diff --git a/changelog.d/1095-access-fund-period.fixed.md b/changelog.d/1095-access-fund-period.fixed.md
new file mode 100644
index 000000000..722930a8e
--- /dev/null
+++ b/changelog.d/1095-access-fund-period.fixed.md
@@ -0,0 +1 @@
+The UK spine reads an implausible calendar-month access-fund award as the annual award it almost certainly is (microcosm#1095, from the review of #1100). The FRS weeklyises `ACCSSAMT` from the reported amount and its period code `ACCSSPD`, and on the 2024-25 tab the calendar-month amounts run up to the size of a whole annual award, so an award read as monthly could annualise far above every award the survey records as annual; #1089 had to sign an input-mass exclusion for it when calibration took its household to the weight bound, and #1100 retired that exclusion once the household moved off the bound, leaving the record itself unchanged. A calendar-month award (code 5) that annualises to more than twice the largest annual-coded award (code 52) on the same tab is now read as its per-period amount, `ACCSSAMT` × 52/12, which repairs fewer than 10 awards on the 2024-25 tab and leaves large but plausible monthly payments as reported; every other award stays as the FRS weeklyised it, including the few with no period code. The threshold moves with the tab's largest annual-coded award, so a tab with calendar-month awards but no annual-coded award now refuses rather than repairing nothing, and the stage's checkpoint evidence records the paid awards by period code, the threshold and how many awards were repaired (from the review of microcosm#1121).
diff --git a/changelog.d/1095-is-blind-registration.added.md b/changelog.d/1095-is-blind-registration.added.md
new file mode 100644
index 000000000..383ca26c9
--- /dev/null
+++ b/changelog.d/1095-is-blind-registration.added.md
@@ -0,0 +1,21 @@
+The UK spine now sets `is_blind` from the FRS blind registration question (uk-data#523, microcosm#1095). Before, the column was never written, so it was False for everyone and took policyengine-uk's default.
+
+A person is blind when `SPCREG1` is 1, meaning registered blind or severely sight impaired with the local authority or social services. Registration follows the consultant ophthalmologist's certificate that the engine's `is_blind` names. The adult and child tabs ask the same question, so children are read too.
+
+Partial-sight registration (`SPCREG2`) does not meet the severely-sight-impaired test, so it is not read. A person who was not asked the question (blank `SPCREG1`) is not blind.
+
+In FRS 2024-25, 29 adult records (57.3k at the `gross4` design weights) are registered blind. Fewer than 10 child records are, so that count is suppressed.
+
+On the locked policyengine-uk 2.100.0, `is_blind` is read by:
+
+- the TV licence concession;
+- the Disabled Students' Allowance qualifying condition;
+- the Council Tax Reduction non-dependant exemption;
+- the Tax-Free Childcare age limit and higher amount for a disabled child.
+
+`person.is_blind` is a net-new export column. It joins the `uk_export_surface` allow-list (`gates.json` and `UK_ALLOWED_EXTRA_EXPORT_COLUMNS`) and the spine graph's root person booleans. The following are regenerated:
+
+- `source_stages.json`;
+- the H2 spine fixture, whose synthetic adult and child tabs now carry `SPCREG1`;
+- the coverage manifest;
+- the mirrored gate digests.
diff --git a/changelog.d/1095-lcfs-household-counts.fixed.md b/changelog.d/1095-lcfs-household-counts.fixed.md
new file mode 100644
index 000000000..24a450e0b
--- /dev/null
+++ b/changelog.d/1095-lcfs-household-counts.fixed.md
@@ -0,0 +1 @@
+The UK spine's LCFS consumption stage names its household adult and child counts for what they are, and its stage notes load in full (microcosm#1095, the rest of uk-data#486). The LCFS G018 and G019 counts were renamed to the engine's person flags `is_adult` and `is_child`, and the recipient summed those engine flags per household; policyengine-uk#1896 deprecates them. The predictors are now `household_adult_count` and `household_child_count`, and the recipient counts its members from `age` with the survey's age-18 split, so the values and the imputation are unchanged. The stage's `notes` were a plain YAML scalar containing " #890", which YAML reads as the start of a comment, so the 642 characters after it were dropped at load; they are now quoted.
diff --git a/changelog.d/1095-mixed-age-pension-credit-saving.added.md b/changelog.d/1095-mixed-age-pension-credit-saving.added.md
new file mode 100644
index 000000000..5af72e107
--- /dev/null
+++ b/changelog.d/1095-mixed-age-pension-credit-saving.added.md
@@ -0,0 +1,11 @@
+The Pension Credit stage stores the SI 2019/37 mixed-age couple saving (`has_mixed_age_couple_pension_credit_saving`, pe-uk#1940, uk-data#519, microcosm#1095). The value is the engine's own default, evaluated once at the survey-year period on the post-SPI receipts.
+
+The default reads a birth year computed from age and the model year. microcosm holds ages fixed, so the default would drop one more protected cohort each later model year.
+
+The flag holds for a mixed-age couple that meets all of these:
+
+- the older member was born by 5 February 1954;
+- the couple reports Pension Credit, or pension-age Housing Benefit without an income-related legacy benefit;
+- the couple reports no Universal Credit.
+
+The entitlement the stage redraws take-up on reads the same value.
diff --git a/changelog.d/1095-pension-age-housing-benefit-spending.added.md b/changelog.d/1095-pension-age-housing-benefit-spending.added.md
new file mode 100644
index 000000000..ea9a8769d
--- /dev/null
+++ b/changelog.d/1095-pension-age-housing-benefit-spending.added.md
@@ -0,0 +1,11 @@
+The UK national calibration binds Great Britain pension-age Housing Benefit spending (`dwp.hb.amount_pension_age`, microcosm#1095). The row reads the DWP benefit expenditure and caseload tables, Spring Forecast 2026 (PolicyEngine/chronicle#280 / #282), on the line "Housing Benefit over Pension Credit qualifying age". It binds the calendar-2025 window: three twelfths of the FY2024-25 outturn (£6,851.0m) plus nine twelfths of the FY2025-26 forecast (£7,114.7m), which gives £7,048.8m. The model column sums `housing_benefit` over benefit units whose eldest adult is 66 or over, in Great Britain households, the same age rule as the pension-age caseload rows. The row has its own `dwp_benefit_expenditure` family, which allows source projections.
+
+DWP gives each fiscal year its own measure (`expenditure_2024`, `expenditure_2025`). The series identity of `calendar_year_window` includes the measure, so it read the two years as two one-year series and refused the window, which is why #1100 dropped this row. A window selector can now declare `source_measure_id_by_opening_year`, a map from each overlapping year's opening year to its measure:
+
+- A fact matches only the measure that its own year declares.
+- The declared measures form the window's single series.
+- The map must name exactly the window's two years.
+- It cannot be combined with `source_measure_id` or with a list-valued series key, and it is refused under any other value operation.
+- The weights, the two-year requirement and the refusal of a partial window are unchanged.
+
+The national target references, the membership report and the compile-parity signed differences are regenerated. The new row is the only change.
diff --git a/changelog.d/1095-pension-credit-reported-capital.added.md b/changelog.d/1095-pension-credit-reported-capital.added.md
new file mode 100644
index 000000000..349b38296
--- /dev/null
+++ b/changelog.d/1095-pension-credit-reported-capital.added.md
@@ -0,0 +1 @@
+Pension Credit reads the benefit unit's recorded capital (`pension_credit_reported_capital`, pe-uk#2018 and #2070, uk-data#513, microcosm#1095). It takes the same value as Universal Credit's `uc_reported_capital`: FRS `TOTCAPB4`, with SPI-channel units taking their capital redraw. Zero or more replaces every capital source in Pension Credit's assessable capital. The unavailable sentinel −1 is the engine's own default, so the household proxy applies there.
diff --git a/changelog.d/1095-person-types.added.md b/changelog.d/1095-person-types.added.md
new file mode 100644
index 000000000..2b271ca1d
--- /dev/null
+++ b/changelog.d/1095-person-types.added.md
@@ -0,0 +1,6 @@
+The UK spine supplies policyengine-uk's person types (pe-uk#1896, uk-data#524/#486, microcosm#1095). Every FRS person is on the adult table or the child table:
+
+- `is_claimant_or_partner` is adult-table membership, the benefit unit's head and any partner.
+- `is_hbai_dependent_child` is child-table membership.
+
+The engine stops inferring both from ages. The existing claimant derivation already refuses a benefit unit without one or two adult records.
diff --git a/changelog.d/1095-policyengine-uk-2-122.changed.md b/changelog.d/1095-policyengine-uk-2-122.changed.md
new file mode 100644
index 000000000..f9c0ef4f0
--- /dev/null
+++ b/changelog.d/1095-policyengine-uk-2-122.changed.md
@@ -0,0 +1,23 @@
+The UK side of the lock now resolves policyengine-uk 2.122.2 and policyengine-core 3.32.19 (microcosm#1095). Before, it resolved policyengine-uk 2.100.0 and core 3.32.5. The UK extras of microcosm-build, microcosm-data and microcosm-frame now require `policyengine-uk>=2.122.2`. The release lines policyengine-uk added since 2.100.0 are what the remaining #1095 items need, among them:
+
+- pe-uk#1896's person types;
+- #1946's severe disability flag;
+- #2018 and #2070's Pension Credit reported capital;
+- #2006's shared rent and boarders' and lodgers' rent;
+- #1940 and #1904's mixed-age saving;
+- #2140's Child Benefit opt-outs, which #1107's registered-claims export reads.
+
+Under #1086's per-country pins (option 2) the US side does not move: the extras that install policyengine-us keep policyengine-core 3.32.5 in their own fork of the lock, so the certified US default still loads and the US lane runs on the engine it was built with.
+
+Re-derived on the new lock, with no data change:
+
+- the uprating pin stamp (`hmrc_uprating_engine_pins.json`): only the version moves, and the seven parameters are unchanged at every instant;
+- the policyengine-uk concept coverage: 285 inputs (233 before), with the same 40 covered;
+- the concept mapping, reviewed against 2.122.2. Its 40 targets keep their entity, type, unit and input status. The Child Benefit notes now quote 2.122.2's documentation, which says datasets export registered claims, opt-outs included. The liquid financial assets reason now names 2.122.2's other person-level money stocks, `car_list_price` and `lifetime_isa_balance`. It also says that Universal Credit splits household capital by claimant and partner count and counts a Lifetime ISA as person-level capital;
+- the H2 spine fixture's numerical dependency;
+- `APPROVED_UV_LOCK_SHA256`;
+- the CGT stages' declared dependency.
+
+policyengine-uk 2.112.0 replaced `gov.dwp.state_pension.age.male` and `female` with the statutory timetable by date of birth. So the UC take-up population now reads its one State Pension age from that timetable: the whole-year age at which everyone born in a single calendar year reaches it, for the cohort reaching it in the build year. That is 66 for 2024 and 2025, as before. From 2026 the cohort's age is no longer whole years, so the stage refuses rather than approximate.
+
+Forward years: policyengine-uk 2.122.2 models the April 2029 salary-sacrifice cap by adding sacrifice above £2,000 (`salary_sacrifice_returned_to_income`) to `employment_income`, so it also raises adjusted net income from 2029-30. The government's change applies to National Insurance only, so for 2029-30 and later the rules that read adjusted net income (the personal allowance taper, the High Income Child Benefit Charge, Tax-Free Childcare eligibility) see higher incomes for people sacrificing more than £2,000. The 2024-25 build is unaffected. The upstream fix is tracked as pe-uk#2173.
diff --git a/changelog.d/1095-severe-disability-flag.fixed.md b/changelog.d/1095-severe-disability-flag.fixed.md
new file mode 100644
index 000000000..2bfeedaeb
--- /dev/null
+++ b/changelog.d/1095-severe-disability-flag.fixed.md
@@ -0,0 +1,8 @@
+The UK spine stores `is_severely_disabled_for_benefits` as the tax credit severe disability condition (uk-data#494, policyengine-uk#1946, microcosm#1095). That condition is in CTC Regs 2002 reg 8 and WTC Regs 2002 reg 17: the DLA care component at the highest rate, the PIP daily living component at the enhanced rate, or higher-rate Attendance Allowance. The flag is read from the categories the stage derives, so it agrees with them by construction.
+
+On the locked policyengine-uk the engine's formula is the same definition, and an engine-lane test checks them against each other category by category. The old rule did not match that formula in two ways:
+
+- it counted lower-rate Attendance Allowance;
+- it counted any Armed Forces Compensation Scheme payment. FRS code 8 cannot be told apart from Armed Forces Independence Payment, so it no longer counts, a known under-count the uk-data change shares.
+
+The incumbent's coverage manifest marks the flag as required, so the spine keeps storing it rather than leaving the formula to compute it. Dataset runs use the stored value.
diff --git a/changelog.d/1095-shared-rent-and-lodgers.added.md b/changelog.d/1095-shared-rent-and-lodgers.added.md
new file mode 100644
index 000000000..0e661e559
--- /dev/null
+++ b/changelog.d/1095-shared-rent-and-lodgers.added.md
@@ -0,0 +1,17 @@
+The UK spine marks the benefit units that share their household's rent, and records the rent boarders and lodgers pay the householder (pe-uk#2006, uk-data#511/#512/#506, microcosm#1095).
+
+`liable_for_share_of_household_rent` holds for a later benefit unit in a shared household (`HHSTAT` 2) with any of:
+
+- a rent share on its adults' records (`SRENTAMT`);
+- Housing Benefit (`HBOTHAMT`);
+- a linked UC record carrying a housing element (`UCHOUSEL`).
+
+Conventional households are left out, because the survey does not say whether their later units owe the landlord or pay the householder.
+
+`CVPAY`, the rent a boarder or lodger pays the householder, is recorded on the payer. It no longer counts as the payer's property income, the same line microcosm#1081 removes. It now feeds `rent_paid_as_boarder` (`CONVBL` 1, board and lodging) or `rent_paid_as_lodger` (anything else).
+
+In FRS 2024-25 at `gross4` design weights:
+
+- 103 shared households (319.9k weighted) hold 178 liable later benefit units (615.9k).
+- 43 adult records (138.4k) pay the householder, fewer than 10 of them as boarders.
+- £806.7m a year of `CVPAY` leaves property income.
diff --git a/changelog.d/1095-target-references-regenerate.fixed.md b/changelog.d/1095-target-references-regenerate.fixed.md
new file mode 100644
index 000000000..a6ded74d2
--- /dev/null
+++ b/changelog.d/1095-target-references-regenerate.fixed.md
@@ -0,0 +1 @@
+The national target references regenerate from the pinned Chronicle feed again (microcosm#1095). #1089 moved the SPI Table 3.6 employment, self-employment and private-pension amounts by total-income band to HMRC's band growth (`hmrc.itl_2026.total_income_growth_by_total_income_band`), but it did so by editing the generated `uk/target_references.json`. The population contract still declared the flat OBR average-earnings, per-capita mixed-income and private-pension indices, so regenerating restored them, and the feed-gated regeneration test failed on main. The contract now declares the band-growth index for the three targets, and both generated resources are regenerated; the membership report had still recorded the flat-index uprating for the 39 references. The compiled target values are unchanged: the three lowest employment-income bands stay at £17.7bn, £47.5bn and £172.5bn.
diff --git a/changelog.d/1095-uc-start-up-period.added.md b/changelog.d/1095-uc-start-up-period.added.md
new file mode 100644
index 000000000..a98329be7
--- /dev/null
+++ b/changelog.d/1095-uc-start-up-period.added.md
@@ -0,0 +1,39 @@
+The UK spine now sets policyengine-uk's `uc_is_in_startup_period` (uk-data#527, microcosm#1095). Nothing set it before, so the engine's default of False applied to everyone, and the Universal Credit minimum income floor applied from the first assessment period of every self-employed claim. policyengine-uk 2.100.0 already reads the input in `uc_mif_applies`.
+
+The rule follows UC Regs 2013 reg 63(1) as substituted from 23 September 2020 (SI 2019/1152). DWP starts a 12-month start-up period when it finds a claimant in gainful self-employment, and the period is no longer limited to new trades.
+
+A person is in the period when they are self-employed and either of these began less than 12 calendar months before interview (`INTDATE`):
+
+- their benefit unit's UC claim (BENEFIT 95, `UCSTART`, written month/day/year);
+- their first self-employed job (`SEJBLONG`, main job first).
+
+Self-employed means any of these:
+
+- `EMPSTATI` 3 or 4;
+- a held self-employed `JOB` row (`ETYPE` 2-7, no `SEEND`);
+- nonzero `SEINCAM2`.
+
+One self-employed-job answer does not date the trade. When someone was self-employed all year (`SAMESIT` 2, or every `SDEMP` month 3 or 4) and does not describe a business (`JOBBUS` 2), a job under a year old is a new engagement in the same trade (ADM H4102 example 4).
+
+The FRS cannot see two things, both inherited from uk-data's rule:
+
+- **Earlier awards.** A re-claim after the floor applied, or a second period within five years, reads as a start-up period.
+- **A move into the all-work-related-requirements group on an old claim.** This starts a period the flag misses.
+
+**UC records with no claim date.** In FRS 2024-25, 2,114 of 2,402 UC benefit units (88%) carry `UCSTART`. The other 288 have no linked administrative record. For these, the claim recency is drawn, keyed on the benefit unit (`stable_identity_uniforms`, seed 0, salt `uc_is_in_startup_period`). The draw rate is the `GROSS4`-weighted share of self-employed people with a linked claim whose claim began inside the window, 0.324 (uk-data measured 0.320).
+
+The draw is declared on the `frs_spine` stage as `assign_binary_from_rate`. Its notes now name it as the one identity-keyed draw at the root, because only this stage reads the raw claim, interview and job dates.
+
+**Measured on the raw FRS 2024-25 spine at design weights.** 222 person records (488.2k) are in a start-up period, and no child is. Of these:
+
+- 96 records (208.3k) are in benefit units reporting UC, including 17 records (39.6k) in units without a linked claim date;
+- the rest are self-employed people outside UC whose trade is under a year old. The flag matters to them only if they claim.
+
+**SPI-redrawn rows.** These keep their donor's flag only where their employment status or their imputed self-employment income still shows self-employment (`spi_income._refresh_uc_start_up_period`, beside the carer flag refresh). The graph declares the column as a root boolean and as a hidden SPI rewrite.
+
+`person.uc_is_in_startup_period` joins the export-surface allow-list. The following are regenerated:
+
+- `source_stages.json`;
+- the H2 spine fixture, whose synthetic tabs now carry `INTDATE`, `UCSTART`, the self-employment job columns, `SAMESIT` and `SDEMP`, plus linked-recent, linked-old and unlinked UC rows;
+- the coverage manifest's source-manifest pins;
+- the mirrored gate digests.
diff --git a/docs/concept-coverage/policyengine-uk.md b/docs/concept-coverage/policyengine-uk.md
index 0acd3a3b3..a5fb8216e 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 `2.100.0`; concept schema `d824f5e97d58`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand.
+Engine version `2.122.2`; concept schema `d824f5e97d58`. Generated by `tools/refresh_concept_coverage.py`; do not edit by hand.
 
-- Engine inputs: 233
+- Engine inputs: 285
 - Fed by a concept: 40
 - Structural: 11
-- Uncovered: 182
+- Uncovered: 234
 - Mapped but missing from the engine: 0
 
 ## Inputs each concept feeds
@@ -44,19 +44,19 @@ Engine version `2.100.0`; concept schema `d824f5e97d58`. Generated by `tools/ref
 | `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. Its one person-level GBP stock input is student_loan_balance, a debt, and no transform puts a person amount on a household input. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of the household-capital proxy it otherwise sums from all of those capital sources, net of capital other benefit units report, and apportions by benefit-unit adults, so feeding it 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. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it 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. |
 
 ## Inputs no concept covers
 
-### benunit (7)
+### benunit (12)
 
-`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `maximum_extended_childcare_hours_usage`, `tax_free_childcare_meets_uk_connection`, `uc_reported_capital`, `working_tax_credit_run_on`
+`baseline_child_benefit_entitlement`, `baseline_income_support_entitlement`, `baseline_pension_credit_entitlement`, `housing_benefit_board_and_attendance_determination`, `in_specified_or_temporary_accommodation`, `liable_for_share_of_household_rent`, `maximum_extended_childcare_hours_usage`, `meals_included_in_rent`, `pension_credit_reported_capital`, `tax_free_childcare_meets_uk_connection`, `uc_reported_capital`, `working_tax_credit_run_on`
 
-### household (55)
+### household (78)
 
-`accommodation_type`, `alcohol_and_tobacco_consumption`, `brma`, `bus_fare_spending`, `bus_subsidy_spending`, `clothing_and_footwear_consumption`, `communication_consumption`, `corporate_wealth`, `council_tax_band`, `cumulative_non_residential_rent`, `cumulative_residential_rent`, `dfe_education_spending`, `diesel_spending`, `domestic_energy_consumption`, `domestic_rates`, `education_consumption`, `electricity_consumption`, `external_child_payments`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gross_financial_wealth`, `health_consumption`, `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`, `local_authority`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `miscellaneous_consumption`, `net_financial_wealth`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_residential_property_value`, `owned_land`, `petrol_spending`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `restaurants_and_hotels_consumption`, `savings`, `spi_imputed`, `structural_insurance_payments`, `transport_consumption`, `uc_migrated`, `water_and_sewerage_charges`, `would_evade_tv_licence_fee`
+`accommodation_type`, `alcohol_and_tobacco_consumption`, `beer_draught_share`, `beer_litres`, `brma`, `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`, `domestic_energy_consumption`, `domestic_rates`, `education_consumption`, `electricity_consumption`, `external_child_payments`, `food_and_non_alcoholic_beverages_consumption`, `full_rate_vat_expenditure_rate`, `gas_consumption`, `gross_financial_wealth`, `hand_rolling_tobacco_grams_per_week`, `health_consumption`, `heated_tobacco_grams_per_week`, `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`, `local_authority`, `lpg_kg`, `main_residence_value`, `main_residential_property_purchased_is_first_home`, `miscellaneous_consumption`, `mortgage_debt`, `natural_gas_kg`, `net_financial_wealth`, `non_residential_property_value`, `non_residential_rent`, `num_bedrooms`, `num_vehicles`, `other_fermented_draught_share`, `other_fermented_litres`, `other_residential_property_value`, `other_tobacco_grams_per_week`, `owned_land`, `petrol_spending`, `private_pension_wealth`, `property_purchased`, `rail_usage`, `recreation_consumption`, `region`, `restaurants_and_hotels_consumption`, `savings`, `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`, `water_and_sewerage_charges`, `wine_draught_share`, `wine_litres`, `would_evade_tv_licence_fee`
 
-### person (120)
+### person (144)
 
-`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`, `capital_allowances`, `capital_gains_badr`, `capital_gains_carried_interest`, `capital_gains_residential_property`, `care_hours`, `carers_allowance_reported`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `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`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `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`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `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`, `other_deductions`, `other_investment_income`, `other_tax_credits`, `outpatient_visits`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `receives_benefits_in_own_right`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `sic_industry_division`, `ssmg_reported`, `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_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`, `trading_loss`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_is_in_startup_period`, `universal_credit_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`, `charitable_investment_gifts`, `child_benefit_reported`, `child_ema`, `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`, `disabled_students_allowance_eligible_expenses`, `disabled_students_allowance_receives_equivalent_support`, `dla_m_category`, `dla_sc_category`, `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`, `is_about_to_start_paid_work`, `is_apprentice`, `is_before_universal_credit_qualifying_young_person_terminal_date`, `is_blind`, `is_in_approved_training`, `is_looked_after_by_local_authority`, `is_newly_self_employed`, `jsa_contrib_reported`, `jsa_income_reported`, `lifetime_isa_balance`, `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`, `other_deductions`, `other_investment_income`, `other_local_bus_trips`, `other_tax_credits`, `outpatient_visits`, `pension_contributions_via_salary_sacrifice`, `pension_credit_reported`, `pension_income`, `person_benunit_role`, `person_household_role`, `personal_pension_contributions`, `pip_dl_category`, `pip_m_category`, `private_transfer_income`, `receives_benefits_in_own_right`, `receives_limited_capability_for_work_credits`, `rent_paid_as_boarder`, `rent_paid_as_lodger`, `role`, `scottish_funded_early_learning_childcare_start_date_reached`, `sda_reported`, `self_employment_gross_receipts`, `sic_industry_division`, `ssmg_reported`, `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`, `trading_loss`, `travel_grant_abroad_placement`, `travel_grant_clinical_placement`, `travel_grant_eligible_expenses`, `travel_grant_receives_means_tested_nhs_bursary`, `uc_has_limited_capability_for_work`, `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_ineligible_partner`, `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`, `winter_fuel_allowance_reported`, `working_tax_credit_reported`
diff --git a/docs/uk-income-anchors-280.md b/docs/uk-income-anchors-280.md
index 9b2e4bf62..08e32bab6 100644
--- a/docs/uk-income-anchors-280.md
+++ b/docs/uk-income-anchors-280.md
@@ -35,6 +35,16 @@ opening in Y. That is the `calendar_year_window` value operation
 with either year absent, and writes each member's year, weight, value and
 assertion on the spec.
 
+Some publishers give each year its own measure: DWP's benefit expenditure and
+caseload tables carry `expenditure_2024` and `expenditure_2025`, which the
+series identity reads as two series of one year each. A window over such a
+table declares `source_measure_id_by_opening_year` in its selector, mapping each
+of the two years to its measure. A fact matches only the measure its own year
+declares, and the two declared measures form the window's one series. The map
+names exactly the two years of the window, and a selector that declares it
+cannot also declare `source_measure_id` (microcosm#1095: the Great Britain
+pension-age Housing Benefit spending row).
+
 Facts already at a calendar-year period (the DWP monthly cubes) keep their
 calendar-2025 windows; the ESA rows join them below.
 
diff --git a/experiments/1095-uk-data-ports-2-receipts.md b/experiments/1095-uk-data-ports-2-receipts.md
new file mode 100644
index 000000000..d2ffc7a6c
--- /dev/null
+++ b/experiments/1095-uk-data-ports-2-receipts.md
@@ -0,0 +1,187 @@
+# microcosm#1095 receipts: the second ports PR
+
+Plan: `repos/microcosm-1095-ports-2-plan.md` (approved 2026-10-05). Branch `uk-data-ports-1095-2`, off
+main `07bbb656b`, rebased on 2026-10-06 onto main `581b5699e` (which merged #1107), on 2026-10-07
+onto main `a50b9cc0d` (which merged #1081), again on 2026-10-07 onto main `966f0d9d7` (which merged
+#1111, #1118, #1119, #1120 and #1129), and on 2026-10-08 onto main `e3e3d881f` (which merged #1125,
+#1128, #888 and #1115). Each rebase replayed every commit, regenerating the coverage manifest, the
+stage digests and the H2 fixture after each one. Each commit's other changes are line-for-line
+those of its pre-rebase original, with three exceptions:
+- in the second rebase, B4's removal of `CVPAY` from `property_income` became #1081's
+  `frs_property_income`, which already leaves it out, and B4's spine test keeps both rules'
+  assertions;
+- in the third, B0's policyengine-uk concept coverage was regenerated on main's new concept schema
+  (the liquid financial assets concept), still 285 inputs with 40 covered;
+- in the fourth, B0 also reviews that concept's unmapped reason, and #1120's engine test of it,
+  against policyengine-uk 2.122.2. The third rebase had left both on 2.100.0's facts, and CI's
+  `engine-uk` lane failed on the test from then on. 2.122.2 has two more person-level money stocks,
+  `car_list_price` and `lifetime_isa_balance`. Universal Credit now splits household capital by
+  claimant and partner count and counts a Lifetime ISA as person-level capital. The concept stays
+  unmapped.
+
+Max ruled on #1086 on 2026-10-07 (option 2): each country carries its own policyengine-core pin.
+P, a lock-only commit that moves no engine version, opened this branch until it landed on its own
+as #1143 (merged 2026-10-08), as the ruling asked. B0 was replayed onto it and re-locked from its own
+lock, so the UK fork keeps policyengine-uk 2.122.2 with core 3.32.19 and the US fork keeps core
+3.32.5. Nothing else in the lock moved, and every later commit replays unchanged. On 2026-10-08 the
+branch was rebased onto main `4cb43524a`, #1143's merge, whose tree is P's, so the head's tree did not
+change.
+
+Commits, in order:
+
+- P, now #1143 (merged 2026-10-08): per-country policyengine-core pins (#1086). The extras that install
+  policyengine-us pin core 3.32.5 and conflict with the extras that install policyengine-uk, so uv
+  locks the two sides apart and refuses an install that asks for both. A lock-level test reads
+  `uv.lock` through `uv export --frozen`.
+- C1 `4092df00e`: the national target references regenerate from the pinned feed again (#1089 had
+  hand-edited the generated file). Cherry-pickable alone.
+- C2 `a08357994`: a calendar-month access-fund award above twice the largest annual-coded award is
+  read as annual (Vahid's should-fix on #1100).
+- C3 `04fdc3cfc`: the LCFS household counts are named for what they are and counted from `age`; the
+  stage notes are quoted in full.
+- C4 `53c112f23`: Great Britain pension-age Housing Benefit spending (`dwp.hb.amount_pension_age`)
+  at the calendar-2025 window, through the new selector key `source_measure_id_by_opening_year`.
+- C5 `7a67b506d`: `is_blind` from the FRS blind registration question (uk-data#523).
+- C6 `c9d995466`: `uc_is_in_startup_period` from the FRS claim and trade start dates (uk-data#527).
+- B0 `c5cdf2d04`: policyengine-uk 2.122.2 and policyengine-core 3.32.19 on the UK side of the lock.
+- B1 `d7b42f944`: the severe disability flag stored as the tax credit condition (uk-data#494).
+- B2 `6900190ee`: `is_claimant_or_partner` and `is_hbai_dependent_child` from the adult and child
+  tables (uk-data#524/#486).
+- B3 `37775c25d`: `pension_credit_reported_capital` (uk-data#513).
+- B4 `e98503389`: shared rent liability and the rent boarders and lodgers pay (uk-data#511/#512/#506).
+- B5 `3a90d8d0b`: the mixed-age couple Pension Credit saving at survey-year facts (uk-data#519).
+- X `57a0ccf2f`: the West Midlands £12,570–15,000 income-tax cell deferred four weeks in the
+  target-fit register (approved 2026-10-06).
+- Y `caae8de35`: the two UC payment bands policyengine-uk 2.122.2 empties join the uk-data#452
+  measure exclusions (approved 2026-10-07, expiring with the class on 2026-11-26).
+- R1 `a97df8fc6`, from the review of #1121: the access-fund repair is recorded in the root stage's
+  checkpoint evidence (paid awards by period code, the threshold and the repaired count), and a tab
+  with calendar-month awards but no annual-coded award refuses.
+- L `b5c73d12b`: X's register entry names its upstream fix, pe-uk#2174 (the review of #1121
+  agreed the fix belongs in policyengine-uk's `total_income`).
+
+## Where the plan changed during implementation
+
+- **C5, children.** The plan said children stay False. The FRS 2024-25 child table asks the same
+  `SPCREG1` question (fewer than 10 records registered), and the engine reads `is_blind` for
+  children in Tax-Free Childcare, so the rule reads both tables.
+- **C6, the draw.** `frs_spine`'s notes said stochastic flags are absent from the root stage. The
+  start-up period needs raw claim, interview and job dates that only this stage reads, so it now
+  carries one identity-keyed draw, declared as `assign_binary_from_rate`; the notes say so.
+- **B1, mirror rather than stop persisting.** The plan preferred handing the flag to the engine's
+  formula. The incumbent coverage manifest marks it required and the export surface reference holds
+  it, so stopping would need a signed reclassification and an export exclusion. The stored flag now
+  equals the formula by construction (the engine-lane test checks every category), which is also
+  what policyengine-uk documents for datasets.
+- **B2, the UC take-up population.** Max's WIP branch `uk-take-up-per-person-spa` rewrites it to read
+  each person's `is_WA_adult` from the engine. This PR leaves it to that branch; B0 only keeps the
+  current design working (the State Pension age now comes from the timetable by date of birth).
+- **B4, CVPAY.** #1081 merged first (2026-10-07), so B4 is rebased on it and keeps only the payer's
+  `rent_paid_as_boarder` / `rent_paid_as_lodger` and the shared-rent liability. The householder's
+  receipt stays uncredited, as #1081 documents: policyengine-uk 2.122.2 has no householder-side input
+  for it.
+
+## Measurement
+
+Each arm built the spine from its commit in the measurement tree and calibrated it on the national
+role, with local staging only and Chronicle feed 825406f. The control is A9 (`d89a3ddfe`, main's code
+before this PR).
+Arms B2 and head were built from `8e81ddfd5` and `65a08ddb6`, before the rebase onto #1081. Their
+spines therefore lack #1081's property-income rules (losses count as zero, the sub-letting rent is
+floored), which a calibration of the current head would include.
+
+- **Arm E (`52f164c41`, C1–C6 before the rebase).** Loss 0.00702 (A9 0.00715). 99.0% of 1,169 targets
+  are within 10%, ESS 4,848, and all 7 calibrated-seam gates pass.
+  - `dwp.hb.amount_pension_age`: target £7.049bn, design £5.636bn, calibrated £7.052bn.
+  - `obr.vat`: +24.6% (A9 +24.4%), still the watch item.
+  - Input mass passes. `access_fund` sits at £65.9m against the reference's £159.6m; this is the C2
+    repair.
+- **C3 is value-identical.** The 14 non-raked LCFS outputs differ for 137 of 63,936 common households,
+  and every one is explained by an upstream predictor move:
+  - 76 have a member in a UC start-up period (C6 moves UC, and so HBAI net income);
+  - 61 have a registered-blind member (C5 reaches HBAI net income through the Council Tax Reduction
+    non-dependant exemption);
+  - fewer than 10 have a survey-income change.
+
+  The four raked columns move for every household by the global raking factor.
+- **Arm B0 (built as `f6e73f61d`, the engine bump).** Loss 0.00769, 98.4% within 10%, ESS 4,843; 6 of 7 gates
+  pass.
+  - `uk_target_fit` fails on two UC payment bands that hold no benefit unit under policyengine-uk
+    2.122.2: singles at £26,400–27,600 a year and childless couples at £24,000–25,200.
+  - Each rests on fewer than 10 FRS households. The same spine under policyengine-uk 2.100.0 keeps
+    every supporting award in its band, so the engine alone moves them out, through three
+    corrections: the published LHA rates move awards to neighbouring bands, the 2019 mixed-age
+    couple saving keeps couples who report Pension Credit off UC, and Carer Support Payment counts
+    as unearned income. The register already held 19 UC payment-band exclusions of this class
+    (uk-data#452).
+  - `obr.pension_credit` at design weights falls from £4.61bn to £4.13bn.
+  - `obr.vat`: +23.8%.
+- **#1107's rollout checks on arm B0.**
+  - The Child Benefit stage exports registered claims (`opt_out_charge_share` 1).
+  - The fully charged opt-out pool is exhausted: 599.6k families against the 627.0k target, a 27.4k
+    shortfall.
+  - In payment at the stage's design weights: 6.32m families (published 6.87m) and 11.67m children
+    (11.73m).
+  - `obr.child_benefit` calibrates to 0.0%.
+- **Arm B2 (built as `8e81ddfd5`).** Identical fit to B0. The stored severe flag drives only tax credit elements
+  the model does not pay in 2025, and the supplied person types equal the engine's inference.
+- **Arm head (built as `65a08ddb6`).** Loss 0.00765, 98.3% within 10%, ESS 4,957; 6 of 7 gates pass.
+  - `uk_target_fit` fails on the two UC bands.
+  - It also fails on `hmrc.spi_region.income_tax_by_region_12570_15000@E12000005`, West Midlands
+    income tax at £12,570–15,000: target £55.1m, design £339.2m, calibrated +27.4%. The cell was
+    +8.6% on A9, +8.9% on E and +23.6% on B0 and B2.
+  - `obr.vat`: +23.4%.
+- **The West Midlands cell, diagnosed.** The regional cells slice on policyengine-uk's
+  `total_income`, which leaves out `other_investment_income` although `income_tax` charges it as
+  savings interest.
+  - Fewer than 10 SPI records whose income is mostly other investment income sit in this band with
+    their whole tax. The design value is 6.1 times the target (6.0 times on A9; every other
+    region's cell is 0.6–1.1 times).
+  - Banded on total income including other investment income, the cell falls in line with the
+    other regions.
+  - The engine bump moves the solver's pull from +8.9% to +23.6%. A counterfactual arm, the head
+    with CVPAY back in property income (never pushed), lands at +25.4%. So CVPAY is about 2 of the
+    3.8 points from B2 to head, and the rest comes from B3–B5 moving the calibrated weights.
+  - X defers the cell four weeks (expiring 2026-11-03) while policyengine-uk's `total_income` is
+    corrected upstream to include other investment income (pe-uk#2174, filed 2026-10-07).
+- **Y excludes the two UC bands.** Replaying the target-fit gate on the head arm's evidence without
+  them passes, with X in force, and the other six seam gates passed on that arm. The replay keeps
+  the head arm's weights, which were solved with both bands bound; a calibration of the new head,
+  the national build and the certifier have not been run.
+
+## Verification
+
+- Touched-surface tests per commit (counts in each commit's run).
+- B0: the full suite on policyengine-uk 2.122.2, 13,372 passed, 119 skipped. The two failures do
+  not come from the bump: the extras test expects no UK engine, and the live Hugging Face pointer
+  now names `microcosm-uk-2024-25-national`, which fails on main too.
+- Head: the full suite, 13,378 passed, 128 skipped, and the same two environment failures.
+- `ruff check` and `ruff format --check` pass on all changed Python files.
+- `tools/ci_test_plan.py verify` and `tools/graph_acceptance_burndown.py --verify` ok.
+- #1086: on the shared lock, CI's US lane failed one test on both Pythons
+  (`test_batched_scoring_matches_whole_pool_policyengine_us`, a Maryland child-care rate lookup under
+  policyengine-us 2.2.1 on core 3.32.19). With P the US fork stays on core 3.32.5: a US install
+  from the new lock resolves policyengine-us 2.2.1, core 3.32.5 and spm-calculator 1.0.0, and
+  `test_us_post_export_scoring.py` passes there (4 tests, including that one). The lock-level test
+  passes at P and at B0.
+- Review round 1 (2026-10-07): CI's `engine-free` lane failed on `a0f292aec` in
+  `test_fully_armed_battery_evaluates_gate_for_gate`. The battery's register clock (2026-10-04)
+  predated X's approval, so the gate read X as not yet in force. X and Y now move the clock to
+  their approval dates, as the test's note asks. The engine-free CI job's 496 test files were rerun
+  locally on the rebased head (counts in the round's run).
+- Rebase onto `e3e3d881f` (2026-10-08): from the third rebase on, CI's `engine-uk` lane failed on
+  #1120's liquid-asset test (the fourth exception above). On the rebased head the lane's 40 test
+  files pass in full, run past the lane's stop at the first failure: 170 passed. The `engine-free`
+  lane's 509 test files, with `tools/orrery-contract` installed as CI does: 14,929 passed, 133
+  skipped, and the same two environment failures.
+- Review rounds 2 and 3 (2026-10-07 and 2026-10-08): C6's linked share is now a household-weighted
+  average, `np.average` over the linked rows with the `gross4` household weights. It is not a microdf
+  `MicroSeries`, because microdf reaches an environment only with an engine and the FRS spine runs
+  in the engine-free lane. B0's forward-year note cites pe-uk#2173. The FRS spine tests pass (101),
+  and no generated surface moved.
+- Head calibration: queued on 2026-10-08 at tree `c7fe76e8a`, which is this head's tree. Its results
+  are owed before merge.
+
+## Signature drafts
+
+`data/ukds/acceptance/1095-uk-data-ports-2/signed-difference-drafts.md` (not committed until signed).
diff --git a/packages/microcosm-build/pyproject.toml b/packages/microcosm-build/pyproject.toml
index e47eb19f0..b1e7f2f2c 100644
--- a/packages/microcosm-build/pyproject.toml
+++ b/packages/microcosm-build/pyproject.toml
@@ -45,7 +45,7 @@ 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>=2.100.0", "microcosm-data>=0.1,<0.2", "h5py>=3", "tables>=3", "openpyxl>=3.1"]
+uk = ["policyengine-uk>=2.122.2", "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/ledger_targets.py b/packages/microcosm-build/src/microcosm/build/ledger_targets.py
index c0d5e3ae0..5eb7d9d0a 100644
--- a/packages/microcosm-build/src/microcosm/build/ledger_targets.py
+++ b/packages/microcosm-build/src/microcosm/build/ledger_targets.py
@@ -100,6 +100,12 @@
     {-1: 3 / 12, 0: 9 / 12}
 )
 CALENDAR_YEAR_WINDOW_PERIOD_TYPES = frozenset(("fiscal_year", "tax_year"))
+#: A ``calendar_year_window`` selector key for publishers who give each year
+#: its own measure (DWP's benefit expenditure tables: ``expenditure_2024``,
+#: ``expenditure_2025``). It maps each overlapping year, by its opening year,
+#: to the measure that carries it: a fact matches only the measure its own
+#: year declares, and the window reads the declared measures as one series.
+SOURCE_MEASURE_ID_BY_OPENING_YEAR = "source_measure_id_by_opening_year"
 #: Ordered operand roles of ``scaled_by_ratio``: the published cell the
 #: reference selects, then the two national facts whose quotient translates
 #: it (a subset share of the publisher's universe, so the ratio lies in
@@ -267,6 +273,13 @@ def __post_init__(self) -> None:
             _validate_scaled_by_ratio_operands(self.name, self.value_operands)
         if self.value_operation == "calendar_year_window":
             _validate_calendar_year_window_reference(self)
+        elif SOURCE_MEASURE_ID_BY_OPENING_YEAR in self.ledger_selector:
+            raise ValueError(
+                f"LedgerTargetReference {self.name!r}: "
+                f"{SOURCE_MEASURE_ID_BY_OPENING_YEAR!r} composes the years of a "
+                "calendar_year_window; it is refused under value_operation "
+                f"{self.value_operation!r}."
+            )
         if self.value_operation == MONTHLY_WINDOW_COUNT_X_MEAN:
             _validate_count_x_mean_operands(self.name, self.value_operands)
         if self.assertion_policy not in ALLOWED_ASSERTION_POLICIES:
@@ -2680,6 +2693,26 @@ def _validate_calendar_year_window_reference(
             f"LedgerTargetReference {reference.name!r}: calendar_year_window "
             "requires an explicit calendar-year target period."
         )
+    measure_map = reference.ledger_selector.get(SOURCE_MEASURE_ID_BY_OPENING_YEAR)
+    if measure_map is None:
+        return
+    for key in ("source_measure_id", "layout_measure_id"):
+        if reference.ledger_selector.get(key) not in (None, ""):
+            raise ValueError(
+                f"LedgerTargetReference {reference.name!r}: calendar_year_window "
+                f"declares both {key!r} and {SOURCE_MEASURE_ID_BY_OPENING_YEAR!r}; "
+                "the map names each year's measure itself."
+            )
+    if _calendar_year_window_series_count(reference) != 1:
+        raise ValueError(
+            f"LedgerTargetReference {reference.name!r}: calendar_year_window "
+            f"reads {SOURCE_MEASURE_ID_BY_OPENING_YEAR!r} as one series; it "
+            "cannot be combined with a list-valued series key."
+        )
+    # The map's years are checked against the window when it resolves, not
+    # here: a compile may restamp the reference to another period, and a map
+    # without that period's years then defers like any reference without facts.
+    _measure_id_by_opening_year(measure_map)
 
 
 def _calendar_year_window_opening_year(
@@ -2710,8 +2743,11 @@ def _resolve_calendar_year_window_reference_facts(
     months of the year opening in Y-1 and nine of the year opening in Y. The
     resolver takes both facts from one series identity (one publication, one
     measure, one cell) and refuses a window with either year absent, so a
-    partial window never lands as a value. A selector that names several
-    series (a list-valued ``source_concept``, ``source_measure_id`` or
+    partial window never lands as a value. A selector that maps each year to
+    its own measure (``source_measure_id_by_opening_year``) has matched every
+    fact to the measure its year declares, so those measures form the one
+    series and the measure drops out of its identity. A selector that names
+    several series (a list-valued ``source_concept``, ``source_measure_id`` or
     ``source_table``) resolves both years of every one of them, so the window
     of a sum of series is the sum of the windowed series; a series short of a
     year refuses the whole window.
@@ -2719,15 +2755,29 @@ def _resolve_calendar_year_window_reference_facts(
 
     target_year = _calendar_year_from_reference(reference)
     wanted = {target_year + offset for offset in CALENDAR_YEAR_WINDOW_WEIGHTS}
+    measure_map = reference.ledger_selector.get(SOURCE_MEASURE_ID_BY_OPENING_YEAR)
+    if measure_map is not None:
+        declared = sorted(_measure_id_by_opening_year(measure_map))
+        if declared != sorted(wanted):
+            raise ValueError(
+                f"Ledger target reference {reference.name!r}: calendar_year_window "
+                f"of calendar year {target_year} reads the years opening in "
+                f"{sorted(wanted)}; {SOURCE_MEASURE_ID_BY_OPENING_YEAR!r} maps "
+                f"{declared}."
+            )
     expected_series = _calendar_year_window_series_count(reference)
     partitions: dict[tuple[str, ...], dict[int, list[object]]] = {}
     for fact in eligible_matches:
         opening_year = _calendar_year_window_opening_year(fact, reference)
         if opening_year not in wanted:
             continue
-        partitions.setdefault(_selector_period_invariant_key(fact), {}).setdefault(
-            opening_year, []
-        ).append(fact)
+        series_key = _selector_period_invariant_key(
+            fact,
+            include_measure=(
+                SOURCE_MEASURE_ID_BY_OPENING_YEAR not in reference.ledger_selector
+            ),
+        )
+        partitions.setdefault(series_key, {}).setdefault(opening_year, []).append(fact)
     complete = {key: years for key, years in partitions.items() if set(years) == wanted}
     if not complete or len(complete) != expected_series:
         present = sorted({year for years in partitions.values() for year in years})
@@ -3164,11 +3214,17 @@ def _latest_period_selector_match(
     return None
 
 
-def _selector_period_invariant_key(fact: object) -> tuple[str, ...]:
+def _selector_period_invariant_key(
+    fact: object, *, include_measure: bool = True
+) -> tuple[str, ...]:
     return (
         _source_name(fact),
-        _str_at(fact, "observed_measure", "source_measure_id")
-        or _str_at(fact, "layout", "measure_id"),
+        (
+            _str_at(fact, "observed_measure", "source_measure_id")
+            or _str_at(fact, "layout", "measure_id")
+        )
+        if include_measure
+        else "",
         _source_measure_concept(fact),
         _str_at(fact, "geography", "level"),
         _str_at(fact, "geography", "id"),
@@ -3618,6 +3674,10 @@ def _fact_matches_selector(fact: object, selector: Mapping[str, object]) -> bool
             if not _dimension_values_match(fact, expected):
                 return False
             continue
+        if key == SOURCE_MEASURE_ID_BY_OPENING_YEAR:
+            if not _measure_by_opening_year_matches(fact, expected):
+                return False
+            continue
         if expected is None or expected == "":
             continue
         candidates = _selector_candidates(fact, str(key))
@@ -3645,7 +3705,7 @@ def fact_matches_selector(fact: object, selector: Mapping[str, object]) -> bool:
 def selector_field_is_supported(key: str) -> bool:
     """Whether ``key`` is in the closed selector vocabulary."""
 
-    if key in {"dimensions", "dimension_values"}:
+    if key in {"dimensions", "dimension_values", SOURCE_MEASURE_ID_BY_OPENING_YEAR}:
         return True
     try:
         _selector_candidates({}, key)
@@ -3709,6 +3769,46 @@ def _selector_candidates(fact: object, key: str) -> tuple[str, ...]:
     raise ValueError(f"Unsupported Ledger fact selector field {key!r}.")
 
 
+def _measure_id_by_opening_year(expected: object) -> dict[int, str]:
+    """Parse a year-to-measure map: four-digit opening years to measure ids."""
+
+    if not isinstance(expected, Mapping) or not expected:
+        raise ValueError(
+            f"Ledger fact selector field {SOURCE_MEASURE_ID_BY_OPENING_YEAR!r} "
+            "must be a non-empty mapping of opening year to measure id."
+        )
+    measure_by_year: dict[int, str] = {}
+    for year, measure in expected.items():
+        year_text = str(year)
+        if (
+            isinstance(year, bool)
+            or len(year_text) != 4
+            or not year_text.isdigit()
+            or not isinstance(measure, str)
+            or not measure
+        ):
+            raise ValueError(
+                f"Ledger fact selector field {SOURCE_MEASURE_ID_BY_OPENING_YEAR!r} "
+                f"maps {year!r} to {measure!r}; expected a four-digit opening "
+                "year and a non-blank measure id."
+            )
+        measure_by_year[int(year_text)] = measure
+    return measure_by_year
+
+
+def _measure_by_opening_year_matches(fact: object, expected: object) -> bool:
+    """Match an annual fact only to the measure its own opening year declares."""
+
+    measure_by_year = _measure_id_by_opening_year(expected)
+    period_key = _period_key(fact)
+    if not period_key[0] or period_key[1] % 100 != 99:
+        return False
+    measure = measure_by_year.get(period_key[1] // 100)
+    return measure is not None and measure in _selector_candidates(
+        fact, "source_measure_id"
+    )
+
+
 def _dimensions_match(fact: object, expected: object) -> bool:
     if isinstance(expected, list):
         expected_names = {str(name) for name in expected}
diff --git a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json
index 4db168e2b..0abb2ecc9 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/calibration_measure_exclusions.json
@@ -172,6 +172,24 @@
       "approved_on": "2026-10-02",
       "expires_on": "2026-11-26"
     },
+    {
+      "name": "dwp/uc_payment_dist/SINGLE_annual_payment_26_400_to_27_600",
+      "reason": "Engine-moved support: fewer than 10 FRS households support this band, and under policyengine-uk 2.122.2 none of them stays in it (the same spine under 2.100.0 keeps them, and the band sits at -100% with final estimate 0.0 on arms B0, B2 and head). 2.122.2 reads the published LHA rates, which lower the supporting households' housing element and move their awards to the band below. The same class as its 2026-08-26 siblings (uk-data#452): the payment distribution needs support for high awards beyond the would_claim_uc take-up lever.",
+      "tracking": "uk-data#452",
+      "approved_by": "juaristi22",
+      "adjudication": "microcosm#1095 (the B0, B2 and head arms of 2026-10-06 on the second ports PR; same class as the 2026-08-26 microcosm#757 dispositions)",
+      "approved_on": "2026-10-07",
+      "expires_on": "2026-11-26"
+    },
+    {
+      "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_24_000_to_25_200",
+      "reason": "Engine-moved support: fewer than 10 FRS households support this band, and under policyengine-uk 2.122.2 none of them stays in it (the same spine under 2.100.0 keeps them, and the band sits at -100% with final estimate 0.0 on arms B0, B2 and head). 2.122.2 corrects the supporting awards three ways: the 2019 mixed-age couple saving keeps couples who report Pension Credit on it and off UC, the published LHA rates move awards to neighbouring bands, and Carer Support Payment counts as unearned income as Carer's Allowance does. The same class as its 2026-08-26 siblings (uk-data#452): the payment distribution needs support for high awards beyond the would_claim_uc take-up lever.",
+      "tracking": "uk-data#452",
+      "approved_by": "juaristi22",
+      "adjudication": "microcosm#1095 (the B0, B2 and head arms of 2026-10-06 on the second ports PR; same class as the 2026-08-26 microcosm#757 dispositions)",
+      "approved_on": "2026-10-07",
+      "expires_on": "2026-11-26"
+    },
     {
       "name": "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_26_400_to_27_600",
       "reason": "One-sided undershoot: the annual-payment band composition inside the UC caseload is unreachable under the spine's current UC support (the #623-carried catalogue); every cell in this class undershoots on the rebind packet and two sit at -100% with final estimate 0.0. The payment distribution needs element/taper composition support beyond the would_claim_uc take-up lever.",
diff --git a/packages/microcosm-build/src/microcosm/build/uk/gates.json b/packages/microcosm-build/src/microcosm/build/uk/gates.json
index d87d0f2b2..83982de50 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/gates.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/gates.json
@@ -838,6 +838,9 @@
         "allowed_extra_columns": [
           "benunit.child_benefit_opts_out",
           "benunit.frs_benunit_capital",
+          "benunit.has_mixed_age_couple_pension_credit_saving",
+          "benunit.liable_for_share_of_household_rent",
+          "benunit.pension_credit_reported_capital",
           "benunit.uc_deduction_combination",
           "benunit.uc_deduction_random_draw",
           "benunit.uc_deduction_type_random_draw",
@@ -908,6 +911,9 @@
           "person.has_lifetime_isa",
           "person.highest_education",
           "person.is_before_universal_credit_qualifying_young_person_terminal_date",
+          "person.is_blind",
+          "person.is_claimant_or_partner",
+          "person.is_hbai_dependent_child",
           "person.is_in_non_advanced_education",
           "person.is_parent",
           "person.is_uc_claimant",
@@ -924,6 +930,8 @@
           "person.pip_dl_category",
           "person.pip_m_category",
           "person.receives_benefits_in_own_right",
+          "person.rent_paid_as_boarder",
+          "person.rent_paid_as_lodger",
           "person.relationship_to_head",
           "person.salary_sacrifice_asked",
           "person.salary_sacrifice_reported",
@@ -931,6 +939,7 @@
           "person.student_loan_balance",
           "person.student_loan_plan",
           "person.tax_free_childcare_spend_routed_share",
+          "person.uc_is_in_startup_period",
           "person.would_claim_carers_allowance",
           "person.would_claim_marriage_allowance",
           "person.would_claim_scp",
diff --git a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json
index dc37bc970..5ed139578 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/hmrc_uprating_engine_pins.json
@@ -2,7 +2,7 @@
   "schema_version": 1,
   "engine": {
     "package": "policyengine-uk",
-    "version": "2.100.0"
+    "version": "2.122.2"
   },
   "basis": "policyengine-uk parameter ratio between 1 January of the fact's opening year and 1 January of the calibration year (the SPI donor rebasing convention)",
   "written_by": "tools/pin_uk_uprating_engine_values.py",
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 1ffbcc223..7615ebb4d 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/ledger_compile_parity_incumbent_2025_signed_differences.json
@@ -1,11 +1,11 @@
 {
- "compiled_count": 1230,
+ "compiled_count": 1231,
  "counts_by_kind": {
   "calibration_drift": 553,
   "fixture_only": 55,
-  "ledger_only": 648
+  "ledger_only": 649
  },
- "difference_count": 1256,
+ "difference_count": 1257,
  "differences": [
   {
    "fixture_value": 1608000.0,
@@ -581,6 +581,13 @@
    "period": 2025,
    "reason": "Ledger-compiled row has no fixture counterpart at this comparison period."
   },
+  {
+   "kind": "ledger_only",
+   "ledger_value": 7048818510.96767,
+   "name": "dwp.hb.amount_pension_age",
+   "period": 2025,
+   "reason": "Ledger-compiled row has no fixture counterpart at this comparison period."
+  },
   {
    "kind": "ledger_only",
    "ledger_value": 1685702.9166666667,
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 fb490b20d..cef1cb9e4 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
@@ -468,7 +468,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -491,7 +491,7 @@
     "capital_gains"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -514,7 +514,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -536,7 +536,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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)"
@@ -565,7 +565,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -585,7 +585,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -606,7 +606,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -630,7 +630,7 @@
     "capital_gains"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "source_vintages": {
     "conditioning_resource": "hmrc_cgt_conditioning_facts.json",
     "conditioning_resource_sha256": "067ba21c12aeb4e95e6a8a16141dbaa075e8787ed875a005f549cab6e91a6b56",
@@ -719,7 +719,7 @@
     "full_frs_tei_band_unavailable"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "source_vintages": {
     "hmrc_surface": "2023-24",
     "mapped_build_period": "2024",
@@ -760,7 +760,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "source_vintages": {
     "source": "UK Data Service SN 9468 Living Costs and Food Survey 2023-24 household/person tabs and the vendored Chronicle facts for NEED 2023 mean kWh, the FY2024-25 Ofgem cap levels, road-fuel outturn (DESNZ, HMRC, ONS), the DfT VEH1103 licensed-car stock, NTS bus use and the DfT and devolved bus finance tables.",
     "survey": "Living Costs and Food Survey 2023-24"
@@ -786,7 +786,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -810,7 +810,7 @@
     "non_residential_property_value"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "source_vintages": {
     "source": "MHCLG dwellings and ONS UK House Price Index December 2025 regional average prices.",
     "survey": "Public regional property reference"
@@ -837,7 +837,7 @@
     "employment_income"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -870,7 +870,7 @@
     "household_source_id"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -892,7 +892,7 @@
     "student_loan_plan"
    ],
    "source_manifest": "source_stages.json",
-   "source_manifest_sha256": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -914,7 +914,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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"
@@ -949,7 +949,7 @@
    "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": "47a343e212b2a7bba2784b785857905bc3da414e6fac0a529c1dbf4d6fc81c13",
+   "source_manifest_sha256": "5cbf9127728319ea796037f4c4ef6a559aa7b757706bae4bbc7aa133989ceab2",
    "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 52bb8ae46..7422d60a1 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/source_stages.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/source_stages.json
@@ -229,7 +229,7 @@
     },
     {
      "kind": "map_columns",
-     "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095)"
+     "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)"
     },
     {
      "kind": "map_coded_amounts",
@@ -238,6 +238,15 @@
     {
      "kind": "annualize_periodic_amounts",
      "weeks_in_year": 52.17857142857143
+    },
+    {
+     "kind": "assign_binary_from_rate",
+     "output": "uc_is_in_startup_period",
+     "draw": "uc_is_in_startup_period",
+     "assignment_unit": "benefit unit",
+     "eligibility": "benefit units reporting Universal Credit (BENEFIT 95) with no linked claim start date (blank UCSTART)",
+     "rate_derivation": "GROSS4-weighted share of self-employed persons with a linked UC claim whose claim began less than 12 calendar months before INTDATE; a drawn unit's claim counts as inside the window (uk-data#527)",
+     "seed": 0
     }
    ],
    "outputs": [
@@ -252,7 +261,13 @@
     "is_household_head",
     "is_benunit_head",
     "is_parent",
+    "is_blind",
     "is_uc_claimant",
+    "is_claimant_or_partner",
+    "is_hbai_dependent_child",
+    "uc_is_in_startup_period",
+    "rent_paid_as_boarder",
+    "rent_paid_as_lodger",
     "employment_income",
     "self_employment_income",
     "private_pension_income",
@@ -312,6 +327,7 @@
     "frs_benunit_capital",
     "is_married",
     "dependent_children",
+    "liable_for_share_of_household_rent",
     "household_id",
     "region",
     "tenure_type",
@@ -332,6 +348,8 @@
     "external_child_payments"
    ],
    "nonnegative_outputs": [
+    "rent_paid_as_boarder",
+    "rent_paid_as_lodger",
     "age",
     "hours_worked",
     "employment_income",
@@ -357,7 +375,7 @@
     "external_child_payments",
     "care_hours"
    ],
-   "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)."
+   "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882)."
   },
   {
    "stage": "age_tail",
@@ -756,7 +774,7 @@
     "is_enhanced_disabled_for_benefits",
     "is_severely_disabled_for_benefits"
    ],
-   "notes": "Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed."
+   "notes": "Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed. is_severely_disabled_for_benefits is the tax credit severe disability condition on the derived categories (DLA care at the highest rate, PIP daily living at the enhanced rate, or higher-rate Attendance Allowance), so it agrees with policyengine-uk's own formula (pe-uk#1946, uk-data#494); an Armed Forces Compensation Scheme payment alone no longer counts, since FRS code 8 cannot be told apart from Armed Forces Independence Payment."
   },
   {
    "stage": "frs_education",
@@ -3539,8 +3557,6 @@
     {
      "kind": "materialize_rules_engine_predictors",
      "predictors": [
-      "is_adult",
-      "is_child",
       "employment_income",
       "self_employment_income",
       "private_pension_income",
@@ -3550,8 +3566,8 @@
     {
      "kind": "fit_weighted_qrf_chain",
      "predictors": [
-      "is_adult",
-      "is_child",
+      "household_adult_count",
+      "household_child_count",
       "region",
       "employment_income",
       "self_employment_income",
@@ -3885,7 +3901,7 @@
     "gas_consumption",
     "has_fuel_consumption"
    ],
-   "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the"
+   "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2023-24 diary is uprated to the FRS 2024-25 base year by the declared uprate_donor_columns step (microcosm#890), with consumption columns and bus fares uprated by the engine's CPI index (the fare factor survives only where no fare cell levels the column, i.e. Wales), income predictors by the engine's earnings, mixed-income and pension indices, petrol and diesel by the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095)."
   },
   {
    "stage": "etb_vat",
@@ -4606,12 +4622,14 @@
      "kind": "derive",
      "derived": {
       "uc_reported_capital": "frs_benunit_capital",
+      "pension_credit_reported_capital": "frs_benunit_capital",
       "would_claim_uc": "would_claim_uc OR universal_credit_reported_anchor"
      }
     }
    ],
    "outputs": [
-    "uc_reported_capital"
+    "uc_reported_capital",
+    "pension_credit_reported_capital"
    ],
    "rewrites": [
     "frs_benunit_capital",
@@ -4626,6 +4644,12 @@
    "grain": "benunit",
    "artifacts": [],
    "operations": [
+    {
+     "kind": "materialize_rules_engine_predictors",
+     "predictors": [
+      "has_mixed_age_couple_pension_credit_saving"
+     ]
+    },
     {
      "kind": "materialize_rules_engine_predictors",
      "predictors": [
@@ -4665,11 +4689,13 @@
      "residual_rate": "(rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1]"
     }
    ],
-   "outputs": [],
+   "outputs": [
+    "has_mixed_age_couple_pension_credit_saving"
+   ],
    "rewrites": [
     "would_claim_pc"
    ],
-   "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible."
+   "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible."
   },
   {
    "stage": "child_benefit_take_up",
@@ -5266,7 +5292,7 @@
     {
      "role": "policy_parameters",
      "kind": "versioned_parameter_tree",
-     "dependency": "policyengine-uk>=2.100.0 via microcosm-build[uk]",
+     "dependency": "policyengine-uk>=2.122.2 via microcosm-build[uk]",
      "parameters": [
       "gov.hmrc.cgt.annual_exempt_amount"
      ],
@@ -5347,7 +5373,7 @@
     {
      "role": "policy_parameters",
      "kind": "versioned_parameter_tree",
-     "dependency": "policyengine-uk>=2.100.0 via microcosm-build[uk]",
+     "dependency": "policyengine-uk>=2.122.2 via microcosm-build[uk]",
      "parameters": [
       "gov.hmrc.cgt.annual_exempt_amount",
       "gov.hmrc.cgt.badr.lifetime_limit",
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 277463642..2f0729f59 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml
+++ b/packages/microcosm-build/src/microcosm/build/uk/spec/sources.yaml
@@ -368,11 +368,18 @@ stages:
     person_table: adult union child
     household_sort: set_index('household_id').sort_index() before positional household reads
   - kind: map_columns
-    scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095)'
+    scope: 'raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme''s 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit''s UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer''s property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)'
   - kind: map_coded_amounts
     scope: region, tenure, accommodation, council tax band, benefit-code amount mappings (Scotland's Adult and Child Disability Payment codes 117/118 and 121/122 into the PIP and DLA columns they replace; benefit recovery codes 69 and 70 unmapped); adult HOURTOT care-hours band code to weekly care_hours at the band's lower edge (codes 5, 6, 7 and 10 reach the 35-hour Carer's Allowance line); would_claim_carers_allowance from positive reported Carer's Allowance
   - kind: annualize_periodic_amounts
     weeks_in_year: 52.17857142857143
+  - kind: assign_binary_from_rate
+    output: uc_is_in_startup_period
+    draw: uc_is_in_startup_period
+    assignment_unit: benefit unit
+    eligibility: benefit units reporting Universal Credit (BENEFIT 95) with no linked claim start date (blank UCSTART)
+    rate_derivation: GROSS4-weighted share of self-employed persons with a linked UC claim whose claim began less than 12 calendar months before INTDATE; a drawn unit's claim counts as inside the window (uk-data#527)
+    seed: 0
   outputs:
   - person_id
   - person_benunit_id
@@ -385,7 +392,13 @@ stages:
   - is_household_head
   - is_benunit_head
   - is_parent
+  - is_blind
   - is_uc_claimant
+  - is_claimant_or_partner
+  - is_hbai_dependent_child
+  - uc_is_in_startup_period
+  - rent_paid_as_boarder
+  - rent_paid_as_lodger
   - employment_income
   - self_employment_income
   - private_pension_income
@@ -445,6 +458,7 @@ stages:
   - frs_benunit_capital
   - is_married
   - dependent_children
+  - liable_for_share_of_household_rent
   - household_id
   - region
   - tenure_type
@@ -464,6 +478,8 @@ stages:
   - housing_service_charges
   - external_child_payments
   nonnegative_outputs:
+  - rent_paid_as_boarder
+  - rent_paid_as_lodger
   - age
   - hours_worked
   - employment_income
@@ -488,7 +504,7 @@ stages:
   - housing_service_charges
   - external_child_payments
   - care_hours
-  notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).
+  notes: Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).
 - stage: age_tail
   survey: Family Resources Survey 2024-25 and ONS mid-year population estimates
   source: Office for National Statistics mid-year population estimates for the 80+ age bands by sex, via the chronicle ledger facts the calibration register binds (target family ons_population).
@@ -744,7 +760,7 @@ stages:
   - is_disabled_for_benefits
   - is_enhanced_disabled_for_benefits
   - is_severely_disabled_for_benefits
-  notes: Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed.
+  notes: Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed. is_severely_disabled_for_benefits is the tax credit severe disability condition on the derived categories (DLA care at the highest rate, PIP daily living at the enhanced rate, or higher-rate Attendance Allowance), so it agrees with policyengine-uk's own formula (pe-uk#1946, uk-data#494); an Armed Forces Compensation Scheme payment alone no longer counts, since FRS code 8 cannot be told apart from Armed Forces Independence Payment.
 - stage: frs_education
   survey: Family Resources Survey 2024-25
   source: Department for Work and Pensions Family Resources Survey 2024-25, UK Data Service SN 9563, DOI 10.5255/UKDA-SN-9563-1; local licensed 2024_25 tabs.
@@ -2918,16 +2934,14 @@ stages:
     seed: 0
   - kind: materialize_rules_engine_predictors
     predictors:
-    - is_adult
-    - is_child
     - employment_income
     - self_employment_income
     - private_pension_income
     - hbai_household_net_income
   - kind: fit_weighted_qrf_chain
     predictors:
-    - is_adult
-    - is_child
+    - household_adult_count
+    - household_child_count
     - region
     - employment_income
     - self_employment_income
@@ -3194,7 +3208,7 @@ stages:
   - electricity_consumption
   - gas_consumption
   - has_fuel_consumption
-  notes: Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2023-24 diary is uprated to the FRS 2024-25 base year by the declared uprate_donor_columns step (microcosm#890), with consumption columns and bus fares uprated by the engine's CPI index (the fare factor survives only where no fare cell levels the column, i.e. Wales), income predictors by the engine's earnings, mixed-income and pension indices, petrol and diesel by the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level.
+  notes: 'Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent''s WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (María''s rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain''s raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household''s QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk''s seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED''s Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person''s annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household''s non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area''s price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England''s trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame''s eligible trip share beside the publisher''s concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2023-24 diary is uprated to the FRS 2024-25 base year by the declared uprate_donor_columns step (microcosm#890), with consumption columns and bus fares uprated by the engine''s CPI index (the fare factor survives only where no fare cell levels the column, i.e. Wales), income predictors by the engine''s earnings, mixed-income and pension indices, petrol and diesel by the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey''s age-18 split) predict on both sides; the recipient counts its members by age, not through the engine''s is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095).'
 - stage: etb_vat
   survey: Effects of Taxes and Benefits 1977-2024
   source: UK Data Service SN 8856 Effects of Taxes and Benefits household tab and cited VAT anchor resource.
@@ -3738,9 +3752,11 @@ stages:
   - kind: derive
     derived:
       uc_reported_capital: frs_benunit_capital
+      pension_credit_reported_capital: frs_benunit_capital
       would_claim_uc: would_claim_uc OR universal_credit_reported_anchor
   outputs:
   - uc_reported_capital
+  - pension_credit_reported_capital
   rewrites:
   - frs_benunit_capital
   - would_claim_uc
@@ -3751,6 +3767,9 @@ stages:
   grain: benunit
   artifacts: []
   operations:
+  - kind: materialize_rules_engine_predictors
+    predictors:
+    - has_mixed_age_couple_pension_credit_saving
   - kind: materialize_rules_engine_predictors
     predictors:
     - guarantee_credit
@@ -3776,10 +3795,11 @@ stages:
       rate_key: pension_credit_savings_credit_only
     weight_mapping: household_to_benunit
     residual_rate: (rate * entitled - reporters) / (entitled - reporters), weighted, clipped to [0, 1]
-  outputs: []
+  outputs:
+  - has_mixed_age_couple_pension_credit_saving
   rewrites:
   - would_claim_pc
-  notes: Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible.
+  notes: Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible.
 - stage: child_benefit_take_up
   survey: Family Resources Survey 2024-25
   source: HMRC Child Benefit statistics, annual release at August 2025 (percentage of eligible children for whom Child Benefit is claimed by single year of age; families registered and families opted out of payment), applied to PolicyEngine-UK Child Benefit eligibility and adjusted net income after the SPI income chain.
@@ -4255,7 +4275,7 @@ stages:
   artifacts:
   - role: policy_parameters
     kind: versioned_parameter_tree
-    dependency: policyengine-uk>=2.100.0 via microcosm-build[uk]
+    dependency: policyengine-uk>=2.122.2 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
@@ -4320,7 +4340,7 @@ stages:
   artifacts:
   - role: policy_parameters
     kind: versioned_parameter_tree
-    dependency: policyengine-uk>=2.100.0 via microcosm-build[uk]
+    dependency: policyengine-uk>=2.122.2 via microcosm-build[uk]
     parameters:
     - gov.hmrc.cgt.annual_exempt_amount
     - gov.hmrc.cgt.badr.lifetime_limit
diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json b/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json
index 9c4f01877..3b25ba195 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/target_fit_reviewed_exclusions.json
@@ -1,5 +1,13 @@
 {
  "schema_version": 2,
  "description": "Reviewed target-fit exclusions for the UK terminal battery (schema 2 per #610). Each entry defers one calibration target whose |relative error| breaches the 25% release bound, with the defect diagnosed, tracked, and owned elsewhere; the receipt records the reasoning, approver, adjudication reference, approval date, and expiry. The gate keeps the target BOUND in calibration (exclusion applies to the release fence only, never the solve), reports dormant entries, FAILS stale ones (targets back inside the bound), and FAILS expired ones until the adjudication is renewed, so this register cannot rot.",
- "exclusions": {}
+ "exclusions": {
+  "hmrc.spi_region.income_tax_by_region_12570_15000@E12000005@2025": {
+   "reason": "The regional income-tax cells slice on policyengine-uk's total_income, which leaves out other_investment_income although income_tax charges it as savings interest, so an SPI record whose income is mostly other investment income sits in a low band with its whole income tax. Fewer than 10 such records sit in the West Midlands GBP 12,570-15,000 band: the cell's design value is 339.2m against the 55.1m target (6.1 times, and 6.0 times before the microcosm#1095 ports; every other region's cell is 0.6-1.1 times), and banded on total income including other investment income it falls in line with the other regions. The solver pulls the cell to +8.6% before the ports and +8.9% with the engine-free ports; policyengine-uk 2.122.2 lands it at +23.6% and the full ports at +27.4% (about 2 points of the last step come from CVPAY leaving property income). Deferred four weeks while policyengine-uk's total_income is corrected upstream to include other investment income (pe-uk#2174).",
+   "approved_by": "juaristi22",
+   "adjudication": "microcosm#1095 (the head and counterfactual arms of 2026-10-06 on the second ports PR)",
+   "approved_on": "2026-10-06",
+   "expires_on": "2026-11-03"
+  }
+ }
 }
diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json
index 554d47588..afac8edda 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/target_reference_membership.json
@@ -1,11 +1,11 @@
 {
   "source_fact_feed": "chronicle-uk-825406f9-consumer-facts",
   "target_period": 2025,
-  "candidate_count": 1253,
-  "contract_target_count": 340,
-  "active_reference_count": 1230,
+  "candidate_count": 1254,
+  "contract_target_count": 341,
+  "active_reference_count": 1231,
   "status_counts": {
-    "active": 1230,
+    "active": 1231,
     "no_fact_at_or_before_period": 7,
     "signed_excluded": 16
   },
@@ -4072,6 +4072,10 @@
       "geography_level": "country",
       "geography_id": "K03000001"
     },
+    "dwp.hb.amount_pension_age": {
+      "geography_level": "country",
+      "geography_id": "K03000001"
+    },
     "hmrc.salary_sacrifice.it_relief_total": {
       "geography_level": "country",
       "geography_id": "K02000001"
@@ -4281,104 +4285,104 @@
       "name": "hmrc/employment_income_income_band_100_000_to_150_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.23571428571429",
       "value_before_uprating": "82300000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_1_000_000_to_inf",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.19599427753934",
       "value_before_uprating": "35500000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_12_570_to_15_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.05",
       "value_before_uprating": "16900000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_15_000_to_20_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "0.977178423236514",
       "value_before_uprating": "48600000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_150_000_to_200_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.15891089108911",
       "value_before_uprating": "39200000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_20_000_to_30_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.00296442687747",
       "value_before_uprating": "172000000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_200_000_to_300_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "36600000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_30_000_to_40_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "170000000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_300_000_to_500_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "29000000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_40_000_to_50_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "139000000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_50_000_to_70_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "174000000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_500_000_to_1_000_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.22513812154696",
       "value_before_uprating": "23100000000.0"
     },
     {
       "name": "hmrc/employment_income_income_band_70_000_to_100_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "factor": "1.10670439319018",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "118000000000.0"
     },
     {
@@ -4489,104 +4493,104 @@
       "name": "hmrc/self_employment_income_income_band_100_000_to_150_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.23571428571429",
       "value_before_uprating": "4980000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_1_000_000_to_inf",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.19599427753934",
       "value_before_uprating": "19500000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_12_570_to_15_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.05",
       "value_before_uprating": "4810000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_15_000_to_20_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "0.977178423236514",
       "value_before_uprating": "8030000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_150_000_to_200_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.15891089108911",
       "value_before_uprating": "3650000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_20_000_to_30_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.00296442687747",
       "value_before_uprating": "15700000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_200_000_to_300_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "4390000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_30_000_to_40_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "12600000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_300_000_to_500_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "4840000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_40_000_to_50_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "8730000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_50_000_to_70_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "8220000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_500_000_to_1_000_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.22513812154696",
       "value_before_uprating": "6670000000.0"
     },
     {
       "name": "hmrc/self_employment_income_income_band_70_000_to_100_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "factor": "1.0343847100897",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "5360000000.0"
     },
     {
@@ -5009,104 +5013,104 @@
       "name": "hmrc/private_pension_income_income_band_100_000_to_150_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.23571428571429",
       "value_before_uprating": "5430000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_1_000_000_to_inf",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.19599427753934",
       "value_before_uprating": "370000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_12_570_to_15_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.05",
       "value_before_uprating": "4350000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_15_000_to_20_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "0.977178423236514",
       "value_before_uprating": "13600000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_150_000_to_200_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.15891089108911",
       "value_before_uprating": "1810000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_20_000_to_30_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.00296442687747",
       "value_before_uprating": "30200000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_200_000_to_300_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "1570000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_30_000_to_40_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "24900000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_300_000_to_500_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21220604703247",
       "value_before_uprating": "1120000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_40_000_to_50_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.11899038461538",
       "value_before_uprating": "16900000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_50_000_to_70_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "17400000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_500_000_to_1_000_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.22513812154696",
       "value_before_uprating": "640000000.0"
     },
     {
       "name": "hmrc/private_pension_income_income_band_70_000_to_100_000",
       "from": "2023",
       "to": "2025",
-      "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "factor": "1.08769137555276",
+      "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "factor": "1.21466666666667",
       "value_before_uprating": "9470000000.0"
     },
     {
@@ -11566,12 +11570,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 91081771559.55191,
+          "resolved_value": 101699285714.28572,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_b1c634a871c843888ab00545",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-            "factor": "1.10670439319018",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.23571428571429",
             "value_before_uprating": "82300000000.0"
           }
         },
@@ -11581,12 +11585,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 39288005958.251434,
+          "resolved_value": 42457796852.64664,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_16019afa59e224b690877826",
           "uprating": {
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+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.15891089108911",
             "value_before_uprating": "1810000000.0"
           }
         },
@@ -13053,12 +13057,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 32848279541.693417,
+          "resolved_value": 30289525691.699608,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_bafcebe21a05cb0ca19d5e4f",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.00296442687747",
             "value_before_uprating": "30200000000.0"
           }
         },
@@ -13068,12 +13072,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 1707675459.6178367,
+          "resolved_value": 1903163493.8409853,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_de0ed091681463c4ddec5b85",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.21220604703247",
             "value_before_uprating": "1570000000.0"
           }
         },
@@ -13083,12 +13087,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 27083515251.26378,
+          "resolved_value": 27862860576.923077,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_2884a006a8d7c144a8570a7d",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.11899038461538",
             "value_before_uprating": "24900000000.0"
           }
         },
@@ -13098,12 +13102,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 1218214340.6190937,
+          "resolved_value": 1357670772.6763716,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_73e8de3b1ac198cc75c2b696",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.21220604703247",
             "value_before_uprating": "1120000000.0"
           }
         },
@@ -13113,12 +13117,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 18381984246.841682,
+          "resolved_value": 18910937500.0,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_6b29cd5a1d665a00bb3e8046",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.11899038461538",
             "value_before_uprating": "16900000000.0"
           }
         },
@@ -13128,12 +13132,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 18925829934.61806,
+          "resolved_value": 21135200000.0,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_8f911d955de4f80e87dc69d8",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.21466666666667",
             "value_before_uprating": "17400000000.0"
           }
         },
@@ -13143,12 +13147,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 696122480.3537678,
+          "resolved_value": 784088397.7900552,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_a13e6165bec82537c5c2883c",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.22513812154696",
             "value_before_uprating": "640000000.0"
           }
         },
@@ -13158,12 +13162,12 @@
           "matched_fact_count_overall": 1,
           "matched_fact_count_at_or_before_period": 1,
           "resolved_period": 2025,
-          "resolved_value": 10300437326.484657,
+          "resolved_value": 11502893333.333332,
           "resolved_fact_period": "2023",
           "resolved_fact_key": "ledger_aggregate_fact_v2_0a1db235b87cad00eeddbfd8",
           "uprating": {
-            "index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-            "factor": "1.08769137555276",
+            "index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+            "factor": "1.21466666666667",
             "value_before_uprating": "9470000000.0"
           }
         }
@@ -29173,6 +29177,21 @@
         }
       ]
     },
+    "dwp.hb.amount_pension_age": {
+      "status": "active",
+      "candidates": [
+        {
+          "name": "dwp.hb.amount_pension_age",
+          "status": "active",
+          "matched_fact_count_overall": 2,
+          "matched_fact_count_at_or_before_period": 2,
+          "resolved_period": 2025,
+          "resolved_value": 7048818510.96767,
+          "resolved_fact_period": "2025",
+          "resolved_fact_key": "ledger_aggregate_fact_v2_3631e3adc6a555d6845a1252"
+        }
+      ]
+    },
     "hmrc.salary_sacrifice.it_relief_total": {
       "status": "active",
       "candidates": [
diff --git a/packages/microcosm-build/src/microcosm/build/uk/target_references.json b/packages/microcosm-build/src/microcosm/build/uk/target_references.json
index 202b924c3..d31d3812c 100644
--- a/packages/microcosm-build/src/microcosm/build/uk/target_references.json
+++ b/packages/microcosm-build/src/microcosm/build/uk/target_references.json
@@ -267,6 +267,7 @@
       "dwp.esa_claimants": "dwp.legacy_benefits",
       "dwp.esa_contrib_claimants": "dwp.legacy_benefits",
       "dwp.esa_income_claimants": "dwp.legacy_benefits",
+      "dwp.hb.amount_pension_age": "dwp.legacy_benefits",
       "dwp.hb.households": "dwp.housing_benefit",
       "dwp.hb.households_pension_age": "dwp.legacy_benefits",
       "dwp.hb.households_pension_age_private_rented": "dwp.legacy_benefits",
@@ -577,30 +578,31 @@
       "dwp.attendance_allowance.recipients_england": "Attendance Allowance recipients, England",
       "dwp.attendance_allowance.recipients_wales": "Attendance Allowance recipients, Wales",
       "dwp.benefit_cap.capped_households": "Capped households",
-      "dwp.benefit_cap.capped_households_1000_01_to_1100": "Capped households, £1,000.01 to £1,100 a month",
-      "dwp.benefit_cap.capped_households_100_01_to_200": "Capped households, £100.01 to £200 a month",
-      "dwp.benefit_cap.capped_households_1100_01_to_1200": "Capped households, £1,100.01 to £1,200 a month",
-      "dwp.benefit_cap.capped_households_1200_01_to_1300": "Capped households, £1,200.01 to £1,300 a month",
-      "dwp.benefit_cap.capped_households_1300_01_and_above": "Capped households, £1,300.01 and above a month",
-      "dwp.benefit_cap.capped_households_200_01_to_300": "Capped households, £200.01 to £300 a month",
-      "dwp.benefit_cap.capped_households_300_01_to_400": "Capped households, £300.01 to £400 a month",
-      "dwp.benefit_cap.capped_households_400_01_to_500": "Capped households, £400.01 to £500 a month",
-      "dwp.benefit_cap.capped_households_500_01_to_600": "Capped households, £500.01 to £600 a month",
-      "dwp.benefit_cap.capped_households_600_01_to_700": "Capped households, £600.01 to £700 a month",
-      "dwp.benefit_cap.capped_households_700_01_to_800": "Capped households, £700.01 to £800 a month",
-      "dwp.benefit_cap.capped_households_800_01_to_900": "Capped households, £800.01 to £900 a month",
-      "dwp.benefit_cap.capped_households_900_01_to_1000": "Capped households, £900.01 to £1,000 a month",
-      "dwp.benefit_cap.capped_households_up_to_100": "Capped households, Up to £100 a month",
+      "dwp.benefit_cap.capped_households_1000_01_to_1100": "Capped households, \u00a31,000.01 to \u00a31,100 a month",
+      "dwp.benefit_cap.capped_households_100_01_to_200": "Capped households, \u00a3100.01 to \u00a3200 a month",
+      "dwp.benefit_cap.capped_households_1100_01_to_1200": "Capped households, \u00a31,100.01 to \u00a31,200 a month",
+      "dwp.benefit_cap.capped_households_1200_01_to_1300": "Capped households, \u00a31,200.01 to \u00a31,300 a month",
+      "dwp.benefit_cap.capped_households_1300_01_and_above": "Capped households, \u00a31,300.01 and above a month",
+      "dwp.benefit_cap.capped_households_200_01_to_300": "Capped households, \u00a3200.01 to \u00a3300 a month",
+      "dwp.benefit_cap.capped_households_300_01_to_400": "Capped households, \u00a3300.01 to \u00a3400 a month",
+      "dwp.benefit_cap.capped_households_400_01_to_500": "Capped households, \u00a3400.01 to \u00a3500 a month",
+      "dwp.benefit_cap.capped_households_500_01_to_600": "Capped households, \u00a3500.01 to \u00a3600 a month",
+      "dwp.benefit_cap.capped_households_600_01_to_700": "Capped households, \u00a3600.01 to \u00a3700 a month",
+      "dwp.benefit_cap.capped_households_700_01_to_800": "Capped households, \u00a3700.01 to \u00a3800 a month",
+      "dwp.benefit_cap.capped_households_800_01_to_900": "Capped households, \u00a3800.01 to \u00a3900 a month",
+      "dwp.benefit_cap.capped_households_900_01_to_1000": "Capped households, \u00a3900.01 to \u00a31,000 a month",
+      "dwp.benefit_cap.capped_households_up_to_100": "Capped households, Up to \u00a3100 a month",
       "dwp.esa_claimants": "Employment and Support Allowance claimants",
       "dwp.esa_contrib_claimants": "Employment and Support Allowance contrib claimants",
       "dwp.esa_income_claimants": "Employment and Support Allowance income claimants",
+      "dwp.hb.amount_pension_age": "Housing Benefit spending, pension age",
       "dwp.hb.households": "Housing Benefit claimants, all tenures",
       "dwp.hb.households_pension_age": "Housing Benefit benefit units, pension age",
       "dwp.hb.households_pension_age_private_rented": "Housing Benefit benefit units, pension age, private rented sector",
       "dwp.hb.households_pension_age_social_rented": "Housing Benefit benefit units, pension age, social rented sector",
       "dwp.hb.households_private_rented": "Housing Benefit claimants, private rented",
       "dwp.hb.households_social_rented": "Housing Benefit claimants, social rented",
-      "dwp.jsa_claimants": "Jobseeker’s Allowance claimants",
+      "dwp.jsa_claimants": "Jobseeker\u2019s Allowance claimants",
       "dwp.pension_credit.benefit_units": "Pension Credit benefit units, Great Britain",
       "dwp.pension_credit.benefit_units_65_69": "Pension Credit benefit units, claimant aged 65 to 69",
       "dwp.pension_credit.benefit_units_70_74": "Pension Credit benefit units, claimant aged 70 to 74",
@@ -5778,7 +5780,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£0.01 to £100.00"
+          "monthly_award_amount_bands": "\u00a30.01 to \u00a3100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5808,7 +5810,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£100.01 to £200.00"
+          "monthly_award_amount_bands": "\u00a3100.01 to \u00a3200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5838,7 +5840,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1000.01 to £1100.00"
+          "monthly_award_amount_bands": "\u00a31000.01 to \u00a31100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5868,7 +5870,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1100.01 to £1200.00"
+          "monthly_award_amount_bands": "\u00a31100.01 to \u00a31200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5898,7 +5900,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1200.01 to £1300.00"
+          "monthly_award_amount_bands": "\u00a31200.01 to \u00a31300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5928,7 +5930,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1300.01 to £1400.00"
+          "monthly_award_amount_bands": "\u00a31300.01 to \u00a31400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5958,7 +5960,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1400.01 to £1500.00"
+          "monthly_award_amount_bands": "\u00a31400.01 to \u00a31500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -5988,7 +5990,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1500.01 to £1600.00"
+          "monthly_award_amount_bands": "\u00a31500.01 to \u00a31600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6018,7 +6020,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1600.01 to £1700.00"
+          "monthly_award_amount_bands": "\u00a31600.01 to \u00a31700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6048,7 +6050,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1700.01 to £1800.00"
+          "monthly_award_amount_bands": "\u00a31700.01 to \u00a31800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6078,7 +6080,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1800.01 to £1900.00"
+          "monthly_award_amount_bands": "\u00a31800.01 to \u00a31900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6108,7 +6110,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£1900.01 to £2000.00"
+          "monthly_award_amount_bands": "\u00a31900.01 to \u00a32000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6138,7 +6140,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£200.01 to £300.00"
+          "monthly_award_amount_bands": "\u00a3200.01 to \u00a3300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6168,7 +6170,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£2000.01 to £2100.00"
+          "monthly_award_amount_bands": "\u00a32000.01 to \u00a32100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6198,7 +6200,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£2100.01 to £2200.00"
+          "monthly_award_amount_bands": "\u00a32100.01 to \u00a32200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6228,7 +6230,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£2200.01 to £2300.00"
+          "monthly_award_amount_bands": "\u00a32200.01 to \u00a32300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6258,7 +6260,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£2300.01 to £2400.00"
+          "monthly_award_amount_bands": "\u00a32300.01 to \u00a32400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6288,7 +6290,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£2400.01 to £2500.00"
+          "monthly_award_amount_bands": "\u00a32400.01 to \u00a32500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6318,7 +6320,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£300.01 to £400.00"
+          "monthly_award_amount_bands": "\u00a3300.01 to \u00a3400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6348,7 +6350,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£400.01 to £500.00"
+          "monthly_award_amount_bands": "\u00a3400.01 to \u00a3500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6378,7 +6380,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£500.01 to £600.00"
+          "monthly_award_amount_bands": "\u00a3500.01 to \u00a3600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6408,7 +6410,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£600.01 to £700.00"
+          "monthly_award_amount_bands": "\u00a3600.01 to \u00a3700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6438,7 +6440,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£700.01 to £800.00"
+          "monthly_award_amount_bands": "\u00a3700.01 to \u00a3800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6468,7 +6470,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£800.01 to £900.00"
+          "monthly_award_amount_bands": "\u00a3800.01 to \u00a3900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6498,7 +6500,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, no children",
-          "monthly_award_amount_bands": "£900.01 to £1000.00"
+          "monthly_award_amount_bands": "\u00a3900.01 to \u00a31000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6528,7 +6530,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£0.01 to £100.00"
+          "monthly_award_amount_bands": "\u00a30.01 to \u00a3100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6558,7 +6560,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£100.01 to £200.00"
+          "monthly_award_amount_bands": "\u00a3100.01 to \u00a3200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6588,7 +6590,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1000.01 to £1100.00"
+          "monthly_award_amount_bands": "\u00a31000.01 to \u00a31100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6618,7 +6620,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1100.01 to £1200.00"
+          "monthly_award_amount_bands": "\u00a31100.01 to \u00a31200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6648,7 +6650,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1200.01 to £1300.00"
+          "monthly_award_amount_bands": "\u00a31200.01 to \u00a31300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6678,7 +6680,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1300.01 to £1400.00"
+          "monthly_award_amount_bands": "\u00a31300.01 to \u00a31400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6708,7 +6710,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1400.01 to £1500.00"
+          "monthly_award_amount_bands": "\u00a31400.01 to \u00a31500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6738,7 +6740,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1500.01 to £1600.00"
+          "monthly_award_amount_bands": "\u00a31500.01 to \u00a31600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6768,7 +6770,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1600.01 to £1700.00"
+          "monthly_award_amount_bands": "\u00a31600.01 to \u00a31700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6798,7 +6800,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1700.01 to £1800.00"
+          "monthly_award_amount_bands": "\u00a31700.01 to \u00a31800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6828,7 +6830,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1800.01 to £1900.00"
+          "monthly_award_amount_bands": "\u00a31800.01 to \u00a31900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6858,7 +6860,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£1900.01 to £2000.00"
+          "monthly_award_amount_bands": "\u00a31900.01 to \u00a32000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6888,7 +6890,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£200.01 to £300.00"
+          "monthly_award_amount_bands": "\u00a3200.01 to \u00a3300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6918,7 +6920,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£2000.01 to £2100.00"
+          "monthly_award_amount_bands": "\u00a32000.01 to \u00a32100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6948,7 +6950,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£2100.01 to £2200.00"
+          "monthly_award_amount_bands": "\u00a32100.01 to \u00a32200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -6978,7 +6980,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£2200.01 to £2300.00"
+          "monthly_award_amount_bands": "\u00a32200.01 to \u00a32300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7008,7 +7010,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£2300.01 to £2400.00"
+          "monthly_award_amount_bands": "\u00a32300.01 to \u00a32400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7038,7 +7040,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£2400.01 to £2500.00"
+          "monthly_award_amount_bands": "\u00a32400.01 to \u00a32500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7068,7 +7070,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£300.01 to £400.00"
+          "monthly_award_amount_bands": "\u00a3300.01 to \u00a3400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7098,7 +7100,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£400.01 to £500.00"
+          "monthly_award_amount_bands": "\u00a3400.01 to \u00a3500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7128,7 +7130,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£500.01 to £600.00"
+          "monthly_award_amount_bands": "\u00a3500.01 to \u00a3600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7158,7 +7160,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£600.01 to £700.00"
+          "monthly_award_amount_bands": "\u00a3600.01 to \u00a3700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7188,7 +7190,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£700.01 to £800.00"
+          "monthly_award_amount_bands": "\u00a3700.01 to \u00a3800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7218,7 +7220,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£800.01 to £900.00"
+          "monthly_award_amount_bands": "\u00a3800.01 to \u00a3900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7248,7 +7250,7 @@
         ],
         "dimension_values": {
           "family_type": "Single, with children",
-          "monthly_award_amount_bands": "£900.01 to £1000.00"
+          "monthly_award_amount_bands": "\u00a3900.01 to \u00a31000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7278,7 +7280,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£0.01 to £100.00"
+          "monthly_award_amount_bands": "\u00a30.01 to \u00a3100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7308,7 +7310,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£100.01 to £200.00"
+          "monthly_award_amount_bands": "\u00a3100.01 to \u00a3200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7338,7 +7340,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1000.01 to £1100.00"
+          "monthly_award_amount_bands": "\u00a31000.01 to \u00a31100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7368,7 +7370,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1100.01 to £1200.00"
+          "monthly_award_amount_bands": "\u00a31100.01 to \u00a31200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7398,7 +7400,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1200.01 to £1300.00"
+          "monthly_award_amount_bands": "\u00a31200.01 to \u00a31300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7428,7 +7430,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1300.01 to £1400.00"
+          "monthly_award_amount_bands": "\u00a31300.01 to \u00a31400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7458,7 +7460,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1400.01 to £1500.00"
+          "monthly_award_amount_bands": "\u00a31400.01 to \u00a31500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7488,7 +7490,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1500.01 to £1600.00"
+          "monthly_award_amount_bands": "\u00a31500.01 to \u00a31600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7518,7 +7520,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1600.01 to £1700.00"
+          "monthly_award_amount_bands": "\u00a31600.01 to \u00a31700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7548,7 +7550,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1700.01 to £1800.00"
+          "monthly_award_amount_bands": "\u00a31700.01 to \u00a31800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7578,7 +7580,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1800.01 to £1900.00"
+          "monthly_award_amount_bands": "\u00a31800.01 to \u00a31900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7608,7 +7610,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£1900.01 to £2000.00"
+          "monthly_award_amount_bands": "\u00a31900.01 to \u00a32000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7638,7 +7640,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£200.01 to £300.00"
+          "monthly_award_amount_bands": "\u00a3200.01 to \u00a3300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7668,7 +7670,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£2000.01 to £2100.00"
+          "monthly_award_amount_bands": "\u00a32000.01 to \u00a32100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7698,7 +7700,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£2100.01 to £2200.00"
+          "monthly_award_amount_bands": "\u00a32100.01 to \u00a32200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7728,7 +7730,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£2200.01 to £2300.00"
+          "monthly_award_amount_bands": "\u00a32200.01 to \u00a32300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7758,7 +7760,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£2300.01 to £2400.00"
+          "monthly_award_amount_bands": "\u00a32300.01 to \u00a32400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7788,7 +7790,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£2400.01 to £2500.00"
+          "monthly_award_amount_bands": "\u00a32400.01 to \u00a32500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7818,7 +7820,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£300.01 to £400.00"
+          "monthly_award_amount_bands": "\u00a3300.01 to \u00a3400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7848,7 +7850,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£400.01 to £500.00"
+          "monthly_award_amount_bands": "\u00a3400.01 to \u00a3500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7878,7 +7880,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£500.01 to £600.00"
+          "monthly_award_amount_bands": "\u00a3500.01 to \u00a3600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7908,7 +7910,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£600.01 to £700.00"
+          "monthly_award_amount_bands": "\u00a3600.01 to \u00a3700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7938,7 +7940,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£700.01 to £800.00"
+          "monthly_award_amount_bands": "\u00a3700.01 to \u00a3800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7968,7 +7970,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£800.01 to £900.00"
+          "monthly_award_amount_bands": "\u00a3800.01 to \u00a3900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -7998,7 +8000,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, no children",
-          "monthly_award_amount_bands": "£900.01 to £1000.00"
+          "monthly_award_amount_bands": "\u00a3900.01 to \u00a31000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8028,7 +8030,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£0.01 to £100.00"
+          "monthly_award_amount_bands": "\u00a30.01 to \u00a3100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8058,7 +8060,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£100.01 to £200.00"
+          "monthly_award_amount_bands": "\u00a3100.01 to \u00a3200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8088,7 +8090,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1000.01 to £1100.00"
+          "monthly_award_amount_bands": "\u00a31000.01 to \u00a31100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8118,7 +8120,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1100.01 to £1200.00"
+          "monthly_award_amount_bands": "\u00a31100.01 to \u00a31200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8148,7 +8150,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1200.01 to £1300.00"
+          "monthly_award_amount_bands": "\u00a31200.01 to \u00a31300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8178,7 +8180,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1300.01 to £1400.00"
+          "monthly_award_amount_bands": "\u00a31300.01 to \u00a31400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8208,7 +8210,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1400.01 to £1500.00"
+          "monthly_award_amount_bands": "\u00a31400.01 to \u00a31500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8238,7 +8240,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1500.01 to £1600.00"
+          "monthly_award_amount_bands": "\u00a31500.01 to \u00a31600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8268,7 +8270,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1600.01 to £1700.00"
+          "monthly_award_amount_bands": "\u00a31600.01 to \u00a31700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8298,7 +8300,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1700.01 to £1800.00"
+          "monthly_award_amount_bands": "\u00a31700.01 to \u00a31800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8328,7 +8330,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1800.01 to £1900.00"
+          "monthly_award_amount_bands": "\u00a31800.01 to \u00a31900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8358,7 +8360,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£1900.01 to £2000.00"
+          "monthly_award_amount_bands": "\u00a31900.01 to \u00a32000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8388,7 +8390,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£200.01 to £300.00"
+          "monthly_award_amount_bands": "\u00a3200.01 to \u00a3300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8418,7 +8420,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£2000.01 to £2100.00"
+          "monthly_award_amount_bands": "\u00a32000.01 to \u00a32100.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8448,7 +8450,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£2100.01 to £2200.00"
+          "monthly_award_amount_bands": "\u00a32100.01 to \u00a32200.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8478,7 +8480,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£2200.01 to £2300.00"
+          "monthly_award_amount_bands": "\u00a32200.01 to \u00a32300.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8508,7 +8510,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£2300.01 to £2400.00"
+          "monthly_award_amount_bands": "\u00a32300.01 to \u00a32400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8538,7 +8540,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£2400.01 to £2500.00"
+          "monthly_award_amount_bands": "\u00a32400.01 to \u00a32500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8568,7 +8570,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£300.01 to £400.00"
+          "monthly_award_amount_bands": "\u00a3300.01 to \u00a3400.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8598,7 +8600,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£400.01 to £500.00"
+          "monthly_award_amount_bands": "\u00a3400.01 to \u00a3500.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8628,7 +8630,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£500.01 to £600.00"
+          "monthly_award_amount_bands": "\u00a3500.01 to \u00a3600.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8658,7 +8660,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£600.01 to £700.00"
+          "monthly_award_amount_bands": "\u00a3600.01 to \u00a3700.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8688,7 +8690,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£700.01 to £800.00"
+          "monthly_award_amount_bands": "\u00a3700.01 to \u00a3800.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8718,7 +8720,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£800.01 to £900.00"
+          "monthly_award_amount_bands": "\u00a3800.01 to \u00a3900.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -8748,7 +8750,7 @@
         ],
         "dimension_values": {
           "family_type": "Couple, with children",
-          "monthly_award_amount_bands": "£900.01 to £1000.00"
+          "monthly_award_amount_bands": "\u00a3900.01 to \u00a31000.00"
         },
         "geography_level": "country",
         "geography_id": "K03000001"
@@ -43536,7 +43538,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "Under £20.00"
+          "grouped_amount_of_benefit": "Under \u00a320.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43575,7 +43577,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£100.00 to under £150.00"
+          "grouped_amount_of_benefit": "\u00a3100.00 to under \u00a3150.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43614,7 +43616,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£150.00 to under £200.00"
+          "grouped_amount_of_benefit": "\u00a3150.00 to under \u00a3200.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43653,7 +43655,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£20.00 to under £40.00"
+          "grouped_amount_of_benefit": "\u00a320.00 to under \u00a340.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43692,7 +43694,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£200.00 to under £250.00"
+          "grouped_amount_of_benefit": "\u00a3200.00 to under \u00a3250.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43731,7 +43733,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£250.00 to under £300.00"
+          "grouped_amount_of_benefit": "\u00a3250.00 to under \u00a3300.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43770,7 +43772,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£300.00 and over"
+          "grouped_amount_of_benefit": "\u00a3300.00 and over"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43809,7 +43811,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£60.00 to under £80.00"
+          "grouped_amount_of_benefit": "\u00a360.00 to under \u00a380.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43848,7 +43850,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "New State Pension",
-          "grouped_amount_of_benefit": "£80.00 to under £100.00"
+          "grouped_amount_of_benefit": "\u00a380.00 to under \u00a3100.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43887,7 +43889,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "Under £20.00"
+          "grouped_amount_of_benefit": "Under \u00a320.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43926,7 +43928,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£100.00 to under £150.00"
+          "grouped_amount_of_benefit": "\u00a3100.00 to under \u00a3150.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -43965,7 +43967,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£150.00 to under £200.00"
+          "grouped_amount_of_benefit": "\u00a3150.00 to under \u00a3200.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44004,7 +44006,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£20.00 to under £40.00"
+          "grouped_amount_of_benefit": "\u00a320.00 to under \u00a340.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44043,7 +44045,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£200.00 to under £250.00"
+          "grouped_amount_of_benefit": "\u00a3200.00 to under \u00a3250.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44082,7 +44084,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£250.00 to under £300.00"
+          "grouped_amount_of_benefit": "\u00a3250.00 to under \u00a3300.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44121,7 +44123,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£300.00 and over"
+          "grouped_amount_of_benefit": "\u00a3300.00 and over"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44160,7 +44162,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£40.00 to under £60.00"
+          "grouped_amount_of_benefit": "\u00a340.00 to under \u00a360.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44199,7 +44201,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£60.00 to under £80.00"
+          "grouped_amount_of_benefit": "\u00a360.00 to under \u00a380.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -44238,7 +44240,7 @@
         ],
         "dimension_values": {
           "category_of_pension": "Pre-2016 State Pension",
-          "grouped_amount_of_benefit": "£80.00 to under £100.00"
+          "grouped_amount_of_benefit": "\u00a380.00 to under \u00a3100.00"
         },
         "entity_name": "person",
         "period_type": "month",
@@ -45091,6 +45093,33 @@
       "period_match_policy": "source_window",
       "value_operation": "monthly_window_average"
     },
+    {
+      "name": "dwp.hb.amount_pension_age",
+      "ledger_selector": {
+        "source_name": "dwp",
+        "source_concept": "dwp.benefit_expenditure_amount",
+        "entity_name": "government",
+        "dimension_values": {
+          "benefit_forecast_line": "housing_benefits__housing_benefitover_pension_credit_qualifying_age"
+        },
+        "source_measure_id_by_opening_year": {
+          "2024": "expenditure_2024",
+          "2025": "expenditure_2025"
+        },
+        "geography_level": "country",
+        "geography_id": "K03000001"
+      },
+      "entity": "benunit",
+      "measure": "dwp/hb/amount_pension_age",
+      "family": "dwp_benefit_expenditure",
+      "period": 2025,
+      "metadata": {
+        "contract_target_id": "dwp.hb.amount_pension_age",
+        "measure_kind": "prepared_column"
+      },
+      "assertion_policy": "allow_source_projection",
+      "value_operation": "calendar_year_window"
+    },
     {
       "name": "hmrc.salary_sacrifice.it_relief_total",
       "ledger_selector": {
diff --git a/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json b/packages/microcosm-build/src/microcosm/build/uk/uk_population_targets.json
index 34c63bc39..6ff16117b 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
@@ -830,6 +830,7 @@
       "dwp.hb.households_pension_age": "Post-freeze fact family (DWP Stat-Xplore Housing Benefit, pension age), no uk-data metric (microcosm#1069 c10).",
       "dwp.hb.households_pension_age_social_rented": "Post-freeze fact family (DWP Stat-Xplore Housing Benefit, pension age), no uk-data metric (microcosm#1069 c10).",
       "dwp.hb.households_pension_age_private_rented": "Post-freeze fact family (DWP Stat-Xplore Housing Benefit, pension age), no uk-data metric (microcosm#1069 c10).",
+      "dwp.hb.amount_pension_age": "Post-freeze fact family (DWP benefit expenditure and caseload tables, pension-age Housing Benefit spending), no uk-data metric (microcosm#1095).",
       "hmrc.salary_sacrifice.it_relief_total": "New declaration backed by HMRC Table 6.2 (PolicyEngine/chronicle#302 via #305): the all-rates income-tax relief on salary sacrifice, resolved by the live counterfactual route (microcosm#1069 c11)."
     },
     "suppressed_ancestors": {
@@ -1203,6 +1204,7 @@
       "dwp.hb.households_pension_age",
       "dwp.hb.households_pension_age_social_rented",
       "dwp.hb.households_pension_age_private_rented",
+      "dwp.hb.amount_pension_age",
       "hmrc.salary_sacrifice.it_relief_total"
     ]
   },
@@ -3389,8 +3391,8 @@
       },
       "category_id": "hmrc.survey_of_personal_incomes",
       "label": "Amount by total income band",
-      "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.average_earnings",
-      "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.obr.average_earnings between 1 January 2023 and 1 January 2025, the instants the SPI donor rebasing uses (the engine's OBR indices are calendar-year series, so period 2025 is calendar 2025 for incomes). Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and published facts move to it; the v20 national build measured the held 2023-24 levels as income tax 13 percent under and the state-pension bands 8 to 18 percent over."
+      "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national earnings index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane)."
     },
     {
       "target_id": "hmrc.spi.employment_income.count_by_total_income_band",
@@ -3477,8 +3479,8 @@
       },
       "category_id": "hmrc.survey_of_personal_incomes",
       "label": "Amount by total income band",
-      "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.per_capita.mixed_income",
-      "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.obr.per_capita.mixed_income between 1 January 2023 and 1 January 2025, the instants the SPI donor rebasing uses (the engine's OBR indices are calendar-year series, so period 2025 is calendar 2025 for incomes). Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and published facts move to it; the v20 national build measured the held 2023-24 levels as income tax 13 percent under and the state-pension bands 8 to 18 percent over."
+      "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national mixed-income index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane)."
     },
     {
       "target_id": "hmrc.spi.self_employment_income.count_by_total_income_band",
@@ -3741,8 +3743,8 @@
       },
       "category_id": "hmrc.survey_of_personal_incomes",
       "label": "Amount by total income band",
-      "uprating_index": "policyengine_uk_parameter:gov.economic_assumptions.indices.obr.private_pension_index",
-      "selector_note": "Uprated from the SPI 2023-24 fact to the 2025 calibration period by the pinned engine's gov.economic_assumptions.indices.obr.private_pension_index between 1 January 2023 and 1 January 2025, the instants the SPI donor rebasing uses (the engine's OBR indices are calendar-year series, so period 2025 is calendar 2025 for incomes). Ruled by María on 2026-09-22 (PolicyEngine/chronicle#280 lane): the calibration binds at calendar 2025 and published facts move to it; the v20 national build measured the held 2023-24 levels as income tax 13 percent under and the state-pension bands 8 to 18 percent over."
+      "uprating_index": "hmrc.itl_2026.total_income_growth_by_total_income_band",
+      "selector_note": "Uprated from the SPI 2023-24 fact to calendar 2025 by HMRC's projected total income in the band (Income Tax Liabilities Table 2.5, hmrc.itl_2026), the index the count, total-income and tax rows by band already use: an amount held in a fixed nominal band moves with the band's membership, which a flat national private pension index ignores (microcosm#1063, 2026-10-02). The calibration binds at calendar 2025 under María's ruling of 2026-09-22 (PolicyEngine/chronicle#280 lane)."
     },
     {
       "target_id": "hmrc.spi.private_pension_income.count_by_total_income_band",
@@ -26789,6 +26791,91 @@
       "value_operation": "monthly_window_average",
       "period_match_policy": "source_window"
     },
+    {
+      "target_id": "dwp.hb.amount_pension_age",
+      "family": "dwp_benefit_expenditure",
+      "geography_levels": [
+        "country"
+      ],
+      "measurement": {
+        "entity": "benunit",
+        "concept": "uk.benefits.housing_benefit.amount",
+        "filters": [
+          {
+            "concept": "uk.demographics.age",
+            "operator": ">=",
+            "value": 66
+          },
+          {
+            "concept": "uk.geography.country",
+            "operator": "in",
+            "value": [
+              "england",
+              "scotland",
+              "wales"
+            ]
+          }
+        ]
+      },
+      "bindings": {
+        "policyengine": {
+          "metric_name": "dwp/hb/amount_pension_age",
+          "value_variable": "housing_benefit",
+          "from_entity": "benunit",
+          "filters": [
+            {
+              "variable": "eldest_adult_age",
+              "operator": ">=",
+              "value": 66
+            }
+          ],
+          "household_conditions": [
+            {
+              "variable": "region",
+              "entity": "household",
+              "reduce": "any",
+              "operator": "in",
+              "value": [
+                "NORTH_EAST",
+                "NORTH_WEST",
+                "YORKSHIRE",
+                "EAST_MIDLANDS",
+                "WEST_MIDLANDS",
+                "EAST_OF_ENGLAND",
+                "LONDON",
+                "SOUTH_EAST",
+                "SOUTH_WEST",
+                "WALES",
+                "SCOTLAND"
+              ],
+              "map_to": "benunit"
+            }
+          ],
+          "notes": "DWP benefit expenditure and caseload tables, Spring Forecast 2026 (PolicyEngine/chronicle#280 / #282): the Housing_benefits worksheet line 'Housing Benefit over Pension Credit qualifying age', nominal GBP, which DWP publishes for Great Britain plus residents overseas (Notes tab), so the model column sums Great Britain households only (microcosm#1095, uk-data#490). Bound at the calendar-2025 window of the fiscal years (three twelfths of the FY2024-25 outturn, nine twelfths of the FY2025-26 forecast; calendar_year_window), María's rule of 2026-09-22 that fiscal-year facts take only the months to the end of the calibration calendar year. DWP gives each year its own measure (expenditure_2024, expenditure_2025), so the selector names each year's measure in source_measure_id_by_opening_year. DWP splits Housing Benefit at Pension Credit qualifying age; the engine reads the age of the benefit unit's eldest adult against 66, the State Pension age of everyone who reaches it in 2025, as the pension-age caseload rows do (microcosm#1069 c10, #867)."
+        },
+        "axiom": {
+          "metric_name": "dwp/hb/amount_pension_age",
+          "status": "pending",
+          "value_rule": "uk.benefits.housing_benefit.amount"
+        }
+      },
+      "assertion_policy": "allow_source_projection",
+      "ledger_selector": {
+        "source_name": "dwp",
+        "source_concept": "dwp.benefit_expenditure_amount",
+        "entity_name": "government",
+        "dimension_values": {
+          "benefit_forecast_line": "housing_benefits__housing_benefitover_pension_credit_qualifying_age"
+        },
+        "source_measure_id_by_opening_year": {
+          "2024": "expenditure_2024",
+          "2025": "expenditure_2025"
+        }
+      },
+      "category_id": "dwp.legacy_benefits",
+      "label": "Housing Benefit spending, pension age",
+      "value_operation": "calendar_year_window"
+    },
     {
       "target_id": "hmrc.salary_sacrifice.it_relief_total",
       "family": "hmrc_salary_sacrifice",
diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_disability.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_disability.py
index 78890e152..028414b2b 100644
--- a/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_disability.py
+++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/frs_disability.py
@@ -250,7 +250,10 @@ def derive_frs_disability(
     )
     values["dla_m_category"] = _category(
         _amount(person, "dla_m_reported"),
-        (("LOWER", category_rates.dla_m_lower), ("HIGHER", category_rates.dla_m_higher)),
+        (
+            ("LOWER", category_rates.dla_m_lower),
+            ("HIGHER", category_rates.dla_m_higher),
+        ),
     )
     values["pip_m_category"] = _category(
         _amount(person, "pip_m_reported"),
@@ -266,11 +269,12 @@ def derive_frs_disability(
             ("ENHANCED", category_rates.pip_dl_enhanced),
         ),
     )
-    total = sum(_amount(person, column) for column in UK_DISABILITY_FLAG_REPORTED_COLUMNS)
+    total = sum(
+        _amount(person, column) for column in UK_DISABILITY_FLAG_REPORTED_COLUMNS
+    )
     dla_sc = _amount(person, "dla_sc_reported")
     aa = _amount(person, "attendance_allowance_reported")
     pip_dl = _amount(person, "pip_dl_reported")
-    afcs = _amount(person, "afcs_reported")
     gap = WEEKS_IN_YEAR
     aa_higher = flag_rates.aa_higher * WEEKS_IN_YEAR - gap
     dla_sc_higher = flag_rates.dla_sc_higher * WEEKS_IN_YEAR - gap
@@ -279,9 +283,19 @@ def derive_frs_disability(
     values["is_enhanced_disabled_for_benefits"] = (
         (aa >= aa_higher) | (dla_sc > dla_sc_higher) | (pip_dl >= pip_dl_enhanced)
     )
+    # The tax credit severe disability condition (CTC Regs 2002 reg 8, WTC
+    # Regs 2002 reg 17) on the categories derived above, so the stored flag
+    # agrees with them by construction and with policyengine-uk's formula
+    # (pe-uk#1946, uk-data#494): the DLA care component at the highest rate,
+    # the PIP daily living component at the enhanced rate, or higher-rate
+    # Attendance Allowance. Lower-rate Attendance Allowance does not count,
+    # and FRS code 8 (the Armed Forces Compensation Scheme) cannot be told
+    # apart from Armed Forces Independence Payment, so it no longer counts.
     values["is_severely_disabled_for_benefits"] = (
-        (aa > 0) | (dla_sc >= dla_sc_higher) | (pip_dl >= pip_dl_enhanced) | (afcs > 0)
-    )
+        (values["aa_category"] == "HIGHER")
+        | (values["dla_sc_category"] == "HIGHER")
+        | (values["pip_dl_category"] == "ENHANCED")
+    ).to_numpy(dtype=bool)
     return values
 
 
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 2c0fc1e03..c05c46c50 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
@@ -10,6 +10,7 @@
 import pandas as pd
 
 from microcosm.build.source_manifest import SourceStageSpec
+from microcosm.build.stochastic_assignment import stable_identity_uniforms
 from microcosm.build.trace import sha256_file
 from microcosm.build.uk_runtime.national_frame import (
     uk_national_frame,
@@ -193,7 +194,13 @@
     "is_household_head",
     "is_benunit_head",
     "is_parent",
+    "is_blind",
     "is_uc_claimant",
+    "is_claimant_or_partner",
+    "is_hbai_dependent_child",
+    "uc_is_in_startup_period",
+    "rent_paid_as_boarder",
+    "rent_paid_as_lodger",
     "employment_income",
     "self_employment_income",
     "private_pension_income",
@@ -253,6 +260,7 @@
     "frs_benunit_capital",
     "is_married",
     "dependent_children",
+    "liable_for_share_of_household_rent",
     "household_id",
     "region",
     "tenure_type",
@@ -281,11 +289,16 @@ def __init__(self, raw_dir: str | Path, *, stage: SourceStageSpec) -> None:
         self.raw_dir = Path(raw_dir)
         self.stage = stage
         self._sentinel_mapped_rows: int | None = None
+        self._access_fund: dict[str, object] | None = None
 
     def __call__(self, frame: Frame) -> Frame:
-        result = build_uk_frs_spine_frame(self.raw_dir, stage=self.stage)
+        evidence: dict[str, object] = {}
+        result = build_uk_frs_spine_frame(
+            self.raw_dir, stage=self.stage, evidence=evidence
+        )
         capital = result.table("benunit")["frs_benunit_capital"]
         self._sentinel_mapped_rows = int((capital == UC_CAPITAL_UNAVAILABLE).sum())
+        self._access_fund = dict(evidence["access_fund"])
         return result
 
     @staticmethod
@@ -293,9 +306,10 @@ def output_columns() -> tuple[str, ...]:
         return OUTPUT_COLUMNS
 
     def checkpoint_metadata(self) -> dict[str, object]:
-        """Report how loudly the FRS capital availability rule fired."""
+        """Report how loudly the capital availability rule and the
+        access-fund repair fired."""
 
-        if self._sentinel_mapped_rows is None:
+        if self._sentinel_mapped_rows is None or self._access_fund is None:
             raise RuntimeError("checkpoint metadata requires a completed stage run.")
         return {
             "evidence": {
@@ -304,6 +318,7 @@ def checkpoint_metadata(self) -> dict[str, object]:
                     "unavailable_sentinel": UC_CAPITAL_UNAVAILABLE,
                     "mapped_rows": self._sentinel_mapped_rows,
                 },
+                "access_fund": dict(self._access_fund),
             }
         }
 
@@ -329,8 +344,17 @@ def uk_frs_spine_seed_frame() -> Frame:
     )
 
 
-def build_uk_frs_spine_frame(raw_dir: str | Path, *, stage: SourceStageSpec) -> Frame:
-    """Build the direct raw FRS spine Frame from pinned local tab files."""
+def build_uk_frs_spine_frame(
+    raw_dir: str | Path,
+    *,
+    stage: SourceStageSpec,
+    evidence: dict[str, object] | None = None,
+) -> Frame:
+    """Build the direct raw FRS spine Frame from pinned local tab files.
+
+    ``evidence``, when given, receives the access-fund repair's record under
+    ``"access_fund"`` (see :func:`access_fund_annual`).
+    """
 
     raw_root = Path(raw_dir)
     artifacts = _artifact_by_table(stage)
@@ -341,7 +365,7 @@ def build_uk_frs_spine_frame(raw_dir: str | Path, *, stage: SourceStageSpec) ->
         for table in FRS_SPINE_TABLES
     }
     normalized = {name: _normalize_ids(table) for name, table in tables.items()}
-    frame = _assemble_frame(normalized)
+    frame = _assemble_frame(normalized, evidence=evidence)
     validate_uk_national_frame(frame)
     return frame
 
@@ -413,6 +437,9 @@ def _read_pinned_tab(
     converted = raw.apply(pd.to_numeric, errors="coerce")
     if path.stem == "job" and "salsac" in raw.columns:
         converted["salsac_raw"] = raw["salsac"].astype(str)
+    # UCSTART is a month/day/year date, which numeric conversion would blank.
+    if path.stem == "benefits" and "ucstart" in raw.columns:
+        converted["ucstart_raw"] = raw["ucstart"].astype("string")
     return converted
 
 
@@ -433,7 +460,9 @@ def _normalize_ids(table: pd.DataFrame) -> pd.DataFrame:
     return frame
 
 
-def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame:
+def _assemble_frame(
+    frs: Mapping[str, pd.DataFrame], *, evidence: dict[str, object] | None = None
+) -> Frame:
     person = (
         pd.concat([frs["adult"], frs["child"]], ignore_index=True, sort=False)
         .fillna(0)
@@ -481,12 +510,18 @@ def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame:
     pe_person["is_parent"] = pe_person["person_id"].isin(adult_ids) & pe_person[
         "person_benunit_id"
     ].map(dependent_by_benunit).fillna(0).gt(0)
+    # Registered blind or severely sight impaired with the local authority
+    # (SPCREG1, asked on the adult and child tabs alike). Registration follows
+    # the consultant ophthalmologist's certificate the engine's is_blind
+    # names; partial-sight registration (SPCREG2) does not meet that test
+    # (uk-data#523).
+    pe_person["is_blind"] = _number(person, "spcreg1") == FRS_REGISTERED_YES
 
     pe_person["employment_income"] = _positive(person, "inearns") * WEEKS_IN_YEAR
     pe_person["self_employment_income"] = _positive(person, "seincam2") * WEEKS_IN_YEAR
     _add_private_pension(pe_person, person, frs["pension"])
     _add_accounts(pe_person, person, frs["accounts"])
-    _add_person_income(pe_person, person, household, frs["oddjob"])
+    _add_person_income(pe_person, person, household, frs["oddjob"], evidence=evidence)
     _add_benefits(pe_person, person, frs["benefits"])
     # The engine pays Carer's Allowance on hours or receipt; a dataset keeps it
     # on reported receipt so that care_hours qualifies carers for the UC carer
@@ -502,6 +537,32 @@ def _assemble_frame(frs: Mapping[str, pd.DataFrame]) -> Frame:
     # does not promote a dependent child to a partner, and legal marriage
     # alone does not establish that a partner lives in this benefit unit.
     pe_person["is_uc_claimant"] = frs_uc_claimant_mask(pe_person, pe_benunit)
+    pe_benunit["liable_for_share_of_household_rent"] = (
+        frs_liable_for_share_of_household_rent(
+            benunit_raw, person, household, frs["benefits"]
+        )
+    )
+    boarder_rent, lodger_rent = frs_rent_paid_to_householder(person)
+    pe_person["rent_paid_as_boarder"] = boarder_rent
+    pe_person["rent_paid_as_lodger"] = lodger_rent
+    # policyengine-uk's person types (pe-uk#1896). Every FRS person is on the
+    # adult table or the child table: the adult table holds each benefit
+    # unit's head and any partner (uk-data#524), the child table its HBAI
+    # dependent children (uk-data#486). Supplying both stops the engine
+    # inferring them from ages.
+    # The claimant mask above already refuses a benefit unit without one or
+    # two adult records, so membership is a well-formed role.
+    adult_record = pe_person["person_id"].isin(adult_ids).to_numpy()
+    pe_person["is_claimant_or_partner"] = adult_record
+    pe_person["is_hbai_dependent_child"] = ~adult_record
+    pe_person["uc_is_in_startup_period"] = frs_uc_start_up_period(
+        person,
+        pe_person,
+        pe_benunit,
+        household,
+        job=frs["job"],
+        benefits=frs["benefits"],
+    )
 
     _add_household_columns(pe_household, household, frs)
 
@@ -586,6 +647,8 @@ def _add_person_income(
     person: pd.DataFrame,
     household: pd.DataFrame,
     oddjob: pd.DataFrame,
+    *,
+    evidence: dict[str, object] | None = None,
 ) -> None:
     pe_person["property_income"] = frs_property_income(person, household)
     maintenance_to_self = np.maximum(
@@ -616,7 +679,10 @@ def _add_person_income(
     pe_person["statutory_sick_pay"] = _number(person, "sspadj") * WEEKS_IN_YEAR
     pe_person["statutory_maternity_pay"] = _number(person, "smpadj") * WEEKS_IN_YEAR
     pe_person["student_loans"] = _positive(person, "tuborr")
-    pe_person["access_fund"] = _positive(person, "accssamt") * WEEKS_IN_YEAR
+    access_fund_evidence: dict[str, object] = {}
+    pe_person["access_fund"] = access_fund_annual(person, evidence=access_fund_evidence)
+    if evidence is not None:
+        evidence["access_fund"] = access_fund_evidence
     pe_person["education_grants"] = np.maximum(
         _number(person, "grtdir1") + _number(person, "grtdir2"), 0
     )
@@ -1016,6 +1082,384 @@ def _positive(frame: pd.DataFrame, column: str) -> pd.Series:
     return np.maximum(_number(frame, column), 0)
 
 
+#: HOUSEHOL.HHSTAT 2: a shared household (shared on an equal basis, the head
+#: of household unclear or arbitrary).
+FRS_HHSTAT_SHARED = 2
+#: ADULT.CONVBL 1: the payer's rent to the householder includes meals (a
+#: boarder); anything else is lodging alone (a lodger).
+FRS_CONVBL_BOARD_AND_LODGING = 1
+
+
+def frs_liable_for_share_of_household_rent(
+    benunit: pd.DataFrame,
+    person: pd.DataFrame,
+    household: pd.DataFrame,
+    benefits: pd.DataFrame,
+) -> np.ndarray:
+    """Whether each benefit unit shares liability for its household's rent.
+
+    In a shared household (HHSTAT 2) each benefit unit after the first is
+    asked the rent it pays (SRENTAMT, on its adults' records) and the Housing
+    Benefit it gets (HBOTHAMT). SRENTAMT is asked after state help with the
+    rent, so a unit whose share Universal Credit meets in full can report
+    zero in both; a linked UC record with a housing element (UCHOUSEL on a
+    BENEFIT 95 record) still makes it liable. A later unit with any of the
+    three is one of the people liable for HHRENT, the whole dwelling's rent,
+    which policyengine-uk splits among them (pe-uk#2006, uk-data#512).
+    Conventional households (HHSTAT 1) are left out: their later units may
+    owe the landlord a share or pay the householder as boarders or lodgers,
+    and the survey does not say which.
+    """
+
+    missing = [
+        f"{table}.{column}"
+        for table, frame, column in (
+            ("househol", household, "hhstat"),
+            ("adult", person, "srentamt"),
+            ("benunit", benunit, "hbothamt"),
+            ("benefits", benefits, "uchousel"),
+        )
+        if column not in frame.columns
+    ]
+    if missing:
+        raise KeyError(f"Shared rent liability needs the FRS columns {missing}.")
+    benunit_id = benunit["benunit_id"].to_numpy()
+    shared = (
+        _number(household, "hhstat")
+        .reindex(benunit["household_id"].to_numpy())
+        .eq(FRS_HHSTAT_SHARED)
+        .to_numpy()
+    )
+    share_paid = (
+        _positive(person, "srentamt")
+        .groupby(person["benunit_id"].to_numpy())
+        .sum()
+        .reindex(benunit_id, fill_value=0.0)
+        .to_numpy()
+    )
+    housing_benefit = _positive(benunit, "hbothamt").to_numpy()
+    with_housing_element = benefits.loc[
+        _number(benefits, "benefit").isin(BENEFIT_CODES["universal_credit"])
+        & _number(benefits, "uchousel").gt(0),
+        "benunit_id",
+    ]
+    uc_housing = np.isin(benunit_id, with_housing_element.to_numpy())
+    later_unit = benunit_id % 100 > 1
+    return later_unit & shared & ((share_paid > 0) | (housing_benefit > 0) | uc_housing)
+
+
+def frs_rent_paid_to_householder(person: pd.DataFrame) -> tuple[np.ndarray, np.ndarray]:
+    """Annual rent a boarder and a lodger pays the householder (CVPAY on the payer).
+
+    CONVBL 1 marks board and lodging, a room and at least some meals; anything
+    else is lodging alone (pe-uk#2006's rent_paid_as_boarder and
+    rent_paid_as_lodger, uk-data#506/#511).
+    """
+
+    paid = _positive(person, "cvpay").to_numpy() * WEEKS_IN_YEAR
+    boarder = _number(person, "convbl").eq(FRS_CONVBL_BOARD_AND_LODGING).to_numpy()
+    return np.where(boarder, paid, 0.0), np.where(boarder, 0.0, paid)
+
+
+#: Universal Credit start-up period (UC Regs 2013 reg 63; uk-data#527). The
+#: codes are from the FRS 2024-25 variable listing (SN 9563).
+UC_START_UP_PERIOD_MONTHS = 12
+#: ADULT.EMPSTATI full- and part-time self-employed.
+FRS_SELF_EMPLOYED_EMPSTATI = (3, 4)
+#: ADULT.SDEMP01-12 self employed working full- and part-time.
+FRS_SELF_EMPLOYED_ACTIVITIES = (3, 4)
+#: JOB.ETYPE: every self-employed description (1 is an employee).
+FRS_SELF_EMPLOYED_JOB_ETYPES = (2, 3, 4, 5, 6, 7)
+#: JOB.JOBBUS "A business", against "Job".
+FRS_JOBBUS_BUSINESS = 2
+#: ADULT.SAMESIT "No": the respondent's situation did not change in 12 months.
+FRS_SITUATION_UNCHANGED = 2
+#: The identity-keyed stream that draws the claim recency of a UC record
+#: without a linked claim start date, one draw per benefit unit.
+UC_START_UP_CLAIM_RECENCY_SEED = 0
+UC_START_UP_CLAIM_RECENCY_SALT = "uc_is_in_startup_period"
+_UC_START_UP_PERSON_COLUMNS = (
+    "empstati",
+    "samesit",
+    "seincam2",
+    *(f"sdemp{month:02d}" for month in range(1, 13)),
+)
+_UC_START_UP_JOB_COLUMNS = ("etype", "jobtype", "seend", "sejblong", "jobbus")
+
+
+def frs_interview_dates(household: pd.DataFrame) -> pd.Series:
+    """Interview dates from HOUSEHOL.INTDATE, a SAS date (days since 1960-01-01)."""
+
+    raw = _raw_number(household, "intdate")
+    if raw.isna().any():
+        raise ValueError("FRS INTDATE (interview date) is missing for some households.")
+    return pd.to_datetime(raw, unit="D", origin="1960-01-01")
+
+
+def parse_frs_uc_claim_start(raw: pd.Series) -> pd.Series:
+    """UC claim start dates from BENEFITS.UCSTART, written month/day/year.
+
+    UCSTART comes from DWP administrative data; it is blank where the survey's
+    UC record has no linked administrative record. Any other value refuses,
+    so a changed format cannot pass silently.
+    """
+
+    text = pd.Series(raw, dtype="string").str.strip()
+    text = text.mask(text == "")
+    parsed = pd.to_datetime(text, format="%m/%d/%Y", errors="coerce")
+    unparsed = text.notna() & parsed.isna()
+    if unparsed.any():
+        raise ValueError(
+            f"{int(unparsed.sum())} FRS UCSTART values are not month/day/year dates."
+        )
+    return parsed
+
+
+def completed_months(later: pd.Series, earlier: pd.Series) -> np.ndarray:
+    """Whole calendar months from ``earlier`` to ``later``; NaN where either is missing."""
+
+    later = pd.DatetimeIndex(later)
+    earlier = pd.DatetimeIndex(earlier)
+    months = (
+        (later.year - earlier.year) * 12
+        + (later.month - earlier.month)
+        - (later.day < earlier.day)
+    )
+    return np.where(later.isna() | earlier.isna(), np.nan, months)
+
+
+def uc_trade_years(
+    years_in_job: np.ndarray,
+    describes_business: np.ndarray,
+    self_employed_all_year: np.ndarray,
+) -> np.ndarray:
+    """Completed years in the trade behind a self-employed job; NaN when unknown.
+
+    SEJBLONG asks someone running a business how long they have run it and
+    anyone else how long they have been in their current self-employed job.
+    For a person self-employed throughout the last 12 months, a job under a
+    year old is a new engagement in the same trade (ADM H4102 example 4 treats
+    a hairdresser turned hairstylist as one trade), so it does not date the
+    trade.
+    """
+
+    years = np.asarray(years_in_job, dtype=float)
+    new_engagement = (
+        (years < 1)
+        & ~np.asarray(describes_business, dtype=bool)
+        & np.asarray(self_employed_all_year, dtype=bool)
+    )
+    return np.where(new_engagement, np.nan, years)
+
+
+def frs_uc_start_up_period(
+    person: pd.DataFrame,
+    pe_person: pd.DataFrame,
+    pe_benunit: pd.DataFrame,
+    household: pd.DataFrame,
+    *,
+    job: pd.DataFrame,
+    benefits: pd.DataFrame,
+) -> np.ndarray:
+    """Whether each person is in a Universal Credit start-up period at interview.
+
+    Reg 63(1), as substituted from 23 September 2020 (SI 2019/1152), starts a
+    12-month start-up period when DWP finds a claimant in gainful
+    self-employment, unless the minimum income floor already applied for the
+    trade; the period is no longer limited to new trades. DWP decides that at
+    the start of a claim or when a new trade is reported, so the period is
+    running when the person is self-employed and either the benefit unit's UC
+    claim (UCSTART) or the trade (SEJBLONG) began less than 12 calendar months
+    before interview (INTDATE). A UC record without a linked claim date is
+    drawn, keyed on its benefit unit, at the survey-weighted share of linked
+    self-employed claimants whose claim began inside the window (uk-data#527).
+    The FRS cannot see earlier awards, so a re-claim after the floor applied,
+    or a second period within five years, reads as a start-up period; nor a
+    move into the all-work-related-requirements group on an old claim, which
+    starts a period the flag misses.
+    """
+
+    missing = [
+        f"{table}.{column}"
+        for table, frame, columns in (
+            ("adult", person, _UC_START_UP_PERSON_COLUMNS),
+            ("job", job, _UC_START_UP_JOB_COLUMNS),
+            ("benefits", benefits, ("ucstart_raw",)),
+        )
+        for column in columns
+        if column not in frame.columns
+    ]
+    if missing:
+        raise KeyError(
+            f"The UC start-up period needs the FRS columns {missing} (uk-data#527)."
+        )
+    interview = frs_interview_dates(household)
+    uc_rows = benefits.loc[
+        _number(benefits, "benefit").isin(BENEFIT_CODES["universal_credit"])
+    ]
+    claim_months = pd.Series(
+        completed_months(
+            interview.reindex(uc_rows["household_id"].to_numpy()),
+            parse_frs_uc_claim_start(uc_rows["ucstart_raw"]),
+        ),
+        index=uc_rows["benunit_id"].to_numpy(),
+    )
+    benunit_ids = pe_benunit["benunit_id"].to_numpy()
+    # One claim per benefit unit; the latest start where its rows disagree.
+    months = claim_months.groupby(level=0).min().reindex(benunit_ids).to_numpy()
+    unlinked = np.isin(benunit_ids, uc_rows["benunit_id"].to_numpy()) & np.isnan(months)
+
+    held = job.loc[
+        _number(job, "etype").isin(FRS_SELF_EMPLOYED_JOB_ETYPES)
+        & ~(_number(job, "seend") > 0)
+    ]
+    # The person's first self-employed job (main job first) is the trade the
+    # start-up period follows.
+    first_job = (
+        held.sort_values(["person_id", "jobtype"])
+        .drop_duplicates("person_id")
+        .set_index("person_id")
+    )
+    person_ids = person["person_id"].to_numpy()
+    main_job_self_employed = (
+        _number(person, "empstati").isin(FRS_SELF_EMPLOYED_EMPSTATI).to_numpy()
+    )
+    calendar = pd.concat(
+        [_number(person, f"sdemp{month:02d}") for month in range(1, 13)], axis=1
+    )
+    self_employed_all_year = main_job_self_employed & (
+        _number(person, "samesit").eq(FRS_SITUATION_UNCHANGED).to_numpy()
+        | calendar.isin(FRS_SELF_EMPLOYED_ACTIVITIES).all(axis=1).to_numpy()
+    )
+    sejblong = _raw_number(first_job, "sejblong")
+    trade_years = uc_trade_years(
+        sejblong.where(sejblong >= 0).reindex(person_ids).to_numpy(),
+        _number(first_job, "jobbus")
+        .eq(FRS_JOBBUS_BUSINESS)
+        .reindex(person_ids, fill_value=False)
+        .to_numpy(),
+        self_employed_all_year,
+    )
+    self_employed = (
+        main_job_self_employed
+        | np.isin(person_ids, held["person_id"].to_numpy())
+        | (_number(person, "seincam2").to_numpy() != 0)
+    )
+
+    person_benunit = pd.Index(benunit_ids).get_indexer(pe_person["person_benunit_id"])
+    if (person_benunit < 0).any():
+        raise ValueError("A person's benefit unit is missing from the FRS spine.")
+    person_months = months[person_benunit]
+    linked = self_employed & ~np.isnan(person_months)
+    weight = (
+        _raw_number(household, "gross4")
+        .reindex(pe_person["person_household_id"].to_numpy())
+        .to_numpy()
+    )
+    # The household-weighted share of linked self-employed claimants whose
+    # claim began inside the window; it treats a missing link as unrelated to
+    # the claim's age.
+    linked_share = (
+        float(
+            np.average(
+                (person_months[linked] < UC_START_UP_PERIOD_MONTHS).astype(float),
+                weights=weight[linked],
+            )
+        )
+        if weight[linked].sum() > 0
+        else 0.0
+    )
+    draws = stable_identity_uniforms(
+        benunit_ids,
+        seed=UC_START_UP_CLAIM_RECENCY_SEED,
+        salt=UC_START_UP_CLAIM_RECENCY_SALT,
+    )
+    claim_in_window = (months < UC_START_UP_PERIOD_MONTHS) | (
+        unlinked & (draws < linked_share)
+    )
+    return self_employed & (claim_in_window[person_benunit] | (trade_years < 1))
+
+
+#: FRS yes code for the local-authority registration questions (SPCREG1-3).
+FRS_REGISTERED_YES = 1
+#: FRS period codes (SN 9563 code frame) for an amount reported per calendar
+#: month and per year.
+FRS_PERIOD_CALENDAR_MONTH = 5
+FRS_PERIOD_YEAR = 52
+#: A calendar-month access-fund award is read as annual only when it
+#: annualises to more than this multiple of the largest annual-coded award, so
+#: a large but plausible monthly payment is left as reported.
+ACCESS_FUND_MONTHLY_REPAIR_MULTIPLE = 2.0
+
+
+def access_fund_annual(
+    person: pd.DataFrame, evidence: dict[str, object] | None = None
+) -> pd.Series:
+    """Annual access-fund award, with one period-code repair.
+
+    The FRS weeklyises ``ACCSSAMT`` from the reported amount and its period
+    code ``ACCSSPD``, and the spine annualises it. An access-fund award is paid
+    per academic year or term, and on the 2024-25 tab the calendar-month
+    amounts run up to the size of a whole annual award: read as monthly, such
+    an award annualises to more than ten times every award the survey records
+    as annual (microcosm#1095, from the review of #1100). So a calendar-month
+    award (code 5) that annualises to more than
+    ``ACCESS_FUND_MONTHLY_REPAIR_MULTIPLE`` times the largest award the same
+    tab records as annual (code 52) is read as the annual award: its
+    per-period amount, ``ACCSSAMT`` x 52/12. On the 2024-25 tab that repairs
+    fewer than 10 awards and leaves large but plausible monthly payments as
+    reported. Every other award stays as the FRS weeklyised it, including the
+    few with no period code on the tab. The threshold moves
+    with the tab's largest annual-coded award, so a tab with calendar-month
+    awards but no annual-coded award refuses rather than repair nothing
+    silently, and so does a tab without the ``ACCSSPD`` column, since the rule
+    cannot be applied to either.
+
+    ``evidence``, when given, records the paid awards by period code, the
+    annual threshold (``None`` when no award is paid) and how many awards
+    were repaired, for the stage's checkpoint evidence.
+    """
+
+    amount = _positive(person, "accssamt")
+    annual = amount * WEEKS_IN_YEAR
+    paid = amount > 0
+    record: dict[str, object] = {
+        "paid_awards": int(paid.sum()),
+        "annual_coded_awards": 0,
+        "calendar_month_awards": 0,
+        "repair_multiple": ACCESS_FUND_MONTHLY_REPAIR_MULTIPLE,
+        "repair_threshold_annual": None,
+        "repaired_awards": 0,
+    }
+    if evidence is not None:
+        evidence.update(record)
+    if not bool(paid.any()):
+        return annual
+    if "accsspd" not in person.columns:
+        raise KeyError("FRS access fund needs the period code ACCSSPD beside ACCSSAMT.")
+    period = _raw_number(person, "accsspd")
+    annual_coded = paid & period.eq(FRS_PERIOD_YEAR)
+    calendar_month = paid & period.eq(FRS_PERIOD_CALENDAR_MONTH)
+    if not bool(annual_coded.any()):
+        if bool(calendar_month.any()):
+            raise ValueError(
+                "FRS access fund has calendar-month awards (ACCSSPD 5) but no "
+                "annual-coded award (ACCSSPD 52), so the calendar-month repair "
+                "has no threshold on this tab."
+            )
+        return annual
+    threshold = ACCESS_FUND_MONTHLY_REPAIR_MULTIPLE * float(annual[annual_coded].max())
+    implausible = calendar_month & annual.gt(threshold)
+    if evidence is not None:
+        evidence.update(
+            annual_coded_awards=int(annual_coded.sum()),
+            calendar_month_awards=int(calendar_month.sum()),
+            repair_threshold_annual=threshold,
+            repaired_awards=int(implausible.sum()),
+        )
+    return annual.where(~implausible, amount * 52 / 12)
+
+
 #: The Water Charges Reduction Scheme's maximum reduction, 2021-27. A council
 #: tax reduction recipient's charges fall by R = 35 x (A/B) - D percentage
 #: points of the gross charge, unless R < 0, on top of the council tax status
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 3adef30c1..731ac9341 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
@@ -54,7 +54,7 @@
 # a unit with every adult at or over State Pension age is never drawn (#882,
 # microcosm#867). The bounds come from the engine at the build instant through
 # ``uk_take_up_population_policy``: State Pension age from the
-# ``gov.dwp.state_pension.age`` parameters and the adult threshold from the
+# ``gov.dwp.state_pension.age`` timetable and the adult threshold from the
 # engine's ``is_adult`` formula (a constant there, pinned by the lockstep test).
 # The stage manifest declares the same population on the ``would_claim_uc``
 # operation; ``assert_take_up_stage_population_declaration`` refuses a manifest
@@ -103,9 +103,10 @@ class UKTakeUpPopulationPolicy:
     """Engine bounds of the Universal Credit take-up population at one instant.
 
     ``adult_age`` is the engine's ``is_adult`` threshold and
-    ``state_pension_age`` its ``gov.dwp.state_pension.age`` value, so a unit is
-    in the population exactly when the engine's ``is_WA_adult`` is true for one
-    of its members.
+    ``state_pension_age`` the age its ``gov.dwp.state_pension.age`` timetable
+    sets for the cohort attaining it in the build year, so a unit is in the
+    population exactly when the engine's ``is_WA_adult`` is true for one of its
+    members (the lockstep test checks every age).
     """
 
     adult_age: int
@@ -132,23 +133,40 @@ def uk_take_up_population_policy(build_period: int | str) -> UKTakeUpPopulationP
     parameters = ParameterNode(
         directory_path=str(Path(policyengine_uk.__file__).parent / "parameters")
     )
-    instant = f"{int(build_period)}-01-01"
-    ages = parameters.gov.dwp.state_pension.age
-    male, female = float(ages.male(instant)), float(ages.female(instant))
-    if male != female or not float(male).is_integer():
+    year = int(build_period)
+    instant = f"{year}-01-01"
+    timetable = parameters.gov.dwp.state_pension.age.age_by_birth_date(instant)
+    # From policyengine-uk 2.112.0 State Pension age follows the statutory
+    # timetable by date of birth (in months; zero where the statute sets a
+    # day instead). The population needs one whole-year age: the age at which
+    # everyone born in a single calendar year attains it, for the cohort that
+    # attains it in the build year.
+    ages = [
+        age
+        for age in range(UK_ENGINE_ADULT_AGE, 100)
+        if (timetable.calc(_birth_dates_in_year(year - age)) == age * 12).all()
+    ]
+    if len(ages) != 1:
         raise ValueError(
             "the take-up population needs one State Pension age for every adult "
-            f"at {instant}; the engine has male {male} and female {female}, so the "
-            "stage must consume sex before it can form the population"
+            f"at {instant}; the engine's timetable gives {ages or 'none'}, so the "
+            "stage must consume dates of birth before it can form the population"
         )
     return UKTakeUpPopulationPolicy(
         adult_age=UK_ENGINE_ADULT_AGE,
-        state_pension_age=int(male),
+        state_pension_age=ages[0],
         instant=instant,
         source="policyengine-uk parameters " + metadata.version("policyengine-uk"),
     )
 
 
+def _birth_dates_in_year(year: int) -> np.ndarray:
+    """Every date of ``year`` as the YYYYMMDD numbers the timetable is keyed by."""
+
+    days = pd.date_range(f"{year}-01-01", f"{year}-12-31", freq="D")
+    return (days.year * 10_000 + days.month * 100 + days.day).to_numpy()
+
+
 def assert_take_up_stage_population_declaration(stage: SourceStageSpec) -> None:
     """Refuse a manifest whose declared UC population differs from the code's."""
 
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 abf61714b..817abd876 100644
--- a/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py
+++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/graph.py
@@ -489,7 +489,11 @@ def owned(self, *, rewrite: bool = False) -> Owned:
     "is_household_head",
     "is_benunit_head",
     "is_parent",
+    "is_blind",
     "is_uc_claimant",
+    "is_claimant_or_partner",
+    "is_hbai_dependent_child",
+    "uc_is_in_startup_period",
     "would_claim_carers_allowance",
 }
 _ROOT_PERSON_INT = {"age"}
@@ -498,6 +502,7 @@ def owned(self, *, rewrite: bool = False) -> Owned:
     "frs_benunit_capital": "float64",
     "is_married": "bool",
     "dependent_children": "int64",
+    "liable_for_share_of_household_rent": "bool",
 }
 _ROOT_HOUSEHOLD_STRING = {
     "region",
@@ -868,10 +873,14 @@ def _cells(
     ),
     "uc_capital_coherence": (
         _Cell("benunit", "uc_reported_capital", "float64"),
+        _Cell("benunit", "pension_credit_reported_capital", "float64"),
         _Cell("benunit", "frs_benunit_capital", "float64"),
         _Cell("benunit", "would_claim_uc", "bool"),
     ),
-    "pension_credit_take_up": (_Cell("benunit", "would_claim_pc", "bool"),),
+    "pension_credit_take_up": (
+        _Cell("benunit", "would_claim_pc", "bool"),
+        _Cell("benunit", "has_mixed_age_couple_pension_credit_saving", "bool"),
+    ),
     "child_benefit_take_up": (
         _Cell("benunit", "would_claim_child_benefit", "bool"),
         _Cell("benunit", "child_benefit_opts_out", "bool"),
@@ -992,6 +1001,9 @@ def _cells(
     # #882: the carer take-up flag follows the refilled Carer's Allowance
     # receipt on the SPI-redrawn rows.
     "would_claim_carers_allowance",
+    # uk-data#527: the UC start-up period holds on a redrawn row only while it
+    # stays self-employed.
+    "uc_is_in_startup_period",
 )
 _STAGE_CELLS = {
     **_STAGE_CELLS,
diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py
index 1ee67b6c1..289fd4278 100644
--- a/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py
+++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/lcfs_consumption.py
@@ -115,8 +115,8 @@
     8: "OTHER",
 }
 HOUSEHOLD_LCFS_RENAMES = {
-    "g018": "is_adult",
-    "g019": "is_child",
+    "g018": "household_adult_count",
+    "g019": "household_child_count",
     "gorx": "region",
     "a124": "num_vehicles",
     "p389p": "hbai_household_net_income",
@@ -196,17 +196,21 @@
 UK_LCFS_DEFAULT_SUPPORT_CLIP_EXEMPT = frozenset(
     {"electricity_consumption", "gas_consumption", "domestic_energy_consumption"}
 )
+#: The LCFS household's adults (G018) and children (G019), by the age-18 split
+#: the survey counts them with. The recipient counts the same split from
+#: ``age`` rather than the engine's person flags ``is_adult`` and ``is_child``,
+#: which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095).
+UK_LCFS_HOUSEHOLD_COUNT_ADULT_AGE = 18
+UK_LCFS_HOUSEHOLD_COUNT_PREDICTORS = ("household_adult_count", "household_child_count")
 UK_LCFS_CONSUMPTION_ENGINE_PREDICTORS = (
-    "is_adult",
-    "is_child",
     "employment_income",
     "self_employment_income",
     "private_pension_income",
     "hbai_household_net_income",
 )
 UK_LCFS_CONSUMPTION_PREDICTORS = (
-    "is_adult",
-    "is_child",
+    "household_adult_count",
+    "household_child_count",
     "region",
     "employment_income",
     "self_employment_income",
@@ -1192,6 +1196,19 @@ def recipient_predictors(frame: Frame, engine: object) -> pd.DataFrame:
             )
         else:
             raise ValueError(f"unsupported LCFS predictor entity {declared!r}.")
+    age = pd.to_numeric(person["age"], errors="raise").to_numpy(dtype=float)
+    adult = age >= UK_LCFS_HOUSEHOLD_COUNT_ADULT_AGE
+    for predictor, members in zip(
+        UK_LCFS_HOUSEHOLD_COUNT_PREDICTORS, (adult, ~adult), strict=True
+    ):
+        counted = (
+            pd.Series(members.astype(float))
+            .groupby(person["person_household_id"].to_numpy())
+            .sum()
+        )
+        result[predictor] = (
+            counted.reindex(household["household_id"]).fillna(0.0).to_numpy()
+        )
     for predictor in ("region", "tenure_type", "accommodation_type"):
         if predictor in household:
             result[predictor] = household[predictor].map(_enum_name).to_numpy()
diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py
index e95767d8b..265c2235d 100644
--- a/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py
+++ b/packages/microcosm-build/src/microcosm/build/uk_runtime/pension_credit_take_up.py
@@ -53,6 +53,10 @@
     "savings_credit",
     "is_pension_credit_eligible",
 )
+#: The SI 2019/37 saving for a mixed-age couple (pe-uk#1940), stored at the
+#: survey year so later model years do not re-derive it from a birth year
+#: that moves with the model year (uk-data#519).
+PENSION_CREDIT_MIXED_AGE_SAVING = "has_mixed_age_couple_pension_credit_saving"
 PENSION_CREDIT_REPORTED_ANCHOR = "pension_credit_reported_anchor"
 PENSION_CREDIT_AGGREGATES = {PENSION_CREDIT_REPORTED_ANCHOR: "pension_credit_reported"}
 #: The component bands, in the order DWP reports take-up, with their rules and
@@ -116,7 +120,7 @@ def __call__(self, frame: Frame) -> Frame:
 
     @staticmethod
     def output_columns() -> tuple[str, ...]:
-        return ()
+        return (PENSION_CREDIT_MIXED_AGE_SAVING,)
 
     def checkpoint_metadata(self) -> dict[str, object]:
         if self.last_result is None:
@@ -151,13 +155,23 @@ def redraw_pension_credit_take_up(
                 f"Pension Credit take-up {label} columns missing: {missing}."
             )
     period = uk_time_period(frame)
-    materialized = engine.materialize(
-        frame, list(PENSION_CREDIT_ENTITLEMENT_VARIABLES), period
-    )
-    missing = sorted(set(PENSION_CREDIT_ENTITLEMENT_VARIABLES) - set(materialized))
+    # The engine's own default for the mixed-age saving, evaluated once at the
+    # frame's survey-year period on the post-SPI receipts and stored, so the
+    # entitlement below and every later model year read the same answer: a
+    # mixed-age couple whose older member was born by 5 February 1954 and who
+    # report Pension Credit, or pension-age Housing Benefit without an
+    # income-related legacy benefit, and no Universal Credit (uk-data#519).
+    requested = [PENSION_CREDIT_MIXED_AGE_SAVING, *PENSION_CREDIT_ENTITLEMENT_VARIABLES]
+    materialized = engine.materialize(frame, requested, period)
+    missing = sorted(set(requested) - set(materialized))
     if missing:
         raise ValueError(f"Pension Credit take-up engine outputs missing: {missing}.")
     count = len(benunit)
+    benunit[PENSION_CREDIT_MIXED_AGE_SAVING] = _aligned(
+        materialized[PENSION_CREDIT_MIXED_AGE_SAVING],
+        count,
+        PENSION_CREDIT_MIXED_AGE_SAVING,
+    ).astype(bool)
     guarantee = _aligned(materialized["guarantee_credit"], count, "guarantee_credit")
     savings = _aligned(materialized["savings_credit"], count, "savings_credit")
     eligible = _aligned(
@@ -302,6 +316,7 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None:
         )
     kinds = [operation.kind for operation in stage.operations]
     expected_kinds = [
+        "materialize_rules_engine_predictors",
         "materialize_rules_engine_predictors",
         "aggregate_person_to_benunit",
         "assign_component_take_up_residual",
@@ -311,8 +326,9 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None:
             f"{PENSION_CREDIT_TAKE_UP_STAGE_NAME} operation order drifted: "
             f"expected {expected_kinds}, got {kinds}."
         )
-    materialize, aggregate, assign = stage.operations
+    saving, materialize, aggregate, assign = stage.operations
     expected = {
+        "saving": {"predictors": [PENSION_CREDIT_MIXED_AGE_SAVING]},
         "materialize": {
             "predictors": list(PENSION_CREDIT_ENTITLEMENT_VARIABLES),
             "consumed_only": True,
@@ -333,6 +349,7 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None:
         },
     }
     actual = {
+        "saving": dict(saving.parameters),
         "materialize": dict(materialize.parameters),
         "aggregate": dict(aggregate.parameters),
         "assign": dict(assign.parameters),
@@ -342,15 +359,18 @@ def _assert_stage_parameters(stage: SourceStageSpec) -> None:
             f"{PENSION_CREDIT_TAKE_UP_STAGE_NAME} parameters drifted: expected "
             f"{expected}, got {actual}."
         )
-    if stage.outputs != () or stage.rewrites != (PENSION_CREDIT_TAKE_UP_OUTPUT,):
+    if stage.outputs != (PENSION_CREDIT_MIXED_AGE_SAVING,) or stage.rewrites != (
+        PENSION_CREDIT_TAKE_UP_OUTPUT,
+    ):
         raise ValueError(
-            f"{PENSION_CREDIT_TAKE_UP_STAGE_NAME} must declare no new outputs and "
-            "exactly the would_claim_pc rewrite."
+            f"{PENSION_CREDIT_TAKE_UP_STAGE_NAME} must declare exactly the "
+            f"{PENSION_CREDIT_MIXED_AGE_SAVING} output and the would_claim_pc rewrite."
         )
 
 
 __all__ = [
     "PENSION_CREDIT_ENTITLEMENT_VARIABLES",
+    "PENSION_CREDIT_MIXED_AGE_SAVING",
     "PENSION_CREDIT_TAKE_UP_BANDS",
     "PENSION_CREDIT_TAKE_UP_STAGE_NAME",
     "UKPensionCreditTakeUpResult",
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 3775f3aad..93289200b 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
@@ -983,6 +983,7 @@ def impute_uk_spi_income_support(
     person = derive_hmrc_income_auxiliaries(person, row_mask=spi_people)
     person = _refresh_disability_derived_inputs(person, spi_people=spi_people)
     person = _refresh_carer_take_up_input(person, spi_people=spi_people)
+    person = _refresh_uc_start_up_period(person, spi_people=spi_people)
     return UKSPIIncomeImputationResult(
         person=person,
         fit_weight_records=(
@@ -2012,6 +2013,42 @@ def _refresh_carer_take_up_input(
     return person
 
 
+#: employment_status values of the self-employed (FRS EMPSTATI 3 and 4).
+SELF_EMPLOYED_STATUSES = ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED")
+
+
+def _refresh_uc_start_up_period(
+    person: pd.DataFrame, *, spi_people: pd.Series
+) -> pd.DataFrame:
+    """Clear the UC start-up period where a redrawn row is no longer self-employed.
+
+    The root stage dates the period from the donor's claim and trade; a
+    redrawn row keeps the donor's employment status but takes new imputed
+    income, so the flag survives only while that status or the refilled
+    self-employment income still shows self-employment (uk-data#527).
+    """
+
+    if "uc_is_in_startup_period" not in person.columns:
+        return person
+    rows = person.loc[spi_people]
+    still_self_employed = rows["employment_status"].isin(
+        SELF_EMPLOYED_STATUSES
+    ).to_numpy() | (
+        pd.to_numeric(rows["self_employment_income"], errors="coerce")
+        .fillna(0.0)
+        .to_numpy()
+        != 0.0
+    )
+    _assign_spi_values(
+        person,
+        spi_people,
+        "uc_is_in_startup_period",
+        rows["uc_is_in_startup_period"].astype(bool).to_numpy() & still_self_employed,
+        default=False,
+    )
+    return person
+
+
 def _assign_spi_values(
     person: pd.DataFrame,
     spi_people: pd.Series,
diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/terminal_gates.py
index 04b8cf1f7..6920503b2 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
@@ -166,6 +166,9 @@ def __post_init__(self) -> None:
 UK_ALLOWED_EXTRA_EXPORT_COLUMNS: tuple[str, ...] = (
     "benunit.child_benefit_opts_out",
     "benunit.frs_benunit_capital",
+    "benunit.has_mixed_age_couple_pension_credit_saving",
+    "benunit.liable_for_share_of_household_rent",
+    "benunit.pension_credit_reported_capital",
     "benunit.uc_deduction_combination",
     "benunit.uc_deduction_random_draw",
     "benunit.uc_deduction_type_random_draw",
@@ -257,6 +260,9 @@ def __post_init__(self) -> None:
     "person.has_lifetime_isa",
     "person.highest_education",
     "person.is_before_universal_credit_qualifying_young_person_terminal_date",
+    "person.is_blind",
+    "person.is_claimant_or_partner",
+    "person.is_hbai_dependent_child",
     "person.is_in_non_advanced_education",
     "person.is_parent",
     "person.is_uc_claimant",
@@ -273,6 +279,8 @@ def __post_init__(self) -> None:
     "person.pip_dl_category",
     "person.pip_m_category",
     "person.receives_benefits_in_own_right",
+    "person.rent_paid_as_boarder",
+    "person.rent_paid_as_lodger",
     "person.relationship_to_head",
     "person.salary_sacrifice_asked",
     "person.salary_sacrifice_reported",
@@ -280,6 +288,7 @@ def __post_init__(self) -> None:
     "person.student_loan_balance",
     "person.student_loan_plan",
     "person.tax_free_childcare_spend_routed_share",
+    "person.uc_is_in_startup_period",
     "person.would_claim_carers_allowance",
     "person.would_claim_marriage_allowance",
     "person.would_claim_scp",
diff --git a/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py b/packages/microcosm-build/src/microcosm/build/uk_runtime/uc_capital_coherence.py
index 81fe9b30c..1eef83a38 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
@@ -44,7 +44,10 @@
 UC_CAPITAL_REDRAW_OUTPUT = "frs_benunit_capital"
 UC_CAPITAL_REDRAW_SEED = 0
 UC_CAPITAL_REDRAW_SALT = UC_CAPITAL_REDRAW_OUTPUT
-UC_CAPITAL_COHERENCE_OUTPUT_COLUMNS = ("uc_reported_capital",)
+UC_CAPITAL_COHERENCE_OUTPUT_COLUMNS = (
+    "uc_reported_capital",
+    "pension_credit_reported_capital",
+)
 #: Every SPI benefit unit is redrawn: the channel's incomes are SPI draws,
 #: so a copied FRS capital answer no longer belongs to the unit (uk-data#495,
 #: microcosm#1095). Donors are the base units with an available answer.
@@ -220,6 +223,11 @@ def cohere_uc_capital(
     refreshed_would_claim = previous_would_claim | reporter
     benunit[UC_CAPITAL_REDRAW_OUTPUT] = capital
     benunit["uc_reported_capital"] = capital.copy()
+    # Pension Credit reads the same recorded capital (pe-uk#2018 and #2070,
+    # uk-data#513): 0 or more replaces every capital source in its assessable
+    # capital, and the unavailable sentinel -1 is the engine's own default, so
+    # the household proxy applies there.
+    benunit["pension_credit_reported_capital"] = capital.copy()
     benunit["would_claim_uc"] = refreshed_would_claim
 
     result_frame = uk_national_frame(
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 e5a05c0ac..7ce9f8436 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 = (
-    "a31eb026f0091cefa3e6afcd1613e04ac6a232aee752b573350e49d389476cfb"
+    "11338222ee36813af3a2237abb6de9ff12b40cecde530a4a164573a8d751ac9d"
 )
 LEGACY_CAMPAIGN_UV_LOCK_SHA256 = (
     "27f47e385cfa35e2644a37410d1804b361ad9aee123577551c8421547bda65ee"
diff --git a/packages/microcosm-build/tests/engine/uk/test_uk_frs_disability.py b/packages/microcosm-build/tests/engine/uk/test_uk_frs_disability.py
index 64c984df5..cfaf9802d 100644
--- a/packages/microcosm-build/tests/engine/uk/test_uk_frs_disability.py
+++ b/packages/microcosm-build/tests/engine/uk/test_uk_frs_disability.py
@@ -20,3 +20,71 @@ def test_dwp_readers_share_fiscal_converted_tree() -> None:
     assert np.isfinite(category_2024.aa_lower)
     assert category_2024.aa_higher == flags_2024.aa_higher == pytest.approx(108.55)
     assert category_2023.aa_higher == flags_2023.aa_higher == pytest.approx(101.75)
+
+
+def test_stored_severe_flag_matches_the_policyengine_uk_formula() -> None:
+    """On every category, the stored flag is the engine's own formula (pe-uk#1946)."""
+
+    import policyengine_uk
+
+    from microcosm.build.uk_runtime.frs_disability import (
+        uk_dwp_disability_flag_rates,
+    )
+
+    year = 2024
+    rates = uk_dwp_disability_category_rates(year)
+    flags = uk_dwp_disability_flag_rates(year)
+    cases = [
+        ("attendance_allowance_reported", "aa_category", "NONE", 0.0),
+        ("attendance_allowance_reported", "aa_category", "LOWER", rates.aa_lower),
+        ("attendance_allowance_reported", "aa_category", "HIGHER", rates.aa_higher),
+        ("dla_sc_reported", "dla_sc_category", "LOWER", rates.dla_sc_lower),
+        ("dla_sc_reported", "dla_sc_category", "MIDDLE", rates.dla_sc_middle),
+        ("dla_sc_reported", "dla_sc_category", "HIGHER", rates.dla_sc_higher),
+        ("pip_dl_reported", "pip_dl_category", "STANDARD", rates.pip_dl_standard),
+        ("pip_dl_reported", "pip_dl_category", "ENHANCED", rates.pip_dl_enhanced),
+    ]
+    rows = []
+    for column, _, _, weekly in cases:
+        row = dict.fromkeys(
+            (
+                "attendance_allowance_reported",
+                "dla_sc_reported",
+                "dla_m_reported",
+                "pip_m_reported",
+                "pip_dl_reported",
+            ),
+            0.0,
+        )
+        row[column] = weekly * WEEKS_IN_YEAR
+        rows.append(row)
+    stored = derive_frs_disability(
+        pd.DataFrame(rows), category_rates=rates, flag_rates=flags
+    )
+    # The engine reads the same categories the spine stores.
+    assert [
+        stored.loc[index, category_column]
+        for index, (_, category_column, _, _) in enumerate(cases)
+    ] == [category for _, _, category, _ in cases]
+
+    people = {}
+    for index, (_, category_column, category, _) in enumerate(cases):
+        person = {"age": {year: 70 if category_column == "aa_category" else 40}}
+        person[category_column] = {year: category}
+        people[f"p{index}"] = person
+    simulation = policyengine_uk.Simulation(
+        situation={
+            "people": people,
+            "benunits": {
+                f"b{index}": {"members": [name]} for index, name in enumerate(people)
+            },
+            "households": {
+                f"h{index}": {"members": [name]} for index, name in enumerate(people)
+            },
+        }
+    )
+    engine = np.asarray(
+        simulation.calculate("is_severely_disabled_for_benefits", year), dtype=bool
+    )
+
+    assert stored["is_severely_disabled_for_benefits"].tolist() == engine.tolist()
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 90668886f..a3cff51b1 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
@@ -1132,7 +1132,7 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None:
 
         references = {reference.name: reference for reference in spec.target_references}
         assert (
-            len(references) == 1230
+            len(references) == 1231
         )  # PolicyEngine/chronicle#280 lane: 705 + 33 HMRC liabilities rows (Table 2.5, three
         # measures by eleven bands) + 26 SPI savings-interest rows (two measures by
         # thirteen bands) + 360 SPI Table 3.11 region-tier rows (three measures by
@@ -1150,7 +1150,8 @@ def test_uk_target_references_accept_regenerated_contract_fields(self) -> None:
         # the DWP employer contribution total, less the salary-sacrifice users
         # total (the Table 3.8 rows and the DWP employee total left the fit); two
         # Attendance Allowance and three pension-age Housing Benefit rows; the
-        # income-tax relief total on salary sacrifice in place of the amount row
+        # income-tax relief total on salary sacrifice in place of the amount row;
+        # microcosm#1095: Great Britain pension-age Housing Benefit spending
         assert references["obr.esa"].value_operation == "calendar_year_window"
         assert references["dwp.uc.households"].value_operation == (
             "monthly_window_sum_average"
diff --git a/packages/microcosm-build/tests/engine_free/shared/test_ledger_targets.py b/packages/microcosm-build/tests/engine_free/shared/test_ledger_targets.py
index 255018493..dce1af1df 100644
--- a/packages/microcosm-build/tests/engine_free/shared/test_ledger_targets.py
+++ b/packages/microcosm-build/tests/engine_free/shared/test_ledger_targets.py
@@ -13,12 +13,14 @@
     apply_ledger_target_profile,
     compile_ledger_target_references,
     constraint_bound_shadowing_dimensions,
+    fact_matches_selector,
     hierarchy_seed_from_catalog,
     ledger_target_registry_parity_report,
     period_values_semantically_equal,
     reference_fact_selectors,
     select_ledger_targets,
     select_ledger_targets_from_jsonl,
+    selector_field_is_supported,
     target_spec_from_ledger_reference,
 )
 from microcosm.calibrate import (
@@ -4945,3 +4947,177 @@ def test__given_a_malformed_window_reference__then_it_is_refused(
 ) -> None:
     with pytest.raises(ValueError, match=message):
         _cy_window_reference(**overrides)
+
+
+def _per_year_measure_facts():
+    # DWP's benefit expenditure tables give each fiscal year its own measure.
+    return [
+        _cy_window_fact(
+            key=f"{measure}_{year}",
+            value=value,
+            opening_year=year,
+            measure_id=f"{measure}_{year}",
+            assertion="observation" if year < 2025 else "source_projection",
+            period_type="fiscal_year",
+        )
+        for measure, values in (
+            ("expenditure", {2023: 6.25, 2024: 6.85, 2025: 7.11, 2026: 7.27}),
+            ("caseload", {2024: 1.104, 2025: 1.109}),
+        )
+        for year, value in values.items()
+    ]
+
+
+_PER_YEAR_MEASURE_SELECTOR = {
+    "source_name": "irs_soi",
+    "geography_level": "country",
+    "geography_id": "0100000US",
+    "source_measure_id_by_opening_year": {
+        "2024": "expenditure_2024",
+        "2025": "expenditure_2025",
+    },
+}
+
+
+def test__given_a_year_to_measure_map__then_calendar_year_window_reads_one_series() -> (
+    None
+):
+    registry = compile_ledger_target_references(
+        _per_year_measure_facts(),
+        [_cy_window_reference(ledger_selector=_PER_YEAR_MEASURE_SELECTOR)],
+        country="uk",
+    )
+
+    spec = registry.specs[0]
+    assert spec.value == pytest.approx(0.25 * 6.85 + 0.75 * 7.11)
+    assert spec.metadata["ledger_calendar_year_window_series"] == "1"
+    assert spec.metadata["ledger_value_formula"] == (
+        "3/12 * FY2024 + 9/12 * FY2025 (the months to the end of calendar year 2025)"
+    )
+    members = json.loads(spec.metadata["ledger_calendar_year_window_members"])
+    assert {year: member["fact_key"] for year, member in members.items()} == {
+        "2024": "ledger.aggregate_fact.v2:expenditure_2024",
+        "2025": "ledger.aggregate_fact.v2:expenditure_2025",
+    }
+    assert spec.metadata["ledger_resolved_assertion"] == "source_projection"
+
+
+def test__given_per_year_measures_without_the_map__then_calendar_year_window_refuses() -> (
+    None
+):
+    # Each year's measure is its own series, so neither carries both years.
+    selector = {
+        key: value
+        for key, value in _PER_YEAR_MEASURE_SELECTOR.items()
+        if key != "source_measure_id_by_opening_year"
+    }
+
+    with pytest.raises(ValueError, match="from one series; found \\[2024, 2025\\]"):
+        compile_ledger_target_references(
+            _per_year_measure_facts(),
+            [_cy_window_reference(ledger_selector=selector)],
+            country="uk",
+        )
+
+
+def test__given_a_year_to_measure_map__then_a_fact_matches_only_its_own_year() -> None:
+    facts = {
+        (fact["layout"]["measure_id"], fact["period"]["value"]): fact
+        for fact in _per_year_measure_facts()
+    }
+    # A measure stamped at another year matches no entry of the map.
+    misdated = {
+        **facts[("expenditure_2025", 2025)],
+        "period": {
+            "type": "fiscal_year",
+            "value": 2024,
+        },
+    }
+
+    assert selector_field_is_supported("source_measure_id_by_opening_year")
+    assert fact_matches_selector(
+        facts[("expenditure_2024", 2024)], _PER_YEAR_MEASURE_SELECTOR
+    )
+    assert fact_matches_selector(
+        facts[("expenditure_2025", 2025)], _PER_YEAR_MEASURE_SELECTOR
+    )
+    assert not fact_matches_selector(misdated, _PER_YEAR_MEASURE_SELECTOR)
+    assert not fact_matches_selector(
+        facts[("expenditure_2026", 2026)], _PER_YEAR_MEASURE_SELECTOR
+    )
+    assert not fact_matches_selector(
+        facts[("caseload_2025", 2025)], _PER_YEAR_MEASURE_SELECTOR
+    )
+
+
+@pytest.mark.parametrize(
+    ("overrides", "message"),
+    [
+        (
+            {"source_measure_id": "expenditure_2025"},
+            "declares both 'source_measure_id'",
+        ),
+        (
+            {
+                "source_measure_id_by_opening_year": {
+                    "FY2024": "expenditure_2024",
+                    "2025": "expenditure_2025",
+                }
+            },
+            "four-digit opening year",
+        ),
+        ({"source_measure_id_by_opening_year": {}}, "non-empty mapping"),
+        ({"source_concept": ["a", "b"]}, "list-valued series key"),
+    ],
+)
+def test__given_a_malformed_year_to_measure_map__then_the_window_refuses(
+    overrides, message
+) -> None:
+    with pytest.raises(ValueError, match=message):
+        _cy_window_reference(
+            ledger_selector={**_PER_YEAR_MEASURE_SELECTOR, **overrides}
+        )
+
+
+def test__given_a_map_of_other_years__then_calendar_year_window_refuses_at_resolution() -> (
+    None
+):
+    reference = _cy_window_reference(
+        ledger_selector={
+            **_PER_YEAR_MEASURE_SELECTOR,
+            "source_measure_id_by_opening_year": {
+                "2023": "expenditure_2023",
+                "2024": "expenditure_2024",
+            },
+        }
+    )
+
+    with pytest.raises(ValueError, match="reads the years opening in \\[2024, 2025\\]"):
+        compile_ledger_target_references(
+            _per_year_measure_facts(), [reference], country="uk"
+        )
+
+
+def test__given_a_mapped_window_restamped_to_another_year__then_it_defers() -> None:
+    # A compile restamps references to its own period (the 2023 parity
+    # receipt); the map then names no year the window reads, so the row has
+    # no fact at or before the period, the way any unpublished year defers.
+    reference = replace(
+        _cy_window_reference(ledger_selector=_PER_YEAR_MEASURE_SELECTOR), period=2023
+    )
+
+    with pytest.raises(ValueError, match="at or before target period"):
+        compile_ledger_target_references(
+            _per_year_measure_facts(), [reference], country="uk"
+        )
+
+
+def test__given_a_year_to_measure_map_outside_a_window__then_reference_refuses() -> (
+    None
+):
+    with pytest.raises(
+        ValueError, match="composes the years of a calendar_year_window"
+    ):
+        _cy_window_reference(
+            ledger_selector=_PER_YEAR_MEASURE_SELECTOR, value_operation="identity"
+        )
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py
index 51de977b4..9c12fdf3a 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_battery_bindings.py
@@ -60,8 +60,11 @@
 #: 2026-10-21, the same entries) so the suite never drifts across an approval
 #: or expiry boundary. Move it forward when a register gains a later approval.
 # The committed registers are evaluated as of this date; the microcosm#1063
-# c9 input-mass and QRF-tail entries take force on 2026-10-02.
-CLOCK = date(2026, 10, 4)
+# c9 input-mass and QRF-tail entries take force on 2026-10-02, the
+# microcosm#1095 West Midlands target-fit deferral on 2026-10-06 and its two
+# UC payment-band measure exclusions on 2026-10-07. The earliest expiry is
+# 2026-10-15.
+CLOCK = date(2026, 10, 7)
 
 VALIDATE_REFERENCE = (
     "microcosm.build.uk_runtime.weighted_integrity."
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_disability.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_disability.py
index d282e2e59..5b4a9fe52 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_disability.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_disability.py
@@ -48,7 +48,79 @@ def test_disability_flag_operator_asymmetry_and_afcs() -> None:
     )
 
     assert result["is_enhanced_disabled_for_benefits"].tolist() == [False, False]
-    assert result["is_severely_disabled_for_benefits"].tolist() == [True, True]
+    # DLA care at the highest rate counts; an Armed Forces Compensation Scheme
+    # payment alone does not, since FRS code 8 cannot be told apart from Armed
+    # Forces Independence Payment (pe-uk#1946, uk-data#494).
+    assert result["is_severely_disabled_for_benefits"].tolist() == [True, False]
+
+
+@pytest.mark.parametrize(
+    ("attendance_allowance", "severe"),
+    [(0.0, False), (10.0, False), (20.0, True)],
+)
+def test_severe_flag_counts_only_higher_rate_attendance_allowance(
+    attendance_allowance: float, severe: bool
+) -> None:
+    person = pd.DataFrame(
+        {
+            "attendance_allowance_reported": [attendance_allowance * WEEKS_IN_YEAR],
+            "dla_sc_reported": [0.0],
+            "dla_m_reported": [0.0],
+            "pip_m_reported": [0.0],
+            "pip_dl_reported": [0.0],
+        }
+    )
+
+    result = derive_frs_disability(
+        person, category_rates=_category_rates(), flag_rates=_flags()
+    )
+
+    assert result["is_severely_disabled_for_benefits"].tolist() == [severe]
+
+
+def test_severe_flag_is_the_tax_credit_condition_on_the_categories() -> None:
+    """Every row's flag equals the category rule, across the rate boundaries."""
+
+    rates = _category_rates()
+    offsets = (0.0, 0.5, -0.5, -0.99, -1.0, -1.01, -1.5)
+    rows = []
+    for column, levels in (
+        ("attendance_allowance_reported", (rates.aa_lower, rates.aa_higher)),
+        (
+            "dla_sc_reported",
+            (rates.dla_sc_lower, rates.dla_sc_middle, rates.dla_sc_higher),
+        ),
+        ("pip_dl_reported", (rates.pip_dl_standard, rates.pip_dl_enhanced)),
+    ):
+        for level in levels:
+            for offset in offsets:
+                row = dict.fromkeys(
+                    (
+                        "attendance_allowance_reported",
+                        "dla_sc_reported",
+                        "dla_m_reported",
+                        "pip_m_reported",
+                        "pip_dl_reported",
+                    ),
+                    0.0,
+                )
+                row[column] = (level + offset) * WEEKS_IN_YEAR
+                rows.append(row)
+    person = pd.DataFrame(rows)
+
+    result = derive_frs_disability(person, category_rates=rates, flag_rates=_flags())
+
+    expected = (
+        (result["aa_category"] == "HIGHER")
+        | (result["dla_sc_category"] == "HIGHER")
+        | (result["pip_dl_category"] == "ENHANCED")
+    )
+    assert result["is_severely_disabled_for_benefits"].tolist() == expected.tolist()
+    # Not vacuous: middle-rate DLA care and standard PIP daily living rows
+    # are present, and the flag takes both values.
+    assert (result["dla_sc_category"] == "MIDDLE").any()
+    assert (result["pip_dl_category"] == "STANDARD").any()
+    assert expected.any() and not expected.all()
 
 
 @pytest.mark.parametrize("operation_index", [0, 1])
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_frs_spine.py
index 88a55f868..e6a44747b 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
@@ -42,8 +42,12 @@
     WEEKS_IN_YEAR,
     UKFRSSpineStageTransform,
     _add_benefits,
+    access_fund_annual,
     build_uk_frs_spine_frame,
+    completed_months,
+    parse_frs_uc_claim_start,
     scottish_water_and_sewerage_weekly,
+    uc_trade_years,
     uk_frs_spine_seed_frame,
 )
 from microcosm.build.uk_runtime.national_frame import (
@@ -61,6 +65,10 @@
 _TOOL_PATH = Path(spine_build.__file__)
 
 
+#: The fixture households' interview date, 15 October 2024, as a SAS date.
+_FIXTURE_INTERVIEW_SAS_DATE = 23664
+
+
 def _load_tool():
     spec = importlib.util.spec_from_file_location("build_uk_frs_spine", _TOOL_PATH)
     assert spec is not None
@@ -182,6 +190,10 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
         "MORTINT": 7.0,
         "STRUINS": 8.0,
         **{f"CHRGAMT{i}": float(i) for i in range(1, 10)},
+        # 15 October 2024 as a SAS date (days since 1 January 1960).
+        "INTDATE": float(_FIXTURE_INTERVIEW_SAS_DATE),
+        # A conventional household (HHSTAT 1), not a shared one.
+        "HHSTAT": 1,
     }
     household_1 = {
         **household_2,
@@ -229,6 +241,11 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
         "R02": 7,
         "MARITAL": 1,
         "EMPSTATI": 5,
+        "SAMESIT": 1,
+        **{f"SDEMP{month:02d}": 5 for month in range(1, 13)},
+        "SRENTAMT": "",
+        # CVPAY below is rent paid to the householder as a lodger (CONVBL 2).
+        "CONVBL": 2,
         "MJOBSECT": 1,
         "SIC": 84,
         "FTED": 2,
@@ -264,6 +281,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
         "SMPADJ": 0.5,
         "TUBORR": 500.0,
         "ACCSSAMT": 1.0,
+        "ACCSSPD": 52.0,
         "GRTDIR1": 2.0,
         "GRTDIR2": 3.0,
         # heartval is on the adult tape too; the three school columns are not.
@@ -313,6 +331,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "FAMTYPB2": 5,
                 "DEPCHLDB": 0,
                 "TOTCAPB4": 222.0,
+                "HBOTHAMT": 0.0,
             },
             {
                 "SERNUM": 1,
@@ -320,6 +339,7 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "FAMTYPB2": 7,
                 "DEPCHLDB": 1,
                 "TOTCAPB4": 111.0,
+                "HBOTHAMT": 0.0,
             },
         ],
         "househol": [household_2, household_1],
@@ -374,6 +394,11 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "DEDUC1": 2.0,
                 "SPNAMT": 3.0,
                 "SALSAC": "1",
+                "ETYPE": 1,
+                "JOBTYPE": 1,
+                "SEEND": "",
+                "SEJBLONG": "",
+                "JOBBUS": 1,
             }
         ],
         "benefits": [
@@ -384,6 +409,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 14,
                 "VAR2": 1,
                 "BENAMT": 2.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
             {
                 "SERNUM": 1,
@@ -392,6 +419,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 14,
                 "VAR2": 2,
                 "BENAMT": 3.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
             {
                 "SERNUM": 1,
@@ -400,6 +429,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 16,
                 "VAR2": 3,
                 "BENAMT": 4.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
             {
                 "SERNUM": 1,
@@ -408,6 +439,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 16,
                 "VAR2": 4,
                 "BENAMT": 5.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
             {
                 "SERNUM": 1,
@@ -416,6 +449,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 6,
                 "VAR2": 0,
                 "BENAMT": 6.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
             {
                 "SERNUM": 1,
@@ -424,6 +459,8 @@ def _fixture_tables() -> dict[str, list[dict[str, object]]]:
                 "BENEFIT": 3,
                 "VAR2": 0,
                 "BENAMT": 7.0,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             },
         ],
         "maint": [
@@ -959,6 +996,7 @@ def test_manifest_stage_and_runtime_agree_on_artifacts_and_operations() -> None:
         "map_columns",
         "map_coded_amounts",
         "annualize_periodic_amounts",
+        "assign_binary_from_rate",
     }
     assert set(stage.outputs) == set(UKFRSSpineStageTransform.output_columns())
 
@@ -1021,6 +1059,24 @@ def test_root_stage_reports_capital_sentinel_mapping_count(tmp_path: Path) -> No
     }
 
 
+def test_root_stage_reports_the_access_fund_repair(tmp_path: Path) -> None:
+    # Both fixture adults report an annual-coded award of 1 a week, so the
+    # threshold is twice that, annualised, and nothing is repaired.
+    stage = _write_fixture(tmp_path)
+    transform = UKFRSSpineStageTransform(tmp_path, stage=stage)
+
+    transform(uk_frs_spine_seed_frame())
+
+    assert transform.checkpoint_metadata()["evidence"]["access_fund"] == {
+        "paid_awards": 2,
+        "annual_coded_awards": 2,
+        "calendar_month_awards": 0,
+        "repair_multiple": 2.0,
+        "repair_threshold_annual": pytest.approx(2 * WEEKS_IN_YEAR),
+        "repaired_awards": 0,
+    }
+
+
 def test_benunit_capital_maps_unavailable_raw_values_to_named_sentinel(
     tmp_path: Path,
 ) -> None:
@@ -1071,8 +1127,12 @@ def test_direct_person_mapping_values_are_ported(tmp_path: Path) -> None:
     assert adult["tax_free_savings_income"] == pytest.approx(1 * WEEKS_IN_YEAR)
     assert adult["savings_interest_income"] == pytest.approx(3.5 * WEEKS_IN_YEAR)
     assert adult["dividend_income"] == pytest.approx(3 * WEEKS_IN_YEAR)
-    # ROYYR1 2 only: CVPAY is not income and household 1 does not sub-let.
+    # ROYYR1 2 only: CVPAY (1 a week) is rent the adult pays as a lodger
+    # (rent_paid_as_lodger, pe-uk#2006), not their own income, and household 1
+    # does not sub-let.
     assert adult["property_income"] == pytest.approx(2 * WEEKS_IN_YEAR)
+    assert adult["rent_paid_as_lodger"] == pytest.approx(WEEKS_IN_YEAR)
+    assert adult["rent_paid_as_boarder"] == 0
     assert adult["maintenance_income"] == pytest.approx(3 * WEEKS_IN_YEAR)
     assert adult["miscellaneous_income"] == pytest.approx(41 * WEEKS_IN_YEAR)
     assert adult["private_transfer_income"] == pytest.approx(57 * WEEKS_IN_YEAR)
@@ -1169,6 +1229,177 @@ def test_household_subrent_column_floors_missing_value_codes(
     assert household.loc[2, "subrent"] == pytest.approx(6 * WEEKS_IN_YEAR)
 
 
+def test_is_blind_reads_blind_registration_on_the_adult_and_child_tabs(
+    tmp_path: Path,
+) -> None:
+    """``is_blind`` is SPCREG1 registration, never partial sight (uk-data#523)."""
+
+    tables = _fixture_tables()
+    adult_1 = next(row for row in tables["adult"] if row["SERNUM"] == 1)
+    adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2)
+    # Registered blind; registered partially sighted only; and the child tab
+    # asks the same registration question.
+    adult_1.update({"SPCREG1": 1, "SPCREG2": 2})
+    adult_2.update({"SPCREG1": 2, "SPCREG2": 1})
+    tables["child"][0]["SPCREG1"] = 1
+    stage = _write_fixture(tmp_path / "registered", tables)
+
+    person = build_uk_frs_spine_frame(tmp_path / "registered", stage=stage).table(
+        "person"
+    )
+    blind = person.set_index("person_id")["is_blind"]
+
+    assert blind.dtype == bool
+    assert blind.to_dict() == {1001: True, 1002: True, 2001: False}
+
+    # A person the question was not asked of (blank SPCREG1) is not blind.
+    stage = _write_fixture(tmp_path / "unasked")
+    person = build_uk_frs_spine_frame(tmp_path / "unasked", stage=stage).table("person")
+    assert not person["is_blind"].any()
+
+
+def test_person_types_follow_the_adult_and_child_tables(tmp_path: Path) -> None:
+    """Claimant or partner is the adult table, HBAI dependent child the child
+    table (uk-data#524, uk-data#486), whatever the ages say."""
+
+    tables = _fixture_tables()
+    # A 19-year-old on the child table stays a dependent child, and a
+    # 17-year-old partner on the adult table is a claimant's partner.
+    tables["child"][0]["AGE"] = 19
+    tables["adult"].append(
+        {
+            **tables["adult"][0],
+            "PERSON": 2,
+            "UPERSON": 2,
+            "HRPID": 0,
+            "AGE": 17,
+            "MARITAL": 2,
+            "RELHRP": 2,
+            "R01": 2,
+            "R02": "",
+        }
+    )
+    stage = _write_fixture(tmp_path, tables)
+
+    person = (
+        build_uk_frs_spine_frame(tmp_path, stage=stage)
+        .table("person")
+        .set_index("person_id")
+    )
+
+    assert person["is_claimant_or_partner"].to_dict() == {
+        1001: True,
+        1002: False,
+        2001: True,
+        2002: True,
+    }
+    assert (
+        person["is_hbai_dependent_child"] == ~person["is_claimant_or_partner"]
+    ).all()
+    assert person["is_claimant_or_partner"].dtype == bool
+
+
+def test_a_benefit_unit_with_three_adult_records_refuses(tmp_path: Path) -> None:
+    tables = _fixture_tables()
+    for number in (2, 3):
+        tables["adult"].append(
+            {**tables["adult"][0], "PERSON": number, "UPERSON": number, "HRPID": 0}
+        )
+    stage = _write_fixture(tmp_path, tables)
+
+    # The claimant mask refuses it, so adult-table membership is always a
+    # single claimant or a couple (uk-data#524).
+    with pytest.raises(ValueError, match="one or two claimants per benefit unit"):
+        build_uk_frs_spine_frame(tmp_path, stage=stage)
+
+
+def _shared_household_tables(
+    *, hhstat: int, srentamt: object = "", hbothamt: float = 0.0, uchousel: object = ""
+) -> dict[str, list[dict[str, object]]]:
+    """Household 2 with a second benefit unit (its own adult) beside the first."""
+
+    tables = _fixture_tables()
+    household_2 = next(row for row in tables["househol"] if row["SERNUM"] == 2)
+    household_2["HHSTAT"] = hhstat
+    adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2)
+    tables["adult"].append(
+        {
+            **adult_2,
+            "BENUNIT": 2,
+            "PERSON": 2,
+            "UPERSON": 1,
+            "HRPID": 0,
+            "SRENTAMT": srentamt,
+        }
+    )
+    tables["benunit"].append(
+        {
+            "SERNUM": 2,
+            "BENUNIT": 2,
+            "FAMTYPB2": 5,
+            "DEPCHLDB": 0,
+            "TOTCAPB4": 50.0,
+            "HBOTHAMT": hbothamt,
+        }
+    )
+    tables["benefits"].append(
+        {**_uc_row(2, ""), "BENUNIT": 2, "PERSON": 2, "UCHOUSEL": uchousel}
+    )
+    return tables
+
+
+@pytest.mark.parametrize(
+    ("hhstat", "answers", "liable"),
+    [
+        (2, {"srentamt": 40.0}, True),
+        (2, {"hbothamt": 25.0}, True),
+        (2, {"uchousel": 60.0}, True),
+        (2, {}, False),
+        (1, {"srentamt": 40.0}, False),
+    ],
+)
+def test_a_later_unit_of_a_shared_household_shares_its_rent(
+    tmp_path: Path, hhstat: int, answers: dict, liable: bool
+) -> None:
+    """uk-data#512: shared households only, later units with rent evidence."""
+
+    tables = _shared_household_tables(hhstat=hhstat, **answers)
+    stage = _write_fixture(tmp_path, tables)
+
+    benunit = (
+        build_uk_frs_spine_frame(tmp_path, stage=stage)
+        .table("benunit")
+        .set_index("benunit_id")
+    )
+
+    assert benunit["liable_for_share_of_household_rent"].to_dict() == {
+        101: False,
+        201: False,
+        202: liable,
+    }
+
+
+@pytest.mark.parametrize(("convbl", "boarder"), [(1, True), (2, False), ("", False)])
+def test_rent_paid_to_the_householder_splits_on_meals(
+    tmp_path: Path, convbl: object, boarder: bool
+) -> None:
+    tables = _fixture_tables()
+    adult_1 = next(row for row in tables["adult"] if row["SERNUM"] == 1)
+    adult_1.update({"CVPAY": 80.0, "CONVBL": convbl})
+    stage = _write_fixture(tmp_path, tables)
+
+    adult = (
+        build_uk_frs_spine_frame(tmp_path, stage=stage)
+        .table("person")
+        .set_index("person_id")
+        .loc[1001]
+    )
+
+    paid = 80.0 * WEEKS_IN_YEAR
+    assert adult["rent_paid_as_boarder"] == pytest.approx(paid if boarder else 0.0)
+    assert adult["rent_paid_as_lodger"] == pytest.approx(0.0 if boarder else paid)
+
+
 @pytest.mark.parametrize("couple_has_children", [False, True])
 def test_uc_claimant_input_uses_frs_membership_not_age_or_marriage(
     tmp_path: Path, couple_has_children: bool
@@ -2438,6 +2669,7 @@ def test_e8_manifest_seeds_all_reach_the_build_sidecar_harvester() -> None:
         [stages[name] for name in tool._uk_spine_stage_names(spec)]
     )
 
+    assert declared["frs_spine"] == {"uc_is_in_startup_period": 0}
     assert declared["cgt_incidence_clone"] == {"cgt_prior_amount": 0}
     assert declared["nts_bus_travel"] == {"local_bus_use_band": 0}
     assert declared["was_lisa"] == {"has_lifetime_isa": 0, "lifetime_isa_balance": 0}
@@ -2704,6 +2936,209 @@ def test_retired_cells_cannot_reintroduce_the_incumbent_zeroing(self) -> None:
         assert result > 0
 
 
+class TestAccessFundAnnual:
+    """The access-fund award: ACCSSAMT annualised, with the calendar-month repair.
+
+    An access-fund award is paid per academic year or term; a calendar-month
+    award that annualises to more than twice every award the tab records as
+    annual is read as the annual award (microcosm#1095, from the review of
+    #1100).
+    """
+
+    @staticmethod
+    def _frame(rows: list[tuple[float, float]]) -> pd.DataFrame:
+        return pd.DataFrame(rows, columns=["accssamt", "accsspd"])
+
+    def test_awards_are_annualised_as_the_frs_weeklyised_them(self) -> None:
+        frame = self._frame([(10.0, 52.0), (5.0, 5.0), (0.0, float("nan"))])
+        assert access_fund_annual(frame).tolist() == pytest.approx(
+            [10.0 * WEEKS_IN_YEAR, 5.0 * WEEKS_IN_YEAR, 0.0]
+        )
+
+    def test_monthly_award_above_every_annual_award_is_read_as_annual(self) -> None:
+        # 100 a week from a calendar-month code is about 433 a month; annualised
+        # it is far above the largest annual-coded award, so it is that award.
+        frame = self._frame([(20.0, 52.0), (100.0, 5.0)])
+        result = access_fund_annual(frame)
+        assert result.iloc[0] == pytest.approx(20.0 * WEEKS_IN_YEAR)
+        assert result.iloc[1] == pytest.approx(100.0 * 52 / 12)
+
+    def test_a_monthly_award_within_twice_the_largest_annual_stays(self) -> None:
+        # Annualised, 30 a week is 1.5 times the largest annual-coded award:
+        # large for a monthly payment, but plausible, so it stays as reported.
+        frame = self._frame([(20.0, 52.0), (30.0, 5.0)])
+        assert access_fund_annual(frame).iloc[1] == pytest.approx(30.0 * WEEKS_IN_YEAR)
+
+    def test_monthly_awards_without_an_annual_award_refuse(self) -> None:
+        # The threshold is twice the tab's largest annual-coded award, so a
+        # tab with calendar-month awards and none coded annual cannot set it
+        # (microcosm#1095 review round 1): refuse rather than repair nothing.
+        frame = self._frame([(100.0, 5.0), (3.0, 5.0)])
+        with pytest.raises(ValueError, match="no annual-coded award"):
+            access_fund_annual(frame)
+
+    def test_awards_with_neither_code_need_no_threshold(self) -> None:
+        frame = self._frame([(10.0, 1.0), (5.0, float("nan"))])
+        evidence: dict[str, object] = {}
+        assert access_fund_annual(frame, evidence=evidence).tolist() == pytest.approx(
+            [10.0 * WEEKS_IN_YEAR, 5.0 * WEEKS_IN_YEAR]
+        )
+        assert evidence["repair_threshold_annual"] is None
+        assert evidence["repaired_awards"] == 0
+
+    def test_the_repair_is_recorded_as_evidence(self) -> None:
+        frame = self._frame([(20.0, 52.0), (100.0, 5.0), (30.0, 5.0), (0.0, 5.0)])
+        evidence: dict[str, object] = {}
+        access_fund_annual(frame, evidence=evidence)
+        assert evidence == {
+            "paid_awards": 3,
+            "annual_coded_awards": 1,
+            "calendar_month_awards": 2,
+            "repair_multiple": 2.0,
+            "repair_threshold_annual": pytest.approx(2 * 20.0 * WEEKS_IN_YEAR),
+            "repaired_awards": 1,
+        }
+
+    def test_no_paid_award_records_an_empty_repair(self) -> None:
+        evidence: dict[str, object] = {}
+        access_fund_annual(self._frame([(0.0, float("nan"))]), evidence=evidence)
+        assert evidence["paid_awards"] == 0
+        assert evidence["repair_threshold_annual"] is None
+
+    def test_an_award_without_a_period_code_stays_as_weeklyised(self) -> None:
+        # The 2024-25 tab carries a few awards with no period code; the FRS
+        # has weeklyised them already, and only a calendar-month code can be
+        # repaired.
+        frame = self._frame([(20.0, 52.0), (100.0, float("nan"))])
+        assert access_fund_annual(frame).iloc[1] == pytest.approx(100.0 * WEEKS_IN_YEAR)
+
+    def test_a_tab_without_the_period_column_refuses(self) -> None:
+        frame = pd.DataFrame({"accssamt": [10.0]})
+        with pytest.raises(KeyError, match="ACCSSPD"):
+            access_fund_annual(frame)
+
+
+def _uc_row(sernum: int, ucstart: str) -> dict[str, object]:
+    return {
+        "SERNUM": sernum,
+        "BENUNIT": 1,
+        "PERSON": 1,
+        "BENEFIT": 95,
+        "VAR2": 0,
+        "BENAMT": 1.0,
+        "UCSTART": ucstart,
+    }
+
+
+def _self_employed_job(
+    sernum: int, *, years: object, business: bool
+) -> dict[str, object]:
+    return {
+        "SERNUM": sernum,
+        "BENUNIT": 1,
+        "PERSON": 1,
+        "DEDUC1": 0.0,
+        "SPNAMT": 0.0,
+        "SALSAC": "2",
+        "ETYPE": 2 if business else 4,
+        "JOBTYPE": 1,
+        "SEEND": "",
+        "SEJBLONG": years,
+        "JOBBUS": 2 if business else 1,
+    }
+
+
+def _start_up_flags(tmp_path: Path, tables) -> dict[int, bool]:
+    stage = _write_fixture(tmp_path, tables)
+    person = build_uk_frs_spine_frame(tmp_path, stage=stage).table("person")
+    assert person["uc_is_in_startup_period"].dtype == bool
+    return person.set_index("person_id")["uc_is_in_startup_period"].to_dict()
+
+
+class TestUCStartUpPeriod:
+    """The UC start-up period from claim and trade start dates (uk-data#527)."""
+
+    def test_completed_months_counts_whole_calendar_months(self) -> None:
+        later = pd.Series(pd.to_datetime(["2025-03-15", "2025-03-14", "2025-03-31"]))
+        earlier = pd.Series(pd.to_datetime(["2024-03-15", "2024-03-15", "2025-02-28"]))
+
+        assert completed_months(later, earlier).tolist() == [12.0, 11.0, 1.0]
+        missing = pd.Series(pd.to_datetime([None, None, None]))
+        assert np.isnan(completed_months(later, missing)).all()
+
+    def test_claim_start_reads_month_day_year_and_refuses_other_spellings(
+        self,
+    ) -> None:
+        parsed = parse_frs_uc_claim_start(pd.Series(["03/31/2024", " ", None]))
+
+        assert parsed.iloc[0] == pd.Timestamp("2024-03-31")
+        assert parsed.iloc[1:].isna().all()
+        with pytest.raises(ValueError, match="not month/day/year"):
+            parse_frs_uc_claim_start(pd.Series(["2024-03-31"]))
+
+    def test_a_new_engagement_in_a_year_round_trade_does_not_date_it(self) -> None:
+        years = uc_trade_years(
+            np.array([0.0, 0.0, 0.0, 3.0]),
+            describes_business=np.array([False, True, False, False]),
+            self_employed_all_year=np.array([True, True, False, True]),
+        )
+
+        assert np.isnan(years[0])
+        assert years[1:].tolist() == [0.0, 0.0, 3.0]
+
+    def test_a_recent_claim_or_a_new_business_starts_the_period(
+        self, tmp_path: Path
+    ) -> None:
+        tables = _fixture_tables()
+        # Household 1: a claim three months before interview; its adult is
+        # self-employed through SEINCAM2, its child is not.
+        tables["benefits"].append(_uc_row(1, "07/15/2024"))
+        # Household 2: a claim two years old, but a business started this year.
+        tables["benefits"].append(_uc_row(2, "09/15/2022"))
+        tables["job"].append(_self_employed_job(2, years=0, business=True))
+
+        flags = _start_up_flags(tmp_path, tables)
+
+        assert flags == {1001: True, 1002: False, 2001: True}
+
+    @pytest.mark.parametrize(("business", "expected"), [(True, True), (False, False)])
+    def test_a_year_round_traders_new_self_employed_job_is_not_a_new_trade(
+        self, tmp_path: Path, business: bool, expected: bool
+    ) -> None:
+        tables = _fixture_tables()
+        adult_2 = next(row for row in tables["adult"] if row["SERNUM"] == 2)
+        adult_2.update({"EMPSTATI": 3, "SAMESIT": 2})
+        tables["benefits"].append(_uc_row(2, "09/15/2022"))
+        tables["job"].append(_self_employed_job(2, years=0, business=business))
+
+        assert _start_up_flags(tmp_path, tables)[2001] is expected
+
+    @pytest.mark.parametrize(
+        ("linked_start", "expected"), [("07/15/2024", True), ("09/15/2022", False)]
+    )
+    def test_an_unlinked_claim_is_drawn_at_the_linked_share(
+        self, tmp_path: Path, linked_start: str, expected: bool
+    ) -> None:
+        # The one linked self-employed claim sets the share to 1 or 0, so the
+        # unlinked unit's draw is certain either way.
+        tables = _fixture_tables()
+        tables["benefits"].append(_uc_row(1, linked_start))
+        tables["benefits"].append(_uc_row(2, ""))
+
+        assert _start_up_flags(tmp_path, tables)[2001] is expected
+
+    def test_without_the_raw_date_columns_the_spine_refuses(
+        self, tmp_path: Path
+    ) -> None:
+        tables = _fixture_tables()
+        for row in tables["job"]:
+            del row["SEJBLONG"]
+        stage = _write_fixture(tmp_path, tables)
+
+        with pytest.raises(KeyError, match="job.sejblong"):
+            build_uk_frs_spine_frame(tmp_path, stage=stage)
+
+
 def test_in_kind_benefits_map_from_the_raw_person_tapes(tmp_path: Path) -> None:
     """The four in-kind benefit columns, ported at #686.
 
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py
index 712c841df..c83f660a8 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_lcfs_consumption.py
@@ -15,6 +15,7 @@
     assign_recipient_has_fuel,
     clean_lcfs_consumption_table,
     derive_energy_from_lcfs,
+    recipient_predictors,
     support_clip_to_donor,
 )
 from microcosm.build.uk_runtime.national_frame import uk_national_frame
@@ -338,6 +339,7 @@ def variable_metadata(self, name):
                 "person_id": [1, 2, 3],
                 "person_benunit_id": [1, 2, 3],
                 "person_household_id": [10, 20, 30],
+                "age": [40.0, 35.0, 70.0],
             }
         ),
         benunit=pd.DataFrame({"benunit_id": [1, 2, 3]}),
@@ -722,8 +724,6 @@ def variable_metadata(self, name):
         def materialize(self, frame, variables, period):
             rows = len(frame.table("household"))
             values = {
-                "is_adult": np.full(rows, 2.0),
-                "is_child": np.zeros(rows),
                 "employment_income": rng.uniform(0.0, 5e4, rows),
                 "self_employment_income": rng.uniform(0.0, 5e3, rows),
                 "private_pension_income": rng.uniform(0.0, 1e4, rows),
@@ -881,3 +881,51 @@ class Stage:
         operations = ()
 
     assert fuel_litres_audit(draws, weights=weights, stage=Stage()) is None
+
+
+def test_recipient_counts_adults_and_children_by_age_not_engine_flags() -> None:
+    """The LCFS G018/G019 split is age 18, which the recipient counts from age.
+
+    The engine's is_adult and is_child flags are deprecated (policyengine-uk
+    #1896), so the household counts never ask the engine for them
+    (uk-data#486, microcosm#1095).
+    """
+
+    from types import SimpleNamespace
+
+    person = pd.DataFrame(
+        {
+            "person_id": [1, 2, 3, 4, 5],
+            "person_benunit_id": [1, 1, 1, 2, 2],
+            "person_household_id": [1, 1, 1, 2, 2],
+            "age": [40.0, 10.0, 70.0, 17.0, 18.0],
+        }
+    )
+    household = pd.DataFrame(
+        {
+            "household_id": [1, 2],
+            "num_vehicles": [1, 0],
+            "household_gross_income": [3e4, 2e4],
+            "household_weight": [1.0, 1.0],
+        }
+    )
+    frame = uk_national_frame(
+        person=person,
+        benunit=pd.DataFrame({"benunit_id": [1, 2]}),
+        household=household,
+        time_period="2024",
+    )
+    requested: list[str] = []
+
+    class _Engine:
+        def variable_metadata(self, name):
+            return SimpleNamespace(entity="household")
+
+        def materialize(self, frame, variables, period):
+            requested.extend(variables)
+            return {name: np.zeros(2) for name in variables}
+
+    result = recipient_predictors(frame, _Engine())
+    assert result["household_adult_count"].tolist() == [2.0, 1.0]
+    assert result["household_child_count"].tolist() == [1.0, 1.0]
+    assert not {"is_adult", "is_child"} & set(requested)
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 0b264a3f1..197e7af48 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
@@ -576,7 +576,9 @@ def test_exclusion_applier_warns_within_week_of_expiry():
     "hmrc.salary_sacrifice.": 3,
     "_1_000_000_to_inf": 10,
     "slc.": 5,
-    "dwp/uc_payment_dist/": 19,
+    # microcosm#1095 (2026-10-07): two bands whose few supporting awards
+    # policyengine-uk 2.122.2 moves out join the uk-data#452 class.
+    "dwp/uc_payment_dist/": 21,
     "obr.universal_credit_": 2,
     # microcosm#890 E1 (2026-09-11): the all-road-users obr.fuel_duties row
     # is signed out of the reference surface (target_reference_signed_
@@ -635,7 +637,7 @@ def test_exclusion_applier_warns_within_week_of_expiry():
 def test_packaged_exclusions_load():
     exclusions = load_uk_calibration_measure_exclusions()
     names = [entry["name"] for entry in exclusions]
-    assert len(names) == len(set(names)) == 62
+    assert len(names) == len(set(names)) == 64
     band_h_region_cells = [
         entry
         for entry in exclusions
@@ -717,6 +719,20 @@ def test_packaged_exclusions_load():
         assert "support" in entry["reason"]
     renewed = [e for e in today if e not in thin]
     assert sorted(e["name"] for e in renewed) == sorted(_A16_UNREACHABLE_ROWS[:2])
+    # The engine bump of microcosm#1095 (policyengine-uk 2.122.2) moves every
+    # supporting award out of two more payment bands: the published LHA rates,
+    # the 2019 mixed-age couple saving and Carer Support Payment as unearned
+    # income. They join the class with its expiry.
+    engine_moved = [e for e in exclusions if e["approved_on"] == "2026-10-07"]
+    assert sorted(e["name"] for e in engine_moved) == [
+        "dwp/uc_payment_dist/COUPLE_NO_CHILDREN_annual_payment_24_000_to_25_200",
+        "dwp/uc_payment_dist/SINGLE_annual_payment_26_400_to_27_600",
+    ]
+    for entry in engine_moved:
+        assert entry["tracking"] == "uk-data#452"
+        assert entry["expires_on"] == "2026-11-26"
+        assert "microcosm#1095" in entry["adjudication"]
+        assert "policyengine-uk 2.122.2" in entry["reason"]
     for entry in renewed:
         assert entry["expires_on"] == "2026-11-03", entry["name"]
         assert entry["tracking"] == a16_issues[entry["name"]], entry["name"]
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py
index d1ed33339..42116254d 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_national_calibration.py
@@ -53,7 +53,7 @@
 from microcosm.calibrate.artifacts import decode_problem
 from microcosm.frame import EntitySchema, Frame, MassChange, WeightKind, Weights
 
-ACTIVE_REFERENCE_COUNT = 1230
+ACTIVE_REFERENCE_COUNT = 1231
 
 
 def _uc_reference(**overrides) -> LedgerTargetReference:
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py
index 9f9957ac8..d02836f8c 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_age_benefit_targets.py
@@ -85,6 +85,37 @@ def test_pension_age_housing_benefit_reads_the_detail_tenure_cells() -> None:
         )
 
 
+def test_pension_age_housing_benefit_spending_reads_the_dwp_calendar_window() -> None:
+    target = _targets()["dwp.hb.amount_pension_age"]
+    selector = target["ledger_selector"]
+    assert selector["source_concept"] == "dwp.benefit_expenditure_amount"
+    assert selector["dimension_values"] == {
+        "benefit_forecast_line": (
+            "housing_benefits__housing_benefitover_pension_credit_qualifying_age"
+        )
+    }
+    # DWP's outturn and forecast tables give each fiscal year its own
+    # measure, so the calendar-2025 window names the measure of each year.
+    assert target["value_operation"] == "calendar_year_window"
+    assert selector["source_measure_id_by_opening_year"] == {
+        "2024": "expenditure_2024",
+        "2025": "expenditure_2025",
+    }
+    assert "source_measure_id" not in selector
+    assert target["assertion_policy"] == "allow_source_projection"
+    assert target["family"] == "dwp_benefit_expenditure"
+    binding = target["bindings"]["policyengine"]
+    assert binding["value_variable"] == "housing_benefit"
+    assert binding["filters"] == [
+        {"variable": "eldest_adult_age", "operator": ">=", "value": 66}
+    ]
+    # Great Britain, the scope of DWP's line (uk-data#490).
+    (condition,) = binding["household_conditions"]
+    assert condition["variable"] == "region"
+    assert "NORTHERN_IRELAND" not in condition["value"]
+    assert len(condition["value"]) == 11
+
+
 def test_winter_fuel_recipients_are_a_diagnostic_never_a_target() -> None:
     contract = load_uk_population_contract()
     assert "dwp.winter_fuel_payment.recipients" not in {
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py
index 26b096dde..57a0bba01 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_pension_credit_take_up.py
@@ -14,6 +14,7 @@
 from microcosm.build.uk_runtime.national_frame import uk_national_frame
 from microcosm.build.uk_runtime.pension_credit_take_up import (
     PENSION_CREDIT_ENTITLEMENT_VARIABLES,
+    PENSION_CREDIT_MIXED_AGE_SAVING,
     PENSION_CREDIT_TAKE_UP_STAGE_NAME,
     UKPensionCreditTakeUpStageTransform,
     _assert_stage_parameters,
@@ -129,7 +130,9 @@ def materialize(self, frame, variables, period):
         benunit = frame.table("benunit")
         ids = benunit["benunit_id"].to_numpy()
         # 1: Guarantee Credit; 2: Savings Credit only; 3: not entitled.
+        # Unit 2 is a mixed-age couple keeping the SI 2019/37 saving.
         return {
+            PENSION_CREDIT_MIXED_AGE_SAVING: ids == 2,
             "guarantee_credit": np.where(ids == 1, 40.0, 0.0),
             "savings_credit": np.where(ids == 2, 15.0, 0.0),
             "is_pension_credit_eligible": ids != 3,
@@ -170,7 +173,13 @@ def test_redraw_reads_engine_entitlement_once_and_rewrites_only_would_claim_pc()
         frame, engine=engine, contract=load_uk_take_up_contract()
     )
 
-    assert engine.calls == [(PENSION_CREDIT_ENTITLEMENT_VARIABLES, "2024")]
+    # One materialization at the frame's survey-year period, the saving first.
+    assert engine.calls == [
+        (
+            (PENSION_CREDIT_MIXED_AGE_SAVING, *PENSION_CREDIT_ENTITLEMENT_VARIABLES),
+            "2024",
+        )
+    ]
     after = result.frame.table("benunit")
     # Unit 3 reports Pension Credit the engine finds it is not entitled to: it
     # keeps claiming, and the receipt counts it.
@@ -184,9 +193,13 @@ def test_redraw_reads_engine_entitlement_once_and_rewrites_only_would_claim_pc()
             result.frame.table(entity), frame.table(entity), check_exact=True
         )
     pd.testing.assert_frame_equal(
-        after.drop(columns=["would_claim_pc"]),
+        after.drop(columns=["would_claim_pc", PENSION_CREDIT_MIXED_AGE_SAVING]),
         frame.table("benunit").drop(columns=["would_claim_pc"]),
     )
+    # The saving is stored as the engine's survey-year default; the
+    # entitlement it fed stays consumed.
+    assert after[PENSION_CREDIT_MIXED_AGE_SAVING].tolist() == [False, True, False]
+    assert after[PENSION_CREDIT_MIXED_AGE_SAVING].dtype == bool
     for name in PENSION_CREDIT_ENTITLEMENT_VARIABLES:
         assert name not in after
     evidence = result.evidence()
@@ -201,7 +214,7 @@ def test_committed_manifest_declares_the_stage_and_its_rewrite() -> None:
     stage = _stage()
 
     assert stage.grain == "benunit"
-    assert stage.outputs == ()
+    assert stage.outputs == (PENSION_CREDIT_MIXED_AGE_SAVING,)
     assert stage.rewrites == ("would_claim_pc",)
     _assert_stage_parameters(stage)
     transform = UKPensionCreditTakeUpStageTransform(stage=stage, engine=_StubEngine())
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py
index 49fe9fd5f..2a0863515 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_population_targets.py
@@ -40,7 +40,13 @@
     "parameter_gated_threshold",
     "baseline_flag_crosstab",
 }
-PROJECTION_FAMILIES = {"obr", "slc_borrowers", "scotgov_social_security", "hmrc_itl"}
+PROJECTION_FAMILIES = {
+    "obr",
+    "slc_borrowers",
+    "scotgov_social_security",
+    "hmrc_itl",
+    "dwp_benefit_expenditure",
+}
 NATIONAL_SELECTOR_KEYS = {
     "aggregate_fact_key",
     "source_name",
@@ -54,6 +60,7 @@
     "period_type",
     "period_value",
     "layout_groupby_value_id",
+    "source_measure_id_by_opening_year",
 }
 LOCAL_SELECTOR_KEYS = {"source_name", "source_measure_id", "record_set_spec_id"}
 POLICYENGINE_BINDING_KEYS = {
@@ -194,8 +201,9 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None:
     # three pension-age Housing Benefit caseload targets; the four Table 3.8
     # contribution targets and the DWP employee total leave the fit again (their
     # relief-mechanism and gross definitions do not match the engine's columns;
-    # microcosm#1069 c9 arm).
-    assert len(resource["targets"]) == 391
+    # microcosm#1069 c9 arm); microcosm#1095 adds DWP's Great Britain
+    # pension-age Housing Benefit spending.
+    assert len(resource["targets"]) == 392
 
     providers = resource["hierarchy"]["providers"]
     categories = resource["hierarchy"]["categories"]
@@ -209,10 +217,10 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None:
     target_ids = [target["target_id"] for target in resource["targets"]]
     registry_scope = resource["registry_parity"]["scope_target_ids"]
     profile_scope = resource["profile_parity"]["scope_target_ids"]
-    assert len(registry_scope) == 340
+    assert len(registry_scope) == 341
     assert len(profile_scope) == 51
-    assert target_ids[:340] == registry_scope
-    assert target_ids[340:] == profile_scope
+    assert target_ids[:341] == registry_scope
+    assert target_ids[341:] == profile_scope
 
     parity = resource["registry_parity"]
     assert parity["pinned_ref"] == "12a1e028afeef08d8b2d74ee03fd9de3a78b2dd3"
@@ -226,7 +234,7 @@ def test_uk_population_targets_shape_order_and_registry_accounting() -> None:
     assert mapped_target_ids | set(unmapped_declarations) == set(registry_scope)
     assert all(reason for reason in unmapped_declarations.values())
     assert len(mapped_target_ids) == 192
-    assert len(unmapped_declarations) == 148
+    assert len(unmapped_declarations) == 149
     suppressed_ancestors = parity["suppressed_ancestors"]
     assert len(suppressed_ancestors) == 5
     assert set(suppressed_ancestors).isdisjoint(parity["mapped"])
@@ -345,7 +353,7 @@ def test_uk_population_targets_have_unique_target_ids() -> None:
     resource = _load()
 
     target_ids = [target["target_id"] for target in resource["targets"]]
-    assert len(target_ids) == 391
+    assert len(target_ids) == 392
     assert len(target_ids) == len(set(target_ids))
 
 
@@ -813,7 +821,7 @@ def test_uc_payment_bands_share_administrative_family_but_keep_source_window() -
 
 def test_paid_joint_diagnostics_do_not_add_active_targets() -> None:
     targets = _load()["targets"]
-    assert len(targets) == 391
+    assert len(targets) == 392
     assert not any(
         f.get("variable") == "uc_calibration_child_entitlement"
         for target in targets
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 1c37cadb0..649b85302 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_spi_income.py
@@ -94,6 +94,36 @@ def test_spi_carer_take_up_refresh_follows_the_refilled_receipt() -> None:
     )
 
 
+def test_spi_uc_start_up_period_holds_only_while_the_row_stays_self_employed() -> None:
+    """A redrawn row keeps its donor's start-up period only while self-employed."""
+
+    person = pd.DataFrame(
+        {
+            "employment_status": [
+                "FT_EMPLOYED",
+                "FT_SELF_EMPLOYED",
+                "FT_EMPLOYED",
+                "FT_EMPLOYED",
+            ],
+            # Row 20 keeps a self-employed status, row 30 keeps imputed
+            # self-employment income, row 40 is a base row whose flag stays.
+            "self_employment_income": [0.0, 0.0, 800.0, 0.0],
+            "uc_is_in_startup_period": [True, True, True, True],
+        },
+        index=[10, 20, 30, 40],
+    )
+    spi_people = pd.Series([True, True, True, False], index=person.index)
+
+    result = spi_income._refresh_uc_start_up_period(person, spi_people=spi_people)
+
+    assert result["uc_is_in_startup_period"].tolist() == [False, True, True, True]
+    assert result["uc_is_in_startup_period"].dtype == bool
+    bare = pd.DataFrame({"employment_status": ["FT_EMPLOYED"]}, index=[1])
+    assert "uc_is_in_startup_period" not in spi_income._refresh_uc_start_up_period(
+        bare, spi_people=pd.Series([True], index=[1])
+    )
+
+
 def test_finite_numeric_diagnostic_names_columns_and_counts() -> None:
     frame = pd.DataFrame(
         {
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_target_references.py
index 9a0fbfaad..1bcf8666c 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
@@ -64,7 +64,7 @@
 
 _TEST_PATHS = paths_for("microcosm-build")
 
-ACTIVE_REFERENCE_COUNT = 1230
+ACTIVE_REFERENCE_COUNT = 1231
 REGION_TIER_LEVEL = {code: level for level, code in UK_REGION_TIER}
 UK_DATA_REPO = "policyengine-" + "uk-data"
 
@@ -750,7 +750,7 @@ def test_uk_target_reference_membership_report_is_packaged() -> None:
     assert membership["target_period"] == 2025
     assert membership["active_reference_count"] == ACTIVE_REFERENCE_COUNT
     assert membership["status_counts"] == {
-        "active": 1230,
+        "active": 1231,
         "no_fact_at_or_before_period": 7,
         "signed_excluded": 16,
     }
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 a9637942d..f7d96a543 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
@@ -545,8 +545,22 @@ def test_committed_target_fit_register_retains_only_live_deferrals() -> None:
     # is retired with the microcosm#1095 ports: with FRS respondents keeping
     # their reported dividends and SPI draws going to claimants and partners
     # only, the cell fits at +11.7 % on the rebased head, and the gate fails
-    # the deferral as stale.
-    assert register == {}
+    # the deferral as stale. The West Midlands 12,570-15,000 cell is deferred
+    # on the second microcosm#1095 ports (2026-10-06, four weeks): the
+    # regional cells slice on the engine's total_income, which leaves out the
+    # other investment income that income tax charges, and on policyengine-uk
+    # 2.122.2 the solver's pull on the cell stops at +27.4 %. The fix is
+    # upstream in policyengine-uk's total_income (pe-uk#2174).
+    assert set(register) == {
+        "hmrc.spi_region.income_tax_by_region_12570_15000@E12000005@2025"
+    }
+    deferral = register[
+        "hmrc.spi_region.income_tax_by_region_12570_15000@E12000005@2025"
+    ]
+    assert deferral.approved_on == "2026-10-06"
+    assert deferral.expires_on == "2026-11-03"
+    assert "microcosm#1095" in deferral.adjudication
+    assert "pe-uk#2174" in deferral.reason
 
 
 # Aggregate errors from the fresh UC #882 development run: 1,500 epochs with
@@ -571,7 +585,7 @@ def test_restored_fit_checks_leave_empty_payment_tail_cells_blocked() -> None:
             **empty_tail,
         },
         reviewed_exclusions=uk_default_target_fit_reviewed_exclusions(),
-        now=date(2026, 10, 2),
+        now=date(2026, 10, 6),
     )
 
     assert not fit.passed
@@ -593,7 +607,7 @@ def test_restored_fit_checks_apply_if_a_later_run_breaches_again(
     fit = uk_target_fit_gate(
         {name: relative_error},
         reviewed_exclusions=uk_default_target_fit_reviewed_exclusions(),
-        now=date(2026, 10, 2),
+        now=date(2026, 10, 6),
     )
 
     assert fit.passed is passes
@@ -608,7 +622,7 @@ def test_observed_liability_has_no_retired_cash_exemption() -> None:
     fit = uk_target_fit_gate(
         {"hmrc.cgt.liability_total@2025": 0.30},
         reviewed_exclusions=register,
-        now=date(2026, 10, 2),
+        now=date(2026, 10, 6),
     )
     assert not fit.passed
     assert fit.details["failing_targets"] == {"hmrc.cgt.liability_total@2025": 0.30}
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py
index 937062935..c496b7704 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_capital_coherence.py
@@ -24,7 +24,7 @@ def test_manifest_declares_exact_redraw_seed_and_output() -> None:
     assert redraw.parameters["investment_income_band_edges"] == list(
         UC_CAPITAL_INCOME_BAND_EDGES
     )
-    assert stage.outputs == ("uc_reported_capital",)
+    assert stage.outputs == ("uc_reported_capital", "pension_credit_reported_capital")
     assert stage.rewrites == ("frs_benunit_capital", "would_claim_uc")
 
 
@@ -88,6 +88,11 @@ def test_or_refresh_truth_table_and_same_capital_source() -> None:
     np.testing.assert_array_equal(
         benunit["uc_reported_capital"], benunit["frs_benunit_capital"]
     )
+    # Pension Credit reads the same recorded capital, the unavailable
+    # sentinel included (pe-uk#2018, uk-data#513).
+    np.testing.assert_array_equal(
+        benunit["pension_credit_reported_capital"], benunit["frs_benunit_capital"]
+    )
     assert result.refreshed_would_claim_count == 4
 
 
diff --git a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py
index 4bfe15d9d..725768878 100644
--- a/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py
+++ b/packages/microcosm-build/tests/engine_free/uk/test_uk_uc_family.py
@@ -349,16 +349,18 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case
     ]
     full_registry = TargetRegistry(specs, country="uk")
     active = [spec for spec in specs if spec.name not in excluded]
-    # 81 active / 19 excluded: the 2026-09-21 A16 retirements returned the
+    # 79 active / 21 excluded: the 2026-09-21 A16 retirements returned the
     # LONE_PARENT 10,800-12,000 payment-band row (measured inside the fence
     # on the v20 national build), and microcosm#1063 excluded the two thin
     # cells whose one or two supporting households left the band when the
     # larger donor block reordered the rows the later positional draws read
     # (SINGLE 28,800-30,000 and COUPLE_NO_CHILDREN 22,800-24,000, the
-    # uk-data#452 class).
+    # uk-data#452 class). microcosm#1095 excluded two more whose few
+    # supporting awards policyengine-uk 2.122.2 moves out (SINGLE
+    # 26,400-27,600 and COUPLE_NO_CHILDREN 24,000-25,200).
     assert len(specs) == 100
-    assert len(active) == 81
-    assert len({s.name for s in specs} & excluded) == 19
+    assert len(active) == 79
+    assert len({s.name for s in specs} & excluded) == 21
     registry = TargetRegistry(active, country="uk")
     result = materialize_uk_ledger_targets(
         adapter, registry, period=2025, band_edge_registry=full_registry
@@ -368,7 +370,7 @@ def test_full_uc_payment_registry_matches_independent_relationship_and_band_case
         adapter.to_frame(), registry.to_target_set(), weight_entity="household"
     )
     assert not problem.skipped
-    assert len(problem.names) == 81
+    assert len(problem.names) == 79
     assert not {f"{name}@2025" for name in excluded}.intersection(problem.names)
     expected_family = np.asarray(expected_families)[bu.benunit_id.to_numpy()]
     uc = bu.universal_credit.to_numpy()
diff --git a/packages/microcosm-data/pyproject.toml b/packages/microcosm-data/pyproject.toml
index ac747255a..1ce772267 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>=2.98.0"]
+uk = ["policyengine-uk>=2.122.2"]
 
 [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 883e83e51..32dee6aa3 100644
--- a/packages/microcosm-data/src/microcosm/data/contract.py
+++ b/packages/microcosm-data/src/microcosm/data/contract.py
@@ -416,13 +416,13 @@ def compatibility_claim_declarer_error(declared_by: object) -> str | None:
 # fingerprint derives from the manifest digest. Editing the spec moves all
 # three here in the same reviewed change.
 _UK_GATE_BATTERY_POLICY_SHA256 = (
-    "49bac274b87f743371a78f1bea6b19b703cb5eceb2efcfdea483c41fddcfc63d"
+    "6be9d4f71cc00815c9ad2c93f0d58f42d7c3ba6417cb8a2ab31ad9bf4c8f73ea"
 )
 _UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = (
-    "dd9a99f66bba9dd9e4a0eee3b506d6fa8e2119db7e61178b21a034e6e4d82173"
+    "8e635a5f341d304b87f277a68c15e1069972836c8858ed69115b972bf6d70b67"
 )
 _UK_GATE_BATTERY_SPEC_FINGERPRINT = (
-    "f1b1fb935fde60d0d0febbda70fee1c7fb88310d1ee3cffbdb1d59410ddbb35c"
+    "fea3ba9ad91bd163cd08542872e1bb7627716cb6e4579f6f87de5c8cf83b5eea"
 )
 #: Spec entry id -> the legacy gate name whose observable detail checks
 #: apply unchanged (the battery re-keys the report by entry id; the gate
@@ -867,10 +867,10 @@ def _uk_line_cut_tag_re(release_id: str) -> re.Pattern[str] | None:
     },
     "release_cut": {
         "gates_manifest_sha256": (
-            "70a0add2714853b18c9e7e75efe051a17c4e5e3c2ae0351943a9b434802e00b7"
+            "b08c43d431859f03845327f5a321047adb32e74e78035a5fceca35346e5234f6"
         ),
         "policy_sha256": (
-            "1201c699695cbd7a3d5faf54e55e6557a50a094245013aa928e5b57fdc94fc00"
+            "5711e4f07003aa548de9a1c9b67e3949bc99294a4a7302f956735983f8b67d4c"
         ),
     },
 }
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 b1e037b65..feb52a889 100644
--- a/packages/microcosm-data/tests/engine_free/shared/test_contract.py
+++ b/packages/microcosm-data/tests/engine_free/shared/test_contract.py
@@ -127,13 +127,13 @@ def _trusted_terminal_gate_signing_key(monkeypatch) -> None:
 UK_GATE_BATTERY_PRODUCER = "microcosm.build.gate_battery"
 UK_GATE_BATTERY_SIGNING_KEY_ENV = "MICROCOSM_UK_TERMINAL_GATE_SIGNING_KEY"
 UK_GATE_BATTERY_POLICY_SHA256 = (
-    "49bac274b87f743371a78f1bea6b19b703cb5eceb2efcfdea483c41fddcfc63d"
+    "6be9d4f71cc00815c9ad2c93f0d58f42d7c3ba6417cb8a2ab31ad9bf4c8f73ea"
 )
 UK_GATE_BATTERY_GATES_MANIFEST_SHA256 = (
-    "dd9a99f66bba9dd9e4a0eee3b506d6fa8e2119db7e61178b21a034e6e4d82173"
+    "8e635a5f341d304b87f277a68c15e1069972836c8858ed69115b972bf6d70b67"
 )
 UK_GATE_BATTERY_SPEC_FINGERPRINT = (
-    "f1b1fb935fde60d0d0febbda70fee1c7fb88310d1ee3cffbdb1d59410ddbb35c"
+    "fea3ba9ad91bd163cd08542872e1bb7627716cb6e4579f6f87de5c8cf83b5eea"
 )
 UK_GATE_BATTERY_DEGENERATE_EVIDENCE_SHA256 = (
     "6f0243bcda09dad26945376230c44ec3cf55d4e417c3a25e29bae8c59bc1a69d"
diff --git a/packages/microcosm-frame/pyproject.toml b/packages/microcosm-frame/pyproject.toml
index fef74475a..0eda37f1a 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>=2.98.0"]
+uk = ["policyengine-uk>=2.122.2"]
 # 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_concepts.py b/packages/microcosm-frame/src/microcosm/frame/adapters/policyengine_uk_concepts.py
index 97fe4bf00..b86e95299 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
@@ -32,7 +32,7 @@
 # ---------------------------------------------------------------------------
 #
 # Which PolicyEngine-UK inputs each engine-neutral concept feeds
-# (microcosm.frame.concepts), reviewed against policyengine-uk 2.100.0. Every
+# (microcosm.frame.concepts), reviewed against policyengine-uk 2.122.2. 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``);
@@ -115,10 +115,13 @@
         "The UK build does not set it, so the engine default (True) applies.",
     ),
     "child_benefit": (
-        "Whether this benefit unit would claim Child Benefit if eligible. "
-        "Generated stochastically in the dataset using take-up rates.",
+        "Whether this benefit unit would claim Child Benefit if eligible, "
+        "including registered claimants who opt out of payment. ... Datasets "
+        "must export claims rather than claims excluding opt-outs.",
         "The UK build forces reported recipients to take up and draws the rest "
-        "(frs_take_up.py); a threshold alone has no such anchor.",
+        "(frs_take_up.py), and on an engine with opt-outs the Child Benefit "
+        "redraw exports registered claims, opt-outs included "
+        "(child_benefit_take_up.py); a threshold alone has no such anchor.",
     ),
     "extended_childcare": (
         "Whether this family would claim extended childcare entitlement if eligible",
@@ -182,7 +185,7 @@ def _uk_take_up(program: str, entity: str) -> InputBinding:
 
 POLICYENGINE_UK_CONCEPT_MAPPING = ConceptMapping(
     engine="policyengine-uk",
-    engine_version="2.100.0",
+    engine_version="2.122.2",
     entity_correspondence={"person": "person", "household": "household"},
     input_declaration=InputDeclaration.ENGINE_TYPED,
     bindings=(
@@ -412,11 +415,12 @@ def _uk_take_up(program: str, entity: str) -> InputBinding:
             TakeUpThreshold(program="uk.child_benefit_opt_out"),
             AlignmentRelation.APPROXIMATE,
             (
-                "Engine documentation: 'Whether this family would opt out of "
-                "receiving Child Benefit payments. Generated stochastically in "
-                "the dataset using opt-out rates.' (default False). True when "
-                "the opt-out draw falls below the opt-out rate; the unit takes "
-                "its reference member's draw."
+                "Engine documentation: 'Whether a family would opt out of Child "
+                "Benefit payments because of the High Income Child Benefit "
+                "Charge. This is independent of would_claim_child_benefit and "
+                "never identifies a claim by itself. ...' (default False). True "
+                "when the opt-out draw falls below the opt-out rate; the unit "
+                "takes its reference member's draw."
             ),
             group_rule=GroupRule.REFERENCE_MEMBER,
         ),
@@ -509,20 +513,28 @@ def _uk_take_up(program: str, entity: str) -> InputBinding:
             "('Household liquid savings'), corporate_wealth ('Wealth held in "
             "corporations directly or through investment funds'), "
             "gross_financial_wealth and net_financial_wealth are household "
-            "inputs. Its one person-level GBP stock input is "
-            "student_loan_balance, a debt, and no transform puts a person "
-            "amount on a household input. savings and corporate_wealth are two "
-            "of Universal Credit's capital sources "
-            "(gov.dwp.universal_credit.means_test.capital.sources). "
+            "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. savings and corporate_wealth are two of "
+            "Universal Credit's capital sources "
+            "(gov.dwp.universal_credit.means_test.capital.sources), and "
+            "lifetime_isa_countable_capital, the Lifetime ISA at its surrender "
+            "value, is its one person-level source (capital.person_sources). "
             "uc_reported_capital (benefit unit; 'Claimant-level capital for "
             "Universal Credit when household-level asset data cannot be "
             "attributed across multiple benefit units', default -1) is "
             "Universal Credit capital: when it is zero or more, "
-            "uc_assessable_capital takes it in place of the household-capital "
-            "proxy it otherwise sums from all of those capital sources, net of "
-            "capital other benefit units report, and apportions by benefit-unit "
-            "adults, so feeding it would be a modelling choice for a UK build, "
-            "not a mapping of this concept."
+            "uc_assessable_capital takes it in place of both the "
+            "household-capital proxy it otherwise sums from the household "
+            "sources, net of capital other benefit units report, and "
+            "apportions by claimant and partner count, and the person-level "
+            "sources of the unit's claimant and partner, so feeding it 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 "
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 7bd48d588..ea65b4c66 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
@@ -112,10 +112,12 @@ 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 only person-level GBP stock
-    # is a debt. uc_reported_capital is a benefit-unit input, unset at
-    # -1, that the uc_assessable_capital formula reads in place of a household
-    # proxy apportioned by benefit-unit adults.
+    # 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.
+    # uc_reported_capital is a benefit-unit input, unset at -1, that the
+    # uc_assessable_capital formula reads in place of both a household proxy
+    # apportioned by claimant and partner count and the person-level sources.
     inputs = set(engine.variables())
     person_stocks = {
         name
@@ -124,7 +126,11 @@ def test_the_liquid_asset_reason_holds(engine) -> None:
         and engine._variable(name).unit == "currency-GBP"
         and engine.variable_metadata(name).entity == "person"
     }
-    assert person_stocks == {"student_loan_balance"}
+    assert person_stocks == {
+        "car_list_price",
+        "lifetime_isa_balance",
+        "student_loan_balance",
+    }
     for name in ("savings", "gross_financial_wealth", "net_financial_wealth"):
         assert name in inputs, name
         assert engine.variable_metadata(name).entity == "household", name
@@ -134,13 +140,19 @@ def test_the_liquid_asset_reason_holds(engine) -> None:
     assert "uc_assessable_capital" not in inputs
     source = inspect.getsource(engine._variable("uc_assessable_capital").get_formula())
     assert "uc_reported_capital" in source
-    assert 'add(benunit, period, ["is_adult"])' in source
+    assert 'add(benunit, period, ["is_uc_claimant"])' in source
+    assert "p.capital.person_sources" in source
     assert "loan" in engine._variable("student_loan_balance").label.lower()
+    duty = engine._variable("car_vehicle_excise_duty").get_formula()
+    assert 'person("car_list_price", period)' in inspect.getsource(duty)
+    documentation = engine._variable("lifetime_isa_balance").documentation
+    assert "gross_financial_wealth and net_financial_wealth" in documentation
 
 
 def test_the_liquid_asset_reason_names_the_household_wealth_inputs(engine) -> None:
     # Shares and funds sit on the household too, in corporate_wealth, which
-    # Universal Credit counts as capital alongside savings.
+    # Universal Credit counts as capital alongside savings. Its one
+    # person-level source is the Lifetime ISA at its surrender value.
     reason = MAPPING.unmapped["fact:person.liquid_financial_assets"]
     assert engine.variable_metadata("corporate_wealth").entity == "household"
     assert engine._variable("corporate_wealth").quantity_type == "stock"
@@ -148,6 +160,8 @@ def test_the_liquid_asset_reason_names_the_household_wealth_inputs(engine) -> No
     universal_credit = engine._tax_benefit_system().parameters.gov.dwp.universal_credit
     sources = universal_credit.means_test.capital.sources("2026-01-01")
     assert {"savings", "corporate_wealth"} <= set(sources)
+    person_sources = universal_credit.means_test.capital.person_sources("2026-01-01")
+    assert list(person_sources) == ["lifetime_isa_countable_capital"]
     source = inspect.getsource(engine._variable("uc_assessable_capital").get_formula())
     assert "p.capital.sources" in source
     for name in (
@@ -155,9 +169,11 @@ def test_the_liquid_asset_reason_names_the_household_wealth_inputs(engine) -> No
         "corporate_wealth",
         "gross_financial_wealth",
         "net_financial_wealth",
+        "lifetime_isa_countable_capital",
     ):
         assert name in reason, name
-    assert "one person-level GBP stock input is student_loan_balance" in reason
+    assert "inputs are student_loan_balance, a debt; car_list_price" in reason
+    assert "and lifetime_isa_balance" in reason
 
 
 def test_weekly_hours_divides_annual_hours_by_52(engine) -> None:
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 25b416202..ac9626aaf 100644
--- a/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json
+++ b/packages/microcosm-frame/tests/golden/concept-coverage/policyengine-uk.json
@@ -393,8 +393,8 @@
   }
  ],
  "engine": "policyengine-uk",
- "engine_version": "2.100.0",
- "input_count": 233,
+ "engine_version": "2.122.2",
+ "input_count": 285,
  "schema_sha256": "d824f5e97d58292d62f65ffae60bc8654408711344b638d4e3caa95d6c2e8d1e",
  "structural_inputs": [
   {
@@ -515,6 +515,14 @@
    "entity": "benunit",
    "name": "baseline_pension_credit_entitlement"
   },
+  {
+   "entity": "household",
+   "name": "beer_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "beer_litres"
+  },
   {
    "entity": "person",
    "name": "blind_persons_allowance"
@@ -541,7 +549,15 @@
   },
   {
    "entity": "household",
-   "name": "bus_fare_spending"
+   "name": "bus_fare_spending_reported"
+  },
+  {
+   "entity": "person",
+   "name": "bus_in_london_trips"
+  },
+  {
+   "entity": "person",
+   "name": "bus_pass_eligible"
   },
   {
    "entity": "household",
@@ -563,6 +579,30 @@
    "entity": "person",
    "name": "capital_gains_residential_property"
   },
+  {
+   "entity": "person",
+   "name": "car_co2_emissions"
+  },
+  {
+   "entity": "person",
+   "name": "car_engine_size"
+  },
+  {
+   "entity": "person",
+   "name": "car_first_registration_date"
+  },
+  {
+   "entity": "person",
+   "name": "car_fuel_type"
+  },
+  {
+   "entity": "person",
+   "name": "car_is_ved_exempt"
+  },
+  {
+   "entity": "person",
+   "name": "car_list_price"
+  },
   {
    "entity": "person",
    "name": "care_hours"
@@ -571,6 +611,10 @@
    "entity": "person",
    "name": "carers_allowance_reported"
   },
+  {
+   "entity": "household",
+   "name": "cash_isa"
+  },
   {
    "entity": "person",
    "name": "charitable_investment_gifts"
@@ -607,6 +651,22 @@
    "entity": "person",
    "name": "childcare_grant_receives_postgraduate_loan"
   },
+  {
+   "entity": "household",
+   "name": "cider_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "cider_litres"
+  },
+  {
+   "entity": "household",
+   "name": "cigarettes_per_week"
+  },
+  {
+   "entity": "household",
+   "name": "cigars_grams_per_week"
+  },
   {
    "entity": "household",
    "name": "clothing_and_footwear_consumption"
@@ -615,6 +675,10 @@
    "entity": "household",
    "name": "communication_consumption"
   },
+  {
+   "entity": "household",
+   "name": "consumer_debt"
+  },
   {
    "entity": "household",
    "name": "corporate_wealth"
@@ -751,6 +815,10 @@
    "entity": "household",
    "name": "gross_financial_wealth"
   },
+  {
+   "entity": "household",
+   "name": "hand_rolling_tobacco_grams_per_week"
+  },
   {
    "entity": "household",
    "name": "health_consumption"
@@ -759,6 +827,10 @@
    "entity": "person",
    "name": "healthy_start_vouchers"
   },
+  {
+   "entity": "household",
+   "name": "heated_tobacco_grams_per_week"
+  },
   {
    "entity": "household",
    "name": "household_furnishings_consumption"
@@ -767,6 +839,10 @@
    "entity": "household",
    "name": "household_owns_tv"
   },
+  {
+   "entity": "benunit",
+   "name": "housing_benefit_board_and_attendance_determination"
+  },
   {
    "entity": "person",
    "name": "housing_benefit_reported"
@@ -795,6 +871,10 @@
    "entity": "household",
    "name": "in_rural_fuel_duty_relief_area"
   },
+  {
+   "entity": "benunit",
+   "name": "in_specified_or_temporary_accommodation"
+  },
   {
    "entity": "person",
    "name": "incapacity_benefit_reported"
@@ -843,10 +923,22 @@
    "entity": "person",
    "name": "jsa_income_reported"
   },
+  {
+   "entity": "benunit",
+   "name": "liable_for_share_of_household_rent"
+  },
+  {
+   "entity": "person",
+   "name": "lifetime_isa_balance"
+  },
   {
    "entity": "household",
    "name": "local_authority"
   },
+  {
+   "entity": "household",
+   "name": "lpg_kg"
+  },
   {
    "entity": "person",
    "name": "lump_sum_income"
@@ -883,6 +975,10 @@
    "entity": "benunit",
    "name": "maximum_extended_childcare_hours_usage"
   },
+  {
+   "entity": "benunit",
+   "name": "meals_included_in_rent"
+  },
   {
    "entity": "household",
    "name": "miscellaneous_consumption"
@@ -891,6 +987,14 @@
    "entity": "person",
    "name": "miscellaneous_income"
   },
+  {
+   "entity": "household",
+   "name": "mortgage_debt"
+  },
+  {
+   "entity": "household",
+   "name": "natural_gas_kg"
+  },
   {
    "entity": "household",
    "name": "net_financial_wealth"
@@ -931,10 +1035,22 @@
    "entity": "person",
    "name": "other_deductions"
   },
+  {
+   "entity": "household",
+   "name": "other_fermented_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "other_fermented_litres"
+  },
   {
    "entity": "person",
    "name": "other_investment_income"
   },
+  {
+   "entity": "person",
+   "name": "other_local_bus_trips"
+  },
   {
    "entity": "household",
    "name": "other_residential_property_value"
@@ -943,6 +1059,10 @@
    "entity": "person",
    "name": "other_tax_credits"
   },
+  {
+   "entity": "household",
+   "name": "other_tobacco_grams_per_week"
+  },
   {
    "entity": "person",
    "name": "outpatient_visits"
@@ -959,6 +1079,10 @@
    "entity": "person",
    "name": "pension_credit_reported"
   },
+  {
+   "entity": "benunit",
+   "name": "pension_credit_reported_capital"
+  },
   {
    "entity": "person",
    "name": "pension_income"
@@ -1007,6 +1131,10 @@
    "entity": "person",
    "name": "receives_benefits_in_own_right"
   },
+  {
+   "entity": "person",
+   "name": "receives_limited_capability_for_work_credits"
+  },
   {
    "entity": "household",
    "name": "recreation_consumption"
@@ -1015,6 +1143,14 @@
    "entity": "household",
    "name": "region"
   },
+  {
+   "entity": "person",
+   "name": "rent_paid_as_boarder"
+  },
+  {
+   "entity": "person",
+   "name": "rent_paid_as_lodger"
+  },
   {
    "entity": "household",
    "name": "restaurants_and_hotels_consumption"
@@ -1035,14 +1171,34 @@
    "entity": "person",
    "name": "sda_reported"
   },
+  {
+   "entity": "person",
+   "name": "self_employment_gross_receipts"
+  },
   {
    "entity": "person",
    "name": "sic_industry_division"
   },
+  {
+   "entity": "household",
+   "name": "sparkling_cider_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "sparkling_cider_litres"
+  },
   {
    "entity": "household",
    "name": "spi_imputed"
   },
+  {
+   "entity": "household",
+   "name": "spirits_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "spirits_litres"
+  },
   {
    "entity": "person",
    "name": "ssmg_reported"
@@ -1059,6 +1215,10 @@
    "entity": "person",
    "name": "statutory_sick_pay"
   },
+  {
+   "entity": "household",
+   "name": "stocks_and_shares_isa"
+  },
   {
    "entity": "household",
    "name": "structural_insurance_payments"
@@ -1087,6 +1247,10 @@
    "entity": "person",
    "name": "tax_free_childcare_on_adoption_leave"
   },
+  {
+   "entity": "person",
+   "name": "tax_free_childcare_on_carers_leave"
+  },
   {
    "entity": "person",
    "name": "tax_free_childcare_on_qualifying_leave"
@@ -1135,10 +1299,46 @@
    "entity": "person",
    "name": "travel_grant_receives_means_tested_nhs_bursary"
   },
+  {
+   "entity": "person",
+   "name": "uc_has_limited_capability_for_work"
+  },
+  {
+   "entity": "person",
+   "name": "uc_has_offer_of_paid_work_starting_by_end_of_next_assessment_period"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_adopter_in_first_year"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_foster_parent_or_new_friend_or_family_carer"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_in_pregnancy_or_post_confinement_period"
+  },
   {
    "entity": "person",
    "name": "uc_is_in_startup_period"
   },
+  {
+   "entity": "person",
+   "name": "uc_is_ineligible_partner"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_responsible_foster_parent_of_child_under_one"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_student_with_no_work_related_requirements"
+  },
+  {
+   "entity": "person",
+   "name": "uc_is_temporarily_absent_from_claimant_household"
+  },
   {
    "entity": "household",
    "name": "uc_migrated"
@@ -1155,6 +1355,14 @@
    "entity": "household",
    "name": "water_and_sewerage_charges"
   },
+  {
+   "entity": "household",
+   "name": "wine_draught_share"
+  },
+  {
+   "entity": "household",
+   "name": "wine_litres"
+  },
   {
    "entity": "person",
    "name": "winter_fuel_allowance_reported"
@@ -1176,7 +1384,7 @@
  "unmapped_concepts": {
   "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.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. Its one person-level GBP stock input is student_loan_balance, a debt, and no transform puts a person amount on a household input. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of the household-capital proxy it otherwise sums from all of those capital sources, net of capital other benefit units report, and apportions by benefit-unit adults, so feeding it 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. savings and corporate_wealth are two of Universal Credit's capital sources (gov.dwp.universal_credit.means_test.capital.sources), and lifetime_isa_countable_capital, the Lifetime ISA at its surrender value, is its one person-level source (capital.person_sources). uc_reported_capital (benefit unit; 'Claimant-level capital for Universal Credit when household-level asset data cannot be attributed across multiple benefit units', default -1) is Universal Credit capital: when it is zero or more, uc_assessable_capital takes it in place of both the household-capital proxy it otherwise sums from the household sources, net of capital other benefit units report, and apportions by claimant and partner count, and the person-level sources of the unit's claimant and partner, so feeding it 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/benunit.csv b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/benunit.csv
index 021f7bb2b..32e3ae1f1 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/benunit.csv
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/benunit.csv
@@ -1,136 +1,136 @@
-benunit_id,frs_benunit_capital,is_married,dependent_children
-101,1.0_1000000000000000e+02,True,0
-201,1.0_2000000000000000e+02,True,0
-301,1.0_3000000000000000e+02,True,0
-401,1.0_4000000000000000e+02,True,0
-501,1.0_5000000000000000e+02,True,1
-601,1.0_6000000000000000e+02,True,0
-701,1.0_7000000000000000e+02,True,0
-801,1.0_8000000000000000e+02,True,0
-901,1.0_9000000000000000e+02,True,0
-1001,1.1_0000000000000000e+02,True,1
-1101,1.1_1000000000000000e+02,True,0
-1201,1.1_2000000000000000e+02,True,0
-1301,1.1_3000000000000000e+02,True,0
-1401,1.1_4000000000000000e+02,True,0
-1501,1.1_5000000000000000e+02,True,1
-1601,1.1_6000000000000000e+02,True,0
-1701,1.1_7000000000000000e+02,True,0
-1801,1.1_8000000000000000e+02,True,0
-1901,1.1_9000000000000000e+02,True,0
-2001,1.2_0000000000000000e+02,True,1
-2101,1.2_1000000000000000e+02,True,0
-2201,1.2_2000000000000000e+02,True,0
-2301,1.2_3000000000000000e+02,True,0
-2401,1.2_4000000000000000e+02,True,0
-2501,1.2_5000000000000000e+02,True,1
-2601,1.2_6000000000000000e+02,True,0
-2701,1.2_7000000000000000e+02,True,0
-2801,1.2_8000000000000000e+02,True,0
-2901,1.2_9000000000000000e+02,True,0
-3001,1.3_0000000000000000e+02,True,1
-3101,1.3_1000000000000000e+02,True,0
-3201,1.3_2000000000000000e+02,True,0
-3301,1.3_3000000000000000e+02,True,0
-3401,1.3_4000000000000000e+02,True,0
-3501,1.3_5000000000000000e+02,True,1
-3601,1.3_6000000000000000e+02,True,0
-3701,1.3_7000000000000000e+02,True,0
-3801,1.3_8000000000000000e+02,True,0
-3901,1.3_9000000000000000e+02,True,0
-4001,1.4_0000000000000000e+02,True,1
-4101,1.4_1000000000000000e+02,True,0
-4201,1.4_2000000000000000e+02,True,0
-4301,1.4_3000000000000000e+02,True,0
-4401,1.4_4000000000000000e+02,True,0
-4501,1.4_5000000000000000e+02,True,1
-4601,1.4_6000000000000000e+02,True,0
-4701,1.4_7000000000000000e+02,True,0
-4801,1.4_8000000000000000e+02,True,0
-4901,1.4_9000000000000000e+02,True,0
-5001,1.5_0000000000000000e+02,True,1
-5101,1.5_1000000000000000e+02,True,0
-5201,1.5_2000000000000000e+02,True,0
-5301,1.5_3000000000000000e+02,True,0
-5401,1.5_4000000000000000e+02,True,0
-5501,1.5_5000000000000000e+02,True,1
-5601,1.5_6000000000000000e+02,True,0
-5701,1.5_7000000000000000e+02,True,0
-5801,1.5_8000000000000000e+02,True,0
-5901,1.5_9000000000000000e+02,True,0
-6001,1.6_0000000000000000e+02,True,1
-6101,1.6_1000000000000000e+02,True,0
-6201,1.6_2000000000000000e+02,True,0
-6301,1.6_3000000000000000e+02,True,0
-6401,1.6_4000000000000000e+02,True,0
-6501,1.6_5000000000000000e+02,True,1
-6601,1.6_6000000000000000e+02,True,0
-6701,1.6_7000000000000000e+02,True,0
-6801,1.6_8000000000000000e+02,True,0
-6901,1.6_9000000000000000e+02,True,0
-7001,1.7_0000000000000000e+02,True,1
-7101,1.7_1000000000000000e+02,True,0
-7201,1.7_2000000000000000e+02,True,0
-7301,1.7_3000000000000000e+02,True,0
-7401,1.7_4000000000000000e+02,True,0
-7501,1.7_5000000000000000e+02,True,1
-7601,1.7_6000000000000000e+02,True,0
-7701,1.7_7000000000000000e+02,True,0
-7801,1.7_8000000000000000e+02,True,0
-7901,1.7_9000000000000000e+02,True,0
-8001,1.8_0000000000000000e+02,True,1
-8101,1.8_1000000000000000e+02,True,0
-8201,1.8_2000000000000000e+02,True,0
-8301,1.8_3000000000000000e+02,True,0
-8401,1.8_4000000000000000e+02,True,0
-8501,1.8_5000000000000000e+02,True,1
-8601,1.8_6000000000000000e+02,True,0
-8701,1.8_7000000000000000e+02,True,0
-8801,1.8_8000000000000000e+02,True,0
-8901,1.8_9000000000000000e+02,True,0
-9001,1.9_0000000000000000e+02,True,1
-9101,1.9_1000000000000000e+02,True,0
-9201,1.9_2000000000000000e+02,True,0
-9301,1.9_3000000000000000e+02,True,0
-9401,1.9_4000000000000000e+02,True,0
-9501,1.9_5000000000000000e+02,True,1
-9601,1.9_6000000000000000e+02,True,0
-9701,1.9_7000000000000000e+02,True,0
-9801,1.9_8000000000000000e+02,True,0
-9901,1.9_9000000000000000e+02,True,0
-10001,2.0_0000000000000000e+02,True,1
-10101,2.0_1000000000000000e+02,True,0
-10201,2.0_2000000000000000e+02,True,0
-10301,2.0_3000000000000000e+02,True,0
-10401,2.0_4000000000000000e+02,True,0
-10501,2.0_5000000000000000e+02,True,1
-10601,2.0_6000000000000000e+02,True,0
-10701,2.0_7000000000000000e+02,True,0
-10801,2.0_8000000000000000e+02,True,0
-10901,2.0_9000000000000000e+02,True,0
-11001,2.1_0000000000000000e+02,True,1
-11101,2.1_1000000000000000e+02,True,0
-11201,2.1_2000000000000000e+02,True,0
-11301,2.1_3000000000000000e+02,True,0
-11401,2.1_4000000000000000e+02,True,0
-11501,2.1_5000000000000000e+02,True,1
-11601,2.1_6000000000000000e+02,True,0
-11701,2.1_7000000000000000e+02,True,0
-11801,2.1_8000000000000000e+02,True,0
-11901,2.1_9000000000000000e+02,True,0
-12001,2.2_0000000000000000e+02,True,1
-12101,2.2_1000000000000000e+02,True,0
-12201,2.2_2000000000000000e+02,True,0
-12301,2.2_3000000000000000e+02,True,0
-12401,2.2_4000000000000000e+02,True,0
-12501,2.2_5000000000000000e+02,True,1
-12601,2.2_6000000000000000e+02,True,0
-12701,2.2_7000000000000000e+02,True,0
-12801,2.2_8000000000000000e+02,True,0
-12901,2.2_9000000000000000e+02,True,0
-13001,2.3_0000000000000000e+02,True,1
-13101,2.3_1000000000000000e+02,True,0
-13201,2.3_2000000000000000e+02,True,0
-13301,2.3_3000000000000000e+02,True,0
-13401,2.3_4000000000000000e+02,True,0
-13501,2.3_5000000000000000e+02,True,1
+benunit_id,frs_benunit_capital,is_married,dependent_children,liable_for_share_of_household_rent
+101,1.0_1000000000000000e+02,True,0,False
+201,1.0_2000000000000000e+02,True,0,False
+301,1.0_3000000000000000e+02,True,0,False
+401,1.0_4000000000000000e+02,True,0,False
+501,1.0_5000000000000000e+02,True,1,False
+601,1.0_6000000000000000e+02,True,0,False
+701,1.0_7000000000000000e+02,True,0,False
+801,1.0_8000000000000000e+02,True,0,False
+901,1.0_9000000000000000e+02,True,0,False
+1001,1.1_0000000000000000e+02,True,1,False
+1101,1.1_1000000000000000e+02,True,0,False
+1201,1.1_2000000000000000e+02,True,0,False
+1301,1.1_3000000000000000e+02,True,0,False
+1401,1.1_4000000000000000e+02,True,0,False
+1501,1.1_5000000000000000e+02,True,1,False
+1601,1.1_6000000000000000e+02,True,0,False
+1701,1.1_7000000000000000e+02,True,0,False
+1801,1.1_8000000000000000e+02,True,0,False
+1901,1.1_9000000000000000e+02,True,0,False
+2001,1.2_0000000000000000e+02,True,1,False
+2101,1.2_1000000000000000e+02,True,0,False
+2201,1.2_2000000000000000e+02,True,0,False
+2301,1.2_3000000000000000e+02,True,0,False
+2401,1.2_4000000000000000e+02,True,0,False
+2501,1.2_5000000000000000e+02,True,1,False
+2601,1.2_6000000000000000e+02,True,0,False
+2701,1.2_7000000000000000e+02,True,0,False
+2801,1.2_8000000000000000e+02,True,0,False
+2901,1.2_9000000000000000e+02,True,0,False
+3001,1.3_0000000000000000e+02,True,1,False
+3101,1.3_1000000000000000e+02,True,0,False
+3201,1.3_2000000000000000e+02,True,0,False
+3301,1.3_3000000000000000e+02,True,0,False
+3401,1.3_4000000000000000e+02,True,0,False
+3501,1.3_5000000000000000e+02,True,1,False
+3601,1.3_6000000000000000e+02,True,0,False
+3701,1.3_7000000000000000e+02,True,0,False
+3801,1.3_8000000000000000e+02,True,0,False
+3901,1.3_9000000000000000e+02,True,0,False
+4001,1.4_0000000000000000e+02,True,1,False
+4101,1.4_1000000000000000e+02,True,0,False
+4201,1.4_2000000000000000e+02,True,0,False
+4301,1.4_3000000000000000e+02,True,0,False
+4401,1.4_4000000000000000e+02,True,0,False
+4501,1.4_5000000000000000e+02,True,1,False
+4601,1.4_6000000000000000e+02,True,0,False
+4701,1.4_7000000000000000e+02,True,0,False
+4801,1.4_8000000000000000e+02,True,0,False
+4901,1.4_9000000000000000e+02,True,0,False
+5001,1.5_0000000000000000e+02,True,1,False
+5101,1.5_1000000000000000e+02,True,0,False
+5201,1.5_2000000000000000e+02,True,0,False
+5301,1.5_3000000000000000e+02,True,0,False
+5401,1.5_4000000000000000e+02,True,0,False
+5501,1.5_5000000000000000e+02,True,1,False
+5601,1.5_6000000000000000e+02,True,0,False
+5701,1.5_7000000000000000e+02,True,0,False
+5801,1.5_8000000000000000e+02,True,0,False
+5901,1.5_9000000000000000e+02,True,0,False
+6001,1.6_0000000000000000e+02,True,1,False
+6101,1.6_1000000000000000e+02,True,0,False
+6201,1.6_2000000000000000e+02,True,0,False
+6301,1.6_3000000000000000e+02,True,0,False
+6401,1.6_4000000000000000e+02,True,0,False
+6501,1.6_5000000000000000e+02,True,1,False
+6601,1.6_6000000000000000e+02,True,0,False
+6701,1.6_7000000000000000e+02,True,0,False
+6801,1.6_8000000000000000e+02,True,0,False
+6901,1.6_9000000000000000e+02,True,0,False
+7001,1.7_0000000000000000e+02,True,1,False
+7101,1.7_1000000000000000e+02,True,0,False
+7201,1.7_2000000000000000e+02,True,0,False
+7301,1.7_3000000000000000e+02,True,0,False
+7401,1.7_4000000000000000e+02,True,0,False
+7501,1.7_5000000000000000e+02,True,1,False
+7601,1.7_6000000000000000e+02,True,0,False
+7701,1.7_7000000000000000e+02,True,0,False
+7801,1.7_8000000000000000e+02,True,0,False
+7901,1.7_9000000000000000e+02,True,0,False
+8001,1.8_0000000000000000e+02,True,1,False
+8101,1.8_1000000000000000e+02,True,0,False
+8201,1.8_2000000000000000e+02,True,0,False
+8301,1.8_3000000000000000e+02,True,0,False
+8401,1.8_4000000000000000e+02,True,0,False
+8501,1.8_5000000000000000e+02,True,1,False
+8601,1.8_6000000000000000e+02,True,0,False
+8701,1.8_7000000000000000e+02,True,0,False
+8801,1.8_8000000000000000e+02,True,0,False
+8901,1.8_9000000000000000e+02,True,0,False
+9001,1.9_0000000000000000e+02,True,1,False
+9101,1.9_1000000000000000e+02,True,0,False
+9201,1.9_2000000000000000e+02,True,0,False
+9301,1.9_3000000000000000e+02,True,0,False
+9401,1.9_4000000000000000e+02,True,0,False
+9501,1.9_5000000000000000e+02,True,1,False
+9601,1.9_6000000000000000e+02,True,0,False
+9701,1.9_7000000000000000e+02,True,0,False
+9801,1.9_8000000000000000e+02,True,0,False
+9901,1.9_9000000000000000e+02,True,0,False
+10001,2.0_0000000000000000e+02,True,1,False
+10101,2.0_1000000000000000e+02,True,0,False
+10201,2.0_2000000000000000e+02,True,0,False
+10301,2.0_3000000000000000e+02,True,0,False
+10401,2.0_4000000000000000e+02,True,0,False
+10501,2.0_5000000000000000e+02,True,1,False
+10601,2.0_6000000000000000e+02,True,0,False
+10701,2.0_7000000000000000e+02,True,0,False
+10801,2.0_8000000000000000e+02,True,0,False
+10901,2.0_9000000000000000e+02,True,0,False
+11001,2.1_0000000000000000e+02,True,1,False
+11101,2.1_1000000000000000e+02,True,0,False
+11201,2.1_2000000000000000e+02,True,0,False
+11301,2.1_3000000000000000e+02,True,0,False
+11401,2.1_4000000000000000e+02,True,0,False
+11501,2.1_5000000000000000e+02,True,1,False
+11601,2.1_6000000000000000e+02,True,0,False
+11701,2.1_7000000000000000e+02,True,0,False
+11801,2.1_8000000000000000e+02,True,0,False
+11901,2.1_9000000000000000e+02,True,0,False
+12001,2.2_0000000000000000e+02,True,1,False
+12101,2.2_1000000000000000e+02,True,0,False
+12201,2.2_2000000000000000e+02,True,0,False
+12301,2.2_3000000000000000e+02,True,0,False
+12401,2.2_4000000000000000e+02,True,0,False
+12501,2.2_5000000000000000e+02,True,1,False
+12601,2.2_6000000000000000e+02,True,0,False
+12701,2.2_7000000000000000e+02,True,0,False
+12801,2.2_8000000000000000e+02,True,0,False
+12901,2.2_9000000000000000e+02,True,0,False
+13001,2.3_0000000000000000e+02,True,1,False
+13101,2.3_1000000000000000e+02,True,0,False
+13201,2.3_2000000000000000e+02,True,0,False
+13301,2.3_3000000000000000e+02,True,0,False
+13401,2.3_4000000000000000e+02,True,0,False
+13501,2.3_5000000000000000e+02,True,1,False
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 fbddbf143..d259bfa3e 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
@@ -252,7 +252,7 @@
     "cgt_residential_split": {
       "artifacts": [
         {
-          "dependency": "policyengine-uk>=2.100.0 via microcosm-build[uk]",
+          "dependency": "policyengine-uk>=2.122.2 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",
@@ -1042,8 +1042,8 @@
           "locator": "househol.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "d6c1b0f31e20980c3effb033fba5ba76527788c750e0051b0176e3bcb29bc89d",
-          "size_bytes": 15915,
+          "sha256": "c16fdc0dfbe254127a1decd9ebd796cc0584ecd0cf9c372da6143f5e223227bb",
+          "size_bytes": 17280,
           "table": "househol",
           "vintage": "synthetic-h2"
         }
@@ -1086,7 +1086,7 @@
       "artifacts": [],
       "grain": "person",
       "nonnegative_outputs": [],
-      "notes": "Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed.",
+      "notes": "Consumes E2 reported disability amount carriers. The five internal amount carriers are retained through E7 and stripped at E10; export allowlists stay fail-closed. is_severely_disabled_for_benefits is the tax credit severe disability condition on the derived categories (DLA care at the highest rate, PIP daily living at the enhanced rate, or higher-rate Attendance Allowance), so it agrees with policyengine-uk's own formula (pe-uk#1946, uk-data#494); an Armed Forces Compensation Scheme payment alone no longer counts, since FRS code 8 cannot be told apart from Armed Forces Independence Payment.",
       "operations": [
         {
           "kind": "derive",
@@ -1122,8 +1122,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         },
@@ -1133,8 +1133,8 @@
           "locator": "child.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "fd24138e8c97aa641b126ed03b867e6ea177d22cbcc8c1e53b44530ba045724a",
-          "size_bytes": 2123,
+          "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2",
+          "size_bytes": 2185,
           "table": "child",
           "vintage": "synthetic-h2"
         }
@@ -1238,8 +1238,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         }
@@ -1281,8 +1281,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         },
@@ -1292,8 +1292,8 @@
           "locator": "benefits.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "e438d4e81045c45caa93a1d6f8462a484429f510647c267b67ae3cefc05a1acc",
-          "size_bytes": 14946,
+          "sha256": "d79e0f4a8fed6ca6aeda4de928a3ca27f66c12274ac9669806e93e36af9b1844",
+          "size_bytes": 18064,
           "table": "benefits",
           "vintage": "synthetic-h2"
         },
@@ -1303,8 +1303,8 @@
           "locator": "job.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "0f787336f16448b3b9ec0b7ca46ca50da12b4fa90892161c0054fa89282189d7",
-          "size_bytes": 2643,
+          "sha256": "f54570510234710ec5e89e67be70139ae2b1033bc1cbcb6bd1a9c7d5a2e7804a",
+          "size_bytes": 3861,
           "table": "job",
           "vintage": "synthetic-h2"
         },
@@ -1499,8 +1499,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         }
@@ -1610,8 +1610,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         },
@@ -1621,8 +1621,8 @@
           "locator": "child.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "fd24138e8c97aa641b126ed03b867e6ea177d22cbcc8c1e53b44530ba045724a",
-          "size_bytes": 2123,
+          "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2",
+          "size_bytes": 2185,
           "table": "child",
           "vintage": "synthetic-h2"
         },
@@ -1632,8 +1632,8 @@
           "locator": "househol.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "d6c1b0f31e20980c3effb033fba5ba76527788c750e0051b0176e3bcb29bc89d",
-          "size_bytes": 15915,
+          "sha256": "c16fdc0dfbe254127a1decd9ebd796cc0584ecd0cf9c372da6143f5e223227bb",
+          "size_bytes": 17280,
           "table": "househol",
           "vintage": "synthetic-h2"
         }
@@ -1835,8 +1835,8 @@
           "locator": "adult.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "79801434b27f0b0b2f82af0f4d0750688c53aabda049bc2c30994fc35b338a5f",
-          "size_bytes": 24966,
+          "sha256": "e716297716a3fa0fee2118ef95da64b82a8115b933988158c9b3660fe642d0ad",
+          "size_bytes": 30240,
           "table": "adult",
           "vintage": "synthetic-h2"
         },
@@ -1846,8 +1846,8 @@
           "locator": "benefits.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "e438d4e81045c45caa93a1d6f8462a484429f510647c267b67ae3cefc05a1acc",
-          "size_bytes": 14946,
+          "sha256": "d79e0f4a8fed6ca6aeda4de928a3ca27f66c12274ac9669806e93e36af9b1844",
+          "size_bytes": 18064,
           "table": "benefits",
           "vintage": "synthetic-h2"
         },
@@ -1857,8 +1857,8 @@
           "locator": "benunit.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "2a1dacb6dcdb3c498fc9297f566e1367b7ae5191da5970b0300e3e873ce48f86",
-          "size_bytes": 2094,
+          "sha256": "55354eaa18ef88d1ccc37c906f2321b57bc0c975c51bc445534cf5c0bdae60f0",
+          "size_bytes": 2643,
           "table": "benunit",
           "vintage": "synthetic-h2"
         },
@@ -1868,8 +1868,8 @@
           "locator": "child.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "fd24138e8c97aa641b126ed03b867e6ea177d22cbcc8c1e53b44530ba045724a",
-          "size_bytes": 2123,
+          "sha256": "1ce507a1363e7f44c9ed3f81eaa207f9c92e6801a7db722e37fe953e21dddef2",
+          "size_bytes": 2185,
           "table": "child",
           "vintage": "synthetic-h2"
         },
@@ -1901,8 +1901,8 @@
           "locator": "househol.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "d6c1b0f31e20980c3effb033fba5ba76527788c750e0051b0176e3bcb29bc89d",
-          "size_bytes": 15915,
+          "sha256": "c16fdc0dfbe254127a1decd9ebd796cc0584ecd0cf9c372da6143f5e223227bb",
+          "size_bytes": 17280,
           "table": "househol",
           "vintage": "synthetic-h2"
         },
@@ -1912,8 +1912,8 @@
           "locator": "job.tab",
           "role": "frs_table",
           "runtime_sha256_required": true,
-          "sha256": "0f787336f16448b3b9ec0b7ca46ca50da12b4fa90892161c0054fa89282189d7",
-          "size_bytes": 2643,
+          "sha256": "f54570510234710ec5e89e67be70139ae2b1033bc1cbcb6bd1a9c7d5a2e7804a",
+          "size_bytes": 3861,
           "table": "job",
           "vintage": "synthetic-h2"
         },
@@ -1975,6 +1975,8 @@
       ],
       "grain": "household",
       "nonnegative_outputs": [
+        "rent_paid_as_boarder",
+        "rent_paid_as_lodger",
         "age",
         "hours_worked",
         "employment_income",
@@ -2000,7 +2002,7 @@
         "external_child_payments",
         "care_hours"
       ],
-      "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).",
+      "notes": "Root E2 spine assembly. It carries direct raw mappings only; education-grant aggregate and council-tax reported fields remain raw carriers for E3. Benefit take-up, BRMA/LHA assignment, stochastic flags, and imputations are intentionally absent from this stage, except one identity-keyed draw (uk-data#527). The UC start-up period needs the raw claim, interview and job dates only this stage reads, so a UC record without a linked claim date takes its claim recency from a benefit-unit-keyed draw at the linked share. The 2024-25 househol.tab is not sernum-ordered; the declared household sort guards stable identity. care_hours carries the FRS banded care-hours code as weekly hours (lower band edge) and would_claim_carers_allowance follows reported receipt, so the engine keeps Carer's Allowance on reported claimants while the hours qualify carers for the Universal Credit carer element (#882).",
       "operations": [
         {
           "delimiter": "\t",
@@ -2024,7 +2026,7 @@
         },
         {
           "kind": "map_columns",
-          "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095)"
+          "scope": "raw-to-column E2 mappings and FRS claimant-role derivation (is_benunit_head OR is_parent OR no dependent children within the benefit unit) to is_uc_claimant; benunit.TOTCAPB4 -> frs_benunit_capital with blank, nonnumeric, or negative raw values -> named -1 unavailable sentinel (mapped-row count reported in stage evidence); Scottish water_and_sewerage_charges is the charge paid, (CWATAMT1 + CSEWAMT1) less the CTDISC/CT25D50D status discount, or where CTREB is 1 less the larger of that discount and the Water Charges Reduction Scheme's 35% (the full-reduction case), and CWATAMTD where CWATAMT1 is not positive (uk-data#499, microcosm#1095); access_fund is ACCSSAMT annualised, except that a calendar-month award (ACCSSPD 5) that annualises to more than twice the largest annual-coded (ACCSSPD 52) award on the tab is read as the annual award, ACCSSAMT x 52/12 (microcosm#1095), and a tab with calendar-month awards but no annual-coded award refuses, since the threshold comes from that award; the stage evidence records the awards by period code, the threshold and the repaired count; is_blind is SPCREG1 = 1, registered blind or severely sight impaired with the local authority, read on the adult and child tabs alike, while partial-sight registration (SPCREG2) is not read (uk-data#523); uc_is_in_startup_period holds for a self-employed person (EMPSTATI 3 or 4, a held self-employed JOB row with ETYPE 2-7 and no SEEND, or nonzero SEINCAM2) whose benefit unit's UC claim (BENEFIT 95, UCSTART month/day/year) or whose first self-employed job (SEJBLONG, main job first) began less than 12 calendar months before INTDATE, except that a self-employed-job answer under a year from someone self-employed all year (SAMESIT 2 or every SDEMP month 3 or 4) who does not describe a business (JOBBUS 2) is a new engagement and does not date the trade (UC Regs 2013 reg 63, uk-data#527); is_claimant_or_partner is adult-table membership and is_hbai_dependent_child child-table membership (uk-data#524, uk-data#486), the claimant derivation already refusing a benefit unit without one or two adult records; CVPAY, the rent a boarder or lodger pays the householder, is not the payer's property income but rent_paid_as_boarder where CONVBL is 1 and rent_paid_as_lodger otherwise, and liable_for_share_of_household_rent holds for a later benefit unit of a shared household (HHSTAT 2) with a rent share (SRENTAMT), Housing Benefit (HBOTHAMT) or a linked UC housing element (UCHOUSEL on BENEFIT 95) (pe-uk#2006, uk-data#512)"
         },
         {
           "kind": "map_coded_amounts",
@@ -2033,6 +2035,15 @@
         {
           "kind": "annualize_periodic_amounts",
           "weeks_in_year": 52.17857142857143
+        },
+        {
+          "assignment_unit": "benefit unit",
+          "draw": "uc_is_in_startup_period",
+          "eligibility": "benefit units reporting Universal Credit (BENEFIT 95) with no linked claim start date (blank UCSTART)",
+          "kind": "assign_binary_from_rate",
+          "output": "uc_is_in_startup_period",
+          "rate_derivation": "GROSS4-weighted share of self-employed persons with a linked UC claim whose claim began less than 12 calendar months before INTDATE; a drawn unit's claim counts as inside the window (uk-data#527)",
+          "seed": 0
         }
       ],
       "outputs": [
@@ -2047,7 +2058,13 @@
         "is_household_head",
         "is_benunit_head",
         "is_parent",
+        "is_blind",
         "is_uc_claimant",
+        "is_claimant_or_partner",
+        "is_hbai_dependent_child",
+        "uc_is_in_startup_period",
+        "rent_paid_as_boarder",
+        "rent_paid_as_lodger",
         "employment_income",
         "self_employment_income",
         "private_pension_income",
@@ -2107,6 +2124,7 @@
         "frs_benunit_capital",
         "is_married",
         "dependent_children",
+        "liable_for_share_of_household_rent",
         "household_id",
         "region",
         "tenure_type",
@@ -2261,7 +2279,7 @@
     "hmrc_cgt_asset_type_spine": {
       "artifacts": [
         {
-          "dependency": "policyengine-uk>=2.100.0 via microcosm-build[uk]",
+          "dependency": "policyengine-uk>=2.122.2 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",
@@ -3457,7 +3475,7 @@
         "gas_consumption",
         "has_fuel_consumption"
       ],
-      "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (Mar\u00eda's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the",
+      "notes": "Ports the incumbent LCFS consumption QRF, including NEED energy raking, with adjudicated weighted fits and an identity-keyed seed-0 fuel flag. The vehicle count is a numeric QRF predictor on both sides (LCFS a124 on the donor, the was_wealth draw of WAS vcarnr8 on the recipient, both clipped to 0-5), which retires the incumbent's WAS-to-LCFS has-fuel bridge (microcosm#890 A). The recipient fuel flag is num_vehicles > 0 times a draw at the fuel-buyer share from the vendored DfT VEH1103 licensed-car stock (one minus the battery-electric and fuel-cell share, 2024, Great Britain; plug-in hybrids count as fuel buyers), replacing the hand-copied NTS share (microcosm#890 E2). Energy support clipping is exempt because NEED raking governs those columns. Bus fares are clipped to donor support before the pricing step (Mar\u00eda's rulings 2026-09-15 and 2026-09-17), which bounds the region no area prices (Wales keeps the chain's raw draw) while the priced regions move beyond donor support afterwards; for that reason the column carries no committed support bound for the terminal gate. Domestic energy is raked in kWh to the NEED 2024 shape at the DESNZ level and priced at the average prices paid (microcosm#890 F, chronicle#270), where both NEED rakes take their mean-kWh margins from the vendored NEED 2024 tables (England and Wales by income band, tenure, property type and region; Scotland by income band, tenure and property type with the all-dwellings mean as its region margin; Northern Ireland untouched), the declared price_domestic_energy step converts spend to kWh and back at each household's QEP price region through the committed crosswalk using the FY2024-25 average variable unit cost and fixed cost across payment methods, standard metering, including VAT (Northern Ireland gas at the UK average because QEP publishes no Northern Ireland gas row), a gas fixed cost only for connected households, the gas connection imposed on the recipient frame at the published share of each region (DESNZ subnational gas meters over electricity meters, 2024) by disconnecting gas-positive households in an identity-keyed uniform order, skipping a household whose weight would overshoot the published share (Northern Ireland has no subnational row and keeps the positive-gas rule), and after the four-margin NEED rake one factor per fuel scales the kWh so the prior-weighted frame total equals the DESNZ Energy Trends domestic consumption at actual temperature summed over the four FY2024-25 quarters; NEED gas means are per gas-metered household, so the gas kWh margins are raked over connected households only while electricity is raked over every household, and the energy_rake gate recomputes every NEED mean, the DESNZ level and the connection shares from the vendored rows. The disconnection walk's seed-0 uniform order makes every group of households (dwelling type, income band, support channel) lose the same expected share of its drawn connected mass, so only the level of the drawn connection moves; the smallest-drawn-gas-first order it replaces piled the disconnections onto low-draw flats once the SPI support households drew gas from their own incomes, and NEED's Scottish property-type means then implied a level 2.6 % above the Scottish all-dwellings mean, a margin conflict the rake could not close. Bus fares are priced from journeys (microcosm#930), where the nts_bus_travel stage imputes each person's annual bus-in-London and other-local-bus trips and their concessionary eligibility, and the declared price_bus_journeys step sets bus_fare_spending as the sum over the household's non-eligible persons of trips times boardings per resident trip (the published BUS01 journeys over the published NTS0705a trips per resident times the ONS population) times the yield per fare-paying boarding (BUS05ai receipts over journeys less concessionary journeys), per area; bus-in-London trips take the London area's price for every resident, other-local-bus trips the price of the residence area (England outside London; Scotland and Northern Ireland from their own receipts and journeys with England's trip rate as the declared proxy); Wales is unpriced and keeps the raw draw. The chain still draws bus_fare_spending so later targets condition on the same raw draw; the emitted column is the priced value. The receipt records every factor, the frame-implied boardings beside the published ones and the frame's eligible trip share beside the publisher's concessionary boarding share, so the prior-weight fit of the bound receipts targets is read as a composition check, never a level the stage set; the #890 incidence draw and fare rake are retired. The 2023-24 diary is uprated to the FRS 2024-25 base year by the declared uprate_donor_columns step (microcosm#890), with consumption columns and bus fares uprated by the engine's CPI index (the fare factor survives only where no fare cell levels the column, i.e. Wales), income predictors by the engine's earnings, mixed-income and pension indices, petrol and diesel by the DESNZ pump-price times HMRC litres per head outturn from the vendored road_fuel_anchors.json; the energy columns are exempt because the NEED 2024 shape at the DESNZ level and the QEP average prices paid set their level. The household adult and child counts (LCFS G018 and G019, the survey's age-18 split) predict on both sides; the recipient counts its members by age, not through the engine's is_adult and is_child flags, which policyengine-uk#1896 deprecates (uk-data#486, microcosm#1095).",
       "operations": [
         {
           "annualization_weeks": 52.17857142857143,
@@ -3611,8 +3629,6 @@
         {
           "kind": "materialize_rules_engine_predictors",
           "predictors": [
-            "is_adult",
-            "is_child",
             "employment_income",
             "self_employment_income",
             "private_pension_income",
@@ -3628,8 +3644,8 @@
           "kind": "fit_weighted_qrf_chain",
           "n_estimators": 4,
           "predictors": [
-            "is_adult",
-            "is_child",
+            "household_adult_count",
+            "household_child_count",
             "region",
             "employment_income",
             "self_employment_income",
@@ -4353,8 +4369,14 @@
       "artifacts": [],
       "grain": "benunit",
       "nonnegative_outputs": [],
-      "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible.",
+      "notes": "Runs after the SPI income chain and UC capital coherence, before CGT cloning (microcosm#1069 R9). It first stores the engine's own default for the SI 2019/37 mixed-age couple saving (has_mixed_age_couple_pension_credit_saving, pe-uk#1940), evaluated once at the survey-year period on the post-SPI receipts, so later model years do not re-derive it from a birth year that moves with the model year (uk-data#519). One temporary engine materialization gives each benefit unit's pre-take-up Pension Credit entitlement (guarantee_credit, savings_credit, is_pension_credit_eligible; consumed only). Entitled units fall into DWP's take-up components, Guarantee Credit (with or without Savings Credit) and Savings Credit only; within a component a reporter claims and the other entitled units claim at r = (t * E - R) / (E - R), t being DWP's financial year ending 2024 caseload take-up (69% and 37%), E the component's entitled units and R its reporters, weighted at the stage's household weights. A unit with no entitlement claims only if it reports. The uniform draw is frs_take_up's would_claim_pc identity stream, whose early draw this rewrite replaces; units with every adult at or over State Pension age are the only ones the engine finds eligible.",
       "operations": [
+        {
+          "kind": "materialize_rules_engine_predictors",
+          "predictors": [
+            "has_mixed_age_couple_pension_credit_saving"
+          ]
+        },
         {
           "consumed_only": true,
           "kind": "materialize_rules_engine_predictors",
@@ -4394,7 +4416,9 @@
           "weight_mapping": "household_to_benunit"
         }
       ],
-      "outputs": [],
+      "outputs": [
+        "has_mixed_age_couple_pension_credit_saving"
+      ],
       "rewrites": [
         "would_claim_pc"
       ],
@@ -5002,6 +5026,7 @@
         },
         {
           "derived": {
+            "pension_credit_reported_capital": "frs_benunit_capital",
             "uc_reported_capital": "frs_benunit_capital",
             "would_claim_uc": "would_claim_uc OR universal_credit_reported_anchor"
           },
@@ -5009,7 +5034,8 @@
         }
       ],
       "outputs": [
-        "uc_reported_capital"
+        "uc_reported_capital",
+        "pension_credit_reported_capital"
       ],
       "rewrites": [
         "frs_benunit_capital",
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab
index c5833093a..3f0b19383 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/adult.tab
@@ -1,136 +1,136 @@
-SERNUM	BENUNIT	PERSON	AGE80	AGE	SEX	TOTHOURS	HRPID	UPERSON	RELHRP	R01	R02	R03	R04	R05	R06	R07	R08	R09	R10	R11	R12	R13	R14	MARITAL	EMPSTATI	MJOBSECT	SIC	FTED	TYPEED2	EDUCQUAL	TRAIN	EMAAMT	CHEMAAMT	INEARNS	SEINCAM2	MNTUS1	MNTUSAM1	MNTAMT1	MNTAMT2	CVPAY	ROYYR1	ROYYR2	ROYYR3	ROYYR4	ALLPAY2	ALLPAY3	ALLPAY4	CHAMTERN	CHAMTTST	APAMT	APDAMT	PAREAMT	REDAMT	SLREPAMT	SSPADJ	SMPADJ	TUBORR	ACCSSAMT	GRTDIR1	GRTDIR2	HEARTVAL
-1	1	1	0	80	2	21	1	1																2	2	2	11	2	0	2	10	0.0	0.0	101.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-2	1	1	0	22	1	22	1	1																3	3	1	12	2	0	3	10	0.0	0.0	102.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-3	1	1	0	23	2	23	1	1																1	4	2	13	2	0	4	10	0.0	0.0	103.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-4	1	1	0	24	1	24	1	1																2	5	1	14	2	0	5	10	0.0	0.0	104.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-5	1	1	0	25	2	25	1	1			7													3	6	2	15	2	0	6	10	0.0	0.0	105.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-6	1	1	0	26	1	26	1	1																1	7	1	16	2	0	7	10	0.0	0.0	106.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-7	1	1	0	27	2	27	1	1																2	8	2	17	2	0	8	10	0.0	0.0	107.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-8	1	1	0	28	1	28	1	1																3	1	1	18	2	0	9	10	0.0	0.0	108.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-9	1	1	0	29	2	29	1	1																1	2	2	19	2	0	10	10	0.0	0.0	109.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-10	1	1	0	30	1	30	1	1			7													2	3	1	20	2	0	11	10	0.0	0.0	110.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-11	1	1	0	31	2	31	1	1																3	4	2	21	2	0	12	10	0.0	0.0	111.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-12	1	1	0	32	1	32	1	1																1	5	1	22	2	0	13	10	0.0	0.0	112.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-13	1	1	0	33	2	33	1	1																2	6	2	23	2	0	14	10	0.0	0.0	113.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-14	1	1	0	34	1	34	1	1																3	7	1	24	2	0	15	10	0.0	0.0	114.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-15	1	1	0	35	2	35	1	1			7													1	8	2	25	2	0	16	10	0.0	0.0	115.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-16	1	1	0	36	1	36	1	1																2	1	1	26	2	0	17	10	0.0	0.0	116.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-17	1	1	0	37	2	37	1	1																3	2	2	27	2	0	18	10	0.0	0.0	117.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-18	1	1	0	38	1	38	1	1																1	3	1	28	2	0	1	10	0.0	0.0	118.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-19	1	1	0	39	2	39	1	1																2	4	2	29	2	0	2	10	0.0	0.0	119.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-20	1	1	0	40	1	40	1	1			7													3	5	1	30	2	0	3	10	0.0	0.0	120.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-21	1	1	0	41	2	41	1	1																1	6	2	31	2	0	4	10	0.0	0.0	121.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-22	1	1	0	42	1	42	1	1																2	7	1	32	2	0	5	10	0.0	0.0	122.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-23	1	1	0	43	2	43	1	1																3	8	2	33	2	0	6	10	0.0	0.0	123.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-24	1	1	0	44	1	44	1	1																1	1	1	34	2	0	7	10	0.0	0.0	124.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-25	1	1	0	45	2	20	1	1			7													2	2	2	35	2	0	8	10	0.0	0.0	125.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-26	1	1	0	46	1	21	1	1																3	3	1	36	2	0	9	10	0.0	0.0	126.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-27	1	1	0	47	2	22	1	1																1	4	2	37	2	0	10	10	0.0	0.0	127.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-28	1	1	0	48	1	23	1	1																2	5	1	38	2	0	11	10	0.0	0.0	128.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-29	1	1	0	49	2	24	1	1																3	6	2	39	2	0	12	10	0.0	0.0	129.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-30	1	1	0	50	1	25	1	1			7													1	7	1	40	2	0	13	10	0.0	0.0	130.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-31	1	1	0	51	2	26	1	1																2	8	2	41	2	0	14	10	0.0	0.0	131.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-32	1	1	0	52	1	27	1	1																3	1	1	42	2	0	15	10	0.0	0.0	132.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-33	1	1	0	53	2	28	1	1																1	2	2	43	2	0	16	10	0.0	0.0	133.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-34	1	1	0	54	1	29	1	1																2	3	1	44	2	0	17	10	0.0	0.0	134.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-35	1	1	0	55	2	30	1	1			7													3	4	2	45	2	0	18	10	0.0	0.0	135.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-36	1	1	0	56	1	31	1	1																1	5	1	46	2	0	1	10	0.0	0.0	136.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-37	1	1	0	57	2	32	1	1																2	6	2	47	2	0	2	10	0.0	0.0	137.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
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-91	1	1	0	56	2	36	1	1																2	4	2	21	2	0	2	10	0.0	0.0	191.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-92	1	1	0	57	1	37	1	1																3	5	1	22	2	0	3	10	0.0	0.0	192.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-93	1	1	0	58	2	38	1	1																1	6	2	23	2	0	4	10	0.0	0.0	193.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
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-97	1	1	0	62	2	42	1	1																2	2	2	27	2	0	8	10	0.0	0.0	197.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-98	1	1	0	63	1	43	1	1																3	3	1	28	2	0	9	10	0.0	0.0	198.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
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-106	1	1	0	71	1	26	1	1																2	3	1	36	2	0	17	10	0.0	0.0	206.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
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-134	1	1	0	44	1	29	1	1																3	7	1	64	2	0	9	10	0.0	0.0	234.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
-135	1	1	0	45	2	30	1	1			7													1	8	2	65	2	0	10	10	0.0	0.0	235.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	2.0	3.0	5.0
+SERNUM	BENUNIT	PERSON	AGE80	AGE	SEX	TOTHOURS	HRPID	UPERSON	RELHRP	R01	R02	R03	R04	R05	R06	R07	R08	R09	R10	R11	R12	R13	R14	MARITAL	EMPSTATI	SAMESIT	SRENTAMT	CONVBL	SDEMP01	SDEMP02	SDEMP03	SDEMP04	SDEMP05	SDEMP06	SDEMP07	SDEMP08	SDEMP09	SDEMP10	SDEMP11	SDEMP12	MJOBSECT	SIC	FTED	TYPEED2	EDUCQUAL	TRAIN	EMAAMT	CHEMAAMT	INEARNS	SEINCAM2	MNTUS1	MNTUSAM1	MNTAMT1	MNTAMT2	CVPAY	ROYYR1	ROYYR2	ROYYR3	ROYYR4	ALLPAY2	ALLPAY3	ALLPAY4	CHAMTERN	CHAMTTST	APAMT	APDAMT	PAREAMT	REDAMT	SLREPAMT	SSPADJ	SMPADJ	TUBORR	ACCSSAMT	ACCSSPD	SPCREG1	SPCREG2	GRTDIR1	GRTDIR2	HEARTVAL
+1	1	1	0	80	2	21	1	1																2	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	11	2	0	2	10	0.0	0.0	101.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+2	1	1	0	22	1	22	1	1																3	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	12	2	0	3	10	0.0	0.0	102.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+3	1	1	0	23	2	23	1	1																1	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	13	2	0	4	10	0.0	0.0	103.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+4	1	1	0	24	1	24	1	1																2	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	14	2	0	5	10	0.0	0.0	104.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+5	1	1	0	25	2	25	1	1			7													3	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	15	2	0	6	10	0.0	0.0	105.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+6	1	1	0	26	1	26	1	1																1	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	16	2	0	7	10	0.0	0.0	106.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+7	1	1	0	27	2	27	1	1																2	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	17	2	0	8	10	0.0	0.0	107.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+8	1	1	0	28	1	28	1	1																3	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	18	2	0	9	10	0.0	0.0	108.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+9	1	1	0	29	2	29	1	1																1	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	19	2	0	10	10	0.0	0.0	109.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+10	1	1	0	30	1	30	1	1			7													2	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	20	2	0	11	10	0.0	0.0	110.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+11	1	1	0	31	2	31	1	1																3	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	21	2	0	12	10	0.0	0.0	111.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+12	1	1	0	32	1	32	1	1																1	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	22	2	0	13	10	0.0	0.0	112.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+13	1	1	0	33	2	33	1	1																2	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	23	2	0	14	10	0.0	0.0	113.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+14	1	1	0	34	1	34	1	1																3	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	24	2	0	15	10	0.0	0.0	114.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+15	1	1	0	35	2	35	1	1			7													1	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	25	2	0	16	10	0.0	0.0	115.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+16	1	1	0	36	1	36	1	1																2	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	26	2	0	17	10	0.0	0.0	116.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+17	1	1	0	37	2	37	1	1																3	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	27	2	0	18	10	0.0	0.0	117.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+18	1	1	0	38	1	38	1	1																1	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	28	2	0	1	10	0.0	0.0	118.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+19	1	1	0	39	2	39	1	1																2	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	29	2	0	2	10	0.0	0.0	119.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+20	1	1	0	40	1	40	1	1			7													3	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	30	2	0	3	10	0.0	0.0	120.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+21	1	1	0	41	2	41	1	1																1	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	31	2	0	4	10	0.0	0.0	121.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+22	1	1	0	42	1	42	1	1																2	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	32	2	0	5	10	0.0	0.0	122.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+23	1	1	0	43	2	43	1	1																3	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	33	2	0	6	10	0.0	0.0	123.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+24	1	1	0	44	1	44	1	1																1	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	34	2	0	7	10	0.0	0.0	124.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+25	1	1	0	45	2	20	1	1			7													2	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	35	2	0	8	10	0.0	0.0	125.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+26	1	1	0	46	1	21	1	1																3	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	36	2	0	9	10	0.0	0.0	126.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+27	1	1	0	47	2	22	1	1																1	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	37	2	0	10	10	0.0	0.0	127.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+28	1	1	0	48	1	23	1	1																2	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	38	2	0	11	10	0.0	0.0	128.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+29	1	1	0	49	2	24	1	1																3	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	39	2	0	12	10	0.0	0.0	129.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+30	1	1	0	50	1	25	1	1			7													1	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	40	2	0	13	10	0.0	0.0	130.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+31	1	1	0	51	2	26	1	1																2	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	41	2	0	14	10	0.0	0.0	131.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+32	1	1	0	52	1	27	1	1																3	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	42	2	0	15	10	0.0	0.0	132.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+33	1	1	0	53	2	28	1	1																1	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	43	2	0	16	10	0.0	0.0	133.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+34	1	1	0	54	1	29	1	1																2	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	44	2	0	17	10	0.0	0.0	134.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+35	1	1	0	55	2	30	1	1			7													3	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	45	2	0	18	10	0.0	0.0	135.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	1	2.0	3.0	5.0
+36	1	1	0	56	1	31	1	1																1	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	46	2	0	1	10	0.0	0.0	136.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
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+119	1	1	0	29	2	39	1	1																3	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	49	2	0	12	10	0.0	0.0	219.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+120	1	1	0	30	1	40	1	1			7													1	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	50	2	0	13	10	0.0	0.0	220.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+121	1	1	0	31	2	41	1	1																2	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	51	2	0	14	10	0.0	0.0	221.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+122	1	1	0	32	1	42	1	1																3	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	52	2	0	15	10	0.0	0.0	222.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+123	1	1	0	33	2	43	1	1																1	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	53	2	0	16	10	0.0	0.0	223.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+124	1	1	0	34	1	44	1	1																2	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	54	2	0	17	10	0.0	0.0	224.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+125	1	1	0	35	2	20	1	1			7													3	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	55	2	0	18	10	0.0	0.0	225.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+126	1	1	0	36	1	21	1	1																1	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	56	2	0	1	10	0.0	0.0	226.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+127	1	1	0	37	2	22	1	1																2	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	57	2	0	2	10	0.0	0.0	227.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+128	1	1	0	38	1	23	1	1																3	1	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	58	2	0	3	10	0.0	0.0	228.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+129	1	1	0	39	2	24	1	1																1	2	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	59	2	0	4	10	0.0	0.0	229.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+130	1	1	0	40	1	25	1	1			7													2	3	1		2	3	3	3	3	3	3	3	3	3	3	3	3	1	60	2	0	5	10	0.0	0.0	230.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
+131	1	1	0	41	2	26	1	1																3	4	2		1	3	3	3	3	3	3	3	3	3	3	3	3	2	61	2	0	6	10	0.0	0.0	231.0	1.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+132	1	1	0	42	1	27	1	1																1	5	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	62	2	0	7	10	0.0	0.0	232.0	2.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+133	1	1	0	43	2	28	1	1																2	6	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	63	2	0	8	10	0.0	0.0	233.0	3.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	1	2	2.0	3.0	5.0
+134	1	1	0	44	1	29	1	1																3	7	1		2	1	1	1	1	1	1	1	1	1	1	1	1	1	64	2	0	9	10	0.0	0.0	234.0	4.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	2	2.0	3.0	5.0
+135	1	1	0	45	2	30	1	1			7													1	8	2		1	1	1	1	1	1	1	1	1	1	1	1	1	2	65	2	0	10	10	0.0	0.0	235.0	0.0	2	1.0	2.0	1.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0	11.0	12.0	13.0	20.0	2.0	1.0	0.5	500.0	1.0	52.0	2	1	2.0	3.0	5.0
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab
index 5670db119..5cfcc21c0 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benefits.tab
@@ -1,946 +1,991 @@
-SERNUM	BENUNIT	PERSON	BENEFIT	VAR2	BENAMT
-1	1	1	14	1	2.0
-1	1	1	14	2	3.0
-1	1	1	16	3	4.0
-1	1	1	16	4	5.0
-1	1	1	5	0	1.0
-1	1	1	6	0	6.0
-1	1	1	3	0	7.0
-2	1	1	14	1	2.0
-2	1	1	14	2	3.0
-2	1	1	16	3	4.0
-2	1	1	16	4	5.0
-2	1	1	5	0	1.0
-2	1	1	6	0	6.0
-2	1	1	3	0	7.0
-3	1	1	14	1	2.0
-3	1	1	14	2	3.0
-3	1	1	16	3	4.0
-3	1	1	16	4	5.0
-3	1	1	5	0	1.0
-3	1	1	6	0	6.0
-3	1	1	3	0	7.0
-4	1	1	14	1	2.0
-4	1	1	14	2	3.0
-4	1	1	16	3	4.0
-4	1	1	16	4	5.0
-4	1	1	5	0	1.0
-4	1	1	6	0	6.0
-4	1	1	3	0	7.0
-5	1	1	14	1	2.0
-5	1	1	14	2	3.0
-5	1	1	16	3	4.0
-5	1	1	16	4	5.0
-5	1	1	5	0	1.0
-5	1	1	6	0	6.0
-5	1	1	3	0	7.0
-6	1	1	14	1	2.0
-6	1	1	14	2	3.0
-6	1	1	16	3	4.0
-6	1	1	16	4	5.0
-6	1	1	5	0	1.0
-6	1	1	6	0	6.0
-6	1	1	3	0	7.0
-7	1	1	14	1	2.0
-7	1	1	14	2	3.0
-7	1	1	16	3	4.0
-7	1	1	16	4	5.0
-7	1	1	5	0	1.0
-7	1	1	6	0	6.0
-7	1	1	3	0	7.0
-8	1	1	14	1	2.0
-8	1	1	14	2	3.0
-8	1	1	16	3	4.0
-8	1	1	16	4	5.0
-8	1	1	5	0	1.0
-8	1	1	6	0	6.0
-8	1	1	3	0	7.0
-9	1	1	14	1	2.0
-9	1	1	14	2	3.0
-9	1	1	16	3	4.0
-9	1	1	16	4	5.0
-9	1	1	5	0	1.0
-9	1	1	6	0	6.0
-9	1	1	3	0	7.0
-10	1	1	14	1	2.0
-10	1	1	14	2	3.0
-10	1	1	16	3	4.0
-10	1	1	16	4	5.0
-10	1	1	5	0	1.0
-10	1	1	6	0	6.0
-10	1	1	3	0	7.0
-11	1	1	14	1	2.0
-11	1	1	14	2	3.0
-11	1	1	16	3	4.0
-11	1	1	16	4	5.0
-11	1	1	5	0	1.0
-11	1	1	6	0	6.0
-11	1	1	3	0	7.0
-12	1	1	14	1	2.0
-12	1	1	14	2	3.0
-12	1	1	16	3	4.0
-12	1	1	16	4	5.0
-12	1	1	5	0	1.0
-12	1	1	6	0	6.0
-12	1	1	3	0	7.0
-13	1	1	14	1	2.0
-13	1	1	14	2	3.0
-13	1	1	16	3	4.0
-13	1	1	16	4	5.0
-13	1	1	5	0	1.0
-13	1	1	6	0	6.0
-13	1	1	3	0	7.0
-14	1	1	14	1	2.0
-14	1	1	14	2	3.0
-14	1	1	16	3	4.0
-14	1	1	16	4	5.0
-14	1	1	5	0	1.0
-14	1	1	6	0	6.0
-14	1	1	3	0	7.0
-15	1	1	14	1	2.0
-15	1	1	14	2	3.0
-15	1	1	16	3	4.0
-15	1	1	16	4	5.0
-15	1	1	5	0	1.0
-15	1	1	6	0	6.0
-15	1	1	3	0	7.0
-16	1	1	14	1	2.0
-16	1	1	14	2	3.0
-16	1	1	16	3	4.0
-16	1	1	16	4	5.0
-16	1	1	5	0	1.0
-16	1	1	6	0	6.0
-16	1	1	3	0	7.0
-17	1	1	14	1	2.0
-17	1	1	14	2	3.0
-17	1	1	16	3	4.0
-17	1	1	16	4	5.0
-17	1	1	5	0	1.0
-17	1	1	6	0	6.0
-17	1	1	3	0	7.0
-18	1	1	14	1	2.0
-18	1	1	14	2	3.0
-18	1	1	16	3	4.0
-18	1	1	16	4	5.0
-18	1	1	5	0	1.0
-18	1	1	6	0	6.0
-18	1	1	3	0	7.0
-19	1	1	14	1	2.0
-19	1	1	14	2	3.0
-19	1	1	16	3	4.0
-19	1	1	16	4	5.0
-19	1	1	5	0	1.0
-19	1	1	6	0	6.0
-19	1	1	3	0	7.0
-20	1	1	14	1	2.0
-20	1	1	14	2	3.0
-20	1	1	16	3	4.0
-20	1	1	16	4	5.0
-20	1	1	5	0	1.0
-20	1	1	6	0	6.0
-20	1	1	3	0	7.0
-21	1	1	14	1	2.0
-21	1	1	14	2	3.0
-21	1	1	16	3	4.0
-21	1	1	16	4	5.0
-21	1	1	5	0	1.0
-21	1	1	6	0	6.0
-21	1	1	3	0	7.0
-22	1	1	14	1	2.0
-22	1	1	14	2	3.0
-22	1	1	16	3	4.0
-22	1	1	16	4	5.0
-22	1	1	5	0	1.0
-22	1	1	6	0	6.0
-22	1	1	3	0	7.0
-23	1	1	14	1	2.0
-23	1	1	14	2	3.0
-23	1	1	16	3	4.0
-23	1	1	16	4	5.0
-23	1	1	5	0	1.0
-23	1	1	6	0	6.0
-23	1	1	3	0	7.0
-24	1	1	14	1	2.0
-24	1	1	14	2	3.0
-24	1	1	16	3	4.0
-24	1	1	16	4	5.0
-24	1	1	5	0	1.0
-24	1	1	6	0	6.0
-24	1	1	3	0	7.0
-25	1	1	14	1	2.0
-25	1	1	14	2	3.0
-25	1	1	16	3	4.0
-25	1	1	16	4	5.0
-25	1	1	5	0	1.0
-25	1	1	6	0	6.0
-25	1	1	3	0	7.0
-26	1	1	14	1	2.0
-26	1	1	14	2	3.0
-26	1	1	16	3	4.0
-26	1	1	16	4	5.0
-26	1	1	5	0	1.0
-26	1	1	6	0	6.0
-26	1	1	3	0	7.0
-27	1	1	14	1	2.0
-27	1	1	14	2	3.0
-27	1	1	16	3	4.0
-27	1	1	16	4	5.0
-27	1	1	5	0	1.0
-27	1	1	6	0	6.0
-27	1	1	3	0	7.0
-28	1	1	14	1	2.0
-28	1	1	14	2	3.0
-28	1	1	16	3	4.0
-28	1	1	16	4	5.0
-28	1	1	5	0	1.0
-28	1	1	6	0	6.0
-28	1	1	3	0	7.0
-29	1	1	14	1	2.0
-29	1	1	14	2	3.0
-29	1	1	16	3	4.0
-29	1	1	16	4	5.0
-29	1	1	5	0	1.0
-29	1	1	6	0	6.0
-29	1	1	3	0	7.0
-30	1	1	14	1	2.0
-30	1	1	14	2	3.0
-30	1	1	16	3	4.0
-30	1	1	16	4	5.0
-30	1	1	5	0	1.0
-30	1	1	6	0	6.0
-30	1	1	3	0	7.0
-31	1	1	14	1	2.0
-31	1	1	14	2	3.0
-31	1	1	16	3	4.0
-31	1	1	16	4	5.0
-31	1	1	5	0	1.0
-31	1	1	6	0	6.0
-31	1	1	3	0	7.0
-32	1	1	14	1	2.0
-32	1	1	14	2	3.0
-32	1	1	16	3	4.0
-32	1	1	16	4	5.0
-32	1	1	5	0	1.0
-32	1	1	6	0	6.0
-32	1	1	3	0	7.0
-33	1	1	14	1	2.0
-33	1	1	14	2	3.0
-33	1	1	16	3	4.0
-33	1	1	16	4	5.0
-33	1	1	5	0	1.0
-33	1	1	6	0	6.0
-33	1	1	3	0	7.0
-34	1	1	14	1	2.0
-34	1	1	14	2	3.0
-34	1	1	16	3	4.0
-34	1	1	16	4	5.0
-34	1	1	5	0	1.0
-34	1	1	6	0	6.0
-34	1	1	3	0	7.0
-35	1	1	14	1	2.0
-35	1	1	14	2	3.0
-35	1	1	16	3	4.0
-35	1	1	16	4	5.0
-35	1	1	5	0	1.0
-35	1	1	6	0	6.0
-35	1	1	3	0	7.0
-36	1	1	14	1	2.0
-36	1	1	14	2	3.0
-36	1	1	16	3	4.0
-36	1	1	16	4	5.0
-36	1	1	5	0	1.0
-36	1	1	6	0	6.0
-36	1	1	3	0	7.0
-37	1	1	14	1	2.0
-37	1	1	14	2	3.0
-37	1	1	16	3	4.0
-37	1	1	16	4	5.0
-37	1	1	5	0	1.0
-37	1	1	6	0	6.0
-37	1	1	3	0	7.0
-38	1	1	14	1	2.0
-38	1	1	14	2	3.0
-38	1	1	16	3	4.0
-38	1	1	16	4	5.0
-38	1	1	5	0	1.0
-38	1	1	6	0	6.0
-38	1	1	3	0	7.0
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-111	1	1	3	0	7.0
-112	1	1	14	1	2.0
-112	1	1	14	2	3.0
-112	1	1	16	3	4.0
-112	1	1	16	4	5.0
-112	1	1	5	0	1.0
-112	1	1	6	0	6.0
-112	1	1	3	0	7.0
-113	1	1	14	1	2.0
-113	1	1	14	2	3.0
-113	1	1	16	3	4.0
-113	1	1	16	4	5.0
-113	1	1	5	0	1.0
-113	1	1	6	0	6.0
-113	1	1	3	0	7.0
-114	1	1	14	1	2.0
-114	1	1	14	2	3.0
-114	1	1	16	3	4.0
-114	1	1	16	4	5.0
-114	1	1	5	0	1.0
-114	1	1	6	0	6.0
-114	1	1	3	0	7.0
-115	1	1	14	1	2.0
-115	1	1	14	2	3.0
-115	1	1	16	3	4.0
-115	1	1	16	4	5.0
-115	1	1	5	0	1.0
-115	1	1	6	0	6.0
-115	1	1	3	0	7.0
-116	1	1	14	1	2.0
-116	1	1	14	2	3.0
-116	1	1	16	3	4.0
-116	1	1	16	4	5.0
-116	1	1	5	0	1.0
-116	1	1	6	0	6.0
-116	1	1	3	0	7.0
-117	1	1	14	1	2.0
-117	1	1	14	2	3.0
-117	1	1	16	3	4.0
-117	1	1	16	4	5.0
-117	1	1	5	0	1.0
-117	1	1	6	0	6.0
-117	1	1	3	0	7.0
-118	1	1	14	1	2.0
-118	1	1	14	2	3.0
-118	1	1	16	3	4.0
-118	1	1	16	4	5.0
-118	1	1	5	0	1.0
-118	1	1	6	0	6.0
-118	1	1	3	0	7.0
-119	1	1	14	1	2.0
-119	1	1	14	2	3.0
-119	1	1	16	3	4.0
-119	1	1	16	4	5.0
-119	1	1	5	0	1.0
-119	1	1	6	0	6.0
-119	1	1	3	0	7.0
-120	1	1	14	1	2.0
-120	1	1	14	2	3.0
-120	1	1	16	3	4.0
-120	1	1	16	4	5.0
-120	1	1	5	0	1.0
-120	1	1	6	0	6.0
-120	1	1	3	0	7.0
-121	1	1	14	1	2.0
-121	1	1	14	2	3.0
-121	1	1	16	3	4.0
-121	1	1	16	4	5.0
-121	1	1	5	0	1.0
-121	1	1	6	0	6.0
-121	1	1	3	0	7.0
-122	1	1	14	1	2.0
-122	1	1	14	2	3.0
-122	1	1	16	3	4.0
-122	1	1	16	4	5.0
-122	1	1	5	0	1.0
-122	1	1	6	0	6.0
-122	1	1	3	0	7.0
-123	1	1	14	1	2.0
-123	1	1	14	2	3.0
-123	1	1	16	3	4.0
-123	1	1	16	4	5.0
-123	1	1	5	0	1.0
-123	1	1	6	0	6.0
-123	1	1	3	0	7.0
-124	1	1	14	1	2.0
-124	1	1	14	2	3.0
-124	1	1	16	3	4.0
-124	1	1	16	4	5.0
-124	1	1	5	0	1.0
-124	1	1	6	0	6.0
-124	1	1	3	0	7.0
-125	1	1	14	1	2.0
-125	1	1	14	2	3.0
-125	1	1	16	3	4.0
-125	1	1	16	4	5.0
-125	1	1	5	0	1.0
-125	1	1	6	0	6.0
-125	1	1	3	0	7.0
-126	1	1	14	1	2.0
-126	1	1	14	2	3.0
-126	1	1	16	3	4.0
-126	1	1	16	4	5.0
-126	1	1	5	0	1.0
-126	1	1	6	0	6.0
-126	1	1	3	0	7.0
-127	1	1	14	1	2.0
-127	1	1	14	2	3.0
-127	1	1	16	3	4.0
-127	1	1	16	4	5.0
-127	1	1	5	0	1.0
-127	1	1	6	0	6.0
-127	1	1	3	0	7.0
-128	1	1	14	1	2.0
-128	1	1	14	2	3.0
-128	1	1	16	3	4.0
-128	1	1	16	4	5.0
-128	1	1	5	0	1.0
-128	1	1	6	0	6.0
-128	1	1	3	0	7.0
-129	1	1	14	1	2.0
-129	1	1	14	2	3.0
-129	1	1	16	3	4.0
-129	1	1	16	4	5.0
-129	1	1	5	0	1.0
-129	1	1	6	0	6.0
-129	1	1	3	0	7.0
-130	1	1	14	1	2.0
-130	1	1	14	2	3.0
-130	1	1	16	3	4.0
-130	1	1	16	4	5.0
-130	1	1	5	0	1.0
-130	1	1	6	0	6.0
-130	1	1	3	0	7.0
-131	1	1	14	1	2.0
-131	1	1	14	2	3.0
-131	1	1	16	3	4.0
-131	1	1	16	4	5.0
-131	1	1	5	0	1.0
-131	1	1	6	0	6.0
-131	1	1	3	0	7.0
-132	1	1	14	1	2.0
-132	1	1	14	2	3.0
-132	1	1	16	3	4.0
-132	1	1	16	4	5.0
-132	1	1	5	0	1.0
-132	1	1	6	0	6.0
-132	1	1	3	0	7.0
-133	1	1	14	1	2.0
-133	1	1	14	2	3.0
-133	1	1	16	3	4.0
-133	1	1	16	4	5.0
-133	1	1	5	0	1.0
-133	1	1	6	0	6.0
-133	1	1	3	0	7.0
-134	1	1	14	1	2.0
-134	1	1	14	2	3.0
-134	1	1	16	3	4.0
-134	1	1	16	4	5.0
-134	1	1	5	0	1.0
-134	1	1	6	0	6.0
-134	1	1	3	0	7.0
-135	1	1	14	1	2.0
-135	1	1	14	2	3.0
-135	1	1	16	3	4.0
-135	1	1	16	4	5.0
-135	1	1	5	0	1.0
-135	1	1	6	0	6.0
-135	1	1	3	0	7.0
+SERNUM	BENUNIT	PERSON	BENEFIT	VAR2	BENAMT	UCSTART	UCHOUSEL
+1	1	1	14	1	2.0		
+1	1	1	14	2	3.0		
+1	1	1	16	3	4.0		
+1	1	1	16	4	5.0		
+1	1	1	5	0	1.0		
+1	1	1	6	0	6.0		
+1	1	1	3	0	7.0		
+2	1	1	14	1	2.0		
+2	1	1	14	2	3.0		
+2	1	1	16	3	4.0		
+2	1	1	16	4	5.0		
+2	1	1	5	0	1.0		
+2	1	1	6	0	6.0		
+2	1	1	3	0	7.0		
+3	1	1	14	1	2.0		
+3	1	1	14	2	3.0		
+3	1	1	16	3	4.0		
+3	1	1	16	4	5.0		
+3	1	1	5	0	1.0		
+3	1	1	6	0	6.0		
+3	1	1	3	0	7.0		
+3	1	1	95	0	8.0	01/15/2022	50.0
+4	1	1	14	1	2.0		
+4	1	1	14	2	3.0		
+4	1	1	16	3	4.0		
+4	1	1	16	4	5.0		
+4	1	1	5	0	1.0		
+4	1	1	6	0	6.0		
+4	1	1	3	0	7.0		
+5	1	1	14	1	2.0		
+5	1	1	14	2	3.0		
+5	1	1	16	3	4.0		
+5	1	1	16	4	5.0		
+5	1	1	5	0	1.0		
+5	1	1	6	0	6.0		
+5	1	1	3	0	7.0		
+6	1	1	14	1	2.0		
+6	1	1	14	2	3.0		
+6	1	1	16	3	4.0		
+6	1	1	16	4	5.0		
+6	1	1	5	0	1.0		
+6	1	1	6	0	6.0		
+6	1	1	3	0	7.0		
+6	1	1	95	0	8.0		
+7	1	1	14	1	2.0		
+7	1	1	14	2	3.0		
+7	1	1	16	3	4.0		
+7	1	1	16	4	5.0		
+7	1	1	5	0	1.0		
+7	1	1	6	0	6.0		
+7	1	1	3	0	7.0		
+8	1	1	14	1	2.0		
+8	1	1	14	2	3.0		
+8	1	1	16	3	4.0		
+8	1	1	16	4	5.0		
+8	1	1	5	0	1.0		
+8	1	1	6	0	6.0		
+8	1	1	3	0	7.0		
+9	1	1	14	1	2.0		
+9	1	1	14	2	3.0		
+9	1	1	16	3	4.0		
+9	1	1	16	4	5.0		
+9	1	1	5	0	1.0		
+9	1	1	6	0	6.0		
+9	1	1	3	0	7.0		
+9	1	1	95	0	8.0	07/01/2024	50.0
+10	1	1	14	1	2.0		
+10	1	1	14	2	3.0		
+10	1	1	16	3	4.0		
+10	1	1	16	4	5.0		
+10	1	1	5	0	1.0		
+10	1	1	6	0	6.0		
+10	1	1	3	0	7.0		
+11	1	1	14	1	2.0		
+11	1	1	14	2	3.0		
+11	1	1	16	3	4.0		
+11	1	1	16	4	5.0		
+11	1	1	5	0	1.0		
+11	1	1	6	0	6.0		
+11	1	1	3	0	7.0		
+12	1	1	14	1	2.0		
+12	1	1	14	2	3.0		
+12	1	1	16	3	4.0		
+12	1	1	16	4	5.0		
+12	1	1	5	0	1.0		
+12	1	1	6	0	6.0		
+12	1	1	3	0	7.0		
+12	1	1	95	0	8.0	01/15/2022	
+13	1	1	14	1	2.0		
+13	1	1	14	2	3.0		
+13	1	1	16	3	4.0		
+13	1	1	16	4	5.0		
+13	1	1	5	0	1.0		
+13	1	1	6	0	6.0		
+13	1	1	3	0	7.0		
+14	1	1	14	1	2.0		
+14	1	1	14	2	3.0		
+14	1	1	16	3	4.0		
+14	1	1	16	4	5.0		
+14	1	1	5	0	1.0		
+14	1	1	6	0	6.0		
+14	1	1	3	0	7.0		
+15	1	1	14	1	2.0		
+15	1	1	14	2	3.0		
+15	1	1	16	3	4.0		
+15	1	1	16	4	5.0		
+15	1	1	5	0	1.0		
+15	1	1	6	0	6.0		
+15	1	1	3	0	7.0		
+15	1	1	95	0	8.0		50.0
+16	1	1	14	1	2.0		
+16	1	1	14	2	3.0		
+16	1	1	16	3	4.0		
+16	1	1	16	4	5.0		
+16	1	1	5	0	1.0		
+16	1	1	6	0	6.0		
+16	1	1	3	0	7.0		
+17	1	1	14	1	2.0		
+17	1	1	14	2	3.0		
+17	1	1	16	3	4.0		
+17	1	1	16	4	5.0		
+17	1	1	5	0	1.0		
+17	1	1	6	0	6.0		
+17	1	1	3	0	7.0		
+18	1	1	14	1	2.0		
+18	1	1	14	2	3.0		
+18	1	1	16	3	4.0		
+18	1	1	16	4	5.0		
+18	1	1	5	0	1.0		
+18	1	1	6	0	6.0		
+18	1	1	3	0	7.0		
+18	1	1	95	0	8.0	07/01/2024	
+19	1	1	14	1	2.0		
+19	1	1	14	2	3.0		
+19	1	1	16	3	4.0		
+19	1	1	16	4	5.0		
+19	1	1	5	0	1.0		
+19	1	1	6	0	6.0		
+19	1	1	3	0	7.0		
+20	1	1	14	1	2.0		
+20	1	1	14	2	3.0		
+20	1	1	16	3	4.0		
+20	1	1	16	4	5.0		
+20	1	1	5	0	1.0		
+20	1	1	6	0	6.0		
+20	1	1	3	0	7.0		
+21	1	1	14	1	2.0		
+21	1	1	14	2	3.0		
+21	1	1	16	3	4.0		
+21	1	1	16	4	5.0		
+21	1	1	5	0	1.0		
+21	1	1	6	0	6.0		
+21	1	1	3	0	7.0		
+21	1	1	95	0	8.0	01/15/2022	50.0
+22	1	1	14	1	2.0		
+22	1	1	14	2	3.0		
+22	1	1	16	3	4.0		
+22	1	1	16	4	5.0		
+22	1	1	5	0	1.0		
+22	1	1	6	0	6.0		
+22	1	1	3	0	7.0		
+23	1	1	14	1	2.0		
+23	1	1	14	2	3.0		
+23	1	1	16	3	4.0		
+23	1	1	16	4	5.0		
+23	1	1	5	0	1.0		
+23	1	1	6	0	6.0		
+23	1	1	3	0	7.0		
+24	1	1	14	1	2.0		
+24	1	1	14	2	3.0		
+24	1	1	16	3	4.0		
+24	1	1	16	4	5.0		
+24	1	1	5	0	1.0		
+24	1	1	6	0	6.0		
+24	1	1	3	0	7.0		
+24	1	1	95	0	8.0		
+25	1	1	14	1	2.0		
+25	1	1	14	2	3.0		
+25	1	1	16	3	4.0		
+25	1	1	16	4	5.0		
+25	1	1	5	0	1.0		
+25	1	1	6	0	6.0		
+25	1	1	3	0	7.0		
+26	1	1	14	1	2.0		
+26	1	1	14	2	3.0		
+26	1	1	16	3	4.0		
+26	1	1	16	4	5.0		
+26	1	1	5	0	1.0		
+26	1	1	6	0	6.0		
+26	1	1	3	0	7.0		
+27	1	1	14	1	2.0		
+27	1	1	14	2	3.0		
+27	1	1	16	3	4.0		
+27	1	1	16	4	5.0		
+27	1	1	5	0	1.0		
+27	1	1	6	0	6.0		
+27	1	1	3	0	7.0		
+27	1	1	95	0	8.0	07/01/2024	50.0
+28	1	1	14	1	2.0		
+28	1	1	14	2	3.0		
+28	1	1	16	3	4.0		
+28	1	1	16	4	5.0		
+28	1	1	5	0	1.0		
+28	1	1	6	0	6.0		
+28	1	1	3	0	7.0		
+29	1	1	14	1	2.0		
+29	1	1	14	2	3.0		
+29	1	1	16	3	4.0		
+29	1	1	16	4	5.0		
+29	1	1	5	0	1.0		
+29	1	1	6	0	6.0		
+29	1	1	3	0	7.0		
+30	1	1	14	1	2.0		
+30	1	1	14	2	3.0		
+30	1	1	16	3	4.0		
+30	1	1	16	4	5.0		
+30	1	1	5	0	1.0		
+30	1	1	6	0	6.0		
+30	1	1	3	0	7.0		
+30	1	1	95	0	8.0	01/15/2022	
+31	1	1	14	1	2.0		
+31	1	1	14	2	3.0		
+31	1	1	16	3	4.0		
+31	1	1	16	4	5.0		
+31	1	1	5	0	1.0		
+31	1	1	6	0	6.0		
+31	1	1	3	0	7.0		
+32	1	1	14	1	2.0		
+32	1	1	14	2	3.0		
+32	1	1	16	3	4.0		
+32	1	1	16	4	5.0		
+32	1	1	5	0	1.0		
+32	1	1	6	0	6.0		
+32	1	1	3	0	7.0		
+33	1	1	14	1	2.0		
+33	1	1	14	2	3.0		
+33	1	1	16	3	4.0		
+33	1	1	16	4	5.0		
+33	1	1	5	0	1.0		
+33	1	1	6	0	6.0		
+33	1	1	3	0	7.0		
+33	1	1	95	0	8.0		50.0
+34	1	1	14	1	2.0		
+34	1	1	14	2	3.0		
+34	1	1	16	3	4.0		
+34	1	1	16	4	5.0		
+34	1	1	5	0	1.0		
+34	1	1	6	0	6.0		
+34	1	1	3	0	7.0		
+35	1	1	14	1	2.0		
+35	1	1	14	2	3.0		
+35	1	1	16	3	4.0		
+35	1	1	16	4	5.0		
+35	1	1	5	0	1.0		
+35	1	1	6	0	6.0		
+35	1	1	3	0	7.0		
+36	1	1	14	1	2.0		
+36	1	1	14	2	3.0		
+36	1	1	16	3	4.0		
+36	1	1	16	4	5.0		
+36	1	1	5	0	1.0		
+36	1	1	6	0	6.0		
+36	1	1	3	0	7.0		
+36	1	1	95	0	8.0	07/01/2024	
+37	1	1	14	1	2.0		
+37	1	1	14	2	3.0		
+37	1	1	16	3	4.0		
+37	1	1	16	4	5.0		
+37	1	1	5	0	1.0		
+37	1	1	6	0	6.0		
+37	1	1	3	0	7.0		
+38	1	1	14	1	2.0		
+38	1	1	14	2	3.0		
+38	1	1	16	3	4.0		
+38	1	1	16	4	5.0		
+38	1	1	5	0	1.0		
+38	1	1	6	0	6.0		
+38	1	1	3	0	7.0		
+39	1	1	14	1	2.0		
+39	1	1	14	2	3.0		
+39	1	1	16	3	4.0		
+39	1	1	16	4	5.0		
+39	1	1	5	0	1.0		
+39	1	1	6	0	6.0		
+39	1	1	3	0	7.0		
+39	1	1	95	0	8.0	01/15/2022	50.0
+40	1	1	14	1	2.0		
+40	1	1	14	2	3.0		
+40	1	1	16	3	4.0		
+40	1	1	16	4	5.0		
+40	1	1	5	0	1.0		
+40	1	1	6	0	6.0		
+40	1	1	3	0	7.0		
+41	1	1	14	1	2.0		
+41	1	1	14	2	3.0		
+41	1	1	16	3	4.0		
+41	1	1	16	4	5.0		
+41	1	1	5	0	1.0		
+41	1	1	6	0	6.0		
+41	1	1	3	0	7.0		
+42	1	1	14	1	2.0		
+42	1	1	14	2	3.0		
+42	1	1	16	3	4.0		
+42	1	1	16	4	5.0		
+42	1	1	5	0	1.0		
+42	1	1	6	0	6.0		
+42	1	1	3	0	7.0		
+42	1	1	95	0	8.0		
+43	1	1	14	1	2.0		
+43	1	1	14	2	3.0		
+43	1	1	16	3	4.0		
+43	1	1	16	4	5.0		
+43	1	1	5	0	1.0		
+43	1	1	6	0	6.0		
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+108	1	1	14	2	3.0		
+108	1	1	16	3	4.0		
+108	1	1	16	4	5.0		
+108	1	1	5	0	1.0		
+108	1	1	6	0	6.0		
+108	1	1	3	0	7.0		
+108	1	1	95	0	8.0	07/01/2024	
+109	1	1	14	1	2.0		
+109	1	1	14	2	3.0		
+109	1	1	16	3	4.0		
+109	1	1	16	4	5.0		
+109	1	1	5	0	1.0		
+109	1	1	6	0	6.0		
+109	1	1	3	0	7.0		
+110	1	1	14	1	2.0		
+110	1	1	14	2	3.0		
+110	1	1	16	3	4.0		
+110	1	1	16	4	5.0		
+110	1	1	5	0	1.0		
+110	1	1	6	0	6.0		
+110	1	1	3	0	7.0		
+111	1	1	14	1	2.0		
+111	1	1	14	2	3.0		
+111	1	1	16	3	4.0		
+111	1	1	16	4	5.0		
+111	1	1	5	0	1.0		
+111	1	1	6	0	6.0		
+111	1	1	3	0	7.0		
+111	1	1	95	0	8.0	01/15/2022	50.0
+112	1	1	14	1	2.0		
+112	1	1	14	2	3.0		
+112	1	1	16	3	4.0		
+112	1	1	16	4	5.0		
+112	1	1	5	0	1.0		
+112	1	1	6	0	6.0		
+112	1	1	3	0	7.0		
+113	1	1	14	1	2.0		
+113	1	1	14	2	3.0		
+113	1	1	16	3	4.0		
+113	1	1	16	4	5.0		
+113	1	1	5	0	1.0		
+113	1	1	6	0	6.0		
+113	1	1	3	0	7.0		
+114	1	1	14	1	2.0		
+114	1	1	14	2	3.0		
+114	1	1	16	3	4.0		
+114	1	1	16	4	5.0		
+114	1	1	5	0	1.0		
+114	1	1	6	0	6.0		
+114	1	1	3	0	7.0		
+114	1	1	95	0	8.0		
+115	1	1	14	1	2.0		
+115	1	1	14	2	3.0		
+115	1	1	16	3	4.0		
+115	1	1	16	4	5.0		
+115	1	1	5	0	1.0		
+115	1	1	6	0	6.0		
+115	1	1	3	0	7.0		
+116	1	1	14	1	2.0		
+116	1	1	14	2	3.0		
+116	1	1	16	3	4.0		
+116	1	1	16	4	5.0		
+116	1	1	5	0	1.0		
+116	1	1	6	0	6.0		
+116	1	1	3	0	7.0		
+117	1	1	14	1	2.0		
+117	1	1	14	2	3.0		
+117	1	1	16	3	4.0		
+117	1	1	16	4	5.0		
+117	1	1	5	0	1.0		
+117	1	1	6	0	6.0		
+117	1	1	3	0	7.0		
+117	1	1	95	0	8.0	07/01/2024	50.0
+118	1	1	14	1	2.0		
+118	1	1	14	2	3.0		
+118	1	1	16	3	4.0		
+118	1	1	16	4	5.0		
+118	1	1	5	0	1.0		
+118	1	1	6	0	6.0		
+118	1	1	3	0	7.0		
+119	1	1	14	1	2.0		
+119	1	1	14	2	3.0		
+119	1	1	16	3	4.0		
+119	1	1	16	4	5.0		
+119	1	1	5	0	1.0		
+119	1	1	6	0	6.0		
+119	1	1	3	0	7.0		
+120	1	1	14	1	2.0		
+120	1	1	14	2	3.0		
+120	1	1	16	3	4.0		
+120	1	1	16	4	5.0		
+120	1	1	5	0	1.0		
+120	1	1	6	0	6.0		
+120	1	1	3	0	7.0		
+120	1	1	95	0	8.0	01/15/2022	
+121	1	1	14	1	2.0		
+121	1	1	14	2	3.0		
+121	1	1	16	3	4.0		
+121	1	1	16	4	5.0		
+121	1	1	5	0	1.0		
+121	1	1	6	0	6.0		
+121	1	1	3	0	7.0		
+122	1	1	14	1	2.0		
+122	1	1	14	2	3.0		
+122	1	1	16	3	4.0		
+122	1	1	16	4	5.0		
+122	1	1	5	0	1.0		
+122	1	1	6	0	6.0		
+122	1	1	3	0	7.0		
+123	1	1	14	1	2.0		
+123	1	1	14	2	3.0		
+123	1	1	16	3	4.0		
+123	1	1	16	4	5.0		
+123	1	1	5	0	1.0		
+123	1	1	6	0	6.0		
+123	1	1	3	0	7.0		
+123	1	1	95	0	8.0		50.0
+124	1	1	14	1	2.0		
+124	1	1	14	2	3.0		
+124	1	1	16	3	4.0		
+124	1	1	16	4	5.0		
+124	1	1	5	0	1.0		
+124	1	1	6	0	6.0		
+124	1	1	3	0	7.0		
+125	1	1	14	1	2.0		
+125	1	1	14	2	3.0		
+125	1	1	16	3	4.0		
+125	1	1	16	4	5.0		
+125	1	1	5	0	1.0		
+125	1	1	6	0	6.0		
+125	1	1	3	0	7.0		
+126	1	1	14	1	2.0		
+126	1	1	14	2	3.0		
+126	1	1	16	3	4.0		
+126	1	1	16	4	5.0		
+126	1	1	5	0	1.0		
+126	1	1	6	0	6.0		
+126	1	1	3	0	7.0		
+126	1	1	95	0	8.0	07/01/2024	
+127	1	1	14	1	2.0		
+127	1	1	14	2	3.0		
+127	1	1	16	3	4.0		
+127	1	1	16	4	5.0		
+127	1	1	5	0	1.0		
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+128	1	1	14	1	2.0		
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+128	1	1	16	3	4.0		
+128	1	1	16	4	5.0		
+128	1	1	5	0	1.0		
+128	1	1	6	0	6.0		
+128	1	1	3	0	7.0		
+129	1	1	14	1	2.0		
+129	1	1	14	2	3.0		
+129	1	1	16	3	4.0		
+129	1	1	16	4	5.0		
+129	1	1	5	0	1.0		
+129	1	1	6	0	6.0		
+129	1	1	3	0	7.0		
+129	1	1	95	0	8.0	01/15/2022	50.0
+130	1	1	14	1	2.0		
+130	1	1	14	2	3.0		
+130	1	1	16	3	4.0		
+130	1	1	16	4	5.0		
+130	1	1	5	0	1.0		
+130	1	1	6	0	6.0		
+130	1	1	3	0	7.0		
+131	1	1	14	1	2.0		
+131	1	1	14	2	3.0		
+131	1	1	16	3	4.0		
+131	1	1	16	4	5.0		
+131	1	1	5	0	1.0		
+131	1	1	6	0	6.0		
+131	1	1	3	0	7.0		
+132	1	1	14	1	2.0		
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+132	1	1	16	3	4.0		
+132	1	1	16	4	5.0		
+132	1	1	5	0	1.0		
+132	1	1	6	0	6.0		
+132	1	1	3	0	7.0		
+132	1	1	95	0	8.0		
+133	1	1	14	1	2.0		
+133	1	1	14	2	3.0		
+133	1	1	16	3	4.0		
+133	1	1	16	4	5.0		
+133	1	1	5	0	1.0		
+133	1	1	6	0	6.0		
+133	1	1	3	0	7.0		
+134	1	1	14	1	2.0		
+134	1	1	14	2	3.0		
+134	1	1	16	3	4.0		
+134	1	1	16	4	5.0		
+134	1	1	5	0	1.0		
+134	1	1	6	0	6.0		
+134	1	1	3	0	7.0		
+135	1	1	14	1	2.0		
+135	1	1	14	2	3.0		
+135	1	1	16	3	4.0		
+135	1	1	16	4	5.0		
+135	1	1	5	0	1.0		
+135	1	1	6	0	6.0		
+135	1	1	3	0	7.0		
+135	1	1	95	0	8.0	07/01/2024	50.0
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benunit.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benunit.tab
index 7060c839c..09845a494 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benunit.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/benunit.tab
@@ -1,136 +1,136 @@
-SERNUM	BENUNIT	FAMTYPB2	DEPCHLDB	TOTCAPB4
-1	1	5	0	101.0
-2	1	5	0	102.0
-3	1	5	0	103.0
-4	1	5	0	104.0
-5	1	5	1	105.0
-6	1	5	0	106.0
-7	1	5	0	107.0
-8	1	5	0	108.0
-9	1	5	0	109.0
-10	1	5	1	110.0
-11	1	5	0	111.0
-12	1	5	0	112.0
-13	1	5	0	113.0
-14	1	5	0	114.0
-15	1	5	1	115.0
-16	1	5	0	116.0
-17	1	5	0	117.0
-18	1	5	0	118.0
-19	1	5	0	119.0
-20	1	5	1	120.0
-21	1	5	0	121.0
-22	1	5	0	122.0
-23	1	5	0	123.0
-24	1	5	0	124.0
-25	1	5	1	125.0
-26	1	5	0	126.0
-27	1	5	0	127.0
-28	1	5	0	128.0
-29	1	5	0	129.0
-30	1	5	1	130.0
-31	1	5	0	131.0
-32	1	5	0	132.0
-33	1	5	0	133.0
-34	1	5	0	134.0
-35	1	5	1	135.0
-36	1	5	0	136.0
-37	1	5	0	137.0
-38	1	5	0	138.0
-39	1	5	0	139.0
-40	1	5	1	140.0
-41	1	5	0	141.0
-42	1	5	0	142.0
-43	1	5	0	143.0
-44	1	5	0	144.0
-45	1	5	1	145.0
-46	1	5	0	146.0
-47	1	5	0	147.0
-48	1	5	0	148.0
-49	1	5	0	149.0
-50	1	5	1	150.0
-51	1	5	0	151.0
-52	1	5	0	152.0
-53	1	5	0	153.0
-54	1	5	0	154.0
-55	1	5	1	155.0
-56	1	5	0	156.0
-57	1	5	0	157.0
-58	1	5	0	158.0
-59	1	5	0	159.0
-60	1	5	1	160.0
-61	1	5	0	161.0
-62	1	5	0	162.0
-63	1	5	0	163.0
-64	1	5	0	164.0
-65	1	5	1	165.0
-66	1	5	0	166.0
-67	1	5	0	167.0
-68	1	5	0	168.0
-69	1	5	0	169.0
-70	1	5	1	170.0
-71	1	5	0	171.0
-72	1	5	0	172.0
-73	1	5	0	173.0
-74	1	5	0	174.0
-75	1	5	1	175.0
-76	1	5	0	176.0
-77	1	5	0	177.0
-78	1	5	0	178.0
-79	1	5	0	179.0
-80	1	5	1	180.0
-81	1	5	0	181.0
-82	1	5	0	182.0
-83	1	5	0	183.0
-84	1	5	0	184.0
-85	1	5	1	185.0
-86	1	5	0	186.0
-87	1	5	0	187.0
-88	1	5	0	188.0
-89	1	5	0	189.0
-90	1	5	1	190.0
-91	1	5	0	191.0
-92	1	5	0	192.0
-93	1	5	0	193.0
-94	1	5	0	194.0
-95	1	5	1	195.0
-96	1	5	0	196.0
-97	1	5	0	197.0
-98	1	5	0	198.0
-99	1	5	0	199.0
-100	1	5	1	200.0
-101	1	5	0	201.0
-102	1	5	0	202.0
-103	1	5	0	203.0
-104	1	5	0	204.0
-105	1	5	1	205.0
-106	1	5	0	206.0
-107	1	5	0	207.0
-108	1	5	0	208.0
-109	1	5	0	209.0
-110	1	5	1	210.0
-111	1	5	0	211.0
-112	1	5	0	212.0
-113	1	5	0	213.0
-114	1	5	0	214.0
-115	1	5	1	215.0
-116	1	5	0	216.0
-117	1	5	0	217.0
-118	1	5	0	218.0
-119	1	5	0	219.0
-120	1	5	1	220.0
-121	1	5	0	221.0
-122	1	5	0	222.0
-123	1	5	0	223.0
-124	1	5	0	224.0
-125	1	5	1	225.0
-126	1	5	0	226.0
-127	1	5	0	227.0
-128	1	5	0	228.0
-129	1	5	0	229.0
-130	1	5	1	230.0
-131	1	5	0	231.0
-132	1	5	0	232.0
-133	1	5	0	233.0
-134	1	5	0	234.0
-135	1	5	1	235.0
+SERNUM	BENUNIT	FAMTYPB2	DEPCHLDB	TOTCAPB4	HBOTHAMT
+1	1	5	0	101.0	0.0
+2	1	5	0	102.0	0.0
+3	1	5	0	103.0	0.0
+4	1	5	0	104.0	0.0
+5	1	5	1	105.0	0.0
+6	1	5	0	106.0	0.0
+7	1	5	0	107.0	0.0
+8	1	5	0	108.0	0.0
+9	1	5	0	109.0	0.0
+10	1	5	1	110.0	0.0
+11	1	5	0	111.0	0.0
+12	1	5	0	112.0	0.0
+13	1	5	0	113.0	0.0
+14	1	5	0	114.0	0.0
+15	1	5	1	115.0	0.0
+16	1	5	0	116.0	0.0
+17	1	5	0	117.0	0.0
+18	1	5	0	118.0	0.0
+19	1	5	0	119.0	0.0
+20	1	5	1	120.0	0.0
+21	1	5	0	121.0	0.0
+22	1	5	0	122.0	0.0
+23	1	5	0	123.0	0.0
+24	1	5	0	124.0	0.0
+25	1	5	1	125.0	0.0
+26	1	5	0	126.0	0.0
+27	1	5	0	127.0	0.0
+28	1	5	0	128.0	0.0
+29	1	5	0	129.0	0.0
+30	1	5	1	130.0	0.0
+31	1	5	0	131.0	0.0
+32	1	5	0	132.0	0.0
+33	1	5	0	133.0	0.0
+34	1	5	0	134.0	0.0
+35	1	5	1	135.0	0.0
+36	1	5	0	136.0	0.0
+37	1	5	0	137.0	0.0
+38	1	5	0	138.0	0.0
+39	1	5	0	139.0	0.0
+40	1	5	1	140.0	0.0
+41	1	5	0	141.0	0.0
+42	1	5	0	142.0	0.0
+43	1	5	0	143.0	0.0
+44	1	5	0	144.0	0.0
+45	1	5	1	145.0	0.0
+46	1	5	0	146.0	0.0
+47	1	5	0	147.0	0.0
+48	1	5	0	148.0	0.0
+49	1	5	0	149.0	0.0
+50	1	5	1	150.0	0.0
+51	1	5	0	151.0	0.0
+52	1	5	0	152.0	0.0
+53	1	5	0	153.0	0.0
+54	1	5	0	154.0	0.0
+55	1	5	1	155.0	0.0
+56	1	5	0	156.0	0.0
+57	1	5	0	157.0	0.0
+58	1	5	0	158.0	0.0
+59	1	5	0	159.0	0.0
+60	1	5	1	160.0	0.0
+61	1	5	0	161.0	0.0
+62	1	5	0	162.0	0.0
+63	1	5	0	163.0	0.0
+64	1	5	0	164.0	0.0
+65	1	5	1	165.0	0.0
+66	1	5	0	166.0	0.0
+67	1	5	0	167.0	0.0
+68	1	5	0	168.0	0.0
+69	1	5	0	169.0	0.0
+70	1	5	1	170.0	0.0
+71	1	5	0	171.0	0.0
+72	1	5	0	172.0	0.0
+73	1	5	0	173.0	0.0
+74	1	5	0	174.0	0.0
+75	1	5	1	175.0	0.0
+76	1	5	0	176.0	0.0
+77	1	5	0	177.0	0.0
+78	1	5	0	178.0	0.0
+79	1	5	0	179.0	0.0
+80	1	5	1	180.0	0.0
+81	1	5	0	181.0	0.0
+82	1	5	0	182.0	0.0
+83	1	5	0	183.0	0.0
+84	1	5	0	184.0	0.0
+85	1	5	1	185.0	0.0
+86	1	5	0	186.0	0.0
+87	1	5	0	187.0	0.0
+88	1	5	0	188.0	0.0
+89	1	5	0	189.0	0.0
+90	1	5	1	190.0	0.0
+91	1	5	0	191.0	0.0
+92	1	5	0	192.0	0.0
+93	1	5	0	193.0	0.0
+94	1	5	0	194.0	0.0
+95	1	5	1	195.0	0.0
+96	1	5	0	196.0	0.0
+97	1	5	0	197.0	0.0
+98	1	5	0	198.0	0.0
+99	1	5	0	199.0	0.0
+100	1	5	1	200.0	0.0
+101	1	5	0	201.0	0.0
+102	1	5	0	202.0	0.0
+103	1	5	0	203.0	0.0
+104	1	5	0	204.0	0.0
+105	1	5	1	205.0	0.0
+106	1	5	0	206.0	0.0
+107	1	5	0	207.0	0.0
+108	1	5	0	208.0	0.0
+109	1	5	0	209.0	0.0
+110	1	5	1	210.0	0.0
+111	1	5	0	211.0	0.0
+112	1	5	0	212.0	0.0
+113	1	5	0	213.0	0.0
+114	1	5	0	214.0	0.0
+115	1	5	1	215.0	0.0
+116	1	5	0	216.0	0.0
+117	1	5	0	217.0	0.0
+118	1	5	0	218.0	0.0
+119	1	5	0	219.0	0.0
+120	1	5	1	220.0	0.0
+121	1	5	0	221.0	0.0
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+123	1	5	0	223.0	0.0
+124	1	5	0	224.0	0.0
+125	1	5	1	225.0	0.0
+126	1	5	0	226.0	0.0
+127	1	5	0	227.0	0.0
+128	1	5	0	228.0	0.0
+129	1	5	0	229.0	0.0
+130	1	5	1	230.0	0.0
+131	1	5	0	231.0	0.0
+132	1	5	0	232.0	0.0
+133	1	5	0	233.0	0.0
+134	1	5	0	234.0	0.0
+135	1	5	1	235.0	0.0
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab
index f2c60ca0b..7576f7b24 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/child.tab
@@ -1,28 +1,28 @@
-SERNUM	BENUNIT	PERSON	AGE80	AGE	SEX	TOTHOURS	HRPID	UPERSON	RELHRP	R01	R02	R03	R04	R05	R06	R07	R08	R09	R10	R11	R12	R13	R14	MARITAL	FTED	TYPEED2	EDUCQUAL	TRAIN	EMAAMT	CHEMAAMT	FSMVAL	FSFVVAL	FSBVAL	HEARTVAL
-5	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-10	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-15	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-20	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-25	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-30	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-35	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-40	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-45	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-50	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-55	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-60	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-65	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-70	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-75	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-80	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-85	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-90	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-95	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-100	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-105	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-110	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-115	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-120	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-125	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-130	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
-135	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0
+SERNUM	BENUNIT	PERSON	AGE80	AGE	SEX	TOTHOURS	HRPID	UPERSON	RELHRP	R01	R02	R03	R04	R05	R06	R07	R08	R09	R10	R11	R12	R13	R14	MARITAL	FTED	TYPEED2	EDUCQUAL	TRAIN	EMAAMT	CHEMAAMT	FSMVAL	FSFVVAL	FSBVAL	HEARTVAL	SPCREG1
+5	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+10	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+15	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+20	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+25	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+30	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+35	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+40	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+45	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	1
+50	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+55	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+60	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+65	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+70	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+75	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+80	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+85	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+90	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	1
+95	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+100	1	2	0	12	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+105	1	2	0	9	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+110	1	2	0	14	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+115	1	2	0	11	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+120	1	2	0	8	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+125	1	2	0	13	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+130	1	2	0	10	1		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	2
+135	1	2	0	15	2		0	0	3	3														2	1	2	86	9	0.0	1.0	3.0	1.0	2.0	4.0	1
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/househol.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/househol.tab
index c03bc276b..05317d9ec 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/househol.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/househol.tab
@@ -1,136 +1,136 @@
-SERNUM	GROSS4	GVTREGNO	PTENTYP2	TYPEACC	BEDROOM6	CTANNUAL	CTBAND	CTDISC	CT25D50D	CTREB	CTREBAMT	ADULTH	HRPNUM	CSEWAMT	CWATAMTD	CWATAMT1	CSEWAMT1	WATSEWRT	NIRATLIA	RT2REBAM	HHRENT	SUBRENT	TENTYP2	MORTINT	STRUINS	CHRGAMT1	CHRGAMT2	CHRGAMT3	CHRGAMT4	CHRGAMT5	CHRGAMT6	CHRGAMT7	CHRGAMT8	CHRGAMT9
-1	1.01	11	6	2	2	910.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-2	1.02	12	5	3	3	920.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-3	1.03	1	6	4	4	930.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-4	1.04	8	5	1	1	940.0	5	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-5	1.05	11	6	2	2	950.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-6	1.06	12	5	3	3	960.0	7	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-7	1.07	1	6	4	4	970.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-8	1.08	8	5	1	1	980.0	2	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-9	1.09	11	6	2	2	990.0	3	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-10	1.1	12	5	3	3	1000.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-11	1.11	1	6	4	4	1010.0	5	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-12	1.12	8	5	1	1	1020.0	6	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-13	1.13	11	6	2	2	1030.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-14	1.1400000000000001	12	5	3	3	1040.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-15	1.15	1	6	4	4	1050.0	2	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-16	1.16	8	5	1	1	1060.0	3	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-17	1.17	11	6	2	2	1070.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-18	1.18	12	5	3	3	1080.0	5	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-19	1.19	1	6	4	4	1090.0	6	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-20	1.2	8	5	1	1	1100.0	7	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-21	1.21	11	6	2	2	1110.0	1	1	1	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-22	1.22	12	5	3	3	1120.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-23	1.23	1	6	4	4	1130.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-24	1.24	8	5	1	1	1140.0	4	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-25	1.25	11	6	2	2	1150.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-26	1.26	12	5	3	3	1160.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-27	1.27	1	6	4	4	1170.0	7	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-28	1.28	8	5	1	1	1180.0	1	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-29	1.29	11	6	2	2	1190.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-30	1.3	12	5	3	3	1200.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-31	1.31	1	6	4	4	1210.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-32	1.32	8	5	1	1	1220.0	5	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-33	1.33	11	6	2	2	1230.0	6	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-34	1.34	12	5	3	3	1240.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-35	1.35	1	6	4	4	1250.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-36	1.3599999999999999	8	5	1	1	1260.0	2	1	2	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-37	1.37	11	6	2	2	1270.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-38	1.38	12	5	3	3	1280.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-39	1.3900000000000001	1	6	4	4	1290.0	5	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-40	1.4	8	5	1	1	1300.0	6	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-41	1.41	11	6	2	2	1310.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-42	1.42	12	5	3	3	1320.0	1	1	1	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-43	1.43	1	6	4	4	1330.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-44	1.44	8	5	1	1	1340.0	3	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-45	1.45	11	6	2	2	1350.0	4	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-46	1.46	12	5	3	3	1360.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-47	1.47	1	6	4	4	1370.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-48	1.48	8	5	1	1	1380.0	7	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-49	1.49	11	6	2	2	1390.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-50	1.5	12	5	3	3	1400.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-51	1.51	1	6	4	4	1410.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-52	1.52	8	5	1	1	1420.0	4	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-53	1.53	11	6	2	2	1430.0	5	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-54	1.54	12	5	3	3	1440.0	6	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-55	1.55	1	6	4	4	1450.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-56	1.56	8	5	1	1	1460.0	1	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-57	1.5699999999999998	11	6	2	2	1470.0	2	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-58	1.58	12	5	3	3	1480.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-59	1.5899999999999999	1	6	4	4	1490.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-60	1.6	8	5	1	1	1500.0	5	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-61	1.6099999999999999	11	6	2	2	1510.0	6	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-62	1.62	12	5	3	3	1520.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-63	1.63	1	6	4	4	1530.0	1	1	2	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-64	1.6400000000000001	8	5	1	1	1540.0	2	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-65	1.65	11	6	2	2	1550.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-66	1.6600000000000001	12	5	3	3	1560.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-67	1.67	1	6	4	4	1570.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-68	1.6800000000000002	8	5	1	1	1580.0	6	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-69	1.69	11	6	2	2	1590.0	7	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-70	1.7	12	5	3	3	1600.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-71	1.71	1	6	4	4	1610.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-72	1.72	8	5	1	1	1620.0	3	1	2	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-73	1.73	11	6	2	2	1630.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-74	1.74	12	5	3	3	1640.0	5	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-75	1.75	1	6	4	4	1650.0	6	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-76	1.76	8	5	1	1	1660.0	7	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-77	1.77	11	6	2	2	1670.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-78	1.78	12	5	3	3	1680.0	2	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-79	1.79	1	6	4	4	1690.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-80	1.8	8	5	1	1	1700.0	4	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-81	1.81	11	6	2	2	1710.0	5	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-82	1.8199999999999998	12	5	3	3	1720.0	6	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-83	1.83	1	6	4	4	1730.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-84	1.8399999999999999	8	5	1	1	1740.0	1	1	1	1	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-85	1.85	11	6	2	2	1750.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-86	1.8599999999999999	12	5	3	3	1760.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-87	1.87	1	6	4	4	1770.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-88	1.88	8	5	1	1	1780.0	5	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-89	1.8900000000000001	11	6	2	2	1790.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-90	1.9	12	5	3	3	1800.0	7	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-91	1.9100000000000001	1	6	4	4	1810.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-92	1.92	8	5	1	1	1820.0	2	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-93	1.9300000000000002	11	6	2	2	1830.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-94	1.94	12	5	3	3	1840.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-95	1.95	1	6	4	4	1850.0	5	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-96	1.96	8	5	1	1	1860.0	6	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-97	1.97	11	6	2	2	1870.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-98	1.98	12	5	3	3	1880.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-99	1.99	1	6	4	4	1890.0	2	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-100	2.0	8	5	1	1	1900.0	3	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-101	2.01	11	6	2	2	1910.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-102	2.02	12	5	3	3	1920.0	5	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-103	2.0300000000000002	1	6	4	4	1930.0	6	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-104	2.04	8	5	1	1	1940.0	7	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-105	2.05	11	6	2	2	1950.0	1	1	1	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-106	2.06	12	5	3	3	1960.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-107	2.0700000000000003	1	6	4	4	1970.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-108	2.08	8	5	1	1	1980.0	4	1	2	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-109	2.09	11	6	2	2	1990.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-110	2.1	12	5	3	3	2000.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-111	2.1100000000000003	1	6	4	4	2010.0	7	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-112	2.12	8	5	1	1	2020.0	1	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-113	2.13	11	6	2	2	2030.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-114	2.1399999999999997	12	5	3	3	2040.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-115	2.15	1	6	4	4	2050.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-116	2.16	8	5	1	1	2060.0	5	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-117	2.17	11	6	2	2	2070.0	6	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-118	2.1799999999999997	12	5	3	3	2080.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-119	2.19	1	6	4	4	2090.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-120	2.2	8	5	1	1	2100.0	2	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-121	2.21	11	6	2	2	2110.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-122	2.2199999999999998	12	5	3	3	2120.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-123	2.23	1	6	4	4	2130.0	5	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-124	2.24	8	5	1	1	2140.0	6	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-125	2.25	11	6	2	2	2150.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-126	2.26	12	5	3	3	2160.0	1	1	2	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-127	2.27	1	6	4	4	2170.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-128	2.2800000000000002	8	5	1	1	2180.0	3	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-129	2.29	11	6	2	2	2190.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-130	2.3	12	5	3	3	2200.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-131	2.31	1	6	4	4	2210.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-132	2.3200000000000003	8	5	1	1	2220.0	7	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-133	2.33	11	6	2	2	2230.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-134	2.34	12	5	3	3	2240.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
-135	2.35	1	6	4	4	2250.0	3	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0
+SERNUM	GROSS4	GVTREGNO	PTENTYP2	TYPEACC	BEDROOM6	CTANNUAL	CTBAND	CTDISC	CT25D50D	CTREB	CTREBAMT	ADULTH	HRPNUM	CSEWAMT	CWATAMTD	CWATAMT1	CSEWAMT1	WATSEWRT	NIRATLIA	RT2REBAM	HHRENT	SUBRENT	TENTYP2	MORTINT	STRUINS	CHRGAMT1	CHRGAMT2	CHRGAMT3	CHRGAMT4	CHRGAMT5	CHRGAMT6	CHRGAMT7	CHRGAMT8	CHRGAMT9	INTDATE	HHSTAT
+1	1.01	11	6	2	2	910.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23665.0	1
+2	1.02	12	5	3	3	920.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23666.0	1
+3	1.03	1	6	4	4	930.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23667.0	1
+4	1.04	8	5	1	1	940.0	5	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23668.0	1
+5	1.05	11	6	2	2	950.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23669.0	1
+6	1.06	12	5	3	3	960.0	7	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23670.0	1
+7	1.07	1	6	4	4	970.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23671.0	1
+8	1.08	8	5	1	1	980.0	2	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23672.0	1
+9	1.09	11	6	2	2	990.0	3	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23673.0	1
+10	1.1	12	5	3	3	1000.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23674.0	1
+11	1.11	1	6	4	4	1010.0	5	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23675.0	2
+12	1.12	8	5	1	1	1020.0	6	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23676.0	1
+13	1.13	11	6	2	2	1030.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23677.0	1
+14	1.1400000000000001	12	5	3	3	1040.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23678.0	1
+15	1.15	1	6	4	4	1050.0	2	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23679.0	1
+16	1.16	8	5	1	1	1060.0	3	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23680.0	1
+17	1.17	11	6	2	2	1070.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23681.0	1
+18	1.18	12	5	3	3	1080.0	5	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23682.0	1
+19	1.19	1	6	4	4	1090.0	6	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23683.0	1
+20	1.2	8	5	1	1	1100.0	7	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23684.0	1
+21	1.21	11	6	2	2	1110.0	1	1	1	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23685.0	1
+22	1.22	12	5	3	3	1120.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23686.0	2
+23	1.23	1	6	4	4	1130.0	3	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23687.0	1
+24	1.24	8	5	1	1	1140.0	4	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23688.0	1
+25	1.25	11	6	2	2	1150.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23689.0	1
+26	1.26	12	5	3	3	1160.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23690.0	1
+27	1.27	1	6	4	4	1170.0	7	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23691.0	1
+28	1.28	8	5	1	1	1180.0	1	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23692.0	1
+29	1.29	11	6	2	2	1190.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23693.0	1
+30	1.3	12	5	3	3	1200.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23694.0	1
+31	1.31	1	6	4	4	1210.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23695.0	1
+32	1.32	8	5	1	1	1220.0	5	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23696.0	1
+33	1.33	11	6	2	2	1230.0	6	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23697.0	2
+34	1.34	12	5	3	3	1240.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23698.0	1
+35	1.35	1	6	4	4	1250.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23699.0	1
+36	1.3599999999999999	8	5	1	1	1260.0	2	1	2	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23700.0	1
+37	1.37	11	6	2	2	1270.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23701.0	1
+38	1.38	12	5	3	3	1280.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23702.0	1
+39	1.3900000000000001	1	6	4	4	1290.0	5	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23703.0	1
+40	1.4	8	5	1	1	1300.0	6	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23704.0	1
+41	1.41	11	6	2	2	1310.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23705.0	1
+42	1.42	12	5	3	3	1320.0	1	1	1	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23706.0	1
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+109	2.09	11	6	2	2	1990.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23773.0	1
+110	2.1	12	5	3	3	2000.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23774.0	2
+111	2.1100000000000003	1	6	4	4	2010.0	7	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23775.0	1
+112	2.12	8	5	1	1	2020.0	1	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23776.0	1
+113	2.13	11	6	2	2	2030.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23777.0	1
+114	2.1399999999999997	12	5	3	3	2040.0	3	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23778.0	1
+115	2.15	1	6	4	4	2050.0	4	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23779.0	1
+116	2.16	8	5	1	1	2060.0	5	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23780.0	1
+117	2.17	11	6	2	2	2070.0	6	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23781.0	1
+118	2.1799999999999997	12	5	3	3	2080.0	7	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23782.0	1
+119	2.19	1	6	4	4	2090.0	1	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23783.0	1
+120	2.2	8	5	1	1	2100.0	2	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23664.0	1
+121	2.21	11	6	2	2	2110.0	3	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23665.0	2
+122	2.2199999999999998	12	5	3	3	2120.0	4	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23666.0	1
+123	2.23	1	6	4	4	2130.0	5	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23667.0	1
+124	2.24	8	5	1	1	2140.0	6	2		1	1.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23668.0	1
+125	2.25	11	6	2	2	2150.0	7	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23669.0	1
+126	2.26	12	5	3	3	2160.0	1	1	2	1	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23670.0	1
+127	2.27	1	6	4	4	2170.0	2	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23671.0	1
+128	2.2800000000000002	8	5	1	1	2180.0	3	2		1	2.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23672.0	1
+129	2.29	11	6	2	2	2190.0	4	1	1	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23673.0	1
+130	2.3	12	5	3	3	2200.0	5	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23674.0	1
+131	2.31	1	6	4	4	2210.0	6	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23675.0	1
+132	2.3200000000000003	8	5	1	1	2220.0	7	1	1	2	0.0	1	1		0.0	4.0	5.0	3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23676.0	2
+133	2.33	11	6	2	2	2230.0	1	2		1	1.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23677.0	1
+134	2.34	12	5	3	3	2240.0	2	2		1	2.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23678.0	1
+135	2.35	1	6	4	4	2250.0	3	1	2	2	0.0	1	1		0.0			3.0	0.0	0.0	5.0	0.0	5	7.0	8.0	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	23679.0	1
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab
index 7520fc9a2..0c718cef2 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/frs_raw/job.tab
@@ -1,136 +1,136 @@
-SERNUM	BENUNIT	PERSON	DEDUC1	SPNAMT	SALSAC	JOBSECT
-1	1	1	2.0	3.0	1	2
-2	1	1	2.0	3.0	1	1
-3	1	1	2.0	3.0	1	2
-4	1	1	2.0	3.0	1	1
-5	1	1	2.0	3.0	1	2
-6	1	1	2.0	3.0	1	1
-7	1	1	2.0	3.0	1	2
-8	1	1	2.0	3.0	1	1
-9	1	1	2.0	3.0	1	2
-10	1	1	2.0	3.0	1	1
-11	1	1	2.0	3.0	1	2
-12	1	1	2.0	3.0	1	1
-13	1	1	2.0	3.0	1	2
-14	1	1	2.0	3.0	1	1
-15	1	1	2.0	3.0	1	2
-16	1	1	2.0	3.0	1	1
-17	1	1	2.0	3.0	1	2
-18	1	1	2.0	3.0	1	1
-19	1	1	2.0	3.0	1	2
-20	1	1	2.0	3.0	1	1
-21	1	1	2.0	3.0	1	2
-22	1	1	2.0	3.0	1	1
-23	1	1	2.0	3.0	1	2
-24	1	1	2.0	3.0	1	1
-25	1	1	2.0	3.0	1	2
-26	1	1	2.0	3.0	1	1
-27	1	1	2.0	3.0	1	2
-28	1	1	2.0	3.0	1	1
-29	1	1	2.0	3.0	1	2
-30	1	1	2.0	3.0	1	1
-31	1	1	2.0	3.0	1	2
-32	1	1	2.0	3.0	1	1
-33	1	1	2.0	3.0	1	2
-34	1	1	2.0	3.0	1	1
-35	1	1	2.0	3.0	1	2
-36	1	1	2.0	3.0	1	1
-37	1	1	2.0	3.0	1	2
-38	1	1	2.0	3.0	1	1
-39	1	1	2.0	3.0	1	2
-40	1	1	2.0	3.0	1	1
-41	1	1	2.0	3.0	1	2
-42	1	1	2.0	3.0	1	1
-43	1	1	2.0	3.0	1	2
-44	1	1	2.0	3.0	1	1
-45	1	1	2.0	3.0	1	2
-46	1	1	2.0	3.0	1	1
-47	1	1	2.0	3.0	1	2
-48	1	1	2.0	3.0	1	1
-49	1	1	2.0	3.0	1	2
-50	1	1	2.0	3.0	1	1
-51	1	1	2.0	3.0	1	2
-52	1	1	2.0	3.0	1	1
-53	1	1	2.0	3.0	1	2
-54	1	1	2.0	3.0	1	1
-55	1	1	2.0	3.0	1	2
-56	1	1	2.0	3.0	1	1
-57	1	1	2.0	3.0	1	2
-58	1	1	2.0	3.0	1	1
-59	1	1	2.0	3.0	1	2
-60	1	1	2.0	3.0	1	1
-61	1	1	2.0	3.0	1	2
-62	1	1	2.0	3.0	1	1
-63	1	1	2.0	3.0	1	2
-64	1	1	2.0	3.0	1	1
-65	1	1	2.0	3.0	1	2
-66	1	1	2.0	3.0	1	1
-67	1	1	2.0	3.0	1	2
-68	1	1	2.0	3.0	1	1
-69	1	1	2.0	3.0	1	2
-70	1	1	2.0	3.0	1	1
-71	1	1	2.0	3.0	1	2
-72	1	1	2.0	3.0	1	1
-73	1	1	2.0	3.0	1	2
-74	1	1	2.0	3.0	1	1
-75	1	1	2.0	3.0	1	2
-76	1	1	2.0	3.0	1	1
-77	1	1	2.0	3.0	1	2
-78	1	1	2.0	3.0	1	1
-79	1	1	2.0	3.0	1	2
-80	1	1	2.0	3.0	1	1
-81	1	1	2.0	3.0	1	2
-82	1	1	2.0	3.0	1	1
-83	1	1	2.0	3.0	1	2
-84	1	1	2.0	3.0	1	1
-85	1	1	2.0	3.0	1	2
-86	1	1	2.0	3.0	1	1
-87	1	1	2.0	3.0	1	2
-88	1	1	2.0	3.0	1	1
-89	1	1	2.0	3.0	1	2
-90	1	1	2.0	3.0	1	1
-91	1	1	2.0	3.0	1	2
-92	1	1	2.0	3.0	1	1
-93	1	1	2.0	3.0	1	2
-94	1	1	2.0	3.0	1	1
-95	1	1	2.0	3.0	1	2
-96	1	1	2.0	3.0	1	1
-97	1	1	2.0	3.0	1	2
-98	1	1	2.0	3.0	1	1
-99	1	1	2.0	3.0	1	2
-100	1	1	2.0	3.0	1	1
-101	1	1	2.0	3.0	1	2
-102	1	1	2.0	3.0	1	1
-103	1	1	2.0	3.0	1	2
-104	1	1	2.0	3.0	1	1
-105	1	1	2.0	3.0	1	2
-106	1	1	2.0	3.0	1	1
-107	1	1	2.0	3.0	1	2
-108	1	1	2.0	3.0	1	1
-109	1	1	2.0	3.0	1	2
-110	1	1	2.0	3.0	1	1
-111	1	1	2.0	3.0	1	2
-112	1	1	2.0	3.0	1	1
-113	1	1	2.0	3.0	1	2
-114	1	1	2.0	3.0	1	1
-115	1	1	2.0	3.0	1	2
-116	1	1	2.0	3.0	1	1
-117	1	1	2.0	3.0	1	2
-118	1	1	2.0	3.0	1	1
-119	1	1	2.0	3.0	1	2
-120	1	1	2.0	3.0	1	1
-121	1	1	2.0	3.0	1	2
-122	1	1	2.0	3.0	1	1
-123	1	1	2.0	3.0	1	2
-124	1	1	2.0	3.0	1	1
-125	1	1	2.0	3.0	1	2
-126	1	1	2.0	3.0	1	1
-127	1	1	2.0	3.0	1	2
-128	1	1	2.0	3.0	1	1
-129	1	1	2.0	3.0	1	2
-130	1	1	2.0	3.0	1	1
-131	1	1	2.0	3.0	1	2
-132	1	1	2.0	3.0	1	1
-133	1	1	2.0	3.0	1	2
-134	1	1	2.0	3.0	1	1
-135	1	1	2.0	3.0	1	2
+SERNUM	BENUNIT	PERSON	DEDUC1	SPNAMT	SALSAC	JOBSECT	ETYPE	JOBTYPE	SEEND	SEJBLONG	JOBBUS
+1	1	1	2.0	3.0	1	2	1	1			2
+2	1	1	2.0	3.0	1	1	4	1		2.0	1
+3	1	1	2.0	3.0	1	2	4	1		0.0	2
+4	1	1	2.0	3.0	1	1	1	1			1
+5	1	1	2.0	3.0	1	2	1	1			2
+6	1	1	2.0	3.0	1	1	1	1			1
+7	1	1	2.0	3.0	1	2	1	1			2
+8	1	1	2.0	3.0	1	1	1	1			1
+9	1	1	2.0	3.0	1	2	1	1			2
+10	1	1	2.0	3.0	1	1	4	1		1.0	1
+11	1	1	2.0	3.0	1	2	4	1		2.0	2
+12	1	1	2.0	3.0	1	1	1	1			1
+13	1	1	2.0	3.0	1	2	1	1			2
+14	1	1	2.0	3.0	1	1	1	1			1
+15	1	1	2.0	3.0	1	2	1	1			2
+16	1	1	2.0	3.0	1	1	1	1			1
+17	1	1	2.0	3.0	1	2	1	1			2
+18	1	1	2.0	3.0	1	1	4	1		0.0	1
+19	1	1	2.0	3.0	1	2	4	1		1.0	2
+20	1	1	2.0	3.0	1	1	1	1			1
+21	1	1	2.0	3.0	1	2	1	1			2
+22	1	1	2.0	3.0	1	1	1	1			1
+23	1	1	2.0	3.0	1	2	1	1			2
+24	1	1	2.0	3.0	1	1	1	1			1
+25	1	1	2.0	3.0	1	2	1	1			2
+26	1	1	2.0	3.0	1	1	4	1		2.0	1
+27	1	1	2.0	3.0	1	2	4	1		0.0	2
+28	1	1	2.0	3.0	1	1	1	1			1
+29	1	1	2.0	3.0	1	2	1	1			2
+30	1	1	2.0	3.0	1	1	1	1			1
+31	1	1	2.0	3.0	1	2	1	1			2
+32	1	1	2.0	3.0	1	1	1	1			1
+33	1	1	2.0	3.0	1	2	1	1			2
+34	1	1	2.0	3.0	1	1	4	1		1.0	1
+35	1	1	2.0	3.0	1	2	4	1		2.0	2
+36	1	1	2.0	3.0	1	1	1	1			1
+37	1	1	2.0	3.0	1	2	1	1			2
+38	1	1	2.0	3.0	1	1	1	1			1
+39	1	1	2.0	3.0	1	2	1	1			2
+40	1	1	2.0	3.0	1	1	1	1			1
+41	1	1	2.0	3.0	1	2	1	1			2
+42	1	1	2.0	3.0	1	1	4	1		0.0	1
+43	1	1	2.0	3.0	1	2	4	1		1.0	2
+44	1	1	2.0	3.0	1	1	1	1			1
+45	1	1	2.0	3.0	1	2	1	1			2
+46	1	1	2.0	3.0	1	1	1	1			1
+47	1	1	2.0	3.0	1	2	1	1			2
+48	1	1	2.0	3.0	1	1	1	1			1
+49	1	1	2.0	3.0	1	2	1	1			2
+50	1	1	2.0	3.0	1	1	4	1		2.0	1
+51	1	1	2.0	3.0	1	2	4	1		0.0	2
+52	1	1	2.0	3.0	1	1	1	1			1
+53	1	1	2.0	3.0	1	2	1	1			2
+54	1	1	2.0	3.0	1	1	1	1			1
+55	1	1	2.0	3.0	1	2	1	1			2
+56	1	1	2.0	3.0	1	1	1	1			1
+57	1	1	2.0	3.0	1	2	1	1			2
+58	1	1	2.0	3.0	1	1	4	1		1.0	1
+59	1	1	2.0	3.0	1	2	4	1		2.0	2
+60	1	1	2.0	3.0	1	1	1	1			1
+61	1	1	2.0	3.0	1	2	1	1			2
+62	1	1	2.0	3.0	1	1	1	1			1
+63	1	1	2.0	3.0	1	2	1	1			2
+64	1	1	2.0	3.0	1	1	1	1			1
+65	1	1	2.0	3.0	1	2	1	1			2
+66	1	1	2.0	3.0	1	1	4	1		0.0	1
+67	1	1	2.0	3.0	1	2	4	1		1.0	2
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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 8f4c88f6c..6c041aa79 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/person.csv
@@ -1,163 +1,163 @@
-person_id,person_benunit_id,person_household_id,age,gender,marital_status,hours_worked,care_hours,is_household_head,is_benunit_head,is_parent,employment_income,self_employment_income,private_pension_income,tax_free_savings_income,savings_interest_income,dividend_income,property_income,maintenance_income,miscellaneous_income,private_transfer_income,lump_sum_income,student_loan_repayments,statutory_sick_pay,statutory_maternity_pay,student_loans,access_fund,education_grants,healthy_start_vouchers,free_school_breakfasts,free_school_fruit_veg,free_school_meals,child_benefit_reported,income_support_reported,housing_benefit_reported,attendance_allowance_reported,dla_sc_reported,dla_m_reported,iidb_reported,carers_allowance_reported,sda_reported,afcs_reported,ssmg_reported,pension_credit_reported,child_tax_credit_reported,working_tax_credit_reported,state_pension_reported,winter_fuel_allowance_reported,incapacity_benefit_reported,universal_credit_reported,pip_m_reported,pip_dl_reported,jsa_contrib_reported,jsa_income_reported,esa_contrib_reported,esa_income_reported,bsp_reported,would_claim_carers_allowance,council_tax_benefit_reported,maintenance_expenses,childcare_expenses,personal_pension_contributions,employee_pension_contributions,pension_contributions_via_salary_sacrifice,salary_sacrifice_reported,salary_sacrifice_asked,is_uc_claimant,__stratum__
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diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/sources/schema.json
index c5bfafbdf..c4c88701c 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
@@ -4,7 +4,8 @@
       "benunit_id": "int64",
       "dependent_children": "int64",
       "frs_benunit_capital": "float64",
-      "is_married": "bool"
+      "is_married": "bool",
+      "liable_for_share_of_household_rent": "bool"
     },
     "household": {
       "accommodation_type": "string",
@@ -58,6 +59,9 @@
       "incapacity_benefit_reported": "float64",
       "income_support_reported": "float64",
       "is_benunit_head": "bool",
+      "is_blind": "bool",
+      "is_claimant_or_partner": "bool",
+      "is_hbai_dependent_child": "bool",
       "is_household_head": "bool",
       "is_parent": "bool",
       "is_uc_claimant": "bool",
@@ -79,6 +83,8 @@
       "private_pension_income": "float64",
       "private_transfer_income": "float64",
       "property_income": "float64",
+      "rent_paid_as_boarder": "float64",
+      "rent_paid_as_lodger": "float64",
       "salary_sacrifice_asked": "float64",
       "salary_sacrifice_reported": "float64",
       "savings_interest_income": "float64",
@@ -91,6 +97,7 @@
       "student_loan_repayments": "float64",
       "student_loans": "float64",
       "tax_free_savings_income": "float64",
+      "uc_is_in_startup_period": "bool",
       "universal_credit_reported": "float64",
       "winter_fuel_allowance_reported": "float64",
       "working_tax_credit_reported": "float64",
diff --git a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json
index 6892c99aa..98b5fcc8f 100644
--- a/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json
+++ b/packages/microcosm-graph/tests/fixtures/parity/uk_spine/uk_spine.json
@@ -1 +1 @@
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the cells materialized by cgt_residential_split.","id":"cgt_residential_split.owned","inputs":[],"kernel":"uk.claim@1","mass":"conserve","outputs":[{"column":"household_is_cgt_residential_clone","dtype":"bool","entity":"household","ownership":"produced","rows":"all"},{"column":"cgt_residential_clone_index","dtype":"int64","entity":"household","ownership":"produced","rows":"all"},{"column":"cgt_residential_probability","dtype":"float64","entity":"person","ownership":"produced","rows":"all"},{"column":"capital_gains_residential_property","dtype":"float64","entity":"person","ownership":"produced","rows":"all"}],"params":{"materialized_expand_outputs":["household.household_is_cgt_residential_clone","household.cgt_residential_clone_index","person.cgt_residential_probability","person.capital_gains_residential_property"]},"population":"cgt_residential_split","sources":[],"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 hmrc_cgt_asset_type_spine.","id":"hmrc_cgt_asset_type_spine","inputs":[{"columns":["frs_benunit_capital"],"entity":"benunit","rows":"all"},{"columns":["corporate_wealth","gross_financial_wealth","other_residential_property_value","region","stocks_and_shares_isa"],"entity":"household","rows":"all"},{"columns":["age","capital_gains","capital_gains_residential_property","dividend_income","property_income","self_employment_income"],"entity":"person","rows":"all"}],"kernel":"uk.stage.hmrc_cgt_asset_type_spine@1","mass":"conserve","outputs":[{"column":"capital_gains_asset_type","dtype":"string","entity":"person","ownership":"produced","rows":"all"},{"column":"capital_gains_badr","dtype":"float64","entity":"person","ownership":"produced","rows":"all"}],"params":{"numerical_dependencies":[["policyengine-uk","2.122.2"],["policyengine-core","3.32.19"],["numpy","2.4.6"],["pandas","3.0.3"],["scikit-learn","1.8.0"],["quantile-forest","1.4.2"]],"stage":"hmrc_cgt_asset_type_spine","stage_contract_sha256":"826e18043928f5dea5ab9fc128f737a3c6978c23cb61c789ce41b9ecb2d34263","time_period":"2024"},"population":"cgt_residential_split","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 salary_sacrifice.","id":"salary_sacrifice","inputs":[{"columns":["frs_benunit_capital","is_married","dependent_children","liable_for_share_of_household_rent","would_claim_child_benefit","child_benefit_opts_out","would_claim_pc","would_claim_uc","would_claim_tfc","would_claim_extended_childcare","would_claim_universal_childcare","would_claim_targeted_childcare","would_claim_uc_childcare","maximum_extended_childcare_hours_usage","benunit_source_id","benunit_support_channel","benunit_support_clone_index","uc_reported_capital","pension_credit_reported_capital","has_mixed_age_couple_pension_credit_saving","uc_deduction_random_draw","uc_deduction_type_random_draw","uc_latent_deduction_rate","uc_deduction_combination"],"entity":"benunit","rows":"all"},{"columns":["region","tenure_type","accommodation_type","num_bedrooms","council_tax_reported","council_tax_band","council_tax_rebate","council_tax_single_adult_raw","water_and_sewerage_charges","domestic_rates","rent","subrent","mortgage_interest_repayment","mortgage_capital_repayment","structural_insurance_payments","housing_service_charges","external_child_payments","ons_household_type","council_tax","household_owns_tv","would_evade_tv_licence_fee","main_residential_property_purchased_is_first_home","property_purchased","brma","source_household_id","source_year","source_household_key","household_source_id","household_support_channel","household_support_clone_index","household_is_spi_synthetic","household_is_spi_income_band_donor","spi_income_band_donor_lower_bound","owned_land","property_wealth","corporate_wealth","private_pension_wealth","gross_financial_wealth","net_financial_wealth","main_residence_value","other_residential_property_value","non_residential_property_value","savings","num_vehicles","cash_isa","stocks_and_shares_isa","mortgage_debt","consumer_debt","household_lifetime_isa_balance","household_local_bus_trips","food_and_non_alcoholic_beverages_consumption","alcohol_and_tobacco_consumption","clothing_and_footwear_consumption","housing_water_and_electricity_consumption","household_furnishings_consumption","health_consumption","transport_consumption","communication_consumption","recreation_consumption","education_consumption","restaurants_and_hotels_consumption","miscellaneous_consumption","petrol_spending","diesel_spending","bus_fare_spending","domestic_energy_consumption","electricity_consumption","gas_consumption","has_fuel_consumption","full_rate_vat_expenditure_rate","dfe_education_spending","rail_subsidy_spending","bus_subsidy_spending","rail_usage","household_is_cgt_support_copy","cgt_support_copies","cgt_support_copy_index","household_is_capital_gains_clone","household_is_cgt_residential_clone","cgt_residential_clone_index"],"entity":"household","rows":"all"},{"columns":["age","gender","marital_status","hours_worked","care_hours","is_household_head","is_benunit_head","is_parent","is_blind","is_uc_claimant","is_claimant_or_partner","is_hbai_dependent_child","uc_is_in_startup_period","rent_paid_as_boarder","rent_paid_as_lodger","self_employment_income","private_pension_income","tax_free_savings_income","savings_interest_income","dividend_income","property_income","maintenance_income","miscellaneous_income","private_transfer_income","lump_sum_income","student_loan_repayments","statutory_sick_pay","statutory_maternity_pay","student_loans","access_fund","education_grants","healthy_start_vouchers","free_school_breakfasts","free_school_fruit_veg","free_school_meals","council_tax_benefit_reported","maintenance_expenses","childcare_expenses","personal_pension_contributions","salary_sacrifice_reported","salary_sacrifice_asked","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","bsp_reported","relationship_to_head","ons_family_role","ons_family_index","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","legacy_jobseeker_proxy","esa_health_condition_proxy","esa_support_group_proxy","disabled_students_allowance_eligible_expenses","would_claim_marriage_allowance","would_claim_scp","attends_private_school_random_draw","tax_free_childcare_spend_routed_share","hmrc_spi_pay","hmrc_spi_unemployment_benefit_income","hmrc_spi_incapacity_benefit_income","ossben_identifiable_subset","srp_regular_code5","employer_pension_contributions","person_source_id","person_support_channel","person_support_clone_index","person_is_spi_income_band_carrier","charitable_investment_gifts","gift_aid","other_investment_income","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","hmrc_spi_employed_income","hmrc_spi_total_earned_income","hmrc_spi_total_investment_income","hmrc_spi_assessable_income","student_loan_balance","has_lifetime_isa","lifetime_isa_balance","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"],"entity":"person","rows":"all"}],"kernel":"uk.stage.salary_sacrifice@1","mass":"conserve","outputs":[{"column":"pension_contributions_via_salary_sacrifice","dtype":"float64","entity":"person","ownership":"produced","rewrite":true,"rows":"all"},{"column":"employee_pension_contributions","dtype":"float64","entity":"person","ownership":"produced","rewrite":true,"rows":"all"},{"column":"employment_income","dtype":"float64","entity":"person","ownership":"produced","rewrite":true,"rows":"all"},{"column":"salary_sacrifice_pre_conversion_pay","dtype":"float64","entity":"person","ownership":"produced","rows":"all"}],"params":{"numerical_dependencies":[["policyengine-uk","2.122.2"],["policyengine-core","3.32.19"],["numpy","2.4.6"],["pandas","3.0.3"],["scikit-learn","1.8.0"],["quantile-forest","1.4.2"]],"stage":"salary_sacrifice","stage_contract_sha256":"051e5ad02ae8f22986db97f513ad3d7dcc06da81f4cfa7ec5d01428f37748360","time_period":"2024"},"population":"cgt_residential_split","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 student_loans.","id":"student_loans","inputs":[{"columns":["frs_benunit_capital"],"entity":"benunit","rows":"all"},{"columns":["region"],"entity":"household","rows":"all"},{"columns":["age","current_education","highest_education","salary_sacrifice_pre_conversion_pay","student_loan_repayments","student_loans"],"entity":"person","rows":"all"}],"kernel":"uk.stage.student_loans@1","mass":"conserve","outputs":[{"column":"student_loan_plan","dtype":"string","entity":"person","ownership":"produced","rows":"all"}],"params":{"numerical_dependencies":[["policyengine-uk","2.122.2"],["policyengine-core","3.32.19"],["numpy","2.4.6"],["pandas","3.0.3"],["scikit-learn","1.8.0"],["quantile-forest","1.4.2"]],"stage":"student_loans","stage_contract_sha256":"f2936c5e22a0e9b097437e3f3aabaf06c4561d5c25a9b7c05e720a6553f73bbc","time_period":"2024"},"population":"cgt_residential_split","sources":["frs"],"structural":"none","weights":null}],"sources":[{"codec":"csv-tables","description":"Content-bound UK FRS and donor fixture/source bundle.","name":"frs"}]}
diff --git a/tools/graph_uk_spine_fixture.py b/tools/graph_uk_spine_fixture.py
index ac537fbd9..dd4f92a22 100644
--- a/tools/graph_uk_spine_fixture.py
+++ b/tools/graph_uk_spine_fixture.py
@@ -364,6 +364,11 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                 "MORTINT": 7.0,
                 "STRUINS": 8.0,
                 **{f"CHRGAMT{i}": float(i) for i in range(1, 10)},
+                # Interviews from 15 October 2024 (SAS dates, days since
+                # 1 January 1960).
+                "INTDATE": float(23664 + household_id % 120),
+                # Every eleventh household is shared (HHSTAT 2).
+                "HHSTAT": 2 if household_id % 11 == 0 else 1,
             }
         )
         benunits.append(
@@ -373,6 +378,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                 "FAMTYPB2": 5,
                 "DEPCHLDB": int(has_child),
                 "TOTCAPB4": 100.0 + household_id,
+                "HBOTHAMT": 0.0,
             }
         )
         adult = {
@@ -392,6 +398,15 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
             **({"R02": 7} if has_child else {}),
             "MARITAL": 1 + household_id % 3,
             "EMPSTATI": 1 + household_id % 8,
+            "SAMESIT": 2 if household_id % 2 else 1,
+            "SRENTAMT": "",
+            # CVPAY (below) is board and lodging on odd households, lodging alone
+            # on even ones.
+            "CONVBL": 1 if household_id % 2 else 2,
+            **{
+                f"SDEMP{month:02d}": 3 if household_id % 8 in (2, 3) else 1
+                for month in range(1, 13)
+            },
             "MJOBSECT": 1 + household_id % 2,
             "SIC": 10 + household_id % 80,
             "FTED": 2,
@@ -425,6 +440,11 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
             "SMPADJ": 0.5,
             "TUBORR": 500.0,
             "ACCSSAMT": 1.0,
+            "ACCSSPD": 52.0,
+            # Registered blind (SPCREG1 1) on every seventh adult; the
+            # partial-sight registration (SPCREG2) is never read.
+            "SPCREG1": 1 if household_id % 7 == 0 else 2,
+            "SPCREG2": 1 if household_id % 5 == 0 else 2,
             "GRTDIR1": 2.0,
             "GRTDIR2": 3.0,
             "HEARTVAL": 5.0,
@@ -456,6 +476,7 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                     "FSFVVAL": 1.0,
                     "FSBVAL": 2.0,
                     "HEARTVAL": 4.0,
+                    "SPCREG1": 1 if household_id % 9 == 0 else 2,
                 }
             )
         accounts.extend(
@@ -488,6 +509,8 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                 "BENEFIT": benefit,
                 "VAR2": variant,
                 "BENAMT": amount,
+                "UCSTART": "",
+                "UCHOUSEL": "",
             }
             for benefit, variant, amount in (
                 (14, 1, 2.0),
@@ -499,6 +522,21 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                 (3, 0, 7.0),
             )
         )
+        if household_id % 3 == 0:
+            # Universal Credit on every third household: a claim linked to a
+            # recent start, one linked to an old start, and one unlinked.
+            benefits.append(
+                {
+                    "SERNUM": household_id,
+                    "BENUNIT": 1,
+                    "PERSON": 1,
+                    "BENEFIT": 95,
+                    "VAR2": 0,
+                    "BENAMT": 8.0,
+                    "UCSTART": ("07/01/2024", "01/15/2022", "")[household_id % 9 // 3],
+                    "UCHOUSEL": 50.0 if household_id % 2 else "",
+                }
+            )
         jobs.append(
             {
                 "SERNUM": household_id,
@@ -508,6 +546,14 @@ def _frs_tables() -> dict[str, pd.DataFrame]:
                 "SPNAMT": 3.0,
                 "SALSAC": "1",
                 "JOBSECT": 1 + household_id % 2,
+                # Self-employed ("working for myself") where EMPSTATI is.
+                "ETYPE": 4 if household_id % 8 in (2, 3) else 1,
+                "JOBTYPE": 1,
+                "SEEND": "",
+                "SEJBLONG": float(household_id % 3)
+                if household_id % 8 in (2, 3)
+                else "",
+                "JOBBUS": 2 if household_id % 2 else 1,
             }
         )
         pensions.append(
diff --git a/uv.lock b/uv.lock
index 83cada2f2..468830f7b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -801,7 +801,7 @@ us = [
     { name = "h5py" },
     { name = "microcosm-data" },
     { name = "microunit" },
-    { name = "policyengine-core" },
+    { name = "policyengine-core", version = "3.32.5", source = { registry = "https://pypi.org/simple" } },
     { name = "policyengine-us" },
     { name = "tables" },
 ]
@@ -829,7 +829,7 @@ 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 = ">=2.100.0" },
+    { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.122.2" },
     { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" },
     { name = "pyarrow", specifier = ">=15" },
     { name = "pyyaml", specifier = ">=6" },
@@ -892,7 +892,7 @@ uk = [
     { name = "policyengine-uk" },
 ]
 us = [
-    { name = "policyengine-core" },
+    { name = "policyengine-core", version = "3.32.5", source = { registry = "https://pypi.org/simple" } },
     { name = "policyengine-us" },
 ]
 
@@ -908,7 +908,7 @@ 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 = ">=2.98.0" },
+    { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.122.2" },
     { name = "policyengine-us", marker = "extra == 'us'", specifier = ">=2.2.1,<3" },
 ]
 provides-extras = ["us", "uk"]
@@ -982,7 +982,7 @@ axiom = [
 ]
 policyengine = [
     { name = "microunit" },
-    { name = "policyengine-core" },
+    { name = "policyengine-core", version = "3.32.5", source = { registry = "https://pypi.org/simple" } },
     { name = "policyengine-us" },
 ]
 uk = [
@@ -1007,7 +1007,7 @@ 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 = ">=2.98.0" },
+    { name = "policyengine-uk", marker = "extra == 'uk'", specifier = ">=2.122.2" },
     { name = "policyengine-us", marker = "extra == 'policyengine'", specifier = ">=2.2.1,<3" },
     { name = "tables", marker = "extra == 'axiom'", specifier = ">=3" },
 ]
@@ -1559,6 +1559,14 @@ wheels = [
 name = "policyengine-core"
 version = "3.32.5"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
 dependencies = [
     { name = "dpath" },
     { name = "h5py" },
@@ -1582,19 +1590,54 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c6/d9/72c3707465e9fe77e6f7a55ea64b49a287cbf875b78f6e862d945df26975/policyengine_core-3.32.5-py3-none-any.whl", hash = "sha256:9d8c162d5fe5c784ae16c885da3ffbfc39a536add0e111133144d8edd555935f", size = 245925, upload-time = "2026-09-10T04:16:47.153Z" },
 ]
 
+[[package]]
+name = "policyengine-core"
+version = "3.32.19"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+dependencies = [
+    { name = "dpath" },
+    { name = "h5py" },
+    { name = "huggingface-hub" },
+    { name = "ipython" },
+    { name = "microdf-python" },
+    { name = "numexpr" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "plotly" },
+    { name = "psutil" },
+    { name = "pytest" },
+    { name = "pyvis" },
+    { name = "requests" },
+    { name = "sortedcontainers" },
+    { name = "standard-imghdr" },
+    { name = "wheel" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/be/c4/27ac7c91c84b4a341c830f91200156741a92a45fac65a8e869d6090dedf4/policyengine_core-3.32.19.tar.gz", hash = "sha256:d4c3619fa5ff9ba860e5547ba224c01b9982ae9c73545eb566a9245458ec86a6", size = 526979, upload-time = "2026-10-06T09:42:25.906Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/09/30e4d18f9d7649a32c60e1c116d43a51e53d470c37fafbb057510f486f7a/policyengine_core-3.32.19-py3-none-any.whl", hash = "sha256:409f2f04667cb30f9145910e0f1d244203b623b41df49fe790743f261b8381ab", size = 267934, upload-time = "2026-10-06T09:42:24.369Z" },
+]
+
 [[package]]
 name = "policyengine-uk"
-version = "2.100.0"
+version = "2.122.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "microdf-python" },
-    { name = "policyengine-core" },
+    { name = "policyengine-core", version = "3.32.19", source = { registry = "https://pypi.org/simple" } },
     { name = "pydantic" },
     { name = "tables" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/b4/6b/a84c78706f2f0c4be25a65034e624a1b1dc4e6e99e21291078b039bba30e/policyengine_uk-2.100.0.tar.gz", hash = "sha256:a7666023cc406eca0d2399dbe159d7712e61ff4bccedf841143ef3e85643e644", size = 1283060, upload-time = "2026-09-22T16:03:21.998Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/07/62/4378fd8e493df42fef30c58924dbb0f7ca8111c927302f6e60be862d67d0/policyengine_uk-2.122.2.tar.gz", hash = "sha256:0083793f1d398feef28a64db5508d5a64bcd7834feeade6cde1055d419d505ed", size = 1976779, upload-time = "2026-10-06T02:10:56.786Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7c/54/f24bcee4879cda9920e1a837eca087502b73fa43e531940f86295baa1d67/policyengine_uk-2.100.0-py3-none-any.whl", hash = "sha256:ec1be1e181d26a57446292aa188b3cc1ad96b3951bf4ff0436580bfc9cd40b1a", size = 2106162, upload-time = "2026-09-22T16:03:20.37Z" },
+    { url = "https://files.pythonhosted.org/packages/63/11/09948c900ca6d58c1050c89b9c93d8f5884244aa6ac95661ef1a2ee9eb9a/policyengine_uk-2.122.2-py3-none-any.whl", hash = "sha256:14cefcedce43ce4a37d659a0ff17ad05695de0dc8e0c5f6e2259a69944cb5558", size = 3118697, upload-time = "2026-10-06T02:10:54.896Z" },
 ]
 
 [[package]]
@@ -1604,7 +1647,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "microdf-python" },
     { name = "pandas" },
-    { name = "policyengine-core" },
+    { name = "policyengine-core", version = "3.32.5", source = { registry = "https://pypi.org/simple" } },
     { name = "spm-calculator" },
     { name = "tables" },
     { name = "tqdm" },