policyengine-us-data 1.75.3

A package to create representative microdata for the US.

PolicyEngine US Data

Installation

While it is possible to install via PyPi:

pip install policyengine-us-data

the recommended installation is

pip install -e .[dev]

which installs the development dependencies in a reference-only manner (so that changes to the package code will be reflected immediately); policyengine-us-data is a dev package and not intended for direct access.

SSA Data Sources

The following SSA data sources are used in this project:

• Latest Trustee's Report (2025) - Source for social_security_aux.csv (extracted via extract_ssa_costs.py)
• Single Year Supplementary Tables (2025) - Long-range demographic and economic projections
• Single Year Age Demographic Projections (2024 - latest published) - Source for SSPopJul_TR2024.csv population data

Pipeline Overview

PolicyEngine constructs its representative household datasets through a multi-step pipeline. Public survey data is merged, stratified, and cloned to geographic variants per household. Each clone is simulated through PolicyEngine US with stochastic take-up, then calibrated via L0-regularized optimization against administrative targets at the national, state, and congressional district levels, producing geographically representative datasets.

The Enhanced CPS (make data-legacy) produces a national-only calibrated dataset. For the current geography-specific pipeline, see docs/calibration.md.

The repo currently contains two calibration tracks:

• Legacy Enhanced CPS (make data-legacy), which uses the older EnhancedCPS / build_loss_matrix() path for national-only calibration.
• Unified calibration (docs/calibration.md), which uses storage/calibration/policy_data.db and the sparse matrix + L0 pipeline for current national and geography-specific builds.

For detailed calibration usage, see docs/calibration.md and modal_app/README.md.

Running the Full Pipeline

The pipeline runs as sequential steps in Modal:

make pipeline   # prints the steps below

# 1. Build data (CPS/PUF/ACS → source-imputed stratified CPS)
make build-data-modal

# 2. Build calibration matrices (CPU, ~10h)
make build-matrices

# 3. Fit weights (GPU, county + national in parallel)
make calibrate-both

# 4. Build H5 files (state/district/city + national in parallel)
make stage-all-h5s

# 5. Promote to versioned HF paths
make promote

Building the Paper

Prerequisites

The paper requires a LaTeX distribution (e.g., TeXLive or MiKTeX) with the following packages:

• graphicx (for figures)
• amsmath (for mathematical notation)
• natbib (for bibliography management)
• hyperref (for PDF links)
• booktabs (for tables)
• geometry (for page layout)
• microtype (for typography)
• xcolor (for colored links)

On Ubuntu/Debian, you can install these with:

sudo apt-get install texlive-latex-base texlive-latex-recommended texlive-latex-extra texlive-fonts-recommended

On macOS with Homebrew:

brew install --cask mactex

Building

To build the paper:

make paper

To clean LaTeX build files:

make clean-paper

The output PDF will be at paper/main.pdf.

Building the Documentation

Prerequisites

The documentation uses Jupyter Book 2 (pre-release) with MyST. To install:

# Install Jupyter Book 2 pre-release
pip install --pre "jupyter-book==2.*"

# Install MyST CLI
npm install -g mystmd

Building

To build and serve the documentation locally:

cd docs
myst start

Or alternatively from the project root:

jupyter book start docs

Both commands will start a local server at http://localhost:3001 where you can view the documentation.

The legacy Makefile command:

make documentation

Note: The Makefile uses the older jb command syntax which may not work with Jupyter Book 2. Use myst start or jupyter book start docs instead.