Selection and Parallel Trends

Arbeitspapier

One of the perceived advantages of difference-in-differences (DiD) methods is that they do not explicitly restrict how units select into treatment. However, when justifying DiD, researchers often argue that the treatment is "quasi-randomly" assigned. We investigate what selection mechanisms are compatible with the parallel trends assumptions underlying DiD. We derive necessary and sufficient conditions for parallel trends that clarify whether and how selection can depend on time-invariant and time-varying unobservables. We also suggest a menu of interpretable primitive sufficient conditions for parallel trends, thereby providing the formal underpinnings for justifying DiD based on contextual information about selection into treatment. We provide results for both separable and nonseparable outcome models and show that this distinction has implications for the use of covariates in DiD analyses. Building on our analysis of nonseparable models, we connect DiD to the literature on nonparametric identification in panel models.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 9910

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Thema: causal inference ; conditional parallal trends ; covariates ; difference-in-differences ; selection mechanism ; time-invariant and time-varying unobservables ; treatment effects

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Ghanem, Dalia ; Sant'Anna, Pedro H. C. ; Wüthrich, Kaspar
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/265945
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ