Racial Bias in Police Traffic Stops: White Residents’ County-Level Prejudice and Stereotypes Are Related to Disproportionate Stopping of Black Drivers

Author:

Stelter Marleen12ORCID,Essien Iniobong23,Sander Carsten1,Degner Juliane1

Affiliation:

1. Social Psychology Department, Universität Hamburg

2. Institute of Psychology, FernUniversität in Hagen

3. Department of Social and Organisational Psychology of Social Work, Leuphana Universität Lüneburg

Abstract

Racial disparities in policing are well documented, but the reasons for such disparities are often debated. In the current research, we weighed in on this debate using a regional-level bias framework: We investigated the link between racial disparities in police traffic stops and regional-level racial bias, employing data from more than 130 million police traffic stops in 1,413 U.S. counties and county-level measures of racial bias from more than 2 million online respondents. Compared with their population share in county demographics, Black drivers were stopped at disproportionate rates in the majority of counties. Crucially, disproportionate stopping of Black drivers was higher in counties with higher levels of racial prejudice by White residents ( rs = .07−.36). Furthermore, county-level aggregates of White people’s threat-related stereotypes were less consistent in predicting disproportionate stopping ( rs = .00−.19). These observed relationships between regional-level bias and racial disparities in policing highlight the importance of the context in which police operate.

Publisher

SAGE Publications

Subject

General Psychology

Reference68 articles.

1. Amodio D. M., Devine P. G. (2006). Stereotyping and evaluation in implicit race bias: Evidence for independent constructs and unique effects on behavior. Journal of Personality and Social Psychology, 91(4), 652–661. https://doi.org/10.1037/0022-3514.91.4.652

2. Auguie B. (2017). gridExtra: Miscellaneous functions for “grid” graphics (Version 2.3) [Computer software]. https://CRAN.R-project.org/package=gridExtra

3. Aust F., Barth M. (2020). papaja: Create APA manuscripts with R Markdown (Version 0.1.0.9997) [Computer software]. https://github.com/crsh/papaja

4. Bartoń K. (2020). MuMIn: Multi-model inference (Version 1.43.17) [Computer software]. https://CRAN.R-project.org/package=MuMIn

5. Bates D., Mächler M., Bolker B., Walker S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01

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