Researchers Build Deregulation Index Using LLMs
Danilo Cascaldi-Garcia and Matteo Iacoviello (March 2026) construct a US news-based deregulation index spanning 1960–2025 using large language models to semantically classify newspaper articles. The index, validated against human labels and Federal Register measures, leads formal regulatory records by nearly one year and decomposes by sector and policy stage. The authors find positive deregulation shocks raise investment, productivity, stock prices, profits, and GDP.
Key Points
- 1Constructs 1960–2025 US deregulation index using LLM semantic classification of newspaper articles
- 2Validates index against Federal Register, finding it leads formal records by nearly one year
- 3Demonstrates deregulation shocks increase investment, productivity, profits, stock returns, and GDP
Scoring Rationale
Strong methodological novelty and Fed-affiliated credibility; broad scope and clear empirical effects support a high impact score.
Sources
Public references used for this report.
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