Journal of Financial Economics Vol. 144 No. 2 2022
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.
- DOI
- 10.1016/j.jfineco.2022.01.004
- Volume
- 144
- Issue
- 2
- Pages
- 370-395
- Language
- en
- Sources
- bibtex:phds-export.bib openalex crossref