Revisiting Event-Study Designs: Robust and Efficient Estimation

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ID: 305182
2024
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Abstract
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.
Reference Key
openalex_W3196836501 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kirill Borusyak, Xavier Jaravel, Jann Spiess
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Journal The Review of Economic Studies
Year 2024
DOI
10.1093/restud/rdae007
URL
https://academic.oup.com/restud/advance-article-pdf/doi/10.1093/restud/rdae007/56588588/rdae007.pdf
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