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

🤖 Plain-English Summary

We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs.

🔑 Key Findings

• 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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