Data and Code for: Event-Studies with a Continuous Treatment

Principal Investigator(s):  View help for Principal Investigator(s) Brantly Callaway, University of Georgia; Andrew Goodman-Bacon, Opportunity and Inclusive Growth Institute, Federal Reserve Bank of Minneapolis; Pedro H. C. Sant'Anna, Emory University

Version:  View help for Version V1

Name File Type Size Last Modified
  codes 05/01/2024 12:28:AM
  data 05/01/2024 01:02:AM
  plots 05/01/2024 12:42:AM
CGBS_AEAPP.pdf application/pdf 255.9 KB 04/30/2024 06:48:PM
Event-Studies-with-a-Continuous-Treatment.Rproj text/plain 205 bytes 04/30/2024 07:14:PM
README.html text/html 610.4 KB 04/30/2024 06:48:PM
README.md text/x-web-markdown 1.5 KB 04/30/2024 07:04:PM
README.pdf application/pdf 86 KB 04/30/2024 08:26:PM

Project Citation: 

Callaway, Brantly, Goodman-Bacon, Andrew, and Sant’Anna, Pedro H. C. Data and Code for: Event-Studies with a Continuous Treatment. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-05-06. https://doi.org/10.3886/E201785V1

Project Description

Summary:  View help for Summary This paper builds on the identification results and estimation tools for continuous DiD designs in Callaway, Goodman-Bacon, and Sant'Anna (2023) to discuss aggregation strategies for event studies with continuous treatments. Estimates from continuous designs are functions of the treatment dosage/intensity variable. Nonparametric plots of these functions show heterogeneity across doses, but not heterogeneity over time. Event-study-type plots of aggregated parameters achieve the opposite. We describe how partially aggregating across treatment doses and event time can lead to readable yet nuanced figures that reflect how causal effects evolve over time, potentially in different parts of the treatment dose distribution.

Scope of Project

Subject Terms:  View help for Subject Terms Causal Inference; Difference-in-Differences; Event-Study
JEL Classification:  View help for JEL Classification
      C01 Econometrics
      C14 Semiparametric and Nonparametric Methods: General
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 2001 – 2014
Universe:  View help for Universe Counties
Data Type(s):  View help for Data Type(s) observational data; program source code


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V1 [2024-05-06]

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