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

Abstract

Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the presence of treatment effect heterogeneity. Given the pronounced use of staggered treatment designs in applied corporate finance and accounting research, this finding potentially impacts a large swath of prior findings in these fields. We survey the nascent literature and document how and when such bias arises from treatment effect heterogeneity. We apply recently proposed methods to a set of prior published results, and find that correcting for the bias induced by the staggered nature of policy adoption frequently impacts the estimated effect from standard difference-in-difference studies. In many cases, the reported effects in prior research become indistinguishable from zero.