Narratives to Numbers: Large Language Models and Economic Policy Uncertainty

This study evaluates large language models as estimable classifiers and clarifies how modeling choices shape downstream measurement error. Revisiting the Economic Policy Uncertainty index, we show that contemporary classifiers substantially outperform dictionary rules, better track human audit assessments, and extend naturally to noisy historical and multilingual news. We use these tools to construct a new nineteenth-century U.S. index from more than 360 million newspaper articles and exploratory cross-country indices with a single multilingual model. Taken together, our results show that LLMs can systematically improve text-derived measures and should be integrated as explicit measurement tools in empirical economics.