POLICE PROFESSIONALISM AND RACIAL DISPARITIES IN … · This dissertation examines the prevalence and causes of such discrimination, seeking to better understand and increase police

POLICE PROFESSIONALISM AND RACIAL DISPARITIES IN ARREST RATES: AN EXAMINATION OF POLICE DISCRIMINATION, DISCRETION, AND

DIVERSITY

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF SOCIOLOGY

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Katharina Roesler

June 2017

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/rh272ww3914

© 2017 by Katharina Hannah Roesler. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/rh272ww3914

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Cristobal Young, Primary Adviser


Michael Rosenfeld


C. Matthew Snipp

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost for Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

iv

Abstract

It is highly debated in both academia and civil discourse whether U.S. police

agencies are professional, performing their duties impersonally and expertly. In

particular, there is much concern that U.S. police agencies discriminate against black

men. This dissertation examines the prevalence and causes of such discrimination,

seeking to better understand and increase police professionalism.

Using data from the 2013 National Incident-Based Reporting System (NIBRS)

and the 2013 Law Enforcement Management and Administrative Statistics (LEMAS),

this dissertation employs multilevel modeling to estimate black and white criminal

offenders’ relative arrest likelihoods across different police agencies. The use of

national data on criminal offenses allows me to condition on criminal behavior and

examine police treatment of black and white men net of criminal behavior. This is a

novel contribution that greatly improves our understanding of police discrimination in

the U.S.

My analyses of 956,434 criminal offenders reported for nine common offenses

do not find evidence that police agencies discriminate against black offenders. In fact,

black men are often less likely than white men to face arrest after committing the same

criminal offense. This is the case even in police agencies that use more discretion

when investigating reported offenses and with more officers of color.

These findings suggest that black men’s over-representation in the criminal

justice system cannot be overcome simply by reducing police discretion and

increasing police diversity. Moreover, arrest rate disparities are likely to persist even

in the absence of police discrimination against black men, since 35 percent of reported

v

male offenders are black. Overcoming racial disparities in arrest rates will require

multipronged interventions that account for the complexity of this issue, including

racial disparities in criminal behavior reported by victims, witnesses, and police

officers. It is possible that criminal reports are exceedingly biased, which would help

explain these findings, as well as the large racial disparities in arrest rates observed

today.

vi

Acknowledgments

This dissertation would not have been written without the help and

encouragement of many people, particularly Cristobal Young, Michael Rosenfeld,

Matt Snipp, Xueguang Zhou, David Sklansky, Rob Parker, Shelley Correll, Bogdan

State, Jessica Santana, Jared Furuta, Anna Boch, Christof Brandtner, Naejin Kwak,

and Yan Michalevsky. Thank you especially to Cristobal and Michael for advising me

on this project and more generally. In addition, many thanks to the developers who

provided the Python and R packages used, as well as the incredibly generous users of

StackOverflow.

And thanks most of all to my family for reminding me that I am fortunate to

have so many opportunities, and for enabling me to use them.

vii

TABLE OF CONTENTS Abstract ........................................................................................................ iv Acknowledgments ....................................................................................... vi Table of Contents ........................................................................................ vii List of Tables ............................................................................................... viii List of Illustrations ...................................................................................... x Chapter 1: Introduction ............................................................................. 1

Introduction ............................................................................................. 1 Outline ..................................................................................................... 2

Chapter 2: Police Discrimination .............................................................. 4

Data and Methods ................................................................................... 14 Results ..................................................................................................... 27 Sensitivity Analysis ................................................................................ 32 Discussion ............................................................................................... 33

Chapter 3: Police Discretion ...................................................................... 36

Data and Methods ................................................................................... 43 Results ..................................................................................................... 48 Discussion ............................................................................................... 54

Chapter 4: Police Diversity ........................................................................ 58

Data and Methods ................................................................................... 62 Results ..................................................................................................... 66 Discussion ............................................................................................... 69

Chapter 5: Conclusion ................................................................................ 73

Review of Major Findings and Contributions ........................................ 73 Directions for Future Research ............................................................... 74 Context of Findings ................................................................................. 75

Appendix ...................................................................................................... 77

Problem Statement .................................................................................. 77 Models and Data ..................................................................................... 78 Results ..................................................................................................... 79

List of References ........................................................................................ 83 Tables ........................................................................................................... 93

viii

List of Tables Table 2.1. Offender Counts and Arrest Rates of the Twenty Most

Common Offenses ............................................................................. 93 Table 2.2: Uniform Crime Reports (UCR) Data on Criminal Offenses

and Arrests ......................................................................................... 94 Table 2.3: Law Enforcement Agencies ......................................................... 94 Table 2.4: Counties ....................................................................................... 95 Table 2.5. Logistic Regression Models Predicting each Male Offender’s

Arrest – Offenses with Victims .......................................................... 96 Table 2.6. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Offenses with Victims ....................................... 97 Table 2.7. Logistic Regression Models Predicting each Male Offender’s

Arrest – Offenses without Victims .................................................... 98 Table 2.8. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Offenses without Victims .................................. 99 Table 2.9. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Offenses with Victims who Knew the Offender ............................................................................................ 100

Table 3.1. Offenders ...................................................................................... 101 Table 3.2. Percent of Arrested Offenders who were Arrested on Day of

Offense .............................................................................................. 101 Table 3.3. Law Enforcement Agencies ......................................................... 102 Table 3.4. Intraclass Correlation Coefficients ............................................... 102 Table 3.5. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Agency Arrest Rates .......................................... 103 Table 3.6. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Agency Same-Day Arrest Rates for Drugs/Narcotics Offenses .................................................................. 104

Table 4.1. Offense Counts and Arrest Rates by Agency Percentage of

Officers White .................................................................................... 105

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Table 4.2. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Offenses with Victims ....................................... 106 Table 4.3. Multilevel Logistic Regression Models Predicting each Male

Offender’s Arrest – Offenses without Victims .................................. 107

x

List of Illustrations Figure 2.1: Percent of Male Offenders Arrested by Offense ........................ 12 Figure 2.2: Percent of Black versus White Offenders Arrested in each

Police Agency .................................................................................... 13 Figure 3.1: Agency Arrest Rates by Offense ................................................. 42 Figure 3.2: Drugs/Narcotics Offenders Arrested by Agency Arrest Rate ..... 49 Figure 3.3: Drugs/Narcotics Offenders Arrested by Agency Same-Day

Arrests ................................................................................................ 52 Figure 4.1: Offender Arrest Rates by Percent Officers White ....................... 64 Figure A.1. !" Estimates ................................................................................ 80 Figure A.2. !# Estimates ................................................................................ 81

1

Chapter 1: Introduction Introduction

Over the past few years, there has been a resurgence of concern over racial

inequality in the U.S. criminal justice system. The “Black Lives Matter” movement

has been at the center of this resurgence, responding to several cases in which black

Americans have died at the hands of police. Academic research has also contributed

to this movement, documenting inequalities and recommending policy changes

(Alexander 2010; Goffman 2009, 2014). Such inequalities occur at many points of

contact with the criminal justice system, including emergency response, investigation,

arrest, indictment, pretrial detention, bail determination, legal defense, trial, and

sentencing.

This dissertation focuses on two of the earliest points of contact with the

criminal justice system: criminal investigation and arrest. Racial disparities in arrest

are enormous and greatly contribute to inequalities at later stages of contact (Close and

Mason 2007; Gelman, Fagan, and Kiss 2007; Goel, Rao, and Shroff 2016; Kochel,

Wilson, and Mastrofski 2011; Petrocelli, Piquero, and Smith 2003). In addition, arrest

rate disparities suggest that police departments may be discriminating against black

men.

Much of the concern about the U.S. criminal justice system centers on

unprofessional policing – policing that is personal, impulsive, and often

discriminatory. This dissertation utilizes criminal offenders’ likelihood of arrest as a

measure of police treatment, contributing to our understanding of police

professionalism and discrimination.

2

Gender and race are both highly relevant to policing practices, as many

Americans believe that women are less violent than men and that black people are

more violent than white people. In addition, the effects of gender and race interact, as

black men are often considered particularly violent. In order to isolate racial

discrimination from gender-based discrimination, I examine only the policing of men.

This is in keeping with many other studies of policing and racial bias (Dixon 2008;

Eitle, Stolzenberg, and D’Alessio 2005; Freeman et al. 2011; Eberhardt et al. 2004;

Oliver 1999; Saperstein and Penner 2010).

Outline

In the following chapter, I examine the prevalence of police professionalism,

looking for evidence of discrimination against black men throughout the United

States. Using national data on criminal offenses, I condition on criminal behavior to

measure police treatment, asking whether black and white offenders have similar

likelihoods of arrest per reported criminal offense, or whether police agencies are

more likely to arrest black offenders. This analysis contribute to our understanding of

racial disparities in arrest rates, which may result from disparities in reported criminal

behavior and/or police discrimination.

Next I examine the role of police discretion, which is hypothesized to enable

unprofessional policing. I focus on drug offense investigations, which often afford

officers enormous amounts of discretion, in order to understand the impact of police

discretion on professionalism.

In the final empirical chapter, I examine the impact of police department racial

diversity on professionalism and discrimination. I hypothesize that police agencies

3

with relatively more white officers are less professional and more likely to arrest

black, rather than white, criminal offenders.

Together, these chapters contribute to our understanding of police

professionalism and the impacts of police discretion and diversity on discrimination.

As one of the first nationwide studies on police discrimination, this dissertation

extends previous research on policing and lays the groundwork for further

examinations of police professionalism in the U.S.

4

Chapter 2: Police Discrimination Introduction

In this chapter, I look for evidence of police discrimination against black men.

Given that 35 percent of reported offenders are black, one would expect 35 percent of

arrested men to be black when police agencies are professional. However, if police

officers are unprofessional, even more than 35 percent of arrested men will be black,

and black offenders would have a greater likelihood of being arrested than white

offenders. I consider these two possibilities to understand the extent of police

professionalism. In short, I ask whether black men are arrested more often than the

frequency of their participation in criminal activity would predict.

Witness, victim, and police reports of crimes include a description of the

offenders’ race, providing reasonably good data on the race of criminal offenders,

regardless of whether there is an arrest. Rather than looking simply at the

“unconditional” or overall rate of arrest for black and white individuals, this

dissertation looks at the rate of arrest in crimes reportedly committed by black men

versus those committed by white men. In other words, I look at the probability of

arrest conditional on criminal activity, or the rate of arrest per crime committed.

Police Professionalism The central question motivating this dissertation is whether police departments

are behaving professionally, enforcing the law in an impersonal and expert manner.

One can think of the ideal police agency as a modern bureaucracy in Max Weber’s

conception (Weber 1921/1968). According to Weber, in a modern bureaucracy,

5

employees are interchangeable experts well-suited to their roles. The organization has

written rules and clear hierarchies, and operates in an efficient and rational manner.

I envision police professionalism similarly. Professional police agencies treat

all residents equally, following their own policies and those of the state to expertly

perform their duties. Police officers are hired due to skill and trained to behave

similarly, to be interchangeable as agents of the police force.

There is widespread concern that U.S. police agencies currently operate

unprofessionally, treating black residents more harshly than white residents. In this

chapter, I look for evidence of such discrimination. I use criminal offenders’

likelihood of arrest as a measure of police treatment, asking whether black and white

offenders have equal likelihoods of arrest. If this is the case, then police

discrimination may not be a major contributor to arrest rate disparities. However, if

black offenders are disproportionately likely to be arrested, then it is possible that

police agencies are discriminating against them.

In either case, it is possible that many forms of bias operate at many stages of

the criminal justice system. Whether or not I find evidence of unprofessional policing,

the enormous racial disparities in incarceration rates likely result from myriad forms

of discrimination. For instance, there is evidence of unequal treatment in prosecution,

bail decisions, convictions, sentencing, and parole hearings (Alexander 2010). This

chapter examines one particular form of discrimination at the national level,

complementing previous research to document the complex phenomenon of racial

discrimination.

6

“Preference-Based” Discrimination Several theories and branches of research suggest that unprofessional policing

is common. Subconscious bias against black men is widespread among U.S. adults,

and police officers may share this bias (Dixon 2008; Freeman et al. 2011; Eberhardt et

al. 2004; Oliver 1999; Saperstein and Penner 2010). Although officer training may

help alleviate these biases (Correll et al. 2007), it may be unable to do so entirely.

Thus officers’ biases may cause them to be more suspicious of black individuals and

to focus their investigations on black, rather than white, suspects.

Officers may hold stereotypes of black men as criminal before becoming

officers, or may adopt such stereotypes after encountering a disproportionate number

of black offenders. For instance, socioeconomic differences or a departmental focus

on black neighborhoods may cause officers to observe primarily black offenders. In

addition, senior officers may “teach” new officers to discriminate against black men,

either directly or indirectly (such as by describing “suspicious” clothes, cars, and

behaviors).

Such conscious and subconscious biases may cause officers to engage in

“preference-based discrimination,” in which they disproportionately investigate black,

rather than white, residents. This discrimination is “preference-based” in the sense

that it is personally motivated and does not increase crime detection or community

safety.

Several studies have found that police officers engage in preference-based

discrimination against black men (Antonovics and Knight 2009; Engel and Calnon

2004; Hernández-Murillo and Knowles 2004). In particular, there is a broad literature

7

on “driving while black” documenting the disproportionately high rates at which black

men are stopped while driving (Close and Mason 2007; Harris 1999; Lundman and

Kaufman 2003; Rojek, Rosenfeld, and Decker 2012; but also see Alpert, MacDonald,

and Dunham 2005; Grogger and Ridgeway 2006; Worden, McLean, and Wheeler). In

addition, many Americans believe that police officers discriminate against black men

(Weitzer and Tuch 2002), and newspaper accounts and cellphone video footage of

police misconduct are now commonplace. However, there are almost 4 million crimes

reported to police each year in America, and 1.6 million arrests. Alarming as some

individual cases have been, such cases do not show whether or not discriminatory

policing is widespread.

Departmental Discrimination

In addition to personally discriminating against black men, police officers may

inadvertently discriminate against them by following department orders. That is,

police agencies may disproportionately target black neighborhoods, as well as crimes

more often committed by black people (Cureton 2000: 705). By implementing

departmental priorities, even unbiased officers may disadvantage black residents.

One example of such departmental discrimination was found in Seattle, where

police departments disproportionately focus on drugs more often sold by black

individuals and on neighborhoods with relatively more black residents (Beckett,

Nyrop, and Pfingst 2006). They do so because drugs, particularly crack cocaine, are

assumed to be more prevalent in black neighborhoods (Beckett et al. 2005).

8

Civilian Discrimination

Lastly, police discrimination may emerge as a result of civilian discrimination.

Conflict theory argues that powerful individuals use state apparatuses, including the

police, to maintain their political, economic, and social power (Chambliss 2001).

These individuals criminalize those who challenge their power, explicitly or

symbolically, particularly when this power is threatened.

One means of criminalization is the stereotyping of black men as criminal.

White people may spread such stereotypes, causing witnesses and victims to

incorrectly categorize criminal offenders as black, rather than white (Eberhardt et al.

2004; Freeman et al. 2011; Saperstein and Penner 2010). In addition, these

stereotypes may cause individuals to more frequently report black men to the police

and to overlook white criminal offenders.

Even if only a few white people intentionally criminalize black men, apathy on

the part of others could result in substantial discrimination. For instance, although

most white Americans do not consciously wish to criminalize black people, they are

more supportive of punitive criminal justice policies when they primarily impact black

people (Hetey and Eberhardt 2014). Given mass incarceration, such apathy could

produce enormous racial disparities in arrest and incarceration rates.

In addition to enabling the criminalization of black men, white people may be

more effective at resisting the criminalization of their own communities. Consider the

black neighborhood in Philadelphia described by Alice Goffman (2009, 2014). Its

residents are unable to divert law enforcement attention away from their community,

while nearby white neighborhoods enjoy a positive relationship with the police. In the

9

black neighborhood, police officers routinely break down doors and search walkers-

by. Yet it is difficult to imagine such policing in a wealthy white neighborhood,

where issues are usually addressed with non-criminal measures. For instance, drug

use in a wealthy white neighborhood might result in the establishment of a

rehabilitation center, rather than the arrest of drug users.

In these ways, racial and class inequalities may result in “civilian

discrimination.” Even if police officers are unbiased, they may inadvertently

discriminate against black men, given the racial conflict in their communities and the

nation at large.

Equal Treatment

One line of research argues that police officers focus on black individuals only

when doing so maximizes crime prevention. This theory argues that black men are

arrested more often than white men simply because they are more likely to engage in

criminal behavior (Beaver et al. 2013; Felson, Deane, and Armstrong 2008; Felson

and Kreager 2015; Raudenbush, Johnson, and Sampson 2003; Sampson, Morenoff,

and Raudenbush 2005; but see also Pollock, Oliver, and Menard 2012; Wright and

Younts 2009).

Research supporting this theory finds that targeting racial minorities during

investigation maximizes the likelihood of arrest (Becker 2004; Knowles, Persico, and

Todd 2001; Persico and Todd 2006; but see also Simoiu, Corbett-Davies, and Goel

forthcoming). However, these studies assume that all arrests are legitimate, which

may not be the case.

10

Nonetheless, there is some evidence that police officers treat black and white

people equally given their behavior. For instance, officers stop similar proportions of

black drivers during the day, when they can observe drivers’ race, as at night, when

they cannot, suggesting that their decisions are purely determined by drivers’ behavior

(Grogger and Ridgeway 2006; Worden, McLean, and Wheeler 2012; but see also

Close and Mason 2007; Harris 1999; Lundman and Kaufman 2003; Rojek, Rosenfeld,

and Decker 2012). According to this line of research, arrest rate disparities are due

only to differences in criminal behavior, and black and white men enjoy equal police

treatment.

Hypotheses

Although one line of research argues that officers treat black and white men

equally, other research suggests three potential mechanisms causing police

discrimination against black men. First, police officers may be personally biased

against black men and engage in “preference-based” discrimination. Second, police

departments may have priorities that disadvantage black communities, engaging in

“departmental discrimination.” Lastly, white people may engage in “civilian

discrimination,” using their greater political, social, and economic capital to direct law

enforcement actions towards black men.

Thus we have compelling reasons to believe that police officers and agencies

discriminate against black men, but also evidence suggesting that they do not. I

hypothesize that there is widespread discrimination against black men, creating arrest

rate disparities unaccounted for by criminal behavior. My null hypothesis is that black

11

and white criminal offenders are equally likely to be arrested, holding constant

characteristics of their offense and the investigating police agency.

Initial Evidence

Table 2.1 presents counts of criminal offenders and their arrest rates for the

twenty most frequently reported offenses, along with the percentages of offenders and

arrestees who are black. Across all offenses, 48.5 percent of offenders are arrested,

but arrest rates vary widely by offense and offender race.

[TABLE 2.1]

Figure 2.1 shows black and white offenders’ arrest rates for the nine offenses

analyzed. These offenses were chosen because they are the most common offenses for

which an offender’s race is often observed (i.e., not burglary) with a clear definition of

the crime (i.e., not “other theft”). On average, black offenders are less likely than

white offenders to be arrested. Drug offenses are a notable exception, as black

offenders are slightly more likely than white offenders to be arrested for

drugs/narcotics violations and similarly likely to be arrested for drug equipment

violations.

12

Figure 2.1. Percent of Male Offenders Arrested by Offense

However, Figure 2.1 may be misleading, as a few agencies with many

offenders may skew the relative percentages of black and white offenders arrested.

For this reason, Figure 2.2 shows each police agency’s percentage of offenders

arrested, with the percentage of white offenders arrested on the x-axis, and the

percentage of black offenders arrested on the y-axis. The red lines show where these

percentages are equal, such that agencies above them arrest relatively more black than

white offenders.

13

Figure 2.2. Percent of Black versus White Offenders Arrested in each Police Agency

For most offenses, agencies are fairly evenly distributed above and below the

red line. In addition, for robbery, aggravated assault, and shoplifting, it seems that

more agencies arrest relatively more white than black offenders than vice versa. Drug

offenses are again a notable outlier, with similarly high arrest rates for both black and

white offenders.

Although Table 2.1 and Figures 2.1 and 2.2 help describe black and white

men’s arrest rates across police agencies, the patterns they reveal may be spurious.

14

For instance, black offenders may be less likely than white offenders to be arrested

because their victims are more often black, and offenses with black victims are more

likely to lead to arrest. For this reason, I will estimate offenders’ likelihood of arrest

using multivariate regression models that hold constant important characteristics of the

offense and investigating police agency. My null hypothesis is that black and white

criminal offenders are equally likely to be arrested when these factors are held

constant.

Data and Methods

In order to test this hypothesis, I use data on police agencies and the criminal

offenses reported to them.1 The most comprehensive data on reported criminal

offenses come from the 2013 National Incidence-Based Reporting System (NIBRS),

while the 2013 Law Enforcement Management and Administrative Statistics

(LEMAS) survey provides the most detailed and geographically diverse data on law

enforcement agencies available.

I merge these sources to create a data set of criminal offenders: individuals

reported to police agencies for allegedly committing a crime. Following each report,

police officers investigate the offense, searching for an offender matching the given

description. If the police arrest someone, the offender is recorded as arrested in the

data set.

National Incidence-Based Reporting System (NIBRS)

1 Please see crimedata.io for the data and coded used to clean and analyze them.

15

The National Incidence-Based Reporting System (NIBRS) provides the most

comprehensive national data on criminal offenders publicly available2. It is part of the

FBI’s Uniform Crime Reporting (UCR) Program and was developed in the 1980s to

improve upon UCR’s longer-standing summary data. UCR data are of police

agencies’ arrest counts for eight serious crimes, for which reason they cannot be used

to predict criminal offenders’ likelihood of arrest. NIBRS data, on the other hand,

include information on criminal offenders, their offenses and victims, and whether or

not they were arrested in the same year as the offence was reported. NIBRS data are

at the incident level and include information on 57 crimes (11 of which are available

only for arrested individuals). Therefore, NIBRS data enable the prediction of

individual criminal offenders’ likelihood of arrest in relation to their personal

characteristics and characteristics of their victims.

The FBI collects NIBRS data from state, local, and campus law enforcement

agencies on a monthly basis, producing an annual file maintained and distributed by

the National Archive of Criminal Justice Data (NACJD) and the Inter-University

Consortium for Political and Social Research (ICPSR). I use the NIBRS extract file

for criminal offenders reported in 2013 (NACJD 2015).

Law enforcement agencies report these data on a voluntary basis, potentially

leading to substantial selection bias (Addington 2008). In 2013, 6,070 of about 12,500

local law enforcement agencies provided NIBRS data and only 638 of these agencies

also report LEMAS data, as discussed below. Fortunately, nearly all U.S. agencies 2 The National Academies of Sciences, Engineering, and Medicine are currently assessing the current state of data on crime and will make recommendations for future efforts at national crime data (National Academies 2017). However, for the time being, NIBRS data are the best source of incident-level data on crime.

16

report aggregate offense and arrest statistics via the Uniform Crime Reporting (UCR)

Program, enabling us to compare the agencies sampled in this dissertation to all U.S.

agencies.

[TABLE 2.2]

Table 2.2 compares the agencies examined in this dissertation (“Data Set”

columns) to UCR data for all law enforcement agencies (“U.S.” columns). The

agencies examined in this dissertation are very similar to all U.S. agencies in terms of

the offenses reported to them, with comparable percentages of aggravated assault,

motor vehicle theft, larceny, burglary, murder, rape, and robbery offenses. However,

the agencies examined tend to have more reported offenses and more arrests, likely

because they serve larger populations (as shown in Table 2.4). In addition, they tend

to arrest more black people and fewer white people than the average U.S. agency.

Law Enforcement Management and Administrative Statistics (LEMAS)

In order to examine police agency characteristics, I use data from the Law

Enforcement Management and Administrative Statistics (LEMAS) survey, which the

U.S. Department of Justice’s Bureau of Justice Statistics (BJS) conducts once every

five to ten years. In 2013, BJS contacted a nationally representative sample of law

enforcement agencies and received responses from 88.8 percent of agencies, resulting

in 2,059 completed questionnaires (BJS 2015; Reaves 2015).

Of these agencies, 638 local agencies also replied to the NIBRS survey

conducted by the FBI. These 638 agencies comprise this dissertation’s sample, as I

have data on both their characteristics and the criminal offenses reported to them.

17

Although they are only about 5 percent of local law enforcement agencies, they are

largely representative of all U.S. law enforcement agencies, as show below.

[TABLE 2.3]

Table 2.3 compares the agencies examined here (labeled “Data Set”) to those

representative of the entire U.S. (labeled “U.S.”). On several measures, the law

enforcement agencies analyzed are representative of all local U.S. agencies. For

instance, 91.6 percent of officers in the data set’s agencies are men, compared to 91.5

percent of officers in all local agencies sampled by LEMAS. Similarly, the agencies

analyzed and all U.S. agencies have comparable rates of assigning officers to beats,

requiring additional training, and requiring more than a high school degree of new

hires.

However, the agencies analyzed here also differ from the larger sample of U.S.

agencies on several key measures. Most notably, 89.7 percent of officers in the data

set’s agencies are white, while only 83.0 percent of officers in all U.S. agencies are

white. In addition, the agencies analyzed tend to have fewer full-time officers and

smaller budgets than the average local agency.

18

American Community Survey (ACS)

Ideally, the agencies analyzed will be representative both of U.S. police

agencies and of U.S. communities more generally. I use the 2009-2013 American

Community Survey (ACS) to compare the counties sampled to all U.S. counties. In

particular, I compare counties’ racial demographics, unemployment rates, mean

incomes, and percentages of black and white residents earning below $10,000,

$20,000, and $40,000. In addition, U.S. counties’ voting records in the 2012

presidential election (McCain v. Obama) are taken from CQPress.com (CQ Press

2015).

[TABLE 2.4]

Table 2.4 demonstrates that on most demographic measures, the counties

examined here are representative of the U.S. as a whole, with comparable racial

demographics, unemployment rates, and black and white poverty rates. However, the

counties sampled have much larger populations, as well as moderately higher incomes

and fewer Republican voters. On the whole, my analyses likely generalize to the

entire United States, particularly more densely populated areas.

Merged Data Set

The data analyzed here include detailed descriptions of criminal offenses, the

individuals who allegedly committed them, and the police agencies that investigated

them. This data set comprises 1,299,614 black and white male criminal offenders

reported to 638 police agencies in 2013. 46.5 percent of these offenders are black, and

19

47.7 percent of black offenders were arrested in 2013, compared to 56.1 percent of

white offenders. Offenders have a mean age of 30 years.

Agencies vary along many dimensions that likely affect arrest rates, such as

population and income. In addition, the offenses reported to them may differ greatly,

warranting different responses. For this reason, it is important to consider individual

offenders’ likelihood of arrest, net of their offense and agency characteristics.

These characteristics include those of the victim and the community in which

the offense occurred. Research has shown that offenses in which the victim was black

are less likely to result in arrest (Briggs and Opsal 2012; Howerton 2006; Smith,

Visher, and Davidson 1984; but also see Taylor, Holleran, and Topalli 2009), while

offenses in which the victim and the offender knew one another are more likely to

result in arrest (Roberts 2007). In addition, it is likely that residents’ income and

crime rates impact arrest rates (Allen 1996; Borg and Parker 2001; Doerner and

Doerner 2012; Paré, Felson, and Ouimet 2007; Wakefield and Uggen 2010).

Offender Variables

When a crime is reported to the police, an “offender” is created in the data set.

If an arrest is made in connection with the reported crime, the “offender” is coded as

having been arrested. It is not known in the dataset whether the arrestee is ultimately

convicted. Therefore, I make the simplifying assumption that the arrestee is the same

person as the true offender. This is probably correct for most offenders, as most

criminal charges lead to conviction in both state and federal courts (SCPS 2011;

USDOJ 2013). According to a study that surveyed police administrators, sheriffs,

20

county prosecutors, public defenders, and judges in Ohio, only about 0.5 percent of

convictions are wrongful (Huff, Rattner, and Sagarin 1996). In addition, only about

40 to 80 prisoners are exonerated each year (Gross and Shaffer 2012). Thus it is

reasonable to assume that arrests are usually of the person reported to police.

Arrested – The dependent variable is whether a reported offender is arrested in the

same year as the offense. It is a binary variable coded 1 if an offender is arrested, and

0 if not. It is coded as missing if an offender is arrested for a crime other than the one

reported, as it is unknown whether the offender would eventually have been arrested

for the reported offense. This variable is the outcome.

Offender Black – This measure is coded 1 if the offender is black and 0 if he is white.

It is based on victims’, witnesses’, and officers’ perceptions of offenders’ race and

ethnicity, and multiracial and Hispanic offenders are excluded to simplify analysis and

because data on Hispanic identity is often missing. It may be that offenders are often

“incorrectly” perceived as black, and that officers arrest white men when looking for

black men. In addition, officers may look for one reportedly black man and find a

different black man, as offense reports become self-fulfilling prophecies.

To determine the face validity of this measure, I compare offenders’ and

arrestees’ race. Offender race is recorded both when the offense is reported and when

the offender is arrested (if this occurs). 98.3 percent of arrested offenders perceived to

be black after arrest were also recorded as black prior to arrest, while 98.0 percent of

arrested offenders perceived as white after arrest were also considered white prior to

arrest. This indicates that individuals’ perceptions of offenders’ race prior to their

21

arrest are highly comparable to their perceptions after offenders’ arrest. Although we

can never be certain that arrests are accurate, this measure of offender race is

plausible.

Victim Variables

For violent offenses (simple and aggravated assault, intimidation, and

robbery), offender records include descriptions of up to three victims. Unfortunately,

NIBRS data are at the incident level and do not specify precisely which offenses each

offender committed against each victim. That is, for incidents with multiple offenders

or multiple victims, there is no guarantee that a given offender committed a particular

offense against a particular victim. For this reason, analyses of violent offenses

examine only offenses in which a single offender had a single victim, following

previous research (Eitle, Stolzenberg, and D’Alessio 2005).

Victim Black – Whether the victim was black (1) or white (0). Following Eitle,

Stolzenberg, and D’Alessio (2005), only offenders with black or white victims are

included in analyses of violent crimes in order to enable the comparison of victim and

offender race.

Victim Known – Whether the victim and offender knew one another.

Agency Variables

Two factors that likely affect arrest rates are communities’ crime rates and

socioeconomic characteristics, as discussed above. For this reason, I include measures

22

of the total number of offenders reported to police agencies and counties’ mean

incomes.

Although I would have liked to include many more measures of agency and

county characteristics, including police officer demographics and population, these

measures are strongly correlated with one another and cannot be included

simultaneously. For this reason, only the two measures described are included, and

their coefficients should be interpreted with caution.

Offender Count – Total number of offenders reported to the agency in 2013.

Offender Count / Full-Time Officer – Total number of offenders, divided by total

number of full-time officers. This measure captures officers’ workload and is

included in supplemental models not shown in this chapter. Results with this measure

are identical to results with the offender count measure instead.

County Mean Income – Mean household income of the county in which the agency is

located, in thousands of dollars. The standardized measure appears normally

distributed, and models including the standardized log measure give equivalent results.

All continuous measures are grand-mean centered and standardized by

dividing their centered measure by twice its standard deviation. This standardization

enables the comparison of coefficients for continuous and binary variables and

mitigates problems of collinearity (Gelman and Hill 2007).

23

Data Limitations

First, there may be substantial selection bias in which agencies submit their

data to the FBI and in which people are reported as criminal offenders. Victims,

witnesses, and officers may “incorrectly” categorize offenders as black (Eberhardt et

al. 2004; Freeman et al. 2011; Saperstein and Penner 2010), which is particularly

problematic if crimes more likely to lead to arrest are also considered more

stereotypically “black.” Similarly, it is possible that black people are more likely to be

falsely reported to the police. If this is the case, then black “offenders” will be less

likely to be arrested simply because they are less likely to be criminal offenders. In

addition, offender gender and race are often unobserved3, further increasing the

probability of selection bias in the sample examined.

Another issue is that offenders are often arrested immediately upon suspicion

of a crime, rather than after a witness or victim has reported them. Unfortunately,

offenders who are “reported” after being arrested are indistinguishable from offenders

reported prior to arrest.4 Thus predicting arrest is a bit disingenuous, given that some

offenders are only “reported” after they are arrested. Fortunately, such “retroactive

reporting” is unlikely to occur for certain offenses, such as robbery, intimidation, and

assault, as these crimes usually occur prior to investigation.

Lastly, it is possible that arrested individuals have not committed the crimes

with which they are charged. This is a problem inherent to any study of policing,

which would be best mitigated with additional information on conviction rates. 3 79.5 percent of offenders are of an unknown gender, and 3.6 of male offenders are of an unknown race. 4 This has been confirmed in correspondence with ICPSR and the FBI.

24

Unfortunately, NIBRS data are anonymous, for which reason conviction data cannot

be included. However, as previously discussed, most defendants charged with a

criminal offense are convicted, for which reason one would expect that most arrested

individuals committed the reported offense.

Models

I include the measures described above in multivariate analyses of offenders’

likelihood of arrest. In order to determine whether black and white male criminal

offenders are equally likely to be arrested, I predict whether an offender is arrested,

given his race, victim characteristics (if applicable), and crime rate and mean income

of the community in which he committed the offense.

The most common approach to predicting a binary response is to estimate a

logistic regression model. However, offenders are located within many of the same

agencies and counties, for which reason the measures examined are not independent

across individuals. One common method of accounting for such clustering is to adjust

the variance-covariance matrix and calculate robust Huber-White standard errors

(Harrell, Jr. 2017; Jacobs et al. 2002; Kerrissey and Schofer 2013; White 1980).

Unfortunately, this method increases the robustness of the standard errors but does not

guarantee that the coefficients themselves are consistent (Freeman 2006; Greene 2002:

674; King and Roberts 2017)5. Nonetheless, this method is useful as a first attempt to

model the likelihood of arrest, before proceeding to more complex models.

5 Please see the Appendix for a more thorough discussion of this issue.

25

Thus I first estimate the following logistic regression model:

log((*+)

1 − ((*+)= !0 +!"2"

where log 3(45)"63(45)

is an offender’s log odds of being arrested, ((*+) is an offender’s

probability of being arrested, !0 is the intercept, and !" is the linear increase in the log

odds of arrest resulting from two standard deviations’ increase in 2". One can

exponentiate each coefficient !+ to calculate the odds ratio 7+/(1 − 7+), representing

the multiplicative increase in the likelihood of arrest resulting from two standard

deviations’ increase in 2+.

More concretely, I estimate the following model:

9 = !0 + !":;<=> + !#?@=A@B:;<=> + !C?@=A@BDEFGE +

!HIJJKELKMNOK7FMAKL + !PQK<ERE=FBK

where 9 is the log odds that an offender is arrested, !0 is the intercept, and !" is the

effect of an offender being black, rather than white. Models for violent offenses

include effect terms for victim characteristics, while offenses without victims do not.

!H and !P are agency-level measures, by definition clustered by agency.

As mentioned above, robust standard errors do not fully account for the

hierarchical nature of the data (Greene 2002: 674). For instance, arrest rates may vary

across agencies for a variety of unknown reasons, leading to spurious relationships

between independent variables clustered by agency and the likelihood of arrest.

For this reason, I also estimate multilevel models, also known as mixed-

effects, varying effects, or hierarchical linear models, with criminal offenders as the

26

first level and police agencies as the second level (Bryk, Raudenbush, and Congdon

1996; Cohen et al. 2003; Gelman and Hill 2007; Hayes 2006). More precisely, I

estimate mixed effects logistic regression models, which are a type of generalized

linear mixed model specific to binary response variables.

Generalized linear mixed models are generalized in the sense that they do not

require normally distributed response variables, and mixed in the sense that they

include both fixed and random effects (Winter 2013). Fixed effects are those most

familiar to sociologists, such as the coefficients generated by ordinary least squared

(OLS) linear regression models. They are fixed in that they presume the relationship

between variables to be systematic and constant across groups.

Random, or “varying,” effects account for seemingly random variation across

groups that is not understood by the researcher. For instance, police agencies may

have different arrest rates for a variety of reasons, some of which the researcher is

unaware of and cannot model explicitly. By including a varying intercept for police

agency, the researcher allows offenders’ baseline probability of arrest to vary across

agencies. This is analogous to having a dummy variable for each agency, while also

enabling the examination of agency-level measures.

I use the R package “lme4” (Bates et al. 2015; R Core Team 2015) to estimate

a generalized linear mixed model with a logistic link function. I predict offenders’ log

odds of arrest given several offender, victim, and agency characteristics.

27

Abstractly, the model is as follows:

log((*+)

1 − ((*+)= S+ + TU +!"2"+ + !#2#+

where log 3(45)"63(45)

is an offender’s log odds of being arrested, ((*+) is an offender’s

probability of being arrested, S+ is an intercept, TU is an intercept that varies across

agencies, !" is an effect of an offender characteristic, and !# is an effect of an agency

characteristic.

More precisely, I estimate the following model:

9+ = S+ + TU + !"+:;<=>+ + !#+?@=A@B:;<=>+ + !C+?@=A@BDEFGE+ +

!H+IJJKELKMNOK7FMAKL+ + !P+QK<ERE=FBK+

where 9+ is the log odds that an offender is arrested, S+ is an intercept, TU is an

intercept varying across police agencies, !" is the effect of an offender being black,

rather than white, and !H is the effect of the number of offenders reported to the

offender’s agency. Models for violent offenses include terms for victim

characteristics, while offenses without victims do not.

Results – Offenses with Victims

Table 2.5 presents results from logistic regression models estimating male

criminal offenders’ log odds of arrest for offenses with victims. All standard errors

are robust Huber/White standard errors to account for clustering by agency. Four

offenses are examined: simple assault, aggravated assault, intimidation, and robbery.

For each offense, the model on the left predicts offenders’ log odds of arrest using

28

only their race (black or white), while the model on the right includes controls for

victim race, whether the offender and victim knew one another, the number of

offenders reported to the police agency, and county mean income.

[TABLE 2.5]

For each offense, black offenders are significantly less likely than white

offenders to be arrested before holding other factors constant. For instance, black

robbery offenders are 0.476 times as likely as white robbery offenders to be arrested.

However, once victim race, relationship to offender, crime rate, and mean income are

accounted for, this association is drastically reduced. For instance, holding these

factors constant, black robbery offenders are 0.707 times as likely as white robbery

offenders to be arrested.

Very similar patterns are present for aggravated assault, simple assault, and

intimidation, as black offenders are less likely than white offenders to be arrested, but

less so once other factors have been taken into account. For intimidation, this

relationship disappears completely once characteristics of the victim and location are

taken into account.

Thus the offense and community characteristics examined explain part of the

association between race and likelihood of arrest. This is consistent with the lower

AIC and BIC values in the models including them, compared to those without them.

How do these characteristics relate to offenders’ likelihood of arrest? In

general, offenses with black victims are less likely to result in arrest, as suggested by

6 K60.WX 7 K60.CX

29

prior research (Briggs and Opsal 2012; Howerton 2006; Smith, Visher, and Davidson

1984; but also see Taylor, Holleran, and Topalli 2009). In addition, offenses in which

the victim and offender knew one another are more likely to result in arrest, which is

also consistent with prior findings (Roberts 2007). However, contrary to previous

work (Allen 1996; Doerner and Doerner 2012; Paré, Felson, and Ouimet 2007), I find

that offenders are less likely to be arrested in agencies with more reported offenders

and more likely to be arrested in counties with higher mean incomes.

However, it is possible that these single-level models are biased by the non-

independence of observations within police agencies. One method of assessing the

degree of clustering is to calculate the intraclass correlation coefficient (ICC): the

proportion of variation in the outcome that is between clusters. Large values indicate

that outcomes within clusters are very similar, while outcomes vary greatly across

clusters.

ICC values for offender arrest are 0.22, 0.16, 0.18, and 0.34 for robbery,

aggravated assault, simple assault, and intimidation, indicating that a relatively large

portion of the variation in arrest is explained by police agency. For this reason, I also

estimate multilevel logistic regression models with intercepts varying across agencies.

Table 2.6 presents these models, which include a varying intercept, as well as

the non-varying terms included in the models in Table 2.5. Once again, it appears that

black offenders are considerably less likely than white offenders to be arrested.

However, the magnitude of this effect is greatly reduced, particularly in the models

without controls, as the varying intercept accounts for unobserved differences in

agencies overlooked in the models in Table 2.5.

30

For instance, black robbery offenders are 0.658 times as likely as white robbery

offenders to be arrested when no other factors are taken into account, and 0.729 times

as likely after accounting for victim and agency characteristics. Once again,

intimidation is the only offense for which offender race is unrelated to the probability

of arrest, regardless of controls. In fact, including controls does not unambiguously

improve model fit for intimidation, in contrast to other offenses.

[TABLE 2.6]

I again find that offenses with black victims and in higher crime agencies are

less likely to result in arrest, while offenses where the victim and offender know each

other and in higher income counties are more likely to result in arrest.

Results – Offenses without Victims

Having examined offenses with victims, I turn now to offenses without

victims. Here, the relationship between offenders’ race and likelihood of arrest is not

quite as consistent. Table 2.7 shows results from logistic regression models,

indicating that race may be unrelated to the likelihood of arrest for weapon and drug

offenses. Unlike for offenses with victims, the relationships between crime rate and

income with arrest likelihood differ between offenses.

[TABLE 2.7]

8 K60.HC 9 K60.CC

31

However, as previously discussed, these coefficients may be biased by non-

independence within police agencies, for which reason more trust should be placed in

multilevel models with an intercept varying across agencies.

Table 2.8 presents such multilevel logistic regression models, whose results are

more coherent than those of the single-level models. For weapon offenses,

shoplifting, and vandalism, black offenders are less likely than white offenders to be

arrested, even once crime rates and county income are held constant. The comparable

magnitudes of this relationship before and after holding these factors constant suggests

that any difference in arrest likelihood due to agency is already captured by the

intercept varying across agencies. In fact, compared to the magnitude of each of the

fixed effects, the varying intercept has considerable variation, ranging from 0.72 to

3.11 across offenses, suggesting that offenders’ arrest likelihoods vary greatly across

agencies.

[TABLE 2.8]

Perhaps the most striking finding of the models shown in Table 8 is that there

is no association between offender race and arrest likelihood for drug offenses. This

may be because the many drug offenders are only observed when they are caught

(Paré, Felson, and Ouimet 2007; Figure 2.2). However, many police agencies

nonetheless arrest fewer than 80 percent of drug offenders, suggesting that something

specific to drug offenses accounts for this difference. In the following chapter I

examine police discretion, which is often greater in drug investigations than other

types of investigations, in order to better understand the relationship between offender

race and arrest.

32

For now, let us note that for most offenses, black offenders are less likely than

white offenders to be arrested for weapon, shoplifting, and vandalism offenses. This

difference is greatest for shoplifting, as black shoplifting offenders are 0.6210 times as

likely as white offenders to be arrested. As discussed, controlling for crime rate and

mean income does not substantially change estimates or model fit.

Sensitivity Analysis One concern is that black reported offenders are less likely to actually be

offenders, or to be black. If Americans often report black men to the police despite

their not committing a crime, then black reported offenders will often not be arrested

simply because they are not offenders. To determine how greatly this issue affects my

results, I estimate offenders’ likelihood of arrest for offenses in which the victim knew

the offender. In these cases, it is much less likely that reported offenders did not

commit a crime and that their race has been misperceived.

Table 2.9 presents results from multilevel logistic regression models predicting

offenders’ arrest for offenses in which the victim knew the offender. If the results are

similar to those in Tables 2.5 and 2.6, which are also for robbery, simple and

aggravated assault, and intimidation, then we should have greater confidence that bias

in reporting does not significantly skew my results. By contrast, if black offenders are

relatively more likely to be arrested when the victim knew them, then reporting bias

may compromise my findings.

10 K60.HY

33

[TABLE 2.9]

The race coefficient in Table 2.9 is quite similar to those in Tables 2.5 and 2.6

for simple assault, aggravated assault, and intimidation. However, black offenders are

slightly less likely to be arrested, relative to white offenders, for offenses in which the

victim knew them, rather than for all offenses. This can be seen by the somewhat

smaller offender race coefficient in Table 2.9 as compared to those in Tables 2.5 and

2.6. In the model with victim and agency controls, it is even statistically insignificant,

although it appears similar in magnitude to the general coefficients.

Nonetheless, it appears that the relationship between race and likelihood of

arrest is quite similar for offenders whose victim knew them as for all offenders

combined. This suggests that reporting bias may not entirely account for my findings

that black offenders tend to be less likely than white offenders to be arrested.

Discussion

For most of the criminal offenses examined, both sets of models – single-level

and multilevel – refute the null hypothesis that black and white offenders are equally

likely to be arrested. Surprisingly, they show that black offenders are usually less

likely to be arrested, although this relationship is diminished when certain

characteristics of the offense and police agency are held constant.

Based on these data, I find no evidence that police officers discriminate against

black men when investigating robbery, aggravated and simple assault, intimidation,

weapon offenses, shoplifting, vandalism, or drug offenses. This is not to say that such

34

discrimination is not commonplace – there is simply no evidence of it in the criminal

reports available in 2013.

It is quite likely that crucial factors related to both offender race and the

likelihood of arrest are omitted, and that discrimination is present but hidden by these

factors. Unfortunately, several measures of offense, police agency, and community

characteristics are strongly correlated and could not be examined here due to

multicollinearity. Additional factors may never be measured, such as an offender’s

familiarity with the criminal justice system. Holding such measures constant, perhaps

we would find that police agencies discriminate against black men.

Perhaps the greatest question this analysis leaves unanswered is how to

interpret available data on reported offenders. According to NIBRS data, 35 percent

of male offenders are black, compared to 13 percent of U.S. men (NIBRS 2013). This

begs the question: are reported offenses representative of all offenses? Or are black

people more likely to be reported to the police than white people who behave

similarly? Future research might examine the processes leading to a criminal report

and ideally utilize more detailed data that includes a description of the report itself.

Conclusion

This chapter asks whether police agencies behave professionally, treating black

and white male criminal offenders similarly. I find no evidence of discrimination

against black men for any of the nine most common offenses. However, this study

suffers from several limitations and in no way shows that discrimination does not

exist.

35

In addition, it is important to remember that many forms of discrimination

operate throughout the criminal justice system. Black and white men are often treated

differently during prosecution, bail determination, legal representation, criminal

hearings, and sentencing (Alexander 2010). Such discrimination contributes to the

vast overrepresentation of black men in prison and must not be overlooked.

One of the most striking findings of this chapter is perhaps the most obvious:

over 45 percent of all black and white male offenders in 2013 were black. This is

more than three times the percentage of black and white people in the U.S. who are

black (ACS 2013). This means that even if police agencies are completely

professional, enormous racial disparities in investigation, arrest, and imprisonment

will emerge. Unprofessional policing cannot be the sole contributor to these

disparities. In fact, it cannot even be the primary contributor, given the

disproportionate number of black offenders reported to police every day.

Nonetheless, it remains important to determine the extent of police

professionalism in the U.S. Although this chapter finds no evidence of

unprofessionalism at the national level, it may be that discrimination operates on a

more granular level. For instance, certain types of police agencies may discriminate

against black men, while others do not. Or it may be that certain types of criminal

investigation enable discrimination, while others do not. The following two chapters

consider these possibilities, further increasing our understanding of police

professionalism and discrimination.

36

Chapter 3: Police Discretion Introduction Having examined the overall prevalence of police discrimination, I now

consider one factor that may increase it: police discretion. Police discretion is the

extent to which police officers use their personal judgment when deciding whom to

investigate. At its heart, discretion is about trust and is only problematic when we do

not trust officers’ judgment and suspect it conflicts with professional policing

(Goldstein 1963; Tomic and Hakes 2008). On the other hand, when we trust officers’

intentions and ability to behave professionally, granting them discretion merely

increases their efficacy (Pepinsky 1984).

Many Americans do not trust police officers and believe they discriminate

against black men. This distrust stems from a long history of police discrimination

and has been renewed by recent filmed instances of police brutality (Weitzer and Tuch

1999; Weitzer 2002). This chapter lends an ear to this distrust and asks whether

officer discretion is associated with unprofessional policing.

Police Discretion

Police professionalism is the extent to which the police treat all residents

equally. Professional police officers do not allow their personal biases to influence

their behavior, even when they have the power to do so. By contrast, unprofessional

officers are more lenient towards certain individuals than others and do not enforce the

law with an even hand. Their actions are whimsical and may even fail to enforce the

law:

37

… when the police are called to a domestic incident, for which policy tells

them to separate and counsel the parties but not to write up an assault, it never

happened, as far as official records are concerned. Nor did drunken blows

among friends when the responding officer decides that a drive home is a more

appropriate response than an arrest. (Tonry 1995: 56)

How does an officer decide what an “appropriate” response is? In the second example

above, a professional officer would use objective criteria, such as the severity of the

assault, to reach a decision. An unprofessional officer, on the other hand, would rely

more on personal judgment and emotion, driving a sympathetic suspect home and an

unsympathetic one to the police station.

In this situation, the officer exercises discretion, choosing among several

possible responses. Both driving the suspect home and driving him/her to the station

are equally acceptable to the officer’s supervisor, acquaintances, and own sensibility,

and the officer is largely free to decide how to respond as he or she wishes.

Such discretion enables officers to selectively enforce the law, opening the

door to unprofessional policing, including racial discrimination. In fact, several

studies have found that police officers are more likely to discriminate against black

suspects when they have greater discretion (Blumstein 1982; Powell 1990; Tomic and

Hakes 2008). For instance, in his examination of arrest and incarceration rate racial

disparities, which were enormous even in 1982, Blumstein (1982) states, “… as the

seriousness of the offense decreases, blacks are disproportionately represented in

prison. This does suggest that blacks become increasingly disadvantaged as the

amount of permissible criminal-justice discretion increases” (1280). Thus we have

38

reason to suspect that police discretion leads to unprofessional policing, particularly

discrimination against black men.

Discretion in Drug Investigations In the 1970s, President Richard Nixon declared drug abuse America’s number

one public enemy, and in the 1980s, President Ronald Reagan officially announced a

“War on Drugs” (Bertram et al. 1996). Drug use became seen primarily as a criminal

issue, and penalties for drug offenses increased drastically. Law enforcement

resources to fight drug offenses grew, and many more Americans were imprisoned for

drug possession and distribution (Boyd 2002; Carson 2015; Flanagan, van Alstyne,

and Gottfredson 1982: 486, 488).

This War on Drugs grants police agencies considerable discretion when

investigating drug offenses. Officers are encouraged to search for drug offenses where

they believe they might be occurring, without clear guidance as to where to search.

Consequently, they often stop and search individuals whom they suspect of possessing

or distributing drugs due to their appearance, behavior, or location. They make these

decisions using discretion, with few consequences resulting from biased investigation.

Discretion and Discrimination

Why might officers investigate drug offenses in a biased manner? First,

officers may consciously or subconsciously believe that black residents are more

likely to commit drug offenses and disproportionately focus investigations on black

residents (Eberhardt et al. 2004; Freeman et al. 2011; Saperstein and Penner 2010).

Similarly, they may believe that neighborhoods with more black residents have greater

39

drug use (Beckett et al. 2005; Beckett, Nyrop, and Pfingst 2006). Second, community

pressure and legal constraints may cause officers to focus on drug offenses committed

in public places, rather than on private property. Because black drug offenders are

more likely than white offenders to operate in public, a focus on public places

disproportionately targets black offenders (Bertram et al. 1996). This may help

explain why black men are imprisoned at vastly greater rates than white men for drug

offenses, despite similar rates of drug use and distribution (Beckett, Nyrop, and

Pfingst 2006; Carson 2014; Fellner 2000; Tonry and Melewski 2008; Mosher 2001;

SAMHSA 2014: 26; USSC 1995).

On the other hand, police officers may behave professionally, even when

granted considerable discretion. They may exercise discretion only to investigate

neighborhoods with high rates of drug offenses, which may happen to be

disproportionately black. If this is the case, then limiting officers’ discretion will only

impede their efficacy and fail to decrease discrimination.

Differences in Discretion In order to understand the role of discretion on police professionalism and

discrimination, I compare offenders’ treatment across different police agencies. I

examine drug investigations, because agencies differ widely in the amount of

discretion they grant officers investigating drug offenses. Some agencies primarily

respond to drug offenses after they are reported by concerned residents, while other

agencies frequently patrol neighborhoods they consider “at risk,” encouraging officers

to stop and search residents they consider suspicious. For instance, New York City

recently engaged in a controversial “stop and frisk” strategy that encouraged officers

40

to frequently stop and search individuals walking in certain (mostly minority)

neighborhoods.

I utilize this variation in department behavior to evaluate the impact of

discretion on police professionalism. When officers proactively look for drug

offenses, such as by searching young men on the street, they immediately arrest any

drug offenders they find. On the other hand, when officers investigate reported drug

offenses, they may take several days to apprehend offenders, if they manage to do so

at all. For this reason, I operationalize discretionary policing in terms of 1) the time

between an offense and the resulting arrest, and 2) the percentage of offenders who are

arrested. Police departments in which officers exercise more discretion during drug

investigations are likely to have more same-day arrests and higher arrest rates. While

imperfect, these measures capture two important dimensions of discretionary policing

in drug investigations.

Same-Day Arrests When police officers and agencies proactively look for criminal offenses, they

use discretion when deciding whom to investigate. Such discretion may enable

discrimination, such as when officers in New York focus disproportionately stop and

frisk racial minorities (Goel, Rao, and Shroff 2016). One result of proactive policing

is the simultaneous observation of an offense and the resulting arrest. For instance,

when an officer stops and searches an individual and finds narcotics, she immediately

arrests the offender. This is a same-day arrest, as the offense and the arrest occurred

on the same day.

41

On the other hand, when officers react to reported criminal offenses, they

exercise less discretion in deciding whom to investigate. In such cases, they must look

for a particular offender matching the reported description and often cannot arrest the

offender on the same day as the offense. For instance, if a witness calls a police

agency and reports a drug sale that occurred on a certain day, then it is likely that the

offender will not be arrested on the same day as the offense. In fact, the offender may

never be arrested, as the officer may not succeed in finding the offender and evidence

of the offense.

Agency Arrest Rates For this reason, arrest rates themselves may be an indication of discretionary

policing. When police agencies proactively search for drug offenses, they are likely to

arrest nearly all of the offenders they encounter. On the other hand, when agencies

respond to reports of drug offenses, they may fail to arrest many offenders.

Thus police agencies with higher arrest rates likely engage in more

discretionary policing than agencies (Alexander 2010). This is consistent with drug

offenses’ high arrest rates compared to other offenses, as shown in Figure 3.1.

42

Figure 3.1. Agency Arrest Rates by Offense

Hypotheses

I use agencies’ arrest rates and same-day arrest rates to test whether agencies

exercising more discretion are also more likely to discriminate against black men. My

hypotheses are as follows:

1. Black offenders will be relatively more likely than white offenders to be

arrested in police agencies with higher arrest rates.


arrested in police agencies with more same-day arrests.

43

Data and Methods I utilize the same data as in chapter 2: NIBRS data on criminal offenders,

LEMAS data on police agencies, and ACS data on county demographics. However, I

examine only two offenses, drugs/narcotics and drug equipment offenses, due to the

frequent discretionary character of their investigations, as previously discussed.

Tables 3.1-3.3 describe these two subsets, labeled “Drugs/Narcotics” and

“Drug Equipment.” Table 3.1 describes these offenders, particularly their arrest rates

and racial demographics. It is noteworthy that there are many more drugs/narcotics

than drug equipment offenders, and the majority of each were arrested. In fact, the

majority of drug offenders were arrested on the same day as their offense, suggesting

that police officers may be proactively looking for drug offenses rather than

responding to reported offenses.

A disproportionate number of drug offenders are black, particularly for

drugs/narcotics offenses (43 percent). However, black and white drug offenders have

similar rates of arrest.

[TABLE 3.1]

I argue that police officers more often exercise discretion when making same-

day than other-day arrests and that black offenders are more likely to be arrested when

officers exercise discretion. To confirm that this hypothesis is plausible, I first see

how often black and white offenders are arrested on the day of their offense rather

than at a later date. Table 3.2 shows that for drugs/narcotics offenses, 89.4 percent of

white offenders who were arrested were arrested on the day of their offense, compared

44

to 91.1 percent of arrested black offenders. A similar difference is present for drug

equipment offenses, where 92.6 of white and 94.3 percent of black arrested offenders

were arrested on the day of their offense.

[TABLE 3.2]

These differences are statistically significant within offenses (p < 0.001, two-

tailed t-tests), indicating that black arrested offenders are more often arrested on the

day of their offense than are white arrested offenders. However, these differences may

result from differences between offenders or the agencies policing them, rather than

discretionary policing. Multivariate analyses are needed to distinguish discretionary

policing from other factors.

[TABLE 3.3]

Two factors related to both offender race and discretionary policing are crime

rates and community socioeconomic status (Alexander 2010; Allen 1996; Borg and

Parker 2001; Paré, Felson, and Ouimet 2007; Sobol 2010; Wakefield and Uggen

2010). Table 3.3 shows these measures’ means and standard deviations for each

offense. Crime rate is simply the number of offenses reported to the agency, while

socioeconomic status is mean household income in the given county.

Agencies vary greatly along these measures. For instance, agencies in the

drugs/narcotics data set have an average of 262 reported offenders, with a standard

deviation of 591. In addition, they arrest 73.4 percent of drugs/narcotics offenders on

average, with a standard deviation of 21.4 percent.

45

Offender Variables

Arrested – The dependent variable is whether an individual who committed a crime in

2013 is also arrested for that crime in 2013. It is a binary variable coded as 1 if an

offender is listed as arrested, and 0 if not. It is coded as missing if an offender is

arrested for a crime other than the one reported, as it is unknown whether the offender

would eventually have been arrested for the reported offense.

Offender Black – This measure is coded 1 if the offender is black and 0 if he is white.

It is based on victims’, witnesses’, and officers’ perceptions of offenders’ race and

ethnicity. Multiracial and Hispanic offenders are excluded.

Agency Variables

When police officers proactively look for offenders, rather than responding to

resident reports, they exercise considerable discretion in deciding where to look. They

are also more likely to arrest observed offenders and to do so on the same day as the

offense, as they only observe offenses during their commission. Thus agency arrest

rates and same-day arrest rates are proxies for discretionary policing and can be used

as indirect measures of it.

Arrest Rate – Percentage of reported offenders who were arrested in 2013:

#<MMKNAKL#EFA<MMKNAKL + #<MMKNAKL

46

Same-Day Arrest Rate – Percentage of arrested offenders who were arrested on the

day of their offense:

#<MMKNAKLN<BKL<*#<MMKNAKLN<BKL<* + #<MMKNAKL;<AKML<*

Two factors that likely affect arrest rates and discretionary policing are

communities’ crime rates and socioeconomic characteristics, as previously discussed.

For this reason, I include measures of the total number of offenders reported to police

agencies and counties’ mean household incomes.

Offender Count – Total number of drugs/narcotics (or drug equipment, as appropriate)

offenders reported to the agency in 2013.

County Mean Income – Mean household income of the county in which the agency is

located, in thousands of dollars. The standardized measure appears normally

distributed.

Models

I include the measures described above in multivariate analyses of offenders’

likelihood of arrest. In order to determine whether black and white criminal offenders

are equally likely to be arrested in high- versus low-discretion investigations, I predict

whether an offender is arrested, given his race and the crime rate and mean income of

the community in which he committed the offense. I include an interaction term for

the multiplicative effect of an offender being black and 1) an agency arresting many

offenders or 2) an agency arresting many offenders on the same day as the offense.

47


logistic regression model. However, offenders are located within many of the same

police agencies, for which reason the outcome and independent variables may not be

independent across individuals. One method to assess the extent of this non-

independence is to calculate the intraclass correlation coefficient (ICC): the proportion

of variation in an outcome that is between groups (rather than within them). Large

ICC values indicate that offenders are very similar within agencies and vary greatly

across agencies.

[TABLE 3.4]

Table 3.4 presents intraclass correlation coefficients for each offense when

predicting offenders’ race and arrest outcome. These coefficients indicate that a

moderate amount of variation in offender race and offender arrest outcome is

explained by agency. That is, offender race and likelihood of arrest are not

independent within agencies.

As previously discussed, robust standard errors do not fully address this issue

(Freeman 2006; Greene 2002: 674; King and Roberts 2017).11 For this reason, I

estimate multilevel logistic regression models with intercepts that vary across police

agencies. I estimate these models for drugs/narcotics and drug equipment offenders

separately, as the offenses may be investigated differently.

11 Please see the Appendix for a discussion of this issue and simulation demonstrating it.

48

I predict each offender’s likelihood of arrest given his race, agency crime rate

and county mean income, agency same-day arrest rate, and the interaction of his race

and agency’s arrest rate or same day-arrest rate. This last term is the coefficient of

greatest interest, as I hypothesize that it is positive. If it is positive, then black

offenders have a relatively greater likelihood of arrest in agencies with more arrests or

with more same-day arrests.

Thus I estimate the following model:

9+ = S+ + TU + !":;<=>+ + !#9MMKNAO<AKU + !C:;<=>+×9MMKNAO<AKU +

!HIJJKELKMNOK7FMAKLU + !PQK<ERE=FBKU

where 9+ is the log odds that an offender is arrested, S+ is an intercept, TU is an

intercept varying across police agencies, !" is the effect of an offender being black,

rather than white, !# is the effect of an agency’s arrest rate or same-day arrest rate, !C

is the interaction effect of interest, and !H is the effect of the number of offenders

reported to each agency j.

Results – Agency Arrest Rates Before estimating these models, I compare black and white offenders’ arrest

rates in relation to agency arrest rates. I look for visual evidence of an interaction

effect, evidence that agencies that arrest more offenders are also more likely to arrest

black than white offenders.

Figure 3.2 suggests that such an interaction is not present for drugs/narcotics

offenses. Each point is an agency, and the x-axis represents the percentage of the

agency’s reported offenders who were arrested. The y-axis is the percentage of

49

reported offenders who were arrested, and light and dark orange points represent white

and black offenders’ percentages, respectively. Lines show local regressions.

If agencies with higher arrest rates disproportionately arrest black offenders,

one would expect the dashed line to rise more steeply than the solid line. However,

the dashed and solid lines overlap almost completely; there does not seem to be a

multiplicative interaction between agency arrest rate and offender race. This pattern is

very similar for drug equipment offenses, which are not shown here in the interest of

space.

Figure 3.2. Drugs/Narcotics Offenders Arrested by Agency Arrest Rate

50

This speaks against the first hypothesis: that agencies with higher arrest rates

arrest relatively more black offenders. However, multivariate analyses are needed to

control for other factors and outcome non-independence within agency (clustering).

Table 3.5 presents such an analysis, showing estimates from multilevel logistic

regression models predicting whether an offender is arrested. The coefficients of

interest are for offender race, agency arrest rate, and their interaction. This interaction

term represents the additional increase in the log odds of arrest for black offenders due

to two standard deviations’ increase in an agency’s percentage of offenders arrested.

For drugs/narcotics, agency arrest rate has a standard deviation of 16. Thus a positive

interaction term of magnitude 0.4 would indicate that increasing an agency’s arrest

rate by 32 percentage points would increase black offenders’ odds of arrest 49

percent12 more than white offenders’.

[TABLE 3.5]

However, we do not observe such an interaction, nor even a main effect for

offender race on the probability of arrest. For both drugs/narcotics and drug

equipment violations, the only large and significant term is for agency arrest rate. For

instance, increasing the arrest rate by 32 percentage points increases all

drugs/narcotics offenders’ odds of arrest by a factor of 7.13 Similarly, an increase of

52 percentage points in agency arrest rate increases drug equipment offenders’ odds of

12 K0.H = 1.49 13 K".\X = 7.10

51

arrest by a factor of 17.14 This is intuitive, as offenders are by definition more likely

to be arrested in agencies that arrest a greater proportion of offenders.15

Thus I find no support for the hypothesis that agencies with higher arrest rates

are relatively more likely to arrest black offenders.

Results – Same-Day Arrest Rates

My second hypothesis is that agencies with higher same-day arrest rates arrest

relatively more black offenders. To visually assess the plausibility of this hypothesis,

I compare drugs/narcotics offender arrest rates across police agencies, as shown in

Figure 3.3. The x-axis shows the percentage of arrests made on the day of the offense,

and the y-axis shows the percentage of offenders arrested. Circles and triangles

represent each agency’s percentage of white and black offenders arrested, and solid

and dashed lines show local regressions.

14 K#.Y" = 16.61 15 Please note that the random intercepts in models including agency arrest rates as 0 with 0 variance, as the baseline likelihood of arrest in each agency is already held constant.

52

Figure 3.3. Drugs/Narcotics Offenders Arrested by Agency Same-Day Arrests

If agencies with more same-day arrests are relatively more likely to arrest

black offenders, we would expect the slope of the dashed line to increase relative to

the slope of the solid line. That is, we would expect black offenders to be relatively

more likely to be arrested in agencies with more same-day arrests.

We do not observer such a pattern, as the dashed line does not become

relatively more steep than the solid line as same-day arrest rates increase. This

suggests that agencies with more same-day arrests may not be more likely to arrest

black drugs/narcotics and drug equipment offenders (latter not shown here, but very

similar).

53

However, this analysis may be misleading for two reasons. First, each point

represents a police agency, which is a single observation in the local regression. Yet

agencies vary greatly in how many offenders they investigate, and there may be an

interaction between offender race and same-day arrest rates for most offenders that is

overlooked when giving small agencies equal weight as large agencies.

Second, police agencies may differ in ways that obscure an interaction. As

previously discussed, differences in community crime rates and socioeconomic well-

being across agencies may be associated with offender race, same-day arrest rates, and

offender likelihood of arrest. For this reason, I turn now to multilevel logistic

regression models holding constant these agency characteristics and offenders’

baseline likelihood of arrest in each agency.

Table 3.6 presents the results from these models, which predict offenders’

arrest given their race, agency same-day arrest rate, and the interaction of offender

race and same-day arrest rate. The positive interaction term represents the change in

the log odds of arrest for black offenders due to two standard deviations’ increase in

an agency’s percentage of arrests that were on the day of the offense. For

drugs/narcotics, agencies’ same-day arrest rate has a standard deviation of 9.8 across

all offenders. Thus a positive interaction term of magnitude 0.2 would indicate that

increasing the arrest rate by 19.6 percentage points would increase black offenders’

odds of arrest by 22 percent.16

[TABLE 3.6]

16 K0.# = 1.22

54

However, the interaction terms for drugs/narcotics and drug equipment are

small and non-significant, indicating that agencies with more same-day arrests are not

relatively more likely to arrest black offenders. In addition, there is no evidence that

black offenders are more likely than white offenders to be arrested for drugs/narcotics

offenses, although they are slightly more likely to be arrested for drug equipment

offenses. Holding constant agency crime rate, county mean income, and agency same-

day arrest rates, black drug equipment offenders are 1.0617 times more likely than

white offenders to be arrested.

The impact of same-day arrest rates is comparable for drugs/narcotics and drug

equipment offenses. For drugs/narcotics, a 20 percentage point increase in an

agency’s same-day arrest rate is associated with a 1.2 factor increase in offenders’

odds of arrest18. For drug equipment, a 24 percentage point increase in same-day

arrests is related to a 1.3 factor decrease in offenders’ odds of arrest.19

Discussion

When police officers use their discretion to choose which residents to

investigate, they are likely to arrest the offenders they observe and remain unaware of

those they do not. Thus their overall and same-day arrest rates will be high, as the

only “reported” offenders are those whom they thought to investigate.

Such discretionary policing may enable unprofessional policing, including

racial discrimination, as officers may focus their investigations disproportionately on

black men. They may do so because they consciously or subconsciously hold

17 K0.# = 1.06 18 K0.0X = 1.22 19 "

]^_.`= "

0.WH= 1.3

55

stereotypes of black men as criminal, believe that neighborhoods with more black men

have more drug use, or because black drug offenders are more likely to operate in

public. Whatever the underlying cause, police discretion may enable unprofessional

policing.

In this chapter, I use agency arrest rates and same-day arrest rates to look for

evidence of a relationship between discretionary and unprofessional policing. I

examine drug offenses because they are common sites of discretionary policing, as

officers often stop, search, and frisk residents in search of drugs when no offense has

been reported.

I find no evidence that discretionary policing is associated with unprofessional

policing. However, this analysis suffers from several limitations and should be

viewed as an exploratory study. In addition, it is important to remember that whether

or not police discretion is shown to increase unprofessionalism, discrimination may be

widespread and discretionary policing may be undesirable.

Limitations The most severe limitation of this chapter is the lack of direct measures of

discretionary policing. Unfortunately, NIBRS data on reported offenses do not

describe the character of each investigation, and LEMAS data on law enforcement

agencies do not describe investigation policies. For this reason, I used arrest rates and

same-day arrest rates as proxies for discretionary policing, which is problematic for

two reasons. First, they may not be positively related to discretionary policing.

Second, they may be associated with other factors that are overlooked in this analysis.

For instance, if arrest rates are higher in neighborhoods with more powder cocaine use

56

and if police officers disproportionately focus on white residents in such

neighborhoods, then arrest rates will be lower for black offenders in high arrest rate

neighborhoods. Yet this relationship will be spurious.

In addition, this chapter has many of the same limitations as the first empirical

chapter, as it uses the same data. There may be considerable selection bias in which

agencies report their data to the FBI, as well as in which offenses are reported to law

enforcement. Further, there is no guarantee that arrests are legitimate or even of the

reported offender. This is a problem common to research on crime, as data are often

anonymized and unverifiable. To the extent possible, I have compared these data to

other publicly available data on crime and verified their plausibility.20

Conclusion To my knowledge, this is the first study of police discretion and discrimination

at the national level. Although there is some research on discretion and an enormous

literature on discrimination, there are few studies that examine their interrelation, and

none that do so with nationally representative data. National data is crucial, as studies

of particular locations are not generalizable and may suffer from file drawer

syndrome.21

This study is a first attempt to leverage national data to understand the

relationship between discretionary and professional policing. As a first attempt, it

examines only a single form of police discretion (investigation) and a single negative

20 Similar offense/arrest rates were found in data from Uniform Crime Reports, National Crime Victimization Survey, and Bureau of Justice analyses of NIBRS data. 21 If many geographically limited studies are conducted, it is likely that only those with positive findings will be published, skewing our understanding of this relationship.

57

outcome that may result (arrest). Further research is needed to understand other forms

of discretion, such as the discretion that officers and district attorneys have when

deciding how to charge arrested individuals. In addition, future research might

examine the impact of discretion on police professionalism more generally, expanding

beyond arrest rate disparities. For instance, one might examine the relationship

between discretion and complaints against officers, use of force, and resident and

officer attitudes. In addition, one might consider potential benefits of discretionary

policing, such as the humane enforcement of inflexible laws (Pepinsky 1984).

Whatever future research may find, it is important to remember that police

discretion is only one of many potential causes of unprofessional policing. Even if

discretion does not contribute to unprofessional policing, there are many forms of

discrimination occurring in the criminal justice system today. Black and white men

are often treated differently at many stages of contact, including prosecution, bail

determination, legal representation, criminal hearings, and sentencing hearings

(Alexander 2010). These findings in no way contradict this knowledge, which is

important to consider in its own right.

58

Chapter 4: Police Diversity

Introduction The essence of police professionalism is the impersonal and skillful

performance of police duties. Professional police officers are interchangeable, as they

perform their duties as required, no more and no less. Unprofessional officers, on the

other hand, behave idiosyncratically, according to their own preferences.

Researchers and concerned citizens have long suspected that U.S. police

officers are not interchangeable, arguing that officers’ personal characteristics, such as

race and gender, influence their behavior. In particular, they argue that racially

homogenous, largely white police departments are less professional than racially

diverse departments (Ashkenas and Park 2015; Black 1971; Brown and Frank 2006;

Sherman 1980).

Why might this be the case? First, white officers may treat black residents less

professionally than do other officers. White officers may be more consciously or

subconsciously biased against black individuals and investigate them more intensely.

Second, police departments with more officers of color may foster more professional

cultures where racial bias and discrimination are not tolerated. In this way, racial

diversity may impact both black and white officers’ behavior and reduce

discrimination throughout the department.

Officer Race The first reason to expect police departments with more non-white officers to

behave more professionally is that white officers and officers of color may have

59

different amounts of bias against black men. Many white people held racist beliefs a

few decades ago, and they may privately hold such beliefs today.

However, it is possible that white people are not racially biased, or that white

and non-white people have comparable levels of implicit bias against black men

(Dixon 2008; Freeman et al. 2011; Eberhardt et al. 2004; Oliver 1999; Saperstein and

Penner 2010). Such implicit bias may operate in a variety of ways. For instance,

officers may subconsciously find black people more suspicious than white people and

subsequently investigate them more rigorously. They may consider their movements

more indicative of criminal behavior, for which reason they more often stop, question,

or search them.

In addition, officers may interpret situations very differently depending on a

suspect’s race. For instance, if an officer finds marijuana in a white teenager’s car, he

may focus on his youth and assume that he is a “good boy” who made a mistake. He

may warn him of the drug’s adverse health effects and release him with a warning. By

contrast, if the officer finds marijuana in a black teenager’s car, he may react much

more harshly. He may assume that he is a “bad boy” who needs to be disciplined and

arrest him. Such bias may never be observed, and the officer himself may not realize

he is treating black and white people differently.

If white officers have more implicit bias than officers of color, then they will

more often discriminate against black men. However, most research has found that

black and white officers treat black men similarly (Anwar and Fang 2006; Brown and

Frank 2006; Eitle, Stolzenberg, and D’Alessio 2005; Hindelang 1978, 1981; National

Research Council 2004; Sherman 1980; Wilbanks 1987; Worden 1989; but see also

60

Donohue and Levitt 2001). In addition, research on police department culture finds

that officers of different races behave similarly.

Blue Blood

According to “blue blood” theory, police officers are similarly socialized when

they enter a police force (Conlon 2004; Sklansky 2007). Officers share a common

“Police Subculture Schema,” due to their shared difficulties and estrangement from

the public. If this subculture tolerates discrimination against black residents, then

white and non-white officers will discriminate to the same degree. Conversely, if the

subculture is professional, then both black and white officers will behave

professionally.

Thus “blue blood” theory implies that department diversity is unrelated to

police professionalism. White and non-white officers may be equally professional (or

unprofessional), and increasing officer diversity will neither increase nor decrease

police professionalism.

Police Culture However, even if black and white officers behave similarly, diversity may

shape police department culture and all officers’ behavior. The past few decades have

seen dramatic increases in police officer diversity in terms of gender, race, and

sexuality, and the “organizational effects” of this diversity may include increased

professionalism (Sklansky 2006).

Diversity may increase professionalism by enabling one-on-one interactions

between black and white officers. According to intergroup contact theory, such

61

interactions could increase mutual understanding and respect (Allport 1954; Pettigrew

1998). In addition, police diversity could create social fragmentation, displacing a

previously dominant police culture favoring white individuals (Sklansky 2006). Thus,

even though white and non-white officers may behave similarly, increasing officer

diversity may decrease discrimination by changing police culture.

Hypothesis


arrested in police agencies with relatively more white officers.

We have reason to believe that police departments with greater racial diversity

are more likely to be professional. First, officers of color may be less likely than

white officers to discriminate against black men. Second, diverse police departments

may have more professional cultures that are less tolerant of discrimination.

However, it is also possible that department diversity does not impact

professionalism and that all police officers have “blue blood” and share a common

subculture that is independent of diversity. This chapter considers these two

possibilities and asks whether more racially diverse police departments are more

professional than less diverse departments.

The first two empirical chapters of this dissertation examined the extent to

which police officers treat all residents equally. However, police professionalism also

requires that different police officers treat residents similarly. This chapter asks

whether officers truly are interchangeable, or whether police diversity increases

62

professionalism. Together with the first two chapters, this chapter increases our

understanding of police professionalism and its role in the criminal justice system.

Data and Methods

I utilize the same data as in chapter 2: NIBRS data on criminal offenders,

LEMAS data on police agencies, and ACS data on county demographics. I examine

all nine of the most common offenses for which offender race is often reported:

robbery, simple and aggravated assault, intimidation, weapon violations, shoplifting,

vandalism, drugs/narcotics, and drug equipment violations.

Variables Percent Officers White – A police agency’s percentage of full-time officers who are

white.

All other variables have been previously described in chapters 2 and 3. All

continuous measures are grand-mean centered and standardized by dividing their

centered measure by twice its standard deviation. This standardization enables the

comparison of coefficients for continuous and binary variables and mitigates problems

of collinearity (Gelman and Hill 2007).

Descriptive Statistics

This chapter examines the relationship between offender race (black versus

white) and police agency racial diversity (lower percentage of white officers). One

way to examine this relationship is to tabulate offenders’ arrest rates by agencies’

percentage of officers white. Table 4.1 compares agencies with fewer than 75 percent

63

of officers white to agencies with 75 to 87 percent of officers white and agencies with

more than 87 percent of officers white. These cutoff points were chosen to compare

roughly equal proportions of offenders.22

[TABLE 4.1]

Table 4.1 suggests that agencies with more white officers do not arrest

relatively more black offenders. For instance, in agencies with fewer than 75 percent

of officers white, 72.7 percent of simple assault offenders are black, while only 68.3

percent of simple assault arrestees are black. This results in a ratio of 1.06 percent

offenders to percent arrestees black, indicating that black simple assault offenders are

less likely to be arrested than are white simple assault offenders. In agencies with

more than 87 percent of officers white, 30.5 percent of simple assault offenders are

black, but only 26.0 percent of simple assault arrestees are black, yielding a percent

offenders-to-arrestees black ratio of 1.17. If anything, black offenders are relatively

more likely than white offenders to be arrested in more diverse agencies.

One notable exception is drug offenses, which have very similar percent

offenders-to-arrestees black ratios across levels of agency racial diversity. For

instance, drugs/narcotics offenders have ratios of 0.99, 0.99, and 0.96 percent

offenders to percent arrestees black in agencies with fewer than 75 percent officers

white, 75 to 87 percent officers white, and more than 87 percent officers white,

respectively.

22 Keep in mind that agencies vary widely in their number of criminal offenders, and that although agencies have a mean of 89.7 percent of officers white, 73.2 percent of offenders are in agencies with fewer than 89.7 percent of officers white.

64

Figure 4.1 Offender Arrest Rates by Percent Officers White

Nonetheless, on the whole, there is no evidence that agencies with relatively

more white officers arrest relatively more black offenders. However, this tabulation

captures only some of the variation in officer diversity, and it may be that using

smaller intervals reveals a relationship between officer diversity and arrest rate

differences. For this reason, I also plot black and white offenders’ arrest rates for 2-

65

percentage-point intervals, as shown in Figure 4.1. The lines show local regression

fits, with black and white arrest rates shown with dashed and solid lines, respectively.

The lines are quite close and parallel to one another, suggesting that black and white

offenders have similar arrest rates for each level of police diversity. In addition, a

comparison of black and white arrest rates within each agency reveals no interaction

between officer diversity and offender race (not shown).

However, it is possible that police agency racial diversity and black and white

offenders’ relative arrest rates are both related to other factors that mask their true

relationship. For instance, offenses with black victims might be less likely to result in

arrest, more likely to be committed by a black offender, and more likely to be

investigated by an agency with more white officers. The relationship between officer

diversity and victim race would disguise an interaction between offender race and

officer diversity.

In addition, offenders’ likelihood of arrest might vary across police agencies

for unknown reasons, adding noise and/or bias to bivariate analyses that do not

account for agency differences. For these reasons, I estimate multilevel logistic

regression models that hold constant several relevant factors and account for variation

in offenders’ likelihood of arrest across agencies. These models are described below.

66

Models

I predict each offenders’ log odds of arrest given several offender, victim, and

agency characteristics. In particular, I estimate the following model:

9 = !0 + !"IJJKELKM:;<=> + !#?@=A@B:;<=> + !C?@=A@BDEFGE +

!HIJJ@=KMNbℎ@AK + !PIJJKELKM:;<=>×IJJ@=KMNbℎ@AK

where 9 is the log odds that an offender is arrested, !0 is the intercept, and !" is the

effect of an offender being black, rather than white. Models for violent offenses

include effect terms for victim characteristics, while offenses without victims do not.

The coefficient of interest is !P, which represents the increase in the log odds

of arrest for black offenders due to two standard deviations’ increase in an agency’s

percentage of officers who are white. The standard deviation of percent of officers

white is about 17 for each type of offense, so a !P coefficient of 1 would mean that

increasing the percentage of white officers from 90 (the mean) to 95 is associated with

a factor 1.1623 increase in black offenders’ odds of arrest. In addition, increasing the

percentage of white officers by 5 percentage points would increase all offenders’ odds

of arrest by a factor of K0."X×de.

Results

I find that there is no such relationship between police diversity and black and

white criminal offenders’ relative arrest likelihood for any of the following offenses:

robbery, simple and aggravated assault, intimidation, weapon violations, shoplifting,

23 P

#×"W= 0.147. K0."HW = 1.16

67

vandalism, and drugs/narcotics and drug equipment violations. Given prior research

on simple assault (Eitle, Stolzenberg, and D’Alessio 2005) and drug offenses

(Alexander 2010), the following discussion focuses on these particular offenses, but

the lack of results is consistent across all nine offenses examined.

Tables 4.2 and 4.3 present results from multilevel logistic regression models

estimating offenders’ log odds of arrest for violent and victimless crimes, respectively.

All models include terms for offender characteristics, as well as for victim

characteristics if applicable. The second model for each offense also includes the

interaction of offender race and agency percentage of officers white. All models

include a varying intercept and a varying coefficient for offender race. The variances

of the varying intercepts are quite large, indicating that offenders have very different

log odds of arrest across agencies, for which reason multilevel models are appropriate.

Offenses with Victims

Table 4.2 presents results from multilevel logistic regression models predicting

male offenders’ log odds of arrest as a function of their race, victim characteristics,

and police agency racial diversity. For robbery, simple assault, and aggravated assault

black offenders are significantly less likely than white offenders to be arrested. For

instance, the second model shows that black simple assault offenders are 0.924 times as

likely to be arrested as white simple assault offenders, holding constant victim

characteristics. However, there is no such association for intimidation offenses.

24 K60.""

68

Intimidation is also the only offense for which there is a statistically significant

(p < 0.05) association between officer racial diversity and offender likelihood of

arrest. However, for most offenses, there appears to be a positive relationship between

the percent of officers who are white and offenders’ likelihood of arrest, as can be

seen in Figure 4.1 above.

[TABLE 4.2]

Having examined the main effects of offender race and agency diversity, let us

turn to our primary coefficient of interest: their multiplicative association with the

likelihood of arrest. This interaction is not significant for any of the offenses

examined. In fact, models with such an interaction term do not fit the data better than

identical models without an interaction term. This can be seen with the AIC, BIC, and

log likelihood measures shown for each pair of nested models.

Offenses without Victims

A similar picture emerges for offenses without victims: weapon, shoplifting,

vandalism, drugs/narcotics, and drug equipment offenses. Table 4.3 presents

estimates from multilevel logistic regression models predicting offenders’ log odds of

arrest for these five offenses. The second model for each offense includes a term for

the percentage of officers who are white in each agency, as well as an interaction term

between offender race and percent of officers who are white.

[TABLE 4.3]

69

For drugs/narcotics and drug equipment offenses, black offenders are less

likely than white offenders to be arrested. For instance, black vandalism offenders are

0.8725 times as likely as white vandalism offenders to be arrested. In contrast, there is

no significant association between offender race and the likelihood of arrest for

drugs/narcotics and drug equipment offenders.

For shoplifting, it appears that black offenders are more likely to be arrested in

agencies with more white officers. That is, an increase of 32 percentage points in an

agency’s officers who are white is associated with a factor 1.126 increase in black

offenders’ odds of arrest. This association is very small, as a 32 percentage point

increase white officers is enormous. In addition, it is most likely meaningless, as

Figure 4.1 shows that there is no linear increase in black offenders’ relative likelihood

of arrest as agencies’ percentage of officers white increases. Similarly, the negative

interaction term for drug equipment offenses is likely spurious, as there is no apparent

relationship between the percentage of officers who are white and black and white

drug equipment offenders’ relative arrest rates.

Discussion These results suggest that agencies with relatively more white officers are not

relatively more likely to arrest black, rather than white, offenders. Less racially

diverse agencies are not more likely to discriminate against black men than their more

diverse counterparts.

25 K60."H 26 K0."

70

These findings are relevant to both the academic literature and the current

national debate on police diversity. In particular, they indicate that increasing police

agency racial diversity, while a worthwhile goal for many reasons, is unlikely to

alleviate current racial disparities in arrest rates.

In addition, they increase our understanding of police professionalism.

Researchers and concerned citizens often argue that overwhelmingly white police

departments are less professional than racially diverse departments (Ashkenas and

Park 2015; Black 1971; Brown and Frank 2006; Sherman 1980), yet I find no

evidence for this claim. Instead, I find that officers are interchangeable, as predicted

by blue blood theory and research on implicit bias.

However, this chapter has several limitations, and further research is urgently

needed to better understand the relationship between police diversity and

professionalism.

Limitations This analysis faces several limitations, particularly its lack of detailed data on

each officer. Unfortunately, national data on police officer characteristics is not

available, and I was unable to examine individual officers’ investigations and arrest

rates. For this reason, these findings may be victim to the ecological fallacy, as white

officers may discriminate more against black men yet tend to be in agencies that

discriminate less against black men. Further research is needed to better understand

individual officers’ investigations and arrest decisions.

71

Another issue is omitted variable bias, which may disguise existing

relationships or render found relationships spurious. An ideal study design would be

experimental, randomly assigning offenders to be black or white and randomly

distributing them across police agencies. A more realistic study design would hold

constant every factor related to offender race, agency demographics, and offender

arrest. However, such factors are often unknown, unmeasured, and highly correlated

with one another. For instance, for offenses without victims, models including

controls for crime rate and income did not converge, due to multicollinearity.

Given these limitations, these findings should be interpreted cautiously.

Further research is needed to determine their robustness and complement their

shortcomings.

Conclusion Limited as they are, these findings improve our understanding of police

diversity and professionalism in the U.S. Leveraging several recent data sources, I

provide a broad examination of the role of police diversity on professionalism. I find

that increasing officer diversity is not a silver bullet and is unlikely to increase police

professionalism.

However, it is important to keep in mind that unprofessional policing is only

one factor contributing to racial inequalities in the criminal justice system. Many

forms of discrimination at many stages of contact with the criminal justice system

contribute to these inequalities and should not be overlooked. Black men are often

treated unfairly when they encounter police officers, are indicted, (cannot) hire

lawyers, (cannot) post bail, are convicted, sentenced, or are (not) released on parole

72

(Alexander 2010). Even if police officers perform their duties with the utmost

professionalism, there is ample reason to suspect that racial disparities in incarceration

are unwarranted and that the criminal justice system urgently needs reform.

73

Chapter 5: Conclusion

Review of Major Findings and Contributions This dissertation examines police professionalism in the U.S., searching for

evidence of unprofessional policing and examining its relation to police discretion and

diversity. These analyses increase our understanding of police professionalism at a

time when many Americans doubt its existence.

I first examine the overall prevalence of police professionalism, testing the

hypothesis that police agencies on average discriminate against black men. I find no

evidence for this hypothesis, occasionally even finding that black offenders are less

likely than white offenders to be arrested.

Although I find no evidence of widespread discrimination, previous research

suggests that police discretion and lack of diversity both decrease professionalism.

For this reason, I look for evidence that discretionary policing is associated with

discrimination against black men, operationalizing discretionary policing as

investigations in which offenders are more often arrested, particularly on the day of

their offense. Focusing on drug offenses, where discretionary policing is common, I

find that discretionary policing and discrimination are unrelated.

Next, I test the hypothesis that less diverse police forces are less professional

and find that police diversity is unrelated to discrimination. This suggests that

increasing police diversity, while worthwhile for many reasons, may have little effect

on police discrimination.

Together, these chapters tell a consistent story: discrimination is not present in

all police agencies and cannot be quickly overcome by changing these specific one or

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two factors. The processes that cause and sustain discrimination are likely subtle and

present only in certain areas and situations. Further research is urgently needed to

explore these areas and extend these findings.

Directions for Future Research

Perhaps the most promising avenue of future research is simply the

continuation of most current research: micro-level analyses of police professionalism

within individual cities or states. Although this dissertation finds no evidence of

unprofessionalism at the national level, it may be that discrimination is common

locally in several locations and types of agencies. For instance, it may be that rural

areas have considerable discrimination, while urban areas (those oversampled by

NIBRS) tend not to. Or it may be that certain types of police investigations or officers

engage in discrimination, while others do not. For instance, officers with little

experience and training may discriminate against black men, while most officers do

not. Further research is needed to examine such hypotheses and understand police

discrimination more fully.

Future research might also examine the quality of data on criminal offenses, as

current data may be substantially biased by reporting bias and police agencies’

willingness to cooperate with the FBI. Analyses such as that of the National

Academies of Sciences, Engineering, and Medicine (National Academies 2017) are

needed to better understand and improve data sources like the Uniform Crime Reports

(UCR) and the National Incident-Based Reporting System (NIBRS).

75

Context of Findings

Lastly, it is crucial to view these findings within the wider context of research

on the U.S. criminal justice system. Although these findings suggest that police

discrimination is not ever-present in the entire U.S., there may well be discrimination

in certain police investigations, and certainly in other parts of the criminal justice

system.

In addition, discrimination is not the only issue facing the U.S. criminal justice

system. The sheer scale of mass incarceration and its racial inequalities is staggering

and not to be forgotten. The United States has the second highest incarceration rate in

the world, and mass incarceration affects black people much more than it does white

people. Almost 20 percent of black and 3 percent of white men have been imprisoned

by the time they are 30 years old, and 33 percent of black and 6 percent of white men

are likely to be incarcerated during their lifetime (Bonczar 2003; Bonczar and Beck

1997; Pettit and Western 2004). These racial disparities further widen existing

socioeconomic and racial inequalities while diminishing the legitimacy of poor and

black Americans (Western 2013; Wildeman 2009; Wakefield and Wildeman 2013).

This dissertation asks whether black and white men reported for a criminal

offense are equally likely to be arrested, whether U.S. policing is professional. In a

way, this is asking whether U.S. policing is legitimate, or at least equally legitimate

for black and white men. However, this question may be beside the point, as we may

find it unacceptable for our society to imprison one percent of its population and 20

percent of a historically disadvantaged ethnic group. Perhaps the appropriate question

is not, “Which people are arrested,” but, “Why are we arresting so many people?”

76

How do other countries manage to have low crime rates without mass incarceration?

How did the U.S. manage to do so in previous decades?

These questions are important and should be kept in mind when considering

this dissertation’s findings. Although I find no evidence of unprofessional policing,

mass incarceration alone threatens the legitimacy and efficacy of the criminal justice

system and further exacerbates racial inequalities. We must never lose sight of this

reality and never stop pushing for criminal justice reform.

77

Appendix Problem Statement


logistic regression model. However, it is often the case, as it is here, that observations

are clustered within groups that share certain characteristics. For instance, criminal

offenders are investigated by many of the same agencies, which have a single police

chief, budget, and crime rate. Thus many offenders have identical agency-level

characteristics and artificially low standard errors for their estimated coefficients.

One common solution is to adjust the variance-covariance matrix of the

specified error term and compute a robust Huber-White standard error for each

estimate (Harrell, Jr. 2017; Jacobs et al. 2002; Kerrissey and Schofer 2013; White

1980). However, although robust standard errors account for residuals’ low variances,

they do not adjust for observations’ dependence (Freeman 2006; Greene 2002: 674;

King and Roberts 2017).

Why might observations be dependent on one another? In this case, offenders

investigated by the same police agency may have similar likelihoods of arrest, as

certain agencies have higher arrest rates than others. Perhaps more distressingly,

offenders own characteristics, such as race, gender, and offense severity, may be

related to those of other offenders reported to the same agency. In short, observations

within a group may be more similar to one another than to observations in other

groups.

78

Yet if groups differ in their likelihood of the outcome, then factors associated

with certain groups will appear to be related to the outcome. This will bias

coefficients in an unknown direction that cannot be detected. In addition, groups’

varying rate of the outcome will add noise to the data and bias estimates downwards.

Here I focus on the second issue, comparing several common modeling approaches.

Models and Data

Single-Level Logistic Regression with a Group Dummy One method of accounting for each group’s association with the outcome is to

include a dummy variable for group. However, this method precludes the estimation

of group-level terms. In my case, it prevents an analysis of the impact of an agency’s

percentage of officers who are white on offenders’ likelihood of arrest.

Multilevel Logistic Regression An alternative to the dummy variable approach is a multilevel model with a

varying intercept for each group (Gelman and Hill 2007: 240). This model estimates

an intercept for each group, while still allowing for group-level terms.

Data

In order to assess the potential magnitude of the outcome clustering issue and

the ability of each model to remedy it, I generate a data set with known associations

79

between each variable. To make the data comparable to those analyzed in this

dissertation, I generate 180,000 observations within 600 groups, each of size 300.

I generate a standard normal random variable, 2", a Bernoulli random variable,

2#, (7 = 0.3), a constant j+, group constant k+U, and error term l+ introducing

randomness. Using these variables, I generate an outcome m, which is a Bernoulli

random variable, where each *+ has probability 7+ of being 1, and 7+ has the following

property:

log7+

1 − 7+= j+ + k+U +!"2" + !#2#

I generate a data set for several random variables k, each of which is normal

with mean 0 but has a different standard deviation. Importantly, the sum of the

variances of k and l the same across data sets, holding constant observations’ total

error. This makes outcome dependence within groups the only distinguishing factor

between data sets, rendering them comparable.

Results

Using these data, for which I know the true relationships between 2", 2#, and

m, I evaluate each model’s performance. Figures A.1 and A.2 show their estimated

coefficients for !" and !#, respectively. Circles, triangles, and squares show estimates

for single-level models without a dummy, single-level models with a dummy, and

multilevel models with a varying intercept. Lines show the true association with the

outcome.

80

Figure A.1. no Estimates

The x-axis represents the standard deviation of the group-level intercept. The

greater this standard deviation is, the more that groups’ baseline likelihood of the

outcome differs (and the less that error is random within groups). This is analogous to

intraclass correlation coefficients (ICC) for the outcome, and the x-axis can be thought

of as ranging from an ICC near 0 to an ICC of almost 1.

Figure A.1 shows that single-level logistic regression models without a group

dummy variable consistently underestimate the magnitude of the association between

2" and the outcome. This is precisely the problem anticipated, as group-level

variations in the outcome are not captured by the model. Similarly, Figure A.2 shows

81

that this model underestimates !#, the association between the binary variable 2# and

the outcome.

Figure A.2. np Estimates

When there is little group-level clustering (to the left of the x-axis), each model

performs equally poorly, as observations’ random errors bias estimates downwards.

However, as more of the error is associated with group membership, the single-level

model with a dummy variable and multilevel model with a varying intercept increase

in accuracy. In fact, their estimates are highly similar, although not identical.

Recalling the issue of estimating group-level coefficients in models with a

dummy for group, I conclude that multilevel models are ideal for analyzing data with

82

outcomes clustered within groups. Single-level logistic regression models fail to

capture each group’s outcome likelihood and to accurately estimate independent

variables’ association with the outcome.

83

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Tables

Table 2.1. Offender Counts and Arrest Rates of the Twenty Most Common Offenses

Offense Offense Count Arrest Count % Arrested % Offenders

Black % Arrestees

Black % Offenders /

% Arrestees Black All 1,299,614 630,257 48.5 45.2 40.8 1.11 Simple Assault 270,118 113,722 42.1 47.4 41.2 1.15 Drugs Narcotics 223,048 146,216 65.6 42.3 43.8 0.97 Shoplifting 117,656 60,660 51.6 40.8 34.0 1.2 Vandalism 116,307 22,335 19.2 40.2 31.2 1.29 Other Theft 100,374 19,914 19.8 37.0 30.6 1.21 Burglary 91,651 25,310 27.6 46.1 42.4 1.09 Drug Equipment 86,969 22,926 26.4 25.4 24.4 1.04 Aggravated Assault 79,322 30,110 38.0 52.6 43.3 1.22 Intimidation 76,491 14,497 19.0 45.6 36.4 1.25 Robbery 69,067 12,274 17.8 79.6 67.8 1.17 Weapon 44,780 16,755 37.4 57.2 55.1 1.04 Building Theft 38,257 5,426 14.2 39.5 32.1 1.23 Theft From Auto 29,660 6,573 22.2 38.3 31.4 1.22 Auto Theft 26,719 5,693 21.3 43.9 40.2 1.09 False Pretenses 23,771 5,167 21.7 38.2 32.5 1.18 Forgery 17,931 4,513 25.2 39.6 37.8 1.05 Stolen Property 17,362 6,801 39.2 37.7 42.7 0.88 Forcible Rape 12,429 2,221 17.9 43.7 42.0 1.04 Forcible Fondling 11,607 2,383 20.5 31.0 29.3 1.06 Note: Data are from merged NIBRS-LEMAS data set. “% Offenders Black” is the percentage of black and white male offenders who are black. No multi-racial, other race, or Hispanic offenders are included. Arrestees for a given offense are included only if they were also reported for that offense.

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Table 2.2. Uniform Crime Reports (UCR) Data on Criminal Offenses and Arrests

Data Set Mean Data Set S.D. Data Set N U.S. Mean U.S. S.D. U.S. N

Total Arrests 14,944.1 32,918.0 600 5,450.4 28,357.9 11,883 Total Offenses 3,984.0 9,565.3 600 1,096.9 5,159.8 11,883 % Aggravated Assault 35.8 11.5 600 33.4 17.4 11,883 % Motor Vehicle Theft 3.2 2.3 600 4.0 7.7 11,883 % Larceny 46.5 12.0 600 46.4 18.5 11,883 % Burglary 12.2 6.1 600 14.0 10.3 11,883 % Murder 0.1 0.2 600 0.1 1.3 11,883 % Rape 1.0 0.9 600 1.0 2.4 11,883 % Robbery 1.3 1.4 600 1.0 1.9 11,883 Percent Arrests Black 21.2 21.5 600 16.1 20.6 11,883 Percent Arrests White 76.6 21.4 600 81.5 21.1 11,883 Percent Arrests Asian 0.8 2.0 600 0.8 2.4 11,883 Percent Arrests Native American 1.4 6.2 600 1.6 7.0 11,883 Percent Arrests Hispanic 0.0 0.0 600 0.0 0.0 11,883 Percent Arrests Non-Hispanic 0.0 0.0 600 0.0 0.0 11,883 Note: Data are from 2013 Uniform Crime Reports (UCR). “Data Set” columns represent UCR data for the agencies sampled in this dissertation’s dataset. Male and female offenders are included, as are multi-racial, other race, and Hispanic offenders.

Table 2.3. Law Enforcement Agencies


Continuous Measures % Officers White 89.7 15.8 638 83.0 23.0 2,049 % Officers Black 4.3 8.4 638 5.8 11.8 2,049 % Officers Male 91.6 7.2 638 91.5 7.9 2,054 Full-Time Sworn Officers 106.6 253.7 638 146.1 898.5 2,059 Total Budget ($M) 14.0 33.2 599 21.4 122.6 1,923 Binary Measures % Assign Officers to Beats 57.4 - 626 57.7 - 2,021 % Use Body Cameras 32.7 - 637 27.8 - 2,050 % Require More than HS 16.6 - 632 17.7 - 2,031 % Without Additional Training 14.9 - 631 17.2 - 2,037 Note: “Data Set” columns represent the local agencies sampled, while “U.S.” columns represent all local law enforcement agencies for which LEMAS 2013 data are available.

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Table 2.4. Counties


Total Population (K) 214.7 361.4 367 99.1 316.5 3,143 Male Population (K) 105.4 177.4 367 48.8 155.4 3,143 % Residents White 84.7 11.7 367 83.8 16.7 3,143 % Residents Black 7.8 10.7 367 9.0 14.5 3,143 % 2012 Pres. Votes Republican 55.6 13.6 309 59.4 15.1 3,163 Unemployment Rate (Single Race) 8.9 3.0 367 8.9 3.9 3,143 White Unemployment 8.1 2.6 367 7.9 3.3 3,143 Black Unemployment 16.8 14.0 363 16.9 18.0 2,831 Mean Household Income (K) 65.1 15.9 367 59.5 14.3 3,143 % White Households < 10K 6.6 2.6 367 7.1 3.1 3,143 % Black Households < 10K 15.6 13.9 361 16.4 17.9 2,671 % White Households < 20K 17.7 5.8 367 19.6 6.3 3,143 % Black Households < 20K 32.5 18.9 361 34.8 24.2 2,671 % White Households < 40K 39.4 9.7 367 42.6 9.6 3,143 % Black Households < 40K 57.9 20.4 361 60.1 26.2 2,671 Note: “Data Set” columns represent counties in the data analyzed here, while “U.S.” columns represent all U.S. counties in the American Community Survey (ACS). “Percent Votes Republican” represents the percentage of a county’s voters who voted for the Republican candidate (John McCain) in the 2012 presidential election, as provided by CQPress.com (CQ Press 2015). All other measures are derived from 2009-2013 ACS data.

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Table 2.5. Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses with Victims

Robbery Aggravated Assault Simple Assault Intimidation Offender Black (ref = White) -0.76*** -0.36*** -0.71*** -0.23*** -0.63*** -0.22*** -0.52* 0.06

(0.15) (0.08) (0.13) (0.05) (0.10) (0.03) (0.22) (0.06) Victim Black (ref = White) -0.13 -0.27*** -0.28*** -0.09

(0.08) (0.05) (0.04) (0.09) Victim Knew Offender 0.65*** 0.87*** 0.61*** -0.17

(0.12) (0.06) (0.08) (0.13) Offenders Reported to Agency -0.65* -0.67*** -0.48** -1.45***

(0.27) (0.19) (0.17) (0.32) County Mean Income 0.41** 0.44*** 0.49*** 0.55***

(0.14) (0.10) (0.09) (0.13) Constant -0.80*** -1.36*** 0.35*** -0.55*** 0.18*** -0.48*** -1.17*** -1.41*** (0.09) (0.15) (0.06) (0.08) (0.05) (0.08) (0.11) (0.17) N 8,228 8,228 30,807 30,807 150,776 150,776 44,473 44,473 AIC 8,228.6 7,880.6 41,746.7 39,973.4 204,934.5 199,729.3 43,948.6 41,057.5 BIC 8,242.6 7,922.7 41,763.4 40,023.4 204,954.3 199,788.9 43,966.1 41,109.7 Log Likelihood (df) -4,112.3 (1) -3,934.3 (5) -20,871.4 (1) -19,980.7 (5) -102,465.2 (1) -99,858.7 (5) -21,972.3 (1) -20,522.7 (5) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS. Only black and white male offenders are included, and only when there was a single victim and a single offense committed. Agencies with fewer than 10 offenders for the given offense are excluded. Huber-White standard errors are shown to account for clustering by agency. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 2.6. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses with Victims

Robbery Aggravated Assault Simple Assault Intimidation Offender Black (ref = White) -0.43*** -0.33*** -0.23*** -0.15*** -0.20*** -0.11*** -0.03 0.04

(0.07) (0.08) (0.03) (0.04) (0.01) (0.02) (0.03) (0.04) Victim Black (ref = White) -0.12 -0.15*** -0.15*** -0.12**

(0.07) (0.04) (0.02) (0.04) Victim Knew Offender 0.70*** 0.95*** 0.69*** -0.08

(0.06) (0.04) (0.02) (0.05) Offenders Reported to Agency -0.71* -1.09*** -0.75*** -1.18**


(0.11) (0.07) (0.07) (0.10) Constant -0.72*** -1.42*** 0.57*** -0.66*** 0.33*** -0.57*** -1.28*** -1.67*** (0.08) (0.15) (0.05) (0.11) (0.04) (0.09) (0.07) (0.18) Offenders 8,228 8,228 30,807 30,807 150,776 150,776 44,473 44,473 Agencies 398 398 747 747 840 840 727 727 Agency Intercept Variance 0.76 0.62 0.89 0.82 1.16 1.10 2.32 2.17 AIC 7,637.2 7,511.8 38,067.8 37,458.0 183,575.5 182,728.1 33,260.7 33,236.6 BIC 7,658.2 7,560.9 38,092.8 37,516.3 183,605.3 182,797.6 33,286.8 33,297.6 Log Likelihood (df) -3,815.6 (3) -3,748.9 (7) -19,030.9 (3) -18,722.0 (7) -91,784.8 (3) -91,357.1 (7) -16,627.3 (3) -16,611.3 (7) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS. Only black and white male offenders are included, and only when there was a single victim and a single offense committed. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05.

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Table 2.7. Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses without Victims

Weapon Shoplifting Vandalism Drugs / Narcotics Drug Equipment Offender Black (ref = White) -0.05 -0.36*** -0.65*** -0.53*** -0.61*** -0.36*** 0.12 0.02 -0.01 -0.07

(0.18) (0.10) (0.08) (0.06) (0.14) (0.07) (0.10) (0.07) (0.10) (0.08) Offenders Reported to Agency 0.85*** -0.49** -0.66* 0.37 0.22

(0.22) (0.17) (0.32) (0.20) (0.20) County Mean Income -0.19 -0.22* 0.34*** 0.11 -0.01

(0.11) (0.09) (0.10) (0.11) (0.19) Constant 0.58*** 0.79*** 0.60*** 0.56*** -0.92*** -1.06*** 1.18*** 1.23*** 0.61*** 0.63*** (0.07) (0.09) (0.07) (0.07) (0.07) (0.10) (0.06) (0.08) (0.08) (0.09) N 21,940 21,940 97,016 97,016 84,466 84,466 156,272 156,272 31,322 31,322 AIC 28,789.2 27,951.7 129,361.4 127,854.9 92,117.4 90,435.7 167,044.5 166,238.0 40,670.8 40,594.6 BIC 28,805.2 27,983.7 129,380.3 127,892.8 92,136.1 90,473.1 167,064.4 166,277.9 40,687.5 40,628.0 Log Likelihood (df) -14,392.6 (1) -13,971.9 (3) -64,678.7 (1) -63,923.5 (3) -46,056.7 (1) -45,213.9 (3) -83,520.2 (1) -83,115.0 (3) -20,333.4 (1) -20,293.3 (3) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS, and only black and white male offenders are included. Agencies with fewer than 10 offenders for the given offense are excluded. Huber-White standard errors are shown to account for clustering by agency. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 2.8. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses without Victims

Weapon Shoplifting Vandalism Drugs / Narcotics Drug Equipment Black (ref = White) -0.21*** -0.22*** -0.48*** -0.48*** -0.15*** -0.15*** 0.02 0.02 0.03 0.03

(0.04) (0.04) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.04) (0.04) Offenders Reported to Agency 0.33 -0.96** -0.70** 0.48* 0.52

(0.25) (0.30) (0.27) (0.24) (0.37) County Mean Income 0.01 -0.02 0.35*** 0.16 -0.03

(0.09) (0.10) (0.08) (0.10) (0.17) Constant 0.65*** 0.77*** 0.70*** 0.42*** -1.06*** -1.29*** 1.28*** 1.43*** 0.46*** 0.57*** (0.05) (0.11) (0.06) (0.11) (0.05) (0.10) (0.05) (0.08) (0.09) (0.12) Offenders 21,940 21,940 97,016 97,016 84,466 84,466 156,272 156,272 31,322 31,322 Agencies 335 335 444 444 587 587 686 686 392 392 Agency Intercept Variance 0.73 0.72 1.48 1.44 1.21 1.15 1.58 1.56 3.11 3.09

AIC 25,718.2 25,720.6 114,887.6 114,882.2 79,941.2 79,920.5 142,827.7 142,826.5 31,207.7 31,209.7 BIC 25,742.2 25,760.6 114,916.1 114,929.6 79,969.2 79,967.2 142,857.6 142,876.3 31,232.7 31,251.5 Log Likelihood (df) -12,856.1 (3) -12,855.3 (5) -57,440.8 (3) -57,436.1 (5) -39,967.6 (3) -39,955.3 (5) -71,410.8 (3) -71,408.3 (5) -15,600.8 (3) -15,599.9 (5) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS, and only black and white male offenders are included. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 2.9. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses with Victims who Knew the Offender

Robbery Aggravated Assault Simple Assault Intimidation Offender Black (ref = White) -0.26* -0.21 -0.24*** -0.15*** -0.19*** -0.10*** 0.01 0.05

(0.11) (0.13) (0.03) (0.04) (0.01) (0.02) (0.04) (0.05) Victim Black (ref = White) -0.05 -0.13** -0.14*** -0.08

(0.12) (0.04) (0.02) (0.05) Offenders Reported to Agency -0.54 -0.94*** -0.74*** -1.17**


(0.11) (0.07) (0.07) (0.10) Constant -0.54*** -0.79*** 0.64*** 0.30** 0.36*** 0.11 -1.31*** -1.77*** (0.11) (0.16) (0.05) (0.11) (0.04) (0.09) (0.07) (0.18) Offenders 2,951 2,951 26,846 26,846 141,662 141,662 40,323 40,323 Agencies 337 337 736 736 838 838 715 715 Agency Intercept Variance 0.99 0.76 0.96 0.87 1.19 1.11 2.38 2.23 AIC 3,165.3 3,153.0 32,772.6 32,740.1 171,655.7 171,576.8 29,935.1 29,917.4 BIC 3,183.3 3,188.9 32,797.2 32,789.3 171,685.3 171,636.0 29,960.9 29,969.0 Log Likelihood (df) -1,579.7 (3) -1,570.5 (6) -16,383.3 (3) -16,364.0 (6) -85,824.9 (3) -85,782.4 (6) -14,964.5 (3) -14,952.7 (6) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS. Only black and white male offenders are included, and only when there was a single victim and a single offense committed. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 3.1. Drug Offenders Drugs / Narcotics Drug Equipment Offenders 188,275 35,812 % Arrested 77.4 62.6 % Arrested on Same Day 68.1 55.9 % Arrested on Other Day 14.6 10.3 % Offenders Black 43.3 25.1 % Black Offenders Arrested 78.5 61.6 % White Offenders Arrested 76.6 63.0 Note: Data are from 2013 NIBRS. Only black and white male offenders are included. Offenders arrested for other offenses are omitted, as are police agencies with fewer than 10 offenders. Table 3.2. Percent of Arrested Offenders who were Arrested on Day of Offense Offender Race Drugs / Narcotics Drug Equipment White 89.4 92.6 Black 91.1 94.3 Note: Data are from 2013 NIBRS. Only black and white male offenders are included. Offenders arrested for other offenses are omitted, as are police agencies with fewer than 10 offenders. Differences between black and white percentages are significant according to a two-tailed t-test (p < 0.001).

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Table 3.3. Law Enforcement Agencies Drugs / Narcotics Drug Equipment Agencies 718 413 Offenders Reported 262 87 (591) (170) Mean Income ($K) 69 70 (18) (16) % Offenders Arrested 73.4 57.0 (21.4) (30.7) % Arrested on Same Day 60.1 47.8 (26.4) (31.8) % Arrested on Other Day 16.0 10.9 (19.9) (18.7) Note: Data are from 2013 NIBRS. Only black and white male offenders are included. Offenders arrested for other offenses are omitted, as are police agencies with fewer than 10 offenders. Standard deviations are shown in parentheses. Table 3.4. Intraclass Correlation Coefficients Drugs / Narcotics Drug Equipment Offender Race 0.224 0.146 Offender Arrest 0.198 0.327 Note: Data are from 2013 NIBRS. Only black and white male offenders are included. Offenders arrested for other offenses are omitted, as are police agencies with fewer than 10 offenders.

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Table 3.5. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Agency Arrest Rates

Drugs / Narcotics Drug Equipment Offender Black (ref = White) 0.02 0.01 -0.00 0.05 0.05 0.05

(0.02) (0.02) (0.02) (0.04) (0.04) (0.04) Offenders Reported to Agency 0.09* 0.09* 0.04 0.04

(0.04) (0.04) (0.04) (0.04) County Mean Income -0.04 -0.05 0.03 0.03

(0.02) (0.02) (0.03) (0.03) Agency Percent Arrested 1.94*** 1.96*** 2.81*** 2.81***

(0.02) (0.02) (0.04) (0.04) Offender Black ´ Percent Arrested -0.05 0.01

(0.03) (0.08) Constant 1.34*** 1.47*** 1.47*** 0.62*** 0.80*** 0.80*** (0.05) (0.02) (0.02) (0.09) (0.02) (0.02) Offenders 150,651 150,651 150,651 29,979 29,979 29,979 Agencies 649 649 649 354 354 354 Agency Intercept Variance 1.33 0.02 0.02 2.58 0.01 0.01 AIC 137,909.3 136,181.0 136,180.2 30,041.1 28,937.4 28,939.4 BIC 137,939.1 136,240.5 136,249.7 30,066.0 28,987.2 28,997.5 Log Likelihood (df) -68,951.7 (3) -68,084.5 (6) -68,083.1 (7) -15,017.5 (3) -14,462.7 (6) -14,462.7 (7) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS. Only black and white male offenders are included. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 3.6. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Agency Same-Day Arrest Rates for Drugs/Narcotics Offenses

Drugs / Narcotics Drugs / Narcotics Same-Day Offender Black (ref = White) 0.02 0.02 0.02 0.05* 0.05* 0.06**

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Offenders Reported to Agency 0.30 0.29 0.32 0.30

(0.20) (0.20) (0.27) (0.26) County Mean Income 0.11 0.11 -0.01 -0.01

(0.09) (0.10) (0.08) (0.08) Percent Arrests Same-Day 0.21** 0.20** -0.29** -0.30**

(0.07) (0.07) (0.09) (0.09) Offender Black ´ Same-Day Arrests 0.04 0.09

(0.03) (0.05) Constant 1.34*** 1.45*** 1.44*** 1.39*** 1.56*** 1.55***

(0.05) (0.07) (0.07) (0.06) (0.11) (0.11) Offenders 150,651 150,651 150,651 87,552 87,552 87,552 Agencies 649 649 649 478 478 478 Agency Intercept Variance 1.33 1.30 1.30 1.37 1.32 1.32 AIC 137,909.3 137,903.0 137,903.6 76,407.4 76,403.1 76,401.9 BIC 137,939.1 137,962.5 137,973.0 76,435.5 76,459.3 76,467.5 Log Likelihood (df) -68,951.7 (3) -68,945.5 (6) -68,944.8 (7) -38,200.7 (3) -38,195.5 (6) -38,193.9 (7) Note: Data are from 2013 NIBRS, 2013 LEMAS, and 2009-2013 ACS. Only black and white male offenders are included. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05

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Table 4.1. Offense Counts and Arrest Rates by Agency Percentage of Officers White

Offense % Officers

White Offense Count Arrest Count

% Offenders Arrested

% Offenders Black

% Arrestees Black

% Offenders / % Arrestees Black

Simple Assault < 75% 119,140 39,949 33.53 72.72 68.34 1.06 75 to 87% 120,776 40,820 33.80 48.33 43.08 1.12 > 87% 125,029 53,403 42.71 30.45 25.99 1.17

Drugs / Narcotics < 75% 51,793 36,170 69.84 64.55 65.43 0.99 75 to 87% 80,984 52,380 64.68 44.90 45.39 0.99 > 87% 97,226 59,371 61.06 26.32 27.55 0.96

Shoplifting < 75% 46,459 25,136 54.10 60.82 55.92 1.09 75 to 87% 68,543 31,539 46.01 42.88 34.91 1.23 > 87% 91,119 58,025 63.68 24.66 20.98 1.18

Vandalism < 75% 40,923 5,726 13.99 70.43 65.18 1.08 75 to 87% 48,919 7,224 14.77 46.83 39.76 1.18 > 87% 51,020 10,812 21.19 25.79 18.38 1.40

Other Theft < 75% 33,132 5,016 15.14 59.29 55.60 1.07 75 to 87% 36,090 5,058 14.01 41.86 36.79 1.14 > 87% 54,400 11,563 21.26 22.80 17.72 1.29

Intimidation < 75% 37,503 4,483 11.95 75.44 63.24 1.19 75 to 87% 39,163 4,563 11.65 48.97 45.21 1.08 > 87% 27,813 6,282 22.59 26.21 23.24 1.13

Drug Equipment < 75% 13,395 4,186 31.25 42.88 42.09 1.02 75 to 87% 34,451 8,818 25.60 34.57 32.29 1.07 > 87% 46,811 13,025 27.82 13.44 13.77 0.98

Aggravated Assault < 75% 37,989 11,209 29.51 76.96 71.08 1.08 75 to 87% 29,188 9,900 33.92 53.65 49.27 1.09 > 87% 26,406 11,436 43.31 33.54 29.66 1.13

Burglary < 75% 30,296 7,837 25.87 70.08 69.15 1.01 75 to 87% 29,049 6,206 21.36 47.38 43.06 1.10 > 87% 32,219 8,999 27.93 28.15 24.97 1.13

Robbery < 75% 37,963 5,740 15.12 88.66 82.63 1.07 75 to 87% 23,416 3,733 15.94 74.21 63.92 1.16 > 87% 12,622 3,446 27.30 57.08 45.33 1.26

Note: Data are from 2013 NIBRS and LEMAS. Percentage of offenders black is the percentages of black and white criminal offenders who are black. No multi-racial, other race, or Hispanic offenders are included. Arrestees for a given offense are only included if they were also reported for that offense. Agencies with fewer than 10 offenders for the given offense are excluded.

106

Table 4.2. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses with Victims

Robbery Aggravated Assault Simple Assault Intimidation

Offender Black (ref = White) -0.33*** -0.30*** -0.15*** -0.14*** -0.11*** -0.11*** 0.04 0.04

(0.07) (0.08) (0.04) (0.04) (0.02) (0.02) (0.04) (0.04) Victim Black (ref = White) -0.12 -0.12 -0.15*** -0.15*** -0.15*** -0.15*** -0.11* -0.11* (0.07) (0.07) (0.04) (0.04) (0.02) (0.02) (0.04) (0.04) Victim Knew Offender 0.78*** 0.78*** 0.95*** 0.95*** 0.70*** 0.70*** -0.01 -0.01 (0.06) (0.06) (0.04) (0.04) (0.02) (0.02) (0.05) (0.05) Officers White 0.13 0.21 0.11 0.14 0.09 0.10 0.38* 0.40**

(0.15) (0.17) (0.10) (0.11) (0.08) (0.08) (0.15) (0.15) Offender Black ´ Officers White -0.15 -0.10 -0.03 -0.06

(0.18) (0.07) (0.03) (0.09) Constant -1.10*** -1.12*** -0.27*** -0.27*** -0.34*** -0.35*** -1.36*** -1.37*** (0.10) (0.10) (0.06) (0.06) (0.05) (0.05) (0.09) (0.09) Offenders 8,677 8,677 32,720 32,720 163,936 163,936 47,432 47,432 Agencies 421 421 778 778 875 875 759 759 Agency Intercept Variance 0.78 0.78 0.91 0.91 1.18 1.18 2.21 2.21 AIC 8,029.1 8,030.4 39,807.1 39,807.0 198,363.7 198,364.3 36,229.7 36,231.2 BIC 8,071.5 8,079.9 39,857.4 39,865.8 198,423.7 198,434.3 36,282.3 36,292.5 Log Likelihood (df) -4,008.6 (6) -4,008.2 (7) -19,897.5 (6) -19,896.5 (7) -99,175.8 (6) -99,175.2 (7) -18,108.8 (6) -18,108.6 (7) Note: Data are from 2013 NIBRS and 2013 LEMAS. Only black and white male offenders are included, and only when there was a single victim and a single offense committed. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05

107

Table 4.3. Multilevel Logistic Regression Models Predicting each Male Offender’s Arrest – Offenses without Victims Weapon Shoplifting Vandalism Drugs / Narcotics Drug Equipment Offender Black -0.31*** -0.30*** -0.49*** -0.49*** -0.14*** -0.14*** 0.02 0.02 0.04 0.03 (ref = White) (0.03) (0.04) (0.02) (0.02) (0.02) (0.02) (0.01) (0.01) (0.03) (0.03) Officers White -0.11 -0.08 0.10 0.06 0.10 0.10 -0.18* -0.18* -0.11 -0.08

(0.10) (0.10) (0.11) (0.11) (0.10) (0.10) (0.09) (0.09) (0.16) (0.16) Offender Black ´ -0.08 0.10** 0.01 -0.00 -0.17* Officers White (0.08) (0.04) (0.05) (0.03) (0.08) Constant 0.69*** 0.69*** 0.67*** 0.68*** -1.07*** -1.07*** 1.31*** 1.31*** 0.37*** 0.37*** (0.06) (0.06) (0.06) (0.06) (0.05) (0.05) (0.05) (0.05) (0.09) (0.09) Offenders 26,843 26,843 104,784 104,784 91,449 91,449 188,275 188,275 35,812 35,812 Agencies 359 359 467 467 615 615 718 718 413 413 Agency Intercept Variance 0.72 0.72 1.42 1.42 1.16 1.16 1.50 1.50 3.25 3.26

AIC 31,029.5 31,030.5 124,993.0 124,987.8 87,074.0 87,075.9 175,142.0 175,144.0 36,753.0 36,750.5 BIC 31,062.3 31,071.4 125,031.2 125,035.6 87,111.6 87,123.1 175,182.6 175,194.7 36,787.0 36,792.9 Log Likelihood(df) -15,510.7(4) -15,510.2(5) -62,492.5(4) -62,488.9(5) -43,533.0(4) -43,533.0(5) -87,567.0(4) -87,567.0(5) -18,372.5(4) -18,370.3(5) Note: Data are from 2013 NIBRS and 2013 LEMAS. Only black and white male offenders are included. Agencies with fewer than 10 offenders for the given offense are excluded. Degrees of freedom (df) include both intercepts. ***p < 0.001, **p < 0.01, *p < 0.05