Three approaches for integrating PolicyEngine tax-benefit calculations with Stata:
Stata 16+ has native Python integration. Install policyengine-us and call it directly:
python:| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>Complete .rac Format with Versioning</title> | |
| <style> | |
| * { box-sizing: border-box; margin: 0; padding: 0; } | |
| body { | |
| font-family: 'SF Mono', 'Fira Code', monospace; | |
| background: #0a0a0f; |
| """ | |
| 85th Percentile Household Income in Los Angeles City | |
| Using 1-year ACS PUMS data for 2023 and 2024 | |
| LA City PUMAs identified from Census ACS API by name matching. | |
| These 23 PUMAs are explicitly labeled as LA City areas. | |
| Note: Some LA City areas may be in shared PUMAs (e.g., 03707, 03748) which | |
| include portions of other cities - these are excluded to avoid overestimation. | |
| """ |
Prepared for Vahid's meeting with ONS firm data team, December 2025
PolicyEngine is exploring building an OLG (Overlapping Generations) model for the UK, similar to OG-USA/OG-Core but with greater firm-level heterogeneity. We also want to expand our firm microsimulation capabilities for tax policy analysis (VAT, corporation tax, business rates).
This document outlines what firm-level data would be most valuable from ONS.