Party and Gender Stereotypes in Campaign Attacks
Research on negative campaigning has largely overlooked the role of stereotypes. In this study, we argue that the gender and partisan stereotypes associated with traits and policy issues interact with a candidate’s gender and partisanship to shape the effectiveness of campaign attacks. We draw on expectancy-violation theory to argue that candidates may be evaluated more harshly when attacks suggest the candidate has violated stereotypic assumptions about their group. Thus, attacks on a candidate’s “home turf,” or those traits or issues traditionally associated with their party or gender, may be more effective in reducing support for the attacked candidate. We use two survey experiments to examine the effects of stereotype-based attacks—a Trait Attack Study and an Issue Attack Study. The results suggest that female candidates are particularly vulnerable to trait based attacks that challenge stereotypically feminine strengths. Both male and female candidates proved vulnerable to attacks on policy issues stereotypically associated with their party and gender, but the negative effects of all forms of stereotype-based attacks were especially large for democratic women. Our results offer new insights into the use of stereotypes in negative campaigning and their consequences for the electoral fortunes of political candidates.
KeywordsNegative campaigning Stereotypes Traits Issue ownership Vote choice Gender Partisanship
Data collection for the Trait Attack Study was funded by the 2015 Carrie Chapman Catt Prize. The authors would like to thank Angie Bos, Monica Schneider, Bas Van Doorn, J. Celeste Lay, Menaka Philips, the Gender and Political Psychology Writing Group, the Tulane Political Science Junior Scholar Research Group for their comments on drafts of this project, and our anonymous reviewers for their careful and constructive feedback. A previous draft of this paper was presented at West Virginia University and The College of Wooster. All data and code needed for replication is available on the Harvard Dataverse at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OBGAHG.
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