| --- |
| 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](https://github.com/PolicyEngine/policyengine-us), built by the |
| [`populace`](https://github.com/PolicyEngine/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 |
|
|
| ```bash |
| pip install 'populace-data[us]' |
| ``` |
|
|
| ```python |
| 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: |
|
|
| ```python |
| 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](https://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](https://populace.dev); loader and registry: |
| [github.com/PolicyEngine/populace](https://github.com/PolicyEngine/populace) |
| (`packages/populace-data`). |
|
|
