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Revisiting Event Study Designs: Robust and Efficient Estimation

Kirill Borusyak, Xavier Jaravel, Jann Spiess

arXiv 27 Aug 2021 · Econometrics · publishedThe Review of Economic Studies (2024) · 1,853 citations (OpenAlex)

arXiv:2108.12419 · PDF · DOI · OpenAlex · Extracted main text

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 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.

Citation extraction

53
references
147
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appendix boundary found by appendix_command · 51% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Broda, Christian, Parker, Jonathan A (2014) The economic stimulus payments of 2008 and the aggregate demand for consumption1.000123100%
2Roth, Jonathan (2022) Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends1.00093100%
3Sun, Liyang, Abraham, Sarah (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects0.90916675%
4Callaway, Brantly, Sant'Anna, Pedro H.C (2021) Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment0.9098475%
5(2022) Difference-in-Differences Estimators of Intertemporal Treatment Effects0.8947371%
6(2020) Two-way fixed effects estimators with heterogeneous treatment effects0.8749667%
7Laibson, David, Maxted, Peter, Moll, Benjamin (2022) A Simple Mapping from MPCs to MPXs0.87482100%
8Wooldridge, Jeffrey M (2021) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Event Study Estimators0.8435360%
9Parker, Jonathan A., Souleles, Nicholas S., Johnson, David S., McCle… (2013) Consumer Spending and the Economic Stimulus Payments of 20080.81142100%
10Marcus, Michelle, Sant'Anna, Pedro H.C (2020) The role of parallel trends in event study settings: An application to environmental economics0.7374275%

Showing the top 10 of 53 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey1.000363
2When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators1.00095
3Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study1.00083
4What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature1.00053
5Sequential Synthetic Difference in Differences1.00053
6Efficient Difference-in-Differences and Event Study Estimators0.946136
7Cohort-Anchored Robust Inference for Event-Study with Staggered Adoption0.92853
8Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators0.92854
9What Do We Get from Two-Way Fixed Effects Regressions? Implications from Numerical Equivalence0.92843
10Difference-in-Differences with Spatial Spillovers0.92843