Discrimination affects hiring, mating and voting decisions.
Whilst discrimination in elections mainly relates to gender or race, we introduce a novel source of discrimination: candidate resemblance. When candidates' partisanship is not known, voters select those that resemble most elected co-partisans. Using a machine learning algorithm for face comparison, we find a stronger resemblance effect for Republicans compared to Democrats in the US. This happens because Republicans have a higher within-party facial resemblance than Democrats, even when accounting for gender and race. We find a similar pattern in the UK, where Conservative MPs are more similar looking to each other than Labour. Using a survey experiment, we find that Tory voters reward resemblance, while there is no similar effect for Labour. We estimate that facial dissimilarity decreases the candidate's re-election probability by 5-14 percentage points. The results are consistent with an interpretation of this behaviour as a form of statistical discrimination.
Raluca L. Pahontu is Fellow in European and International Politics and Policy. She earned her DPhil in Political Science from Nuffield College, University of Oxford in 2020. Raluca L. Pahontu is a political economist working on issues related to risk and insurance, inequality, distributional conflict and voting behaviour in Europe and the United States in the post-war period.
Toni Rodon is Assistant Professor at the Universitat Pompeu Fabra.