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Replication Data for: Can Close Election Regression Discontinuity Designs Identify Effects of Winning Politician Characteristics?

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

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Title Replication Data for: Can Close Election Regression Discontinuity Designs Identify Effects of Winning Politician Characteristics?
 
Identifier https://doi.org/10.7910/DVN/4MZQYH
 
Creator Marshall, John
 
Publisher Harvard Dataverse
 
Description Politician characteristic regression discontinuity (PCRD) designs leveraging close elections are widely used to isolate effects of an elected politician characteristic on downstream outcomes. Unlike standard regression discontinuity designs, treatment is defined by a predetermined characteristic that could affect a politician's victory margin. I prove that, by conditioning on politicians who win close elections, PCRD estimators identify the effect of the specific characteristic of interest and all compensating differentials---candidate-level characteristics that ensure elections remain close between candidates that differ in the characteristic of interest. Avoiding this asymptotic bias generally requires assuming either that the characteristic of interest does not affect candidate vote shares or that no compensating differential affects the outcome. Since theories of voting behavior suggest that neither strong assumption usually holds, I further analyze the implications for interpreting continuity tests and consider if and how covariate adjustment, bounding, and recharacterizing treatment can mitigate the post-treatment bias afflicting PCRD designs.
 
Subject Social Sciences
Regression discontinuity design
Politician characteristics
Causal inference
Post-treatment bias
 
Contributor Marshall, John
 
Source The data was compiled by hand by the author from the original papers (downloaded online, e.g. using JSTOR) and Google Scholar (last accessed March 8, 2022).