PolicyEngine scorecard

A template for comparing PolicyEngine/Populace estimates against every external score or analysis the model can plausibly run — as a filterable, honest scorecard. Every external number appears alongside its PolicyEngine counterpart with the delta and any concept-mismatch annotation; the misses stay on the page.

Instance 1: Urban Institute's State of the Safety Net — nine programs (SNAP, SSI, TANF, WIC, CCDF, housing, LIHEAP, EITC, refundable CTC) × eligibility/participation metrics × US + 50 states + DC + demographic subgroups, plus the SPM poverty full-participation counterfactual.

How it works

sources/<source-id>/
  source.json       registry entry: what the source is, method, period
  raw/              the source's own published data, as fetched
  adapter.py        raw → tidy external rows (one schema for all sources)
  annotations.json  concept-mismatch annotations; every one traces to
                    docs/, engine metadata, or a measured diagnostic
pipeline/
  compute_counterparts.py  PE metrics on the certified Populace artifact
                           (policyengine.py managed_microsimulation, Build P)
  build_comparison.py      join + derive rates/deltas → data/comparison.json
app/                       the scorecard UI (bun + vite + react + ui-kit)
docs/                      replication assessment + engine mechanics audit

Tidy row schema: {source, program, metric, subgroup, variant, geography, unit_concept, period, value}. The PE side emits weighted counts only; every rate and delta is derived in one place (build_comparison.py).

Status taxonomy — honesty made structural:

status	meaning
comparable	PE measures the same concept
constructed	PE approximates the concept via a documented construction
concept_mismatch	PE value exists but measures a different concept
pe_gap	the model/artifact cannot produce this today
not_computed	producible but not yet in the pipeline
suppressed	the source suppressed the cell

Instance 2: baseline moments — average tariff rates (Yale, TPC)

The scorecard's second claim class, baseline_moment: current-law statistics published by external modelers, captured with the same honesty machinery as reform scores. First population: average US tariff rates —

• Yale Budget Lab tariff-rate-tracker (weighted_etr): effective statutory rates × fixed 2024 import weights, vintage pinned at the 2026-06-09 publication (commit 39d394d).
• TPC Tracking Trump Tariffs: average statutory rate × fixed 2025 weights, ex-AD/CVD, Datawrapper datasets version-pinned (aO4iG v44, MC81F v43 incl. by-authority types).
• Our side: the ex-post collections rate (Σ calculated duty / Σ customs value, monthly) from the Microcosm import-entry margins (exact-reconciled; microcosm #620) — today a concept_mismatch counterpart by design, with the fixed-base-vs-contemporaneous gap annotated; same-construct replications (our rates under each tracker's own definition) are the staged next counterparts, and by-authority decomposition arrives with the full-schedule rate generator (P5 charter).

Pipeline (additive; does not touch the Instance-1 build):

python sources/yale-tariff-tracker/adapter.py
python sources/tpc-tariffs/adapter.py
python pipeline/compute_tariff_counterparts.py   # parquet if present, else the committed extract
python pipeline/build_moments.py                 # -> app/public/data/moments.json

Reproducing

Python side (requires the policyengine.py-managed environment; heavy — respect the machine-wide sim lock):

python sources/urban-sotsn/adapter.py
simlock -- .venv-pe/bin/python pipeline/compute_counterparts.py
python pipeline/build_comparison.py

App:

cd app && bun install && bun dev

Architecture for N sources

See docs/ARCHITECTURE.md — the registry/adapter contract and the divergence-diagnosis stage that classifies each material delta as PE gap, external-model issue, concept mismatch, or data vintage, and drafts the fix.