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Revisiting Event-Study Designs: Robust and Efficient Estimation
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DOI：10.1093/restud/rdae007.png)
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摘要
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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.
Keyword:
Difference-in-differences
Efficiency
Marginal propensity to consume
期刊
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6.4
论文数:
2.5K
被引数:
2.1W
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