Narratives to Numbers: Large Language Models and Economic Policy Uncertainty

We study the implications of adopting machine-generated data in economic analysis. Rather than taking the data-generating process as given and correcting estimates econometrically, we evaluate how upstream modeling and researcher choices shape downstream estimates. Revisiting the Economic Policy Uncertainty index, we find that large language models fine-tuned on human audit labels identify nearly twice as many auditor-flagged articles as keyword-based alternatives and are robust to noisy text and linguistic drift. Varying classifier, threshold, and aggregation choices produces substantial differences not only in performance but in what the index captures, reshaping the relationship between policy uncertainty and returns.