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Differences in Appearance-Based Trait Inferences for Male and Female Political Candidates

Ditonto, Tessa and Mattes, Kyle (2018) 'Differences in Appearance-Based Trait Inferences for Male and Female Political Candidates.', Journal of Women, Politics & Policy, 39 (4). pp. 430-450.


Studies show that automatic trait inferences can predict outcomes of actual elections, but these studies generally include male candidates only. Substantial evidence also shows that female candidates are subject to gender-based stereotypes, which can lead to differences in how men and women candidates are evaluated. This article combines these two literatures to compare the effects of competence, threat, and attractiveness inferences in elections that include women. We use experimental data in which candidate pairs from state and local US elections were judged on these three traits and examine whether those ratings are predictive of election outcomes. We find that although competence matters most for elections involving only men, attractiveness predicts winners in women-only elections. In mixed-gender races, competence inferences predict success when the female candidate is perceived as more competent than the male candidate. Finally, unlike men, women benefit from being perceived as physically threatening in mixed-gender races.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial 4.0.
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Publisher statement:This is an Accepted Manuscript version of the following article, accepted for publication in Journal of Women, Politics & Policy. Ditonto, Tessa & Mattes, Kyle (2018). Differences in Appearance-Based Trait Inferences for Male and Female Political Candidates. Journal of Women, Politics & Policy 39(4): 430-450. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Date accepted:No date available
Date deposited:15 October 2021
Date of first online publication:09 November 2018
Date first made open access:15 October 2021

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