Jennifer Cryer

While voters may form negative impressions of candidates that do not immediately fit traditional expectations, equal electoral success rates demonstrate that voters are not biased against women and racial minority candidates. I argue that candidates address voter perceptions through their campaign strategy and messages. Thus, a key dimension of campaign strategy is the extent to which perceptions of candidate gender and racial identity may encourage, or even necessitate, the use of differing campaign message strategies.This paper seeks to determine whether candidate profile characteristics might account for the seeming discrepancy between voter bias and electoral outcomes. I use large-scale observational and experimental messages to show how candidate identity, communication—particularly image-managing messages that express individualistic work-ethic and perseverance—and signals of competency affect voter perceptions. In a preliminary forced-choice paired-conjoint and a menu-based choice experiment (n=500), I find that constituents make judgments regarding vote choice, candidate issue competencies, and character using identity. Nevertheless, respondents positively respond to other dimensions of candidate profiles, specifically messages of work-ethic and previous political experience. In particular, Image-Management is consistently shown to be associated with a significant positive increase in these evaluations. In the menu-based survey experiment, I find that when respondents craft the candidates they would consider ideal and worthy of their vote, women and racial minorities are often recommended to pursue messages of Image-Management and higher educational attainment. Taken together, these findings suggest that candidate messaging may provide an account for why women and racial minority candidates are able to obtain electoral success despite evidence of voter biases. This work provides cutting-edge analysis of strategic campaign messaging and puts to the test its effectiveness in attenuating voter biases.