Adopting a Multidimensional Approach to Assessing Contextual Predictors of Regional Implicit Gender-Career Stereotyping

This study adopts a multidimensional approach to assess whether geographic areas with greater gender representation and integration (even distribution of employed men and women) have less traditional implicit gender-career stereotyping (IGS). Contact with and exposure to working women (counterstereotypic exemplars) may reduce regional IGS. However, this may depend on the type of career positions, as stereotypic (feminine careers) gender-career representation may exacerbate IGS. Researchers analyzed data from the U.S. Census’ 2010 to 2019 American Community Survey (metropolitan statistical areas: N = 338) and Project Implicit ( N = 689,455). Using multilevel modeling, representation and integration negatively predicted traditional IGS in that region. Furthermore, the proportion of working women in stereotypically masculine careers negatively predicted IGS, whereas employed women in feminine careers positively predicted IGS. Findings support environmental models of implicit associations and imply that enhancing the proportion of working women in masculine careers may be a method to addressing implicit gender bias.