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Integrating the Literature on Police Use of Deadly Force and Police Lethal Victimization: How Does Place Impact Fatal Police–Citizen Encounters?

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Abstract

Objectives

Use of lethal force by police officers has incited riots, inspired social movements, and engendered socio-political debate. Police officers also assume a high level of risk during police–citizen encounters. Yet, existing studies tend to center on these two phenomena independently. Additionally, the under-utilization of multilevel research in these areas of inquiry has hampered attempts to empirically disentangle the individual, agency, and contextual correlates of fatal police–citizen encounters. This study integrates the predominantly distinct research bases on these phenomena to examine the contexts in which police use lethal force, relative to the contexts in which officers are killed in the line of duty.

Methods

Data were compiled on 6416 citizen fatalities and 709 officer fatalities distributed across 1735 agencies and 1506 U.S. places from 2000 to 2016. A series of three-level logistic regression models examined the civilian and officer characteristics, organizational factors, and contextual features that impacted the odds of citizen fatalities by the police relative to police lethal victimization.

Results

Findings indicated that structural disadvantage increased the odds of police lethal victimization relative to citizen fatalities by the police. Moreover, this contextual effect was, in part, a product of increased firearm usage by citizens who killed police in more disadvantaged areas.

Conclusions

A more complete understanding of fatal police–citizen encounters requires considering police use of lethal force and police lethal victimization concurrently in their broader social contexts.

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Notes

  1. Census designated places represent settled concentrations of the population within a state that are identifiable by name to locals, yet may or may not be legally incorporated. Places are smaller geographic units of analysis than counties and include cities, towns, villages, or boroughs that are defined in coordination with local officials (see U.S. Bureau of the Census 1994).

  2. As law enforcement jurisdiction boundaries are not established by the U.S. Census, agencies are not perfectly nested within other census geographies—agencies may cross multiple places, and places may be served by multiple agencies. As such, we linked each agency to the single census place listed in 2012 Law Enforcement Agency Identifiers Crosswalk (LEAIC) data file, enabling us to nest agencies within places. Most sample places contained a single agency (87.72%, N = 1321), with a range of 1–10 agencies per place. Due to the lack of formalized nesting structure of agencies within places, we conducted two sensitivity analyses. We: (1) nested incidents within agencies in a two-level model, excluding the place-level; and (2) nested incidents within places in a two-level model. The results were substantively unchanged using this approach, lending credence to our modeling strategy and results.

  3. We also note that the SHR appeared to over-report the deaths of white citizens, although this may be an artifact of the FBI’s failure to adequately measure ethnicity. To compare the SHR to the Washington Post and Fatal Encounters data which include a separate category for Hispanic citizens, we weighted counts of Hispanic citizens by the proportion of Hispanic citizens that self-identified as white, black, or other race in the U.S. Census (2010). It is likely, however, that reports of citizen race in the SHR do not correspond to an individual’s self-identified race. We therefore advise caution in interpreting these racial/ethnic differences across the SHR and crowd-sourced data.

  4. Cases in ODMP that were not verified by LEOKA were excluded from the analysis.

  5. The average response rate reported by LEMAS is over 90%.

  6. Basic personnel information for smaller agencies not included in the LEMAS data during the time period was appended from the 2008 Census of State and Local Law Enforcement Agencies (CSLLEA) data.

  7. As part of the UCR, state and local law enforcement agencies submit monthly counts of 43 different crimes to the FBI for compilation. While Part I crimes reported to the police at the county level are typically used to construct aggregate crime rates, these agency-level arrest data are more appropriate for measuring misdemeanors and other non-index crimes. Regarding the WONDER data, the CDC compiles county-level mortality and population data for all U.S. residents from 1999 to 2016.

  8. Three incidents of citizen fatalities had invalid ORI identifiers, while 200 were missing census place codes. For police fatalities by citizens, 49 incidents were excluded due to missing census place codes. As a result, our analysis represents approximately 96.5% of all fatal police–citizen encounters identified by LEOKA and the SHR.

  9. In multiple officer and/or citizen incidents, demographic characteristics are measured as: the mean age; whether at least one female officer or citizen was involved; and the majority race of the officers or citizens involved. Cases with no clear majority race were coded into the other category.

  10. Although these offenses have been substantiated in prior research as a general indicator of a proactive policing style, some have questioned the reliability of the UCR for lower level crimes (see Sampson and Cohen 1988).

  11. The last three items in the scale were revere-coded.

  12. We also allowed the slope of firearm as lethal weapon, π11jk, to vary randomly across places via a random coefficients model: π11jk = γ1100 + µ110k. Subsequently, we included a random slope of firearm as lethal weapon in the cross-level interaction model: π11jk = γ1100 + γ1101DIS + µ110k. The results, discussed below, were substantively consistent with the results from the models without a random slope of firearm as lethal weapon.

  13. Diagnostic tests suggest that this model is a good fit for the data. Hosmer and Lemeshow’s statistic and the Link test failed to reject the null (p = 0.18 and p = 0.57, respectively). Classification tests indicated that 92.34% of events were predicted correctly based on covariates. The average Pregibon’s (1981) influence statistic was 0.007 (maximum 0.809) and the average Pregibon’s (1981) leverage statistic was 0.006 (maximum 0.118), suggesting that no observations were unduly influencing results. All variance inflation factors were below 1.90.

  14. We acknowledge the debate over whether odds ratios (or exponentiated log-odds regression coefficients) from a multilevel model are directly comparable to the odds ratios from a single-level logistic regression model, in particular because the odds ratios from a multilevel model may be more accurately interpreted as the effects on the median odds of the dependent variable, whereas the odds ratios from a single-level model are readily interpreted as the effects on the mean odds of the dependent variables (Agresti 2013). Nonetheless, odds ratios are readily calculated from coefficient estimates in multilevel models due to ease of interpretation relative to log-odds (Raudenbush and Bryk 2002). We follow this conventional approach.

  15. In a random coefficients model, the slope of firearm as lethal weapon varied randomly across place (µ110k = 5.24, χ2 = 143.83, p < .001). In a cross-level interaction model, the interaction between firearm as lethal weapon and concentrated disadvantage was significant (OR = .38, 95% CI = .23, .63, p < .001), consistent with the results presented for the fixed cross-level interaction coefficient above; and the slope of firearm as lethal weapon was reduced by 16% from 5.24 to 4.39 (µ110k = 4.39, χ2 = 131.47, p < .01).

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Authors and Affiliations

  1. School of Criminology and Criminal Justice, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Emma E. Fridel, Keller G. Sheppard & Gregory M. Zimmerman

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  1. Emma E. Fridel
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Appendices

Appendix 1

See Table 3.

Table 3 Do incidents recorded in the SHR and in crowd-sourced databases occur in similar contexts?
Full size table

Appendix 2

See Table 4.

Table 4 Comparison of citizen and agency characteristics for citizen fatalities by the police reported by the SHR and crowd-sourced databases, 2014–2016
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Appendix 3

See Table 5.

Table 5 Descriptive statistics for LEMAS sample, by officer-involved deaths, officer fatalities, and in totality
Full size table

Appendix 4

See Table 6.

Table 6 Three-level logistic regression model predicting the odds of citizen fatalities by police relative to police lethal victimization as a function of additional law enforcement management and administrative statistics (LEMAS) data (N = 5995 incidents, 1002 agencies, 905 places)
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Appendix 5

See Table 7.

Table 7 Descriptive analysis of the situational characteristics and criminal backgrounds of citizens killing police officers across levels of concentrated disadvantage
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Fridel, E.E., Sheppard, K.G. & Zimmerman, G.M. Integrating the Literature on Police Use of Deadly Force and Police Lethal Victimization: How Does Place Impact Fatal Police–Citizen Encounters?. J Quant Criminol 36, 957–992 (2020). https://doi.org/10.1007/s10940-019-09438-5

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