What’s trending in difference-in-differences? A synthesis of the recent econometrics literature

• Published in: Journal of econometrics Vol. 235; no. 2; pp. 2218 - 2244
• Main Authors: Roth, Jonathan, Sant’Anna, Pedro H.C., Bilinski, Alyssa, Poe, John
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2023
• Subjects: area; Causal Inference; Clustering; Difference-in-differences; econometrics; Parallel trends; Sensitivity Analysis; Staggered Treatment timing; temporal variation; Treatment Effect Heterogeneity; wills; Causal Inference; Staggered Treatment timing; Sensitivity Analysis; Parallel trends; Treatment Effect Heterogeneity; Difference-in-differences; Clustering
• ISSN: 0304-4076; 1872-6895
• DOI: 10.1016/j.jeconom.2023.03.008

This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of “canonical” assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on (i) multiple periods and variation in treatment timing, (ii) potential violations of parallel trends, or (iii) alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.