Data and Code For: Difference-in-Differences Designs: A Practitioner's Guide

Principal Investigator(s):  View help for Principal Investigator(s) Andrew Baker, UC Berkeley ; Brantly Callaway, University of Georgia; Scott Cunningham, Baylor University; Andrew Goodman-Bacon, Federal Reserve Bank of Minneapolis; Pedro Sant'Anna, Emory University

Version:  View help for Version V1

Name File Type Size Last Modified
  JEL-DiD-main 01/02/2026 08:58:AM

Project Citation: 

Baker, Andrew, Callaway, Brantly, Cunningham, Scott, Goodman-Bacon, Andrew, and Sant’Anna, Pedro. Data and Code For: Difference-in-Differences Designs: A Practitioner’s Guide. Nashville, TN: American Economic Association [publisher], 2026. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2026-03-25. https://doi.org/10.3886/E239070V1

Project Description

Summary:  View help for Summary Difference-in-differences (DiD) is arguably the most popular quasi-experimental research design. Its canonical form, with two groups and two periods, is well-understood. However, empirical practices can be ad hoc when researchers go beyond that simple case. This article provides an organizing framework for discussing different types of DiD designs and their associated DiD estimators. It discusses covariates, weights, handling multiple periods, and staggered treatments. The organizational framework, however, applies to other extensions of DiD methods as well.

Scope of Project

Subject Terms:  View help for Subject Terms Econometrics; Difference-in-Differences; Causal Inference
JEL Classification:  View help for JEL Classification
      C01 Econometrics
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 2009 – 2019
Universe:  View help for Universe US adult population ages 20-64.
Data Type(s):  View help for Data Type(s) administrative records data; census/enumeration data; observational data
Collection Notes:  View help for Collection Notes All data are publicly available from the CDC, the Census, the Bureau of Labor Statistics, or (for policy variable) from the Kaiser Family Foundation.

Methodology

Weights:  View help for Weights Population weights are used in some analyses
Unit(s) of Observation:  View help for Unit(s) of Observation county/year
Geographic Unit:  View help for Geographic Unit county

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Published Versions

V1 [2026-03-25]

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Dublin Core

DDI 2.5

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