "lifetime_isa_balance": rng.uniform(100, 20_000, len(person)), + } + ) + return household, person, recipients + + +def _run(spec, donor, statistics, seed: int = 1): + household, person, recipients = _population(seed) + cash_balance, stocks_balance, receipt = stage.split_household_isa_balances( + person=person, + household=household, + donor=donor, + seed=spec.split_seed, + salt=spec.split_salt, + minimum_age=spec.minimum_age, + maximum_adults=spec.maximum_donor_adults, + ) + cash, stocks, lifetime, result = stage.impute_isa_subscriptions( + recipients, + cash_balance=cash_balance, + stocks_balance=stocks_balance, + statistics=statistics, + spec=spec, + ) + return ( + household, + person, + recipients, + cash_balance, + stocks_balance, + cash, + stocks, + lifetime, + result, + ) + + +def test_the_declaration_is_read_and_refuses_drift(): + spec = stage.ISASubscriptionsSpec.from_stage(isa_stage_spec()) + assert spec.minimum_age == 18 and spec.overall_limit_gbp == 20_000 + assert (spec.base_tax_year, spec.build_tax_year) == (2023, 2024) + for kind, key, value in ( + ("allocate_isa_subscribers", "selection_order", ["cash_only"]), + ("draw_isa_subscription_amounts", "within_band", "tilted"), + ("split_both_type_subscriptions", "anchor", "table_9_4_cash_residual"), + ("grow_isa_subscriptions", "people", "table_9_4_amount_growth"), + ("grow_isa_subscriptions", "amounts", "table_9_4_amount_growth_capped"), + # Nobody aged 48 or 49 can hold a Lifetime ISA in 2024-25. + ("draw_lifetime_isa_payments", "maximum_age", 49), + ("draw_lifetime_isa_payments", "outputs", ["lifetime_isa_bonus"]), + ("grow_isa_subscriptions", "to_tax_year", 2025), + ("classify_isa_facts_with_reviewed_fence", "calibration_permitted", True), + ("classify_isa_facts_with_reviewed_fence", "fenced_fact_count", 1000), + ): + declared = isa_stage_spec() + operations = tuple( + SourceOperationSpec(op.kind, {**op.parameters, key: value}) + if op.kind == kind + else op + for op in declared.operations + ) + with pytest.raises(stage.ISASubscriptionsError): + stage.ISASubscriptionsSpec.from_stage( + dataclasses.replace(declared, operations=operations) + ) + + +def test_the_donor_pins_person_values_to_the_household_aggregates(spec): + household, person = synthetic_was_isa_tabs() + donor = stage.clean_was_isa_person_donor( + person, household, columns=spec.columns, maximum_adults=4 + ) + for shares, holds in ( + (donor.cash_shares, donor.holds_cash), + (donor.stocks_and_shares_shares, donor.holds_stocks_and_shares), + ): + totals = np.asarray([share.sum() for share in shares]) + assert np.allclose(totals[holds], 1.0) and np.allclose(totals[~holds], 0.0) + broken = person.copy() + broken.loc[broken.index[0], "DVCISAvR8"] += 50.0 + with pytest.raises(stage.ISASubscriptionsError, match="do not sum"): + stage.clean_was_isa_person_donor( + broken, household, columns=spec.columns, maximum_adults=4 + ) + negative = person.copy() + negative.loc[negative.index[0], "DVIISAvR8"] = -1.0 + with pytest.raises(stage.ISASubscriptionsError, match="non-negative"): + stage.clean_was_isa_person_donor( + negative, household, columns=spec.columns, maximum_adults=4 + ) + + +def test_person_balances_sum_exactly_to_the_household_draws(spec, donor): + household, person, *_ = _population() + cash, stocks, receipt = stage.split_household_isa_balances( + person=person, + household=household, + donor=donor, + seed=0, + salt="split", + minimum_age=18, + maximum_adults=4, + ) + sums = ( + pd.DataFrame( + {"cash": cash, "stocks": stocks, "household": person["person_household_id"]} + ) + .groupby("household")[["cash", "stocks"]] + .sum() + .reindex(household["household_id"]) + .fillna(0.0) + ) + assert np.allclose(sums["cash"], household["cash_isa"], rtol=0, atol=1e-6) + assert np.allclose( + sums["stocks"], household["stocks_and_shares_isa"], rtol=0, atol=1e-6 + ) + assert (cash >= 0).all() and (stocks >= 0).all() + minors = person["age"].to_numpy(float) < 18 + has_adult = person.groupby("person_household_id")["age"].transform("max") >= 18 + assert not ((cash > 0) & minors & has_adult.to_numpy()).any() + + +def test_the_imputation_keeps_every_invariant(spec, donor, statistics): + *_, recipients, cash_balance, stocks_balance, cash, stocks, lifetime, result = _run( + spec, donor, statistics + ) + assert all(count == 0 for count in result["checks"]["violations"].values()), result[ + "checks" + ] + combined = cash + stocks + assert ((combined + lifetime) <= 20_000 + 1e-6).all() + assert (lifetime <= 4_000 + 1e-6).all() + assert ((cash > 0) <= (cash_balance > 0)).all() and ( + (stocks > 0) <= (stocks_balance > 0) + ).all() + ages = recipients["age"].to_numpy(float) + assert not ((combined > 0) & (ages < 18)).any() + assert not ( + (lifetime > 0) & ((ages > 47) | ~recipients["holds_lifetime_isa"].to_numpy()) + ).any() + split = result["both_type_split"] + assert split["saturated"] is None + assert split["stocks_and_shares_achieved_gbp"] == pytest.approx( + split["stocks_and_shares_target_gbp"] + ) + growth = result["growth"] + # 2024-25 meets Table 9.4's own amounts (María's ruling of 29 October 2026). + assert growth["cash_target_gbp"] == statistics.amount_subscribed[("cash", 2024)] + assert ( + growth["stocks_and_shares_target_gbp"] + == (statistics.amount_subscribed[("stocks_and_shares", 2024)]) + ) + assert growth["cash_achieved_gbp"] == pytest.approx( + growth["cash_target_gbp"], rel=1e-9 + ) + assert growth["stocks_and_shares_achieved_gbp"] == pytest.approx( + growth["stocks_and_shares_target_gbp"], rel=1e-9 + ) + + +def test_band_amounts_sit_inside_their_bands_with_an_exact_top(statistics): + rng = np.random.default_rng(3) + rows = np.arange(400) + weights = rng.uniform(1, 5, 400) + shares = np.asarray( + [ + statistics.subscribers("cash_only", HMRC_ISA_INCOME_BAND_IDS[3], band) + for band in stage.HMRC_ISA_SUBSCRIPTION_BAND_IDS + ] + ) + amounts = stage.draw_band_amounts( + rows, + weights=weights, + balance=rng.uniform(0, 50_000, 400), + person_ids=rows + 1, + band_shares=shares, + balance_rank_weight=0.5, + seed=0, + salt="t", + key="k", + ) + assert (amounts > 0).all() and (amounts <= 20_000).all() + top = amounts == 20_000 + assert weights[top].sum() / weights.sum() == pytest.approx( + shares[-1] / shares.sum(), abs=0.02 + ) + assert not ((amounts > 18_000) & (amounts < 20_000) & (amounts >= 19_999.999)).any() + + +class _UKEngine: + country = "uk" + + +def _stage_frame(seed: int = 1, order: np.ndarray | None = None): + """The synthetic population as the 2024 frame the whole transform reads.""" + + household, person, recipients = _population(seed) + holds = recipients["holds_lifetime_isa"].to_numpy() + person = person.assign( + person_benunit_id=person["person_household_id"].to_numpy() * 10, + **dict.fromkeys(UK_CGT_TAXABLE_INCOME_PROXY_COMPONENTS, 0.0), + has_lifetime_isa=holds, + lifetime_isa_balance=np.where( + holds, recipients["lifetime_isa_balance"].to_numpy(), 0.0 + ), + ) + person["employment_income"] = recipients["total_income"].to_numpy() + if order is not None: + person = person.iloc[order].reset_index(drop=True) + weights = ( + recipients.groupby("household_id")["weight"] + .first() + .reindex(household["household_id"]) + .to_numpy() + ) + return uk_national_frame( + person=person, + benunit=pd.DataFrame({"benunit_id": household["household_id"] * 10}), + household=household, + household_weights=weights, + weight_kind=WeightKind.DESIGN, + time_period="2024", + ) + + +def _transform(statistics): + household, person = synthetic_was_isa_tabs() + return stage.UKISASubscriptionsStageTransform( + stage=isa_stage_spec(), + engine=_UKEngine(), + donor_household=household, + donor_person=person, + statistics=statistics, + ) + + +def _declared_gate_parameters(gate_id: str) -> dict: + from microcosm.build.country_spec import load_country_spec + + entry = next( + gate for gate in load_country_spec("uk").gates.gates if gate.id == gate_id + ) + return dict(entry.parameters) + + +def test_the_whole_stage_writes_its_columns_within_every_invariant(statistics): + from microcosm.build.uk_runtime.stage_health import uk_stage_health_gate + + frame = _stage_frame() + transform = _transform(statistics) + result = transform(frame) + person = result.table("person") + for column in stage.UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS: + values = person[column].to_numpy(float) + assert np.isfinite(values).all() and (values >= 0).all(), column + assert (person[stage.CASH_SUBSCRIPTIONS] > 0).any() + assert (person[stage.LIFETIME_ISA_PAYMENTS] > 0).any() + receipt = transform.last_receipt + assert all(count == 0 for count in receipt["checks"]["violations"].values()) + assert receipt["balance_split"]["max_abs_household_sum_error_gbp"] <= 0.01 + # Every class is selected, and the Lifetime ISA total is reached, not named. + for isa_type in stage.CLASS_ORDER: + assert receipt["selection"][isa_type]["base"]["selected_weight"] > 0, isa_type + assert receipt["lifetime_isa"]["saturated"] is None + assert ( + 0 + < receipt["lifetime_isa"]["paying_weight"] + <= (receipt["lifetime_isa"]["eligible_holder_weight"]) + ) + # Households, weights and every earlier person column pass through. + pd.testing.assert_frame_equal(result.table("household"), frame.table("household")) + assert np.array_equal( + result.weights_for("household").values, frame.weights_for("household").values + ) + pd.testing.assert_frame_equal( + person[list(frame.table("person").columns)], frame.table("person") + ) + # Both declared entries pass at their declared tolerances. + for gate_id in ( + "uk_stage_isa_subscriptions_invariants", + "uk_stage_isa_subscriptions", + ): + parameters = _declared_gate_parameters(gate_id) + gate = uk_stage_health_gate( + evidence=receipt, + stage=parameters["stage"], + check=parameters["check"], + parameters=parameters, + ) + assert gate.passed, (gate_id, gate.failures) + + +def test_reordering_the_rows_moves_no_draw(statistics): + """Every draw is keyed on identity, so row order moves none. + + Adding or removing rows can move others' amounts (the rakes, band walks and + growth factors read the whole population), so only order is invariant. + """ + + columns = list(stage.UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS) + transform = _transform(statistics) + first = transform(_stage_frame()).table("person") + order = np.random.default_rng(7).permutation(len(first)) + second = transform(_stage_frame(order=order)).table("person") + pd.testing.assert_frame_equal( + first.set_index("person_id")[columns].sort_index(), + second.set_index("person_id")[columns].sort_index(), + check_exact=False, + rtol=0, + atol=1e-6, + ) + + +def test_the_both_split_reports_saturation(): + combined = np.full(10, 10_000.0) + weights = np.ones(10) + share, receipt = stage.split_both_type_amounts( + combined, + np.full(10, 0.5), + weights, + stocks_and_shares_target=-1.0, + share_floor=0.02, + ) + assert receipt["saturated"] == "stocks_and_shares_residual_below_reach" + assert (share > 0).all() and (share < 1).all() + share, receipt = stage.split_both_type_amounts( + combined, + np.full(10, 0.5), + weights, + stocks_and_shares_target=30_000.0, + share_floor=0.02, + ) + assert receipt["saturated"] is None + assert float((combined * (1 - share)).sum()) == pytest.approx(30_000.0) + + +@pytest.mark.parametrize( + ("cash_target", "stocks_target"), + [ + # Below the starting totals: the factor falls under one. + (30_000.0, 12_000.0), + # Above them: the rest rise, capped at the limit. + (75_000.0, 18_000.0), + ], +) +def test_amounts_scale_to_the_build_year_holding_the_limit(cash_target, stocks_target): + cash = np.asarray([19_000.0, 5_000.0, 0.0, 10_000.0, 8_000.0, 19_000.0]) + stocks = np.asarray([1_000.0, 0.0, 8_000.0, 5_000.0, 2_000.0, 0.0]) + weights = np.ones(6) + scaled_cash, scaled_stocks, receipt = stage.scale_to_build_year_amounts( + cash, + stocks, + weights, + cash_target=cash_target, + stocks_target=stocks_target, + limit=20_000.0, + taper_from=18_000.0, + ) + # The subscriber at the limit stays exactly where they were. + assert (scaled_cash[0], scaled_stocks[0]) == (19_000.0, 1_000.0) + assert ((scaled_cash + scaled_stocks) <= 20_000 + 1e-6).all() + assert scaled_cash.sum() == pytest.approx(cash_target) + assert scaled_stocks.sum() == pytest.approx(stocks_target) + factor = receipt["cash_factor"] + rising = bool(cash_target > cash.sum()) + assert (factor > 1) is rising + # Above GBP 18,000 the amount moves on the line from the scaled GBP 18,000 + # to the limit, so a factor below one leaves no empty range under it. + start = factor * 18_000.0 + expected = start + (19_000.0 - 18_000.0) * (20_000.0 - start) / 2_000.0 + assert scaled_cash[5] == pytest.approx(min(expected, 20_000.0)) + if not rising: + assert factor * 19_999.0 < scaled_cash[5] < 20_000.0 + + +def test_a_non_finite_amount_is_a_violation(spec): + recipients = pd.DataFrame({"weight": np.ones(3), "age": [30.0, 40.0, 50.0]}) + cash = np.asarray([1_000.0, np.nan, 0.0]) + zeros = np.zeros(3) + checks = stage._checks( + recipients, + cash, + zeros, + zeros, + np.asarray([stage.CASH_ONLY, stage.CASH_ONLY, ""], dtype=object), + np.full(3, 5_000.0), + zeros, + spec, + ) + # NaN compares false, so only the finiteness count catches it. + assert checks["violations"]["non_finite_values"] == 1 + assert checks["violations"]["over_overall_limit"] == 0 + + +def test_lifetime_isa_paying_ages_follow_the_2017_opening(): + # Opened at 18 to 39 from 6 April 2017; payments stop at 50. + assert lifetime_isa_paying_ages(2017) == (18, 40) + assert lifetime_isa_paying_ages(2024) == (18, 47) + assert lifetime_isa_paying_ages(2026) == (18, 49) + assert lifetime_isa_paying_ages(2030) == (18, 49) + with pytest.raises(ValueError): + lifetime_isa_paying_ages(2016) + + +def test_band_ids_parse_and_the_loader_refuses_gaps(): + assert isa_band_bounds("subscription_20000_to_20000") == (20000, 20000) + assert isa_band_bounds("income_150001_plus") == (150001, None) + with pytest.raises(HMRCISAStatisticsError): + isa_band_bounds("income_banded") + payload = synthetic_isa_statistics_payload() + payload["rows"] = [ + row for row in payload["rows"] if ".table9_9." not in row["source_record_id"] + ] + with pytest.raises(HMRCISAStatisticsError, match="lack"): + load_hmrc_isa_statistics(payload=payload, verify_feed_identity=False) + payload = synthetic_isa_statistics_payload() + for row in payload["rows"]: + if row["source_record_id"].endswith( + "table9_7.ty2023.all.subscribers.all.all_subscriptions" + ): + row["value"] += 1_000.0 + with pytest.raises(HMRCISAStatisticsError): + load_hmrc_isa_statistics(payload=payload, verify_feed_identity=False) + + +def test_the_committed_declaration_carries_the_tested_operations(): + """The packaged manifest declares exactly what these tests exercise.""" + + from microcosm.build.country_spec import load_country_spec + from test_support.microcosm_build.uk_isa_statistics import ( + ISA_FACTS_ARTIFACT, + ISA_STAGE_OPERATIONS, + ) + + stages = load_country_spec("uk").sources.stage_map() + committed = stages["isa_subscriptions"] + assert [ + {"kind": operation.kind, **dict(operation.parameters)} + for operation in committed.operations + ] == list(ISA_STAGE_OPERATIONS) + assert committed.outputs == stage.UK_ISA_SUBSCRIPTIONS_OUTPUT_COLUMNS + assert ( + committed.nonnegative_outputs + == stage.UK_ISA_SUBSCRIPTIONS_NONNEGATIVE_OUTPUT_COLUMNS + ) + roles = {artifact["role"]: artifact for artifact in committed.artifacts} + assert roles["isa_statistics_facts"] == ISA_FACTS_ARTIFACT + 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] + assert list(stages)[-1] == "isa_subscriptions" + + +def test_no_calibration_target_reads_an_isa_fact(): + """The reviewed fence keeps every vendored ISA fact measured only. + + The stage draws subscribers and amounts to these cells, so a target compiled + from them would be circular (María's ruling of 28 October 2026). Targets + select facts by concept, so the contract and both compiled reference surfaces + must name none of the vendored ISA concepts, nor any concept under their + prefixes, nor the ISA record sets (review of microcosm#1421). + """ + + import json + from importlib.resources import files + + package = files("microcosm.build.uk") + vendored = json.loads( + package.joinpath("hmrc_isa_statistics_facts.json").read_text("utf-8") + ) + concepts = {row["concept"] for row in vendored["rows"]} + assert {"hmrc.isa_amount_subscribed", "hmrc.isa_subscribers"} <= concepts + prefixes = ("hmrc.isa_", "hmrc.lisa_", "hmrc.cash_isa_") + assert all(concept.startswith(prefixes) for concept in concepts) + record_sets = ( + "hmrc.isa_statistics_2026", + "hmrc.lisa_statistics_2026", + "hmrc.cash_isa_limit_reduction_2026", + ) + for resource in ( + "uk_population_targets.json", + "target_references.json", + "local_target_references.json", + ): + text = package.joinpath(resource).read_text("utf-8") + for name in (*prefixes, *record_sets): + # As the start of a JSON string: a concept or record-set id. + assert f'"{name}' not in text, (resource, name) 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 6d7508a7d1..6e8fa3d3e8 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 @@ -19,6 +19,10 @@ load_uk_calibration_measure_exclusions, ) from microcosm.calibrate import TargetRegistry, TargetSpec +from microcosm.frame.adapters.policyengine_uk import ( + UK_ISA_INCOME_CONTRACT_ATTR, + UK_LEGACY_ISA_INCOME_CONTRACT, +) class FrameStub: @@ -1970,9 +1974,23 @@ def test_the_resolver_routes_high_value_home_measures_to_households( def _savings_frame() -> FrameStub: - """Gross interest with its ISA part: a saver, an ISA-only saver, a non-saver.""" + """The producer's additive layout: a saver, an ISA-only saver, a non-saver.""" + + frame = FrameStub() + frame._tables["person"] = frame._tables["person"].assign( + savings_interest_income=[700.0, 0.0, 0.0], + individual_savings_account_interest_income=[300.0, 300.0, 0.0], + ) + return frame + + +def _legacy_savings_frame() -> FrameStub: + """The same people in the legacy gross layout (the enhanced FRS reference).""" frame = FrameStub() + frame.metadata = { + UK_ISA_INCOME_CONTRACT_ATTR: json.dumps(dict(UK_LEGACY_ISA_INCOME_CONTRACT)) + } frame._tables["person"] = frame._tables["person"].assign( savings_interest_income=[1_000.0, 300.0, 0.0], tax_free_savings_income=[300.0, 300.0, 0.0], @@ -1980,18 +1998,31 @@ def _savings_frame() -> FrameStub: return frame -def test_savings_interest_outside_isas_is_the_stored_gross_less_its_isa_part(): - """SPI Table 3.7 counts recipients of taxable interest (microcosm#1095).""" +@pytest.mark.parametrize( + ("frame", "route"), + [ + (_savings_frame, "frame_columns:savings_interest_income"), + ( + _legacy_savings_frame, + "frame_columns:savings_interest_income_less_tax_free_savings_income", + ), + ], +) +def test_savings_interest_outside_isas_counts_isa_interest_once(frame, route): + """SPI Table 3.7 counts recipients of taxable interest (microcosm#1095). + + Each layout reads the frame's declared contract (microcosm#1360). + """ # No engine: the stored columns are read off the frame, never uprated. - values, route = compute_uk_measure_input( - _savings_frame(), None, "person", UK_SAVINGS_INTEREST_OUTSIDE_ISAS, 2025 + values, measured = compute_uk_measure_input( + frame(), None, "person", UK_SAVINGS_INTEREST_OUTSIDE_ISAS, 2025 ) assert values.tolist() == [700.0, 0.0, 0.0] - assert route == "frame_columns:savings_interest_income_less_tax_free_savings_income" + assert measured == route with pytest.raises(KeyError, match="person-only"): compute_uk_measure_input( - _savings_frame(), None, "household", UK_SAVINGS_INTEREST_OUTSIDE_ISAS, 2025 + frame(), None, "household", UK_SAVINGS_INTEREST_OUTSIDE_ISAS, 2025 ) 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 3acba5258c..cc02204d5c 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 @@ -107,12 +107,20 @@ def test_the_stage_needs_the_stored_draw_and_writes_attendance_once(): store_private_school_attendance(stored, engine=engine, calibration_year=2025) -def test_the_stage_is_the_last_of_the_spine(): +def test_only_the_isa_subscriptions_follow_the_stage(): """No later stage copies or re-keys a person, so the stored flags reach - the release as the engine set them.""" - - stages = [stage.stage for stage in load_country_spec("uk").sources.stages] - assert stages[-1] == "private_school_attendance" + the release as the engine set them. Only the ISA subscriptions follow it, + writing person ISA columns on the rows they are given (microcosm#1415).""" + + 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"] + for stage in later: + assert PRIVATE_SCHOOL_ATTENDANCE_OUTPUT not in { + *stage.outputs, + *stage.rewrites, + } def test_the_stage_refuses_drifted_parameters(): diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py index 48080a95a5..ed82c874d6 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_release_input_coverage_manifest.py @@ -49,9 +49,14 @@ def test_known_gap_register_records_post_candidate_restoration_separately() -> N gaps = _resource("efrs_parity_known_gaps.json") assert gaps["known_gaps"] == {} # microcosm#1106: property_wealth is formula-owned in policyengine-uk, so - # the release leaves it for the engine to derive. It is a declared - # exclusion in its own section, never parity debt in known_gaps. - assert set(gaps["engine_derived_exclusions"]) == {"property_wealth"} + # the release leaves it for the engine to derive, and so is + # tax_free_savings_income under the additive ISA income contract + # (microcosm#1360). Each is a declared exclusion in its own section, never + # parity debt in known_gaps. + assert set(gaps["engine_derived_exclusions"]) == { + "property_wealth", + "tax_free_savings_income", + } assert gaps["engine_derived_exclusions"]["property_wealth"]["reason"] == ( "formula-owned in policyengine-uk and derived from persisted " "components; a persisted copy overrides the formula and is not uprated" @@ -125,24 +130,26 @@ def test_promoted_manifest_requires_the_full_reference_surface() -> None: reference = _resource("efrs_parity_reference.json") manifest = _resource("release_input_coverage_manifest.json") assert manifest["counts"] == { - "required": 144, - "reviewed_exclusion": 1, + "required": 143, + "reviewed_exclusion": 2, "total": 145, } assert set(manifest["columns"]) == set(reference["nonzero_shares"]) - # Every reference input is required except the engine-derived total; its - # three persisted components stay required (microcosm#1106). + # Every reference input is required except the engine-derived totals; their + # persisted components stay required (microcosm#1106, microcosm#1360). excluded = { name: entry for name, entry in manifest["columns"].items() if entry != {"status": "required"} } - assert set(excluded) == {"property_wealth"} - assert excluded["property_wealth"]["status"] == "reviewed_exclusion" + assert set(excluded) == {"property_wealth", "tax_free_savings_income"} + for name in excluded: + assert excluded[name]["status"] == "reviewed_exclusion" for component in ( "main_residence_value", "other_residential_property_value", "non_residential_property_value", + "savings_interest_income", ): assert manifest["columns"][component] == {"status": "required"} assert manifest["restoration_evidence"] == { @@ -412,9 +419,10 @@ def test_engine_derived_exclusions_require_a_signature(tmp_path) -> None: committed = load_efrs_engine_derived_exclusions() assert [(gap.variable, gap.approved_by) for gap in committed] == [ - ("property_wealth", "juaristi22") + ("property_wealth", "juaristi22"), + ("tax_free_savings_income", "juaristi22"), ] - assert committed[0].approved_on == "2026-10-11" + assert [gap.approved_on for gap in committed] == ["2026-10-11", "2026-10-28"] assert all(gap.approved_by is None for gap in load_efrs_parity_known_gaps()) unsigned = tmp_path / "ledger.json" 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 46ec7753eb..ca36548984 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 @@ -120,8 +120,11 @@ # private-school attendance ranks households on net income (which counts # the childcare entitlements). "childcare_copy_take_up", - # microcosm#1095: the stored private-school attendance runs last. + # microcosm#1095: the stored private-school attendance runs after them. "private_school_attendance", + # microcosm#1415: the ISA subscriptions run last, on final incomes; nothing + # reads their outputs. + "isa_subscriptions", ] UK_SOURCE_STAGE_NAMES = [ "frs_spine", @@ -308,6 +311,7 @@ def test_country_stage_plan_assembles_spine_plan(self) -> None: "salary_sacrifice": _identity, "student_loans": _identity, "private_school_attendance": _identity, + "isa_subscriptions": _identity, "childcare_copy_take_up": _identity, "age_tail": _identity, "frs_relationships": _identity, @@ -642,9 +646,11 @@ def test_e3_operation_kinds_are_declared_in_order(self) -> None: "record_mass_conservation_receipt", ] assert [op.kind for op in stages["was_lisa"].operations] == [ + "verify_vendored_fact_resource", "clean_was_lisa_donor", "materialize_rules_engine_predictors", "impute_lifetime_isa_ownership", + "anchor_lifetime_isa_ownership", "impute_lifetime_isa_balance", "support_clip", "cap_lifetime_isa_to_financial_wealth", 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 ce1ce77416..06afdf9573 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 @@ -839,17 +839,19 @@ def test_taxable_income_mask_applies_the_no_record_rule() -> None: "state_pension_reported": 0.0, "employment_status": "UNEMPLOYED", "savings_interest_income": 0.0, - "tax_free_savings_income": 0.0, + "individual_savings_account_interest_income": 0.0, "dividend_income": 0.0, "property_income": 0.0, "miscellaneous_income": 0.0, } rows = [ dict(columns), # nothing at all + # ISA interest is not taxable: 900 outside ISAs stays under the floor + # (microcosm#1360). { **columns, - "savings_interest_income": 1_500.0, - "tax_free_savings_income": 600.0, + "savings_interest_income": 900.0, + "individual_savings_account_interest_income": 600.0, }, {**columns, "dividend_income": 1_200.0}, {**columns, "employment_status": "FT_SELF_EMPLOYED"}, 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 cdb7253c36..ed8a3e7901 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 @@ -41,8 +41,8 @@ def test_observed_analogues_read_the_twin_and_derive_taxable_interest() -> None: {column: [0.0, 5.0] for column in analogue_source_columns()}, index=[10, 11], ) - person["savings_interest_income"] = [100.0, 20.0] - person["tax_free_savings_income"] = [30.0, 50.0] + # Interest outside ISAs, floored at zero (microcosm#1360). + person["savings_interest_income"] = [70.0, -30.0] person["dividend_income"] = [7.0, 0.0] observed = observed_analogues(person) assert list(observed.columns) == list(analogue_targets()) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_support.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_support.py index 6af30dfa77..5672e79fe8 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_support.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_support.py @@ -241,7 +241,10 @@ def test_spi_variable_surfaces_include_efrs_stage1_and_stage2_fixes() -> None: assert "universal_credit_reported" in FRS_ONLY_SPI_FILL_PERSON_COLUMNS assert "housing_benefit_reported" in FRS_ONLY_SPI_FILL_PERSON_COLUMNS assert "employee_pension_contributions" in FRS_ONLY_SPI_FILL_PERSON_COLUMNS - assert "tax_free_savings_income" in FRS_ONLY_SPI_FILL_PERSON_COLUMNS + assert ( + "individual_savings_account_interest_income" in FRS_ONLY_SPI_FILL_PERSON_COLUMNS + ) + assert "tax_free_savings_income" not in FRS_ONLY_SPI_FILL_PERSON_COLUMNS def _certified_like_dead_spi_support(): 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 e3df84d08d..15dc71994e 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 @@ -172,10 +172,13 @@ def _apply_pending_roster_transformations( # stage, before the private-school attendance. assert "childcare_copy_take_up" not in roster roster.append("childcare_copy_take_up") - # microcosm#1095 re-mint pending: the stored private-school attendance is - # the last stage of the spine. + # microcosm#1095 re-mint pending: the stored private-school attendance + # 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. + assert "isa_subscriptions" not in roster + roster.append("isa_subscriptions") return tuple(roster) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_checkpoints.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_checkpoints.py index fde2d96465..f6a6453c91 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_checkpoints.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_stage_checkpoints.py @@ -9,6 +9,7 @@ from __future__ import annotations import hashlib +import json from pathlib import Path import pandas as pd @@ -27,6 +28,10 @@ load_uk_stage_predecessor, uk_stage_metadata, ) +from microcosm.frame.adapters.policyengine_uk import ( + UK_ISA_INCOME_CONTRACT, + UK_ISA_INCOME_CONTRACT_ATTR, +) _PIPELINE = StagePipeline( ( @@ -115,7 +120,14 @@ def test_stage_metadata_refuses_reserved_key_collisions() -> None: frame = _frame() metadata = uk_stage_metadata(frame, extra={"seed": 42}) - assert metadata[UK_FRAME_METADATA_KEY] == {"time_period": "2023"} + # The frame metadata carries the ISA income contract beside the period + # (microcosm#1360). + assert metadata[UK_FRAME_METADATA_KEY] == { + "time_period": "2023", + UK_ISA_INCOME_CONTRACT_ATTR: json.dumps( + dict(UK_ISA_INCOME_CONTRACT), sort_keys=True + ), + } assert metadata["seed"] == 42 with pytest.raises(ValueError, match="reserved"): 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 f26b3d139d..06e697074f 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 @@ -533,6 +533,15 @@ def test_chronicle_household_reference_surface_is_complete() -> None: assert not any(name.startswith("external:") for name in names) +#: The June 2026 BUS0415a release blended over policyengine-uk's model years, +#: as test_uk_bus_targets.py derives it (microcosm#1331). London's fares were +#: held, and DfT's unrounded March 2026 value lifts them a little. +_LONDON_FARE_FACTOR = (196.1 * 270 + 196.10649454164795 * 95) / 365 / 196.1 +_OUTSIDE_LONDON_FARE_FACTOR = ( + 210.5 * 86 + 213.6 * 92 + 214.3 * 92 + 217.95569364121366 * 95 +) / (181.6 * 86 + 185.7 * 92 + 185.9 * 92 + 206.4 * 90 + 210.5 * 5) + + def test_childcare_and_bus_references_compile_with_declared_provenance() -> None: root = _TEST_PATHS.repository feed = root / STABLE_UK_FACT_FEED_NAME @@ -593,10 +602,13 @@ def test_childcare_and_bus_references_compile_with_declared_provenance() -> None "dfe.funded_childcare.working_parent_children_age_3": 278_640.0, "dfe.funded_childcare.universal_nursery_children_age_3": 565_954.0, "dft.bus_fare_receipts.england": pytest.approx( - 3_417_388_656.43538 * 204.125 / 193.125 + 1_347_434_943.01459 * _LONDON_FARE_FACTOR + + 2_069_953_713.42079 * _OUTSIDE_LONDON_FARE_FACTOR ), "dft.bus_net_support.england": 3_024_904_320.8399997, - "dft.bus_fare_receipts.london": 1_347_434_943.01459, + "dft.bus_fare_receipts.london": pytest.approx( + 1_347_434_943.01459 * _LONDON_FARE_FACTOR + ), "dft.bus_net_support.london": 1_130_214_000.0, "scotgov.bus.passenger_revenue": 391_000_000.0, "scotgov.bus.government_support": 499_000_000.0, diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py index 7d8a25dfb7..c8059e3f8d 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_terminal_gates.py @@ -429,8 +429,10 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: # The boundary drops the reviewed export exclusions, the internal # disability carriers, the salary-sacrifice pre-conversion pay carrier, # the landlord carrier, the FRS bonus carrier (microcosm#1340), the - # in-household care hours carrier (microcosm#1418) and the value the - # regional home values replaced (microcosm#1112), and nothing else. + # in-household care hours carrier (microcosm#1418), the value the + # regional home values replaced (microcosm#1112) and the household ISA + # totals policyengine-uk sums from the person balances (microcosm#1415), + # and nothing else. from microcosm.build.uk_runtime.frs_disability import ( UK_INTERNAL_DISABILITY_REPORTED_COLUMNS, ) @@ -457,6 +459,7 @@ def test_export_candidate_columns_strip_ids_and_carry_the_weight() -> None: | {f"person.{FRS_BONUS_CARRIER_COLUMN}"} | {f"person.{FRS_IN_HOUSEHOLD_CARE_HOURS_COLUMN}"} | {f"household.{RANK_BASIS_COLUMN}"} + | {"household.cash_isa", "household.stocks_and_shares_isa"} ) # A frame without the column passes through untouched. assert uk_release_export_frame(exported) is exported diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa.py index 0e097eceba..e2f21136ef 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa.py @@ -12,7 +12,14 @@ from microcosm.build.country_spec import load_country_spec from microcosm.build.source_manifest import SourceStageSpec -from microcosm.build.uk_runtime.national_frame import uk_national_frame +from microcosm.build.uk_runtime.hmrc_isa_statistics import ( + HMRCISAStatisticsError, + load_hmrc_isa_statistics, +) +from microcosm.build.uk_runtime.national_frame import ( + uk_national_frame, + uk_time_period, +) from microcosm.build.uk_runtime.stage_health import uk_stage_health_gate from microcosm.build.uk_runtime.was_lisa import ( BALANCE_COLUMN, @@ -36,6 +43,11 @@ recipient_predictors, ) from microcosm.build.uk_runtime.was_wealth import UK_WAS_ENGINE_PREDICTOR_ENTITIES +from test_support.microcosm_build.uk_isa_statistics import ( + ISA_FACTS_VERIFY_OPERATION, + LISA_ANCHOR_OPERATION, + synthetic_isa_statistics_payload, +) from test_support.paths import paths_for from tools.graph_uk_spine_fixture import _was_donor, _was_person_donor @@ -139,7 +151,7 @@ def variable_metadata(self, name): return SimpleNamespace(entity=UK_WAS_ENGINE_PREDICTOR_ENTITIES[name]) def materialize(self, frame, variables, period): - assert period == "2023" + assert period == uk_time_period(frame) tables = { entity: frame.table(entity) for entity in ("person", "benunit", "household") } @@ -211,7 +223,7 @@ def _donor(): return clean_was_lisa_donor(_was_person_donor(), _was_donor(), columns=_columns()) -def _recipient_frame(households: int = 64): +def _recipient_frame(households: int = 64, time_period: str = "2024"): """FRS-like recipients in the synthetic donor's ranges (the fixture's scale).""" persons: list[dict[str, object]] = [] @@ -279,7 +291,7 @@ def _recipient_frame(households: int = 64): person=pd.DataFrame(persons), benunit=pd.DataFrame(benunits), household=pd.DataFrame(homes), - time_period="2023", + time_period=time_period, ) @@ -513,12 +525,28 @@ def test_cap_scales_a_household_over_its_financial_wealth_pro_rata() -> None: # -------------------------------------------------------------------------- +def _statistics(lifetime_accounts: float = 0.0): + """Synthetic ISA facts whose 2024-25 Lifetime ISA accounts are as given. + + The synthetic payload carries 1,100 Lifetime ISA accounts at scale 1; a + target of zero leaves the drawn holders as they are. + """ + + return load_hmrc_isa_statistics( + payload=synthetic_isa_statistics_payload( + scale=max(lifetime_accounts, 1e-9) / 1_100.0 + ), + verify_feed_identity=False, + ) + + def _transform() -> UKWASLISAStageTransform: return UKWASLISAStageTransform( stage=_committed_stage(), engine=_FakeEngine(), donor_household=_was_donor(), donor_person=_was_person_donor(), + statistics=_statistics(), ) @@ -566,7 +594,10 @@ def test_stage_writes_the_declared_outputs_with_receipts() -> None: "realised", "financial_wealth_coherence", "population", + "anchored_holders_after_cap", } + # A zero target leaves the drawn holders as they are (microcosm#1415). + assert evidence["ownership_model"]["anchor"]["logit_shift"] == 0.0 assert evidence["support_clip"]["columns"][BALANCE_COLUMN]["clipped_high_rows"] == 0 assert ( evidence["financial_wealth_coherence"]["households_over_financial_wealth"] >= 0 @@ -591,6 +622,75 @@ def test_stage_writes_the_declared_outputs_with_receipts() -> None: } +def _counted_holders(frame) -> float: + person = frame.table("person") + weights = ( + pd.Series( + np.asarray(frame.weights_for("household").values, dtype=float), + index=frame.table("household")["household_id"].to_numpy(), + ) + .reindex(person["person_household_id"].to_numpy()) + .to_numpy(dtype=float) + ) + ages = person["age"].to_numpy(dtype=float) + counted = person[OWNERSHIP_COLUMN].to_numpy(bool) & (ages >= 18) & (ages <= 47) + return float(weights[counted].sum()) + + +def test_the_declared_anchor_adds_holders_up_to_hmrc_payers() -> None: + frame = _recipient_frame(households=400) + drawn = _transform()(frame) + before = _counted_holders(drawn) + target = 1.5 * before + statistics = _statistics(target) + transform = UKWASLISAStageTransform( + stage=_committed_stage(), + engine=_FakeEngine(), + donor_household=_was_donor(), + donor_person=_was_person_donor(), + statistics=statistics, + ) + + result = transform(frame) + + person = result.table("person") + anchored = person[OWNERSHIP_COLUMN].to_numpy(bool) + assert not ( + drawn.table("person")[OWNERSHIP_COLUMN].to_numpy(bool) & ~anchored + ).any() + np.testing.assert_array_equal(anchored, person[BALANCE_COLUMN].to_numpy() > 0) + evidence = transform.checkpoint_metadata()["evidence"] + achieved = evidence["anchored_holders_after_cap"] + assert achieved == pytest.approx(_counted_holders(result)) + anchor = evidence["ownership_model"]["anchor"] + assert anchor["target_holders"] == pytest.approx(target) + # The cap clears holders exactly where the anchor predicted it would. + assert achieved == pytest.approx(anchor["holders_after"]) + assert target <= achieved <= target + 5.0 + assert anchor["logit_shift"] > 0 + assert anchor["removed_holders"] == 0 + gate = uk_stage_health_gate( + evidence=evidence, + stage=UK_WAS_LISA_STAGE_NAME, + check="was_lisa_anchor", + parameters={"maximum_relative_deviation": 5.0 / target}, + ) + assert gate.passed, gate.failures + + +def test_the_anchor_maps_hmrc_facts_to_the_build_year_only() -> None: + transform = UKWASLISAStageTransform( + stage=_committed_stage(), + engine=_FakeEngine(), + donor_household=_was_donor(), + donor_person=_was_person_donor(), + statistics=_statistics(), + ) + + with pytest.raises(HMRCISAStatisticsError, match="build period 2024"): + transform(_recipient_frame(time_period="2023")) + + def test_stage_is_deterministic() -> None: first = _transform()(_recipient_frame()).table("person") second = _transform()(_recipient_frame()).table("person") @@ -623,6 +723,13 @@ def test_committed_declaration_carries_the_tested_parameters() -> None: assert by_kind["impute_lifetime_isa_ownership"] == OWNERSHIP assert by_kind["impute_lifetime_isa_balance"] == {**BALANCE, "n_estimators": 100} assert by_kind["cap_lifetime_isa_to_financial_wealth"] == CAP + # microcosm#1415: the anchor to HMRC's payers and its facts check. + assert by_kind["anchor_lifetime_isa_ownership"] == { + key: value for key, value in LISA_ANCHOR_OPERATION.items() if key != "kind" + } + assert by_kind["verify_vendored_fact_resource"] == { + key: value for key, value in ISA_FACTS_VERIFY_OPERATION.items() if key != "kind" + } assert by_kind["aggregate_person_to_household"] == { "method": "sum", "aggregates": {HOUSEHOLD_BALANCE_COLUMN: BALANCE_COLUMN}, @@ -674,7 +781,13 @@ def test_committed_support_bounds_match_the_stage_pins() -> None: ) payload = json.loads(resource.read_text(encoding="utf-8")) stage = load_country_spec("uk").sources.stage_map()[UK_WAS_LISA_STAGE_NAME] - pins = {artifact["role"]: artifact["sha256"] for artifact in stage.artifacts} + # The microdata tabs are pinned by digest; the vendored ISA facts are a + # packaged resource (microcosm#1415). + pins = { + artifact["role"]: artifact["sha256"] + for artifact in stage.artifacts + if "sha256" in artifact + } assert set(payload["bounds"]) == set(UK_WAS_LISA_SUPPORT_CLIP_COLUMNS) assert payload["source"]["household_tab_sha256"] == pins["was_qrf_donor"] diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa_anchor.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa_anchor.py new file mode 100644 index 0000000000..db3baa6aa1 --- /dev/null +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_was_lisa_anchor.py @@ -0,0 +1,140 @@ +"""The Lifetime ISA ownership anchor to HMRC's 2024-25 payers (microcosm#1415).""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pytest + +from microcosm.build.stochastic_assignment import stable_identity_uniforms +from microcosm.build.uk_runtime.was_lisa import ( + OwnershipAnchorSpec, + OwnershipImputation, + OwnershipModelSpec, + WASLISAError, + anchor_lifetime_isa_ownership, +) +from test_support.microcosm_build.uk_isa_statistics import LISA_ANCHOR_OPERATION + +SPEC = OwnershipModelSpec( + output="has_lifetime_isa", + seed=0, + salt="was_lisa:has_lifetime_isa", + penalty_c=1.0, + solver="lbfgs", + max_iter=5000, + age_group_lower_bounds=(0, 25, 35, 45, 55), + log1p_predictors=(), + indicator_predictors=(), + count_predictors=(), + minimum_age=18, +) +ANCHOR_PARAMETERS = { + key: value for key, value in LISA_ANCHOR_OPERATION.items() if key != "kind" +} +ANCHOR = OwnershipAnchorSpec.from_parameters(ANCHOR_PARAMETERS) + + +def _ownership(n: int = 4000, seed: int = 2): + rng = np.random.default_rng(seed) + recipient = pd.DataFrame( + {"person_id": np.arange(n) + 1, "age": rng.integers(0, 90, n).astype(float)} + ) + probability = np.where(recipient["age"] >= 18, rng.uniform(0.001, 0.05, n), 0.0) + uniforms = stable_identity_uniforms( + recipient["person_id"].to_numpy(), seed=SPEC.seed, salt=SPEC.salt + ) + holds = (recipient["age"].to_numpy() >= 18) & (uniforms < probability) + ownership = OwnershipImputation( + holds=holds, probability=probability, receipt={}, fit_weight_records=() + ) + weights = rng.uniform(100, 300, n) + survives = rng.random(n) < 0.95 + return recipient, ownership, weights, survives + + +def _counted(recipient, holds, weights, survives) -> float: + ages = recipient["age"].to_numpy() + counted = (ages >= 18) & (ages <= 47) + return float(weights[holds & survives & counted].sum()) + + +def _anchor(target, recipient, ownership, weights, survives): + return anchor_lifetime_isa_ownership( + ownership, + recipient, + spec=SPEC, + anchor=ANCHOR, + target_holders=target, + person_weights=weights, + survives_cap=survives, + ) + + +def test_the_anchor_reaches_the_target_without_removing_holders(): + recipient, ownership, weights, survives = _ownership() + before = _counted(recipient, ownership.holds, weights, survives) + target = 2.0 * before + anchored = _anchor(target, recipient, ownership, weights, survives) + after = _counted(recipient, anchored.holds, weights, survives) + assert target <= after <= target + weights.max() + assert not (ownership.holds & ~anchored.holds).any() + ages = recipient["age"].to_numpy() + added = anchored.holds & ~ownership.holds + assert ((ages[added] >= 18) & (ages[added] <= 47)).all() + receipt = anchored.receipt["anchor"] + assert receipt["removed_holders"] == 0 + assert receipt["logit_shift"] > 0 + assert receipt["saturated"] is None + assert receipt["holders_before"] == pytest.approx(before) + assert receipt["holders_after"] == pytest.approx(after) + + +def test_holders_outside_the_counted_ages_do_not_count(): + recipient, ownership, weights, survives = _ownership() + ages = recipient["age"].to_numpy() + # Nobody older than 47 can hold one in 2024-25 (opened at 18 to 39 since + # April 2017), so the base model's older holders are not HMRC's payers. + older = ownership.holds & survives & (ages > 47) + assert older.any() + anchored = _anchor(0.0, recipient, ownership, weights, survives) + assert anchored.receipt["anchor"]["holders_before"] == pytest.approx( + _counted(recipient, ownership.holds, weights, survives) + ) + + +def test_holders_at_or_above_the_target_are_left_as_drawn(): + recipient, ownership, weights, survives = _ownership() + before = _counted(recipient, ownership.holds, weights, survives) + anchored = _anchor(0.5 * before, recipient, ownership, weights, survives) + np.testing.assert_array_equal(anchored.holds, ownership.holds) + np.testing.assert_array_equal(anchored.probability, ownership.probability) + receipt = anchored.receipt["anchor"] + assert receipt["logit_shift"] == 0.0 + assert receipt["saturated"] is None + assert receipt["holders_added_weight"] == 0.0 + + +def test_an_unreachable_target_is_named(): + recipient, ownership, weights, survives = _ownership() + anchored = _anchor(10.0 * weights.sum(), recipient, ownership, weights, survives) + receipt = anchored.receipt["anchor"] + assert receipt["saturated"] == "target_above_reach" + assert receipt["removed_holders"] == 0 + assert not (ownership.holds & ~anchored.holds).any() + + +@pytest.mark.parametrize( + "change", + [ + {"target": "holders_from_was"}, + {"tax_year": 2023}, + {"counted_ages": {"minimum": 18, "maximum": 45}}, + # 48 and 49 may pay in by law, but nobody that old holds one in 2024-25. + {"counted_ages": {"minimum": 18, "maximum": 49}}, + {"ages": {"minimum": 40, "maximum": 30}}, + ], +) +def test_the_anchor_declaration_is_checked(change): + with pytest.raises(WASLISAError): + OwnershipAnchorSpec.from_parameters({**ANCHOR_PARAMETERS, **change}) diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py index 21589063ea..aff3e143bc 100644 --- a/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py +++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_weighted_integrity.py @@ -557,17 +557,22 @@ def test_committed_exclusion_registers_load() -> None: # microcosm#1106 (signed 2026-10-11): the release leaves it to the engine, # so the reference carries a column the candidate no longer ships. The # QRF tail register went with its gate (microcosm#1276). + # tax_free_savings_income joins with microcosm#1360 (signed 2026-10-28): + # under the additive ISA income contract the candidate stores ISA interest + # apart, and the engine sums it into the column the reference stores. assert set(input_mass["efrs-post-calibration"]) == { "dfe_education_spending", "property_wealth", + "tax_free_savings_income", } for record in input_mass["efrs-post-calibration"].values(): assert record.approved_by == "juaristi22" - assert record.approved_on in {"2026-10-02", "2026-10-11"} - assert record.expires_on in {"2027-04-02", "2027-04-11"} + assert record.approved_on in {"2026-10-02", "2026-10-11", "2026-10-28"} + assert record.expires_on in {"2027-04-02", "2027-04-11", "2027-04-28"} assert ( "microcosm#1063" in record.adjudication or "microcosm#1106" in record.adjudication + or "microcosm#1360" in record.adjudication ) assert uk_default_input_mass_reviewed_exclusions() is ( uk_default_input_mass_reviewed_exclusions() diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml index da3a5eefd1..90d3ccad8b 100644 --- a/packages/microcosm-data/pyproject.toml +++ b/packages/microcosm-data/pyproject.toml @@ -37,7 +37,7 @@ us = ["policyengine-us>=2.2.1,<3", "policyengine-core==3.32.5"] # The UK artifact lives in a PRIVATE repo (UK Data Service licence): loads # require an authenticated HF token with access. The loader surfaces the # 401 with that explanation rather than retrying. -uk = ["policyengine-uk>=2.162.14"] +uk = ["policyengine-uk>=2.164.2"] [project.urls] Homepage = "https://microcosm.institute" diff --git a/packages/microcosm-data/src/microcosm/data/contract.py b/packages/microcosm-data/src/microcosm/data/contract.py index 0c976fe17e..27856156e9 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 = ( - "2cba4eea2a100807d7beac76df4fa753a81be869af13859ab91de6ab27096e6d" + "29c866ba365999cfd39a5ddceb94e159396759e575a8ac99de619e3f03525653" ) _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "1cb27600d7520fa5d049be0fec0898cd0f512c395918ee6f4cc9b9c9478713a2" + "a9e1187a619794b9e159b209a6f6476a24260791df6bb14ae72f9f9bfcaa7514" ) _UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "80c3b49acd8c039c82b247997e7bc25aa7e8e22cfbfb424362274c2cbbd9c895" + "6bd701aa0257a7faad66f5ee8f80f8e4afb664298b70c7093e5b0e220677049d" ) #: Spec entry id -> the legacy gate name whose observable detail checks #: apply unchanged (the battery re-keys the report by entry id; the gate @@ -501,6 +501,7 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: ), "uk_stage_was_wealth_support": ("stage_health", "transferred"), "uk_stage_was_lisa_support": ("stage_health", "transferred"), + "uk_stage_was_lisa_anchor": ("stage_health", "transferred"), "uk_stage_property_components": ("stage_health", "transferred"), "uk_stage_was_wealth_coherence": ("stage_health", "transferred"), "uk_stage_nts_bus_travel_support": ("stage_health", "transferred"), @@ -558,6 +559,8 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "uk_stage_property_purchases_fit": ("stage_health", "transferred"), "uk_stage_uc_legacy_closure_take_up_contract": ("stage_health", "transferred"), "uk_stage_frs_person_draws_apprentices": ("stage_health", "transferred"), + "uk_stage_isa_subscriptions_invariants": ("stage_health", "transferred"), + "uk_stage_isa_subscriptions": ("stage_health", "transferred"), "uk_stage_age_tail_targets": ("stage_health", "assembled"), "uk_stage_frs_relationships_composition": ("stage_health", "assembled"), "uk_ledger_compile_parity_local_incumbent_2025": ( @@ -625,6 +628,7 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "uk_input_mass_parity", "uk_stage_was_wealth_support", "uk_stage_was_lisa_support", + "uk_stage_was_lisa_anchor", "uk_stage_property_components", "uk_stage_was_wealth_coherence", "uk_stage_nts_bus_travel_support", @@ -664,6 +668,8 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: "uk_stage_property_purchases_fit", "uk_stage_uc_legacy_closure_take_up_contract", "uk_stage_frs_person_draws_apprentices", + "uk_stage_isa_subscriptions_invariants", + "uk_stage_isa_subscriptions", "uk_stage_age_tail_targets", "uk_stage_frs_relationships_composition", "uk_cgt_projection_entrants", @@ -674,7 +680,7 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None: # canonical hash; this pins the wrapped digest so the entry's evidence line # still binds the enhanced-FRS incumbent totals. _UK_GATE_BATTERY_INPUT_MASS_EVIDENCE_SHA256 = ( - "82a5fa0c7dec5ac5c169f0b8b2f4acc6b302e80637dc698d3247e75a6d133933" + "24bb96f93de3bda45425b797a2f5069df7477bf3a56616080cc04376beab56e5" ) # The degenerate binding's evidence payload digests the resolved exclusion # records; for a release that must be the committed register, so its digest @@ -890,6 +896,8 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: "uk_stage_hmrc_cgt_asset_type_spine_summary", "uk_stage_hmrc_cgt_gains_spine_summary", "uk_stage_hmrc_spi_income_spine_identity", + "uk_stage_isa_subscriptions", + "uk_stage_isa_subscriptions_invariants", "uk_stage_lcfs_consumption_basis", "uk_stage_lcfs_consumption_bus_pricing", "uk_stage_lcfs_consumption_energy_rake", @@ -912,6 +920,7 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None: "uk_stage_uc_deduction_attributes", "uk_stage_uc_legacy_closure_take_up_contract", "uk_stage_uc_take_up", + "uk_stage_was_lisa_anchor", "uk_stage_was_lisa_support", "uk_stage_was_wealth_coherence", "uk_stage_was_wealth_support", @@ -955,26 +964,26 @@ 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": ( - "75684f2b3e1ce0dd8fdb6e220cb4336a4e845c2802f67f507d27385346a4b790" + "06f62df651d151e586d6102ed2110b3b2348bd881826d4d6d66621e4ab8abb1f" ), "policy_sha256": ( - "53a93836d048a4e652b943d866e64801f5b29b41b92965e0641a9387f8cb67e5" + "390a79297f7817def126c7805f492328c447e6a0d6d376dc8b946b98b9f96725" ), }, "calibration_seam": { "gates_manifest_sha256": ( - "12f79bcc5eea788a589fc81c9d6d6ed7282d4d289c7fff99b7818afeb2eac21e" + "fc75904db7b9574a65ec38b8542f151702f110dd09a99b823d59e86aa47f6d9d" ), "policy_sha256": ( - "98b174b57459d1f3a553e9a15efc8a47aaf322470207a448224c1c61bd19b621" + "322c96ce9632577262861012d10cbe0bb1924f68e2595042e75ac9af808c5143" ), }, "release_cut": { "gates_manifest_sha256": ( - "32db04cc11945031c609ff183a75e991856cb9b47057b95db4c408fb6b32ccc3" + "c44ce8629cfc9a48e6aaeb9d77012c4fad382b8ef0e232476b9bc8c8e83dbefc" ), "policy_sha256": ( - "08ecff88f63133e776ab20950f6e82541997e3e0d9d44b768cdd670e0807c3f0" + "b335bef4e20b9803b60e728bfb2f045d282d16309429b00d72bf86dd4f4bafd8" ), }, } 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 773ae7395f..37e33febf5 100644 --- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py +++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py @@ -115,6 +115,13 @@ "approved_on": "2026-10-11", "expires_on": "2027-04-11", }, + "tax_free_savings_income": { + "reason": "The candidate no longer ships person.tax_free_savings_income: policyengine-uk derives it as the sum of individual_savings_account_interest_income, which the candidate stores apart from non-ISA interest under the additive ISA income contract, and a persisted copy overrides that sum and is never uprated (microcosm#1360). The enhanced-FRS reference stores it in the legacy gross layout, so the column is present in the reference and absent from the candidate, which this gate otherwise fails whatever the tolerance. Non-ISA savings_interest_income stays compared; it falls below the reference's gross total by the ISA part.", + "approved_by": "juaristi22", + "adjudication": "microcosm#1360 folded into microcosm#1418's PR B (ruling 2026-10-28 in session)", + "approved_on": "2026-10-28", + "expires_on": "2027-04-28", + }, } GIT_COMMIT = "5fa48f07436a806ad75ff76fd22cfb8613bddbe0" DATASET_SHA = "d" * 64 @@ -213,19 +220,19 @@ 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 = ( - "2cba4eea2a100807d7beac76df4fa753a81be869af13859ab91de6ab27096e6d" + "29c866ba365999cfd39a5ddceb94e159396759e575a8ac99de619e3f03525653" ) UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = ( - "1cb27600d7520fa5d049be0fec0898cd0f512c395918ee6f4cc9b9c9478713a2" + "a9e1187a619794b9e159b209a6f6476a24260791df6bb14ae72f9f9bfcaa7514" ) UK_GATE_BATTERY_SPEC_FINGERPRINT = ( - "80c3b49acd8c039c82b247997e7bc25aa7e8e22cfbfb424362274c2cbbd9c895" + "6bd701aa0257a7faad66f5ee8f80f8e4afb664298b70c7093e5b0e220677049d" ) UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = ( "f246388de42654dd68dbbb40c42282001b5b0a8bf48976162bcb1199085cb1d3" ) UK_GATE_BATTERY_INPUT_MASS_EVIDENCE_SHA256 = ( - "82a5fa0c7dec5ac5c169f0b8b2f4acc6b302e80637dc698d3247e75a6d133933" + "24bb96f93de3bda45425b797a2f5069df7477bf3a56616080cc04376beab56e5" ) #: Spec entry id -> (neutral gate name, phase, legacy detail-schema name). #: microcosm#1240: the two report-only high-value homes gates. @@ -246,6 +253,7 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: ), "uk_stage_was_wealth_support": ("stage_health", "transferred", None), "uk_stage_was_lisa_support": ("stage_health", "transferred", None), + "uk_stage_was_lisa_anchor": ("stage_health", "transferred", None), "uk_stage_property_components": ("stage_health", "transferred", None), "uk_stage_was_wealth_coherence": ("stage_health", "transferred", None), "uk_stage_uc_deduction_attributes": ( @@ -359,6 +367,8 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None: "transferred", None, ), + "uk_stage_isa_subscriptions_invariants": ("stage_health", "transferred", None), + "uk_stage_isa_subscriptions": ("stage_health", "transferred", None), "uk_stage_hmrc_cgt_asset_type_spine_summary": ( "stage_health", "transferred", @@ -1392,6 +1402,7 @@ def _gate_battery_payload( stage_health_stages = { "uk_stage_was_wealth_support": "was_wealth", "uk_stage_was_lisa_support": "was_lisa", + "uk_stage_was_lisa_anchor": "was_lisa", "uk_stage_property_components": "property_components", "uk_stage_was_wealth_coherence": "was_wealth", "uk_stage_uc_deduction_attributes": "uc_deduction_attributes", @@ -1431,6 +1442,8 @@ def _gate_battery_payload( "uk_stage_property_purchases_fit": "property_purchases", "uk_stage_uc_legacy_closure_take_up_contract": "uc_legacy_closure_take_up", "uk_stage_frs_person_draws_apprentices": "frs_person_draws", + "uk_stage_isa_subscriptions_invariants": "isa_subscriptions", + "uk_stage_isa_subscriptions": "isa_subscriptions", "uk_stage_age_tail_targets": "age_tail", "uk_stage_frs_relationships_composition": "frs_relationships", } diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml index 0b993a6a4e..2d1ce2b05e 100644 --- a/packages/microcosm-frame/pyproject.toml +++ b/packages/microcosm-frame/pyproject.toml @@ -22,7 +22,7 @@ policyengine = [ "policyengine-core==3.32.5", "microunit>=0.1.0", ] -uk = ["policyengine-uk>=2.162.14"] +uk = ["policyengine-uk>=2.164.2"] # The Axiom adapter's PyPI-resolvable dependencies. The engine itself # (axiom-rules-engine + its dense native extension) is not on PyPI yet and # installs from a checkout; see microcosm/frame/adapters/axiom.py. diff --git a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py index 8d0368a629..cd932507ca 100644 --- a/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py +++ b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk.py @@ -7,6 +7,7 @@ from __future__ import annotations +import json from collections.abc import Mapping, Sequence from importlib import metadata from pathlib import Path @@ -31,23 +32,91 @@ "SUPPLIED_ROLE_INPUTS", "UK_ISA_INCOME_CONTRACT", "UK_ISA_INCOME_CONTRACT_ATTR", + "UK_LEGACY_ISA_INCOME_CONTRACT", "UK_SCHEMA", + "frame_isa_income_contract", + "uk_isa_income_contract_from", "validate_uc_claimant_input", ] UK_SCHEMA = EntitySchema(group_entities=("benunit", "household")) -#: How the frame's savings interest holds ISA interest, declared to -#: policyengine-uk (its ``isa_income_contract``, from 2.162.2): the frame -#: stores ``savings_interest_income`` gross, ISA interest included, with the -#: ISA part in ``tax_free_savings_income``. policyengine-uk reads that as -#: ``legacy_gross`` and takes the ISA part off when it loads the dataset; -#: declaring it keeps that reading if the engine's default for a dataset that -#: declares nothing changes (microcosm#1095). +#: How the producer's savings interest holds ISA interest, declared to +#: policyengine-uk (its ``isa_income_contract``, from 2.162.2): non-ISA interest +#: in ``savings_interest_income`` and ISA interest in +#: ``individual_savings_account_interest_income``, each projected by its own +#: index (microcosm#1360). ``tax_free_savings_income`` is the engine's sum of +#: the ISA parts; a stored copy would override it and never be uprated, so the +#: producer never stores it. UK_ISA_INCOME_CONTRACT: Mapping[str, object] = MappingProxyType( + {"version": 1, "representation": "additive", "projection": "additive"} +) +#: How policyengine-uk reads a dataset that declares no contract: gross +#: ``savings_interest_income`` with the ISA part in ``tax_free_savings_income``, +#: the enhanced FRS's layout and Microcosm's before microcosm#1360. +UK_LEGACY_ISA_INCOME_CONTRACT: Mapping[str, object] = MappingProxyType( {"version": 1, "representation": "legacy_gross", "projection": "legacy_gross"} ) -#: The H5 root attribute policyengine-uk reads that contract from. +#: The H5 root attribute policyengine-uk reads the contract from, and the frame +#: metadata key that carries it. UK_ISA_INCOME_CONTRACT_ATTR = "policyengine_uk.isa_income_contract" +#: The (representation, projection) pairs policyengine-uk accepts at version 1. +_ISA_INCOME_CONTRACT_PAIRS = frozenset( + { + ("legacy_gross", "legacy_gross"), + ("additive", "legacy_gross"), + ("additive", "additive"), + } +) + + +def uk_isa_income_contract_from(value: object) -> dict[str, object]: + """Read a declared ISA income contract, a mapping or its JSON. + + Anything but one of policyengine-uk's version-1 pairs fails, so a frame's + reading of its savings columns is never inferred from their amounts. + """ + + if isinstance(value, bytes): + value = value.decode("utf-8") + if isinstance(value, str): + try: + value = json.loads(value) + except json.JSONDecodeError as exc: + raise ValueError( + f"ISA income contract {value!r} is not valid JSON." + ) from exc + if not isinstance(value, Mapping): + raise ValueError( + f"ISA income contract must be a mapping, got {type(value).__name__}." + ) + contract = dict(value) + version = contract.get("version") + pair = (contract.get("representation"), contract.get("projection")) + if ( + set(contract) != {"version", "representation", "projection"} + or type(version) is not int + or version != 1 + or pair not in _ISA_INCOME_CONTRACT_PAIRS + ): + raise ValueError(f"Unsupported ISA income contract {contract!r}.") + return {"version": 1, "representation": pair[0], "projection": pair[1]} + + +def frame_isa_income_contract(bundle: Frame) -> dict[str, object]: + """The contract a frame's savings columns follow. + + A frame loaded from an H5 carries the file's declaration (one declaring + nothing reads as legacy gross); a frame assembled in process holds the + producer's columns and declares the producer's contract. + """ + + metadata = getattr(bundle, "metadata", None) or {} + declared = metadata.get(UK_ISA_INCOME_CONTRACT_ATTR) + if declared is None: + return dict(UK_ISA_INCOME_CONTRACT) + return uk_isa_income_contract_from(declared) + + _PERSON_TABLE = "person" _GROUP_TABLES = ("benunit", "household") _DTYPE_KIND_BY_VALUE_TYPE: dict[type, str] = { @@ -227,7 +296,7 @@ def _build_dataset(self, bundle: Frame, period: int | str) -> Any: benunit=tables["benunit"].copy(), household=tables["household"].copy(), fiscal_year=int(period), - isa_income_contract=dict(UK_ISA_INCOME_CONTRACT), + isa_income_contract=frame_isa_income_contract(bundle), ) 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 085e59a16e..8c524f6843 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 @@ -35,7 +35,7 @@ # --------------------------------------------------------------------------- # # Which PolicyEngine-UK inputs each engine-neutral concept feeds -# (microcosm.frame.concepts), reviewed against policyengine-uk 2.162.14. Every +# (microcosm.frame.concepts), reviewed against policyengine-uk 2.164.2. Every # target is a pure input (in ``PolicyEngineUKEngine.variables``). The UK # loader also accepts some formula-owned variables as overrides # (``employment_income``, ``state_pension_reported``, ``is_household_head``); @@ -364,8 +364,8 @@ ), "ISA dividends and the cash interest held in non-cash ISAs " "(policyengine-uk 2.162.2). The FRS records ISA interest as one total, " - "which the frame supplies in tax_free_savings_income, so the of-which " - "detail stays zero. Until 2027-28 that changes nothing; from 2027-28 the " + "which the frame supplies in individual_savings_account_interest_income " + "(microcosm#1360), so the of-which detail stays zero. Until 2027-28 that changes nothing; from 2027-28 the " "detail sets isa_cash_interest_flat_rate_charge, the final charge on cash " "interest in non-cash ISAs, so projections from that year show no such " "charge. It records no ISA dividends " @@ -375,18 +375,17 @@ ), **dict.fromkeys( ( - "lifetime_isa_payments", "lifetime_isa_withdrawals", "lifetime_isa_withdrawals_towards_first_home", "lifetime_isa_first_time_buyer", "lifetime_isa_terminally_ill", "lifetime_isa_months_since_first_payment", ), - "The Lifetime ISA's flows (policyengine-uk 2.162.2). Neither the FRS nor " - "the WAS records payments into or withdrawals from a Lifetime ISA (the " - "was_lisa stage draws holdings), so payments and withdrawals stay zero: " - "the 25% government bonus and the withdrawal charge are zero for every " - "account, and the bonus HM Treasury pays is not modelled " + "The Lifetime ISA's withdrawals and the conditions on them " + "(policyengine-uk 2.162.2). Neither the FRS nor the WAS records " + "withdrawals from a Lifetime ISA (the was_lisa stage draws holdings and " + "isa_subscriptions the payments, microcosm#1415), so withdrawals stay " + "zero and the withdrawal charge is zero for every account " "(microcosm#1095; for María's signature).", ), **dict.fromkeys( @@ -435,7 +434,7 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: POLICYENGINE_UK_CONCEPT_MAPPING = ConceptMapping( engine="policyengine-uk", - engine_version="2.162.14", + engine_version="2.164.2", entity_correspondence={"person": "person", "household": "household"}, input_declaration=InputDeclaration.ENGINE_TYPED, bindings=( @@ -532,15 +531,15 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: AlignmentRelation.NARROW_MATCH, ( "Engine label from 2.162.2: 'Savings interest outside tax-free " - "accounts'. The frame stores it gross, ISA interest included, " - "with the ISA part in tax_free_savings_income; it carries no " - "ISA income contract, so policyengine-uk reads it as legacy " - "gross and subtracts tax_free_savings_income when it loads the " - "file. Securities and National Savings interest belong to " - "other_investment_income, which stays uncovered. ISA interest " - "(individual_savings_account_interest_income) is no longer an " - "engine input from 2.162.2: a formula supplies it, and the " - "frame's tax_free_savings_income overrides it." + "accounts'. The frame stores it so, under the additive ISA " + "income contract it declares (microcosm#1360): ISA interest is " + "stored apart in individual_savings_account_interest_income, a " + "total the engine's formula takes first, and " + "tax_free_savings_income adds it. A legacy gross file (the " + "enhanced FRS) stores interest gross with the ISA part in " + "tax_free_savings_income, which policyengine-uk subtracts when " + "it loads the file. Securities and National Savings interest " + "belong to other_investment_income, which stays uncovered." ), ), bind( @@ -796,7 +795,13 @@ def _uk_take_up(program: str, entity: str) -> InputBinding: "describe a company the person owns, for Universal Credit's " "regulation 77 (owned_company_capital, owned_company_holding_value " "and owned_company_trade_assets); every one defaults to no such " - "company, and the build sets none. savings and share_like_capital are two " + "company, and the build sets none. Since policyengine-uk 2.164.0 " + "cash_isa_balance and stocks_and_shares_isa_balance hold the " + "person's ISAs, and the household cash_isa and " + "stocks_and_shares_isa sum them; the isa_subscriptions stage " + "splits the WAS household totals into them (microcosm#1415), a " + "split of one wrapper's balance, not of liquid financial assets. " + "savings and share_like_capital are two " "of Universal Credit's capital sources " "(gov.dwp.universal_credit.means_test.capital.sources); since " "policyengine-uk 2.147.0 share_like_capital is corporate_wealth unless " diff --git a/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py b/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py index 472752874a..db056d49dc 100644 --- a/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py +++ b/packages/microcosm-frame/tests/engine/uk/test_concept_mapping_policyengine_uk.py @@ -131,8 +131,9 @@ def test_the_mapping_notes_formula_ownership_claims_hold(engine) -> None: def test_the_liquid_asset_reason_holds(engine) -> None: # Financial wealth is a household input. The person-level GBP stocks are a - # debt, a car's list price and the Lifetime ISA balance, which the engine - # documents as already inside the household's financial wealth. + # debt, a car's list price, the Lifetime ISA balance, which the engine + # documents as already inside the household's financial wealth, and the + # cash and stocks-and-shares ISA balances the household ISA totals sum. # uc_reported_capital defaults to -1 since policyengine-uk 2.136.1: set to # zero or more it is a unit's complete capital. benunit_reported_capital, # a benefit-unit input unset at -1, replaces the household financial proxy @@ -155,7 +156,15 @@ def test_the_liquid_asset_reason_holds(engine) -> None: "owned_company_capital", "owned_company_holding_value", "owned_company_trade_assets", + # The person's ISAs (2.164.0), which the household cash_isa and + # stocks_and_shares_isa sum; the isa_subscriptions stage writes them + # (microcosm#1415). + "cash_isa_balance", + "stocks_and_shares_isa_balance", } + for name in ("cash_isa", "stocks_and_shares_isa"): + assert name not in inputs, name + assert engine.variable_metadata(name).entity == "household", name for name in ("savings", "gross_financial_wealth", "net_financial_wealth"): assert name in inputs, name assert engine.variable_metadata(name).entity == "household", name diff --git a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json index 9aa2cb6d4e..6c78f1d393 100644 --- a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json +++ b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json @@ -405,8 +405,8 @@ } ], "engine": "policyengine-uk", - "engine_version": "2.162.14", - "input_count": 357, + "engine_version": "2.164.2", + "input_count": 360, "schema_sha256": "5a55669d0447a686692911a6327ae298f19741dde29559f2654a75807878a141", "structural_inputs": [ { @@ -636,8 +636,12 @@ "name": "carers_allowance_reported" }, { - "entity": "household", - "name": "cash_isa" + "entity": "person", + "name": "cash_isa_balance" + }, + { + "entity": "person", + "name": "cash_isa_subscriptions" }, { "entity": "person", @@ -927,6 +931,10 @@ "entity": "benunit", "name": "housing_benefit_has_main_phase_esa" }, + { + "entity": "benunit", + "name": "housing_benefit_permitted_work_disregard" + }, { "entity": "person", "name": "housing_benefit_reported" @@ -1504,8 +1512,12 @@ "name": "statutory_sick_pay" }, { - "entity": "household", - "name": "stocks_and_shares_isa" + "entity": "person", + "name": "stocks_and_shares_isa_balance" + }, + { + "entity": "person", + "name": "stocks_and_shares_isa_subscriptions" }, { "entity": "household", @@ -1681,7 +1693,7 @@ "fact:household.reference_person_id": "is_household_head is formula-owned (the oldest member). The UK build overrides it with the FRS household reference person (frs_spine.py), an override the loader accepts, but it is not an input, so the mapping does not target it. Every housing input is household-level, so nothing is allocated to the reference person.", "fact:household.source_household_id": "Original donor household lineage is build provenance and weight diagnostic support, not a policyengine-uk rules input.", "fact:person.legal_marital_status": "policyengine-uk derives marital status from benefit-unit composition; marital_status and is_married are formula-owned.", - "fact:person.liquid_financial_assets": "policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. savings and share_like_capital are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources); since policyengine-uk 2.147.0 share_like_capital is corporate_wealth unless its components, the household inputs directly_held_shares and unit_and_investment_trusts, add up to more; the WAS stage writes both on its corporate_wealth less the stocks-and-shares ISA, so they and the ISA sum to it (microcosm#1095). Universal Credit's one person-level source is lifetime_isa_countable_capital, the Lifetime ISA at its surrender value (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either would be a modelling choice for a UK build, not a mapping of this concept.", + "fact:person.liquid_financial_assets": "policyengine-uk holds financial wealth on the household: savings ('Household liquid savings'), corporate_wealth ('Wealth held in corporations directly or through investment funds'), gross_financial_wealth and net_financial_wealth are household inputs, and no transform puts a person amount on a household input. Its person-level GBP stock inputs are student_loan_balance, a debt; car_list_price, the price vehicle excise duty's expensive-car supplement tests; and lifetime_isa_balance ('Current value of the person's Lifetime ISAs, cash and stocks and shares together'), one savings wrapper, which the engine documents as already inside gross_financial_wealth and net_financial_wealth. Since policyengine-uk 2.130.0 three more describe a company the person owns, for Universal Credit's regulation 77 (owned_company_capital, owned_company_holding_value and owned_company_trade_assets); every one defaults to no such company, and the build sets none. Since policyengine-uk 2.164.0 cash_isa_balance and stocks_and_shares_isa_balance hold the person's ISAs, and the household cash_isa and stocks_and_shares_isa sum them; the isa_subscriptions stage splits the WAS household totals into them (microcosm#1415), a split of one wrapper's balance, not of liquid financial assets. savings and share_like_capital are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources); since policyengine-uk 2.147.0 share_like_capital is corporate_wealth unless its components, the household inputs directly_held_shares and unit_and_investment_trusts, add up to more; the WAS stage writes both on its corporate_wealth less the stocks-and-shares ISA, so they and the ISA sum to it (microcosm#1095). Universal Credit's one person-level source is lifetime_isa_countable_capital, the Lifetime ISA at its surrender value (capital.person_sources). uc_reported_capital (benefit unit, default -1) is Universal Credit capital; since policyengine-uk 2.136.1 a value of zero or more set directly is the unit's complete countable capital, in place of every source, property included. benunit_reported_capital, a benefit-unit input ('Savings and investments of the benefit unit's adults (the claimant and any partner) as recorded'), replaces the financial sources only: the household pool it otherwise sums from the household sources, net of what other benefit units report or override, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner. Property is valued separately (uc_property_capital) and shared across every claimant and partner in the household. Feeding either would be a modelling choice for a UK build, not a mapping of this concept.", "fact:person.partner_person_id": "Partnerships reach policyengine-uk only through benefit-unit membership, a structure the bundle must already carry (the adapter builds no units); is_benunit_head and is_married are formula-owned.", "fact:person.public_pension_income": "The State Pension is computed from state_pension_reported, which is formula-owned: the loader accepts it as an override, but it is not an input." } 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 3ae2157306..80ecf64e5f 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 @@ -176,6 +176,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -256,6 +257,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -290,7 +292,7 @@ "cgt_residential_split": { "artifacts": [ { - "dependency": "policyengine-uk>=2.162.14 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.164.2 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -425,6 +427,7 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], @@ -2265,7 +2268,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", "other_investment_income", @@ -2362,7 +2365,7 @@ "loss_relief", "ni_class_4_losses_brought_forward", "private_pension_income", - "tax_free_savings_income", + "individual_savings_account_interest_income", "savings_interest_income", "dividend_income", "other_investment_income", @@ -2464,7 +2467,7 @@ "maximum_extended_childcare_hours_usage", "max_free_entitlement_hours_used" ], - "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.162.14's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30.", + "notes": "Identity-keyed seed 0 streams replace the incumbent sequential take-up seed 100; salts are output variable names. Reported positive receipt anchors are transient and consumed only. The Universal Credit draw's population is the benefit units with a UC claimant or partner (is_uc_claimant) aged 18 or over and under Pension Credit qualifying age (66 for everyone who can reach it in this release), the age and role conditions of policyengine-uk 2.164.2's is_uc_eligible; the engine lockstep test pins it and names the three engine cases not mirrored (16- and 17-year-old claimants under the UC Regs 2013 reg 8 exceptions, mixed-age couples on the Pension Credit route, and from 2.128.0 pension-age families moved onto Universal Credit when their legacy benefit closes, SI 2014/1230 reg 60A). So the contract rate is a share of those units, an anchor outside them stays true, and any other unit is never drawn; a unit with no claimant or partner of that age keeps its reported Housing Benefit, which a claim it cannot make no longer zeroes (#882, microcosm#867, microcosm#1095). The universal childcare flag is nested under the working-parent one. A unit that claims the extended hours claims the universal hours, and any other unit claims them at the contract's universal_childcare rate, the rate conditional on not claiming the extended hours; the extended draw is unchanged. childcare_copy_take_up later re-draws the childcare flags and hours of the support and CGT copies on their own benefit units. A child of DfE age 0 or 1 (its age on 31 December of the fit's model year, the age DfE counts by) in a unit that claims the working-parent hours uses them at its age's contract rate, extended_childcare_age_0_usage or extended_childcare_age_1_usage (DfE registers far fewer of the youngest eligible children than of those aged 2 to 4, and fewer at 9 to 11 months than at age 1); a child that does not gets max_free_entitlement_hours_used 0, so the engine funds it no hours, and every other person keeps the engine's default of 30.", "operations": [ { "aggregates": { @@ -2645,7 +2648,7 @@ "hmrc_cgt_asset_type_spine": { "artifacts": [ { - "dependency": "policyengine-uk>=2.162.14 via microcosm-build[uk]", + "dependency": "policyengine-uk>=2.164.2 via microcosm-build[uk]", "dependency_discipline": "deferred inside uk_cgt_policy_parameters and uk_cgt_badr_parameters; the base package never imports policyengine-uk at import time", "instant_rule": "raw dated parameter files evaluated at 1 June of the build period's tax year", "kind": "versioned_parameter_tree", @@ -2854,10 +2857,11 @@ "private_pension_income", "property_income", "savings_interest_income", + "individual_savings_account_interest_income", "dividend_income", "miscellaneous_income" ], - "components_semantics": "Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted.", + "components_semantics": "Persisted leaves of the model's total_income concept (ITA 2007 s.23); state_pension_reported stands in for social_security_income, whose other taxable benefits are not persisted; reliefs such as pension contributions and Gift Aid are not deducted. ISA interest is exempt but stays beside non-ISA interest, as gross interest held it before microcosm#1360, so the proxy is unchanged.", "fail_on_missing_component": true, "kind": "taxable_income_proxy" }, @@ -3239,7 +3243,7 @@ ], "rebase_income_to_build_period": true, "recipient_income_rule": { - "drawn": "the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income less tax_free_savings_income), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves", + "drawn": "the twin reports pay, a trade (self_employment_income > 0 or a self-employed main job) or a pension (private_pension_income + state_pension_reported > 0), or more than the floor of taxable interest (savings_interest_income, the interest outside ISAs), dividend_income, property_income and miscellaneous_income together; a reserved band carrier is always drawn; a twin whose only record is the State Pension is drawn, against a tape that mostly holds taxpayers, which can lift its pension leaves", "undrawn": "the twin has no tax record for the tape to condition on; the copy keeps the twin's values at stage 1 and stage 2 (a declared modelling boundary like the under-16 one, microcosm#840)", "unearned_income_floor_gbp": 1000 }, @@ -3272,7 +3276,7 @@ "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", "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 less tax_free_savings_income, 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.", + "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, "population": "uk_frs_raw_spine_base_support_channel claimants and partners (is_uc_claimant) aged 16 and over with taxable income under recipient_income_rule", "predictors": "the stage-1 predictors, encoded identically", @@ -3428,7 +3432,7 @@ "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", @@ -3464,7 +3468,6 @@ "owned_company_income_share" ], "postprocess": { - "gross_savings_interest_income": "stage1 INCBBS draw + stage2 tax_free_savings_income", "refresh_disability_categories": [ "aa_category", "dla_sc_category", @@ -3476,7 +3479,8 @@ "is_disabled_for_benefits", "is_enhanced_disabled_for_benefits", "is_severely_disabled_for_benefits" - ] + ], + "savings_interest_income": "the stage-1 INCBBS draw, interest outside ISAs; individual_savings_account_interest_income is the stage-2 draw beside it (microcosm#1360)" }, "predictors": [ "age", @@ -3814,7 +3818,7 @@ "employer_pension_contributions", "personal_pension_contributions", "pension_contributions_via_salary_sacrifice", - "tax_free_savings_income", + "individual_savings_account_interest_income", "universal_credit_reported", "pension_credit_reported", "child_benefit_reported", @@ -3864,6 +3868,181 @@ "stage": "hmrc_spi_income_spine", "survey": "Survey of Personal Incomes Public Use Tape 2022-23 and HMRC Personal Incomes Tables 3.6/3.7 2023-24" }, + "isa_subscriptions": { + "artifacts": [ + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "hmrc_isa_statistics_facts.json", + "role": "isa_statistics_facts", + "runtime_sha256_required": true + }, + { + "access": "private_local_input", + "doi": "10.5255/UKDA-SN-7215-20", + "filename": "was_round_8_hhold_eul_may_2025_230525.tab", + "kind": "private_microdata", + "locator": "caller-supplied local input", + "role": "was_qrf_donor", + "runtime_sha256_required": true, + "sha256": "18b3eb980c02c99f3d8a3254af859bee31682b2bdc11703877677292b3ce9374", + "size_bytes": 39073613, + "ukds_study_number": 7215 + }, + { + "access": "private_local_input", + "doi": "10.5255/UKDA-SN-7215-20", + "filename": "was_round_8_person_eul_may_2025_230525.tab", + "format": "tab", + "kind": "private_microdata", + "locator": "caller-supplied local input", + "role": "was_person_tab", + "runtime_sha256_required": true, + "sha256": "1ca6fd37d9c677242112e0d7839df8b406eb1611db8322b20771f054e329d71a", + "size_bytes": 356930989, + "ukds_study_number": 7215, + "vintage": "round_8" + } + ], + "grain": "person", + "nonnegative_outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "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 (Mar\u00eda'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.", + "operations": [ + { + "artifact_role": "isa_statistics_facts", + "fail_on_mismatch": true, + "feed_pin": "chronicle_feed.json", + "kind": "verify_vendored_fact_resource", + "mapped_build_period": 2024, + "period_mapping": "individual_tables_grown_to_the_table_9_4_build_year", + "record_sets": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.", + "hmrc.isa_statistics_2026.table9_8.ty2023.", + "hmrc.isa_statistics_2026.table9_9.ty2023.", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2024", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2024", + "hmrc.isa_statistics_2026.table9_6.ty2024", + "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022", + "hmrc.lisa_statistics_2026.table1a.ty2024", + "hmrc.lisa_statistics_2026.table1a.ty2025" + ], + "require_before_source_read": true, + "resource": "hmrc_isa_statistics_facts.json", + "runtime_sha256_required": true, + "source_vintage": "2023-24 individuals; Table 9.4 for 2023-24 and 2024-25" + }, + { + "aggregate_tolerance_gbp": 1, + "household_aggregates": { + "cash_isa": "DVCISAVR8_aggr", + "stocks_and_shares_isa": "DVIISAVR8_aggr" + }, + "household_key": "CASER8", + "household_weight": "R8xshhwgt", + "kind": "clean_was_isa_person_donor", + "non_dependent_code": 2, + "person_columns": { + "age_band": "DVAge17R8", + "cash_isa_value": "DVCISAvR8", + "is_dependent_child": "IsDepR8", + "person_number": "PersonR8", + "stocks_and_shares_isa_value": "DVIISAvR8" + } + }, + { + "kind": "split_household_isa_balances", + "maximum_donor_adults": 4, + "outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance" + ], + "salt": "isa_subscriptions:balance_split", + "seed": 0 + }, + { + "kind": "allocate_isa_subscribers", + "minimum_age": 18, + "salt": "isa_subscriptions:subscribers", + "seed": 0, + "selection_order": [ + "cash_and_stocks_and_shares", + "stocks_and_shares_only", + "cash_only" + ] + }, + { + "balance_rank_weight": 0.5, + "kind": "draw_isa_subscription_amounts", + "outputs": [ + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions" + ], + "salt": "isa_subscriptions:amounts", + "seed": 0, + "within_band": "uniform" + }, + { + "anchor": "table_9_4_stocks_and_shares_residual", + "kind": "split_both_type_subscriptions", + "share_floor": 0.02 + }, + { + "amounts": "table_9_4_amount_level_tapered_to_limit", + "from_tax_year": 2023, + "kind": "grow_isa_subscriptions", + "overall_limit_gbp": 20000, + "people": "table_9_4_account_growth", + "taper_from_gbp": 18000, + "to_tax_year": 2024 + }, + { + "annual_limit_gbp": 4000, + "kind": "draw_lifetime_isa_payments", + "maximum_age": 47, + "minimum_age": 18, + "outputs": [ + "lifetime_isa_payments" + ], + "salt": "isa_subscriptions:lifetime_isa_payments", + "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": "ISA subscriptions per adult from HMRC's ISA tables on the source spine: household weights pass through unchanged and total household mass is conserved." + }, + { + "adjudication": "https://github.com/PolicyEngine/microcosm/issues/1415", + "calibration_permitted": false, + "classification_rationale": "The stage draws subscribers and amounts to these cells, so binding them in the weight solve would be circular. The national build reports the subscription cells after calibration, with the holders (Tables 9.10 to 9.12) and market values (Table 9.6) as independent checks; none blocks a build.", + "fact_fence_id": "isa_facts_imputation_seeds_measured_only", + "fenced_fact_composition": "Table 9.7 2023-24 subscribers by income and subscription band and holders making no further subscription, for all subscribers and each type paid into (624); Table 9.8 by age and sex (325); Table 9.9 by region (70); Table 9.4 accounts and amounts for 2023-24 and 2024-25 (24); Table 9.6 2024-25 market values (19); the impact note's shares of under-65 cash subscribers either side of GBP 12,000 (2); Lifetime ISA Table 1a for 2024-25 and 2025-26 (10)", + "fenced_fact_count": 1074, + "kind": "classify_isa_facts_with_reviewed_fence" + } + ], + "outputs": [ + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions", + "lifetime_isa_payments" + ], + "rewrites": [], + "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" + }, "lcfs_consumption": { "artifacts": [ { @@ -5661,7 +5840,7 @@ ], "grain": "person", "nonnegative_outputs": [], - "notes": "The last spine stage (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.", + "notes": "The last spine stage before the ISA subscriptions (uk-data#568, microcosm#1095). policyengine-uk imputes attends_private_school with a formula that ranks households by net income at their weights and compares a percentile rate, scaled to the national pupil count, with the stored attends_private_school_random_draw. Left to the formula, attendance is re-imputed in every simulation, so a reform to income tax, National Insurance or benefits switches pupils in or out, a population under one million people of weight imputes none (pe-uk#1905), and the calibration and the release read different flags once the weights change. This stage materializes the engine's attendance once, at the calibration year and the spine's design weights, and stores it, as the root stage stores months_since_last_birthday, so the solve and the release read the same flags; no stage after it copies or re-keys a person. Stored attendance no longer responds to income changes or to reforms of the attendance parameters, and a reform to the fee VAT does not move pupils either way in the pinned engine.", "operations": [ { "draw": "attends_private_school_random_draw", @@ -6613,7 +6792,7 @@ "artifacts": [], "grain": "benunit", "nonnegative_outputs": [], - "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.162.14 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base.", + "notes": "Runs after the last universal_credit_reported writer and before CGT cloning. Redraws every SPI benefit unit's capital, since the channel's incomes are SPI draws and the copied FRS answer no longer belongs to the unit (uk-data#495, microcosm#1095), from weighted base-FRS units with available capital in cells of UC reporter status by financial investment income band (interest, dividends and other investment income; property income is excluded because TOTCAPB4 counts accounts and assets) by is_uc_couple (two retained FRS claimant/partner roles, independently of legal is_married) by dependent-children band. A cell with fewer than 20 donors widens in the declared coarsening order, and reporter status is never coarsened. It then applies the monotone reported-UC OR refresh. The recorded capitals add property (uk-data#495, microcosm#1095), since policyengine-uk 2.164.2 replaces every capital source with a recorded figure of 0 or more and takes it as countable, and TOTCAPB4 has no property. WAS records the property for the household, and each means test counts the claimant's and partner's own capital (UC Regs 2013 reg 18; SPCA 2002 s.5). The household reference person's benefit unit therefore holds all of the household's property, and no other unit records any (Mar\u00eda's ruling of 2026-10-12), since that person is the one in whose name the home is owned or rented, the best single reading of the owner. The engine shares household property over every claimant or partner instead, which puts a parent's property on an adult child's claim; a recorded figure consumes its own unit's share, so the engine's shares reach only a unit on the unavailable sentinel, and since policyengine-uk 2.136.1 such a unit takes its share whatever a recording unit beside it holds (the receipt counts any unit left there). The property counts at 90% of its value, market value less 10% for the costs of sale, less the debt secured on it, class by class and floored at zero per class (UC Regs 2013 reg 49(1); SPC Regs 2002 reg 19), as policyengine-uk values Universal Credit property capital. The was_wealth stage draws the debt on other residential and on non-residential property (microcosm#1095), and owned land carries none, as the engine has no input for it (pe-uk#1969). Universal Credit counts other residential and non-residential property, and Pension Credit counts those and owned land. A unit that reports Universal Credit keeps its receipt and records the carrier alone for Universal Credit, since DWP assessed its capital to pay it and the property is imputed without that receipt. frs_benunit_capital stays the financial carrier, and its unavailable sentinel -1 passes through to both. Both recorded figures project to later years by GDP per head, as financial capital does, while policyengine-uk 2.139.1 projects other residential property by house prices, so the other residential property in a recorded figure grows faster than the engine's own projection of it; by the OBR indices the engine carries, it grows 1.5% faster by 2025 and 2.0% by 2026 from a 2024 base.", "operations": [ { "aggregates": { @@ -6640,7 +6819,7 @@ "donor_rows": "base_frs_with_available_capital", "draw": "weighted_empirical", "identity": "benunit_id", - "investment_income": "savings_interest_income + dividend_income + other_investment_income, summed over benefit-unit members", + "investment_income": "savings_interest_income + individual_savings_account_interest_income + dividend_income + other_investment_income, summed over benefit-unit members", "investment_income_band_edges": [ 0, 240, @@ -7001,6 +7180,13 @@ "size_bytes": 356930989, "ukds_study_number": 7215, "vintage": "round_8" + }, + { + "format": "json", + "kind": "public_aggregate_reference", + "resource": "hmrc_isa_statistics_facts.json", + "role": "isa_statistics_facts", + "runtime_sha256_required": true } ], "grain": "person", @@ -7008,8 +7194,33 @@ "lifetime_isa_balance", "household_lifetime_isa_balance" ], - "notes": "Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor's weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner's keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus.", + "notes": "Imputes Lifetime ISA holdings per adult from the WAS round-8 person tab (microcosm#1003). The donor is every non-dependent adult of the person tab joined many-to-one on CASER8 to the cleaned household tab (the was_wealth donor, same pins); the released value DVFLISAvR8 (the ONS-completed FLISAVR8_i) must sum to the household aggregate DVFLISAVR8_aggr on every household or the stage refuses the tab, and a missing or negative value is refused rather than read as a zero balance. Response classes (observed, banded with the ONS value inside the reported band, ONS-imputed value or holding, rule-impossible) are receipted and never used as predictors. The declared credibility rule recodes holders recorded in an age band starting at 45 or above to non-holders and keeps holders above the balance ceiling as holders outside the balance fit (basis on the clean operation). Ownership is a weighted logistic regression with a ridge penalty on the slopes and an unpenalised intercept, so the donor's weighted ownership share is reproduced in-sample, on the age group, the log of person earnings, household net income, gross financial wealth, cash ISA, stocks-and-shares ISA and savings, private renting, sex and the number of children; the house regime gate (gradient boosting) is miscalibrated for an outcome held by under one per cent of adults with about 120 donor holders. Recipients aged 18 or over hold when a uniform keyed on person_id falls below their probability; no upper age limit is imposed. The balance is the regime-gated QRF fitted on the credible holders and drawn at the owner's keyed quantile (predict_positive_from_uniforms), so it is positive exactly when the person holds. Household predictors reuse the was_wealth definitions; tenure is the private-renter flag on both sides (DVPriRntR8 == 1; tenure_type RENT_PRIVATELY), not the engine is_renting. WAS counts LISAs inside gross financial wealth, so a household total above its gross_financial_wealth draw is scaled down pro rata (receipted) and ownership is re-derived from the capped balance; was_wealth columns are never rewritten. Balances are April 2020 to March 2022 pounds from a Great Britain donor and are not uprated; Northern Ireland recipients are predicted from the GB model. WAS records holdings, not annual contributions, and the stage derives no contribution or bonus. HMRC's 2024-25 Lifetime ISA accounts subscribed to (Table 9.4) anchor the holders (microcosm#1415): the ownership logit shifts up on ages 18 to 47, the ages that can hold and pay into one in 2024-25 (opened at 18 to 39 since April 2017), until the holders at those ages who survive the financial-wealth cap equal HMRC's payers, the fewest holders consistent with them; the same keyed uniforms decide ownership, so the anchor only adds holders, and a target it cannot reach is named, not failed. Where the cap binds, an added holder scales the household's balances down pro rata, existing holders' included. Three later stages read the whole frame (uc_reporter_redraw, uc_take_up, pension_credit_take_up), and the Lifetime ISA counts as Universal Credit and Pension Credit capital, so the added holders can move their draws; the pair build counts the benefit units whose entitlement moves (Mar\u00eda's ruling of 29 October 2026).", "operations": [ + { + "artifact_role": "isa_statistics_facts", + "fail_on_mismatch": true, + "feed_pin": "chronicle_feed.json", + "kind": "verify_vendored_fact_resource", + "mapped_build_period": 2024, + "period_mapping": "individual_tables_grown_to_the_table_9_4_build_year", + "record_sets": [ + "hmrc.isa_statistics_2026.table9_7.ty2023.", + "hmrc.isa_statistics_2026.table9_8.ty2023.", + "hmrc.isa_statistics_2026.table9_9.ty2023.", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2023", + "hmrc.isa_statistics_2026.table9_4.accounts.ty2024", + "hmrc.isa_statistics_2026.table9_4.amounts.ty2024", + "hmrc.isa_statistics_2026.table9_6.ty2024", + "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022", + "hmrc.lisa_statistics_2026.table1a.ty2024", + "hmrc.lisa_statistics_2026.table1a.ty2025" + ], + "require_before_source_read": true, + "resource": "hmrc_isa_statistics_facts.json", + "runtime_sha256_required": true, + "source_vintage": "2023-24 individuals; Table 9.4 for 2023-24 and 2024-25" + }, { "age_band_edges": [ 0, @@ -7124,6 +7335,20 @@ "solver": "lbfgs", "standardise": "donor_unweighted_mean_sd" }, + { + "ages": { + "maximum": 47, + "minimum": 18 + }, + "counted_ages": { + "maximum": 47, + "minimum": 18 + }, + "kind": "anchor_lifetime_isa_ownership", + "output": "has_lifetime_isa", + "target": "table_9_4_lifetime_isa_accounts_subscribed", + "tax_year": 2024 + }, { "expected_regime": "positive_only", "kind": "impute_lifetime_isa_balance", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv index bb7e5391f3..6a81e370c6 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv +++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv @@ -1,163 +1,163 @@ 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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 4a475594a0..34ab284d89 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 @@ -9,18 +9,18 @@ "services_including_wages": 68.27759643273899 }, "expenses": { - "all": 32693.389896514316, - "individual": 32366.455997549172, - "partnership": 326.9338989651442 + "all": 32716.81002019269, + "individual": 32389.64191999076, + "partnership": 327.1681002019286 }, "expenses_by_type": { - "legal_management_and_professional_fees": 3913.7986178273254, - "non_residential_finance_costs": 1251.286577334217, - "other_allowable_expenses": 4308.941747511815, - "rent_rates_and_insurance": 3584.512676423584, - "repairs_and_maintenance": 6030.636812565662, - "residential_finance_costs": 12061.273625131324, - "services_including_wages": 1542.939839720388 + "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 }, "landlords": { "all": 136.55519286547798, @@ -28,9 +28,9 @@ "partnership": 1.3655519286547815 }, "receipts": { - "all": 52309.42383442291, - "individual": 51786.32959607868, - "partnership": 523.0942383442307 + "all": 52346.8960323083, + "individual": 51823.42707198522, + "partnership": 523.46896032308 }, "receipts_bands": [ { 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 cb12226432..a755b50aa2 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 @@ -61,6 +61,7 @@ "iidb_reported": "float64", "incapacity_benefit_reported": "float64", "income_support_reported": "float64", + "individual_savings_account_interest_income": "float64", "is_benunit_head": "bool", "is_blind": "bool", "is_claimant_or_partner": "bool", @@ -112,7 +113,6 @@ "statutory_sick_pay": "float64", "student_loan_repayments": "float64", "student_loans": "float64", - "tax_free_savings_income": "float64", "trading_loss": "float64", "uc_is_in_gainful_self_employment": "bool", "uc_is_in_startup_period": "bool", diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/was_person.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/was_person.csv index 8ca1abacf2..68623c09c8 100644 --- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/was_person.csv +++ 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structural UK stage 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_pay","statutory_maternity_pay","statutory_paternity_pay","student_loans","access_fund","education_grants","healthy_start_vouchers","free_school_breakfasts","free_school_fruit_veg","free_school_meals","council_tax_benefit_reported","maintenance_expenses","childcare_expenses","personal_pension_contributions","employee_pension_contributions","pension_contributions_via_salary_sacrifice","salary_sacrifice_reported","salary_sacrifice_asked","frs_bonus_amount","child_benefit_reported","income_support_reported","housing_benefit_reported","attendance_allowance_reported","dla_sc_reported","dla_m_reported","iidb_reported","carers_allowance_reported","would_claim_carers_allowance","sda_reported","afcs_reported","ssmg_reported","pension_credit_reported","child_tax_credit_reported","working_tax_credit_reported","state_pension_reported","winter_fuel_allowance_reported","incapacity_benefit_reported","universal_credit_reported","pip_m_reported","pip_dl_reported","jsa_contrib_reported","jsa_income_reported","esa_contrib_reported","esa_income_reported","esa_includes_support_component","bsp_reported","war_widows_pension_reported","widowed_parents_allowance_reported","relationship_to_head","ons_family_role","ons_family_index","is_looked_after_by_local_authority","employment_status","employment_sector","sic_industry_division","aa_category","dla_sc_category","dla_m_category","pip_m_category","pip_dl_category","is_disabled_for_benefits","is_enhanced_disabled_for_benefits","is_severely_disabled_for_benefits","current_education","highest_education","is_in_non_advanced_education","is_in_approved_training","age_started_or_accepted_current_education_or_training","is_before_universal_credit_qualifying_young_person_terminal_date","adult_ema","child_ema","receives_benefits_in_own_right","reports_outstanding_student_loan","age_left_full_time_education","legacy_jobseeker_proxy","esa_health_condition_proxy","esa_support_group_proxy","disabled_students_allowance_eligible_expenses","max_free_entitlement_hours_used","would_claim_marriage_allowance","would_claim_scp","attends_private_school_random_draw","tax_free_childcare_spend_routed_share","is_apprentice","hmrc_spi_pay","hmrc_spi_unemployment_benefit_income","hmrc_spi_incapacity_benefit_income","ossben_identifiable_subset","srp_regular_code5","employer_pension_contributions","employment_income_annual_share","employment_income_other_spells","self_employment_income_annual_share","self_employment_income_other_spells","private_pension_income_annual_share","contributory_benefits_annual_share","person_source_id","person_support_channel","person_support_clone_index","person_is_spi_income_band_carrier","charitable_investment_gifts","gift_aid","hmrc_spi_employment_benefits","hmrc_spi_employment_expenses","hmrc_spi_other_social_security_income","hmrc_spi_taxable_termination_pay","hmrc_spi_miscellaneous_employment_income","hmrc_spi_other_income","hmrc_spi_state_pension_income","property_finance_costs","hmrc_spi_employed_income","hmrc_spi_total_earned_income","hmrc_spi_total_investment_income","hmrc_spi_assessable_income","person_is_spi_income_recipient","student_loan_balance","has_lifetime_isa","lifetime_isa_balance","property_rental_income","local_bus_use_band","bus_in_london_trips","other_local_bus_trips","local_bus_trips","bus_pass_eligible","local_bus_single_fare_share","a_and_e_visits","admitted_patient_visits","outpatient_visits","nhs_a_and_e_spending","nhs_admitted_patient_spending","nhs_outpatient_spending","capital_gains","cgt_residential_probability","capital_gains_residential_property","capital_gains_asset_type","capital_gains_badr","pension_contributions_via_bonus_sacrifice","salary_sacrifice_pre_conversion_pay","student_loan_plan"],"entity":"person","rows":"all"}],"kernel":"uk.stage.private_school_attendance@1","mass":"conserve","outputs":[{"column":"attends_private_school","dtype":"bool","entity":"person","ownership":"produced","rows":"all"}],"params":{"numerical_dependencies":[["policyengine-uk","2.164.2"],["policyengine-core","3.33.3"],["numpy","2.4.6"],["pandas","3.0.3"],["scikit-learn","1.8.0"],["quantile-forest","1.4.2"]],"stage":"private_school_attendance","stage_contract_sha256":"7e44379c0331fe4c9de05cd76a075c1a6aa22cf0dec6cd2f91b277e70f14a038","time_period":"2024"},"population":"childcare_copy_take_up.boundary","sources":["frs"],"structural":"none","weights":null},{"artifact_outputs":[{"name":"stage_evidence","type":{"name":"microcosm.stage-evidence","schema_version":1}}],"base":null,"citation":"","description":"Run UK spine stage isa_subscriptions.","id":"isa_subscriptions","inputs":[{"columns":["frs_benunit_capital"],"entity":"benunit","rows":"all"},{"columns":["cash_isa","region","stocks_and_shares_isa"],"entity":"household","rows":"all"},{"columns":["age","attends_private_school","dividend_income","employment_income","gender","has_lifetime_isa","individual_savings_account_interest_income","lifetime_isa_balance","loss_relief","miscellaneous_income","private_pension_income","property_income","savings_interest_income","self_employment_income","state_pension_reported"],"entity":"person","rows":"all"}],"kernel":"uk.stage.isa_subscriptions@1","mass":"conserve","outputs":[{"column":"cash_isa_balance","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"stocks_and_shares_isa_balance","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"cash_isa_subscriptions","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"stocks_and_shares_isa_subscriptions","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"lifetime_isa_payments","dtype":"float64","entity":"person","ownership":"produced","rows":"all"}],"params":{"numerical_dependencies":[["policyengine-uk","2.164.2"],["policyengine-core","3.33.3"],["numpy","2.4.6"],["pandas","3.0.3"],["scikit-learn","1.8.0"],["quantile-forest","1.4.2"]],"stage":"isa_subscriptions","stage_contract_sha256":"09637b7e0ed87faf0ddb2d160f171accb1acf3e9b53b708ac2d56c9d92dfe221","time_period":"2024"},"population":"childcare_copy_take_up.boundary","sources":["frs"],"structural":"none","weights":null}],"sources":[{"codec":"csv-tables","description":"Content-bound UK FRS and donor fixture/source bundle.","name":"frs"}]} diff --git a/test_support/microcosm_build/frame_serializer_registry.py b/test_support/microcosm_build/frame_serializer_registry.py index 6e5bec73a4..5beee24bae 100644 --- a/test_support/microcosm_build/frame_serializer_registry.py +++ b/test_support/microcosm_build/frame_serializer_registry.py @@ -226,6 +226,8 @@ def _round_trip_nullable_us_h5(tmp_path: Path, nullable_case: str) -> BooleanRou def _round_trip_uk_single_year(tmp_path: Path, nullable_case: str) -> BooleanRoundTrip: pytest.importorskip("tables") pytest.importorskip("h5py") + from microcosm.frame.adapters.policyengine_uk import UK_ISA_INCOME_CONTRACT + source = _dtype_family_table(nullable_case) before = source.copy(deep=True) path = tmp_path / "uk-single-year.h5" @@ -242,6 +244,8 @@ def _round_trip_uk_single_year(tmp_path: Path, nullable_case: str) -> BooleanRou time_period="2023", weight_kind=WeightKind.DESIGN, mass_log=(), + # Every UK H5 declares its ISA income contract (microcosm#1360). + isa_income_contract=UK_ISA_INCOME_CONTRACT, path=path, ) loaded = _read_uk_national_tables(path)[0]["person"] diff --git a/test_support/microcosm_build/uk_hmrc_calibration.py b/test_support/microcosm_build/uk_hmrc_calibration.py index 3d3be3a976..cf61f3766a 100644 --- a/test_support/microcosm_build/uk_hmrc_calibration.py +++ b/test_support/microcosm_build/uk_hmrc_calibration.py @@ -1,6 +1,7 @@ # ruff: noqa: F401 from __future__ import annotations +from collections.abc import Mapping from pathlib import Path import numpy as np @@ -23,6 +24,7 @@ ) from microcosm.build.uk_runtime.national_frame import ( uk_household_weight_kind, + uk_isa_income_contract, uk_national_frame, uk_time_period, ) @@ -64,7 +66,7 @@ def _feasible_dataset_and_targets() -> tuple[Frame, HMRCIncomeTargetSet]: "person_household_id": row_id, "person_benunit_id": row_id, "state_pension_reported": 0.0, - "tax_free_savings_income": 0.0, + "individual_savings_account_interest_income": 0.0, SPI_HMRC_OTHER_INCOME_COLUMN: 0.0, SPI_HMRC_STATE_PENSION_INCOME_COLUMN: 0.0, } @@ -141,6 +143,7 @@ def _with( person: pd.DataFrame | None = None, household: pd.DataFrame | None = None, time_period: str | None = None, + isa_income_contract: Mapping[str, object] | None = None, ) -> Frame: """Rebuild the national frame with selected tables replaced.""" @@ -156,6 +159,11 @@ def _with( else frame.weights_for("household").values ), mass_log=frame.mass_log, + isa_income_contract=( + uk_isa_income_contract(frame) + if isa_income_contract is None + else isa_income_contract + ), ) diff --git a/test_support/microcosm_build/uk_isa_statistics.py b/test_support/microcosm_build/uk_isa_statistics.py new file mode 100644 index 0000000000..ca04acdd47 --- /dev/null +++ b/test_support/microcosm_build/uk_isa_statistics.py @@ -0,0 +1,378 @@ +"""Synthetic ISA facts, WAS person tabs and the stage declaration (microcosm#1415). + +Values are invented and internally consistent (the 'all' blocks are the sums of the +type blocks, Tables 9.8 and 9.9 restate Table 9.7's totals); they exercise the code, +not HMRC's numbers. +""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import pandas as pd + +from microcosm.build.source_manifest import SourceOperationSpec, SourceStageSpec +from microcosm.build.uk_runtime.hmrc_isa_statistics import ( + HMRC_ISA_AGE_BAND_IDS, + HMRC_ISA_INCOME_BAND_IDS, + HMRC_ISA_REGION_IDS, + HMRC_ISA_STATISTICS_RECORD_SETS, + HMRC_ISA_SUBSCRIPTION_BAND_IDS, + HMRC_ISA_TYPES, +) +from microcosm.build.uk_runtime.isa_subscriptions import ( + UK_ISA_FACT_FENCE, + UK_ISA_SUBSCRIPTIONS_MASS_CONSERVATION_REASON, +) + +PREFIX = "hmrc.isa_statistics_2026." +#: Invented 2023-24 subscriber totals by type, people. +TYPE_TOTALS = { + "cash_only": 9_000.0, + "stocks_and_shares_only": 3_000.0, + "cash_and_stocks_and_shares": 1_000.0, +} +_INCOME_SHARES = np.linspace(1.0, 0.2, len(HMRC_ISA_INCOME_BAND_IDS)) +_BAND_SHARES = np.asarray( + [0.3, 0.12, 0.07, 0.04, 0.05, 0.03, 0.02, 0.03, 0.02, 0.04, 0.28] +) +_AGE_SHARES = np.linspace(0.6, 1.0, len(HMRC_ISA_AGE_BAND_IDS)) +_REGION_SHARES = np.asarray( + [0.5, 1.4, 1.0, 0.9, 1.1, 1.3, 1.8, 2.0, 1.2, 0.6, 0.9, 0.3, 0.02] +) + +#: The vendored-facts artifact and its check, which both stages declare. +ISA_FACTS_ARTIFACT: dict[str, Any] = { + "role": "isa_statistics_facts", + "kind": "public_aggregate_reference", + "resource": "hmrc_isa_statistics_facts.json", + "format": "json", + "runtime_sha256_required": True, +} +ISA_FACTS_VERIFY_OPERATION: dict[str, Any] = { + "kind": "verify_vendored_fact_resource", + "artifact_role": "isa_statistics_facts", + "resource": "hmrc_isa_statistics_facts.json", + "feed_pin": "chronicle_feed.json", + "record_sets": list(HMRC_ISA_STATISTICS_RECORD_SETS), + "source_vintage": "2023-24 individuals; Table 9.4 for 2023-24 and 2024-25", + "mapped_build_period": 2024, + "period_mapping": "individual_tables_grown_to_the_table_9_4_build_year", + "require_before_source_read": True, + "runtime_sha256_required": True, + "fail_on_mismatch": True, +} +#: The ``was_lisa`` anchor to HMRC's 2024-25 Lifetime ISA payers. +LISA_ANCHOR_OPERATION: dict[str, Any] = { + "kind": "anchor_lifetime_isa_ownership", + "output": "has_lifetime_isa", + "target": "table_9_4_lifetime_isa_accounts_subscribed", + "tax_year": 2024, + "ages": {"minimum": 18, "maximum": 47}, + "counted_ages": {"minimum": 18, "maximum": 47}, +} + +ISA_STAGE_OPERATIONS: tuple[dict[str, Any], ...] = ( + ISA_FACTS_VERIFY_OPERATION, + { + "kind": "clean_was_isa_person_donor", + "household_key": "CASER8", + "household_weight": "R8xshhwgt", + "person_columns": { + "person_number": "PersonR8", + "is_dependent_child": "IsDepR8", + "age_band": "DVAge17R8", + "cash_isa_value": "DVCISAvR8", + "stocks_and_shares_isa_value": "DVIISAvR8", + }, + "household_aggregates": { + "cash_isa": "DVCISAVR8_aggr", + "stocks_and_shares_isa": "DVIISAVR8_aggr", + }, + "aggregate_tolerance_gbp": 1.0, + "non_dependent_code": 2, + }, + { + "kind": "split_household_isa_balances", + "outputs": ["cash_isa_balance", "stocks_and_shares_isa_balance"], + "seed": 0, + "salt": "isa_subscriptions:balance_split", + "maximum_donor_adults": 4, + }, + { + "kind": "allocate_isa_subscribers", + "seed": 0, + "salt": "isa_subscriptions:subscribers", + "minimum_age": 18, + "selection_order": [ + "cash_and_stocks_and_shares", + "stocks_and_shares_only", + "cash_only", + ], + }, + { + "kind": "draw_isa_subscription_amounts", + "outputs": ["cash_isa_subscriptions", "stocks_and_shares_isa_subscriptions"], + "seed": 0, + "salt": "isa_subscriptions:amounts", + "within_band": "uniform", + "balance_rank_weight": 0.5, + }, + { + "kind": "split_both_type_subscriptions", + "anchor": "table_9_4_stocks_and_shares_residual", + "share_floor": 0.02, + }, + { + "kind": "grow_isa_subscriptions", + "people": "table_9_4_account_growth", + "amounts": "table_9_4_amount_level_tapered_to_limit", + "from_tax_year": 2023, + "to_tax_year": 2024, + "overall_limit_gbp": 20000.0, + "taper_from_gbp": 18000.0, + }, + { + "kind": "draw_lifetime_isa_payments", + "outputs": ["lifetime_isa_payments"], + "seed": 0, + "salt": "isa_subscriptions:lifetime_isa_payments", + "minimum_age": 18, + "maximum_age": 47, + "annual_limit_gbp": 4000.0, + }, + { + "kind": "record_mass_conservation_receipt", + "entity": "household", + "reason": UK_ISA_SUBSCRIPTIONS_MASS_CONSERVATION_REASON, + "declared_factor": 1.0, + "gate_coupling": ( + "The terminal family gate requires a valid mass-conserving " + "MassChangeRecord carrying exactly this stage-specific reason." + ), + }, + {"kind": "classify_isa_facts_with_reviewed_fence", **UK_ISA_FACT_FENCE}, +) + + +def isa_stage_spec() -> SourceStageSpec: + """The stage declaration the tests run against.""" + + return SourceStageSpec( + stage="isa_subscriptions", + survey="HMRC ISA statistics; Wealth and Assets Survey round 8", + source="synthetic", + grain="person", + artifacts=( + ISA_FACTS_ARTIFACT, + {"role": "was_qrf_donor", "filename": "was_household.tab"}, + {"role": "was_person_tab", "filename": "was_person.tab"}, + ), + operations=tuple( + SourceOperationSpec( + kind=operation["kind"], + parameters={ + key: value for key, value in operation.items() if key != "kind" + }, + ) + for operation in ISA_STAGE_OPERATIONS + ), + outputs=( + "cash_isa_balance", + "stocks_and_shares_isa_balance", + "cash_isa_subscriptions", + "stocks_and_shares_isa_subscriptions", + "lifetime_isa_payments", + ), + ) + + +def synthetic_isa_statistics_payload(scale: float = 1.0) -> dict[str, Any]: + """Every record the loader reads, with invented consistent values.""" + + rows: list[dict[str, Any]] = [] + + def add(record_id: str, value: float) -> None: + rows.append({"source_record_id": record_id, "value": float(value)}) + + income = _INCOME_SHARES / _INCOME_SHARES.sum() + bands = _BAND_SHARES / _BAND_SHARES.sum() + blocks: dict[str, np.ndarray] = {} + for isa_type, total in TYPE_TOTALS.items(): + blocks[isa_type] = np.outer(income, bands) * total * scale + blocks["all"] = sum(blocks[t] for t in HMRC_ISA_TYPES) + for isa_type, cells in blocks.items(): + record_set = f"{PREFIX}table9_7.ty2023.{isa_type}" + rows_with_total = np.vstack([cells, cells.sum(axis=0)]) + for i, band_row in enumerate((*HMRC_ISA_INCOME_BAND_IDS, "all")): + for b, band in enumerate(HMRC_ISA_SUBSCRIPTION_BAND_IDS): + add( + f"{record_set}.subscribers.{band_row}.{band}", rows_with_total[i, b] + ) + subscribers = rows_with_total[i].sum() + add(f"{record_set}.subscribers.{band_row}.all_subscriptions", subscribers) + add( + f"{record_set}.holders.{band_row}.no_further_subscription", + subscribers * 0.8, + ) + measures = { + "cash_only": "cash_only_subscribers", + "stocks_and_shares_only": "stocks_and_shares_only_subscribers", + "cash_and_stocks_and_shares": "cash_and_stocks_and_shares_subscribers", + "all": "all_subscribers", + } + ages = _AGE_SHARES / _AGE_SHARES.sum() + for sex, sex_share in (("male", 0.48), ("female", 0.52), ("all", 1.0)): + record_set = f"{PREFIX}table9_8.ty2023.{sex}" + for a, age in enumerate(HMRC_ISA_AGE_BAND_IDS): + age_share = ages[a] + for isa_type, measure in measures.items(): + total = ( + sum(TYPE_TOTALS.values()) + if isa_type == "all" + else TYPE_TOTALS[isa_type] + ) + add( + f"{record_set}.subscribers.{age}.{measure}", + total * scale * sex_share * age_share, + ) + regions = _REGION_SHARES / _REGION_SHARES.sum() + england = [ + index for index, row in enumerate(HMRC_ISA_REGION_IDS) if row.startswith("E12") + ] + for isa_type, measure in measures.items(): + total = ( + sum(TYPE_TOTALS.values()) if isa_type == "all" else TYPE_TOTALS[isa_type] + ) + for r, row in enumerate(HMRC_ISA_REGION_IDS): + add( + f"{PREFIX}table9_9.ty2023.subscribers.{row}.{measure}", + total * scale * regions[r], + ) + add( + f"{PREFIX}table9_9.ty2023.subscribers.E92000001.{measure}", + total * scale * regions[england].sum(), + ) + accounts = { + 2023: { + "cash": 10_000.0, + "stocks_and_shares": 4_000.0, + "innovative_finance": 10.0, + "lifetime": 900.0, + }, + 2024: { + "cash": 10_800.0, + "stocks_and_shares": 4_800.0, + "innovative_finance": 9.0, + "lifetime": 1_100.0, + }, + } + amounts = { + 2023: { + "cash": 60e6, + "stocks_and_shares": 30e6, + "innovative_finance": 0.1e6, + "lifetime": 2.2e6, + }, + 2024: { + "cash": 80e6, + "stocks_and_shares": 36e6, + "innovative_finance": 0.1e6, + "lifetime": 2.7e6, + }, + } + for year in (2023, 2024): + for component in ( + "cash", + "stocks_and_shares", + "innovative_finance", + "lifetime", + ): + add( + f"{PREFIX}table9_4.accounts.ty{year}.{component}.accounts", + accounts[year][component] * scale, + ) + add( + f"{PREFIX}table9_4.amounts.ty{year}.{component}.amounts", + amounts[year][component] * scale, + ) + add( + f"{PREFIX}table9_4.accounts.ty{year}.all.accounts", + sum(accounts[year].values()) * scale, + ) + add( + f"{PREFIX}table9_4.amounts.ty{year}.all.amounts", + sum(amounts[year].values()) * scale, + ) + for component, value in ( + ("stocks_and_shares", 500e6), + ("cash", 400e6), + ("innovative_finance", 0.5e6), + ("all", 900.5e6), + ): + add(f"{PREFIX}table9_6.ty2024.{component}.all.market_value", value * scale) + add( + f"{PREFIX}table9_6.ty2024.stocks_and_shares.cash_on_deposit.market_value", + 20e6 * scale, + ) + note = "hmrc.cash_isa_limit_reduction_2026.cash_subscribers_under_65.ty2022" + add(f"{note}.under_12000.share_of_cash_subscribers", 78) + add(f"{note}.over_12000.share_of_cash_subscribers", 22) + for year in (2024, 2025): + add( + f"hmrc.lisa_statistics_2026.table1a.ty{year}.tax_year_{year}.house_purchase_withdrawers", + 90.0 * scale, + ) + return {"schema_version": 1, "kind": "uk_vendored_ledger_facts", "rows": rows} + + +def synthetic_was_isa_tabs( + households: int = 120, seed: int = 7 +) -> tuple[pd.DataFrame, pd.DataFrame]: + """WAS round-8 household and person tabs whose person ISA values pin to the aggregates. + + The household tab carries the case number, the weight and the ISA aggregates. + """ + + rng = np.random.default_rng(seed) + people = [] + house_rows = [] + for case in range(1, households + 1): + adults = int(rng.integers(1, 4)) + cash = np.where( + rng.random(adults) < 0.6, rng.integers(500, 30_000, adults), 0 + ).astype(float) + stocks = np.where( + rng.random(adults) < 0.3, rng.integers(500, 60_000, adults), 0 + ).astype(float) + for person in range(adults): + people.append( + { + "CASER8": case, + "PersonR8": person + 1, + "IsDepR8": 2, + "DVAge17R8": int(rng.integers(4, 15)), + "DVCISAvR8": cash[person], + "DVIISAvR8": stocks[person], + } + ) + people.append( + { + "CASER8": case, + "PersonR8": adults + 1, + "IsDepR8": 1, + "DVAge17R8": 1, + "DVCISAvR8": 0.0, + "DVIISAvR8": 0.0, + } + ) + house_rows.append( + { + "CASER8": case, + "R8xshhwgt": float(rng.integers(500, 2_000)), + "DVCISAVR8_aggr": float(cash.sum()), + "DVIISAVR8_aggr": float(stocks.sum()), + } + ) + return pd.DataFrame(house_rows), pd.DataFrame(people) diff --git a/test_support/microcosm_build/uk_spi_income.py b/test_support/microcosm_build/uk_spi_income.py index 78e215b7a9..06745445c8 100644 --- a/test_support/microcosm_build/uk_spi_income.py +++ b/test_support/microcosm_build/uk_spi_income.py @@ -121,7 +121,7 @@ def predict(self, predictors: pd.DataFrame) -> pd.DataFrame: value = 10.0 elif target == "charitable_investment_gifts": value = 2.0 - elif target == "tax_free_savings_income": + elif target == "individual_savings_account_interest_income": value = 5.0 elif target == SPI_HMRC_MISCELLANEOUS_EMPLOYMENT_INCOME_COLUMN: value = -3.0 @@ -220,7 +220,9 @@ def _dead_support( }.items(): if column in person_columns: person_columns[column] = values - person_columns["tax_free_savings_income"] = np.zeros(4, dtype=float) + person_columns["individual_savings_account_interest_income"] = np.zeros( + 4, dtype=float + ) person = pd.DataFrame(person_columns) benunit = pd.DataFrame({"benunit_id": np.arange(201, 205, dtype="int64")}) dead = create_uk_spi_support_tables( diff --git a/test_support/microcosm_build/uk_uc_reporter_redraw.py b/test_support/microcosm_build/uk_uc_reporter_redraw.py index 15b463ba6d..f561b7f2b4 100644 --- a/test_support/microcosm_build/uk_uc_reporter_redraw.py +++ b/test_support/microcosm_build/uk_uc_reporter_redraw.py @@ -171,6 +171,8 @@ def _frame(*, child_only: bool = False): "employment_income": 10_000.0 + benunit_id + member, "self_employment_income": 100.0 * member, "savings_interest_income": 10.0, + # ISA interest apart (microcosm#1360): gross is the sum. + "individual_savings_account_interest_income": 0.0, "dividend_income": 20.0, "property_income": 30.0, "other_investment_income": 40.0, @@ -192,6 +194,7 @@ def _frame(*, child_only: bool = False): "employment_income": 0.0, "self_employment_income": 0.0, "savings_interest_income": 0.0, + "individual_savings_account_interest_income": 0.0, "dividend_income": 0.0, "property_income": 0.0, "other_investment_income": 0.0, diff --git a/tools/build_uk_release_input_coverage_manifest.py b/tools/build_uk_release_input_coverage_manifest.py index 10d6e45b7e..6b28c6c42a 100644 --- a/tools/build_uk_release_input_coverage_manifest.py +++ b/tools/build_uk_release_input_coverage_manifest.py @@ -88,6 +88,18 @@ "approved_by": "juaristi22", "approved_on": "2026-10-11", }, + "tax_free_savings_income": { + "reason": ENGINE_DERIVED_EXCLUSION_REASON, + "tracking_note": ( + "microcosm#1360: under the additive ISA income contract the " + "producer stores ISA interest in " + "individual_savings_account_interest_income, which policyengine-uk " + "adds into tax_free_savings_income; savings_interest_income, now " + "interest outside ISAs, stays required." + ), + "approved_by": "juaristi22", + "approved_on": "2026-10-28", + }, } ENTITY_TABLES = ("person", "benunit", "household") READ_BATCH_SIZE = 12 @@ -916,6 +928,10 @@ def build_manifest( stage_name="was_lisa", candidate_source=candidate_source, ), + "isa_subscriptions": _source_stage_family_coverage_contract( + stage_name="isa_subscriptions", + candidate_source=candidate_source, + ), "nts_bus_travel": _source_stage_family_coverage_contract( stage_name="nts_bus_travel", candidate_source=candidate_source, diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py index 82a4c4769d..c0465f9fe2 100644 --- a/tools/graph_uk_spine_fixture.py +++ b/tools/graph_uk_spine_fixture.py @@ -120,6 +120,9 @@ HMRCPropertyRentalBand, HMRCPropertyRentalFacts, ) +from microcosm.build.uk_runtime.isa_subscriptions import ( + UKISASubscriptionsStageTransform, +) from microcosm.build.uk_runtime.lcfs_consumption import ( BUS_FARE_LCFS_CODES, UKLCFSConsumptionStageTransform, @@ -205,7 +208,7 @@ _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 = 49 +UK_FIXTURE_STAGE_COUNT = 50 _QRF_ESTIMATORS = 4 # These are the complete object-string surface observed in the unchanged @@ -834,13 +837,28 @@ def _was_person_donor() -> pd.DataFrame: financial wealth, which WAS counts them in), one banded value, one ONS-imputed holder, one ONS-imputed non-holder, one self-employment sentinel and one holder in an age band the product rules exclude (recoded - by the stage's credibility rule). + by the stage's credibility rule). The household tab's cash and + stocks-and-shares ISA totals are split across its adults in a few patterns + (one holder, an even split, a lead holder), summing exactly to the + aggregates, for the ISA subscriptions stage (microcosm#1415). """ rows: list[dict[str, float]] = [] for household in range(_DONOR_ROWS): adults = 1 + household % 3 children = household % 3 + cash_total = 7.0 + household + stocks_total = 5.0 + household + cash_shares = { + 1: (1.0,), + 2: (0.5, 0.5) if household % 2 == 0 else (1.0, 0.0), + 3: (0.5, 0.25, 0.25) if household % 4 == 0 else (1.0, 0.0, 0.0), + }[adults] + stocks_shares = { + 1: (1.0,), + 2: (0.0, 1.0) if household % 2 == 0 else (0.5, 0.5), + 3: (0.25, 0.75, 0.0) if household % 4 == 0 else (0.0, 0.0, 1.0), + }[adults] for person in range(adults + children): adult = person < adults band = ( @@ -881,6 +899,10 @@ def _was_person_donor() -> pd.DataFrame: "FLISABR8": 1.0 if banded else -9.0, "flisabr8_iflag": 0.0, "DVFLISAvR8": value, + "DVCISAvR8": cash_total * cash_shares[person] if adult else 0.0, + "DVIISAvR8": ( + stocks_total * stocks_shares[person] if adult else 0.0 + ), } ) return pd.DataFrame(rows) @@ -2121,6 +2143,12 @@ def capture_root(frame: Frame) -> Frame: donor_household=was, donor_person=was_person, ), + "isa_subscriptions": UKISASubscriptionsStageTransform( + stage=stages["isa_subscriptions"], + engine=engine, + donor_household=was, + donor_person=was_person, + ), "nts_bus_travel": UKNTSBusTravelStageTransform( stage=stages["nts_bus_travel"], engine=engine, diff --git a/uv.lock b/uv.lock index 5e79bf04ee..b125c2883e 100644 --- a/uv.lock +++ b/uv.lock @@ -857,7 +857,7 @@ requires-dist = [ { name = "openpyxl", marker = "extra == 'uk'", specifier = ">=3.1" }, { name = "pandas", specifier = ">=2" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.162.14" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.164.2" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, { name = "pyarrow", specifier = ">=15" }, { name = "pyyaml", specifier = ">=6" }, @@ -936,7 +936,7 @@ requires-dist = [ { name = "microcosm-diagnostics", editable = "packages/microcosm-diagnostics" }, { name = "packaging", specifier = ">=24" }, { name = "policyengine-core", marker = "extra == 'us'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.162.14" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.164.2" }, { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" }, ] provides-extras = ["us", "uk"] @@ -1055,7 +1055,7 @@ requires-dist = [ { name = "numpy", specifier = ">=2" }, { name = "pandas", specifier = ">=2.3" }, { name = "policyengine-core", marker = "extra == 'policyengine'", specifier = "==3.32.5" }, - { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.162.14" }, + { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.164.2" }, { name = "policyengine-us", marker = "extra == 'policyengine'", specifier = ">=2.2.1,<3" }, { name = "tables", marker = "extra == 'axiom'", specifier = ">=3" }, ] @@ -1675,7 +1675,7 @@ wheels = [ [[package]] name = "policyengine-uk" -version = "2.162.14" +version = "2.164.2" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "microdf-python" }, @@ -1683,9 +1683,9 @@ dependencies = [ { name = "pydantic" }, { name = "tables" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/d9/09/22249c0c817639a30afb8f1c10689800a8eb24b9ddf576172a97b749bdf0/policyengine_uk-2.162.14.tar.gz", hash = "sha256:af9c465536dbb621347e339bcfc4ca851f3c4592a626b207773c342f93d847c8", size = 3786756, upload-time = "2026-10-27T22:43:11.412Z" } +sdist = { url = "https://files.pythonhosted.org/packages/e5/b2/d0503e1c8f5b092a791411cf66ab9f35cd42fb78f81199dcf1af56af3a19/policyengine_uk-2.164.2.tar.gz", hash = "sha256:3ac770d130cb4bae0c05a62a60eb775e7da5e77f56bacbdd31cf38473690a88f", size = 3910802, upload-time = "2026-10-29T06:56:04.843Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/32/b3/1b080bd095e486144e848b3bdc2d145cdd0390bab4027eda5c6caab68715/policyengine_uk-2.162.14-py3-none-any.whl", hash = "sha256:9538009f3cd83fe12c2a2ece6bd3348b477e99f9e61abf28e21bd20fc9b300d1", size = 5502702, upload-time = "2026-10-27T22:43:08.839Z" }, + { url = "https://files.pythonhosted.org/packages/20/98/7519e6b1f5dfcb79c737aeb9d1e74998ea9f40b406bb271564cf8a744a4d/policyengine_uk-2.164.2-py3-none-any.whl", hash = "sha256:649c03d59d5deb8194690ce7b704206c3ffc90d2cde5661c1a806143a863ae09", size = 5655790, upload-time = "2026-10-29T06:56:02.178Z" }, ] [[package]]