Effective take-up rate analysis plan

Status, October 2026

The analysis below has not been run yet. Stochastic take-up flags are drawn in the data build, not in this repository; the Housing Benefit, Income Support, tax credit and Council Tax Reduction flags are formulas here (see below). Since policyengine.py 6.10.0 (21 October 2026) the certified UK default dataset is Microcosm UK (microcosm_uk_2024_25), built by Microcosm. The Enhanced FRS built by policyengine-uk-data stays loadable by name at release 1.58.1, and it is the dataset this repository’s credentialed tests pin (policyengine_uk/tests/certified_dataset.yaml). Both draw Universal Credit, Pension Credit and Child Benefit take-up anchored on reported receipt, and Microcosm adds some draws the Enhanced FRS lacks (see the table), so the analysis should run on whichever dataset is the certified default at the time.

Why this matters¶

PolicyEngine UK does not compute benefit take-up from scratch. The data build seeds stochastic take-up flags (would_claim_*) for most programmes from target rates (some published take-up estimates, some fitted to caseload or spending, and Universal Credit’s 0.55, which has no cited source in policyengine-uk-data and is held in Microcosm for parity with the earlier pipeline), then runs them through:

Reported anchoring. Universal Credit, Pension Credit and Child Benefit flags are inputs, drawn so that units reporting receipt claim. Housing Benefit, Income Support, the tax credits and Council Tax Reduction instead derive their flags at runtime: on data-built simulations they equal reported receipt, and hand-entered households also claim when claims_all_entitled_benefits is true; see #1621.
SPI integration for income-tax-relevant variables (which can shift the income distribution and therefore who falls into each benefit’s eligible population).
Reweighting to match aggregate caseload and expenditure targets.

Both builds integrate the SPI before reweighting. Pension Credit take-up (both builds), and Child Benefit and Carer’s Allowance take-up (Microcosm), are redrawn after the SPI step.

By the end of the pipeline the effective take-up rate, the share of the eligible population that ends up with would_claim_X == True, can diverge a long way from the seed rate: the initial analysis linked below found 74.9% of the benefit units entitled to Universal Credit in 2026 receiving it, against a 55% seed (Enhanced FRS 2023-24, policyengine-uk 2.53.1). Part of the gap is by construction, because the data builds apply most seed rates to every benefit unit, not only to eligible ones. Some seed rates are published take-up estimates (Pension Credit, Child Benefit, Scottish Child Payment, Marriage Allowance); others were fitted to caseload or spending targets, some at calibrated weights (Tax-Free Childcare in policyengine-uk-data, the Universal Credit childcare element in Microcosm). The real interest is how the post-pipeline shares stack up against published outturn share figures.

Where the seed rates live¶

The take-up rate parameters in this repository (for example gov.dwp.universal_credit.takeup_rate and gov.dwp.housing_benefit.takeup) are reference values: no formula reads them. The rates the data build draws from are in policyengine_uk_data/parameters/take_up/ in policyengine-uk-data and in packages/microcosm-build/src/microcosm/build/uk/take_up_contract.json in Microcosm. The analysis should read the seed rate from the data build that produced the dataset, and check the matching parameter here is in sync. Two already differ: Pension Credit is 0.7 here against 0.62 in policyengine-uk-data from April 2023, and Scottish Child Payment for children aged 6 and over is 0.86 here against 0.85 in both builds.

Programmes to cover¶

Per #1354, with current PolicyEngine UK take-up handling:

Programme	Variable	Take-up flag	Reference parameter here	Handling
Universal Credit	`universal_credit`	`would_claim_uc` (input)	`gov.dwp.universal_credit.takeup_rate`	Drawn in the data build, anchored on reported receipt
UC on legacy closure	`claims_uc_at_legacy_closure`	`would_claim_uc_at_legacy_closure` (input)	–	Drawn in Microcosm at DWP’s Move to Universal Credit claim rates by legacy benefit combination; the Enhanced FRS does not set it, so it defaults to true
UC childcare element	`uc_childcare_element` (claimed via `uc_childcare_element_claimed`)	`would_claim_uc_childcare` (input)	–	Drawn in Microcosm at single and couple rates; the Enhanced FRS does not set it, so it defaults to true
Pension Credit	`pension_credit`	`would_claim_pc` (input)	`gov.dwp.pension_credit.takeup`	Drawn in the data build, anchored on reported receipt
Child Benefit	`child_benefit`	`would_claim_child_benefit` (input)	`gov.hmrc.child_benefit.takeup.overall` and `.by_age`	Drawn in the data build, anchored on reported receipt
Housing Benefit	`housing_benefit`	`would_claim_housing_benefit` (formula)	`gov.dwp.housing_benefit.takeup`	Follows reported receipt (#1621)
Income Support	`income_support`	`would_claim_IS` (formula)	`gov.dwp.income_support.takeup`	Follows reported receipt (#1621); scheme closed 2026-04-01
Income-based JSA / income-related ESA	`jsa_income`, `esa_income`	none	`gov.dwp.JSA.income.takeup`, `gov.dwp.ESA.income.takeup_estimate_fye_2019`	Paid on reported awards that pass capital and work tests; income-based JSA closed 2026-04-01
Child Tax Credit	`child_tax_credit`	`would_claim_CTC` (formula)	`gov.dwp.tax_credits.child_tax_credit.takeup`	Follows reported receipt; scheme ended on 5 April 2025
Working Tax Credit	`working_tax_credit`	`would_claim_WTC` (formula)	`gov.dwp.tax_credits.working_tax_credit.takeup`	Follows reported receipt; scheme ended on 5 April 2025
Council Tax Reduction	`council_tax_reduction`	`would_claim_council_tax_reduction` (formula)	–	Simulated for England’s pension-age scheme, the Scottish and Welsh schemes and five English councils’ working-age schemes, reported reductions elsewhere; on data-built simulations take-up follows reported receipt
Tax-Free Childcare	`tax_free_childcare`	`would_claim_tfc` (input)	–	Drawn in the data build at a flat rate
Universal childcare entitlement	`universal_childcare_entitlement`	`would_claim_universal_childcare` (input)	–	Drawn in the data build at a flat rate
Extended childcare entitlement	`extended_childcare_entitlement`	`would_claim_extended_childcare` (input)	–	Drawn in the data build at a flat rate
Targeted childcare entitlement	`targeted_childcare_entitlement`	`would_claim_targeted_childcare` (input)	–	Drawn in the data build at a flat rate
Marriage Allowance	`marriage_allowance`	`would_claim_marriage_allowance` (input)	`gov.hmrc.income_tax.allowances.marriage_allowance.takeup_rate` (0.5, the post-2019 steady state)	Drawn in the data build at a flat rate
Scottish Child Payment	`scottish_child_payment`	`would_claim_scp` (input)	`gov.social_security_scotland.scottish_child_payment.takeup_rate` (under 6, 6 and over)	Drawn in the data build by age
Carer’s Allowance	`carers_allowance`	`would_claim_carers_allowance` (input)	–	Set from reported receipt in Microcosm, whose main branch since 20 October 2026 also draws a claim at 0.6 for carers outside Scotland whom only the earnings limit keeps from the allowance; the Enhanced FRS does not set it, so it defaults to true
16 to 19 Bursary Fund	`bursary_fund_16_to_19`	`would_claim_bursary_fund_16_to_19` (input)	–	Defaults to true
Student support grants	`adult_dependants_grant`, `childcare_grant`, `parents_learning_allowance`, `travel_grant`, `disabled_students_allowance`	`would_claim_adult_dependants_grant`, `would_claim_childcare_grant`, `would_claim_parents_learning_allowance`, `would_claim_travel_grant`, `would_claim_disabled_students_allowance` (inputs)	–	Default to true
Care to Learn	`care_to_learn`	`would_claim_care_to_learn` (input)	–	Defaults to true
State Pension	`state_pension`	none	–	Paid from reported State Pension (`state_pension_reported`); see state-pension.md
PIP / DLA / AA / SDA	various	none	–	PIP, DLA and AA category inputs set from reported receipt (PIP categories can also come from points); SDA paid at the maximum rate where `sda_reported` is positive; see disability-legacy-benefits.md

Methodology¶

For each programme above, the analysis should produce:

Seed take-up rate: the rate the data build drew from for the analysis year, plus the source (HMRC, DWP or an academic study) and vintage.
Effective take-up rate post-pipeline: counted from the built dataset as sum(would_claim_X * eligible_X * weight) / sum(eligible_X * weight), where eligible_X is computed from the runtime formulas. The numerator counts only eligible units, because would_claim_X can be true for ineligible units too (otherwise one eligible unit and two willing units would give 200%). Where no unit is eligible the denominator is zero; report the rate as undefined (n/a), not 0%.
Published outturn share for the same year: DWP and HMRC statistical publications giving claimant counts and either eligible population counts (where DWP estimates them) or a published take-up rate.
Pipeline drift: effective − seed per programme, with a sign convention so positive means more claimants than seeded.

Most published reference rates change roughly annually (HMRC’s tax credit take-up rates stop at 2017 to 2018). The analysis should be repeatable, ideally as a notebook under docs/book/validation/ so future builds can re-run it and update the gaps table. A notebook needs its own file: entry under Validation in myst.yml to appear in the docs, since only Markdown pages are picked up by folder.

Output¶

A new validation notebook docs/book/validation/take-up-rates.ipynb should:

pull each programme’s seed take-up rate from the data build that produced the dataset,
compute the effective rate against the built dataset,
compare to the most recent published share (DWP’s income-related benefits take-up tables; HMRC’s Child Benefit, Tax-Free Childcare and tax relief statistics; Scottish Government take-up rates; DfE childcare statistics),
flag drifts of more than ±2 percentage points for follow-up.

The first run is the deliverable for #1354. After that the notebook becomes a part of the validation page and gets re-run on each EFO / data refresh.

Reported-anchored take-up is in both data builds for UC, Pension Credit and Child Benefit. The programmes whose flags still follow reported receipt at runtime have no seed draw: their effective rate is the share of eligible units that report receipt, which the analysis should compare with the published rate directly.
The would_claim_* input-only conversion (also in #1621) would give the analysis a cleaner ladder: with all of would_claim_* as inputs, every flag comes from the data build’s take-up stage, and pipeline drift comes only from the steps after it (SPI income imputation and reweighting).

References¶

Issue: #1354.
Initial analysis (UC and CTC): https://gist.github.com/MaxGhenis/763db9278ddecdf310f160a73e138c8a (policyengine-uk 2.53.1; it measured take-up by setting gov.dwp.universal_credit.takeup_rate to 1, which no longer changes take-up on main).
DWP, Income-related benefits: estimates of take-up: reference rates for Pension Credit and pensioners’ Housing Benefit, and (from the financial year ending 2025 edition) a composite Universal Credit (or equivalent benefits) rate.
HMRC, Personal tax credits statistics.
HMRC, Child Benefit: annual release, and Marriage Allowance claimants and cost in HMRC’s tax relief statistics.
Pipeline alignment: #1621.
Cross-links: state-pension.md, disability-legacy-benefits.md, tax-credits.md.