Testing for disparities in traffic stops: Best practices from the Connecticut model

Connecticut's novel approach to collecting and analyzing traffic stop data for evidence of disparate treatment is widely considered to be a model of best practice. Here, we provide an overview of Connecticut's framework, detail solutions to the canonical empirical challenges of analyzing traffic stop, and describe a data‐driven approach to early intervention. Unlike most jurisdictions that simply produce an annual traffic stop report, Connecticut has developed an ongoing system for identifying and mitigating disparity. Connecticut's framework for identifying significant disparities on an annual basis relies on the so‐called “preponderance of evidence” approach. Drawing from the cutting‐edge of the empirical social science literature, this approach applies several, as opposed to a single, rigorous empirical test of disparity. For departments identified as having a disparity, Connecticut has developed a process for intervening on an annual basis. In that process, policing administrators engage with researchers to conduct an empirical exploration into possible contributing factors and enforcement policies. In Connecticut, this approach has transformed what had once been a war of anecdotes into a constructive data‐driven conversation about policy. Variants of the Connecticut Model have recently been adopted by the State of Rhode Island, Oregon, and California. Connecticut's approach provides a useful model and policy framework for states and localities conducting disparity studies of police traffic stops.