Hostname: page-component-76d6cb85b7-hqrjx Total loading time: 0 Render date: 2026-07-14T19:01:50.589Z Has data issue: false hasContentIssue false

What candidate will fight corruption? Gender and anti-corruption stereotypes across European countries

Published online by Cambridge University Press:  12 September 2024

Monika Bauhr*
Affiliation:
Department of Political Science, University of Gothenburg, Goteborg, Sweden
Nicholas Charron
Affiliation:
Department of Political Science, University of Gothenburg, Goteborg, Sweden
Lena Wängnerud
Affiliation:
Department of Political Science, University of Gothenburg, Goteborg, Sweden
*
Corresponding author: Monika Bauhr; Email: Monika.Bauhr@pol.gu.se
Rights & Permissions [Opens in a new window]

Abstract

What candidates do voters perceive as best to combat corruption? While recent studies suggest that parties recruit women in order to restore legitimacy, we know less about whether voters believe that women candidates are better equipped than male candidates to fight corruption. This study suggests that women mayors are seen as more likely to fight corruption, yet that the credibility of both male and female politicians increases if they are ascribed traits traditionally seen as ‘female,’ including being risk averse or specializing in the provision of welfare services. Leveraging the diverse levels of socio-economic development, corruption, and gender equality across 25 EU member countries, our unique conjoint experiment shows support for these claims. Both women and male candidates benefit from being described as risk averse and prioritizing social welfare issues, while outsider status has no effect. Male candidates, however, have a consistent disadvantage, particularly among women voters. Moreover, the effects of candidate gender are strongest in areas of Europe with the highest levels of political gender equality.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of European Consortium for Political Research
Figure 0

Figure 1. Treatment effects on choosing candidate A – marginal means.Note: Dots show the average marginal component effect (AMCE) of the ‘candidate A’ via marginal means (MM) from Logit estimation, whereby the difference in predicted probability for the various treatments (x-axis) represents the AMCE. The baseline support for ‘candidate A’ is 0.339 is highlighted via the dashed vertical line. All candidate B characteristics are also included in the model along with age (not shown). Issue: ‘Inf’ = infrastructure, ‘Edc’ = education, ‘E.D.’ = economic development, ‘H.C.’ = health care. 48,080 individuals and 96,160 total profiles analyzed. Estimates adjusted for design and post-stratification weights and standard errors are clustered by respondent.

Figure 1

Figure 2. Candidate sex and abstention.Note: Dots show the predicted probabilities of Abstention from Logit estimation with 95% confidence intervals. All candidate A and B characteristics are also included in the model (not shown). 48,080 individuals and 96,160 total profiles analyzed. Estimates adjusted for design and post-stratification weights and standard errors are clustered by respondent.

Figure 2

Figure 3. Marginal mean plot of ACIEs – summary of H2.Note: Blue triangle and black circles represent male and female candidates respectively with 95% confidence intervals from respondent-clustered standard errors. Estimates from three separate interaction models with candidate gender – (1) personality type, (2) background status, (3) campaign issue. Dashed line represents the mean, baseline support for ‘candidate A’ (0.3405). Design and post-stratification weights are included.

Figure 3

Figure 4. Conditional treatment effects by respondent’s gender – marginal means.Note: Predicted probability of ‘voting candidate A’ shown on the x-axis, with 95% confidence intervals from respondent-clustered standard errors. Results from interaction models, with marginal mean plots by respondent gender. Design and post-stratification weights are included.

Figure 4

Figure 5. Women’s representation and preferences for women candidates.

Supplementary material: File

Bauhr et al. supplementary material

Bauhr et al. supplementary material
Download Bauhr et al. supplementary material(File)
File 190.4 KB