Revisiting event study designs: robust and efficient estimation

By Kirill Borusyak, Xavier Jaravel, Jann Spiess. 2021

1,690 citations recorded by the source corpus.

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. 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 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.

Subfields: Economics and Econometrics, Gender Studies

View Paper: https://www.semanticscholar.org/paper/1b18ed99b0363bc9ed7b77a2100b235698bfebe2

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Citations in the corpus

Difference-in-Differences Estimators of Intertemporal Treatment Effects (2020, positive): “Sun and Abraham, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2021) in binary-and-staggered designs. In this paper, we consider general designs, where the treatment may neither be binary nor staggered. Such situations are frequent in practice. In a survey of the 100 most-cited papers publish”
Assessing the Effectiveness of Energy Efficiency Measures in the Residential Sector Gas Consumption Through Dynamic Treatment Effects: Evidence from England and Wales (2022, background): “This approach allows us to evaluate the effect observed when certain units in a panel receive a treatment at different moments in time (Borusyak et al., 2021).”
Difference-in-Differences with a Continuous Treatment (2021, positive): “Our decompositions also relate to the literature on TWFE bias in heterogeneous treatment effect settings with a binary treatment (e.g., Borusyak, Jaravel, and Spiess, 2024; de Chaisemartin and D’Haultfoeuille, 2020; Goodman-Bacon, 2021; Sun and Abraham, 2021) but we extend this logic to continuous…”
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, methodology): “This imputation method is the one used by the did imputation Stata command (see Borusyak, 2021) and by the didimputation R command (see Butts, 2021) to compute the estimators proposed by Borusyak et al. (2021).”
What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, neutral): “How does the approach proposed by Callaway and Sant’Anna (2021, CS) compare to that proposed by Borusyak et al. (2021, BJS)?”
Generative AI at Work (2023, positive): “, 2019; de Chaisemartin and D’Haultfœuille, 2020; Sun and Abraham, 2021; Goodman-Bacon, 2021; Callaway and Sant’Anna, 2021; Borusyak et al., 2022). For example, workers may take time to adjust to using the AI system, in which case its impact in the first month may be smaller. Alternatively, the onbo”
Contamination Bias in Linear Regressions (2021, neutral): “…fixed effects regressions (e.g., Goodman-Bacon, 2021; Sun & Abraham, 2021; de Chaisemartin & D’Haultfœuille, 2020, 2022; Callaway & Sant’Anna, 2021; Borusyak et al., 2022; Wooldridge, 2021; Hull, 2018b) are more general—and conceptually distinct—problems.3 Negative weighting arises because…”