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

2022/02/22 by Andrew C. Baker, David F. Larcker, Charles C. Y. Wang · 51 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Gender, Labor, and Family Dynamics #Healthcare Policy and Management

paper · pdf · doi:10.1016/j.jfineco.2022.01.004

openalex publication_date 2022/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

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