UK scorecard: populace_uk_2023 vs enhanced_frs_2024_25 vs admin benchmarks (2026)

cc @juaristi22 — María, could you take a look at why we're off on some of these?

I ran a benchmark scorecard of populace_uk_2023 against enhanced_frs_2024_25 (both simulated for 2026 through policyengine.py, pe.uk.ensure_datasets → Simulation with the pinned UK model version) and compared to approximate admin benchmarks.

Scorecard (2026)

Populace does well on demographics, household counts, taxpayer counts, pension credit, and poverty. The two large misses are:

1. Income tax +27% vs OBR (£419bn vs ~£330bn) — consistent with the +20.8% vs the SPI-anchored benchmark already diagnosed in UK_COVERAGE_PROGRESS.md as distributional.
2. Universal credit at less than half of admin (£39.9bn / 3.3m families vs ~£80–86bn / ~6.7m households) — consistent with #701 (UC not bound in the certified 148-target surface).

Smaller ones worth a look: child benefit ~+30% (though enhanced FRS shares this), council tax ~+20%, state pension ~£10–15bn low (also shared), and Gini a touch high.

Reproduction code

"""Benchmark scorecard for a UK dataset via policyengine.py (year 2026).

Usage: python scorecard.py <dataset-name-or-hf-uri>
e.g.   python scorecard.py populace_uk_2023
"""

import sys

import numpy as np
import policyengine as pe
from policyengine import Simulation

DATASET = sys.argv[1]

d = pe.uk.load_datasets(datasets=[DATASET], years=[2026], data_folder="./data")
dataset = list(d.values())[0]

PERSON_VARS = [
    "age",
    "state_pension",
    "income_tax",
    "national_insurance",
    "employment_income",
    "self_employment_income",
    "dividend_income",
    "pension_income",
    "capital_gains",
]
BENUNIT_VARS = ["universal_credit", "child_benefit", "pension_credit"]
HH_VARS = ["household_net_income", "income_tax", "council_tax", "in_poverty_bhc"]

sim = Simulation(
    dataset=dataset,
    tax_benefit_model_version=pe.uk.model,
    extra_variables={
        "person": PERSON_VARS,
        "benunit": BENUNIT_VARS,
        "household": HH_VARS,
    },
)
sim.ensure()
out = sim.output_dataset.data


def arr(ent, var):
    return np.asarray(getattr(out, ent)[var], float)


pw = arr("person", "person_weight")
bw = arr("benunit", "benunit_weight")
hw = arr("household", "household_weight")

print(f"SCORECARD {DATASET} year 2026")
age = arr("person", "age")
print(f"population_m {pw.sum()/1e6:.2f}")
print(f"children_u16_m {pw[age < 16].sum()/1e6:.2f}")
print(f"age_65plus_m {pw[age >= 65].sum()/1e6:.2f}")
print(f"households_m {hw.sum()/1e6:.2f}")

for v in PERSON_VARS[1:]:
    print(f"{v}_bn {(arr('person', v)*pw).sum()/1e9:.1f}")
it = arr("person", "income_tax")
print(f"taxpayers_m {pw[it > 0].sum()/1e6:.2f}")

for v in BENUNIT_VARS:
    x = arr("benunit", v)
    print(f"{v}_bn {(x*bw).sum()/1e9:.1f}")
    print(f"{v}_families_m {bw[x > 0].sum()/1e6:.2f}")

hni = arr("household", "household_net_income")
print(f"council_tax_bn {(arr('household','council_tax')*hw).sum()/1e9:.1f}")
pov = arr("household", "in_poverty_bhc")
print(f"poverty_bhc_pct {100*(pov*hw).sum()/hw.sum():.1f}")

order = np.argsort(hni)
h, w = hni[order], hw[order]
cw = np.cumsum(w) / w.sum()
inc = h * w
print(f"top1_share_pct {100*inc[cw >= 0.99].sum()/inc.sum():.1f}")
print(f"top10_share_pct {100*inc[cw >= 0.90].sum()/inc.sum():.1f}")
p = np.cumsum(w) / w.sum()
L = np.cumsum(np.clip(inc, 0, None)) / np.clip(inc, 0, None).sum()
print(f"gini {1 - 2*np.trapezoid(L, p):.3f}")

Notes: national_insurance here is the personal-side variable only; benchmarks are approximate (OBR EFO / HMRC / DWP / ONS, nearest year) and meant for orders of magnitude, not precise scoring. All figures are weighted aggregates only — no microdata is included.