CEPR Paper: Framework for Measuring Firms' AI Efforts and Economic Impact

TaniaBabina · x · 2026-08-12

CEPR released a discussion paper (DP21804) by Tania Babina that reviews firm-level AI data and emerging empirical evidence on AI's economic effects.

The paper argues that measurement is central: different AI datasets capture different objects (e.g., invention vs. use, internal capability building vs. outsourcing, realized activity vs. investor perceptions), which can lead to divergent conclusions. The author develops a framework for choosing among these measures and surveys available data sources.

Furthermore, the paper synthesizes evidence on how AI affects firm growth, valuation, productivity, risk, labor, competition, financial markets, and applications, concluding with suggestions for future research.

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