The deadliest local police departments kill 6.91 times more frequently than the least deadly departments, net of risk, in the United States

Abstract

I use data linking counts of homicides by police to police department (PD) and jurisdiction characteristics to estimate benchmarked (i.e. risk-adjusted) police homicide rates in 2008-2017 among the 711 local PDs serving 50,000 or more residents, a sample with demographics resembling all mid-to-large Census places. The benchmarked rate estimates capture PD deadliness by comparing PDs to peers whose officers face similar risks while adjusting for access to trauma care centers to account for differential mortality from deadly force. Compared to existing estimates, differences in benchmarked estimates are more plausibly attributable to policing differences, speaking to whether the force currently used is necessary to maintain safety and public order. I find that the deadliest PDs kill at 6.91 times the benchmarked rate of the least deadly PDs. If the PDs with above-average deadliness instead killed at average rates for a PD facing similar risks, police homicides would decrease by 34.44%. Reducing deadliness to the lowest observed levels would decrease them by 70.04%. These estimates also indicate the percentage of excess police homicides-those unnecessary for maintaining safety-if the baseline agency is assumed to be optimally deadly. Moreover, PD deadliness has a strong, robust association with White/Black segregation and Western regions. Additionally, Black, Hispanic, foreign-born, lower income, and less educated people are disproportionately exposed to deadlier PDs due to the jurisdictions they reside in. Police violence is an important public health concern that is distributed unevenly across US places, contributing to social disparities that disproportionately harm already marginalized communities.

Keywords: criminology; public health; social demography; spatial inequality.

Fig. 1.

Rank-ordered distribution of PD deadliness, measured as the counterfactual rate (per 1 million jurisdiction residents per year) of police homicides under sample average conditions of risk to officers and access to trauma care. The sample includes 711 agencies with jurisdictions of 50,000 or more in 2008–2017. Markers indicate Bayesian PD estimates. Vertical dashes indicate 95% uncertainty intervals. The rank-ordered distribution of unadjusted Bayesian PD estimates is provided as a reference.

Fig. 2.

The PDs with the lowest (least deadly) and highest (most deadly) counterfactual rates (per 1 million jurisdiction residents per year) of police homicides under sample average conditions of risk to officers and access to trauma care. The sample includes 711 agencies with jurisdictions of 50,000 or more in 2008–2017. Markers indicate Bayesian PD estimates. Horizontal dashes indicate 95% uncertainty intervals.

Fig. 3.

Bayesian residual ratios vs. fitted values of PD police homicide rates (per 1 million jurisdiction residents per year) as a function of risk to officers and trauma care access in 2008–2017. Markers are weighted by jurisdiction population and highlighted to indicate the 20 most and least deadly PDs. Bayesian residual ratios are ratios of Bayesian PD police homicide rate estimates to PD fitted values and are the basis for the PD deadliness estimates in Figs. 1 and 2.

Fig. 4.

Demographic disparities in jurisdiction of residence PD deadliness estimates. PD deadliness is estimated as the counterfactual rate (per 1 million jurisdiction residents per year) of police homicides under sample average conditions of risk to officers and access to trauma care. Disparities compare group averages of Bayesian PD deadliness estimates within individuals’ jurisdictions of residence where percentages are relative to the comparison group average. The sample includes 711 agencies with jurisdictions of 50,000 or more in 2008–2017. Horizontal dashes indicate 95% uncertainty intervals.

Fig. 5.

Correlates of standardized PD deadliness estimates, organized by category. Panel A plots each correlate category's contribution to R² in (i) a single-group model including only the correlates in the category, along the vertical axis and (ii) the full model with all correlates, measured as the difference in R² between the full model and a model excluding the category, along the horizontal axis. Panel B plots the linear regression coefficients of correlates, by category, in (i) a bivariate (BV) model, along the vertical axis and (ii) the full model, along the horizontal axis, displaying only correlates with significant correlations in at least one model and estimated coefficients of magnitude0.2 or greater in at least model. All correlates are either binary or standardized continuous variables. The sample includes 711 agencies with jurisdictions of 50,000 or more in 2008–2017. Correlate categories: S, staff (variables coded in the direction of more White, male); P_T, policies regarding training (less training, qualifications); P_R, policies restricting conduct (weaker); P_O, policies regarding oversight (limiting); P_G, policies regulating guns (weaker); L, location (Western, lower population density); C_R, jurisdiction composition of race/ethnicity (less White, foreign-born); C_S, jurisdiction composition of SES (lower); C_O, other jurisdiction composition factors (more children, residential instability); I_R, inequality by race/ethnicity (segregated); I_S, inequality by SES (segregated, higher Gini coefficient); I_RS, inequality by race/ethnicity and SES (White-favoring income disparities).