How much should we trust staggered difference-in-differences estimates?

Andrew C. Baker, David F. Larcker and Charles C.Y. Wang

Journal of Financial Economics, 2022, vol. 144, issue 2, 370-395

Abstract: We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers.

Keywords: Difference in differences; Staggered difference-in-differences; Generalized difference-in-differences; Dynamic treatment effects; Treatment effect heterogeneity (search for similar items in EconPapers)
JEL-codes: C13 C18 C21 C22 C23 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (291)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:144:y:2022:i:2:p:370-395

DOI: 10.1016/j.jfineco.2022.01.004

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