What can LLMs tell us about the ETI?
We investigate how large language models perceive and simulate behavioral responses to tax policy changes, with implications for using LLMs in policy analysis.
Software I've built and research I've published—mostly at the intersection of policy, economics, and AI.
A native macOS menu bar app for tracking memory usage across Claude Code sessions, VS Code workspaces, Chrome tabs, and Python processes.
A VS Code extension for keyboard-driven terminal grid management with project picker and auto-launch for AI coding tools.
AI-powered grant writing and management platform.
Combine GitHub repos, PRs, and issues into single files for AI context.
LLM-powered tool suggesting improvements to PRs based on repo guidelines.
Dashboard for exploring BLS occupational employment and wage data.
We investigate how large language models perceive and simulate behavioral responses to tax policy changes, with implications for using LLMs in policy analysis.
Examining the use of large language models for forecasting policy outcomes under different U.S. presidential administrations using narrative prompting techniques.
A novel approach to microsimulation dataset construction that combines Current Population Survey data with IRS administrative records for improved tax-benefit modeling.
Full research articles built with Jupyter Book, featuring interactive visualizations and executable code.
My recommendations for California elections, with detailed analysis of ballot measures and races.
As CEO of PolicyEngine, I contribute to numerous policy analyses. Visit the PolicyEngine Research page for our full portfolio of published work on tax and benefit policy.
I founded the UBI Center in 2019, an open-source think tank researching universal basic income and cash transfer policies. From 2019 to 2023, the team produced over 60 reports on UBI proposals, child allowances, and carbon dividends.