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Methods and applications are inextricably linked in science, and inparticular in the domain of text-as-data. In this paper, we examine one suchtext-as-data application, an established economic index that measures economicpolicy uncertainty from keyword occurrences in news. This index, which is shownto correlate with firm investment, employment, and excess market returns, hashad substantive impact in both the private sector and academia. Yet, as werevisit and extend the original authors' annotations and text measurements wefind interesting text-as-data methodological research questions: (1) Areannotator disagreements a reflection of ambiguity in language? (2) Doalternative text measurements correlate with one another and with measures ofexternal predictive validity? We find for this application (1) some annotatordisagreements of economic policy uncertainty can be attributed to ambiguity inlanguage, and (2) switching measurements from keyword-matching to supervisedmachine learning classifiers results in low correlation, a concerningimplication for the validity of the index.

Uncertainty over Uncertainty: Investigating the Assumptions, Annotations, and Text Measurements of Economic Policy Uncertainty