Measuring Racial and Ethnic Disparities in Traffic Enforcement with Large-Scale Telematics Data
PNAS: Nexus, 2022.
Replication materials: https://github.com/stanford-policylab/telematics.
Past studies have found that racial and ethnic minorities are more likely than white drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically non-representative areas. In some cities, speeding enforcement was concentrated in predominantly non-white neighborhoods, while, in others, enforcement was concentrated in predominately white neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-white neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity.