Revisiting Event Study Designs: Robust and Efficient Estimation

Kirill Borusyak, Xavier Jaravel and Jann Spiess

Papers from arXiv.org

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 behavior 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 United States, we find that the notional marginal propensity to consume is between 8 and 11 percent 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.

Date: 2021-08, Revised 2024-01
New Economics Papers: this item is included in nep-ecm and nep-isf
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Citations: View citations in EconPapers (162)

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http://arxiv.org/pdf/2108.12419 Latest version (application/pdf)

Related works:
Journal Article: 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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