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

By A. Baker, David F. Larcker, Charles C. Y. Wang. Social Science Research Network, 2021

2,019 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e2776b5135c80abbbd4a08dcb4161acc76cdb4ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “…DID estimators are economically and statistically diﬀerent (see, e.g., the empirical examples in de Chaisemartin and D’Haultfœuille, 2020, 2021a,b; Baker et al., 2022), the previous section also shows a data set where TWFE and heterogeneity-robust DID estimators lead to very similar conclusions.”
Revisiting event study designs: robust and efficient estimation (2021, background): “See also Baker et al. (2021) for evidence that using robust, rather than regression-based, event study estimation matters in other empirical contexts.”