Testing for Racial Discrimination in Police Searches of Motor Vehicles ⇤

In the course of conducting tra c stops, o cers have discretion to search motorists for drugs, weapons, and other contraband. There is concern that these search decisions are prone to racial bias, but it has proven di cult to rigorously assess claims of discrimination. Here we develop a new statistical method—the threshold test—to test for racial discrimination in motor vehicle searches. We use geographic variation in stop outcomes to infer the e↵ective race-specific standards of evidence that o cers apply when deciding whom to search, an approach we formalize with a hierarchical Bayesian latent variable model. This technique mitigates the problems of omitted variables and infra-marginality associated with benchmark and outcome tests for discrimination. On a dataset of 4.5 million police stops in North Carolina, we find that the standard for searching black and Hispanic drivers is considerably lower than the standard for searching white and Asian drivers, a pattern that holds consistently across the 100 largest police departments in the state. ⇤We thank Cheryl Phillips and Vignesh Ramachandran of the Stanford Computational Journalism Lab for compiling the North Carolina tra c stop data, and the John S. and James L. Knight Foundation for partial support of this research. We also thank Stefano Ermon, Avi Feller, Seth Flaxman, Andrew Gelman, Lester Mackey, Jan Overgoor, and Emma Pierson for helpful comments.