Smiles, turnout, candidates, and the winning of district seats

Author:

Asano Masahiko,Patterson Dennis P.

Abstract

Research has shown that a candidate’s appearance affects the support he or she receives in elections. We extend this research in this article in three ways. First, we examine this relationship further in a non-Western context using 2015 local elections in Japan. Next, we show that this positive relationship is more complicated depending on the characteristics of the election under consideration. Specifically, we distinguished election contests by levels of turnout and found that despite a positive relationship between turnout and the extent to which smiling increases a candidate’s support levels, the marginal increase in support declined as turnout increased and, in fact, became negative when some high-turnout threshold was crossed. Finally, we show that the number of candidates competing in an election is negatively related to the impact of a candidate smiling, confirming research conducted by the Dartmouth Group.

Publisher

Cambridge University Press (CUP)

Subject

Public Administration,Social Sciences (miscellaneous),Sociology and Political Science

Reference64 articles.

1. Naturalistic observations of smiling and laughter in human group interactions;Mehu;Behavior,2008

2. 63 We realize that since our analysis is at the aggregate level, it is characterized by inherent limits and that an analysis of these relationships at the individual level would add more insight into these issues.

3. 61 These are happiness, neutral, surprise, anger, and sadness.

4. 60 There were three districts where three candidates ran: Higashiyama-ku (Kyoto city), Fukushima-ku (Osaka city), and Konohama-ku (Osaka city). There was one district in which as many as 29 candidates ran: Minami-ku (Sagamihara city).

5. 56 We calculated the slope of vote share on smile at the mean (mean $=$ 11), a high value of the number of candidates (mean $+$ 1 SD  $=$  17), and at a low value (mean $-$ 1 SD  $=$  6). We then estimated the three equations and found the regression models are statistically significant at the three levels of the number of candidates.

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