Revisiting Event-Study Designs: Robust and Efficient Estimation

• 成果类型: Article
• 署名作者: Borusyak, Kirill; Jaravel, Xavier; Spiess, Jann
• 署名单位: University of California System; University of California Berkeley; University of London; London School Economics & Political Science; Stanford University
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdae007
• 发表日期: 2024
• 页码: 3253-3285
• 关键词: economic stimulus payments; parallel trends

摘要

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.