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

“How much should we trust staggered difference-in-differences estimates?” (2022), by Andrew C. Baker et al. Mathematics research paper. Original abstract below, unchanged. No free PDF was found.

- Authors: Andrew C. Baker, David F. Larcker, Charles C. Y. Wang
- Published: February 22, 2022
- Field: Mathematics
- DOI: https://doi.org/10.1016/j.jfineco.2022.01.004
- Source: https://doi.org/10.1016/j.jfineco.2022.01.004

## 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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