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Replication Data for: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Using Multiple Pre-treatment Periods to Improve Difference-in-Differences and Staggered Adoption Designs
 
Identifier https://doi.org/10.7910/DVN/SLIXNF
 
Creator Egami, Naoki
Yamauchi, Soichiro
 
Publisher Harvard Dataverse
 
Description While a difference-in-differences (DID) design was originally developed with one pre- and one post-treatment period, data from additional pre-treatment periods are often available. How can researchers improve the DID design with such multiple pre-treatment periods under what conditions? We first use potential outcomes to clarify three benefits of multiple pre-treatment periods: (1) assessing the parallel trends assumption, (2) improving estimation accuracy, and (3) allowing for a more flexible parallel trends assumption. We then propose a new estimator, double DID, which combines all the benefits through the generalized method of moments and contains the two-way fixed effects regression as a special case. We show that the double DID requires a weaker assumption about outcome trends and is more efficient than existing DID estimators. We also generalize the double DID to the staggered adoption design where different units can receive the treatment in different time periods. We illustrate the proposed method with two empirical applications, covering both the basic DID and staggered adoption designs. We offer an open-source R package that implements the proposed methodologies.
 
Subject Social Sciences
Panel data analysis
Difference-in-differences
Observational data
Staggered adoption design
 
Contributor Code Ocean