How Much Should We Trust Staggered Difference-In-Differences Estimates?

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 designs, generalized difference-in-differences, dynamic treatment effects

JEL Classification: C13, C18, C21, C22, C23