A Study of Militarization and Use of Force

Home , Baton (law enforcement), Community Policing, Use of force

LIVING IN OCCUPIED TERRITORY: A STUDY OF MILITARIZATION AND USE OF FORCE

Cori Pryor

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF ARTS

May 2020

Committee:

Thomas Mowen, Advisor

Steve Demuth

Danielle Kuhl

ii ABSTRACT

Thomas Mowen, Advisor

Police militarization is happening on a widespread scale across the United States.

However, very little is known about its relationship with use of force. At the same time, there has been a growing focus on community policing. Given the concurrent establishment of both of these trends, it is problematic that we do not know how these two tactics interplay with one another, especially in regard to use of force. Additionally, though force is thought to be a mechanism of social control that is unequally distributed in nonwhite communities, studies examining the link between militarization and use of force have yet to include race/ethnicity into their analysis. This paper attempts to address this important gap in the literature by examining the relationship between militarization and use of force through the lens of minority threat theory. I use data from Law Enforcement Management and Statistics 2013, American

Community Survey 2009, and Uniform Crime Reports 2013, as well as item response theory and multivariate regression techniques to study this relationship. Results show that militarization is positive and significantly related to the number of use of force incidents recorded by an agency.

Additionally, community policing shares a positive and significant relationship with use of force.

However, neither racial demographics nor community policing moderate the relationship between militarization and use of force. These findings stress that law enforcement agencies should proceed with caution when adopting new policing strategies without having a thorough understanding of how they relate to use of force.

iii ACKNOWLEDGMENTS

It takes a village. This project would not be where it is today without the significant guidance of my advisor, Thomas Mowen, and my committee members, Danielle Kuhl and Steve

Demuth. Thank you for all of your comments, your time, and serving as a sound board throughout the writing and analysis process. Also, I’d like to thank my sister, parents, and friends for reading drafts (even if they did not quite understand all of it), being a listening ear, and supporting me.

TABLE OF CONTENTS

Page

INTRODUCTION ...... 1

LITERATURE REVIEW ...... 5

...... Use of Force ...... 5

...... Organizational characteristics ...... 6

...... Community policing ...... 8

...... Environmental/contextual factors ...... 10

...... Minority Threat Theory ...... 11

...... Militarization ...... 13

CURRENT STUDY ...... 17

DATA AND SAMPLE ...... 19

Measures: Dependent Variable ...... 21

Measures: Independent Variables ...... 25

Militarization ...... 25

Community policing ...... 27

Demographics ...... 28

ANALYTICAL STRATEGY ...... 30

Item Response Theory ...... 30

Regression Analyses ...... 35

RESULTS ...... 36

Supplemental Analyses ...... 37

DISCUSSION ...... 39 v

REFERENCES ...... 45 vi

LIST OF TABLES

Table Page

1 Descriptives ...... 23

2 Two parameter logistic item response theory results for militarization ...... 32

3 Two parameter logistic item response theory results for community policing ...... 34

4 A negative binomial regression regressing use of force on predictors ...... 36

INTRODUCTION Police use of force has been under intense public scrutiny and the subject of scholarly research for decades. Recently this attention has increased, resulting in nationwide protests, the creation of the #BlackLivesMatter movement, legislation, and numerous government reports and initiatives dedicated to the topic (United States Department of Justice 2016; United States

Commission on Civil Rights 2018). Central to the issue of police use of force is that this mechanism of social control is unequally distributed and disproportionately affects nonwhite communities. Indeed, research supports the assertion that force is deployed unequally to communities of color (Jacobs and O’Brien 1998; Lersch et al. 2008; Smith and Holmes 2003;

Smith and Holmes 2014). In addition to increased concern over use of force, there has been considerable attention afforded to the concurrent issue of police militarization. Police militarization refers to a multi-dimensional process where law enforcement agencies pattern their tactics around the tenets of militarism: “a set of beliefs, values, and assumptions that stress the use of force and threat of violence as the most appropriate and efficacious means to solve problems” (Kraska 2007:3). Research shows that police militarization is widespread, with policing agencies in the majority of U.S. counties purchasing military equipment (Radil,

Dezzani, and McAden 2017) and orienting their policing efforts around a militarized framework

(Kraska 2007). Importantly, there is growing evidence that police militarization, like use of force, disproportionately affects communities of color (Ajilore 2015; Mummolo 2018). Given the ubiquity of police militarization in the United States along with its potential to perpetuate social inequality, it is crucial to understand the link between militarization and use of force.

While scholars assert that militarization has its roots in the War on Drugs in the 1970s

(Hall and Coyne 2013), the acceleration in acquisition of military equipment by law enforcement is attributed in part to H.R. 3230 , which gave the Secretary of Defense permanent authority to

2 sell or transfer military equipment to local law enforcement agencies (National Defense

Authorization Act for Fiscal Year 1997 1996; James and Else 2014). Law enforcement agencies apply to and receive military equipment through the 1033 Program, which is often the focus of studies regarding militarization (reviewed later in this manuscript). The effect has been widespread: 80% of U.S. counties received transfers of military equipment and those transfers increased from 2006 to 2013 by over 1000% (Radil et al. 2017). Some legislators, citizens and advocacy groups have spoken against militarization, arguing that city streets are not war zones

(Trujillo and Byrnes 2014), while police departments argue that military equipment offers better protection for officers and citizens (Hayes 2017; City of New Hope Minnesota 2019; Welna

2014). Research shows that police militarization may have implications for officer safety (Harris et al. 2017; Mummolo 2018), crime (Bove and Gavrilova 2017; Harris et al. 2017; Mummolo

2018), and use of force against civilians (Williams and Westall 2003). Extant studies also suggest that militarization may harm the public’s perception of police (Mummolo 2018). The latter finding is, perhaps, paradoxical given the growing focus on community-based policing

(Skogan 2006), a framework that has been implemented in police departments with the intention of improving police-community relationships. In fact, research confirms that practitioners implement elements of both militarization and community-based policing (DeMichelle and

Kraska 2001) and scholars agree that these two trends have developed concurrently (Center for

Criminal Justice 1977; DeMichele and Kraska 2001; Wood 2015). Given the ongoing and widespread establishment of both community policing initiatives and militarization of law enforcement, it is problematic that we do not know how these two tactics interplay with one another - especially in regard to police use of force.

Research connecting militarization and use of force is fairly nascent and findings regarding the relationship are inconsistent (see Ariel et al. 2019; Delehanty et al. 2017; Williams and Westall 2003). One reason for the inconsistent findings in prior research may be due to the lack of rigorous theoretical framing and testing. One theoretical perspective that is particularly fruitful to understand the linkages between militarization and use of force across communities in the United States is the minority threat hypotheses (Blalock 1967). Minority threat suggests that communities with greater proportions of racial/ethnicity minorities will experience greater uses of coercive crime control tactics, such as use of force, than predominately White communities

(Blalock 1967; Smith and Holmes 2014) due to concerns around political and economic dominance. In regards to policing specifically, this theoretical orientation has been applied to a diversity of outcomes including police size (Carmichael and Kent 2014; Stults and Baumer

2007), deployment (Mummolo 2018; Roh and Robinson 2009), and sentencing (Caravelis,

Chiricos, and Bale 2011). Although scholars have also applied minority threat to understand use of force (Jacobs and O’Brien 1998; Parker et al. 2005; Smith and Holmes 2014), existing research has generally failed to examine the close linkages between use of force and militarization as a function of threat. Due to the salience of race and ethnicity in issues concerning militarization (Ajilore 2015; Mummolo 2018) and use of force (Smith and Holmes

2014) respectively, it is crucial for an analysis that explores the relationship between the two to include race and ethnicity. Moreover, given the ascendancy of community policing in law enforcement agencies (Skogan 2006), a potential moderation between militarization and community policing needs to be considered.

The current study aims to add to the literature by examining the relationship between militarization and use of force through the lens of minority threat theory. It is guided by four broad research questions: (1) Does police militarization relate to use of force? (2) Does community policing relate to use of force? (3) Is the relationship between militarization and use of force stronger in communities with greater proportions of racial/ethnic minorities? (3) Does community policing moderate the relationship between police militarization and use of force? To answer these questions, I employ multivariate regression techniques and item response theory to analyze a unique and intricate dataset (Pryor et al. 2019) that combines American Community

Survey data from 2009, Uniform Crime Report data from 2013, and Law Enforcement

Management and Statistics 2013 data. The next section overviews the existing literature about use of force, militarization, and minority threat before discussing the current study.

LITERATURE REVIEW

Use of Force

Though police use of force is a relatively rare occurrence (Hickman, Piquero, and Garner

2008) it is often at the forefront of conversations about race and the criminal justice system. Use of force not only shares serious consequences such as bodily injury, trauma, and potential death, but it also carries a heavy social and political weight when it involves a White officer and a person of color (Skolnick and Fyfe 1993). While not all findings support the notion that there are racial differences in the deployment of force based on officer race (Lawton 2007), a good deal of research has demonstrated there are racial disparities in who is on the receiving end of police use of force (Hickman et al. 2008; Lee 2014; Willits and Makin 2018; Worden 1996).

Bittner (1970:40) claimed that force is the defining feature that unites all activities expected of police; that every “conceivable police intervention projects the message that force may be and may have to be used to achieve a desired objective.” For such a politically-charged and potentially fatal instance, there is a surprising amount of ambiguity surrounding its definition and instruction around its use. Not only is there is no single agreed upon definition of use of force, there is also no universal guidance or directives across law enforcement agencies on when force is appropriate (National Institute of Justice 2019). Policies differ from agency to agency, and oftentimes they are vague (Bittner 1970; National Institute of Justice 2019; Waegel 1984).

Additionally, there is currently no official national recording of use of force, but data collection efforts have made great strides in the past several years (Garner et al. 2018). However, differences in findings may be due to the use of different datasets, as well as differing definitions and reporting requirements of use of force by agencies. Regardless of these issues, scholars have

6 dedicated decades of research and inquiry into understanding the determinants of police use of force.

Among the correlates of use of force that scholars have studied entail characteristics of the suspect (Hickman et al. 2008; Nix et al. 2017; Willits and Makin 2018), officer (Menifield,

Shin, and Strother 2019; Paoline, Gau, and Terrill 2016; Paoline and Terrill 2007), agency

(Alpert and MacDonald 2001; Delehanty et al. 2017; White 2001) and the larger environment in which the interaction took place (Kramer and Remster 2018; Lersch et al. 2008). These correlates fall under the broad categories of situational — characteristics of the individual situation in which force was applied, organizational — aspects of the police organization itself, and contextual/environmental — the larger society in which the use of force situation took place.

Situational correlates are outside of the scope of this paper, but a review of organizational and environmental characteristics is below.

Organizational characteristics.

Despite calls by researchers for it (Alpert and McDonald 2001), there has been less scholarly focus on the organizational correlates of use of force. Organizational correlates refer to characteristics of the police agency under which force was employed, such as policies, training hours, and resources. Police work can also be affected by other organizational characteristics, such as structure and culture (Alpert and MacDonald 2001). Wilson (1968) hypothesized that there are different types of police departments, attributable to the chief’s personal philosophies and local politics, that influence officers’ behavior through organizational structure. He suggested that improper force and police aggressiveness were more likely to be found in specific typologies of agencies (Wilson 1968; see also Alpert and MacDonald 2001; Worden 1996).

Another early study disputed this claim and claimed that officers embodied different policing

7 styles within one agency (Worden and McLean 2017). To reconcile these competing viewpoints,

Worden (1996) argued that it was probable that departments develop subcultures where one might dominate and cited the Christopher Commission’s report about the organizational structure of the Los Angeles Police Department (LAPD) as support for Wilson’s (1968) argument. The report asserted that the aggressive interactions between LAPD officers and the public can be traced back to the “assertive style” of law enforcement and incentive structure (Independent

Commission on the Los Angeles Police Department 1991: 97). Worden (1996) claimed that the findings of the report supported the notion that there was indeed a link between formal administrative structure and informal organizational behavior, and further suggested that a department’s organizational structure at least moderately affected the levels at which force was used.

Another aspect of organizational characteristics includes the weapons and training used by officers. Though findings are not entirely consistent (Shjarback and White 2016), studies have demonstrated at least some evidence that training was related to use of force (Bailey 1996; Lee et al. 2010; Smith 2004). Interestingly, Phillips (2015) found that police recruits viewed excessive force as an acceptable means of retaliation against fleeing suspects before engaging in their academy training and suggested extensive training to debunk that line of thinking. In fact, qualitative studies have suggested that the type of training makes an important impact on police trainees. For instance, it follows that when departments outfit officers with military weapons and engage in military-style operations, they would then receive military style training (Delehanty et al. 2017). When he visited a police training session led by military soldiers, Kraska (2001:143) reported the soldiers said of the police trainees that “most of these guys just like to play war; they get a rush out of search-and-destroy missions instead of the bullshit they do normally.” Further,

8 findings have demonstrated a connection between militarization and use of force. For example,

Delehanty and associates (2017) concluded that military transfers from the 1033 program were related to increases in the total use of force counts across the department. On an individual-level,

Ariel et al. (2019) found officers who carried TASERS were more likely to use force than officers who did not carry TASERS.

Perhaps due to practical constraints (Worden 1996), most of the scholarly attention paid to organizational-level correlates has been focused on organizational policies. Studies have shown that organizational policies have a significant impact on police use of force and, importantly, carry with them the potential to reduce it (White 2001). White (2001) studied the effect of both the implementation and subsequent removal of a restrictive deadly force policy in the Philadelphia Police Department. His findings indicated that the department recorded fewer instances of deadly force while the more restrictive policy was in effect.

Community policing.

In recent decades, one of the most widely adopted organizational strategies by law enforcement has been community policing. Following the civil unrest of the 1970s, police reform focused on making officers effective at reducing crime in a more thoughtful and comprehensive way, while also closing the gap between them and the communities they serve (Eve et al. 2003;

Greene 2000; Skogan and Hartnett 1997). Previous reform attempts to make police more accountable resulted in a police force that was isolated from constituents (Greene 2000) and this isolation was thought to have caused impersonal policing (Weisheit, Wells, and Falcone 1994).

Contrary to this impersonal policing, community policing is often implemented as a method by which to increase contact and trust in the community. In fact, the first item the Ferguson report

9 listed as a change necessary to increase community trust is to implement “true” community policing (United States Department of Justice Civil Rights Division 2015).

According to Skogan and Hartnett (1997), community policing involves the decentralization of authority, an emphasis on problem-oriented strategies, a focus on facilitating public involvement in influencing the decision-making priorities of the police, and empowering citizens to take an active role in preventing crime in their own communities. Its promises include empowering the community to resist and prevent crime, mending police-community relations, taking the community’s opinions into account, reorganizing the police organization, and making the police a more comprehensive service by collaborating with other municipal services

(Weisheit, Wells, and Falcone 1994; Greene 2000). Strategies to create these outcomes include surveying community opinions and using that feedback, partnering with community organizations, facilitating beat meetings, assigning officers to patrol the same geographic area, training citizens on police duties, and training police on problem-solving strategies, amongst others (LEMAS 2013; Skogan 2006). Given this diverse list, it is obvious that the way that agencies conceptualize and carry out community policing exists on a wide spectrum. Along with the added difficulty of ambiguity in methods, implementing a successful community policing program is challenging because it requires a complete restructuring of the police organization

(Goldstein 1987).

Some studies show some promising outcomes related to community policing. For example, community policing interventions are associated with more satisfaction with police

(Scheider, Rowell, and Bezdikian 2003), and reduction of fear of crime (Zhao, Schneider and

Thurman 2002). However, how community policing relates to police use of force is still somewhat a mystery to scholars. Prenzler, Porter, and Alpert (2013) found that while no agency

10 specifically adopted “community policing,” there was evidence that training officers in communication skills, diffusing conflict, stress inoculation, and risk assessments helped reduce the number of situations in which force was employed. Additionally, in their overview of violent behavior in policing, Lersch and Mieczowski (2005) suggested that implementing community policing strategies at the organizational level may decrease use of force across police departments. However, a recent finding has countered this suggestion and demonstrated that departments who implement community policing were associated with more sustained use of force complaints (e.g., Pryor et al. 2019). This opposing finding could be for several reasons:

First, it involved a different unit of analysis that the Prenzler, Porter, and Alpert (2013) study

(county versus individual). Additionally, because the Pryor et al. (2019) studied use of force complaints, perhaps agencies who implemented community policing were different from departments who did not in that they were more receptive to citizen complaints, and thus sustained more complaints. Though researchers have begun to explore the relationship between community policing and use of force, these mixed findings highlight the need to further examine it.

Environmental/contextual factors.

In addition to organizational characteristics, research has focused on environmental correlates to police use of force. Environmental or contextual factors refer to the larger society where the use of force incident took place. For example, one strand of macro-level research regarding the correlates of use of force examines the neighborhood context. Terrill and Reisig

(2003) found that police officers were more likely to use higher levels of force when they engaged with suspects in both disadvantaged neighborhoods (see also Terrill 2003) and neighborhoods with higher homicide rates. Studies also discovered that neighborhood racial

11 composition (Lersch et al. 2008; Smith 1986), neighborhood levels of active physical resistance by suspects as reported by police (Lersch et al. 2008), dangerousness of the location (Crawford and Burns 2008), and other structural factors such as segregation and poverty (Kramer and

Remster 2018) were associated with higher levels of use of force.

A second line of macro-level research examines place and city-level correlates of use of force. For example, Alpert and MacDonald (2001) concluded that of all the variables in their models, violent crime rate had the strongest association with reported use of force rates (see also

Klinger et al. 2015). In addition, several studies have examined the relationship between population demographics and levels of force. For example, in studying the determinants of civil rights complaints about police brutality, Holmes (2000) found that municipalities with a larger

Black population had more average annual civil rights criminal complaints. Similarly, Jacobs and

O’Brien (1998) asserted that cities with both a larger Black population and a recent growth in the

Black population had higher levels of deadly force against Black citizens. Studies have also begun to include different races and ethnicities into their analyses. For example, one study found a significant, positive association between percent Black and percent Hispanic and use of force complaints (Smith and Holmes 2014).

Minority Threat Theory

It is difficult to talk about police use of force without mentioning race and ethnicity.

Consequently, the relationship between law enforcement and racial/ethnic minority communities has been the focus of scholarship for decades. Among the most studied theories attempting to explain the linkage between formal social control and minority groups is minority threat theory

(Blalock 1967). Minority threat theory posits that as the size of the minority population increases, the dominant group senses a threat to their political and economic dominance (Blalock

1967). Consequently, law enforcement agencies act to preserve the dominance of the White population by oppressing the Black population using coercive crime control techniques, such as excessive force (Holmes and Smith 2014).

Minority threat theory has been tested across a wide variety of outcomes in criminology, including police size (Carmichael and Kent 2014; Kent and Carmichael 2014; Stults and Baumer

2007), spending (Jackson and Carroll 1981), and crime control tactics such as arrest (Kane

Gustafson, and Bruell 2013), traffic stops (Roh and Robinson 2009) and use of force (Holmes

2000; Jacobs and O’Brien 1998; Parker et al. 2005; Smith and Holmes 2014). Although past research focused mainly on Black populations, more recent studies have examined minority threat in the context of Hispanic and Latino communities (Eitle and Taylor 2008; Kane 2003;

Kane et al. 2013; Smith and Holmes 2003; Smith and Holmes 2014; Holmes et al. 2008), undocumented immigrants (Wang 2012), as well as Asian American and Native American citizens (Sever 2001). Research has also found that population size (Carmichael and Kent 2014), change in population size (Caravelis, Chiricos, Bale 2011; Jacobs and O’Brien 1998), and perceived population size (Wang 2012) share strong relationships with a variety of criminological outcomes, such as perceived criminal threat, sentencing outcomes, and use of force.

Consistent with minority threat theory, militarization would allow for better control of threatening citizens. Indeed, research shows that minority threat may apply to patterns of militarization. For example, SWAT teams were more likely to be deployed in largely African

American communities in one study (Mummolo 2018). Additionally, Raimey and Steidley

(2018) discovered a curvilinear relationship between the relative size of the Black population and participation in the 1033 program. However, not all findings have suggested that minority threat

13 applies to militarization. For example, one study found that counties with a larger African

American population were actually less likely to buy MRAP (mine-resistant ambush protected) vehicles, but counties with greater ethnic residential segregation were more likely to acquire

MRAPs, indicating that segregation may play an important factor in militarization (Ajilore

2015). While previous research has done an exceptional job at extending minority threat theory to study militarization, how militarization affects use of force needs more scholarly attention. To begin exploring this relationship, it is imperative to first understand the history and rise of police militarization in the United States.

Militarization

Kraska (2007:3) defines militarism as “an ideology focused on the best means to solve problems. It is a set of beliefs, values, and assumptions that stress the use of force and threat of violence as the most appropriate and efficacious means to solve problems.” Police militarization then reflects a process where law enforcement agencies pattern their tactics and policing strategies around the tenets of militarism (Kraska 2007). According to Kraska (2007), there are four tangible indicators of militarization: material (martial weaponry, equipment, and advanced technology), cultural (martial language, style, beliefs, and values), organizational (i.e. SWAT, other special operations teams patterned after the military), and operational (patterns of activity modeled after the military).

Empirical studies have found that militarization of police departments is occurring at an accelerating pace and on a widespread scale. For instance, one early study of police militarization found the number of police paramilitary units (PPUs) in departments serving populations of 50,000 or more doubled, and the number of PPUs in smaller jurisdictions increased from 20% to 80%, between 1980 and 1990 (Kraska and Cubellis 1997; Kraska and

Kappeler 1997). Additionally, PPU activity has changed from largely reactive in nature (e.g. responding to bomb threats) to proactive (e.g. drug raids) which has been controversial due to accidental casualties and deaths of innocent civilians involved in their deployment (Kraska and

Kappeler 1997).

Though scholars argue that militarization has its roots in the War on Drugs in the 1970s

(Hall and Coyne 2013) and police have always been militarized to some extent (Kraska 2007), the acceleration in militarization by law enforcement is attributed in part to H.R. 3230 (National

Defense Authorization Act for Fiscal Year 1997), which was signed into law in 1997. This piece of legislation allows the Secretary of Defense to sell or transfer excess military equipment, including MRAPs, assault rifles, grenade launchers, airplanes, helicopters, camouflage and other equipment to local law enforcement agencies (Defense Logistics Agency 2019). As a result, law enforcement agencies who meet requirements set forth by the Law Enforcement Support Office can apply to be a part of the 1033 Program, which allows police agencies to request excess property from the Department of Defense (Defense Logistics Agency 2019). While the number of law enforcement agencies participating in the 1033 Program has vastly increased (Radil et al.

2017), this program has not existed without controversy. After the police response to protests in

Ferguson, Missouri in 2014 saw officers armed with military-grade weapons and gear, the

Obama administration placed restrictions on the 1033 Program. The Trump administration has since lifted those restrictions (Lucas 2017).

Several studies have tested the effects of militarization on officer safety and crime. As far as officer safety, Harris et al. (2017) concluded that purchasing military equipment as part of the

1033 Program was associated with decreased assaults on officers, while another study claimed this relationship was more nuanced and saw different relationships for different equipment

(Carriere and Encinosa 2017). In examining the effect of increased military gear spending on a variety of outcomes, Bove and Gavrilova (2017) found increased county-level drug arrests and decreased street-level crime rates as a result. However, a recent study by Mummolo (2018) that studied the effect of an agency having a SWAT team on various outcomes concluded the opposite, and asserted that having a SWAT team did not improve officer safety and failed to decrease crime rates. Though these studies with opposing findings generally employed similar methodologies, the difference in findings could be due to the different time frames under consideration and different measurements of the independent variable (existence of a SWAT team vs. monetary value of military equipment vs. the amount of military gear purchased).

Studies have also assessed the association between police militarization and the interactions between officers and citizens. For instance, Sousa et al. (2010) found that officers who were armed with TASERs were less likely to discharge their firearm, pepper spray, and baton. Alternatively, a randomized experiment carried out in the U.K. found that carrying a

TASER was associated with increased incidences of use of force (Ariel et al. 2019). These opposite findings could be because U.K. police are routinely unarmed, while U.S. police are armed with a variety of weapons. However, when looking at another aspect of militarization, one study found that SWAT officers and non-SWAT officers showed no significant differences in deployment of force (Williams and Westall 2003). On the other hand, research has found that military transfers were positively related to officer-involved shootings of citizens (Delehanty et al. 2017).

Clearly, research surrounding militarization is rife with contradictory findings. What we do know is that militarization has had a long history in the United States (Hall and Coyne 2013) and militarization is happening on a large scale (Radil et al. 2017). However, studies exploring

16 the relationship between militarization and a variety of criminological outcomes have come to mixed conclusions, perhaps due to different data sources, different operationalizations of militarization, and different units of analysis. Importantly, though there is much concern about it from the public and media (Phillips 2018), we do not quite yet understand the relationship between militarization and use of force. Nor have we adequately studied the role that race plays in that relationship. Additionally, there’s been a dearth of national-level research focusing on the environmental and organizational correlates of use of force. And though studies have focused on tangible measures of militarization, no study has considered a latent trait of militarization of the law enforcement agencies themselves. For example, agencies with a high propensity to militarize may be more likely to say they utilize a drone. Item response theory models (discussed in the subsequent section) take account for the fact that these differences in reports between subjects are not random but reflect differences in an underlying predisposition or latent trait of militarization. This paper attempts to fill this critical gap in the research by exploring the relationship between militarization and use of force through the lens of minority threat theory, using a unique national dataset and item response theory.

CURRENT STUDY

Existing research on use of force has placed a strong emphasis on studying use of force, race/ethnicity, community policing, and militarization separately. Consequently, prior research has failed to provide a national-level analysis that examines that independent and interdependent relationships across these key dimensions. To address this important oversight, this study leverages the minority threat hypothesis (Blalock 1967) to analyze the linkages between police militarization and community policing on use of force. To accomplish this, I use an innovative dataset (discussed in greater detail below) combining elements of the Law Enforcement

Management Administrative Statistics (LEMAS) 2013 data, Uniform Crime Reports (UCR)

2013 data, and American Community Survey (ACS) 2009 data to examine four research questions and corresponding hypotheses.

First, how does militarization of law enforcement agencies relate to use of force? As mentioned above, findings studying the link between militarization and use of force on an individual-level are mixed, but macro-level research suggests militarization may be related to more officer shootings of citizens. Therefore, consistent with previous macro-level research findings (Delehanty et al. 2017), I hypothesize that militarization will be positive and significantly related to use of force. Second, how does community policing associate with use of force? Previous research suggests that community policing may be related to decreases in use of force (Lersch and Mieczkowski 2005; Prenzler, Porter, and Alpert 2013), therefore I expect community policing to be negative and significantly related to use of force. Third, does the relationship between militarization and use of force strengthen in racial/ethnic minority communities? Conforming with minority threat theory, I predict that the effect of militarization on use of force will be stronger in areas with greater proportions of Black and Hispanic

18 populations relative to White populations. And finally, does community policing moderate the relationship between militarization and use of force? I expect community policing to reduce the magnitude of the link between use of force and militarization.

In addition to the theoretical and methodological contributions, findings from this study make key contributions to the literature in several important ways. First, findings will further illuminate the relationship between militarization, community policing and police use of force.

This is critical because the majority of counties in the United States have acquired military equipment (Radil et al. 2017) and the majority of law enforcement agencies have at one point in time implemented community policing into their strategies (Skogan 2006). Several governmental reports have suggested community policing to law enforcement agencies as a way to strengthen police-community relations (President’s Task Force 21st Century Policing, United States

Department of Justice Civil Rights Division 2015) and legislation has made military equipment easily accessible to law enforcement. However, these decisions have been made without reliable information about how these tactics relate to use of force. Finally, police use of force is not only an often-discussed issue amongst academics, but is a very real topic faced by police and citizens themselves, specifically communities of color. Therefore, understanding how organizational policies and characteristics, such as acquiring military equipment and community policing tactics, influence use of force is imperative for making informed policy decisions.

DATA AND SAMPLE

The research described above is a testament to the assertion that in order to adequately study police use of force, data about the law enforcement agency and the broader society the agency is situated in must be taken into account. Both organizational correlates – such as resources, tactics, and weapons– and environmental correlates – such as racial/ethnic composition and crime rate – matter. Therefore, examining each of these separate, yet closely related, factors together as they relate to use of force is integral. Accordingly, a unique feature and key strength of this study is the dataset used. In order to better study police use of force, this analysis required a dataset that includes Law Enforcement Management Administrative Statistics

(LEMAS) 2013 data, Uniform Crime Reports (UCR) 2013 data, and American Community

Survey (ACS) 2009 data. This combination allows the inclusion of not only organizational attributes of law enforcement agencies, but place-level demographic and crime information, in one analysis. Recent research demonstrates that including information from each of these crucial facets provides a fuller picture of the correlates of police use of force (Pryor et al. 2019).

The Law Enforcement Agency Identifiers Crosswalk (LEAIC) file was used to merge these three datasets together. The LEAIC file matches unique identifiers reported in crime data

(Originating Agency Identifier; ORI) and Census data (Federal Information Processing

Standards; FIPS). Any law enforcement agency that is capable of reporting crime information is included in the file, along with their ORI codes and corresponding FIPS identifiers (United

States Bureau of Justice Statistics 2012). The user can then complete a 1:1 merge with crime data using the ORI codes and Census data via the FIPS codes. By allowing researchers to merge together crime data with essentially any variable reported by the Census, this crosswalk file is integral to pushing criminological research forward. By being able to merge these datasets

20 together, scholars and researchers alike can ask questions that were not feasible to study before and provide important demographic contexts in their studies.

The LEMAS survey presents information on a representative sample of non-specialized state and local law enforcement agencies in the United States. LEMAS provides information about technologies used by officers, departmental policies, budgets, demographics, and other important information about state police, local police, and sheriff departments. The sample is based on employment data from the 2008 Bureau of Justice Statistics Census of State and Local

Law Enforcement Agencies. Overall, 2,822 departments comprise the final sample with a response rate of 86%, which includes 2,059 local police departments, 717 sheriffs’ offices, and

46 state law enforcement agencies. All departments were asked to complete an 80-item CJ-44

Survey questionnaire that was administered in 2013 via mail and responses were received via website, email or fax (United States Department of Justice, Bureau of Justice Statistics 2013). It is worth noting that while the dataset is labeled 2013 and was collected in 2013, answers received from agencies were regarding the year 2012.

In order to create an analysis that includes information about the communities the law enforcement agencies in the LEMAS dataset serve, a combination of ACS and UCR data were merged into the master data file. 5-year estimates from ACS in 2009 were captured at the place- level to provide demographic information about racial and ethnic makeup and median income.

The ACS is an ongoing, annual survey that gathers housing, economic, and demographic information about respondents and their households. Using random sampling techniques, ACS surveyors interviewed a total of 1,917,748 households via mail, internet, CATI, and CAPI with a response rate of 98% (United States Census Bureau 2009). For the purposes of this study, these household responses are aggregated at the census place-level. Additionally, 5-year estimates

21 from 2009 were used because the 5-year estimates provide greater statistical reliability for areas with smaller populations. Additionally, only the 5-year estimates are provided for all geographic units, including place. Census “places” refer to either an incorporated place (a legally bounded entity such as a city or town, or have some form of local governance recognized by the state) or a census designated place (a statistical entity that is an area with a concentration of population, housing, and commercial structures identifiable by name that is not within an incorporated place)

(United States Census Bureau, 2013). 5-year estimates from 2009 were used because this project is part of a larger one that necessitates the use of demographic data from 2009. Additionally, the

Census reports fairly steady population estimates from 2009 to 2013. Finally, data from UCR was captured to add crime rates into the analysis. The Uniform Crime Reporting Program includes monthly counts of all official reports of eight index crimes (except for arson) received from victims, officers, or other sources. This excludes crimes that are unfounded or false.

Agencies from 46 states and Washington D.C. forward their data through their state UCR programs, while agencies from the 4 states without state programs forward their statistics directly to the FBI. In 2013, a total of 18,415 city, county, state, university and college, tribal and federal agencies participated in the UCR Program. These data on violent and property crimes known to the police are aggregated at the agency-level and combined to reflect yearly counts (United

States Department of Justice, Bureau of Justice Statistics 2013).

Measures: Dependent Variable

The dependent variable captures the number of use of force incidents. Drawn from the

LEMAS data, agencies were asked “during the 12-month period ending December 31, 2012, how many total use of force incidents did your agency record?” Respondents recorded the number of

22 incidents, and were instructed to write in 0 if there were none. The average number of recorded use of force incidents is 78.93 (SD= 248.3242; range= 0-4,383).

Table 1. Descriptives Variable Mean Standard Minimum Maximum Deviation Dependent Variable Use of force 78.93074 248.3242 0 4383

Militarization Body armor requirement 0.800 0.400 0 1 Airplane 0.008 0.091 0 1 Helicopter 0.034 0.181 0 1 Boat 0.144 0.351 0 1 ATV 0.291 0.455 0 1 Drone 0.003 0.056 0 1 Rifle 0.938 0.241 0 1 Shotgun 0.934 0.249 0 1 Soft projectiles 0.565 0.496 0 1 Handgun 0.998 0.045 0 1 Baton 0.911 0.285 0 1 Other impact weapons 0.472 0.499 0 1 Spray 0.960 0.196 0 1 Other chemicals 0.354 0.478 0 1 Taser 0.836 0.370 0 1 Neck restraint 0.199 0.400 0 1 Takedown 0.930 0.256 0 1 Open hand 0.949 0.221 0 1 Close hand 0.888 0.315 0 1 Leg hobble 0.623 0.485 0 1 Hiring military 0.213 0.409 0 1 SWAT team 0.126 0.332 0 1 Bomb/explosive task force 0.161 0.368 0 1 Terrorism/homeland security 0.349 0.477 0 1 task force Multi-jurisdictional SWAT 0.502 0.500 0 1 team

Community Policing (CP) SARA problem-solving 0.462 0.499 0 1 Community partnership 0.442 0.497 0 1 Same beat 0.581 0.494 0 1 Community survey 0.314 0.464 0 1 CP mission statement 0.796 0.403 0 1 Collaborative-problem solving 0.386 0.487 0 1 CP recruit training 0.705 0.456 0 1 CP in-service training 0.682 0.466 0 1

Table 1. Continued Demographics Percent Hispanic 9.230 15.678 0 100 Percent Black 10.896 18.076 0 100 Crime rate 3,142 3,417 0 69,375 Number of Black officers 20.836 178.851 0 5,557 Ratio of officers to population 220.936 146.616 0 2,794 Median household income 46,136 22,586 9,846 250,001

Measures: Independent Variables

Militarization.

The first category of independent variables comes from the LEMAS 2013 dataset and represents militarization. The variables included in this analysis largely cover the material aspect of militarization described by Kraska (2007), and include weapons, tactics, vehicles, technology and gear used by officers. These variables were included if they fit with Kraska’s (2007:3) conceptualization of militarization: “the process of arming, organizing, planning, training for, threatening, and sometimes implementing violent conflict.”

To create a measure representing militarization, 25 variables were combined using item response theory (IRT) models (further discussed in the analytical strategy section below).

Respondents were asked if either officers were authorized to use or their agency had a body armor requirement (mean=.800; SD=.400; range=0-1), airplanes (mean=.008; SD=.091; range=

0-1), helicopters (mean= .034; SD=.181; range= 0-1), boats (mean=.144; SD=.351; range= 0-1),

ATVs (mean= .291; SD=.455; range= 0-1), drones (mean=.003; SD= .056; range= 0-1), rifles

(mean=.938; SD=.241; range= 0-1), shotguns (mean=.934; SD=.249; range= 0-1), soft projectiles (mean= .565; SD= .496; range= 0-1), handguns (mean=.998; SD= .045; range= 0-1), batons (mean=.911; SD= .285; range= 0-1), other impact weapons (mean=.472; SD= .499; range= 0-1), chemical sprays (mean=.960; SD= .196; range= 0-1), other chemicals (mean= .354;

SD= .478; range= 0-1), TASERs (mean= .836; SD= .370; range= 0-1), neck restraint tactics

(mean= .199; SD= .478; range= 0-1), takedown tactics (mean=.930; SD= .256; range= 0-1), open hand techniques (mean= .949; SD= .221; range= 0-1), close hand techniques (mean= .888; SD=

.315; range= 0-1), leg hobble tactics (mean= .623; SD= .485; range= 0-1), military service as an alternative to education requirements (mean= .213; SD= .409; range= 0-1), SWAT team (mean=

.126; SD= .332; range= 0-1), bomb/explosive disposal task force (mean= .161; SD= .368; range=

0-1), terrorism/homeland security task force (mean= .349; SD= .477; range= 0-1), and participation in a multi-jurisdictional SWAT team (mean= .502; SD= .500; range= 0-1). These are binary variables where 1= Yes and 0= No.

While most of these variables were naturally binary, a few were recoded into dichotomous variables. For instance, respondents were asked “as of January 1, 2013, how did your agency address the following issues, problems or tasks?: Special Operations Unit (e.g.

SWAT, SRT); Terrorism/homeland security; Bomb/explosive disposal.” 1= Apecial unit with full-time personnel, 2= Special unit with part-time personnel, 3= Dedicated personnel, 4= No dedicated personnel, and 5= Not formally addressed. These were recoded into binary variables, where 1= Special unit with either full-time or part-time personnel or just dedicated personnel and

0= There is no special unit or dedicated personnel. This distinguishes agencies who address the issue in some capacity from those who do not. Additionally, respondents were asked if their agency’s written body armor policy includes a requirement that uniformed field/patrol officers must wear body armor in the field at all times. Originally, this variable was categorized as 1=

Yes, 2= No, and -8= Agency has no written policy on body armor. This was recoded into a binary variable, body armor requirement, where 1=Yes, sworn officers are required to wear body armor at all times and 0= No, sworn officers are not required to wear body armor at all times. If there is no written policy, that signals that officers are not required to wear body armor at all times. So, this decision was made to separate agencies where officers are required to wear body armor at all times versus those that have no such requirement. Finally, although respondents had the ability to answer rifles, shotguns, soft projectiles, handguns, batons, other impact weapons, chemical sprays, other chemicals, TASERs, neck restraint tactics, takedown tactics, open hand

27 techniques, close hand techniques, leg hobble tactics 1=All sworn agents are authorized to use,

2=Some sworn agents are authorized to use, or 3= No sworn officers are authorized to use, no agency indicated that some sworn officers were authorized to use those tactics or weapons.

Therefore, these variables occur binary naturally.

Community policing.

The second set of independent variables represent community policing, are also from the

LEMAS data, and are combined into a single measure using IRT (this construction is further discussed in the analytical strategy section). Variables were included if they described community policing tactics (Lawrence and McCarthy 2013). This set of variables includes SARA problem-solving1 (mean= .462; SD= .499; range= 0-1), community partnerships (mean= .442;

SD= .407; range= 0-1), assignment of officers to the same beat (mean= .581; SD= .494; range=

0-1), using community survey information (mean= .314; SD= .464; range= 0-1), having a community policing aspect in the agency’s mission statement (mean= .796; SD= .403; range= 0-

1), using collaborative problem-solving projects as evaluation criteria for officers (mean= .386;

SD= .487; range= 0-1), community policing recruit training (mean= .705; SD= .456; range= 0-

1), and community policing in-service training (mean= .682; SD= .466; range= 0-1). These are binary variables where 1= Yes and 0= No. While most measures were originally dichotomous, a few were reconstructed into binary variables. For example, having a community policing aspect in the agency’s mission statement was recoded from 1= No written statement, 2= Written statement, no community policing component, and 3= Written statement, yes community

1 SARA problem-solving refers to “a problem-solving policing strategy that involves: Scanning: Identifying and prioritizing problems; Analysis: Researching what is known about the problem; Response: Developing solutions to bring about lasting reductions in the number and extent of problems; and Assessment: Evaluating the success of the responses (United States Department of Justice, Bureau of Justice Statistics 2013)”

28 policing component to a binary variable where 1= Written statement, yes community policing component, and 0= All others. As with the body armor requirement variable above, this decision was made to distinguish agencies that do have a community policing component in their mission statement from those that do not, either because there is no written mission statement or because it is not a part of it. Both community policing recruit training and community policing in-service training were recoded from 1=All full-time sworn personnel received at least 8 hours of training on community policing issues, 2= Half or more, 3= Less than half, 4=None to 1= Some or all full-time personnel received at least 8 hours of training or 0= No full-time sworn personnel received at least 8 hours of training. This binary measure was created to separate the agencies who participate in community policing training in some capacity from those who do not.

Demographics.

The final set of variables comes from LEMAS, ACS, and UCR and represents demographics of the agencies and the communities they serve. This includes the number of

Black officers, percent Hispanic population, percent Black population, crime rate, ratio of officers to population, and average income. The number of Black officers is a count variable from the LEMAS data, which denotes how many full-time sworn non-Hispanic Black or African

American personnel are in their agency, according to the respondent (mean= 20.836 SD=

178.851; range= 0-5,557). ACS provides percent Hispanic (mean= 9.230; SD= 15.678; range=

0-100) and percent Black (mean= 10.896; SD= 18.076; range= 0-100) population, which represent the percentage of Hispanic and Black citizens in the census place the law enforcement agency serves. Crime rate from UCR represents the total number of all crimes divided by the served population times 100,000 (mean= 3,142; SD= 3,417; range= 0-69,375; outlier tests are discussed in detail in the supplementary analysis section). Also from UCR, ratio of officers to

29 population indicates the number of full-time sworn law enforcement officers divided by the served population, then multiplied by 100,000. There are on average 220 officers per 100,000 in the population (SD= 146.616; range= 0-2,794). Finally, average income is the average median income at the place-level (mean= 46,136; SD= 22,586; range= 9846-250,001).

ANALYTICAL STRATEGY

Item Response Theory

Creating a measure that represents the militarization of a law enforcement agency or a department’s engagement in community policing presents some challenges. For instance, Kraska

(2007) notes that because police have always been somewhat militaristic, an analysis of militarization should take into account a spectrum on which agencies can fall. An additive scale fails to take this spectrum into account and treats the more common items the same as the perhaps more extreme (e.g. handguns vs. agencies with bomb/explosive task forces). As the descriptive statistics in this manuscript demonstrate, there are clear differences in what is more common to implement in terms of community policing as well. Additionally, measuring militarization and community policing based on observed variables does not account for either of these concepts as a whole. Militarization is the implementation of the ideology of militarism in a variety of ways (Kraska 2007) and similarly, proponents of community policing often emphasize the importance of a philosophical and cultural shift in departments implementing its tenets

(Community Oriented Policing Services 2014). Therefore, including single measures or an additive scale of observed variables, such as weapons or tactics, may not adequately measure the extent to which a department is militarized or its orientation towards community policing as a whole.

To account for these shortcomings, this paper treats militarization and community policing as latent constructs and combines the observed variables into a series of two parameter logistic models (2PLs), a type of IRT model that estimates a latent trait based on observed characteristics (de Ayala 2009). This transforms the analysis from one that examines the association between specific programs or tactics and use of force to one that captures how an

31 agency’s proclivity toward militarization and community policing relate to use of force, respectively (Fisher, Mowen, and Boman 2018). The advantage of 2PL models is that they account for both how much the item varies between police departments (the higher the value, the more the item varies between departments) and how difficult each item is to implement compared to others (a large and negative coefficient signifies that the item is not difficult to implement), based on the extent to which each agency reports the use or implementation of each individual measure (de Ayala 2009; Fisher et al. 2018).

To justify the use of the 2PL models, I compared two models in a sensitivity analysis: one with just the additive scales and one including only the latent trait models. The results were remarkably similar across models, with a few exceptions. However, the analytical sample size dropped in half for the additive scale compared to the latent trait models. The additive scale uses pairwise deletion when constructing its measure, while the IRT models examine a proclivity rather than observed values, so not all items need to be present for a value to exist. To regain statistical power, I imputed data using multiple imputation with chained equations and ran additional models. With the imputed data, the results were consistent across models. Therefore, it is extremely likely that the differences in results were due to a loss of sample size. This points to another advantage of using IRT models.

In sum, there are several benefits to using IRT models over a scale representing observed variables: a larger sample size, taking into account different variable weights, and an all- encompassing measure of the extent of a department’s militarization and community policing.

Therefore, I estimate non-imputed models with the latent traits of militarization and community policing. Descriptive statistics of each of these measures are presented in Table 2 and Table 3.

They are designed to center around a mean of 0 and a standard deviation of 1.

Table 2. Two parameter logistic item response theory results for militarization Variable Coefficient Standard error Body armor requirement Discrimination 0.324 0.070*** Difficulty -4.356 0.921*** Airplane Discrimination 3.877 1.049*** Difficulty 2.680 0.181*** Helicopter Discrimination 2.449 0.316*** Difficulty 2.289 0.131*** Boat Discrimination 0.788 0.089*** Difficulty 2.528 0.248*** ATV Discrimination 0.507 0.065*** Difficulty 1.855 0.237*** Drone Discrimination 1.035 0.468* Difficulty 6.091 2.318** Rifle Discrimination 1.306 0.144*** Difficulty -2.596 0.205*** Shotgun Discrimination 0.332 0.107** Difficulty -8.111 2.543*** Soft projectiles Discrimination 1.821 0.136*** Difficulty -0.228 0.040*** Handgun Discrimination 1.140 0.535* Difficulty -5.991 2.304** Baton Discrimination 0.900 0.107*** Difficulty -2.925 0.290*** Other impact weapons Discrimination 0.787 0.069*** Difficulty 0.160 0.067* Spray Discrimination 1.099 0.151*** Difficulty -3.371 0.360*** Other chemicals Discrimination 1.394 0.101***

Table 2 continued Difficulty 0.583 0.052*** Taser Discrimination 0.737 0.084*** Difficulty -2.442 0.247*** Neck restraint Discrimination 0.820 0.082*** Difficulty 1.921 0.170*** Takedown Discrimination 1.877 0.185*** Difficulty -2.015 0.117*** Open hand Discrimination 3.507 0.530*** Difficulty -1.811 0.087*** Close hand Discrimination 1.696 0.158*** Difficulty -1.737 0.102*** Leg hobble Discrimination 0.828 0.072*** Difficulty -0.695 0.080*** Hiring military Discrimination 0.028 0.074 Difficulty 47.259 125.964 SWAT team Discrimination 0.960 0.100*** Difficulty 2.349 0.200*** Bomb/explosive task force Discrimination 1.263 0.111*** Difficulty 1.672 0.111*** Terrorism/homeland security task force Discrimination 1.126 0.087*** Difficulty 0.698 0.065*** Multi-jurisdictional SWAT team Discrimination 0.184 0.064** Difficulty 0.112 0.286

Table 3. Two parameter logistic item response theory results for community policing Variable Coefficient Standard error SARA problem-solving Discrimination 1.871 0.144*** Difficulty 0.138 0.039*** Community partnerships Discrimination 0.969 0.079*** Difficulty 0.295 0.059*** Same beat Discrimination 0.819 0.073*** Difficulty -0.451 0.072*** Community survey Discrimination 0.934 0.083*** Difficulty 0.987 0.089*** Community policing mission statement Discrimination 1.425 0.116*** Difficulty -1.287 0.080*** Collaborative problem-solving Discrimination 1.834 0.143*** Difficulty 0.403 0.042*** Community policing recruit training Discrimination 1.858 0.162*** Difficulty -0.682 0.054*** Community policing in-service training Discrimination 1.311 0.106*** Difficulty -0.741 0.063***

Regression Analyses

In order to study militarization, community policing, and use of force, this study involves multivariate regression techniques and a series of models investigating potential interaction effects. Negative binomial regressions are used rather than ordinary least squares (OLS) regression models because the dependent variable is a count and significantly skewed to the right, thus violating the normality assumption of OLS. Additionally, with a significant (p < .001) overdispersion parameter, a negative binomial model is preferred over a Poisson. The possibility of using a multilevel model was explored, but an intraclass correlation of .070 showed low variation at level 2 (state). A significant heteroskedasticity test (p < .001) warrants the use of robust standard errors in each model, which would account for the low level of level two variation mentioned above. Finally, after examining variance inflation factors that were all under

2, concerns about potential multicollinearity were mitigated.

In order to address my third and fourth research questions, interaction terms are created and then added into each subsequent model. My third question asks if the strength of the association between militarization and use of force varies by percent Black or percent Hispanic in a population. To test this, an interaction is created between percent Black and militarization and percent Hispanic and militarization. Finally, my fourth hypothesis posits that community policing will weaken the association between militarization and use of force. In order to examine this, an interaction is created between community policing and militarization. All models are fit using Stata 15.1.

RESULTS

Table 4 displays the results from a series of negative binomial regression models which regress the number of use of force incidents on predictors. The main effects model, Model 1, is significant. Militarization is positive and significantly related to the number of use of force incidents recorded by an agency. This indicates that as a department becomes increasingly militarized, more use of force incidents are recorded. These results support Hypothesis 1, which predicts a positive and significant correlation between militarization and use of force.

Surprisingly, Hypothesis 2 is rejected. Community policing also shares a positive and significant relationship with use of force, indicating that as agencies that are more oriented towards community policing report higher instances of use of force.

Table 4. A negative binomial regression regressing use of force on predictors; n = 919. Model 1 Model 2 Model 3 Model 4 b RSE b RSE b RSE b RSE Militarization .841 .108*** .874 .100*** .847 .106*** .860 .111*** Community policing .312 .103** .307 .103** .312 .104** .325 .100*** Percent Hispanic .010 .007 .011 .007 .010 .006 .010 .007 Percent Black .011 .004** .010 .004** .010 .004** .010 .004** Crime rate .000 .000*** .000 .000*** .000 .000*** .000 .000*** Number of Black officers .001 .000*** .001 .000*** .001 .000*** .001 .000*** Officers to population -.003 .000*** -.003 .000*** -.003 .000*** -.003 .000*** Median income -.000 .000 -.000 .000 -.000 .000 -.000 .000 Militarization x Hispanic -- -- -.006 .005 ------Militarization x Black ------.002 .004 -- -- Militarization x ------.138 .075 Community policing

Intercept 3.216 .326*** 3.227 .321*** 3.216 .326*** 3.260 .328***

Model Statistics Wald χ² 804.32*** 820.30*** 878.77*** 850.34*** * p ≤ .05 ** p ≤ .001 *** p ≤ .001

Several of the community demographic variables also share significant relationships with the number of use of force incidents in a police department. Percent Black and the crime rate are positive and significantly correlated with the number of use of force incidents a police department records. This signifies that agencies that serve a larger Black population and are situated in an area with higher crime record more use of force incidents, respectively. However, neither percent Hispanic or median income are statistically significant.

Agency characteristics also play an important role in the model. The number of Black officers in an agency is positive and significantly associated with the number of use of force incidents in a police department. This shows that agencies with a larger number of Black officers record more use of force incidents. Additionally, the ratio of officers-to-population shares a negative and significant relationship with the number of use of force incidents an agency has.

This demonstrates that places with a larger ratio of officers to population report less use of force incidents.

Models 2, 3, and 4 display results of the interactions that investigate Hypotheses 3 and 4.

The results provide no support for either hypothesis. Neither of the interaction terms are statistically significant. This indicates that neither percent Black nor percent Hispanic moderate the relationship between militarization and use of force. Additionally, community policing does not moderate the relationship between militarization and use of force.

Supplemental Analyses

The measure representing crime rate in this study had several outliers on the upper end of its range. To determine if these outliers significantly impacted the results, I performed several outlier tests. These included taking the natural log of the variable, removing observations that were at or beyond 3 standard deviations from the mean, and truncating any observation that was

38 at or beyond 3 standard deviations from the mean. Regardless of variable construction, the results maintained. Additionally, the possibility of a three-way interaction between militarization, community policing, and each of the racial demographic variables (Hispanic and

Black) but the interaction was not significant.

DISCUSSION

Police use of force has been centered in many discussions about policing and this critical issue shows no signs of leaving the discussion anytime soon for researchers, lawmakers, police, or citizens alike. This study seeks to add to this conversation by examining the relationship between a widely adopted and growing trend in policing, militarization, and use of force. Guided by minority threat theory and using an innovative, integrated dataset merging agency and community characteristics, this paper adds both theoretically and methodologically to the use of force literature. Overall, results provide some support for minority threat theory, but several unexpected outcomes emerged. The main findings of the study, limitations, policy implications, and some concluding remarks are included below.

Showing support for Hypothesis 1, Militarization shares a positive and significant relationship with use of force. This finding is in line with other macro-level research that demonstrates a positive correlation between measures of militarization and use of force

(Delehanty et al. 2017). Further, this is a troubling finding for communities and agencies for a variety of reasons. The first is safety of communities that police come into contact with. Past research shows that agencies actually have had to cut back on proactive, military style raids due to controversy over innocent people being killed during the raids (Kraska and Kappeler 1997).

These findings together clearly point to caution from agencies in terms of adopting military equipment and tactics; doing so may very well lead to more force, regardless of crime rate, used by police against citizens. Another important implication is for police departments themselves.

Recent research shows that both militarization (Mummolo 2018) and use of force (Mourtgos &

Adams 2019) may negatively harm the public’s perception of police. In an era where agencies are trying to build relationships between police and communities with community policing

40 strategies (Romero 2018), militarization could be taking away any progress police departments may be making.

Community policing is usually undertaken to strengthen relationships between communities and police. However, like militarization, community policing has been implemented by the majority of police departments without an understanding of how it relates to use of force. While research has yet to come to a definitive conclusion about how community policing and use of force relate to each other (Prenzler, Porter, and Alpert 2013; Lersch and

Mieczowski 2005; Pryor et al. 2019), the results from this study have potentially troubling implications. These findings demonstrate that departments that are more oriented towards community policing record more use of force incidents than those who are not as oriented towards community policing. Thus, Hypothesis 2 is rejected. This finding could be for several reasons. The first has to do with the data being cross-sectional. Perhaps departments who have had issues with officers’ use of force have implemented community policing, but results show no improvement as there just has not been enough time. Additionally, and more worrisome, is the possibility that departments are implementing community policing in a way that does not serve their particular community. Literature shows that community policing is difficult to implement

(Grinc 1994). There is a large range of strategies and communities with unique needs. And not only does there have to be agency buy in, but community dedication as well (Grinc 1994;

Skogan 2006). So perhaps officers are being inadequately trained or administration is having trouble implementing an effective program. The result may be that officers simply have more facetime with the community, where they would just have more opportunities to use force.

Further, another study using the same dataset finds that a department conducting volunteer history screens sustain less use of force complaints than those that do not (Pryor et al. 2019).

Therefore, maybe it is not community policing itself that is beneficial to communities, but hiring officers that show a dedication to involvement with their communities that matters.

Additionally, Hypotheses 3 and 4 were not supported by the results. The relationship between militarization and use of force was not moderated by community racial and ethnic makeup. This finding is somewhat surprising, but past research shows that level of segregation is salient in studies of use of force (Kent and Carmichael 2014; Smith and Holmes 2014), which is impossible to operationalize with this dataset. It is possible that segregation may be playing an important role here that is not being picked up by these models. Alternatively, this nonsignificant finding may instead indicate an important notion: Use of force is applied evenly across all communities. Additionally, community policing does not moderate the relationship between militarization and use of force. This is somewhat expected given the results in the main effects model. Future research should continue to study the nuances in the relationship between racial and ethnic community demographics and use of force, as well as the interplay between community policing and militarization.

In line with minority threat theory that posits that as the number of racial minorities increases the more coercive control techniques are exerted by law enforcement officers, percent

Black was positive and significantly related to use of force. This finding confirms the ongoing and consistent importance of race in any discussion of police use of force. Also in line with previous research (Alpert & MacDonald 2001; Terrill & Reisig 2003), the crime rate was positive and significantly related to use of force. However, percent Hispanic was not related to the number of use of force incidents a police agency reports. Previous studies that found a significant relationship between the size of the Hispanic population and a variety of crime control tactics often did so in the context of places where Hispanics represent a larger “threat”,

42 such as in the Southwestern United States (Holmes et al. 2008). Therefore, this nonsignificant finding could be the result of this national-level dataset masking unique cultural contexts that exist in smaller geographical areas. More research studying smaller geographical areas should be done to better understand the relationship between the Hispanic population and use of force.

There were also several significant relationships between agency characteristics and use of force. For example, the number of Black officers in an agency shared a positive relationship with use of force. This is an interesting finding given the inconclusive nature of the role of officer race in the use of force literature. It could be that this variable is serving as a proxy for larger agencies. In the data, the correlation between the number of Black officers in an agency and total officers in an agency is .917. Intuitively, it makes sense that larger agencies would have more use of force incidents simply due to size. Alternatively, this finding is in line with studies that have shown that Black officers use more force compared to White officers (Ridgeway 2015)

Future research should continue to disentangle the relationship between officer race and use of force. Additionally, these results demonstrate that places with a larger ratio of officers to population record less use of force incidents. This finding is in line with past research that shows larger agencies have a smaller ratio of force to officers than smaller agencies (Garner et al.

2018).

Though innovative methodologically, theoretically, and potentially important for policy implications, this study is not without limitations. First, although this integrated dataset allows researchers to include both agency and community characteristics in their analyses, it is limited in the level of detail of its measures. There is a line of research in the minority threat literature that asserts that the level of segregation (Kent and Carmichael 2014; Smith and Holmes 2014) shares a significant relationship with a variety of crime control tactics, and that multilevel

43 models may help illuminate important relationships (Kane and Gustafson 2013; Lee 2016).

While this study is unable to analyze at that level of nuance, these previous findings point to the importance of taking these factors into account.

A second limitation is that the data are cross-sectional, which limits the ability to establish time order. Additionally, while the LEMAS data are the most recently released from the

Bureau of Justice Statistics, the information is from 2012. Importantly, this is before the Mike

Brown incident and subsequent protests in Ferguson, Missouri, which has been shown to have changed policing (Shjarback et al. 2017). Given the significance of Ferguson and age of the data, research with current longitudinal data is necessary.

Another set of limitations pertains to the measures in the dataset. First, this is the first time LEMAS has collected the number of recorded the number of use of force incidents from law enforcement agencies. Though the LEMAS data represent some of the only nationally representative, department-level data on use of force (Shjarback & White 2015), researchers should use caution and consider supplementing their studies with additional data sources to analyze the correlates of use of force. Additionally, the measures that make up militarization and community policing are not exhaustive. For instance, the community policing construct is only made up of eight measures. Further, this study primarily focuses on the material aspect of militarization, but there are three other dimensions of militarization as identified by Kraska

(2007). Future research should find ways to include the other aspects of militarization in their analyses. Finally, upon investigating multicollinearity, several important variables were excluded from the analysis. Absence of these variables could potentially lead to biased results.

Despite its limitations, this study contributes to the literature and broader society threefold. Theoretically, this paper adds to the literature by analyzing the relationship between

44 militarization and use of force as a function of minority threat. Methodologically, the use of a national-level, integrated dataset that supplies not only agency information, but community contextual information as well, gives researchers access to study a wealth of research questions that could not be asked before. Finally, and perhaps most importantly, the results of this paper stress the importance of having a thorough understanding of how policing tactics inform and influence use of force before implementing them. Something seemingly innocuous, like community policing, or guised as important for officer safety, such as militarization, may actually be harmful for not only communities, but law enforcement officers themselves. The findings of this study illuminate the need to continue to study use of force as police departments adopt new strategies, for the benefit of not only officers, but the communities they serve.

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