Revisiting event study designs: Robust and efficient estimation
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.
Year of publication: |
2022
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Authors: | Borusyak, Kirill ; Jaravel, Xavier ; Spiess, Jann |
Publisher: |
London : Centre for Microdata Methods and Practice (cemmap) |
Saved in:
freely available
Series: | cemmap working paper ; CWP11/22 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 10.47004/wp.cem.2022.1122 [DOI] 1800643624 [GVK] hdl:10419/260392 [Handle] RePEc:ifs:cemmap:11/22 [RePEc] |
Source: |
Persistent link: https://www.econbiz.de/10013253010
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