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Corruption Information and Vote Share: A Meta-Analysis and Lessons for Experimental Design

Published online by Cambridge University Press:  05 June 2020

TREVOR INCERTI Open the ORCID record for TREVOR INCERTI [Opens in a new window]
TREVOR INCERTI*
Affiliation:
Yale University
*
Trevor Incerti, PhD Candidate, Department of Political Science, Yale University. trevor.incerti@yale.edu.
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Abstract

Debate persists on whether voters hold politicians accountable for corruption. Numerous experiments have examined whether informing voters about corrupt acts of politicians decreases their vote share. Meta-analysis demonstrates that corrupt candidates are punished by zero percentage points across field experiments, but approximately 32 points in survey experiments. I argue this discrepancy arises due to methodological differences. Small effects in field experiments may stem partially from weak treatments and noncompliance, and large effects in survey experiments are likely from social desirability bias and the lower and hypothetical nature of costs. Conjoint experiments introduce hypothetical costly trade-offs, but it may be best to interpret results in terms of realistic sets of characteristics rather than marginal effects of particular characteristics. These results suggest that survey experiments may provide point estimates that are not representative of real-world voting behavior. However, field experimental estimates may also not recover the “true” effects due to design decisions and limitations.

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Research Article
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American Political Science Review , Volume 114 , Issue 3 , August 2020 , pp. 761 - 774
Copyright
© American Political Science Association 2020

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Footnotes

I am extremely grateful to Peter Aronow, Alexander Coppock, Angèle Delevoye, Devin Incerti, Joshua Kalla, Daniel Mattingly, Gautam Nair, Susan Rose-Ackerman, Frances Rosenbluth, Radha Sarkar, Tomoya Sasaki, and Fredrik Sävje; participants of the 2019 APSA Corruption and Electoral Behavior Panel; participants at the Yale ISPS Experiments Workshop; the Yale Casual [sic] Inference Lab; and three anonymous reviewers for invaluable feedback and suggestions. Any and all errors are my own. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/HD7UUU.

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