Elected in 100 milliseconds: Appearance-Based Trait Inferences and Voting

All variables of interest (e.g., facial competence, babyfacedness, and attractiveness) ranged between 0 and 1, and represented, for each candidate, the proportion of participants who considered that person to appear more competent, babyfaced, or physically attractive than his or her opponent (see Todorov et al. 2005). As a result, the facial judgments between opposing candidates are perfectly negatively correlated and thus fully redundant. For our analyses, we therefore selected one candidate from each election: the person whose photo happened to be presented on the right side of the survey that was administered to participants (and relative to the candidate’s opponent, whose photo was presented on the left). Because candidate photo positions (right vs. left) were randomly varied across studies, our method for selecting candidates is essentially a random binary process. The differences in vote shares ranged from –1 to +1 and were computed as: (# votes for candidate on the right − # votes for candidate on the left)/(total # of votes). Scores below 0 indicate that the candidate on the left won the election. Scores above 0 indicate that the candidate on the right won the election (a score of 0 would indicate that each candidate received exactly the same number of votes). An alternative analysis, which produces similar results, is to condition the vote share differences and the face judgments on party affiliation (e.g., Democrat vote share and Democrat competence). One disadvantage of this procedure, however, is that it cannot be applied to races where one of the candidates does not belong to one of the two major U.S. parties.

Poutvaara et al. (2009) found that facial competence judgments predicted electoral success for male candidates, but not for female candidates, in Finnish elections. Chiao et al. (2008) found a negative relationship between facial competence and electoral success for male candidates, and no relationship for female candidates, in the 2006 House of Representatives races (although they also found that facial competence positively predicted success for both genders in hypothetical elections). Using the data on U.S. Senate, House, and gubernatorial elections (see Fig. 1), we compared the predictive power of facial competence judgments in elections where one candidate was female and the other male (female-male elections) with those in which both candidates were male (male-male elections). Regressing candidates’ vote shares on their facial competence scores (separately for each election type and gender pairing, and after controlling for several other variables) revealed that, across all three political races, facial competence (positively) predicted vote share equally well for female and male candidates who faced-off against a male candidate (i.e., the regression coefficient for facial competence was positive and significant in every case, except the male-male gubernatorial elections, for which it was marginally significant). In fact, in all three types of elections, the beta coefficient for facial competence was higher for female candidates than for their male counterparts (although this difference was never statistically significant). Thus we found, contrary to Poutvaara et al. (2009) and Chiao et al. (2008), that facial competence was at least as good at predicting electoral success for female candidates as it was for male candidates. Clearly, more studies are needed to determine whether gender moderates the predictive power of facial competence.