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Can a beautiful smile win the vote?

The role of candidates’ physical attractiveness and facial expressions in elections

Published online by Cambridge University Press:  21 September 2021

Lena Masch*
Affiliation:
Humboldt University of Berlin
Anna Gassner
Affiliation:
Heinrich Heine University Düsseldorf
Ulrich Rosar
Affiliation:
Heinrich Heine University Düsseldorf
*
Correspondence: Lena Masch, Department of Psychology, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany. Email: lena.masch@hu-berlin.de

Abstract

Several empirical studies have linked political candidates’ electoral success to their physical appearance. We reexamine the effects of candidates’ physical attractiveness by taking into account emotional facial expressions as measured by automated facial recognition software. The analysis is based on an observational case study of candidate characteristics in the 2017 German federal election. Using hierarchical regression modeling and controlling for candidates’ displays of happiness, consistent effects of physical attractiveness remain. The results suggest that a potential interaction effect between displays of happiness and attractiveness positively affects vote shares. The study emphasizes the importance of considering emotional expressions when analyzing the impact of candidate appearance on electoral outcomes.

Information

Type
Research Notes
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Association for Politics and the Life Sciences
Figure 0

Figure 1. Emotion classifications of candidates’ faces in the German federal election 2017. Figure displays the frequencies of classification results based on the Microsoft emotion detection algorithm for 1,778 portrait pictures of direct candidates.

Figure 1

Table 1. Descriptive statistics of physical attractiveness and happiness according to party membership.

Figure 2

Table 2. Hierarchical regression of direct vote shares on physical attractiveness and happiness.

Figure 3

Figure 2. Interaction between physical attractiveness and happiness on direct vote shares. Figure displays the predicted direct vote shares based on a marginal effect plot with a 95% confidence interval according to happiness (dichotomous) and physical attractiveness.

Supplementary material: File

Masch et al. supplementary material

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