This paper reports on a research project aimed at assessing the impact of data dashboard system implementation with a variety of racial disparity measures, using Michigan State Police traffic stop data, through a mixed-methods approach, and investigated the nuances of program implementation; the paper presents the motivation for performing the study, as well as the research methodology and findings.
The authors conducted a group randomized-controlled trial of an internal dashboard system deployed by the Michigan State Police to determine its effectiveness in reducing traffic stop racial disparities. Informed by a difference-in-differences design, analyses of traffic stop data from 2019–2022 indicated that the dashboard had no impact on traffic stop racial disparities. Additional analyses of traffic stops, crashes, and crime revealed that the dashboard had no “de-policing” effect on traffic patrols, nor were there any significant changes in traffic safety or crime in treatment patrol areas relative to control patrol areas. Qualitative analyses of interview data from more than 40 troopers in the agency revealed unique barriers to program implementation and opportunities for future improvement. In an era of policing where the capacity and demand for data-driven decision-making is on the rise, evidence-based policy and practice can provide police agencies with informed solutions for addressing traffic stop racial disparities. Yet, the increased demand for evidence-based reform is fueled by a relatively low supply of evidence-based research. This study adds to this evidence base by providing unique insights into the effectiveness of a program built specifically to reduce racial disparities in traffic stops, while also highlighting implementation challenges. (Published Abstract Provided)