Facial Similarity Between Voters and Candidates Causes Influence

Public Opinion Quarterly, Vol. 72, No. 5 2008, pp. 935–961

FACIAL SIMILARITY BETWEEN VOTERS AND CANDIDATES CAUSES INFLUENCE JEREMY N. BAILENSON SHANTO IYENGAR NICK YEE NATHAN A. COLLINS

Abstract Social science research demonstrates that people are drawn to others perceived as similar. We extend this finding to political candidates by comparing the relative effects of candidate familiarity as well as partisan, issue, gender, and facial similarity on voters’ evaluations of candidates. In Experiment 1, during the week of the 2006 Florida gubernatorial race, a national representative sample of voters viewed images of two unfamiliar candidates (Crist and Davis) morphed with either themselves or other voters. Results demonstrated a strong preference for facially similar candidates, despite no conscious awareness of the similarity manipulation. In Experiment 2, one week before the 2004 presidential election, a national representative sample of voters evaluated familiar candidates (Bush and Kerry). Strong partisans were unmoved by the facial similarity manipulation, but weak partisans and independents preferred the candidate with whom their own face had been morphed over the candidate morphed with another voter. In Experiment 3, we compared the effects of policy similarity and facial similarity using a set of prospective 2008 presidential candidates. Even though the effects of party and policy similarity dominated, facial similarity proved a significant cue for unfamiliar candidates. Thus, the evidence across the three studies suggests that even in high-profile elections, voters prefer candidates high in facial similarity, but most strongly with unfamiliar candidates.

JEREMY N . BAILENSON , SHANTO IYENGAR AND NICK YEE

are with the Department of Communication, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA. NATHAN A . COLLINS is with the Department of Political Science, Stanford University, 616 Serra St., Stanford, CA 94305, USA. We would like to...