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Gender Stereotypes and Corruption: How Candidates Affect Perceptions of Election Fraud

Published online by Cambridge University Press:  08 August 2014

Tiffany D. Barnes
Affiliation:
University of Kentucky
Emily Beaulieu
Affiliation:
University of Kentucky
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Abstract

How do stereotypes of female candidates influence citizens' perceptions of political fraud and corruption? Because gender stereotypes characterize female politicians as more ethical, honest, and trustworthy than male politicians, there are important theoretical reasons for expecting female politicians to mitigate perceptions of fraud and corruption. Research using observational data, however, is limited in its ability to establish a causal relationship between women's involvement in politics and reduced concerns about corruption. Using a novel experimental survey design, we find that the presence of a female candidate systematically reduces the probability that individuals will express strong suspicion of election fraud in what would otherwise be considered suspicious circumstances. Results from this experiment also reveal interesting heterogeneous effects: individuals who are not influenced by shared partisanship are even more responsive to gender cues; and male respondents are more responsive to those cues than females. These findings have potential implications for women running for office, both with respect to election fraud and corruption more broadly, particularly in low-information electoral settings.

Information

Type
Research Article
Copyright
Copyright © The Women and Politics Research Section of the American Political Science Association 2014 
Figure 0

Table 1. Comparison of descriptive statistics

Figure 1

Table 2. Percentage of respondents who found fraud very likely by treatment

Figure 2

Table 3. Probability of responding fraud is very likely

Figure 3

Figure 1: Partisan hypothesis (H2): Predicted probability of perceiving fraud is very likely.

Notes:Figure 1 graphs the simulated predicted probability of a respondent saying that fraud was very likely The predicted probabilities are based on the Interactive Model in Table 3. For all three treatment groups, strong partisan is set to 0, female is set to 0, and education is held at the sample mean. In order to evaluate if the predicted probabilities are statistically different at the 95% confidence level, we graph 84% confidence intervals for each of the predicted probabilities. An overlap of 84% confidence intervals means that we cannot reject the null hypothesis of no difference at the 95% confidence level. If the confidence intervals do not overlap, we can reject the null hypothesis and conclude that the difference between two predicted probabilities are statistically significant at the 95% confidence level (Julious 2004).
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Figure 2: Respondent sex hypothesis (H3): predicted probability of perceiving fraud is very likely

Note:Figure 2 graphs the simulated predicted probability of a respondent saying that fraud was very likely. The predicted probabilities are based on the Interactive Model in Table 3. For all three treatment groups, Democrat is set to 0, Republican is set to 1, Democratic candidate is set to 1, copartisan is set to 0, strong partisan is set to 0, and education is held at the sample mean. In order to evaluate if the predicted probabilities are statistically different at the 95% confidence level, we graph 84% confidence intervals for each of the predicted probabilities. An overlap of 84% confidence intervals means that we cannot reject the null hypothesis of no difference at the 95% confidence level. If the confidence intervals do not overlap, we can reject the null hypothesis and conclude that the difference between two predicted probabilities are statistically significant at the 95% confidence level (Julious 2004).
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Figure A1: Example of survey experiment as it appeared to survey respondents.

Note: Figure A1 provides an example of what a respondent who received the Democratic condition for candidate's party and the Female treatment for the gender cue would have seen when taking the survey.
Figure 6

Table A1. Response to question “How likely do you think it is that fraud was committed in this case?”