Officer differences in traffic stops of minority drivers

Scott Abrahams

Labour Economics, 2020, vol. 67, issue C

Abstract: This paper uses a finite mixture model to demonstrate that some police officers are more likely than others to stop black drivers. The conclusion is one that though widely believed has proven challenging to establish empirically. By doing so, the paper makes two contributions, one conceptual and one statistical. First, it more closely aligns with the understanding of racial profiling as signifying that black individuals experience more frequent interaction with the police. While disproportional susceptibility to vehicle searches also exemplifies profiling, being pulled over is a much more common margin for potential profiling, which this paper models a tractable way of identifying. Second, studies of secondary decisions such as searches frequently assume that there is no bias in the initial stop decision. An analysis of traffic stops across eight states questions this assumption, concluding that stopped drivers constitute a selected sample. Although bias is theoretically continuous, average behavior actually fits well into two distinct groups, with 30–40% of officers in the group that exhibits a relatively high propensity to stop black drivers. The implication is that race-based policing is more prevalent than the “rotten apples” theory might suggest.

Keywords: Racial profiling; Police bias; Traffic stops; Finite mixture models (search for similar items in EconPapers)
JEL-codes: J15 J7 K42 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:67:y:2020:i:c:s0927537120301160

DOI: 10.1016/j.labeco.2020.101912

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