# Revisiting Event-Study Designs: Robust and Efficient Estimation

“Revisiting Event-Study Designs: Robust and Efficient Estimation” (2024), by Kirill Borusyak et al. Mathematics research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Kirill Borusyak, Xavier Jaravel, Jann Spiess
- Published: January 17, 2024
- Field: Mathematics
- DOI: https://doi.org/10.1093/restud/rdae007
- Free PDF: https://researchonline.lse.ac.uk/id/eprint/123781/1/Jaravel_revisiting-even-study-designs--published.pdf

## 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.

Canonical page: https://nerdi.pages.dev/paper/W3196836501
Read three real papers a day in Nerdi (iPhone): https://apps.apple.com/app/apple-store/id6813826387?pt=128757794&ct=web-paper&mt=8