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Party and Gender Stereotypes in Campaign Attacks

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Abstract

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.

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Notes

  1. Scholars have noted some differences between traits and issues in self-presentation and media and find that substantive attacks on traits are more effective than attacks without substantive content or issue based attacks (Dunaway et al. 2013; Fridkin and Kenney 2011; Brooks and Geer 2007), but little from this research offers a clear guidance as to whether attacks on traits or issue competence will interact more vigorously with candidate characteristics.

  2. Reading a newspaper article likely offers a more conservative test of negative campaign effects relative to viewing a video of an ad, given that negative television ads can amplify emotional responses (Brader 2006). However, it does allow for a clean test of the causal relationship between those attacked and candidate perceptions.

  3. We drew the control condition article from extant scholarship (Krupnikov and Bauer 2014).

  4. Names used in the experimental treatment were drawn from previous experimental research (Holman et al. 2015).

  5. For example, while uncompromising and weak both appear in the database, humility and protective do not, so we focused on the prior set of terms.

  6. Women’s issues have been defined various ways in the literature, but commonly include issues pertaining to children, families, and social welfare generally (Holman 2014), which is consistent with the outcome of our factor analysis.

  7. Modeling the effect of the treatment on these measures through an independent analysis of candidate type also ensures that we are comparing treatment conditions to the appropriate control condition. For example, estimating a regression model of the effect of the conditions on the Vote for the Republican candidates only compares votes in the masculine trait (or issue) attack conditions to the matched Republican control conditions, not all control conditions.

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Acknowledgement

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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Correspondence to Erin C. Cassese.

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Cassese, E.C., Holman, M.R. Party and Gender Stereotypes in Campaign Attacks. Polit Behav 40, 785–807 (2018). https://doi.org/10.1007/s11109-017-9423-7

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