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When Do Voters Punish Corrupt Politicians? Experimental Evidence from a Field and Survey Experiment

Published online by Cambridge University Press:  06 May 2022

Miguel F.P. de Figueiredo Open the ORCID record for Miguel F.P. de Figueiredo [Opens in a new window] ,
F. Daniel Hidalgo  and
Yuri Kasahara Open the ORCID record for Yuri Kasahara [Opens in a new window]
Miguel F.P. de Figueiredo*
Affiliation:
School of Law, University of Connecticut, Hartford, USA
F. Daniel Hidalgo
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, Cambridge, USA
Yuri Kasahara
Affiliation:
Norwegian Institute for Urban and Regional Research, Oslo Metropolitan University, Oslo, Norway.
*
*Corresponding author. Email: mdefig@uconn.edu
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Abstract

When do voters punish corrupt politicians? Heterogeneous views about the importance of corruption can determine whether or not increased information enhances accountability. If partisan cleavages correlate with the importance voters place on corruption, then the consequences of information may vary by candidate, even when voters identify multiple candidates as corrupt. We provide evidence of this mechanism from a field experiment in a mayoral election in Brazil where a reputable interest group declared both candidates corrupt. We distributed fliers in the runoff mayoral election in São Paulo. Informing voters about the challenger's record reduced turnout by 1.9 percentage points and increased the opponent's vote by 2.6 percentage points. Informing voters about the incumbent's record had no effect on behavior. We attribute this divergent finding to differences in how each candidate's supporters view corruption. Using survey data and a survey experiment, we show that the challenger's supporters are more willing to punish their candidate for corruption, while the incumbent's supporters lack this inclination.

Keywords

voting behaviourcorruptioninformationparty identificationexperimental methods
Type
Letter
Information
British Journal of Political Science , Volume 53 , Issue 2 , April 2023 , pp. 728 - 739
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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de Figueiredo et al. Dataset

Dataset

https://doi.org/10.7910/DVN/PQ4JEZ
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de Figueiredo et al. supplementary material

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