Assessing the Effect of Race Bias in Post-traffic Stop Outcomes Using Propensity Scores

In response to community demands, case settlements, and state laws concerning racial profiling, police departments across the nation are collecting data on traffic stops. While the data collection is rapidly moving forward, there are few if any agreed upon methods for analyzing the data. Much of the attention has been on benchmarks for the race distribution of stops and searches. Little empirical work has advanced our understanding of the influence of race in the post-stop activities of police. The present study proposes a propensity score technique to determine the extent to which race bias affects citation rates, search rates, and the duration of the stop. Adjusting for confounding variables using the propensity score offers an alternative to multivariate regression that is more interpretable, less prone to errors in model assumptions, and ultimately easier to present to stakeholders in policing practices. An analysis of traffic stop data from the City of Oakland, California demonstrates the process, presentation, and interpretation of the results that the methodology produces. Ultimately, the study addresses the extent to which race plays a role in officers' use of discretion.