license: mit
pretty_name: populace-us
tags:
- policyengine
- microsimulation
- synthetic-population
- tax-benefit
- united-states
populace-us
The populace-built US population: a calibrated synthetic microdataset for
PolicyEngine-US, built by the
populace stack entirely from
primary sources — the enhanced CPS appears only as the benchmark this file is
scored against, never as a build input. It loads anywhere the enhanced CPS
loads (an API-compatible alternative population), with its own calibrated
weights — and its own strengths and gaps, both documented below.
Load it
pip install 'populace-data[us]'
from policyengine_us import Microsimulation
from populace.data import load
sim = Microsimulation(dataset=load("us", 2024))
sim.calculate("household_net_income", 2024).sum()
Or grab the H5 directly:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="policyengine/populace-us",
filename="populace_us_2024.h5",
repo_type="dataset",
)
How it is built
One HDF5 USSingleYearDataset per year. Every layer comes from a primary
survey or administrative source:
| source | provides |
|---|---|
| Census CPS ASEC | household structure, demographics, incomes, benefits, tenure, hours, occupation flags, health coverage at interview, retirement distributions (DST codes), childcare, prior-year income (longitudinal PERIDNUM join) |
| IRS SOI Public Use File 2015 (uprated) | tax detail: capital gains, dividends, interest, itemized-deduction inputs, QBI/SSTB components, partnership self-employment, estates, tuition |
| Fed SCF 2022 | wealth: bank/stock/bond assets, net worth, mortgage balance hints |
| Census SIPP | tip income for tipped occupations; household vehicles (count and value) |
| CPS-ORG | hourly wage, paid-hourly status, union coverage |
| MEPS-IC parameters | employer-sponsored insurance premiums |
| Census ACS 2022 | rent for renter households |
Imputations use weight-aware quantile-forest models fit on each donor's own
records, and every imputed value is clipped to that donor's realized range
(the support guard) — nothing is anchored to the enhanced CPS. The result is
calibrated to PolicyEngine's administrative target surface (3,704 IRS/Census/
program targets, plus a signed net short-term capital gains target so the
optimizer cannot silently drive a net-negative aggregate to extremes) with a
hard per-record weight bound (max_weight_ratio=50), so no aggregate leans on
a handful of super-weighted records.
Acceptance gates
The build refuses to publish unless every gate passes; this file passed all of them:
- Parity 0: every PolicyEngine input layer the enhanced CPS populates non-degenerately, this file's simulation populates (169 reference layers checked at simulation level).
- Exported-nonzero: all 308 stored columns carry signal — no all-zero scaffolding that would silently mask engine formulas or defaults.
- Calibration: 94.66% of 3,704 targets within 10% (loss 0.022); max household weight 379,623 with zero records above 500k (the enhanced CPS ships 21, max 1.05M).
- Smoke aggregates through
Microsimulation: 332.8M people, $98.0B SNAP, $175.5T net worth (Fed Z.1 ≈ $169T), net short-term capital gains −$77.4B against the −$76.8B PUF-anchored target, tips $53.1B, rent $759.7B.
Validation
Scored by the sound comparison — matched samples (41,314 households), symmetric weight refit on the full administrative target surface, held-out targets never seen by either side's refit. Lower is better.
| metric | populace-us | enhanced CPS |
|---|---|---|
| training loss (2,965 targets) | 0.176 | 1.089 |
| held-out loss (739 unseen targets) | 0.037 | 0.317 |
| full-surface loss (3,704 targets) | 0.213 | 1.406 |
Per individual target the incumbent still wins more often (2,528 of 3,704 to our 1,127, 49 ties): populace wins big where it wins and loses narrowly where it loses. Both facts are the story.
Known gaps
We publish the misses with the hits:
- Net worth runs ~4% above Fed Z.1 ($175.5T vs ≈ $169T): the calibration target ($160T) sits below Z.1 and the achieved total lands between them.
- Investment interest expense is thin ($7.2B against IRS SOI ≈ $24B): the PUF-residual rule populates the layer conservatively; a dedicated SOI calibration target is the roadmap item.
- Per-target wins vs the incumbent: see Validation — aggregate losses are what the comparison gates on, but per-target patterns differ between the two populations. Results are not interchangeable.
The dashboard at populace.dev/dashboard
shows the full per-family calibration fit, the worst-fit targets by name, the
weight distribution, and a live strip while a build chain runs. Methodology
and evidence: populace.dev; loader and registry:
github.com/PolicyEngine/populace
(packages/populace-data).