Revisiting Event Study Designs: Robust and Efficient Estimation
Kirill Borusyak (),
Xavier Jaravel and
Papers from arXiv.org
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. Extensions include time-varying controls, triple-differences, and certain non-binary treatments. We show the practical relevance of these insights 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 2022-04
New Economics Papers: this item is included in nep-ecm and nep-isf
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Working Paper: Revisiting Event Study Designs: Robust and Efficient Estimation (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2108.12419
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