title: Bias in the Flesh: Skin Complexion and Stereotype Consistency in Political Campaigns.
abstract: There is strong evidence linking skin complexion to negative stereotypes and adverse real-world outcomes. We extend these findings to political ad campaigns, in which skin complexion can be easily manipulated in ways that are difficult to detect. Devising a method to measure how dark a candidate appears in an image, this paper examines how complexion varied with ad content during the 2008 presidential election campaign (study 1). Findings show that darker images were more frequent in negative ads-especially those linking Obama to crime-which aired more frequently as Election Day approached. We then conduct an experiment to document how these darker images can activate stereotypes, and show that a subtle darkness manipulation is sufficient to activate the most negative stereotypes about Blacks-even when the candidate is a famous counter-stereotypical exemplar-Barack Obama (study 2). Further evidence of an evaluative penalty for darker skin comes from an observational study measuring affective responses to depictions of Obama with varying skin complexion, presented via the Affect Misattribution Procedure in the 2008 American National Election Study (study 3). This study demonstrates that darker images are used in a way that complements ad content, and shows that doing so can negatively affect how individuals evaluate candidates and think about politics.
authors.affiliation: S olomon M essing is currently Director of Data Labs at the Pew Research Center, Washington, DC, USA, though the research was conducted while he was a Ph.D. candidate at Stanford University, before he joined the Pew Research Center. M aria J abon is a software engineer at LinkedIn, Mountain View, CA, USA. E than P laut is a fellow at Stanford University, Stanford, CA, USA. For helpful feedback and comments, the authors thank Jeremy Bailenson, Matthew DeBell, Justin Grimmer, Shanto Iyengar, Simon Jackman, Jonathan Wand, John Walker, Sean J. Westwood, and POQ 's editors, as well as the anonymous reviewers who provided valuable comments and suggestions. Thanks also to Taylor Young, Truc Nguyen, Alicia Solow-Niederman, Kimberly Rosenblum, and many Mechanical Turk workers who helped code advertisements. Functions to extract image data are available in the R package ImageMetrics. This work was generously supported by a Google grant [1139918-1-GBNKW to S.M.]. All errors are the authors' alone.
authors.forename: Solomon
authors.initials: S
authors.lastname: Messing
authors.affiliation: S olomon M essing is currently Director of Data Labs at the Pew Research Center, Washington, DC, USA, though the research was conducted while he was a Ph.D. candidate at Stanford University, before he joined the Pew Research Center. M aria J abon is a software engineer at LinkedIn, Mountain View, CA, USA. E than P laut is a fellow at Stanford University, Stanford, CA, USA. For helpful feedback and comments, the authors thank Jeremy Bailenson, Matthew DeBell, Justin Grimmer, Shanto Iyengar, Simon Jackman, Jonathan Wand, John Walker, Sean J. Westwood, and POQ 's editors, as well as the anonymous reviewers who provided valuable comments and suggestions. Thanks also to Taylor Young, Truc Nguyen, Alicia Solow-Niederman, Kimberly Rosenblum, and many Mechanical Turk workers who helped code advertisements. Functions to extract image data are available in the R package ImageMetrics. This work was generously supported by a Google grant [1139918-1-GBNKW to S.M.]. All errors are the authors' alone.
authors.forename: Maria
authors.initials: M
authors.lastname: Jabon
authors.affiliation: S olomon M essing is currently Director of Data Labs at the Pew Research Center, Washington, DC, USA, though the research was conducted while he was a Ph.D. candidate at Stanford University, before he joined the Pew Research Center. M aria J abon is a software engineer at LinkedIn, Mountain View, CA, USA. E than P laut is a fellow at Stanford University, Stanford, CA, USA. For helpful feedback and comments, the authors thank Jeremy Bailenson, Matthew DeBell, Justin Grimmer, Shanto Iyengar, Simon Jackman, Jonathan Wand, John Walker, Sean J. Westwood, and POQ 's editors, as well as the anonymous reviewers who provided valuable comments and suggestions. Thanks also to Taylor Young, Truc Nguyen, Alicia Solow-Niederman, Kimberly Rosenblum, and many Mechanical Turk workers who helped code advertisements. Functions to extract image data are available in the R package ImageMetrics. This work was generously supported by a Google grant [1139918-1-GBNKW to S.M.]. All errors are the authors' alone.
authors.forename: Ethan
authors.initials: E
authors.lastname: Plaut