Summary
The published populace-uk calibration (release populace-uk-2023-dd68c73-4aa4b14-20260619T023711Z) contains 149 targets, none of which constrain capital gains. As a result the calibrated weights leave the CGT distribution far from HMRC administrative data — in fact further away than the stock Enhanced FRS on two of the three key statistics.
Measurements
Read directly from populace_uk_2023.h5 (capital_gains weighted by household_weight), compared with the stock Enhanced FRS and HMRC's published CGT statistics:
| Statistic |
populace-uk 2023 |
Enhanced FRS (2026) |
HMRC 2023-24 |
| CGT taxpayers (gains > AEA) |
1.47m |
1.29m |
~378k |
| Gains of those taxpayers |
£96.0bn |
£112.0bn |
~£65.9bn |
| Mean gain per taxpayer |
£65,374 |
£87,158 |
~£174,000 |
So the calibrated dataset has roughly 3.9x HMRC's taxpayer count, with a mean gain about 62% below the administrative figure. The direction is consistent with an unconstrained imputation: gains are spread across too many households in amounts that are individually too small.
Why it matters
Capital gains are one of the places the FRS imputation is furthest from administrative data, and the error is distributional rather than just a level shift. Any analysis of a CGT reform on these weights will overstate how widely the reform is felt — in our case the share of people losing income came out roughly 3x too high (5.0% vs 1.65% once CGT targets were added), and mean gain per payer was off by a factor of two.
The only capital-gains-adjacent item in the target surface is the column household.household_is_capital_gains_clone, which is a feature flag from the imputation rather than a target.
Suggestion
Add HMRC CGT targets to the UK target registry, e.g.:
- total taxable capital gains (HMRC CGT statistics, table 1)
- CGT taxpayer count
- ideally gains by size band, since the concentration matters more than the total for distributional work
For reference, calibrating with just the first two (£70bn gains, 400k taxpayers) alongside held targets for income tax, net income, population and household counts brought all six targets within 0.13% at an ESS cost of only 981 -> 905, so the CGT targets do not appear to be in serious tension with the existing surface.
Happy to share the target definitions we used if helpful.
Summary
The published
populace-ukcalibration (releasepopulace-uk-2023-dd68c73-4aa4b14-20260619T023711Z) contains 149 targets, none of which constrain capital gains. As a result the calibrated weights leave the CGT distribution far from HMRC administrative data — in fact further away than the stock Enhanced FRS on two of the three key statistics.Measurements
Read directly from
populace_uk_2023.h5(capital_gainsweighted byhousehold_weight), compared with the stock Enhanced FRS and HMRC's published CGT statistics:So the calibrated dataset has roughly 3.9x HMRC's taxpayer count, with a mean gain about 62% below the administrative figure. The direction is consistent with an unconstrained imputation: gains are spread across too many households in amounts that are individually too small.
Why it matters
Capital gains are one of the places the FRS imputation is furthest from administrative data, and the error is distributional rather than just a level shift. Any analysis of a CGT reform on these weights will overstate how widely the reform is felt — in our case the share of people losing income came out roughly 3x too high (5.0% vs 1.65% once CGT targets were added), and mean gain per payer was off by a factor of two.
The only capital-gains-adjacent item in the target surface is the column
household.household_is_capital_gains_clone, which is a feature flag from the imputation rather than a target.Suggestion
Add HMRC CGT targets to the UK target registry, e.g.:
For reference, calibrating with just the first two (£70bn gains, 400k taxpayers) alongside held targets for income tax, net income, population and household counts brought all six targets within 0.13% at an ESS cost of only 981 -> 905, so the CGT targets do not appear to be in serious tension with the existing surface.
Happy to share the target definitions we used if helpful.