AI and Distributional Policy Research

A PolicyEngine research initiative examining how economic policies mediate the relationship between AI-driven economic shocks and distributional outcomes.

Overview

This microsite outlines PolicyEngine's interest in modeling the causal chain: AI economic shocks → policy interventions → distributional outcomes (income, consumption, wealth). Rather than forecasting AI's economic impacts or prescribing optimal policies, we provide a framework for analyzing how different policy responses shape distributional outcomes under AI-driven economic change.

Research Questions

• How do economic policies (taxes, transfers, UBI proposals) mediate AI's impact on income, consumption, and wealth distribution?
• How would current policies vs. alternatives (UBI, expanded safety nets, capital taxation) differentially shape distributional outcomes under AI scenarios?
• What are the inequality, poverty, and work incentive effects of different policy responses to AI economic shocks?
• How do these mediation effects vary across regions, demographics, and AI trajectories?

Key Features

This site includes:

1. Research Overview: The challenge, our approach, and why this matters
2. Relevant Research: Summary of academic work on AI economics, labor impacts, inequality, and microsimulation
3. Policy Scenarios: Different policy designs to evaluate (current policy, UBI, safety net expansion, capital taxation, hybrid approaches)
4. Technical Requirements: What's needed to conduct this research (scenario modeling, data, computational infrastructure, validation)
5. Potential Stakeholders: Organizations that might be interested in supporting or collaborating on this work

Geographic Scope

While initial work would focus on the United States using PolicyEngine-US, this research framework could be expanded to:

• United Kingdom (PolicyEngine-UK) - fully operational
• Canada (PolicyEngine-Canada) - partially developed
• Other countries where PolicyEngine models are available

Cross-country comparisons would provide valuable insights into how different tax-benefit systems respond to AI-driven economic change.

Development

Prerequisites

• Node.js >= 22.0.0
• npm

Installation

make install

Or:

npm ci

Running the Development Server

make debug

Or:

npm start

The site will be available at http://localhost:3000.

Building for Production

make build

Or:

npm run build

Testing

make test

Or:

npm test

Code Formatting

Before committing, always format your code:

make format

Or:

npm run lint -- --fix && npx prettier --write .

Project Structure

ai-inequality/
├── public/
│   └── index.html
├── src/
│   ├── components/
│   │   ├── Hero.js              # Landing section
│   │   ├── Hero.css
│   │   ├── Overview.js          # Research overview
│   │   ├── Research.js          # Research summary
│   │   ├── PolicyScenarios.js   # Policy scenarios
│   │   ├── TechnicalRequirements.js
│   │   ├── Stakeholders.js      # Organizations
│   │   ├── Footer.js
│   │   └── Footer.css
│   ├── App.js                   # Main app component
│   ├── App.css
│   ├── index.js                 # Entry point
│   └── index.css
├── package.json
├── Makefile
└── README.md

Live Site

https://policyengine.github.io/ai-inequality/

The site automatically deploys to GitHub Pages on every push to main.

Contributing

This is a PolicyEngine project. Please follow the guidelines in CLAUDE.md for development practices:

1. Use functional React components with hooks
2. Run make format before committing
3. Test your changes with npm test
4. Ensure linting passes with npm run lint -- --max-warnings=0

Contact

Interested in collaborating on this research?

• Email: hello@policyengine.org
• Website: https://policyengine.org
• GitHub: https://github.com/PolicyEngine

License

AGPL-3.0

About PolicyEngine

PolicyEngine is a nonprofit building open-source tax-benefit microsimulation models to make public policy more transparent, accessible, and impactful.