Journal of Financial Economics· 2022Q1
How much should we trust staggered difference-in-differences estimates?
- 3,002citations
- Q1SCImago
- 2022year
Short summary
Staggered difference-in-differences (DiD) regression estimators, widely used for policy impact analysis, are often biased, leading to incorrect conclusions. This paper explains these biases and reviews three alternative estimators that address them.
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Key points
- Staggered DiD estimators are prone to bias, affecting research in finance, accounting, and law.
- Biases in staggered DiD can lead to both Type-I and Type-II errors.
- Three alternative estimators exist to address these biases, each with distinct characteristics.
- Re-analysis of prior studies using alternative estimators shows significant differences in causal estimates and inferences.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of Financial Economics, 2022 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics