Race, driving, and police organization: Modeling moving and nonmoving traffic stops with citizen self-reports of driving practices

Abstract

A rapidly growing body of police scholarship has found evidence of racial disparities in traffic stop patterns using police-generated data. Despite the empirical consensus, the question of whether race inappropriately influences traffic stop patterns remains open, largely as a result of methodological weaknesses. The current article helps to address this issue by employing self-report data about citizens' driving practices and traffic stops. It presents a series of models that predict the likelihood of a self-reported traffic stop disaggregated by police organizational type and the reason for the stop. Results suggest that moving and nonmoving driving practices are associated with the likelihood of police stops for moving and nonmoving reasons, respectively. As expected, differences between local police and state police models emerge. Finally, Black drivers and younger drivers are especially vulnerable to traffic stop risk for nonmoving stops by local police, even after controlling for driving behaviors.