Collaborative Research: Measuring Apparent Race and Ethnicity with Applications to the Study of Discrimination

Literature suggests that darker-skinned, less European-looking Blacks and Latinos face greater discrimination and experience worse socioeconomic outcomes than their lighter, less racially or ethnically "prototypical" counterparts. Attesting to the importance of this line of research, the U.S. Bureau of Labor Statistics and the directors of the General Social Survey and the American National Election Study have begun collecting skin-color data, and the Equal Opportunity Employment Commission has issued guidance about color- and phenotype-discrimination cases under federal law.

However, both the science and the law of phenotype discrimination have been hindered by serious measurement problems. The standard measure of skin color, called the Massey-Martin Scale, is not reliable. There is no generally accepted measure of racial/ethnic prototypicality as such. Social psychologists have measured racial prototypicality using Likert scales and, as coders, convenience samples of college students, but this measurement strategy assumes that everyone sees race in the same way, and that "apparent race" is mainly a function of objective, physical features of the person being observed. Yet social theorists have long argued that race and ethnicity are "socially constructed" categories, and that racial perceptions are likely to be affected by the social status, dress, expressions, and behavior.

To establish solid measurement foundations for research on phenotype discrimination, this project uses Bayesian pairwise comparison models, surveys, and experiments to develop new measures of skin color, apparent racial/ethnic stereotypicality and ancestry. The ancestry measure is designed to capture stable features of the subject’s physical appearance, and the stereotypicality measure to capture the broader set of cultural associations with race/ethnicity.

 

Investigators
Kevin Quinn

Funders
National Science Foundation

Funding Period
2017-2019