A Conditional Likelihood Model of the Relationship Between Officer Features and Rounds Discharged in Police Shootings

Abstract

Assess whether the number of rounds fired in an officer-involved shooting is related to police officer features. The data come from 55 member agencies in the Major Cities Chiefs Association. The full dataset describes 2574 officers involved in 1600 shootings between 2010 and 2018 but only incidents involving multiple officers provide information. Our final dataset included 317 shooting incidents involving 849 officers and 5026 rounds. We match officers on the scene of a shooting incident and develop a conditional truncated Poisson model that eliminates confounding due to time, place, and environment. We use a permutation test to formally assess the strength of the relationship between officer features and shooting rate. We find no officer feature strongly predicts shooting rate. Age at recruitment, age at the time of the shooting, and years of experience all had relative rates nearly equal to 1.0. There was no statistical relationship with an officer being female (p = 0.27), black (p = 0.64), or Hispanic (p = 0.39). Having prior involvement in shootings, prior force complaints, and special assignments appear to elevate the relative rate of shooting, but all confidence intervals included 1.0. Officer features appear to have little or no relationship with shooting rate. These findings correspond with police scholars’ supposition that duty assignment may be more responsible for explaining differences in police use of force than individual officer characteristics. It contrasts with some prior research suggesting that officer race, age at recruitment, and prior performance affect shooting risk. In doing so, these results also lend support to theoretical frameworks emphasizing the role of organizational features and other system-level factors over individual-level explanations for police use of force. The proposed methodology addresses bias due to confounding, but demands a large number of shootings. Expanded participation in multi-agency data collections and including data on all non-shooting officers at the scene of the incident can increase precision.