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Corruption in the Court of Public Opinion: How Voters Perceive and Respond to Candidate Corruption

Abstract

When do voters consider candidates for elected office to be corrupt? If corruption is strongly disfavored by voters, why do corrupt candidates remain popular and keep get-ting reelected? These questions are of great importance for a number of reasons. First, understanding how voters perceive candidate corruption can be predictive in determining electoral outcomes. When evaluating candidates accused of impropriety, success in the voting booth for these candidates can be an indicator of prevailing social norms to-ward corruption. Second, having an empirical foundation for how the public perceives corruption can also serve as an important basis for designing interventions to change such norms, including campaign finance reform and anti-corruption laws. Third, under-standing regional differences in voter perceptions of candidates can lead toward building theories explaining variation in toleration of candidate corruption. Finally, uncovering divergence in how courts, laws, and the public define corruption has implications for the legitimacy of democratic institutions and electoral accountability.

The first chapter analyzes how voters conceptualize the corruption of political candidates, and offers explanatory and predictive frameworks that include the most important variables that factor into the voter’s decision and predict individual voting behavior when a candidate accused of corruption is up for election. The second chapter, co-authored with F. Daniel Hidalgo and Yuri Kasahara, tests the causal effect of informing voters of corruption on voting behavior with a field and survey experiment in a mayoral election. The experiments show voters’ varied responses to corruption information and argues for a novel mechanism that explains the results. The final chapter examines a number of mechanisms that explain why voters support or punish corrupt politicians, raising new research questions for scholars to consider in future research.

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