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Alphabet Soup and Voter Bias: The Effects of Ballot Order Randomization on Gender- and Ethnicity-Based Voter Biases.
Prior work finds that candidates benefit from being listed first on the ballot. Accordingly, in the 1970s many states implemented random alphabets for each election to ensure no candidates systematically benefit. A separate literature examines how cognitive biases shape candidate evaluations and election outcomes for historically marginalized candidates like women and ethnic minorities. This scholarship often finds that these candidates fare worse in low-salience and low-information contexts like on-cycle local elections. Taken together, these literatures produce a natural question: does being listed first benefit historically marginalized candidates more or less than ethnic majority and/or male candidates? We explore these competing hypotheses in two different datasets, one observational and one experimental. In the first, we use data on 25,000 California state and local elections from 1995-2020. We test whether women and ethnic minority candidates are helped or harmed by randomly being listed first on the ballot relative to men and white candidates respectively. We combine this with a survey experiment manipulating hypothetical candidates’ ethnicity and gender through their names, candidate salience through random ordering, and election salience by varying the office the candidates are running for. This experiment enables us to identify the individual-level mechanisms behind these effects.
Sean Freeder, University of North Florida
Justin de Benedictis-Kessner, Harvard University
Rachel Bernhard, Nuffield College