In recent years, the killing of suspects by police and the “militarization” of police have drawn considerable public attention, but there is little analysis of a relationship between the two. In this article, I investigate the possibility that such militarization may lead to an increase in suspect deaths using data on police receipt of surplus military equipment to measure militarization and a newly created database on suspect deaths in all fifty states quarterly from the fourth quarter of 2014 through the fourth quarter of 2016. The data consist of more than eleven thousand agency-quarter observations. I find a positive and significant association between militarization and the number of suspects killed, controlling for several other possible explanations.

On August 9, 2014, a Ferguson, Missouri, police officer shot and killed eighteen-year-old Michael Brown after an encounter, the details of which are still largely unclear. The incident ignited a national debate about police practices in the United States that continues today. The aftermath raised more questions about recent trends in policing, when police officers met protesters dressed in tactical riot gear, wielding automatic weapons, grenade launchers, and tear gas, and confronting them with military-style armored vehicles (Rahall 2015). This incident brought attention to and raised questions about what is sometimes called the “militarization” of police departments in the United States, as well as a possible connection between militarization and the use of lethal violence against suspects. In this paper, I examine the relationship between militarization and the use of lethal force.

How police interact with the public is an important question in a democracy, as the police are the embodiment of the state’s power to deprive citizens of rights—up to and including the right to life. Thus far, despite increasing attention toward the use of lethal force by police (“Don’t Shoot” 2014), there is little research among scholars of political science and public administration on policing (though this trend seems to be changing; see Delehanty et al. 2017; Jennings and Rubado 2017; Nicholson-Crotty, Nicholson-Crotty, and Fernandez 2017; Rivera and Ward 2017) or to determine the effects of militarization on police behavior. There is little empirical evidence to inform the contentious public debate about the behavior of police and the use of lethal force against suspects in situations where such force may not have been necessary. On one side, leaders and representatives of law enforcement claim that the use of lethal force against a suspect is a rare occurrence (Garner et al. 1996), though sometimes unfortunately necessary, and media attention alone is responsible for the perception of excessive use of lethal force. Others have drawn a link between the militarization of police departments and civilian deaths. According to a Washington Post database, police killed 995 people in 2015, 963 in 2016, and 987 in 2017 (Kindy et al. 2015). Although there is only little apparent fluctuation from year to year, there may be characteristics of police departments—such as militarization—that can predict a higher number of deaths within their jurisdiction.

I construct a theoretical argument rooted in classic political science and public administration research on street-level bureaucrats (Wilson 1989) and bureaucratic discretion (Brehm and Gates 1999). I argue that police have a great deal of discretion in deciding how to handle situations they encounter, and militarization affects the decision making of police by moving their preferences toward more violent responses to suspects. Using data on the acquisition of military equipment police departments received through the 1033 military surplus program, which I acquired through a Freedom of Information Act request to the Defense Logistics Agency (DLA), and a new database on police killings of suspects in the United States, I demonstrate an apparent positive and statistically significant association between militarization and the use of lethal force. To be clear, however, my argument is not that the acquisition of military hardware causes militarization or an increase in lethal force by itself. Militarization is a psychological state, for which 1033 equipment transfers are a proxy measure due to the difficulty in capturing a police department’s collective mentality. The 1033 transfers may cause militarization, militarization may cause an increase in 1033 transfers, or there may be some alternative variable that causes increases in both. I argue simply that there is an association between the use of the 1033 program and militarization that makes the former a reasonable proxy variable for the latter. If this theory is correct, then more suspect deaths are a consequence of increased militarization.

This paper makes three important contributions. First, my findings provide empirical evidence to the debate on police militarization. Specifically, I find a positive association between increasing militarization and the frequency of the use of lethal force against suspects. Second, introducing literature on bureaucratic behavior provides a link between police departments as organizations and police officers as individual, street-level actors, whereas prior work on police use of force focuses primarily on either individual officers (Alpert and Dunham 2004) or specific subsets of officers attached to elite units (Kraska and Kappeler 1997). This paper provides a theory and empirical measure of militarization that applies to police departments as a whole but also provides for differing behavior among individual officers. Third, I conduct what appears to be the first national, large-N study of how militarization relates to the use of lethal force, using previously unavailable data to capture the concept of militarization.1

The next section discusses the processes through which police departments may become militarized. Next, I discuss the possible connection between militarization and the use of lethal force. I describe my data and methods after that. Then I describe the results. Finally, the conclusion offers some possible policy recommendations and avenues for future work.

Data and Method To test my hypothesis, I began with a master list of all nonfederal and non-state law enforcement agencies in the United States from the 2008 Census of State and Local Law Enforcement Agencies. The number of suspect killings is from Fatal Encounters,2 a database created with the goal of collecting information on law-enforcement-related deaths. This is currently the most comprehensive database of the use of lethal force by police available. Due to limited data availability for the militarization variable (discussed in the following), I restrict the time period to the fourth quarter of 2014 through the fourth quarter of 2016. Fatal Encounters includes data on the victims of lethal police violence over this time period in all fifty states. Most of the information in Fatal Encounters comes from newspaper articles and other public records, allowing for easy fact-checking and verification. The final data for analysis consist of 11,848 observations of law enforcement agencies with either countywide or subcounty jurisdiction from the fourth quarter of 2014 through the fourth quarter of 2016. My dependent variable for all hypotheses is the number of people the law enforcement agency killed during each quarter over the period of analysis. The source of this variable is the FatalEncounters.org database, which contains records of individual suspects killed by police, aggregated to a count of the number of people a police department killed in each quarter. For example, if three people in the fourth quarter of 2014 died as a result of activity of a particular police department, that agency-quarter observation’s value for the dependent variable will be three. The agency-year count ranges from zero to nine deaths, with a mean of 0.019, and variance of 0.035.3 Fatal Encounters is a free database administered by D. Brian Burghart, former editor/publisher of the Reno News and Review and journalism instructor at the University of Nevada-Reno. Volunteers and paid researchers use media reports and public records to contribute information about the killing of suspects by police that includes the victim’s name, race, age, the location of the incident, the agency responsible, and other incident-specific information. It is still a work-in-progress but is complete for all fifty states and Washington, DC, from 2013 to 2016, and is nearing completion for all years going back to 2000. Although it is a fairly new database, scholars are already using it in studies of lethal force (Delehanty et al. 2017; Jennings and Rubado 2017).

Measuring Militarization Militarization is a somewhat nebulous concept (Wickes 2015) as it involves the psychological state of officers. Kraska (2007) suggests four dimensions of militarization: material, cultural, organizational, and operational. The material dimension focuses on the acquisition of military weapons and equipment by the police and offers an objective way to measure, if indirectly, a potential effect of militarization. The specific policy I use to capture this concept is the federal “1033” program, which allows federal, state, and local law enforcement to acquire surplus military supplies and equipment. In 1997, Congress made the program permanent and expanded its scope to include counter-terrorism (Bailey Grasso 2014). Agency officials may browse an online database or visit warehouses in person to peruse the available equipment, and agencies pay only the cost of transport. The equipment itself is otherwise free of charge (Molina 2014). Figure 1 depicts the total dollar value of hardware that law enforcement agencies received over the period of analysis. Interestingly, the total amount is relatively stable over this time period. Download Open in new tab Download in PowerPoint I obtained data on 1033 program transfers through a Freedom of Information Act request to DLA. DLA maintains a list of all currently outstanding transfers to law enforcement agencies, which updates each quarter. Prior to 2014, however, DLA did not maintain records of past quarters. Agency officials updated and replaced the quarterly database without saving old versions. Beginning in the fourth quarter of 2014, DLA began to save old versions of this database.4 My primary explanatory variable of interest is the value of military hardware each agency possessed in each quarter from the fourth quarter of 2014 through the fourth quarter of 2016, adjusted for inflation to 2016 dollars, divided by ten thousand to keep the coefficient sizes manageable. I lag the variable by one quarter because police departments may request and receive items through the 1033 program at any point in a quarter, so it seems more sensible to lag the variable to adequately capture the level of militarization that it represents. Lagging the variable also helps account for a potentially endogenous relationship between use of the 1033 program and the number of suspects killed in a quarter.5,6 I constructed a militarization variable that accounts for military equipment in a law enforcement agency’s possession by quarter from the fourth quarter of 2014 through the fourth quarter of 2016. I focus on the amount of military equipment law enforcement agencies receive from the Department of Defense as an appropriate measure of police militarization, as it explicitly reflects at least part of a cooperative relationship between the military and police. I use data from DLA, which provides an itemized list, by agency and date, of all such equipment. However, a simple count of the number of items is insufficient to properly capture the concept of militarization. If military equipment represents militarization, different types of equipment likely represent varying levels of militarization. An armored personnel carrier provides a much more striking image than a pair of combat boots. A military rifle is likely somewhere in between, and probably represents a greater level of militarization than an infrared sight. In other words, larger, more high-tech or intimidating equipment should represent more militarization than smaller, low-tech, generic items, and should also be more expensive. I use the dollar value, adjusted for inflation, of each item as a measure of the militarization that item represents.7 It is worth emphasizing that my argument is not necessarily that the 1033 program itself causes an increase in the use of lethal force. Rather, psychological and behavioral changes in police officers cause an increase in the use of lethal force and in the number of suspect deaths. I argue that the 1033 program is a proxy measure that captures the psychological process of militarization. Militarized police departments should request more—and more expensive—military equipment to better carry out their perceived goal of fighting against criminal elements. There is evidence that the 1033 program leads to decreased crime (Bove and Gavrilova 2017; Harris et al. 2017). But like many public policies, there may be negative consequences associated with their implementation. It seems uncontroversial to suggest that the 1033 program probably has some desirable effects with respect to crime control. It also seems uncontroversial to suggest that knowledge of any negative associated consequences is important as well. In a study similar to my own, Delehanty et al. (2017) find that militarization, represented by 1033 program transfers, corresponds to an increase of lethal force incidents. However, they use a sample of only four states and aggregate both suspect deaths and militarization to the county level. Aggregating measures to the county level could lead to incorrect results as the model loses differences between police agencies with subcounty jurisdiction. Some agencies likely receive more than others, or some likely receive more valuable equipment than others, and aggregating to the county loses that variation. Police departments with subcounty jurisdiction perform most policing functions, and there are substantially more of these departments than those with countywide jurisdiction, such as sheriff’s offices. Losing such variation seems quite problematic, so I leave my own data at the agency level.

Control Variables As plausible as the link between militarization and the use of lethal force seems to be, there may be other explanations. It certainly is not the only cause of the use of deadly force. There may be some other factor or factors driving the use of lethal force by police. I explore a brief theoretical basis for each mechanism in the sections that follow, and I describe their inclusion in my analysis as control variables. Population High populations may increase the use of lethal force simply because there are more people for police to encounter. In a high-population area, the per-capita number of police officers will likely be lower than in low population areas (even if the raw number of police officers is higher). That sense of being outnumbered alone may evoke a sense of threat among police officers, who feel a need to protect themselves against the threat. They may react to suspects with lethal force more quickly to avoid becoming the victims of a mob. Moreover, it may be the case that large police departments, which serve communities with larger populations, tend to have less supervision for street-level officers because the larger number of officers stretches the supervisors thin (Nowacki 2015). On the other hand, larger police departments may use lethal force more often simply because there are more officers and more people for them to use such force against. Population, then, serves as a proxy variable for the size of a police department and allows me to account for different behaviors from police departments of different sizes and to account for differences in the number of suspect deaths based on population, which reduces potential bias in model estimates. I obtained populations from the 2013 Law Enforcement Management and Administrative Statistics, which lists the total population each police department served within its jurisdiction in 2012. While these data are two years old at the starting point of my analysis, it is unlikely these numbers changed by any large amount over that period. Poverty There also may be a connection between poverty and the use of lethal force (Hirschfield 2015). There may be two reasons for this. The first is that, to police, poverty suggests danger because officers associate problem places with threats to officer safety (Terrill and Reisig 2003). Impoverished areas tend to also be high crime areas, particularly violent crime (Hsieh and Pugh 1993), leading to officers fearing for their safety when present in these areas. Thus, higher levels of poverty should lead police to use lethal force more often out of a greater perceived need for self-defense. Second, poor people make up a traditionally marginalized demographic (Terrill and Reisig 2003). Police may use lethal force more frequently in high-poverty areas as a means of social control over the poor (Chevigny 1990). I measure poverty as the percentage of the population within a police department’s jurisdiction with income below the poverty line. This information came from the 2015 American Community Survey and is at the level of U.S. Census Place for subcounty police departments and county for county-level departments. Race Research suggests police are more likely to use force, including lethal force, against members of minority racial groups (Terrill and Reisig 2003). The most likely explanation is an extension of the social control argument discussed in the previous paragraph. The difference, however, is that the target of that control is a racial minority rather than the poor. Within the United States, that race is most likely African Americans, given the long history of both legal and social oppression suffered by that group. In addition, police assume African Americans tend toward crime more than whites and view them with greater suspicion (Werthman and Piliavin 1967). It also seems that areas with higher populations of African Americans have a higher frequency of police violence (Jacobs and O’Brien 1998). There may be a similar effect in areas where the largest minority is Hispanic, rather than black. Although officers may not be intentionally or consciously targeting racial minorities, implicit biases that lead to harsher treatment of minorities may still exist (Smith and Alpert 2007). I include the percentage of the population, again by U.S. Census Place, that is African American and the percentage that is Hispanic from the 2010 U.S. Census. For the African American population, I include the proportion that is African American but not Hispanic to avoid overlap between the two variables. Again, while these figures are somewhat dated, it is unlikely that the numbers changed a great deal. Violent Crime This is likely the most obvious alternative explanation for an increased use of lethal force. Violent crime, rather than all crime, should lead to this effect due to the more serious nature of those crimes compared with others, such as property crime. Violent crime presents a much higher potential threat to officer safety than property crime, so violent crime should be a more reliable measure of crime as it relates to the use of lethal force. Police officers should become more aggressive and more punitive when in high violent crime areas because high levels of crime mean an increase in the probability of violent interactions with the public (Bayley and Mendelsohn 1969; Terrill and Reisig 2003). That aggression, then, means more frequent use of lethal force against suspects. For violent crime, I use the number of violent crimes per ten thousand people at the county level. At the time of writing, these appear to be the best available data on violent crime. Although the measures for poverty, crime, and racial minority populations may seem to overlap significantly, they each represent distinct explanations for the use of lethal force by police. Violent crime represents a direct threat to officer safety. Areas with high levels of violent crime mean that officers will likely encounter violent crime more often, which threatens their own safety. Areas with high poverty and large minority populations may experience high levels of violent crime, but police may also be more likely to use lethal force as a means of social control of these groups regardless of crime. Moreover, the correlations between each of these measures are modest at best (the highest being 0.389), so there is no risk of multicollinearity by including them in the model. Budgetary Resources The most frequent participants in the 1033 program seem to be smaller, more rural police departments with fewer resources (Molina 2014). It seems plausible that police departments with greater financial flexibility could potentially use their own departmental resources to purchase equipment that smaller departments receive through the 1033 program. In such a case, these departments would be more militarized than they appear in the data using the 1033 program as a proxy, because they received the same or similar kinds of equipment without using that program. The militarization measure correlates with the total department budget at 0.31, suggesting that this sort of substitution effect is not present. However, budgetary flexibility may still influence the use of lethal force due to its potential effect on hiring. Police departments with less budgetary resources may have more limited options for hiring due to the salaries, training, and so on that they can offer. This may lead some departments to hire officers that are less professional, or to provide new officers with less training, both of which could lead to more incidents of lethal force. Thus, it is important to control for a police department’s budgetary situation. I use the 2013 Law Enforcement Management and Administrative Statistics survey, which asked for each department’s operating budget for the year that included January 1, 2013. I divide this amount by the total number of sworn officers to account for department size, and I divide the resulting amount by ten thousand to keep coefficients manageable. This measure better captures the concept of “budget flexibility,” as such a concept seems to involve both financial resources and department size, rather than simply the size of a department’s budget.8 Countywide Jurisdiction Having countywide versus subcounty jurisdiction may have an impact on the use of lethal force. Most police work is not done at the county level. Countywide police departments operate in more rural areas with lower populations and population densities, which means they may simply come into contact with fewer people. Contact with fewer people means fewer opportunities to use lethal force. However, departments with subcounty jurisdiction perform most police work, operating in larger towns and cities with higher population densities and interacting with more people. I incorporate countywide jurisdiction into my analysis using a binary variable that indicates whether a particular police agency has countywide jurisdiction.

Model Specification The first conclusion one may draw from these data is that the dependent variable is overly dispersed. Using a negative binomial model is appropriate. Second, there are a lot of zeros, because suspects that police officers kill are a relatively small fraction of the total number of people officers encounter in a quarter. I use a zero-inflated negative binomial model to account for the excessive zeros, which, according to the assumptions of the ZINB model, come from a process distinct from a zero count9 (Zeileis, Kleiber, and Jackman 2007). Results of a Vuong test suggest that the zero-inflated negative binomial regression model more closely captures the process that generated these data than a simple negative binomial model (p < .000). See Table 1 for a list of summary statistics. Table 1. Summary Statistics. View larger version

Results Table 2 depicts the results of this model.10 The top section of the table is a truncated count model measuring the impact of each variable on the predicted probabilities of each ascending count level, while the bottom section is a logit model measuring the impact of each variable on the occurrence of an excess zero in the data. Thus, I expect the coefficient for militarization to be positive in the top section and negative in the bottom. The coefficients measure the change in log odds so they are not directly interpretable, but there are some conclusions to take from these results. Table 2. Zero-Inflated Negative Binomial Results. View larger version Militarization has a positive and statistically significant (p < .05) association with the number of lethal force incidents but has no significant association in the zero-inflation model. This suggests that militarization has an effect on the use of lethal force by police, specifically by increasing the number of suspects police kill in a quarter, all else equal. The results provide support for the hypothesis that as militarization increases, so does the number of suspect deaths. The zero-inflation model measures the effect of each variable on the occurrence of a zero in the dependent variable, so the null result suggests militarization has no effect whether police kill any suspect in a particular quarter or not. The relative rarity of both high levels of militarization and of killing suspects (particularly more than one per quarter) may be affecting the calculation of the zero-inflation model. Alternatively, other factors that are significant in the zero-inflation model may account for most of the influence on the change from a zero to a one, while militarization has a stronger influence on moving from one death to a higher number. It may also be the case that militarization does not affect the likelihood of a police department killing no suspects, but for police departments that kill at least one suspect, increased militarization makes them more likely to kill more than one. Substantively, how many deaths should be expected as militarization increases? The raw coefficients for these models cannot answer these questions due to the difficulty of interpretation. I calculated predicted counts with 95 percent confidence intervals for each, presented in Figure 2, which depicts the predicted number according to the model. There is a fairly steady increase in predicted deaths as militarization increases, though the confidence interval widens slightly more at higher levels. Although my measure of militarization does not directly capture the psychological process, it seems safe to conclude that militarization has a positive and significant association with how frequently police kill suspects. Download Open in new tab Download in PowerPoint According to Figure 2, the model predicts one suspect death at a militarization level of around 375 (or $3,750,000). The expected number of deaths increases to two at around five hundred (or $5,000,000). It then doubles to four deaths at around 750 (or $7,500,000). It is important to note, however, that few police departments in the sample reach such high levels of militarization. A value of four hundred in the militarization measure (which corresponds to $4,000,000 in military equipment) is around the 99.5 percentile. While the highest level of militarization in the sample is 1036.0592 (the Houston Police Department in the third quarter of 2016), the extremely skewed nature of this variable means that few police departments even get close to that amount. Still, this result supports the claim that militarization influences the killing of suspects by police. Next are the results for the control variables. There is a positive and significant association between population and the number of lethal force incidents (p < .05) and a negative, significant (p < .001) association between population and the likelihood of a zero. Thus, population seems to both increase the number of suspects killed and decrease the likelihood of a zero. Poverty is not significant in either section, suggesting that lethal force may not be a method of social control of the poor. The percentage of the population that is Hispanic also has no association with the number of suspect deaths in either the count model or the zero-inflation model. Results for the percentage of the population that is black are somewhat counterintuitive. This variable has an insignificant relationship with the number of deaths and with the likelihood of zero deaths. Taken together, the previous two variables seem to suggest that the racial composition of an area has no effect on how often police use lethal force against suspects. The violent crime rate’s result is as suspected. It has a positive and significant association with the total number of suspect deaths (p < .05) but no association in the zero-inflation model. The results suggest, in line with the theoretical argument, that police departments in higher-crime environments will use lethal force more frequently. However, the jurisdictional level of the agency also plays a roll. Having a countywide jurisdiction has not only a negative and significant association with the frequency of suspect deaths (p < .001) but also a negative and significant association with the likelihood of a zero (p < .05). This is a seemingly contradictory result, but perhaps it makes more sense than would be apparent initially. It is possible that agencies with countywide jurisdiction, who may also serve larger populations than subcounty police departments, are more likely to kill at least one suspect in a quarter, but some other factor, such as the higher likelihood of serving primarily rural rather than urban populations, means the total number remains relatively low. Finally, operating budget per officer has no association with the frequency of lethal force.

Conclusion This paper represents an important contribution to a contentious public debate by studying the impact of police militarization on the number of suspects that police officers kill. Results of a zero-inflated negative binomial regression model with a dataset of more than eleven thousand agency-quarter observations support the hypothesis that there is an association between militarization and suspect deaths. In other words, increasing militarization corresponds to more suspect deaths, ceteris paribus. In addition, this paper presents a theoretical argument that combines institutional attributes of police departments and the roles and behavior of individual officers to explain how militarization affects the decision to use lethal force. Other factors also seem to influence the use of lethal force. The variables with a significant effect on the frequency of lethal force are the total population, the rate of violent crime, and the jurisdictional level of the police department. Countywide jurisdiction seems to decrease the number of suspect deaths, while the other variables increase that number. Racial minority populations seem to have no effect. The results of this paper have important implications. If society agrees that increasing the number of people killed by police is undesirable, steps should be taken to reduce the number of suspect deaths either through reducing militarization or, possibly, by reducing the extent to which militarization can affect officer behavior. What steps may counteract this increase in lethal force is a question for future work, but some scholars and activists propose several potential avenues. One potential solution is rethinking the process of training officers so that, for example, they learn to build connections with their community through nonenforcement interactions and to use tactical restraint to minimize the risk of an enforcement action escalating to violence (Stoughton 2014). Other policies regarding oversight of police behavior and strengthening policies on acceptable use of force, as well as consequences for violating those policies, are also possible mechanisms for reducing the use of lethal force.11 More broadly, a new emphasis on principles referred to as Guardian Policing—as opposed to Warrior Policing—seeks to instill values based more on public service through crime prevention and control than on fighting crime (Stoughton 2016). Other potential future directions for research are to continue exploring how race fits into the operation of police departments and the behavior of officers and other aspects outside the scope of this paper: causes of militarization; how officer psychology, such as militarization, affects officer behavior toward suspects and other civilians; how agency-specific training and supervision play a role; and other potentially important topics relating to policing. Improved data resources and possibly experimental research should play a role in expanding this literature. Scholars are now making greater strides in the study of policing, and there is wide latitude for increase in the scope of this research area.

Acknowledgements The author would like to thank Charles Finocchiaro, Tobias Heinrich, Lindsey Hendren, Susan Miller, Alexander Ruder, Christopher Witko, Neal Woods, the Political Research Quarterly (PRQ) editors, and the anonymous reviewers for helpful feedback during the writing process.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes 1.

It is worth noting, however, that Delehanty et al. (2017) conduct a very similar study that reaches a substantively similar conclusion, but with a more limited dataset. I discuss their article in more detail below. 2.

www.fatalencounters.org 3.

I use quarterly data because it is subject to the least amount of aggregation effects. For a supplementary analysis using data aggregated to years, see the online appendix. Results are substantively similar. 4.

See the online appendix for additional information about the Defense Logistics Agency’s (DLA) 1033 data. 5.

As an additional test for endogeneity, I ran two additional ordinary least squares (OLS) models that included a lagged number of suspect deaths and militarization as the dependent variable: one with two-way random effects and one with standard errors clustered by agency. This analysis suggests there is no endogenous relationship between suspect deaths and militarization 6.

It is also worth noting that the military’s level of surplus equipment drives what is available to police through the 1033 program, not the level of demand for such equipment from police departments (Harris et al. 2017). 7.

The online appendix includes models using several different measures of militarization, such as the log of this total dollar value, the total number of items received, and dummy variables indicating whether a police department received items of high value. In models that involve dollar values of equipment in some way, the results remain positive and significant. The total number of items was statistically insignificant. 8.

In addition, total budgets correlate with population at around 0.9, which likely introduces multicollinearity into the model. This budget-per-officer variable correlates with population at around 0.24. 9.

A model that incorporates two-way fixed effects would be desirable to account for unobserved variation between agencies and years. Unfortunately, fixed effects models drop cross-sectional units with no variation in the dependent variable. Because there are a large number of these units (i.e., entities with zero deaths), and because the lack of variation is due to the dependent variable being zero across all years, that leads to a large decrease in the number of observations. Those zeros are theoretically relevant, however, and dropping them distorts the analysis. In addition, having a variance greater than the mean suggests overdispersion in the dependent variable, so I use a zero-inflated negative binomial model for the analysis. These results are robust to several other model specifications. For additional statistical models used as robustness checks, see the online appendix. 10.

While this analysis uses quarterly data, I also run a robustness check using annual data. This alternative model uses the lagged average annual total of the militarization measure and aggregates suspect deaths to the year rather than the quarter, in case using quarterly data somehow biased the results. The conclusions are substantively similar. See the online appendix for this alternative model. 11.

For some examples of proposed policies, see http://www.joincampaignzero.org

Supplemental Material

Supplemental materials for this article are available with the manuscript on the Political Research Quarterly (PRQ) website. Materials for replication are available at https://dataverse.harvard.edu/dataverse/edwardlawsonjr. ORCID iD

Edward Lawson https://orcid.org/0000-0001-5481-776X