The Meta-RCT Approach to Measuring AI's Labor Market Impact

Keywords: Artificial Intelligence

Abstract: How exposed are different occupations to advances in artificial intelligence (AI)? Existing approaches typically infer exposure from patents, expert surveys, or static mappings between AI capabilities and tasks, but each has limitations. I introduce a complementary framework that aggregates evidence from randomized controlled trials (RCTs) in economics and the social sciences, which directly test the causal impact of AI tools on worker performance. Drawing on RCTs across domains such as writing, coding, forecasting, and management, I map their results to O*NET tasks and then aggregate to the occupational level. The outcome is the 'RCT Exposure Index', a novel, empirically grounded measure of AI exposure that captures heterogeneity across tasks and occupations. By anchoring exposure in experimental evidence rather than proxies, the RCT Exposure Index provides a real-time lens on how AI is reshaping the landscape of work.

Submission Number: 185