By Travis Carter, Jedidiah Knode and Scott Wolfe  

This report presents the results from a racial/ethnic disparity analysis of Michigan State Police (MSP) traffic stops conducted in 2021. The goal of the analysis is to identify the extent of racial/ethnic disparities in MSP traffic stop behavior across MSP worksites (i.e., posts). The analyses are based on a leading empirical approach to assessing racial/ethnic disparities in traffic stop behavior—the veil-of-darkness (VOD). The analyses account for important structural differences across posts and their jurisdictions, such as the rate of violent crime and troopers per capita, as well as temporal factors that may shape traffic patterns and stop behavior (e.g., time of day, day of week) to help ensure the results are as informative as possible. Below, we briefly outline the methodology employed and summarize the main findings. When discussing the results from this report, it is important to recognize the difference between “disparity” and “discrimination.” Disparity in these traffic stop analyses refers to differences in racial/ethnic group representation based on presumed visibility of the driver. Disparity cannot identify intent, whereas discrimination inherently involves intent. Therefore, discrimination in traffic stop behavior refers to police officers intentionally stopping individuals based on their status in a racial/ethnic minority group. Discrimination can generate disparities by way of differential treatment of racial/ethnic groups, but disparities may also be the result of nondiscriminatory (e.g., environmental, situational, etc.) factors such as crime prevalence and driving pattern differences. This report and its findings can speak only to the extent of racial/ethnic disparity in MSP traffic stops. The data cannot ascertain whether racially discriminatory practices are occurring within MSP. Although disentangling disparity from bias is critical towards improving police practices, accurately identifying the existence of such disparity and its magnitude is an important precursor to this process. More information on the data collection process is provided in the body of the report. Next, we highlight the main takeaways from the analyses.   

East Lansing:  Michigan Justice Statistics Center School of Criminal Justice Michigan State University, 2022. 33p.