Revisiting Event-Study Designs: Robust and Efficient Estimation

Kirill Borusyak, Xavier Jaravel and Jann Spiess

The Review of Economic Studies, 2024, vol. 91, issue 6, 3253-3285

Abstract: We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects.We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behaviour of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the U.S., we find that the notional marginal propensity to consume is between 8 and 11% in the first quarter—about half as large as benchmark estimates used to calibrate macroeconomic models—and predominantly occurs in the first month after the rebate.

Keywords: Difference-in-differences; Efficiency; Marginal propensity to consume (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (33)

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Related works:
Working Paper: Revisiting Event Study Designs: Robust and Efficient Estimation (2024)
Working Paper: Revisiting event-study designs: robust and efficient estimation (2024)
Working Paper: Revisiting Event Study Designs: Robust and Efficient Estimation (2022)
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