Politics & Gender

Research Article

When Gender and Party Collide: Stereotyping in Candidate Trait Attribution

Danny Hayesa1

a1 American University

Abstract

Research has shown that voters are willing to stereotype candidates on the basis of their gender, which can sometimes pose obstacles and sometimes prove advantageous for female politicians. But the literature is uncertain about how candidate gender interacts with candidate party affiliation to shape voters' perceptions. In this article, I draw on political psychology, the women and politics literature, and recent work on partisan “trait ownership” to suggest that the application of gender stereotypes will be limited by the salience of partisan stereotypes. I use nationally representative survey data and a content analysis of news coverage from the 2006 U.S. Senate elections to test the argument. Focusing on voter evaluations of candidate traits, I find that party stereotypes are more powerful than gender stereotypes, and that assessments of candidate attributes can be affected by news coverage when candidates are portrayed in ways that challenge traditional partisan images. The results suggest that gender stereotyping is limited by the relevance of party stereotypes, and that as the Republican and Democratic parties continue to polarize at the elite level, the importance of partisan stereotyping is likely to increase.

(Online publication June 06 2011)

Danny Hayes is Assistant Professor of Government in the School of Public Affairs at American University, Washington, DC 20016: dhayes@american.edu

Footnotes

Thanks for helpful comments and assistance of various sorts are due to Kristi Andersen, Deb Brooks, Elizabeth Cohen, Johanna Dunaway, Kim Fridkin, Matt Guardino, Jon Hanson, Jen Lawless, Jenn Merolla, Zoe Oxley, Sarah Pralle, Hans Peter Schmitz, Nick Winter, several anonymous reviewers, audiences at the Spring 2008 New York Area Political Psychology Workshop and the 2009 Midwest Political Science Association meeting, and seminar participants at Syracuse University. Thanks also to Daron Shaw and Brian Arbour for help acquiring the data and to Rebekah Liscio for research assistance.

Metrics