Article Review 2 – Achiever Papers

Provide for the 2 articles listed below in 1-2 pages single-spaced (per article) address the following:

1. Include the full reference for the article using the writing style specific to your program on the title page.

a. APA Format

b. Since multiple writing styles are in use within this course, on your title page, please note which style you are using within your assignment. This will help me cater my comments to the style you are using. The style you use need to be the one that is used within your program of study.

2. State the main goal(s) of the study

3. Summarize the research design, and discuss the research method(s) used to answer the research question or assess the hypothesis.

4. Summarize the results of the study.

5. Discuss the advantages and disadvantages of the study’s research design.

6. Provide a discussion on how the study can be moved forward. For example, how can the methods be used in your own research proposal? In what other research might these methods apply? Include two well phrased research questions that could be used in follow-on studies to the one reviewed.

Format: You should have 1-inch margins on all 4 sides of your papers; your title page should include your name and date; you should use 12-point times new roman font throughout.

Things to keep in mind:

Avoid using the first person in formal writing and instead write with an academic voice throughout. Academic voice is usually written in the third person (he, she, it), not first person (I, we) or second person (you). Be consistent in voice and person. See Grammar Girl, “First, Second, and Third Person,” Quick and Dirty Tips for Better Writing, January 20, 2011,

http://grammar.quickanddirtytips.com/first-second-and-third-person.aspx/

. Furthermore, the academic voice avoids abbreviations, contractions, jargon, and slang. Even informal academic discussions are more formal than causal chat among friends.

The body of your work should be made up of no more than 20% direct quotes.

ARTICALS:

1) Parker, Karen F., Richard Stansfield, and Patricia L. McCall. 2016. “Temporal Changes in Racial Violence, 1980 to 2006: A Latent Trajectory Approach.”

Journal of Criminal Justice 47 (December), 1-11

2) Nix, Justin and Scott E. Wolfe. 2016. “Sensitivity to the Ferguson Effect: The Role of Managerial Organizational Justice.”

Journal of Criminal Justice 47 (December):

12-20.

Journal of Criminal Justice

47 (2016) 12–20

Contents lists available at ScienceDirect

Journal of Criminal Justice

Sensitivity to the Ferguson Effect: The role of managerial
organizational justice

Justin Nix a,⁎, Scott E. Wolfe b
a University of Louisville, 2301 South 3rd Street, Louisville, KY 40292, United States
b University of South Carolina, 1305 Greene Street, Columbia, SC 29208, United States

⁎ Corresponding author.
E-mail address: justin.nix@louisville.edu (J. Nix).

http://dx.doi.org/10.1016/j.jcrimjus.2016.06.002
0047-2352/

a b s t r a c t

a r t i c l e i n f o

Article history:
Received 25 May 2016
Received in revised form 27 June 2016
Accepted 28 June 2016
Available online 6 July 2016

Purpose:We argue that the police have been adversely impacted by Ferguson-related negative publicity in ways
beyond the supposed increase in crime (e.g., reduced motivation and increased perception of danger). Further,
we suggest that organizational justice is a key factor that influences officers’ sensitivity to such Ferguson Effects.
Methods: We used a sample of 510 sheriff’s deputies surveyed 6 months after the incident in Ferguson. We ex-
plored whether organizational justice is associated with deputies’ sensitivity to several manifestations of the
Ferguson Effect using OLS and ordered logistic regression models.
Results: The results demonstrated that deputies who believed their supervisors were more organizationally fair
were less likely to feel unmotivated, perceivemore danger, believe their colleagues have been negatively impact-
ed, or feel that US citizens and local residents have become more cynical toward the police in the post-Ferguson
era.
Conclusions: Police supervisorswho use organizational justice as a guidingmanagerial philosophy aremore likely
to shield their officers from the negative work-related outcomes that can follow recent Ferguson-type publicity.
Supervisors should be fair, objective, honest, and respectful when dealing with their subordinates in order to
communicate that the agency has their back even when it may appear the community does not.

Keywords:
Policing
Police management

Organizational justice

Ferguson Effect

1. Organizational justice and sensitivity to the Ferguson Effect

Over the last eighteen months, there has been much debate about
the so-called “Ferguson Effect” on US police. This idea holds that in re-
sponse to heightened scrutiny of the police following the fatal shooting
of unarmed Michael Brown in Ferguson, Missouri in August 2014, offi-
cers are less motivated to aggressively perform their duties and are
pulling back from proactive strategies. Proponents suggest that this
“de-policing” will result in increased crime rates throughout the US.
Themost robust empirical assessment of this argument to date recently
revealed that the Ferguson Effect has not caused increased crime across
the US (Pyrooz, Decker, Wolfe, & Shjarback, 2016; but see also
Rosenfeld, 2016). While this evidence is good news and puts to rest
any worries of a nationwide crime wave (see Mac Donald, 2015),
there may in fact be other ways in which the Ferguson Effect manifests
itself. For instance, research has shown that negative publicity sur-
rounding the police in the aftermath of Ferguson was associated with
lower levels of officer self-legitimacy (Nix & Wolfe, 2015) and reduced
willingness of officers to engage in community partnerships (Wolfe &
Nix, 2016a). These are important findings because extant research has
demonstrated that officers with greater self-legitimacy are more

committed to using procedural justice with citizens (Bradford &
Quinton, 2014) and less reliant on physical force to gain compliance
(Tankebe &Meško, 2015), while community partnerships are an essen-
tial aspect of community and problem-oriented policing (Braga,
Kennedy, Waring, & Piehl, 2001; Gill, Weisburd, Telep, Vitter, &
Bennett, 2014). Thus, while systematic crime rate increases do not
seem to be a direct consequence of the Ferguson Effect, there is reason
to believe that police officers have been adversely impacted by the
Ferguson controversy (and related incidents across the US), which in
turn has implications for crime. In this way, sensitivity to the Ferguson
Effect can be viewed as a negative work-related outcome for officers,
their supervisors and agencies, and the communities they serve.

The problem, however, is that we know very little about what is as-
sociated with officers’ sensitivity to such Ferguson Effects. In other
words, what is it thatmakes a police officer more or less likely to feel af-
fected by negative publicity and public discontent stemming from
Ferguson? This is an important policy question for police agencies and
command staff. What can supervisors do to help prevent their officers
from being adversely impacted by negative publicity stemming from
high-profile incidents like that in Ferguson? Organizational justice the-
ory offers a sound framework for such an understanding
(Cohen-Charash & Spector, 2001; Sheppard, Lewicki, & Minton, 1992).
Within the business management literature, studies have shown that
greater perceived supervisor organizational justice is associated with

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jcrimjus.2016.06.002&domain=pdf

http://dx.doi.org/10.1016/j.jcrimjus.2016.06.002

mailto:justin.nix@louisville.edu

http://dx.doi.org/10.1016/j.jcrimjus.2016.06.002

http://www.sciencedirect.com/science/journal/00472352

13J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

beneficial work-related outcomes such as increased productivity and
greater organizational commitment among employees (Colquitt,
Conlon, Wesson, Porter, & Ng, 2001). And although relatively few stud-
ies have applied the organizational justice framework to the study of
police behavior, the available evidence suggests that officers who per-
ceive their supervisors as being fair are more likely to identify with
their organization, comply with procedures, and hold more favorable
attitudes toward community policing, procedural justice, and the public
more generally (Bradford, Quinton, Myhill, & Porter, 2014; Myhill &
Bradford, 2013; Tankebe, 2014). On the other hand, officerswho believe
their supervisors are unfair express less trust in their agency (Wolfe &
Nix, 2016b) and are more likely to engage in misconduct (Wolfe &
Piquero, 2011). It is with these results in mind that we argue organiza-
tional justice may also be associated with less sensitivity to negative
publicity stemming from Ferguson-related public discontent. Officers
who feel fairly and respectfully treated by their supervisors may be par-
tially shielded from the effects of negative press surrounding their occu-
pation. This is particularly important in agencies across the US that may
not have experienced a high-profile police shooting but are neverthe-
less dealing with the fallout of such events in other jurisdictions. Such
organizational justice likely communicates to officers that they can
trust their agency and supervisors and that theywill be there to support
them in the face of public scrutiny.

Accordingly, the present study considered whether perceived orga-
nizational justice was associated with several different indicators or
manifestations of the Ferguson Effect.We accomplished this using a sur-
vey of sheriff’s deputies (N = 510) employed by an agency in a south-
eastern US metropolis. Multivariate regression equations were
estimated to determine the extent to which organizational justice was
associated with sensitivity to the Ferguson Effect and to rule out the
possible confounding influence of other individual traits (e.g., self-legit-
imacy). Our findings provide valuable insight for police executives who
wish to protect their officers from the public outrage surrounding their
profession in the post-Ferguson era of policing. In this way we are not
interested in finding ways for officers and their agencies to skirt ac-
countability for wrong-doing. Rather, the overarching goal of this
study was to provide empirical evidence concerning the type of police
supervisor actions that can help ensure officers do not become less mo-
tivated, withdraw from their duties, or become less effective cops be-
cause of the threat of media scrutiny and cell phone video recording.
The implications of this study are important from a police policy stand-
point but also because internal fairness within a police agencymay ulti-
mately impact public safety by creating better street cops.

2. The Ferguson Effect

Dating back to the summer of 2014, there have been several highly
publicized fatal encounters between white police officers and unarmed
black citizens. The first occurred in Staten Island, NY, when Eric Garner
died after being placed in a choke hold by NYPD officers. A bystander
captured the incident on video –which includedGarner sayingmultiple
times “I can’t breathe” – and it ultimately went viral on the internet.
Shortly thereafter, in Ferguson, MO, unarmed Michael Brown was shot
and killed by Officer Darren Wilson. This encounter was not captured
on video, but several witnesses claimed that Brown had his arms raised
over his head as if to be surrendering when he was shot. Although the
officer’s use of force was later ruled justified by the US Department of
Justice (i.e., evidence suggested that Brown attempted to grab the
officer’s gun), the incident sparked civil unrest that lasted several
weeks in Ferguson and captured extraordinary media attention.

Eight months later, in North Charleston, SC, cellphone video
emerged ofWalter Scott being shot five times in the back as hewas flee-
ing Officer Michael Slager, who has since been indicted for murder and
is awaiting trial. Just one week after Scott’s death, Freddie Gray went
into a coma while being transported by a Baltimore Police van for pos-
session of an illegal switchblade. The media suggested Gray (who died

from his injuries one week later) had been the victim of a “rough
ride,” and six officers were ultimately indicted for various charges in-
cluding false imprisonment (the knife turned out to be a pocket knife)
and manslaughter.1 Days after Gray’s funeral, televised protests in
downtown Baltimore turned violent: rocks were thrown, fires were
started, patrol cars were destroyed, and many people (including police
officers) sustained injuries. The rioting eventually forced the governor
of Maryland to declare a state of emergency and call in the National
Guard.

Though allegations of excessive use of force against unarmed black
citizens are nothing new (e.g., Rodney King in Los Angeles), these and
related events have resulted in unprecedented levels of police scrutiny
in recent months (Weitzer, 2015). This is due in large part to the advent
of social media and the ease with which citizens can record police be-
havior on cell phones and upload to the Internet for millions to view.
Such continuous negative publicity surrounding the police at a national
level has led some to argue that the police are withdrawing from their
duties in order to avoid being the next viral video on YouTube
(Martinez, 2015; Sutton, 2015) – an argument that has become
known as the “Ferguson Effect.”2 One month after the Baltimore riots,
the Wall Street Journal published an op-ed by Heather Mac Donald
(2015), in which she argued that crime increases being experienced in
several major US cities were precursors to a nationwide crime wave
that is the direct result of the Ferguson Effect and de-policing. Top law
enforcement officials such as St. Louis Chief Sam Dotson (who coined
the term “Ferguson Effect”), FBI Director James Comey and DEA Chief
Chuck Rosenberg, city mayors such as Rahm Emmanuel, and others
have all echoed concerns over de-policing stemming from the Ferguson
Effect.

2.1. The evidence concerning the Ferguson Effect

Until recently, the Ferguson Effect debate has been “long on anec-
dotes and speculation and short on data” (Pyrooz et al., 2016:3). For ex-
ample, the FBI Director warned of the Ferguson Effect and President
Obama argued it may not exist, but both suggested we need data to an-
swer such questions. To determine whether Ferguson was associated
with changes in crime rates at the national level, Pyrooz and his co-au-
thors analyzed monthly UCR Part I offenses in 81 large US cities
12 months before and 12 months after the death of Michael Brown in
Ferguson. They found no evidence of a post-Ferguson change in overall,
violent, or property crime trends – although disaggregated analyses
suggested that robbery rates were on the rise in the post-Ferguson
era. Importantly, they did reveal that a handful of cities—those with
higher than average crime rates, larger African-American populations,
and greater police per capita—experienced increases in violent crime
starting at about the same time as the Ferguson incident. Substantively,
however, themagnitude of such crime rate changeswas quite small. For
example, in the “Ferguson Effect cities” it would take nearly two years to
witness a one-unit increase in homicides, on average. A Ferguson Effect?
Probably – but certainly nothing to sound alarm bells over.3

What Pyrooz and colleagues’ analyses could not speak to, however,
was whether Ferguson and related events have resulted in de-policing.
In a recent report for the 21st Century Cities Initiative at Johns Hopkins
University, Morgan and Pally (2016) explored this possibility
in Baltimore by examining trends in both crime and arrest data from
2010 to 2015, which captures the deaths of both Michael Brown and
Freddie Gray. With respect to crime, the authors found that shootings,
homicides, robberies, carjackings, and automobile thefts all increased
in the three months following Gray’s death. Yet despite these crime in-
creases, the arrest count over the same period declined by 30% (in fact,
arrests had been declining during the 8 months prior to Gray’s arrest,
which is perhaps attributable to the events surrounding Brown’s
death in Ferguson). Thus, the authors found that negative publicity sur-
rounding Gray’s death in Baltimore was associated with both increases
in crime and a slowdown in police activity. Together, these studies

14 J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

suggest that there is no Ferguson Effect on national crime rates; howev-
er, negative publicity stemming from events like Ferguson and Balti-
more do appear to have an effect on police behaviors. Importantly,
such an effect seems to occur regardless of whether a city has experi-
enced a high-profile incident of its own (e.g., Baltimore’s de-policing
after Brown’s death, but before Gray’s death).

Equally important is the possibility that, in response to bothnegative
media attention and public discontent, the police have begun to ques-
tion the confidence they have in their ownmoral authority, or self-legit-
imacy (see Bottoms & Tankebe, 2012). Indeed, Nix and Wolfe (2015)
demonstrated that reduced motivation due to negative publicity in the
months following Ferguson was associated with lower levels of self-le-
gitimacy among officers in their sample. This is especially troubling
given that higher levels of self-legitimacy have been linked to greater
organizational commitment and less dependence on physical force to
gain compliance (Tankebe & Meško, 2015), as well as greater commit-
ment to using procedural fairness (Bradford & Quinton, 2014).

Similarly, Wolfe and Nix (2016a) found that officers who felt less
motivated as a result of negative publicity surrounding lawenforcement
indicated less willingness to engage in community partnerships – a key
component of policing in the community-problem solving era. Impor-
tantly, however, the study also revealed that officers’ lack of willingness
to work with community members was more a result of perceived su-
pervisor unfairness and lack of self-legitimacy. Finally, some commenta-
tors and law enforcement officials have proclaimed that policing has
become more dangerous in the wake of Ferguson due to officers being
more hesitant to use force when the situation calls for it (Canterbury,
2016; Reese, 2014; Safir, 2015). Some have even suggested that the
number of police officers being assaulted and killed in the line of duty
has increased sharply (Hattem, 2015), though empirical evidence sug-
gests otherwise (Maguire, Nix, & Campbell, 2016).

Anecdotes and opinions concerning the Ferguson Effect abound and
many cops argue that it is real. The problemwith most media attention
concerning the Ferguson Effect is that it is treated often as a singular
phenomenon. The reality is that there may be many Ferguson Effects.
While research suggests that a Ferguson Effect on crime rates appears
to be confined to select cities in the US, there are many other conse-
quences experienced by officers that have resulted from negative pub-
licity. In this way, empirical evidence confirms much of the conjecture
and anecdotes. Some cops are less motivated and confident, view the
job as more dangerous, are arresting fewer people for minor offenses,
and are more hesitant to engage with community members in the
post-Ferguson era. Again, it is important to emphasize that social
media contagion has allowed Ferguson-type incidents to be experi-
enced in agencies that have not experienced their own high-profile po-
lice shooting (see Pyrooz et al., 2016). Ultimately, social media has
created a situation where citizens and officers alike can reap the nega-
tive effects of such incidents regardless of geographical proximity. Offi-
cers need to be held accountable for wrongdoing but this evidence
suggests that a sizeable portion of police officers are feeling the ill effects
of intense public scrutiny. These are important observations not only for
police agencies but the communities they serve. Ultimately, officers im-
pacted in this manner are less effective than they should be. This has di-
rect implications for the safety of citizens and the wellbeing of
communities. Unfortunately, we know very little about what factors
are associated with officers’ sensitivity to Ferguson-related negative
publicity. Organizational justice theory offers one possibility for us to
begin to establish an evidence-based understanding of the
phenomenon.

3. Organizational justice

Organizational justice theory has a long history in the businessman-
agement literature (see, e.g., Lind & Tyler, 1988). In fact, several meta-
analyses have demonstrated strong empirical support for the conclu-
sion that employees are more likely to engage in a wide-range of

beneficial work-related behaviors when they perceive their organiza-
tion as fair (Cohen-Charash & Spector, 2001; Colquitt et al., 2001).
There are three primary components to organizational justice, the first
of which is distributive fairness. Employees base their evaluations of su-
pervisors partially on the extent to which they perceive organizational
outcomes, such as salary and promotion decisions, as being distributed
evenhandedly across the organization (i.e., such decisions are not based
on individual characteristics or “who you know”). The second compo-
nent, interactional justice, concerns the degree to which employees
feel they are treated with respect and politeness by supervisors. The
third, and most important, element of organizational justice is proce-
dural fairness. Over and above outcome-based equity, employees look
for supervisory decisions and organizational processes to be handled
in procedurally justmanners—decisions are clearly explained, unbiased,
and allow for employee input.

Given the overlap between the management of cooperate busi-
nesses and police organizations, a wave of organizational justice re-
search in policing contexts has occurred in the past few years. Wolfe
and Piquero (2011), for example, showed that officers were less likely
to engage in misconduct when they viewed their agency and supervi-
sors as organizationally fair. Other research has echoed this finding
and revealed further beneficial outcomes that stem from organizational
justice. Officers are more likely to identify with their agency and its
goals, holdmore favorable views of community policing (and the public
more broadly), use procedural justice, and have higher levels of self-le-
gitimacy when they perceive their supervisors as organizationally fair
(Bradford & Quinton, 2014; Bradford, Quinton, Myhill, & Porter, 2014;
Myhill & Bradford, 2013; Tankebe, 2014; Tankebe & Meško, 2015;
Tyler, Callahan, & Frost, 2007). Relatedly, but using slightly different ter-
minology, recent studies have underscored the importance of “internal
procedural justice” within police departments (Trinkner, Tyler, & Goff,
2016; Van Craen, 2016). The President’s Task Force on 21st Century
Policing (2015) even included internal procedural justice as a corner-
stone of building trust within the community—trust must start from
the inside before being sustained in communities. Taken together, the
literature demonstrates that officers who feel their supervisors are pro-
cedurally fair, distribute outcomes based on objective criteria, and treat
subordinates with respect, engage in more organizational citizenship
behaviors and harbor positive attitudes that are beneficial to both the
agencies they work for and the communities they serve.

With such results in mind, there are several reasons why we would
expect organizational justice to be associated with less sensitivity to the
Ferguson Effect. First, it is important to emphasize that we view sensi-
tivity to the Ferguson Effect as a negative work-related outcome given
the many potential negative consequences of such an orientation. If of-
ficers feel less motivated or believe citizens have worse opinions of the
police in the wake of Ferguson, for example, they may be less likely to
engage in successful crime reduction strategies such as using procedural
justice, community-oriented policing, or order-maintenance policing.
Empirical evidence supports this conclusion (Morgan & Pally, 2016;
Wolfe & Nix, 2016a). On the other hand, officers may be protected
from such negative outcomes when they are treated in a fair manner
by their supervisors. Organizational justice communicates to individual
officers that their supervisors and the broader agency have their
back—they are there to support them.4 Furthermore, being treated fairly
and respectfully by supervisors lets officers know that they have a voice
in their agency and they are a part of the department, not simply a sub-
ordinate employee. Most importantly, supervisors who use organiza-
tional fairness are indicating to officers that “we are in this together”
regarding public scrutiny and Ferguson-related negative media atten-
tion. This sends an important psychological message to officers that if
something does go wrong it will be dealt with fairly.

It is important to determinewhether organizational justice is related
to officers’ sensitivity to the Ferguson Effect because of relatively easy-
to-implement policy implications that would follow. Organizational
fairness can be used as a management philosophy by ensuring that

15J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

supervisors treat officers in a procedurally fair, unbiased, and respectful
manner, and by offering them a voice in decisions. In turn, this strategy
can help stave off any negative psychological effects ofmedia and public
scrutiny. This is important in itself but using organizationally fair super-
vision techniques also has a number of other benefits that come with it
such as creating officers who are more committed to and trusting of
their agency, more willing to work with the community and use proce-
dural justice, and less likely to engage in counterproductive work be-
haviors (e.g., misconduct). Organizational justice is also likely to help
lead to needed reforms in agencies with strained police-community re-
lations. In short, the organizational justice return on investment is great.

4. The current study

Accordingly, the present study explored whether officers’ percep-
tions of organizational fairness within their agency was associated
with their sensitivity to the Ferguson Effect. We examined this question
with a survey of sheriff’s deputies that was conducted about six months
after Michael Brown was killed in Ferguson. This was a time period
when the “Ferguson Effect”was receiving a great deal of attention on so-
cial and conventional media sites and when high ranking officials were
warning of the ill-effects of the phenomenon (see, e.g., Anderson, 2014;
Frizell, 2014; Matt, 2014; Reese, 2014). We use a variety of measures to
explore officers’ attitudes concerning various possible manifestations of
the Ferguson Effect. The purpose of the present study was to provide a
theoretically sophisticated understanding of the correlates of sensitivity
to the Ferguson Effect in order to provide evidence-based police policy
recommendations. The overarching goal was to further demonstrate
the utility of organizational fairness within police agencies.

5. Methods

5.1. Data

Shortly after the beginning of 2015, we surveyed 510 full-time,
sworn sheriff’s deputies employed by an agency in a southeastern US
metropolis (response rate=85%).5 The surveywas administered online
at a password-protected website and participation was encouraged by
ensuring anonymity and securing the endorsement of the agency’s
Deputy Advisory Council – a group of deputies who represent the inter-
est of their colleagues and is very respected throughout the agency. As is
typical of survey research, some respondents returned incomplete sur-
veys, which resulted in a small amount of missing data. We employed
multiple imputation using chained equations (MICE; 10 imputations)
to handle missing data, which is available in Stata 14 (Andridge &
Little, 2010; Fuller & Kim, 2005).

5.2. Dependent variables

We measured sensitivity to the Ferguson Effect with five separate
dependent variables meant to capture various consequences attribut-
able to the Ferguson controversy. Specifically, we considered whether
the respondent felt s/he has been impacted, his/her colleagues have
been affected, and the public has been affected by the Ferguson contro-
versy, respectively, in the wake of Michael Brown’s death and subse-
quent related events in the ensuing months.

5.2.1. Ferguson Effect on self
We presented respondents with eight statements regarding the ex-

tent to which negative publicity had impacted them in the 6 months
leading up to the survey (the survey was administered approximately
6 months after Brown’s death in Ferguson). For example, respondents
were asked to indicate their level agreement (1 = strongly disagree,
2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree) that over the
past 6 months, negative publicity surrounding law enforcement had
“made it more dangerous to be a law enforcement officer,” “made it

less enjoyable to have a career in law enforcement,” and “made it
more difficult for you to be motivated at work.” A complete list of the
items used to measure the effect of negative publicity on respondents
is available in Appendix A. Principal components analysis (PCA) with
varimax rotation demonstrated that the eight items loaded onto two
distinct components – one pertaining to less motivation (λ=4.18, load-
ings N0.66) and the other to increased danger on the job (λ=1.06, load-
ings N0.60). Each component demonstrated adequate internal
consistency (motivation α = 0.87, danger α = 0.71) and, therefore,
were combined into separate additive scales. Less motivation ranges
from 5 to 25, with higher scores indicating the respondent felt less mo-
tivated to do his/her job as a result of negative publicity over the prior
6 months. Increased danger ranges from 3 to 15, with higher scores
reflecting a belief on the part of the respondent that law enforcement
had become more dangerous as a result of negative publicity following
Ferguson and related events. Descriptive statistics for all variables used
in the analyses are presented in Table 1.

5.2.2. Ferguson Effect on colleagues
It is possible that regardless of whether officers believed negative

publicity stemming from the Ferguson controversy had affected them,
they might believe that it had influenced other police officers, including
their colleagues. This is an important consideration given that police of-
ficers routinely rely on their colleagues for backup. For example, officers
may hesitate to stop suspicious persons if they feel their colleagues are
reluctant to use force when it may be necessary. To capture this senti-
ment, we asked respondents to indicate their agreement (1 = strongly
disagree to 5= strongly agree) with statements regarding whether neg-
ative publicity surrounding law enforcement in the previous 6 months
had: (1) made it more difficult for coworkers to do their job, (2) made
it more difficult for coworkers to be motivated at work, (3) caused co-
workers to be less proactive on the job than they were in the past, and
(4) caused coworkers to be more apprehensive about using force even
though it may be necessary. PCA suggested the four items loaded onto
a single component (λ=2.67, loadings N0.76) and Cronbach’s alpha in-
dicated strong internal consistency (α = 0.83). Accordingly, we com-
bined the items into an additive scale ranging from 4 to 20 with
higher scores reflecting a belief that negative publicity surrounding
law enforcement had negatively affected colleagues.

5.2.3. Ferguson Effect on citizens’ views
It is also possible that, in response to the Ferguson-related contro-

versy, officers have come to believe that citizens’ attitudes toward the
police have worsened (see Culhane, Boman, & Schweitzer, 2016). If so,
this could have important ramifications for the police. For example, it
may lead to further immersion into the “us versus them” nature of the
police subculture (Chan, 1996; Neiderhoffer, 1967; Waddington,
1999), which could result in less willingness to work with the commu-
nity to solve problems (Braga et al., 2001). To measure the extent to
which our sample felt this way about US citizens, respondents were
asked to indicate their level of agreement (1 = strongly disagree to
5 = strongly agree) with the following statement: “In general, US citi-
zens’ views of the police have gotten worse in the last 6 months.” Sim-
ilarly, we asked respondents howmuch they agreed that “Over the past
6months, Marie County (pseudonym) residents’ perceptions of law en-
forcement have gotten worse.” This item was used as a fifth dependent
variable, Local citizens, in order to consider the possibility that respon-
dents felt local citizens’ views differed from those of US citizens more
broadly.

5.3. Independent variable

5.3.1. Organizational justice
The independent variable of the present study was organizational

justice, which we measured with 18 items intended to capture each
component of the construct: procedural, distributive, and interactional

Table 1
Descriptive statistics

N M S.D. Min Max

Dependent variables
Less motivation 489 12.85 4.79 5 25
Increased danger 488 10.82 2.52 3 15
Affected colleagues 490 11.74 3.39 4 20
Affected US citizens 504 4.05 0.91 1 5
Affected local citizens 503 2.84 0.99 1 5

Independent variable
Organizational justice 426 63.87 13.54 18 90

Controls
Self-legitimacy 475 20.47 2.89 5 25
Age 438 2.53 1.03 1 4
Male 435 0.76 – 0 1
Minority 430 0.31 – 0 1
Four year degree 438 0.57 – 0 1
Deputya 425 0.69 – 0 1
Patrol 424 0.39 – 0 1
Experience ≥10 years 429 0.60 – 0 1
Military 434 0.39 – 0 1

a “Mid-level supervisor” is the reference category.

16 J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

justice. All items were measured on the same 5-point Likert scale (1 =
strongly disagree to 5 = strongly agree). Procedural justice was mea-
sured by asking respondents the extent to which they agreed with
statements such as “Command staff clearly explains the reasons for
their decisions” and “My agency’s policies are designed to allow em-
ployees to have a voice in agency decisions (e.g., assignment changes,
discipline).” Distributive justice was measured with statements includ-
ing “Landing a good assignment in my agency is based on whom you
know (reverse coded)” and “Command staff treats employees the
same regardless of their gender.” Finally, interactional justice was mea-
sured via statements like “Command staff treats employees with kind-
ness and consideration” and “Generally, command staff treats
employees with respect” (a complete list of the items used to measure
organizational justice is available in Appendix A). These itemswere con-
sistent with those used in prior research (Cohen-Charash & Spector,
2001; Colquitt et al., 2001) and PCA indicated that they loaded onto a
single component (λ = 10.75, factor loadings N0.65). The items also
demonstrated strong internal consistency (α= 0.96) and, accordingly,
were combined into an additive scale with higher scores reflecting
greater perceived organizational justice.

5.4. Controls

In an effort to generate unbiased estimates of the effect of organiza-
tional justice on each of the outcome variables, we controlled for several
individual officer characteristics. To aid inmaintaining respondents’ an-
onymity, we measured age categorically (1 = 21 to 30, 2 = 31 to 40,
3=41 to 50, and 4=51 or older). Gender (1=male), race (1=minor-
ity), and education (1= four-year degree or higher)were dummy coded,
as were rank (1 = deputy), experience (1 = N10 years), and military
background (1= yes). Consistent with the agency’s demographic char-
acteristics, about 76% of respondents weremale and 31%were a minor-
ity. Finally, we controlled for respondents’ level of self-legitimacy
because prior studies have demonstrated its association with a number
of desirable officer attitudes and behaviors (Bradford & Quinton, 2014;
Tankebe & Meško, 2015; Wolfe & Nix, 2016a, 2016b). Respondents
were asked to indicate their level of agreement with the following five
statements: “I have confidence in the authority vested inmeas a lawen-
forcement officer,” “As a lawenforcement officer, I believe I occupy a po-
sition of special importance in society,” “I believe people should always
do what I tell them as long as my orders are lawful,” “I am confident I
have enough authority to do my job well,” and “I believe law enforce-
ment is capable of providing security for all citizens of this county”
(Tankebe, 2014). PCA indicated that the items loaded onto a single

component (λ=2.32; loadings N0.56) and Cronbach’s alpha suggested
adequate internal consistency (α = 0.71). As such, the items were
summed into a scale ranging from 5 to 25, with higher scores indicating
a greater sense of self-legitimacy.

5.5. Analytic strategy

The analysis involved the estimation of a series of multivariate re-
gression equations that examined the role of organizational justice on
officers’ sensitivity to the Ferguson Effect as indicated by five different
outcomevariables. Specifically, Models 1 through 3 in Table 2 used ordi-
nary least squares regression (OLS) to assess whether organizational
justice was associated with less motivation, increased danger, or a
greater sense that public scrutiny had negatively impacted respondents’
colleagues in the months following the incident in Ferguson, net of sta-
tistical controls. OLS was used because these dependent variables ap-
proximated normality (Tabachnick & Fidell, 2013).6 Conversely,
ordered logistic regression was used to estimate the relationship be-
tween organizational justice and respondents’ perceptions of whether
US citizens’ or local residents’ views of the police had gotten worse in
the wake of Ferguson in Models 4 and 5, respectively.7

6. Results

Models 1 and 2 in Table 2 were concerned with the extent to which
respondents felt they had been directly affected by negative publicity
stemming from the Ferguson controversy. Model 1 presents the results
of an OLS model that regressed less motivation onto organizational jus-
tice and nine control variables. For starters, about 29% of the variation
in less motivation was accounted for by the model. Most importantly,
deputies who perceived greater organizational justice on the part of
their agency and its command staff were significantly less likely to re-
port experiencing reduced motivation due to negative publicity sur-
rounding law enforcement in the six months following Michael
Brown’s death in Ferguson (b=−0.18, p b 0.01). In other words, dep-
uties who felt their supervisors treated them fairly and with respect
were less likely to experience reduced motivation from the Ferguson
Effect.

In Model 2, the increased danger scale was regressed onto organi-
zational justice along with each of our control variables. The model
accounted for a moderate amount of variation in this measure of
the Ferguson Effect (R2 = 0.15). Organizational justice (b = −0.07,
p b 0.01) was significantly and negatively associated with a belief
among respondents that law enforcement has become more

Table 2
Sensitivity to Ferguson Effects.

Model 1 (OLS) Model 2 (OLS) Model 3 (OLS) Model 4 (ordered
logistic)

Model 5 (ordered
logistic)

Less motivation Increased danger Affected colleagues Affected US citizens Affected local citizens

b (SE) β b (SE) β b (SE) β b (SE) OR b (SE) OR

Organizational justice −0.18⁎⁎ (0.02) −0.50 −0.07⁎⁎ (0.01) −0.37 −0.10⁎⁎ (0.01) −0.42 −0.03⁎⁎ (0.01) 0.97 −0.06⁎⁎ (0.01) 0.94
Self-legitimacy −0.10 (0.08) −0.06 0.06 (0.05) 0.06 −0.07 (0.06) −0.06 0.08⁎ (0.04) 1.08 0.03 (0.04) 1.03
Age −0.50 (0.27) −0.11 −0.36⁎ (0.15) −0.15 −0.33 (0.19) −0.10 0.01 (0.12) 1.01 −0.04 (0.13) 0.96
Male −0.63 (0.51) −0.05 −0.05 (0.30) −0.01 −0.46 (0.39) −0.06 −0.78⁎⁎ (0.28) 0.46 −0.31 (0.25) 0.74
Minority −0.83 (0.49) −0.08 −0.67⁎ (0.28) −0.12 −0.27 (0.36) −0.04 0.11 (0.23) 1.11 0.29 (0.23) 1.34
Four year degree 0.78 (0.46) 0.08 0.21 (0.27) 0.04 0.43 (0.32) 0.06 0.11 (0.21) 1.11 0.30 (0.21) 1.35
Deputy −0.88 (0.53) −0.08 −0.81⁎⁎ (0.28) −0.15 −0.91⁎ (0.35) −0.12 −0.44 (0.23) 0.65 −0.14 (0.22) 0.87
Patrol −0.15 (0.49) −0.02 −0.27 (0.27) −0.05 −0.02 (0.35) 0.00 0.30 (0.23) 1.35 0.26 (0.22) 1.30
Experience ≥10 years 0.25 (0.61) 0.03 −0.29 (0.31) −0.06 −0.26 (0.42) −0.04 −0.39 (0.27) 0.68 −0.36 (0.27) 0.70
Military 0.29 (0.48) 0.03 0.16 (0.27) 0.03 0.63 (0.35) 0.09 −0.23 (0.23) 0.80 −0.26 (0.22) 0.77
Intercept 28.08⁎⁎ (1.97) – 15.92⁎⁎ (1.19) – 21.28⁎⁎ (1.42) – – – – –
F-test 18.07⁎⁎ 8.85⁎⁎ 10.17⁎⁎ 3.76⁎⁎ 7.07⁎⁎

R2 0.29a 0.15a 0.21a 0.05b 0.07b

N 369 364 370 374 373

Note: All models estimated using MICE (M = 10). Entries are unstandardized regression coefficients (b), robust standard errors (SE), standardized regression coefficients (β), and odds
ratios (OR).
⁎ p b 0.05.
⁎⁎ p b 0.01.
a Mean Adjusted R2.
b Mean McFadden’s R2.

17J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

dangerous in the post-Ferguson era. Here again, organizational jus-
tice on the part of supervisors seemed to protect deputies from the
negative consequences of the Ferguson Effect. It is also worth noting
that compared to white respondents, minorities (b = −0.67,
p b 0.05) were significantly less likely to report an increased sense
of danger in law enforcement. Given that much of the negative pub-
licity surrounding the Ferguson controversy has dealt with the
shooting of unarmed black citizens by white police officers, it is per-
haps not surprising that minority officers are less likely to believe
law enforcement has gotten more dangerous.

Model 3 focused on the extent to which respondents believed
their colleagues had been impacted by negative publicity stemming
from Ferguson. Our third dependent variable – affected colleagues –
was regressed onto organizational justice and each of the control
variables. The model explained 21% of the variation in the outcome
variable. Once again, the organizational justice coefficient
(b =−0.10, p b 0.01) was negative and statistically significant. Dep-
uties who believed their agency and its command staff was fair and
respectful were less likely to feel that their colleagues have been
affected by negative publicity in the months following Ferguson.

Models 4 and 5 explored the extent to which respondents believed
the Ferguson controversy had influenced citizens’ attitudes toward the
police. Using ordered logistic regression, Model 4 regressed our fourth
dependent variable, affected US citizens, onto organizational justice and
the controls. As expected, organizational justice (b = −0.03, p b 0.01)
was significantly and negatively associated with the belief that US citi-
zens’ views of the police had gotten worse in the six months leading
up to the survey. Those deputies who perceived greater organizational
fairness within their agency were less likely to feel that citizens’ views
of the police have gotten worse. In this way, organizationally fair treat-
ment by supervisors seems to protect officers from cynical orientations
about citizens. In Model 5, we explored a similar question but focused
more specifically on respondents’ beliefs about local citizens’ views of
the police. Our final Ferguson Effect variable – affected local citizens –
was regressed onto organizational justice along with the controls.
Again organizational justice (b = −0.06, p b 0.01) was significantly
and negatively associated with the outcome. That is, deputies who per-
ceived greater organizational justicewithin their agencywere less likely
to believe that local citizens’ views of the police had worsened in the

6 months since Ferguson. With the findings in hand, we now turn to a
discussion of the implications surrounding the results.

7. Discussion

There has been much debate over the existence of a Ferguson Effect
on US police over the last year and a half. Although the notion that the
Ferguson Effect is responsible for a nationwide crime trend has been
debunked (Pyrooz et al., 2016; but see Rosenfeld, 2016), a growing
body of evidence suggests that cops have indeed been impacted by con-
tinued negative publicity (Morgan & Pally, 2016; Nix & Wolfe, 2015;
Wolfe & Nix, 2016a). If in fact police officers across the country are
less willing to be proactive on the job in the post-Ferguson era, this is
a serious problem that we need to know more about. For example, re-
ductions in proactive stops, order-maintenance policing, and other
strategies known to be effective in crime reduction may have a more
lagged effect on crime problems in particular communities than recent
research has been able to observe. As such, a question we felt was im-
portant to ask was: what factors are associated with officer sensitivity
to negative publicity stemming from the Ferguson controversy? This
study suggested that respondents’ perceived organizational fairness
on the part of their supervisors was significantly associated with less
sensitivity to five manifestations of the Ferguson Effect. Officers who
felt their agency was fair were less likely to report (1) being unmotivat-
ed, (2) that lawenforcement has becomemore dangerous, (3) that their
colleagues have been impacted by negative publicity, and (4) that citi-
zens’ attitudes (both nationally and locally) toward the police have
worsened. With these results in mind, several issues warrant more de-
tailed discussion.

Organizational justice extends beyond thewalls of the police depart-
ment and ultimately protects cops. Respondents who indicated that
their agency and command staff are fair, objective, honest, and respect-
fulwere less sensitive to negative publicity surrounding their profession
in thewake ofMichael Brown’s death in Ferguson. This finding adds to a
growing body of literature which suggests that internal fairness pro-
duces beneficial outcomes for individual officers, agencies, and the pub-
lic at large (Bradford&Quinton, 2014; Trinkner et al., 2016;Wolfe &Nix,
2016a;Wolfe & Piquero, 2011).We implore researchers to continue ex-
ploring organizational fairness in the police context – perhaps, for

18 J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

example, as it relates to important issues such as embracing evidence-
based practices and adopting new technologies such as body-worn
cameras. It is probable that agencies which emphasize internal fairness
are more likely to generate buy-in from line-level officers on such
matters.

Academic research on the Ferguson Effect has dramatically im-
proved our understanding of thephenomenon. Simply put, scientific ev-
idence is more valuable than conjecture. Research has revealed that
there are important Ferguson-related effects on officers’ orientations to-
ward their job and de-policing behaviors. It is also possible that the neg-
ative publicity surrounding deadly force incidents such as those in
Ferguson and Baltimore have thrust the police into a legitimacy crisis.
That is, in response to the deaths of several unarmedblack citizens in re-
cent years, US citizens may have begun to challenge the legitimacy of
the police (see Bottoms & Tankebe, 2012; Tyler, 1990; Tyler & Huo,
2002). Any look at social media outlets will clearly reveal that a sizable
portion of the American public is questioning the legitimacy of police
use of force, particularly in minority communities. A recent experiment
by Culhane et al. (2016) supports this argument empirically by showing
that citizens are less likely to view police shootings as justified in the
post-Ferguson era. This evidence suggests that the Ferguson-related in-
cidents have also negatively impacted citizens’ orientations toward law
enforcement. At this point, we need more theoretically-grounded and
policy-relevant research on the legitimacy crisis that may be facing
American police. The implications of reduced legitimacy are potentially
far reaching and may impact police-community relations and crime for
generations to come if not adequately addressed (Wolfe, McLean, &
Pratt, 2016).

This issue brings us to the policy implications of our findings. In the
face of public scrutiny of immense proportions, law enforcement agen-
cies are in a position where they must act. Our findings underscore the
importance of organizational fairness from supervisors and agencies
more broadly. This is not necessarily surprising given that decades of re-
searchhas demonstrated the role of organizational justice in the context
of various employment and subordinate settings (Cohen-Charash &
Spector, 2001; Colquitt et al., 2001; Reisig & Bain, 2016). The policy im-
plication of this observation is straightforward: to minimize the impact
of negative publicity and the Ferguson Effect on officers, agencies must
strive to use organizational justice as a guiding principle of their mana-
gerial philosophy. Ensuring that internal policies and procedures are
fair, disciplinary proceedings and hiring/promotion decisions are
based on objective indicators, allowing subordinate officers a voice in
agency decision-making, and treating subordinates with respect and
dignity are some of the ways in which police supervisors can cultivate
a climate of organizational fairness in their agencies. Organizationally
fair treatment sends the message to officers that the agency and its su-
pervisors has their back and communicates to them that they are part of
the agency (rather than simply a subordinate employee). Ultimately,
this allows an agency to create a situation where officers believe that
if something unfortunate occurs on the street, such as having to
shoot a civilian, their agency will support them, provide a fair inves-
tigation process, and not make decisions based on political pressure
or public scrutiny. It is important to note, however, that political
pressure is often an inescapable aspect of law enforcement. As
such, we need to keep in mind that organizational justice on the
part of police agencies cannot be fully separated from the efforts on
the part of local governments (e.g., mayors and city councils) to do
the same thing. Arbitrary decisions from local government officials
have the power to undermine organizational justice within police
agencies. Additionally, advocating for the use of organizational jus-
tice does not absolve officers or agencies from misconduct and
poor community relations. Indeed, police reform is needed in the
US, particularly in certain communities. Ensuring organizational fair-
ness within an agency is one prong to such reform.

Organizational justice training programs need to be developed
with an eye toward helping agencies achieve these goals. Several

training platforms organized by both government (e.g., the Office
of Community-Oriented Policing) and private entities have already
emerged. When such training is implemented within agencies,
there is a need for evaluation research dedicated toward under-
standing what works with such programs and what does not.
Owens, Weisburd, Alpert, and Amendola (2016) recently conducted
an experiment in which they successfully implemented a low-cost
procedural justice-centered training of officers in Seattle. Results re-
vealed that trained officers were less likely to resolve incidents with
arrest or force. An important component of this intervention includ-
ed organizationally fair treatment by supervisors. More training
programs and related evaluation research need to focus on organiza-
tional justice within police agencies in the future.

The importance of organizational justice within police depart-
ments cannot be overstated. Good policing starts inside the walls of
police agencies. Simply put, we cannot expect officers to engage in
procedural justice on the street if they do not receive such treatment
from their own supervisors. Internal procedural justice provides a
model for police officers that is likely to translate into interactions
they have with citizens (Bradford & Quinton, 2014; Van Craen,
2016). Accordingly, organizational justice has direct public safety
consequences. We know that procedural justice policing is safer for
cops and community members alike (i.e., as opposed to relying on
coercive force) and causes citizens to more regularly voluntarily
comply with the law (see Tyler, 1990). Our findings suggest that or-
ganizational justice may also create a situation where cops will not
hesitate to do things on the street that we know are effective at com-
bating crime (e.g., work with community members). Ultimately, this
means that organizational justice translates into safer communities.

While our study is the first of its kind to demonstrate theoretical-
ly-salient and policy-relevant results concerning the correlates of
sensitivity to the Ferguson Effect, there were several things we
could not do that represent opportunities for future work. First, our
measure of the extent to which respondents’ colleagues have been
impacted by negative publicity may suffer from projection. Psycho-
logical projection theory offers a reason to believe that while respon-
dents might be hesitant to indicate having been affected by the
Ferguson controversy, they may project such feelings onto their
peers (Baumeister, Dale, & Sommer, 1998; Boman, Stogner, Miller,
Griffin, & Krohn, 2011). Unfortunately, we are unable to determine
whether this occurred with some of our respondents. One avenue
for future research that may prove fruitful is the collection of social
network data to gauge the extent to which peer effects shape sensi-
tivity to the Ferguson Effect. Second, likemost prior studies, ours was
carried out using cross-sal survey data from a single agency. As such,
the generalizability of our findings may be limited because (1) we
were unable to determine whether respondents’ attitudes have
truly changed post-Ferguson and (2) we cannot be certain that our
findings from this particular agency are generalizable to officers
working in other agencies or other regions of the country. At the
same time, however, it is important to note that our Ferguson Effect
measures were retrospective in nature by asking respondents to in-
dicate how events in the previous six months had impacted them.
Future research using longitudinal designs and/or conducted with
multiple agencies would certainly be fruitful on both theoretical
and practical levels.

In the end, organizational justice should be a cornerstone of all
police departments. The beneficial outcomes – to officers, agencies,
and the public – are numerous. Fortunately, agencies can implement
organizational fairness at little cost. The police profession will un-
doubtedly face continued public scrutiny in the current era of social
media, especially given that officers have the unique power to arrest
and use physical (even deadly) force. In this way, the importance of
our findings is not restricted to reducing sensitivity to the Ferguson
Effect. Rather, our findings suggest that organizational fairness can
encourage officers to continue performing their duties when such

19J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

public scrutiny does occur. Ultimately, this translates into better
cops and safer communities.

Appendix A. Organizational justice and Ferguson Effect items

Organizational justice

My agency’s policies are designed to generate standards so that deci-
sions can be made with consistency.
My agency’s policies are designed to allow employees to have a voice in
agency decisions (e.g. assignment changes, discipline).
My agency’s performance evaluation system is fair.
My agency’s investigation of civilian complaints is fair.
I understand clearly what type of behavior will result in disciplinewith-
in my agency.
Landing a good assignment in my agency is based on whom you know
(reverse coded).
If you work hard, you can get ahead at this agency.
As an organization, my agency can be trusted to do what is right for the
community.
I trust the direction that my department’s command staff is taking our
agency.
I feel confident about top management’s skills.
Command staff considers employees’ viewpoints.
Command staff treats employees with kindness and consideration.
Command staff treats employees the same regardless of their gender.
Command staff treats employees the same regardless of their race or
ethnicity.
Command staff clearly explains the reasons for their decisions.
Command staff clearly explains the reasons the agency makes policy
changes.
Generally, command staff treats employees with respect.
I trust that command staff makes decisions that have the agency’s best
interest in mind.

Ferguson Effects

Less motivation
[Over the past 6 months, negative publicity surrounding law enforce-

ment has]
Made it more difficult for you to be motivated at work.
Caused you to be less proactive on the job than you were in the past.
Caused you to be more apprehensive about using force even though it
may be necessary.
Negatively impacted the way you do your job.
Made it less enjoyable to have a career in law enforcement.

Increased danger
[Over the past 6 months, negative publicity surrounding law enforce-

ment has].
Made it more difficult to do your job.
Made it more dangerous to be a law enforcement officer.
Forced some US law enforcement agencies to make policy changes that
ultimately threaten officer safety.

Affected colleagues
[Over the past 6 months, negative publicity surrounding law enforce-

ment has].
Made it more difficult for your coworkers to do their job.
Made it difficult for your coworkers to be motivated at work.
Caused your coworkers to be less proactive on the job than theywere in
the past.
Caused your coworkers to bemore apprehensive about using force even
though it may be necessary.

Affected US citizens
In general, US citizens’ views toward the police have gotten worse over
the past 6 months.

Affected local citizens
Over the past 6 months, local citizens’ perceptions of law enforcement
have gotten worse.

Notes

1 Officers Nero andGoodsonwere found not guilty of all charges onMay 23, 2016 and
June 23, 2016, respectively. At the time of this writing, the other four officers are still
awaiting trial (includingOfficer Porter, whose first trial resulted in amistrial due to a hung
jury).

2 Others, including NYPD CommissionerWilliam Bratton, have referenced a “YouTube
Effect,”which similarly refers to police officers withdrawing from their duties out of a fear
of being captured on the next video to go viral on the Internet (Davis, 2015).

3 Rosenfeld (2016:2) has since concluded that the homicide increase in 56 large US
cities “was real and nearly unprecedented,” though most of the overall increase was
constrained to 10 cities which had experienced, on average, a 33% increase in homicides.
While there are several possible explanations forwhy these cities experienced suchdrastic
homicide increases, the Ferguson Effect is the most likely, according to Rosenfeld.

4 An anonymous reviewer pointed out that a supervisor might also have an officer’s
back when s/he commits wrongdoing – which is certainly plausible. This would not be a
function of organizational justice, but rather the blue code of silence (see Skolnick,
2002) which is particularly problematic for use of force investigations and the legitimacy
of the police in the eyes of the public. Our use of the term “support” deals does not include
such instances where a supervisor knowingly covers up officer misconduct.

5 This agency – one of the largest in its state – serves Marie County (pseudonym),
which covers over 750 square miles and is home to approximately 401,000 citizens. Ac-
cording to the most recent US Census data, the racial makeup of Marie County is roughly
48% white, 47% black, and 5% other. About one-fourth of the population is under the age
of 18. Themedian household income is $48,674, but note that 16% of the population is liv-
ing in poverty.

6 Less motivation: skewness = 0.37, kurtosis = 2.51; Increased danger: skew-
ness =−0.53, kurtosis = 3.02; Affected colleagues: skewness= 0.13, kurtosis = 2.84.

7 The parallel-lines assumption (i.e., proportional odds) is often violated when using
ordered logistic regression because one or more coefficients in an equation may differ
across values of the outcome measure (Williams, 2006). We used the Brant (1990) test
to assess whether the regression coefficients in the ordered logistic models were similar
across the response categories for each of the dependent variables (see also, Long & Freese,
2006). According to this test, the parallel-lines assumptionwas violated inModels 4 and 5
in Table 2. We reestimated these equations using Stata’s gologit2 command which allows
some regression coefficients to be the same across all values of a dependent variable and
others to differ. A multinomial logit would allow all parameters to vary across the depen-
dent variable but such an equation would lack interpretability and parsimony (Breen,
Luijkx, Muller, & Pollak, 2009; Williams, 2006). The generalized ordered logit robustness
checks revealed substantively similar results as those presented below. For ease of inter-
pretation, we report the ordered logit findings.

References

Anderson, E. (2014, Aug 13).What caused the Ferguson riot exists in somany other cities,
too. The Washington post (Retrieved from) https://www.washingtonpost.com/
posteverything/wp/2014/08/13/what-caused-the-ferguson-riot-exists-in-so-many-
other-cities-too/

Andridge, R. R., & Little, R. J. (2010). A review of hot deck imputation for survey non-re-
sponse. International Statistical Review, 78, 40–64.

Baumeister, R. F., Dale, K., & Sommer, K. L. (1998). Freudian defense mechanisms and em-
pirical findings in modern social psychology: Reaction formation, projection, dis-
placement, undoing, isolation, sublimation, and denial. Journal of Personality, 66(6),
1081–1124.

Boman, J. H., Stogner, J. M., Miller, B. L., Griffin, O. H., & Krohn, M. D. (2011). On the oper-
ational validity of perceptual peer delinquency: Exploring projection and elements
contained in perceptions. Journal of Research in Crime and Delinquency, 49, 601–621.

Bottoms, A., & Tankebe, J. (2012). Beyond procedural justice: A dialogic approach to legit-
imacy in criminal justice. The Journal of Criminal Law and Criminology, 102(1),
119–170.

Bradford, B., & Quinton, P. (2014). Self-legitimacy, police culture and support for demo-
cratic policing in an English constabulary. British Journal of Criminology, 54,
1023–1046.

Bradford, B., Quinton, P., Myhill, A., & Porter, G. (2014). Why do ‘the law’ comply? Proce-
dural justice, group identification and officer motivation in police organizations.
European Journal of Criminology, 11(1), 110–131.

Braga, A. A., Kennedy, D. M.,Waring, E. J., & Piehl, A. M. (2001). Problem-oriented policing,
deterrence, and youth violence: An evaluation of Boston’s operation ceasefire. Journal
of Research in Crime and Delinquency, 38(3), 195–225.

Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal lo-
gistic regression. Biometrics, 46, 1171–1178.

https://www.washingtonpost.com/posteverything/wp/2014/08/13/what-caused-the-ferguson-riot-exists-in-so-many-other-cities-too/

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf9000

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0010

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0015

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0020

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0025

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0030

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0035

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0040

20 J. Nix, S.E. Wolfe / Journal of Criminal Justice 47 (2016) 12–20

Breen, R., Luijkx, R., Muller, W., & Pollak, R. (2009). Nonpersistent inequality in education-
al attainment: Evidence from eight European countries. American Journal of Sociology,
114, 1475–1521.

Canterbury, C. (2016, Feb 12). Letter from National FOP President Canterbury to President
Obama.

Chan, J. (1996). Changing police culture. British Journal of Criminology, 36, 109–134.
Cohen-Charash, Y., & Spector, P. E. (2001). The role of justice in organizations: A meta-

analysis. Organizational Behavior and Human Decision Processes, 86, 278–321.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C., & Ng, K. Y. (2001). Justice at the mil-

lennium: A meta-analytic review of 25 years of organizational justice research.
Journal of Applied Psychology, 86(3), 425–445.

Culhane, S. E., Boman, J. H., & Schweitzer, K. (2016). Public perceptions of the justifiability
of police shootings: The role of body cameras in a pre/post Ferguson experiment.
Police Quarterly. http://dx.doi.org/10.1177/1098611116651403 (Advance online
publication).

Davis, A. C. (2015, Oct 8). ‘YouTube effect’ has left police officers under siege, law enforce-
ment leaders say. The Washington post (Retrieved from) https://www.
washingtonpost.com/news/post-nation/wp/2015/10/08/youtube-effect-has-left-
police-officers-under-siege-law-enforcement-leaders-say/

Frizell, S. (2014, Sep 28). Obama: Ferguson exposed ‘gulf of mistrust’ between cops and
communities. Time (Retrieved from) http://time.com/3441544/obama-ferguson-
gulf-of-mistrust/

Fuller, W. A., & Kim, J. K. (2005). Hot deck imputaiton for the response model. Survey
Methodology, 31, 139–149.

Gill, C., Weisburd, D., Telep, C.W., Vitter, Z., & Bennett, T. (2014). Community-oriented po-
licing to reduce crime, disorder and fear and increase satisfaction and legitimacy
among citizens: A systematic review. Journal of Experimental Criminology, 10,
399–428.

Hattem, J. (2015, May 11). Killings of police officers on the rise. The Hill.
Lind, E. A., & Tyler, T. R. (1988). The social psychology of procedural justice. New York, NY:

Plenum Press.
Long, J. S., & Freese, J. (2006). Regression models for categorical dependent variables using

Stata. College Station, TX: Stata Press.
Mac Donald, H. (2015, May 29). The new nationwide crime wave. The Wall Street Journal

(Retrieved from) http://www.wsj.com/articles/the-new-nationwide-crime-wave-
1432938425

Maguire, E. R., Nix, J., & Campbell, B. A. (2016). A war on cops? The effects of Ferguson on the
number of U. S. police officers murdered in the line of duty. (Manuscript submitted for
publication).

Martinez, M. (2015, May 26). Baltimore’s deadliest month in 15 years: May counts 35 homi-
cides, so far. CNN (Retrieved from) http://www.cnn.com/2015/05/26/us/baltimore-
deadliest-month-violence-since-1999/index.html

Matt, D. (2014, Oct 14). The Ferguson Effect: A cop’s-eye view. New York Post (Retrieved
from) http://nypost.com/2014/10/14/the-ferguson-effect-a-cops-eye-view/

Morgan, S. L., & Pally, J. A. (2016). Ferguson, Gray, and Davis: An analysis of recorded
crime incidents and arrests in Baltimore City, March 2010 through December 2015.
A report written for the 21st Century Cities Initiative at Johns Hopkins University.
(Retrieved from) http://socweb.soc.jhu.edu/faculty/morgan/papers/
MorganPally2016

Myhill, A., & Bradford, B. (2013). Overcoming cop culture? Organizational justice and po-
lice officers’ attitudes toward the public. Policing: An International Journal of Police
Strategies and Management, 36, 338–356. http://dx.doi.org/10.1108/
13639511311329732.

Neiderhoffer, A. (1967). Behind the shield: The police in urban society. Garden City, NY:
Doubleday.

Nix, J., & Wolfe, S. E. (2015). The impact of negative publicity on police self-legitimacy.
Justice Quarterly. http://dx.doi.org/10.1080/07418825.2015.1102954 (Advance online
publication).

Owens, E., Weisburd, D., Alpert, G. P., & Amendola, K. L. (2016). Can you build a better cop?
Experimental evidence on supervision, training, and policing in the community. (Unpub-
lished manuscript).

Pyrooz, D. P., Decker, S. H., Wolfe, S. E., & Shjarback, J. A. (2016). Was there a Ferguson Ef-
fect on crime rates in large U.S. cities? Journal of Criminal Justice, 46, 1–8.

Reese, S. (2014, Nov 27). Police say officers hesitating to use deadly force. The Washington
Times.

Reisig, M. D., & Bain, S. N. (2016). University legitimacy and student compliance with ac-
ademic dishonesty codes: A partial test of the process-based model of self-regulation.
Criminal Justice and Behavior, 43, 83–101.

Rosenfeld, R. (2016). Documenting and explaining the 2015 homicide rise: Research direc-
tions. A report to the National Institute of Justice. Washington, DC: Department of
Justice.

Safir, H. (2015, Jun 5). Police commissioner: Bad old days of crime may be back. Time.
Sheppard, B. H., Lewicki, R. J., & Minton, J. W. (1992). Organizational justice: The search for

fairness in the workplace. Lexington Books/Macmillan.
Skolnick, J. (2002). Corruption and the blue code of silence. Police Practice and Research,

3(1), 7–19.
Sutton, R. (2015). The dangers of de-policing: Will cops just stand down? New York Post.

May 5, (Retrieved from) http://nypost.com/2015/05/05/the-dangers-of-de-policing-
will-cops-just-stand-down/.

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). California
State University Northridge: Harper Collins College Publishers.

Tankebe, J. (2014). Rightful authority: Exploring the structure of police self-legitimacy. In
A. Liebling, J. Shapland, & J. Tankebe (Eds.), Crime, justice and social order: Essays in
honour of A. E. Bottoms. Oxford: Oxford University Press.

Tankebe, J., & Meško, G. (2015). Police self-legitimacy, use of force, and pro-organizational
behavior in Slovenia. In G. Meško, & J. Tankebe (Eds.), Trust and legitimacy in criminal
justice (pp. 261–270). New York, NY: Springer. http://dx.doi.org/10.1007/978-3-319-
09813-5_12.

The President’s Task Force on 21st Century Policing (2015). Final report.Washington, DC:
U.S. Department of Justice.

Trinkner, R. J., Tyler, T. R., & Goff, P. A. (2016). Justice fromwithin: The relations between a
procedurally just organizational climate and police organizational efficiency, en-
dorsement of democratic policing, and officer well-being. Psychology, Public Policy,
and Law. http://dx.doi.org/10.1037/law0000085.

Tyler, T. R. (1990). Why people obey the law. Princeton, NJ: Princeton University Press.
Tyler, T. R., Callahan, P. E., & Frost, J. (2007). Armed, and dangerous(?): Motivating rule

adherence among agents of social control. Law & Society Review, 41, 457–492.
Tyler, T. R., & Huo, Y. J. (2002). Trust in the law: Encouraging public cooperation with the po-

lice and courts. New York, NY: Russell-Sage.
Van Craen, M. (2016). Understanding police officers’ trust and trustworthy behavior: A

work relations framework. European Journal of Criminology, 13, 274–294.
Waddington, P. A. J. (1999). Police (canteen) sub-culture. An appreciation. British Journal

of Criminology, 39(2), 287–309.
Weitzer, R. (2015). American policing under fire: Misconduct and reform. Society, 52(5),

475–480.
Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordi-

nal dependent variables. Stata Journal, 6, 58–82.
Wolfe, S. E., & Nix, J. (2016a). The alleged “Ferguson Effect” and police willingness to en-

gage in community partnership. Law and Human Behavior, 40(1), 1–10. http://dx.doi.
org/10.1037/lhb0000164.

Wolfe, S. E., & Nix, J. (2016b). Police officers’ trust in their agency: Does self-legitimacy pro-
tect against supervisor procedural injustice? (Manuscript submitted for publication).

Wolfe, S. E., & Piquero, A. R. (2011). Organizational justice and policemisconduct. Criminal
Justice and Behavior, 38, 322–353.

Wolfe, S. E., McLean, K., & Pratt, T. C. (2016). I learned it by watching you: Legal socializa-
tion and the intergenerational transmission of legitimacy attitudes. British Journal of
Criminology. http://dx.doi.org/10.1093/bjc/azw038 (Advance online publication).

Justin Nix is an assistant professor in the Department of Criminal Justice at the University
of Louisville. He received his PhD in criminology and criminal justice at the University of
South Carolina. His research is centered in policing with a focus on legitimacy. His work
has recently appeared in Justice Quarterly, Crime & Delinquency, and Journal of Quantitative
Criminology.

Scott E. Wolfe is an assistant professor in the Department of Criminology and Criminal
Justice at the University of South Carolina. He received is PhD in criminology and criminal
justice from Arizona State University. His research focuses on police legitimacy, organiza-
tional justice, and criminological theory. His recent work has appeared in the Journal of
Quantitative Criminology, Justice Quarterly, and Law and Human Behavior.

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0045

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0050

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0055

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0060

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0065

http://dx.doi.org/10.1177/1098611116651403

https://www.washingtonpost.com/news/post-nation/wp/2015/10/08/youtube-effect-has-left-police-officers-under-siege-law-enforcement-leaders-say/

http://time.com/3441544/obama-ferguson-gulf-of-mistrust/

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf9100

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0085

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0090

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf9300

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0095

http://www.wsj.com/articles/the-new-nationwide-crime-wave-1432938425

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf1000

http://www.cnn.com/2015/05/26/us/baltimore-deadliest-month-violence-since-1999/index.html

http://nypost.com/2014/10/14/the-ferguson-effect-a-cops-eye-view/

http://socweb.soc.jhu.edu/faculty/morgan/papers/MorganPally2016

http://dx.doi.org/10.1108/13639511311329732

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0130

http://dx.doi.org/10.1080/07418825.2015.1102954

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0140

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0145

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0150

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0155

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0160

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0165

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0170

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0175

http://nypost.com/2015/05/05/the-dangers-of-de-policing-will-cops-just-stand-down/

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0180

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0185

http://dx.doi.org/10.1007/978-3-319-09813-5_12

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0195

http://dx.doi.org/10.1037/law0000085

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0200

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf9700

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0205

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0210

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0215

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0220

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0225

http://dx.doi.org/10.1037/lhb0000164

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0235

http://refhub.elsevier.com/S0047-2352(16)30049-6/rf0240

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Sensitivity to the Ferguson Effect: The role of managerial organizational justice

1. Organizational justice and sensitivity to the Ferguson Effect
2. The Ferguson Effect
2.1. The evidence concerning the Ferguson Effect
3. Organizational justice
4. The current study
5. Methods
5.1. Data
5.2. Dependent variables
5.2.1. Ferguson Effect on self
5.2.2. Ferguson Effect on colleagues
5.2.3. Ferguson Effect on citizens’ views
5.3. Independent variable
5.3.1. Organizational justice
5.4. Controls
5.5. Analytic strategy
6. Results
7. Discussion
Appendix A. Organizational justice and Ferguson Effect items
Organizational justice
Ferguson Effects
Less motivation
Increased danger
Affected colleagues
Affected US citizens
Affected local citizens

References

Journal of Criminal Justice

47 (2016) 1–11

Contents lists available at ScienceDirect

Journal of Criminal Justice

Temporal changes in racial violence, 1980 to 2006: A latent
trajectory approach

Karen F. Parker a,⁎, Richard Stansfield b, Patricia L. McCall c
a Department of Sociology and Criminal Justice, University of Delaware, Newark, DE 19716, United States
b Department of Sociology, Anthropology and Criminal Justice, Rutgers University, 405-7 Cooper Street, Camden, NJ 08102, United States
c Department of Sociology and Anthropology, North Carolina State University, 1911 Building 365, Raleigh, NC 27695, United States

⁎ Corresponding author.
E-mail addresses: Kparker@udel.edu (K.F. Parker), Rich

(R. Stansfield), Patty_mccall@ncsu.edu (P.L. McCall).

http://dx.doi.org/10.1016/j.jcrimjus.2016.06.001
0047-2352/

Published by Elsevier Ltd.

a b s t r a c t

a r t i c l e i n f o

Article history:
Received 25 April 2016
Received in revised form 22 June 2016
Accepted 23 June 2016
Available online 1 July 2016

Objectives: The study examines the ability of a latent trajectory approach to advance our understanding of the
temporal trends in white and black homicide rates over a critical period, 1980 to 2006. After establishing distinct
trajectories that reveal hidden racial heterogeneity, we estimate which of two dominant arguments concerning
the changes in homicide rates over time: 1)macrostructural conditions and 2) crime control and drug sales—best
explain the latent class race-specific homicide rate memberships at the city level.
Methods:Using homicide data from theUniformCrimeReports alongwith decennial U.S. census data across three
time periods, we employ both latent trajectory and time series approaches.
Results: Our latent trajectory approach identified three unique trends or groupings of cities based on white and
black homicide rates, reflecting “high”, “medium” and “low” temporal homicide trends. Time seriesmodels high-
light variation in which characteristics contributed to the distinct race-specific homicide trends by trajectory
group.
Conclusions: Together, this study reveals hidden heterogeneity among American cities with respect to temporal
trends that inform the current debate about diversity in the location and magnitude of the crime drop as well as
which factors contributed to homicide trends by racial groups. Implications are discussed.

Published by Elsevier Ltd.

Keywords:
Racial violence
Crime drop
Homicide trends
Latent trajectory approach
Macrostructural approach
Crime control strategies
Time series analysis

1. Introduction

Major shifts in national crime trends over the last quarter of the 20th
century, particularly among African-American males, have prompted
criminologists to explore what social, economic and political forces are
driving such changes (Blumstein, 1995). Scholars have specifically doc-
umented the importance of age composition and gains in the economy
(Blumstein & Wallman, 2006; LaFree, 1999; Parker, 2008; Rosenfeld &
Messner, 2009) as explanations for declining crime rates since the
early 1990s (Gartner & Doob, 2010). Strong evidence that the U.S.
crime drop differed in magnitude across locales also led scholars to re-
think the crime drop at local levels (Baumer & Wolff, 2014; Messner
et al., 2007). These investigations revealed that the economy as well
as policy-based factors such as police presence, prison expansion, and
receding illicit drug markets might be key to understanding American
based declines. The role of each factorwithin cities remains hotly debat-
ed however, evidenced by the disagreement surrounding the role of
specialized police strategies in New York City (Rosenfeld & Fornango,

ard.stansfield@rutgers.edu

2014; Weisburd, Telep, & Lawton, 2014; Zimring, 2011). We suggest
that accounting for racial differences could providemore definitive con-
clusions about the role of crime control strategies and structural condi-
tions in the American crime drop.

America’s enduring problem of violence is not equally dispersed
across all cities or all groups. Scholars point to the considerable differ-
ences in the average social and economic conditions of racial and ethnic
groups, in addition to historic and contemporary differences in criminal
justice responses across communities and groups. We examine the ex-
tent to which latent trajectory techniques can inform us about the un-
derlying factors contributing to race-specific U.S. homicide trends
during the latter part of the 20th century and into the early years of
the 21st century. Latent trajectory analyses have been applied primarily
to individual-level longitudinal cohort data to identify distinct offending
trajectories. Few studies have applied this technique to study macro-
level crime trends, but there have been notable exceptions at the street
or neighborhood level (Boggess & Hipp, 2010; Braga, Hureau, &
Papachristos, 2011; Griffiths & Chavez, 2004; Kikuchi & Desmond,
2010; Morris & Slocum, 2012; Weisburd, Bushway, Lum, & Yang,
2004). To date, latent trajectory analysis has rarely been applied to tem-
poral trends in city-level homicide (see Hipp, 2011; McCall, Land, &
Parker, 2011), despite the predominant focus of the crime drop

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http://dx.doi.org/10.1016/j.jcrimjus.2016.06.001

mailto:Patty_mccall@ncsu.edu

http://dx.doi.org/10.1016/j.jcrimjus.2016.06.001

http://www.sciencedirect.com/science/journal/00472352

2 K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

literature on city dynamics. Applying this technique, along with a time
series approach, allows us to identify different city-level trajectories
with unique white and black homicide rate trends, thus allowing us to
capture racial heterogeneity in violence within American cities. Based
on extensive research (see e.g., Baumer & Wolff, 2014; Blumstein &
Wallman, 2006; Levitt, 2004; Parker, 2008; Zimring, 2007 for in-depth
reviews), we know that the crime drop was not universal. For example,
Blumstein and Wallman (2006) discuss different patterns across age
groups and note that the sharpest decline was for young offenders,
while Parker (2008) documents how patterns differ along racial lines.
Moreover, macrostructural research has revealed key factors relevant
to the temporal trends in homicide rates, and race and ethnicity contin-
ue to be among the most important predictors (Hipp, 2011; Peterson &
Krivo, 2010; Parker, 2008).With these literatures inmind, this study ex-
plores how both macrostructural features of cities and crime control
strategies influence race-specific homicide trends overall and by trajec-
tory group classification.

2. Racial violence and macrostructural conditions

At its core, economic and social conditions are key structural features
of urban areas that receive much attention in the macro-level research
on crime. Poverty, unemployment and/or the concentration of these
economic disadvantages have been argued to be among the strongest
predictors of urban homicide regardless ofwhether the level of aggrega-
tion is the community, city, county, or state (Land, McCall, & Cohen,
1990; Phillips, 1997). Family disruption (via divorce) and residential in-
stability are two other consistently strong and robust predictors of ag-
gregate level crime and violence (see Land et al., 1990; McCall, Land, &
Parker, 2010; Pratt & Cullen, 2005; Parker, 2008). For example, Pratt
and Cullen’s (2005) meta-analysis of structural predictors on crime
rates identified family disruption (typically measured as “percent di-
vorced”) among the “strongest and most stable” predictors of crime
rates out of approximately 1984 effects sizes for ecological predictors
in 509 statisticalmodels estimated from 214 differentmacro-level stud-
ies (see Pratt & Cullen, 2005: 403). A key advancement in this literature
is the racial disparities that exist in macro-structural conditions, which
contribute to crime and urban violence (Peterson & Krivo, 2010).

As an example, Sampson and Wilson’s (1995) review of the race-
criminal violence relations literature highlighted how structurally in-
duced disadvantages concentrated in poor black neighborhoods, exac-
erbated by high levels of segregation and the development of
cognitive landscapes that legitimate crime and violence. The unequal
distribution of these conditions all go to the heart of racial differences
in crime rates. As scholars have continued to examine the persisting ef-
fects of structural differences (and cultural responses) between blacks
andwhites, it has become clear that the sources of crime are remarkably
invariant across groups (Peterson & Krivo, 2005, 2010). Rather, these
groups face different social and economic realities which are the key
to understanding the racial gap in crime rates (Hipp, 2011; Ousey,
1999; Parker & McCall, 1999; Parker, 2008). This important aspect of
urban violence has been a strong theme in the literature, albeit largely
missing from the debate about the crimedrop (see Parker, 2008 for sim-
ilar arguments).

Another important consideration in this line of research is how His-
panic population growth in U.S. cities has had profound effects onmany
traditional correlates of crime, such as ethnic heterogeneity, local labor
market conditions, and even the strength of the family (MacDonald &
Sampson, 2012; Martinez, Rosenfeld, & Mares, 2008; Ousey & Kubrin,
2009; Sampson, 2008). Essentially there are ample theoretical reasons
to expect shifts in economic, social and demographic characteristics of
these areas to have important implications for temporal trends in homi-
cide rates (MacDonald & Sampson, 2012; Ousey & Kubrin, 2014;
Sampson, 2008; Wadsworth, 2010). Despite the fact that Hispanics
often reside in communities that are characterized by a variety of
criminogenic factors, research tends to show that the presence of

Hispanic immigrants in urban areas results in either a negligible or neg-
ative effect on crime (see, for example MacDonald, Hipp, & Gill, 2013;
Ousey & Kubrin, 2014) and may have contributed to the violent crime
decline throughout the 1990s (Sampson, 2008; Stowell, Messner,
McGeever, & Raffalovic, 2009; Wadsworth, 2010). For that reason, His-
panic presence should be taken into consideration for its potential influ-
ence on crime rates in general. Accordingly, our research extends these
lines of analyses by applying latent trajectory techniques to white and
black homicide trends 1980–2006 and then further exploring whether
the changes in the composition of American cities, along with shifts in
other macrostructural conditions, have contributed to distinct trajecto-
ry group membership in homicide rates across racial groups.

3. Crime control and changing drug sale patterns

While differences in homicide rates across racial and ethnic groups
have often been attributed to the unequal sorting of economic and social
conditions across racial groups (Sampson, 2013), recent attention has
focused on theways inwhich racial groupmembership and community
may moderate the impact of crime control strategies (Bobo &
Thompson, 2006; Borooah, 2011), and the race- and ethnic-specific
ways that African Americans or Latinos frame their understanding of
police, courts and corrections as result (Unnever & Gabbidon, 2011;
Unnever, Barnes, & Cullen, 2016).

There is little doubt that crime control strategies play a role in tem-
poral trends in homicide rates. Acknowledging the political and legal
changes that occurred as policies shifted to a “get tough on crime” peri-
od beginning in the late 1970s, the U.S. has witnessed unprecedented
increases in imprisonment rates as well as growing police presence on
city streets throughout the 1990s and into the 21st century. In fact,
changes in policing and incarceration are the two most commonly de-
bated factors associated with changes in American crime trends
(Baumer & Wolff, 2014; Blumstein & Wallman, 2006; Levitt, 2004;
Rosenfeld, 2016).

Joining others, the accumulation of evidence concerning the role of
incarceration and police presence on the U.S. crime drop is quite con-
vincing. Scholars have attributed estimates ranging from 10%
(Western, 2006), up to a third of the crime decline (Levitt, 2004) to ris-
ing imprisonment. Levitt (2004) goes on to also highlight the contribu-
tion of policing to the crime decline. While he downplays the role of
policing strategies such as the targeting of crime hotspots, he does ac-
knowledge the role of the size of the police force on the streets. Claims
about the role of crime control strategies like imprisonment and polic-
ing on the American crime drop has been bolstered by others
(Baumer, 2008; Eck & Maguire, 2000; Garland, 2001; Kubrin, Messner,
Deane, McGeever, & Stucky, 2010; Rosenfeld, 2009; Zimring, 2007).
Baumer and Lauritsen (2010), using National Crime Victimization Sur-
vey data, provide further evidence of the role of policing byfinding a sig-
nificant rise in citizen reporting during this time period, which
enhanced the ability of police to effectively respond to crime.

The pressure on criminal justice agencies to remove not only violent
but drug-related offenders from the streets may also partially explain
the declining homicide trend between 1990 and 2000 (Blumstein &
Wallman, 2006). Blumstein (1995) links drug markets, specifically the
emergence of crack cocaine inmany cities through themid-1980s, to vi-
olence in many urban neighborhoods throughout this period. Levitt
(2004) also claims that the stabilizing of drug markets during the
1990s resulted in less urban violence. Ousey and Lee (2002) provide ad-
ditional evidence that the illicit drug market (especially the crack-co-
caine epidemic of this period) that peaked in early 1990s was related
to homicide trends during this same time period.

Of course discussions of punishment policies during this time frame
have raised concerns over the disproportionate effect of the get tough
on drugs era on African Americans, the rates of black incarceration,
and a set of law enforcement practices seen as unfair in many African
American communities (Bobo & Thompson, 2006; Rosenfeld, 2016;

3K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

Unnever & Gabbidon, 2011). The accumulation of evidence suggests
that the criminal justice response through increased police presence
and imprisonment, as well as the receding drug markets, are linked to
declining rates of violence during the 1990s. Recent research, however,
questions whether the criminal justice factors that influence African
American offending are similar to those that influence other racial
groups. The disproportionate use of these practices may have led to
greater divergence in white and black trends. These practices are there-
fore pertinent to our examination of homicide trends among racial
groups. Furthermore, by incorporating a latent trajectory based ap-
proach using separate analyses by racial groups, this study explores
whether macrostructural covariates or crime control strategies lead to
city-level trajectory placement in high homicide offending groups by
race. This research provides additional insights by seeking to under-
stand how these two perspectives impact temporal trends in homicides
across racial groups. Specifically, we address the following research
questions:

1. What is the degree and nature of hidden heterogeneity in temporal
homicide trends? That is, are there distinct latent classes or trajecto-
ries in city-level temporal trends for white and black homicide rates
from 1980 to 2006?

2. Do macrostructural conditions or crime control policies distinguish
group classification? How valuable are these two perspectives in ac-
counting for the differences in the changes in race-specific homicide
rates overall and across latent classes?

4. Data and methods

The homicide rates employed in the trajectory procedure are de-
rived from the FBI’s Supplemental Homicide File (Fox & Swatt, 2009).
Race-specific homicide offenses reported to the police involving single
offender-single victims were divided by the city’s race-specific popula-
tion to calculate thewhite and black offender homicide rates.1 Complete
annual homicide data (1980 to 2006) and covariates (for 1980, 1990,
2000, and 2006) are available for 151 large U.S. cities with populations
of 100,000 or more.2 The sources for covariates included in the study
are: the U.S. Bureau of the Census data (1983, 1994, 2003), American
Communities Survey, FBI’s Uniform Crime Reports (Crime in America),
and the Sourcebook of Criminal Justice Statistics.

4.1. Independent variables

The covariates we employ are based on common measures used in
urban homicide studies (Land et al., 1990; Pratt & Cullen, 2005; McCall
et al., 2011) and contemporary studies of immigration, crime control
policies and changing homicide rates (Baumer & Wolff, 2014;
Blumstein & Rosenfeld, 1998; Feldmeyer, 2010; LaFree, 1999; Levitt,
2004; Sampson, 2008). Prior studies have documented that under-
standing the contextual basis for race and violence requires race-specif-
ic measures of both the dependent variable and key explanatory
variables (Phillips, 2002; Sampson &Wilson, 1995). The structuralmea-
sures utilized in the present analysis are all race-specific and include:
percent of families living below the poverty level, the Gini index (amea-
sure of income inequality), percent of children not livingwith both par-
ents, racial residential segregation (as measured by index of
dissimilarity), the percent of the population of adult males who are di-
vorced, and residential mobility (living in a different residence during
the past 5 years). To capture industrial restructuring from 1980 to
2000, we include a race-specific ratiomeasure of service tomanufactur-
ing industry employment. The percent Hispanic population is also in-
cluded to account for the demographic shift occurring in recent
decades that added to the population heterogeneity of these cities, as
well as the proportion of the population who do not speak English
well or not at all, indicating limited English proficiency.

Crime control policies and arrests for drug sales have surfaced as key
covariates in the crime drop debate (Baumer &Wolff, 2014; Blumstein,
1995; Levitt, 2004; Kubrin et al., 2010; Zimring, 2007), but also impor-
tant indicators leading to the temporal trends in violence over time.
Whilemuch of this literature has focused on total homicide rates, we in-
clude three measures to capture the relevance of these shifts on white
and black homicide trajectories over time. First, the state-level, race-
specific imprisonment rates (measured in 1979, 1989, and 1999) are in-
cluded as a proxy indicator for the incapacitating effects of “get tough on
crime” legislation introduced during these decades. While a state-level
proxy measure of imprisonment is far from ideal, incarceration has
played a key role in the crime drop debate, even though some studies
have reported little to no relationship between changes in incarceration
and crime (see Bird & Grattet, 2016; DeFina & Arvanites, 2002;
Kovandzic & Vieraities, 2006; Raphael & Winter-Ebmer, 2001;
Spelman, 2006, 2009). Because incarceration data at lower levels of ag-
gregation are not available, a state level measure is commonly used by
scholars examining homicide trends (Devine, Sheley, & Smith, 1988;
Marvel & Moody, 1996; McCall, Parker, & MacDonald, 2008; Parker,
2004). Contemporary studies have also pointed to increased police
presence as having an impact on homicide rates (Levitt, 2004; Marvel
& Moody, 1996; Rosenfeld & Fornango, 2014; Weisburd et al., 2014;
Zimring, 2011). Therefore, we include the number of police officers
per capita in our models.

To capture the influence of the drug trade on homicides, we include
a race-specific adult drug sale/manufacture arrest rate. While the use of
arrest data has its limitations, when comparing arrest data with alterna-
tive indicators (e.g., Drug Use Forecasting/Arrestee Drug Abuse Moni-
toring and/or Drug Abuse Warning Network), research has reported
high internal reliability among data sources and that these data sources
yield similar estimates of drug activity when compared to drug arrests
(Baumer, Lauritsen, Rosenfeld, & Wright, 1998; Rosenfeld & Decker,
1993; Warner & Coomer, 2003). Given that the alternative data sources
are limited in sample size, we use drug arrests as a proxy for the level of
drug activities across our sample of large urban cities.3 Drug sale arrest
data have also commonly used in other longitudinal studies of homicide
trends (see Ousey & Kubrin, 2014; Ousey & Lee, 2002; Strom &
MacDonald, 2007). Three other covariates of homicide, total population
size, percent black and southern region, were used to predict group
membership in earlier stages of the latent trajectory estimation proce-
dure. Total population size is, however, later re-introduced into the
time series analysis estimating the effects of macrostructural conditions
and crime control strategies on homicide rates.

Preliminary analyses indicated nonlinear relationships between
race-specific homicide rates and the percent Hispanic population vari-
able. To adjust for this nonlinearity, we used natural logarithmic trans-
formations of the percent Hispanic population. And as commonly
practiced in macro-level analysis, principal components analysis was
conducted to reduce regressor space shared by these variables that
comprised the economic deprivation index (percent family poverty,
Gini index, racial residential segregation and percent of children under
the age of 18 not living with both parents) and to minimize problems
associated with collinearity such as the partialing fallacy. Importantly,
racial residential segregation loaded with economic indicators in the
black models but remained a separate predictor in the white models;
a finding that represents the racial differences in how these measures
tend to concentrate in urban communities and a factor loading scheme
often found by other scholars (Messner & Golden, 1992; Parker &
McCall, 1999; Parker, 2004). Finally, the principal components analysis
identified another index that combined the percentHispanic population
measure with the percent of residents speaking English either “not
well” or “not at all”. These two measures are highly correlated, given
that Hispanics comprise the majority of immigration patterns, as well
as the population that is more likely to retain language loyalty in the
home (Ousey&Kubrin, 2009). Consistentwithmore recent research ex-
amining Hispanic immigration (Martinez et al., 2008; Ousey & Kubrin,

Table 1
Time predictors associated with three trajectory group membership for white and black
homicide rates from 1980 to 2006. Betas, Z-values (in parentheses) and Wald statistic.

White homicide time model (N =
131)

Lowest Medium Highest Wald

Traj group

Traj
group

Linear −0.0013 −0.1423 −0.2444 35.239⁎⁎

(−0.025) (−3.789) (−4.567)
Quadratic −0.1588 −0.3632 −0.5552 61.609⁎⁎

(−1.614) (−5.251) (−5.593)
Cubic −0.0114 0.0645 0.1394 14.987⁎⁎

(−0.274) (2.149) (3.210)
Quartic −0.0114 0.241 0.3313 73.757⁎⁎

(2.212) (6.000) (5.712)
Intercept −10.181 −9.278 −8.402
R2 0.0112 0.0917 0.1433
Overall model R2 0.512⁎

Black homicide time model (N = 144)
Linear −0.122 −0.1637 −0.1768 43.141⁎⁎

(−1.169) (−4.447) (−4.186)
Quadratic −0.0039 −0.1212 −0.1057 65.358⁎⁎

(−0.099) (−6.490) (−4.719)
Cubic 0.0261 0.0564 0.0804 11.122⁎⁎

(0.463) (2.066) (2.512)
Intercept −7.554 −7.006 −6.546
R2 0.000 0.2458 0.0926
Overall model R2 0.436

⁎ p b 0.05.
⁎⁎ p b 0.01.

Fig. 1. Display of homicide trajectory groups by race.

4 K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

2009), this index is referred to as “Hispanic immigration” in ourmodels.
All the indexes are computed as the sum of the variables weighted by
their respective factor scores (Kim & Mueller, 1978).

We used Latent Gold (version 4.0) statistical package to identify la-
tent classes and find evidence of any hidden heterogeneity among the
cities with respect to the temporal trends in white and black homicide
rates over time. If evidence is found, we can then determine the optimal
number of latent groups or classes of cities and the nature of the race-
specific trajectories for city groups. For individuals, this technique has
been used to identify a set of developmental trajectories typically
based on offending patterns and these trends are used to assign individ-
uals with like offending patterns to discrete trajectories or classes. For
our purposes, the latent trajectory estimation procedure identifies cities
that share unique white and black homicide trends into groups or clas-
ses. After cities are classified into discrete groupings, we plot the trajec-
tories for each racial group for visual comparison. Despite the popularity
of trajectory analyses in social science research, there is no agreed upon
best method to identify the number of trajectory groupings or assigning
observations (in this case, cities) to a specific group. We recognize that
different methods used by different researchers could produce differ-
ences in the number of trajectories and assignment of cities to trajectory
groups (Warren, Luo, Halpern-Manners, Raymo, & Palloni, 2015). Nev-
ertheless, this stage of our analysis will allow us to determine if a latent
trajectory estimate procedure is useful when identifying distinct race-
specific homicide trajectories in our sample of large U.S. cities. By apply-
ing this technique, we will learn more about heterogeneity in race-spe-
cific homicide temporal trends, as well as whether cities differ in the
trajectory classification by racial group between 1980 and 2006.

The second stage of our analysis allows us to directly examine tem-
poral trends in race-specific homicide rates. Using a multilevel pooled
cross-sectional time series design, we determine whether race-specific
predictors of macrostructural characteristics or crime control strategies
contribute to explaining the variation in thewithin-city changes in race-
specific homicide rates for the large sample of U.S. cities but also within
the highest and within the lowest latent groups. That is, using annual
data between 1980 and 2006, we estimate cross-sectional time series
models to identify the factors that predict temporal trends for the
highest and lowest latent classes, and report the statistically significant
differences between them. This allows us to extend prior work showing
the value of the trajectory technique (McCall et al., 2011) by (1)
assessing the effect of covariates on the distinct white and black homi-
cide trends and (2) then determining whether predictors contributing
statistically significant variation to those race-specific homicide trends
have significantly different effects on higher rate trends vis a vis lower
rate homicide trends.

5. Results

5.1. Step one: latent class trajectory analysis

Our research employs a latent trajectory technique on white and
black homicide offending rates from 1980 to 2006 in our attempt to es-
timate which cities fall into high versus low trajectory groupings while
allowing for city compositional difference by the racial group. Table 1
displays parameter estimates generated from the latent trajectory esti-
mation procedure for white and black homicide rates. As Bollen and
Curran (2006) discuss, approximating functional forms of trajectories
is best done on the basis of theory, testing hypothesized forms rather
than approximating trends in an exploratoryway. To this end,we tested
fourth-order temporal models because the national trend between
1980 and 2006 had four bends in the temporal curve.

The estimates at the top of the Table 1 represent the white model
with three latent trajectories and a polynomial of the fourth order in
time. The model estimated below it displays those results produced
for African Americans, showing three latent trajectories based with a
third degree polynomial. The complexity of the polynomial models is

necessitated by turning points in the temporal trends that differ for
white and black homicide rates across the decades included in the anal-
ysis (see Fig. 1a and b). Simply put the fitted curves or functions that
best fit the data are different by racial group. That is, the quartic (4th
order polynomial) function was significant for all white trajectory
groups and thus the quartic specification was chosen—adding a slight
increase in homicide rates for whites for the early years of 2000 that

5K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

was not found for the black homicide trajectories. Therefore, the cubic
model was chosen for the black homicide trajectory groups given the
distribution of a polynomial in the 3rd order. A three latent class
model was chosen for both racial groups because of the fit to the data
using BIC scores as a criterion (Raftery, 1995).4 Other more parsimoni-
ousmodels were considered, but the nuances of variation in the tempo-
ral trends were lost with simpler model specifications. Covariates were
used in this estimationprocedure to establish thebaseline, including the
measures of total population size, percent black and a regional code of
South.

Fig. 1a and b display the predicted trends in white and black homi-
cide rates for the three classes of trajectories identified in Table
1—showing white homicide rate trajectories and black homicide rate
trajectories, respectively – in addition to the overall white and black ho-
micide trends for comparison. Based on sample percentages of cities
classified in each group displayed in Fig. 1a, 45% of cities would be con-
sidered in the low white homicide rate group, 35% are classified as me-
dium and 25% of cities are found within the high white homicide rate
group trajectory. In Fig. 1b, 38% of the cities are found in the low black
homicide trajectory group, followed by 36% as medium and 26% cites
in the high black homicide trajectory group.

The graphs also display the relative variation in patterns for these
trajectories across racial groups. That is, there are differences among
the trajectories in the degree of fluctuations, the curves, and themagni-
tude of the homicide rates characterizing each class. From the predicted
trends displayed in Fig. 1a, we see that, by and large, a steeper decreas-
ing slope is associated with the highest white homicide rate trajectory
group throughout the 1980s compared to the average white homicide
trend during that time, with a slight increase beginning around 1987.
In 1993 begins a steady continuous decline for this group through the
end of the 1990s and into the 2000s with an upward turn around
2003. The magnitude of homicide rates characterizing the medium
white group more closely mirror the average white homicide rate for
all cities. In terms of fluctuation, however, the medium and low groups
are parallel, by and large, with muted variation when compared to the
high rate group.

Examining the trends displayed in Fig. 1b, we see a marked contrast
with the trajectories for thewhite homicide rates, driven by a higher av-
erage black homicide rate showing amore noticeable incline during the
1980s, and decline throughout the 1990s. The high and medium black
homicide rate trajectories exhibit this same pattern over time. Both
the high and medium trajectory groups show a steep incline in homi-
cides during the 1980s, followed by a steady decline beginning around
1988, throughout the 1990s and into themid-2000s. The low trajectory

Table 2
Descriptive statistics with sample means (and standard deviations) by trajectory group memb

White models Traj group 1 (low)

Economic disadvantage index⁎ −16.93 (9.54)
Racial segregation (index of diss.)⁎ 50.82 (15.05)
Industrial restructuring⁎ 0.846 (0.345)
Divorced males⁎ 8.19 (1.49)
Hispanic immigration index⁎ 8.09 (6.91)
Incarceration rate⁎ 0.460 (0.145)
Drug sales arrest rate⁎ 40.03 (37.11)
Police presence 196.55 (58.87)
Total pop size 217,595 (126,725)

Black models Traj group 1 (low)
Economic disadvantage index⁎ 44.64 (26.53)
Industrial restructuring⁎ 1.27 (1.29)
Divorced males⁎ 9.64 (2.59)
Hispanic immigration index⁎ 15.34 (16.24)
Incarceration rate⁎ 0.41 (0.148)
Drug sales arrest rate⁎ 36.21 (57.74)
Police presence 177.88 (57.06)
Total pop size 187,749 (116,734)

group, on the other hand, exhibits a steady trend until 1988 and a slight
yet continuous decline over the remaining period. An interesting point
of comparisonwith thewhite high group is the timingwhen the decline
during the 1990s begins; that is, the white high rate group begins their
“crimedecline” in 1993whereas the black high andmedium rate groups
begin their “crime decline” in 1988 (Messner, Deane, Anselin, &
Pearson-Nelson, 2005). Overall, the black homicide trends are higher
than white homicide trends. For example, low-rate black homicide tra-
jectory is still higher in magnitude than the white homicide rate in the
highest trajectory class.

We list the U. S. cities comprising the “low”, “medium” and “high”
white and black homicide trajectory classifications in Appendix 1. Ex-
amining the cities of each classification for commonalities is a difficult
task given the race-specific nature of the analysis, but similarities are
found in a large number of the cities that form the trajectory classes
across racial groups. First “university towns” tend to cluster in the low
homicide rate trajectory groups more so than in medium or high
groups. McCall et al. (2011)study also found a relatively large propor-
tion of “university towns” cluster in the low homicide rate groups.

In terms of the high homicide trajectories, there are commonalities
in the cities that comprise this classification as well. Cities such as Los
Angeles, Atlanta, Chicago, Gary, New Orleans, Detroit, Las Vegas and
NewarkNJ are found in this trajectory across the racial groups. These cit-
ies have garnered significant attention in criminological and economic/
urban sociology literatures, as they are places that have experienced sig-
nificant racial composition and economic shifts over time. Los Angeles
comprises the largest Hispanic (largely Mexican) population, wherein
9% of the entire U.S. population who identifies as Hispanic (foreign but
largely U.S. born) resides (Rytina, 2009). Detroit and Gary are cities
where the removal of manufacturing jobs has contributed to significant
unemployment and economic declines, while New Orleans and Las
Vegas have experienced significant population and economic fluctua-
tion in recent decades. Given the dramatic changes to the urban econo-
my, aswell as the influx of populations, these cities aremore likely to be
found in higher homicide trajectory groups than their counterparts.

Last, Table 2 furthers our examination of the U.S. city based classes
identified in the latent trait analysis by providing descriptive statistical
information for each trajectory group by race. The statistical information
is displayed for whites at the top of the table, followed by African Amer-
icans. For both racial groups, the high trajectory group comprises cities
with the greatest economic deprivation, family disruption and industri-
al restructuring, relative to the low and medium trajectory groups. The
high trajectory groups are also the most residentially segregated cities
racially and have the largest percentage of Hispanics facing language

ership for racial groups.

Traj group 2 (medium) Traj group 3 (high)

−9.83 (8.98) −5.66 (10.95)
59.66 (15.10) 63.32 (12.32)
0.835 (0.273) 0.922 (0.492)
9.27 (1.83) 9.28 (2.33)

17.62 (17.83) 27.46 (18.35)
0.447 (0.132) 0.382 (0.108)
62.23 (64.31) 71.94 (55.33)

229.44 (81.38) 259.64 (102.30)
347,754 (288,995) 966,355 (1,559,522)

Traj group 2 (medium) Traj group 3 (high)
77.98 (16.40) 77.56 (21.07)
1.44 (0.649) 1.64 (1.14)
9.7 (2.19) 10.74 (2.56)

12.76 (12.44) 17.65 (17.81)
0.45 (0.153) 0.41 (0.173)

70.69 (87.40) 125.49 (160.94)
227.51 (64.81) 259.38 (108.87)

459,389 (1,027,725) 592,180 (708,385)

Table 3
Multilevel mixed effects cross-sectional pooled time series regression estimates with [Z
scores] and (Robust standard errors) for white homicide rates within large U.S. cities,
within high and within low latent trajectory classification.

All cities High Traj
class

Low Traj
class

Coefficient comparison
test high vs. low

Macrostructural
conditions
Economic
disadvantage
indexa

0.022⁎⁎ 0.021⁎⁎ 0.007⁎⁎

[8.06] [6.60] [3.02] 3.88⁎⁎

(0.002) (0.003) (0.002)
Racial segregationa 0.012⁎⁎ 0.024⁎⁎ 0.002

[9.76] [6.29] [0.67] 4.92⁎⁎

(0.001) (0.004) (0.002)

Industrial
restructuringa

−0.166⁎⁎ −0.124 −0.237⁎⁎

[−4.30] [−1.81] [−3.84] 1.20
(0.039) (0.069) (0.064)

Divorced malesa 0.092⁎⁎ 0.148⁎⁎ 0.061⁎⁎

[14.68] [10.83] [5.02] 4.72⁎⁎

(0.006) (0.014) (0.012)

Residential
mobility

−0.003⁎ −0.013⁎⁎ −0.001
[−2.26] [−3.34] [−0.05] −3.33⁎⁎

(0.001) (0.003) (0.002)
Hispanic
immigration index

0.032⁎⁎ 0.034⁎⁎ 0.007
[30.52] [20.13] [1.84] 6.04⁎⁎

(0.001) (0.002) (0.004)
Crime control and
arrest for drug sales
Incarceration ratea −0.025 0.396⁎ −0.300

[−0.24] [1.87] [−1.80] 2.58⁎⁎

(0.102) (0.212) (0.167)
Drug sales arrest
ratea

−0.001 −0.001 0.001
[−1.22] [−1.62] [0.82]
(0.001) (0.001) (0.001)

Police presence 0.001 −0.001⁎ 0.001
[0.51] [−2.02] [0.49] −1.41
(0.002) (0.001) (0.001)

Population size
(log)

0.109⁎⁎ −0.053 −0.022
[5.39] [1.65] [−0.50]
(0.020) (0.032) (0.045)

Log likelihood −2205.29 −280.13 −803.55
N 2335/131 446/26 867/59

⁎⁎ p b 0.01.
⁎ p b 0.05.
a Denotes measure is race-specific.

6 K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

barriers. These cities comprise some of the largest population sizes in
our sample, such as Los Angeles and Chicago, and also represent some
of the largest percentages of police officers per capita. Arrests for drug
sales are among the highest for this trajectory group regardless of
race, but the incarceration rate is lower than most other trajectory
groupings. A quick comparison of the means across racial groups sup-
ports previous findings that African Americans face much higher levels
of disadvantage relative to whites (Krivo & Peterson, 2000; Parker &
McCall, 1999; Sampson & Wilson, 1995), supporting the need for a
race-specific investigation of homicide trends (see also Phillips, 2002).

Thus, with regard to our first research question, we find that the
group based trajectory approach produces evidence of unique homicide
trajectories that characterize relatively homogeneous groups of U.S. cit-
ies with respect to trends in homicide rates for the period 1980 to 2006,
as well as evidence of heterogeneity among the entire set of cities with
respect to temporal trends by racial groups. In addition, the fact that the
grouping of cities displays relatively different temporal trends by race
over the time period requires additional research into which macro-
level forces are influencing these classifications of homicide trajectories
revealed in our latent trait analyses. Given evidence of heterogeneity
and significant variation in race-specific homicides, we further investi-
gate these perspectives using a time series approach.

5.2. Step two: multilevel time series analysis

In this stage of the analysis,we usemultilevelmodels in a time series
cross-sectional (TSCS) analysis to determine if eithermacrostructural or
crime control strategies contribute to the within city changes in race-
specific homicide rates over time. This method is applicable to the pres-
ent research questions given the temporal hierarchy in the data, where
measurement occasions are nestedwithin cities (Beck & Katz, 2007). As
Beck and Katz (2007) show, multilevel/random effects models perform
better with TSCS data than alternative models, as they allow for the es-
timation of city-level time-invariant parameters, in addition to observed
time-variant characteristics. After performing time series analysis of the
changes in black and white homicide rates for the large sample of U.S.
cities as a baseline model, we further our investigation by estimating
cross-sectional time seriesmodels for thehighest and also for the lowest
trajectory classes by racial group. This additional analysis allows us to
more closely examine our central research question by identifying the
factors that predict temporal patterns within latent groups and observe
any differences by race. Before discussing the results displayed in Tables
3 and 4, we strengthen our estimation by accounting for two important
statistical issues.

First, we test for the presence of unit roots (or non-stationarity) in
our panel data set. Although there are several tests we could have
used, we chose to use the Harris and Tzavalis (1999) test because our
time dimension is small (21 years, from 1980 to 2000), and we have a
relatively large number of cases in our panel (N = 151). Using the
xtunitroot command in Stata (version 13), we test the null hypothesis
that panels contain unit roots against the alternative hypothesis that
panels are stationary. Cross-sectional means were removed to control
for contemporaneous correlation using the demean option. Statistically
significant results of the HT test lead us to reject the null hypothesis and
conclude that neither the black nor white homicide series contain unit
roots.5 As such there are no obvious non-stationarity issues in either
the black or white homicide trajectories. Second, we provide a formal
test for statistically significant differences between the high and low la-
tent trajectory classes to further our research aimof establishinghetero-
geneity. Using the formula by Paternoster et al. (1998), we calculate and
report the coefficient comparison test of differential impact of predic-
tors on high versus low trajectory classification by racial group. This sta-
tistical test is ideal given the assumption of independent samples.

In Table 3, the panel results show how changes in macrostructural
and crime control strategies are related to the changes in white homi-
cide rates. The results for the full sample of cities are displayed in

Model 1, followed by cross-sectional time series estimates identifying
the factors that contribute to changes in white homicide rates for
those cities within the high rate trajectory (Model 2) and low rate tra-
jectory placements (Model 3). Examining Model 1, we find changes in
all six macrostructural conditions are significantly related to changes
in white homicide rates from 1980 to 2006, whereas none of the
crime control indicators reach statistical significance. Turning attention
toward the latent class specific analyses, we find in Model 2 that most
macrostructural conditions and crime control are significantly related
to changes in white homicide rates within the high rate trajectory
class, with the exception of industrial restructuring and drug sales ar-
rest. On the other hand, in Model 3 only a few structural conditions
(economic disadvantage, industrial restructuring and family disruption)
are related to changes in white homicide rates among those cities in the
low rate trajectory, and crime control and drug sales arrest are unrelat-
ed to changes in white homicide rates in this trajectory. The coefficient
comparison test shows further evidence of the significant differences in
the predictors onwhite homicide rates in the high versus low trajectory
groups. Almost all themacrostructural conditionswere significantly dif-
ferent and had a stronger effect in the high rate model than the low rate
model, and the incarceration rate also had a stronger effect on the high
rate trajectorymodel. This formal test reveals greater evidence of statis-
tically significant differences in macrostructural conditions than among
the crime control and drug sales indicators. For white homicide trends,
the vast majority of the predictors are among the macrostructural con-
ditions vis-a-vis the crime control and drug sales arrest indicators.

Table 4
Multilevel mixed effects cross-sectional pooled time series regression estimates with [Z
scores] and (Robust Standard Errors) for black homicide rateswithin large U.S. cities,with-
in high and within low latent trajectory classification.

All cities High Traj
class
Low Traj
class
Coefficient comparison
test high vs. low

Macrostructural
conditions
Economic
disadvantage
indexa b

0.002⁎ 0.002 −0.000
[2.22] [0.16] [−0.16]
(0.001) (0.001) (0.002)

Industrial
restructuringa

−0.081⁎⁎ −0.072⁎⁎ −0.015
[−4.41] [−3.20] [−0.32] −1.09
(0.018) (0.022) (0.047)

Divorced malesa 0.045⁎⁎ 0.025⁎ −0.001
[7.03] [1.90] [−0.18] 1.53
(0.007) (0.013) (0.011)

Residential
mobility

0.004⁎ 0.010⁎⁎ 0.001
[2.27] [3.57] [0.035] 2.12⁎⁎

(0.002) (0.003) (0.003)
Hispanic
immigration index

−0.001 −0.002 0.002
[−0.12] [−1.21] [1.41]
(0.001) (0.002) (0.002)

Crime control and
arrest for drug sales
Incarceration ratea −0.519⁎⁎ −0.362⁎ −2.00⁎⁎

[−4.36] [−1.96] [−7.79] 5.09⁎⁎

(0.119) (0.194) (0.257)
Drug sales arrest
ratea

0.001⁎⁎ −0.000 0.001⁎⁎

[3.22] [−0.18] [3.05] 1.00
(0.000) (0.000) (0.000)

Police presence 0.001⁎⁎ 0.001⁎⁎ −0.002⁎⁎

[3.38] [2.18] [−2.35] 3.00⁎⁎

(0.000) (0.000) (0.001)
Population size
(log)

0.070⁎⁎ 0.035 −0.339⁎⁎

[3.58] [1.03] [−4.88] 4.97⁎⁎

(0.019) (0.030) (0.069)
Log likelihood −2021.92 −460.55 −650.88
N 2089/144 630/37 616/55

⁎⁎ p b 0.01.
⁎ p b 0.05.
a Denotes measure is race-specific.
b Racial residential segregation is included in economic deprivation index.

7K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

In Table 4, threemodels are provided in our investigation of changes
in black homicide rates. Similar to the white models, Model 1 displays
the cross-sectional time series results for the total sample, while the tra-
jectory specific results are shown in Models 2 and 3. In Model 1, all but
one of the macrostructural conditions and all of the crime control and
drug sales arrest indicators are related to changes in black homicide
rates. The only exception is Hispanic immigration which exhibits a
null effect on black homicide rates. Among those cities found in the
high trajectory class (Model 2), we continue to find macrostructural
conditions and crime control predictors exhibit significant influence
on black homicide trends, although drug sales arrest, economic disad-
vantage and Hispanic immigration do not reach statistical significance.
Finally, in Model 3, none of the structural predictors have a significant
effect, whereas the crime control and drug sale indicators are related
to changes in black homicide rates over time. The formal test for statis-
tical significant differences in the predictors for high versus low trajec-
tory classifications reveal few differences, especially relative to the
tests conducted for differences between the two white models. Specifi-
cally, residential mobility, the incarceration rate and police presence
had a stronger effect in the high black homicide rate model than in
the low rate model. Moreover, differences between the high and low
ratemodels were found for crime control versusmacrostructural condi-
tions in the blackmodels. That is, a greater proportion of the crime con-
trol and drug sales predictors predicted black homicide trends than did
the macrostructural conditions.

Overall, there are some themes or general patterns in the findings
based on the time series model results. First, our results illustrate the

important role of macrostructural conditions and crime control
strategies as they contribute to race-specific temporal trends in
homicide rates. There is significant heterogeneity among American
cities in homicide trends by racial groups and city trajectories. For
example, while these findings suggest macrostructural conditions
are generally more relevant to changes in homicide rates over time
than crime control strategies and drug sales arrests, crime control
and drug sales indicators are more relevant to our understanding of
homicide trends in high trajectory than low trajectory placement
and to black homicide than white homicide. That is, crime control
strategies and drug sales arrest are most relevant to homicide trends
in cities within the highest trajectory classification and for blacks as
compared to whites. Another important theme revealed in this work
are the findings concerning Hispanic immigration. Hispanic immi-
gration is significantly related to changes in white homicide rates
in the time series analyses of overall and high rate trajectory models
but unrelated to changes in black homicide rates regardless of trajectory
classification. The implications of these findings are addressed in the
concluding remarks below.

6. Conclusion

As race remains one of the most robust correlates of homicide and
violence in the U.S., we examined the underlying factors accounting
for the racial differences in U.S. homicide trends during the latter part
of the 20th century and into the early years of the 21st century. Firstly,
we determined if a latent trait approach could advance our understand-
ing of temporal trends in homicide rates during a critical period of time,
1980 to 2006.Moreover, we examinedwhether distinct latent classes or
trajectories in city-level temporal trends differed for white and black
homicide rates. With this objective in mind, we utilized a latent trajec-
tory approach on the temporal trends in city-level race-specific homi-
cide rates from 1980 to 2006. The result of that analysis revealed three
distinct trajectory groups for each racial group studied here, suggesting
a group-based approach was appropriate for identifying hidden hetero-
geneity for race-specific homicide trends at the city-level. This finding
also highlights the benefit of this approach as distinct trends or trajecto-
ries would not be readily apparent using other statistical methods (see
Brame, Paternoster, & Piquero, 2012 for other arguments on the useful-
ness of this particular technique). Additionally, the general shape and
slope of the trajectories emphasize the complexities underlying the
crime drop, especially the stark differences of homicide trends for
each racial group.

In terms of these complexities, for example, the latent trajectory ap-
proach reveals that in our sample of large U.S. cities, a relatively large
proportion of cities fit within the low trajectory classification, where
no evidence of a dramatic “crime drop” of the 1990s was found. In
fact, almost half of the cities were in the white low rate group and
over a third in the African American low homicide rate trajectory.
These lower homicide rate classes, accordingly, were associated with
relatively lower levels of racial isolation, economic disadvantage, family
disruption, and Hispanic immigration when compared to city member-
ship in higher rate trajectories.

Another complexity revealed by the latent trajectory approach is the
different shapes andmagnitudes of the fitted curves across the trajecto-
ries for each racial group. As detailed earlier, the primary distinction
within the race-specific group trajectories is the steepness of the slopes,
but the contrasting trends are striking as well. For the two higher black
homicide rate trajectories, we find an increase at the beginning of the
series and then a steady, continuous decline in homicides from the
late 1980s to 2006 (with the decline for the low rate group beginning
in 1983). On the other hand, we discover a vacillating trend for the
white trajectories—first declining until around 1986, upward until
1994when it begins a “crime decline” and then increasing again around
2003. This finding provides support for those claims that the crime drop
should be examined more closely at the local level, and efforts to

8 K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

understand the nature of the crime drop in one locale as compared to
others warrants attention (Baumer & Wolff, 2014; Zimring, 2007). It
also raises the possibility that some arguments for the crime decline
may better explain trends by racial group (such as enhanced penalties
for crack cocaine related crimes). The application of this research fur-
thers the current crime drop literature by identifying differences that
appear across racial groups even though the high trajectory groups
comprise a similar percentage of the city sample (about a quarter of
the cities) and a number of the same cities (e.g., Los Angeles, Atlanta,
Chicago, Detroit and Dallas).

This finding led us to investigate further the reasons for these
temporal trends by estimating cross-sectional time series models
to identify the factors that predict temporal patterns within latent
groups and to observe any differences by race. That is, in this
paper, we not only reveal hidden heterogeneity in city homicide
trends by race, but we used a time series approach to address the
questions: Do macrostructural conditions or crime control policies
contribute to racial homicide trends? Do these perspectives ex-
plain cities being placed within the highest trajectory classifica-
tion? Do they show important differences by racial group?
Significant effects for both macrostructural conditions and crime
control strategies on trajectory placement were found, with some
interesting caveats.

First, while it is well established that economic disadvantage is
one of the strongest and most consistent predictors of homicide
rates (Land et al., 1990;McCall et al., 2010; Phillips, 2006) and trajec-
tory group membership (Griffiths & Chavez, 2004; McCall et al.,
2011; Stults, 2010), there are significant racial differences in the im-
pact of economic disadvantage on homicide trends once trajectory
membership is accounted for. The economic disadvantage index
had a significant impact on the changes in white homicide rates
across the three models, while this index did not contribute to the
change in black homicide rates within trajectory classification
(only significant in the total model). Another indicator of economic
instability, industrial restructuring, had a statistically significant,
inverse effect on the changes in racial homicide trends in both the
white and black total models, but statistical significance varied
across trajectory and racial groups. That is, industrial restructuring
was inversely related to the change in white homicide rates in the
low trajectory group, but not related to black homicide rates within
this classification. Rather, it contributed to the changes in black
homicide rates for cities placed within a higher trajectory class.
These findings reflect the multiple disadvantages within U.S. cities
that directly limit the socioeconomic wellbeing and resources facing
African Americans, leading to challenges for this group in the form of
heightened violence, but also increasing the probabilities that cities
will be placed within low or high homicide trajectory groups.

For whites, economic disadvantage, racial segregation and industrial
restructuring were each strongly related to homicide trends. Further-
more, based on the coefficient comparison test, these indicators signifi-
cantly distinguished city classification across the trajectory groups. On
the other hand, while economic disadvantage influenced black homi-
cide rates overall (Model 1), industrial restructuring had amore delete-
rious impact for African Americans than whites, contributing to cities
being placed in the highest trajectory classification (Wilson, 1987;
Parker, 2008). Hispanic immigration, on the other hand, contributed
to the change in white homicide trends overall, in addition to white ho-
micides in the highest trajectory group. The Hispanic immigration index
was not related to black homicide trends in any of the three models.
While a finding of racial differences in the estimated effect of Hispanic
population growth is consistent with some recent work (Parker &
Stansfield, 2015), previous studies also lead us to be cautious when
interpreting this claim as we discuss in more detail below
(Steffensmeier, Feldmeyer, Harris, & Ulmer, 2011).

A second major finding is that macrostructural conditions are more
strongly associated with white homicide trends overall and by

trajectory group placement than crime control strategies, yet both
perspectives are equally important in addressing black homicide
trends. In the white models, our measures of incarceration rates,
drug sales arrests and police presence did not reach statistical signif-
icance, with the limited exception of two significant predictors in the
highest trajectory groupmodel only. On the other hand, indicators of
crime control and drug sales had statistically significant effects on
black homicide trends in the overall models and based on trajecto-
ries. The findings, or lack thereof, concerning crime control and
drug sale policies on white homicide trends mirrors cautions in pre-
vious work about a deterrent effect of criminal justice interventions
(Nagin, 2013; Baumer & Wolff, 2014). Our research does suggest,
however, significant racial differences, which have been neglected
in previous studies. While a number of studies report little to no re-
lationship exists between prison growth and crime trends (see
DeFina & Arvanites, 2002; Kovandzic & Vieraities, 2006; Raphael &
Winter-Ebmer, 2001; Spelman, 2006, 2009), such a claim might
only be true for whites. Police presence and drug sales arrests were
positively related to black homicide trends overall and by city trajec-
tory group. These findings are consistent with the notion that cities
with heavy drug trafficking and drug turf wars had high homicide
rates during the 1980s and early 1990s (Blumstein & Wallman,
2006; Ousey & Lee, 2002). Additionally, we find general evidence
that greater police presence influences black, but not white, homi-
cide trends. We surmise that the influence of police presence in the
black models is reflective of the disproportionate police attention
received by low-income and minority communities (Bobo &
Thompson, 2006), however our measure of police presence does
not capture police activity directly. As research on crime trends con-
tinues to expand (including more recent debates about the role of
de-policing activity following Ferguson and other controversial po-
lice use of force incidents) and limitations on existing data are ad-
dressed, more definitive conclusions may emerge about the role of
race in crime control strategies (Rosenfeld, 2016).

There are some limitations to this study, in addition to those
mentioned above. First, our measures of the covariates of homicide
are summary measures only and intercensal measures are derived
by interpolation. Nevertheless, this provides general levels of social
and economic factors that we find to be corroborated for the most
part in this analysis. The purpose is to predict classification of cities
into trajectories with relatively distinct homicide rates for racial
groups and these summary measures serve that purpose. A second
data limitation involves the missing data issues associated with
SHR homicide counts and the inability to distinguish between of-
fenders (or victims) as Hispanic or non-Hispanic. As mentioned
above, we caution against strong conclusions regarding Hispanic im-
migration because it appears the large influx of immigrants in recent
decades may be confounded in the white models as an increasing
number of Hispanics are identified as white in homicide statistics.6

Furthermore, as Hispanic migration increased during this time
frame in many American cities, this could have contributed to con-
founded census estimates of the declining average economic levels
of whites, thereby increasing the odds of those cities being found
within the higher trajectory group.

We also acknowledge our inability to separate out homicide trajec-
tories by age. Data reveal that the crime declines experienced since
the early 1990s included very different patterns in youth violence
trends specifically (Blumstein & Wallman, 2006). Disentangling the
story of city-variations in race-specific homicides by age would be a
complex, yet fruitful endeavor for future research. In addition to age,
there are a number of important ways of disaggregating homicide
trends that might be important for future work. Given the importance
of young people in shaping crime patterns in the US, it is also notable
that this study could not capture youth engagement with conventional
institutions such as higher education and employment among race-spe-
cific groups.

9K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

These limitations aside, the present city-level study demon-
strates that a latent trajectory approach, which illustrates the
relative uniqueness in temporal trends by revealing hidden hetero-
geneity among race-specific homicide trends in a larger sample of
U.S. cities, can play an instrumental role in future research on tem-
poral crime patterns and the racial nature of violence in American
cities specifically. Through revealing hidden heterogeneity in
homicide rates over time by place and racial group, these findings

Appendix 1
List of U.S. cities by trajectory membership for racial groups sorted alphabetically by state and

White trajectory model cities (N = 131)

Low (N = 59) Medium (N = 46) High (N = 26)
Huntsville Mobile Birmingham
Anchorage Tucson Phoenix
Tempe Little Rock Fresno
Berkeley Anaheim Long Beach
Concord Bakersfield Los Angeles
Fremont Oxnard Oakland
Fullerton Pasadena Sacramento
Glendale Riverside San Bernardino
Modesto San Diego Santa Ana
Sunnyvale San Francisco Stockton
Aurora San Jose Bridgeport
Colorado Spring Denver Hartford
New Haven Louisville Atlanta
Stamford Baltimore Chicago
Waterbury Springfield Gary
Columbus Flint New Orleans
Savannah Jackson Detroit
Evansville Kansas City Las Vegas
Fort Wayne St. Louis Newark
Indianapolis city Reno Paterson
South Bend Elizabeth New York
Cedar Rapids Jersey City Columbus city
Des Moines Albuquerque Dallas
Lexington-Fayette Rochester Fort Worth
Baton Rouge Cleveland Houston
Shreveport Dayton San Antonio
Boston Oklahoma City
Worcester Tulsa
Ann Arbor Allentown
Grand Rapids Philadelphia
Lansing Providence
Minneapolis Chattanooga
St. Paul Knoxville
Springfield Memphis
Lincoln Nashville-Davidson
Buffalo Amarillo
Syracuse Austin
Yonkers Beaumont
Charlotte Corpus Christi
Greensboro El Paso
Raleigh Lubbock
Winston-Salem Waco
Cincinnati Portsmouth
Toledo Richmond
Portland Tacoma
Erie Milwaukee
Pittsburgh
Arlington
Garland
Irving
Alexandria
Chesapeake
Hampton
Newport News
Norfolk
Virginia Beach
Seattle
Spokane
Madison

highlight the challenges that must be acknowledged in managing
the future direction of crime trends, as some groups in some cities
will undoubtedly continue on a high criminal trajectory, while
others experience stabilization or declines. Combining trajectory
based procedures with time series analysis in this systematic
study proved to be fruitful to understanding how changes in
urban contexts account for distinct patterns of racial differences
in homicide trends.

city within state.

Black trajectory model cities (N = 144)

Low (N = 55) Medium (N = 52) High (N = 37)
Huntsville Birmingham Phoenix
Anchorage Mobile Little Rock
Mesa Tucson Bakersfield
Tempe Berkeley Long Beach
Anaheim Fresno Los Angeles
Concord Modesto Oakland
Fremont Pasadena San Bernardino
Fullerton Riverside San Francisco
Garden Grove Sacramento Santa Ana
Glendale San Diego Stockton
Huntington Beach Aurora Atlanta
Oxnard Colorado Spring Honolulu
San Jose Denver Chicago
Sunnyvale Bridgeport Gary
Torrance Hartford New Orleans
Lakewood Savannah Baltimore
New Haven Fort Wayne Detroit
Stamford Indianapolis city Flint
Waterbury South Bend Warren
Columbus Louisville Minneapolis
Boise City Baton Rouge Kansas City
Evansville Shreveport St. Louis
Cedar Rapids Boston Las Vegas
Des Moines Grand Rapids Newark
Lexington-Fayette Lansing Albuquerque
Springfield St. Paul Columbus city
Worcester Jackson Philadelphia
Ann Arbor Elizabeth Knoxville
Sterling Heights Jersey City Amarillo
Independence Buffalo Dallas
Springfield New York Fort Worth
Lincoln Rochester San Antonio
Reno Charlotte Salt Lake City
Paterson Winston-Salem Richmond
Syracuse Cleveland Spokane
Yonkers Dayton Tacoma
Greensboro Toledo Milwaukee
Raleigh Oklahoma City
Cincinnati Tulsa
Eugene Portland
Allentown Pittsburgh
Erie Providence
Arlington Chattanooga
Austin Memphis
Beaumont Nashville-Davidson
Corpus Christi Houston
El Paso Lubbock
Garland Waco
Irving Newport News
Pasadena Norfolk
Alexandria Portsmouth
Chesapeake Seattle
Hampton
Virginia Beach
Madison

10 K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

Notes

1 Previous research on race-specific homicide rates has acknowledged some problems
with race-specific homicide data from the SHR, including limitations arising from homi-
cide cases where the offender(s) is unknown and from those SHR data which are submit-
ted at early stages of investigation thereby based on incomplete or inaccurate information
(Messner & Golden, 1992). Given that our homicide counts are disaggregated by race of
the offender, data with multiple imputations was preferred to address item missingness
on offender characteristics. Fox and Swatt (2009) specifically implemented amultiple im-
putation approach based on a log-linear model for incomplete categorical data. Our data
represent the average annual race-specific homicide counts across 5 different imputations
generated by Fox and Swatt (2009). Although our analyses proceed with single offender/
single victim data with imputed offender characteristics, a recent study addressing the
quality and use of race-specific SHR data has concluded that empirical findings and con-
clusions about city-level covariates of race-specific offending rates are less sensitive to
the selection of a specific data source (Messner, Beaulieu, Isles, & Mitchell, 2014).

2 In preparation for estimating a multilevel mixed effects pooled time series analysis
used in this study, annual estimates ofmacro structural predictors and crime control mea-
sures were generated using linear interpolation between 1980, 1990, and 2000 (e.g. Xie,
Lauritsen, & Heimer, 2012).

3 There are few ideal measures for capturing the drug trade that became associated
with the dramatic rise in homicides during the late 1980s and early 1990s. Nevertheless,
this measure is one of the best available for this phenomenon. We include all drug sale/
manufacture arrests because the period covers more than just the 1980s when crack-co-
caine was introduced to the drug market. See Ousey and Lee (2002: 81) for a discussion
of the strengths and weaknesses of using drug arrest rates.

4 We worked with a number of model specifications, making comparisons across
models as to the percent reduction in the BIC statistic as the numbers of classes were in-
creased. In other words, we explored the gain or improvement in model fit provided by
models with larger numbers of classes for purposes of assessing whether amore parsimo-
nious model (fewer classes) could be identified. The model specification shown here was
the best fit to the data.

5 Significant Harris-Tsavalis test statistics reveal that we do not have unit roots in ei-
ther the black homicide series (rho = 0.07, p b 0.001) or white homicide series
(rho = 0.51, p b 0.001). As suggested by Pesaran (2012), we tested each time series
individually.

6 The possibility that Hispanic immigration has confounded white homicide statistics
was established in the work of Steffensmeier et al. (2011) who found evidence of a “His-
panic effect” in Uniform Crime Report (UCR) and National Crime Victimization Survey
(NCVS) data,wherein the growth in theHispanic population leadwhite estimates of crime
(as compared to black) to be confounded with Hispanic offenders. Specifically, they state,
“Hispanic offenders are typically classified in national databases as “white” (approximate-
ly 93%)” (Steffensmeier et al., 2011: 233).

References

Baumer, E. P. (2008). An empirical assessment of the contemporary crime trends puzzle:
A modest step toward a more comprehensive research agenda. In A. Goldberger, & R.
Rosenfeld (Eds.), Understanding Crime Trends. Washington DC: National Academies
Press.

Baumer, E. P., & Lauritsen, J. (2010). Reporting crime to the police: 1973–2005: A multi-
variate analysis of long term trends in NCS and NCVS. Criminology, 48(1), 131–185.

Baumer, E. P., & Wolff, K. T. (2014). Evaluating contemporary crime drops in America,
New York City, and many other places. Justice Quarterly, 31, 5–38.

Baumer, E. P., Lauritsen, J. L., Rosenfeld, R., & Wright, R. (1998). The influence of crack co-
caine on robbery, burglary, and homicide rates: A cross-city, longitudinal analysis.
Journal of Research in Crime and Delinquency, 35, 316–340.

Beck, N., & Katz, J. (2007). Random coefficient models for time-series-cross-section data:
Monte Carlo experiments. Political Analysis, 15, 182–195.

Bird, M., & Grattet, R. (2016). Realignment and recidivism. The Annals of the American
Academy of Political and Social Sciences, 664, 176–195.

Blumstein, A. (1995). Youth violence, guns and the illicit-drug industry. The Journal of
Criminal Law and Criminology, 86, 10–36.

Blumstein, A., & Rosenfeld, R. (1998). Explaining recent trends in U.S. homicide rates. The
Journal of Criminal Law and Criminology, 88(4), 1175–1216.

Blumstein, A., & Wallman, J. (2006). The crime drop in America. Cambridge University
Press.

Bobo, L., & Thompson, V. (2006). Unfair by design: The war on drugs, race, and the legit-
imacy of the criminal justice system. Social Research, 73, 445–472.

Boggess, L., & Hipp, J. (2010). Violent crime, residentially instability and mobility: Does
the relationship differ in minority neighborhoods? Journal of Quantitative
Criminology, 26(3), 351–370.

Bollen, K., & Curran, P. (2006). Latent curve models. Hoboken, NJ: Wiley.
Borooah, V. (2011). Racial disparity in police stop and searches in England and Wales.

Journal of Quantitative Criminology, 27, 453–473.
Braga, A. A., Hureau, D. M., & Papachristos, A. V. (2011). The relevance of micro places to

citywide robbery trends: A longitudinal analysis of robbery incidents at street corners
and block faces in Boston. Journal of Research in Crime and Delinquency, 48(1), 7–32.

Brame, R., Paternoster, R., & Piquero, A. (2012). Thoughts on the analysis of group-based
developmental trajectories in criminology. Justice Quarterly, 29, 469–490.

DeFina, R. H., & Arvanites, T. M. (2002). The weak effect of imprisonment on crime: 1971–
1998. Social Science Quarterly, 83, 635–653.

Devine, J. A., Sheley, J. F., & Smith, M. D. (1988). Macroeconomic and social-control policy
influences on crime rate changes, 1948–1985. American Sociological Review, 53,
407–420.

Eck, J. E., & Maguire, E. R. (2000). Have changes in policing reduced violent crime? An as-
sessment of the evidence. In A. Blumstein, & J. Wallman (Eds.), The crime drop in
America. New York, NY: Cambridge University Press.

Feldmeyer, B. (2010). Segregation and violence: Comparing the effects of residential seg-
regation on Latino and Black violence. The Sociological Quarterly, 51, 600–623.

Fox, J. A., & Swatt, M. L. (2009). Uniform crime reports [United States]: Supplementary ho-
micide reports with multiple imputation, cumulative files, 1976–2007 [computer
file]. 2007. ICPSR24801-v1. In A. Arbor (Ed.), MI: Inter-university consortium for polit-
ical and social research [distributor], 2009-02-24.

Garland, D. (2001). The culture of control: Crime and social order in contemporary society.
Chicago, IL: University of Chicago.

Gartner, R., & Doob, A. (2010). Explaining trends in homicide in Canada? A tour off com-
munities and level of explanations. Unpublished paper. Toronto, Canada: University of
Toronto.

Griffiths, E., & Chavez, J. M. (2004). Communities, street guns and homicide trajectories in
Chicago, 1980–1995: Merging methods for examining homicide trends across space
and time. Criminology, 42, 941–978.

Harris, R. D. F., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the
time dimension is fixed. Journal of Econometrics, 91, 201–226.

Hipp, J. (2011). Spreading the wealth: The effect of the distribution of income and race/
ethnicity across households and neighborhoods on city crime trajectories.
Criminology, 49, 631–665.

Kikuchi, G., & Desmond, S. (2010). A longitudinal analysis of neighborhood crime rates
using latent growth curve modeling. Sociological Perspectives, 53, 127–149.

Kim, J., & Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues.
Beverly Hills: Sage Publications.

Kovandzic, T., & Vieraities, L. M. (2006). The effect of county-level prison population
growth on crime rates. Criminology and Public Policy, 5, 213–244.

Krivo, L., & Peterson, R. D. (2000). The structural context of homicide: Accounting for ra-
cial differences in process. American Sociological Review, 65(4), 547–559.

Kubrin, C. E., Messner, S. F., Deane, G., McGeever, K., & Stucky, T. D. (2010). Proactive po-
licing and robbery rates across U.S. cities. Criminology, 48(1), 57–97.

LaFree, G. (1999). Declining violent crime rates in the 1990s: Predicting crime booms and
busts. Annual Review of Sociology, 25, 145–168.

Land, K. C., McCall, P. L., & Cohen, L. E. (1990). Structural covariates of homicide rates: Are
there any invariances across time and social space? American Journal of Sociology,
95(4), 922–963.

Levitt, S. D. (2004). Understanding why crime fell in the 1990s: Four factors that explain
the decline and six that do not. Journal of Economic Perspectives, 18(1), 163–190.

MacDonald, J. M., & Sampson, R. J. (2012). The world in a city: Immigration and America’s
changing social fabric. The Annals of the American Academy of Political and Social
Science, 641(2), 6–15.

MacDonald, J. M., Hipp, J. R., & Gill, C. (2013). The effects of immigrant concentration on
changes in neighborhood crime rates. Journal of Quantitative Criminology, 29, 191–215.

Martinez, R., Jr., Rosenfeld, R., & Mares, D. (2008). Social disorganization, drug market ac-
tivity, and neighborhood violent crime. Urban Affairs Review, 43, 846–874.

Marvel, T., & Moody, C. (1996). Specification problems, police levels and crime rates.
Criminology, 34, 609–646.

McCall, P. L., Parker, K. F., & MacDonald, J. M. (2008). The dynamic relationship between
homicide rates and social, economic, and political factors from 1970 to 2000. Social
Science Research, 37, 721–735.

McCall, P. L., Land, K. C., & Parker, K. F. (2010). An empirical assessment of what we know
about structural covariates of homicide rates: A return to a classic 20 years later.
Homicide Studies, 14, 219–243.

McCall, P. L., Land, K. C., & Parker, K. F. (2011). Heterogeneity in the rise and decline of city
level homicide rates, 1976–2005: A latent trait approach. Social Science Research,
40(1), 363–378.

Messner, S., & Golden, R. (1992). Racial inequality and racially disaggregated homicide
rates: An assessment of alternative theoretical explanations. Criminology, 30,
421–447.

Messner, S. F., Deane, G., Anselin, L., & Pearson-Nelson, B. (2005). Locating the vanguard in
rising and falling homicide rates across U.S. cities. Criminology, 43(3), 661–696.

Messner, S., Galea, S., Tardiff, K. J., Tracy, M., Bucciarelli, A., Piper, T. M., … Vlahov, D.
(2007). Policing, drugs, and the homicide decline in New York City in the 1990s.
Criminology, 45, 385–414.

Messner, S. F., Beaulieu, M., Isles, S., & Mitchell, L. (2014). Revisiting the quality and use of
race-specific homicide data. Homicide Studies, 18, 151–174.

Morris, N., & Slocum, L. A. (2012). Estimating country-level terrorism trends using group-
based trajectory analysis: Latent class growth analysis and general mixture modeling.
Journal of Quantitative Criminology, 28(1), 103–139.

Nagin, D. (2013). Deterrence: A review of the evidence by a criminologist for economists.
Annual Review of Economics, 5, 83–103.

Ousey, G. C. (1999). Homicide, structural factors and the racial invariance thesis.
Criminology, 37(2), 405–426.

Ousey, G., & Kubrin, C. (2009). Exploring the connection between immigration and crime
rates in US cities. Social Problems, 56, 447–473.

Ousey, G., & Kubrin, C. (2014). Immigration and the changing nature of homicide in US
Cities, 1980–2010. Journal of Quantitative Criminology, 30(3), 453–483.

Ousey, G. C., & Lee, M. R. (2002). Examining the conditional nature of the illicit drug mar-
ket-homicide relationship: A partial test of the theory of contingent causation.
Criminology, 40(1), 73–102.

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0005

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http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0015

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0020

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0025

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0030

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0035

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0040

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0045

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0050

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0055

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0060

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0065

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0070

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0075

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0080

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0085

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0090

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0095

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0100

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0105

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0110

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0115

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0120

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0125

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0130

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0135

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0140

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0145

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0150

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0155

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0160

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0165

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0170

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0175

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0180

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0185

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0190

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0195

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0200

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0205

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0210

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0215

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0220

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0225

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0230

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0235

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0240

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0245

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0250

11K.F. Parker et al. / Journal of Criminal Justice 47 (2016) 1–11

Parker, K. F. (2004). Industrial shift, polarized labor markets and urban violence: Model-
ing the dynamics between economic transformation and disaggregated homicide.
Criminology, 42(3), 619–643.

Parker, K. F. (2008). Unequal crime decline: Theorizing race, urban inequality, and criminal
violence. New York City: NYU Press.

Parker, K. F., & McCall, P. L. (1999). Structural conditions and racial homicide patterns: A
look at the multiple disadvantages in urban areas. Criminology, 37(3), 447–478.

Parker, K. F., & Stansfield, R. (2015). Changing the urban landscape: Interconnections be-
tween racial segregation and Hispanic immigration in the study of race-specific vio-
lence over time. American Journal of Public Health, 105(9), 1796–1805.

Pesaran, H. M. (2012). On the interpretation of panel unit root tests. Economics Letters,
116, 545–546.

Peterson, R. D., & Krivo, L. J. (2005). Macrostructural analyses of race, ethnicity, and vio-
lent crime: Recent lessons and new directions for research. Annual Review of
Sociology, 31, 331–356.

Peterson, R. D., & Krivo, L. J. (2010). Divergent social worlds: Neighborhood crime and the
racial-spatial divide. New York: Russell Sage.

Phillips, J. (1997). Variation in African-American homicide rates: An assessment of poten-
tial explanations. Criminology, 35, 527–559.

Phillips, J. (2002). White, black, and Latino homicide rates: Why the difference? Social
Problems, 49, 349–373.

Phillips, J. A. (2006). Explaining discrepant findings in cross-sectional and longitudinal
analyses: An application to U.S. homicide rates. Social Science Research, 35(4),
948–974.

Pratt, T., & Cullen, F. T. (2005). Assessing macro-level predictors and theories of crime: A
meta-analysis. In M. Tonry (Ed.), Crime and justice: A review of research. Stanford:
Stanford University Press.

Raftery, A. E. (1995). Bayesianmodel selection in social research. SociologicalMethodology,
25, 111–164.

Raphael, S., & Winter-Ebmer, R. (2001). Identifying the effect of unemployment on crime.
Journal of Law and Economics, 44, 259–283.

Rosenfeld, R. (2009). Crime is the problem: Homicide, acquisitive crime and economic
conditions. Journal of Quantitative Criminology, 25(3), 287–306.

Rosenfeld, R. (2016). Documenting and explaining the 2015 homicide rise: Research direc-
tions. Washington, D. C.: National Institute of Justice.

Rosenfeld, R., & Decker, S. H. (1993). Discrepant values, correlated measures: Cross-city
comparisons of self-report and urine tests of cocaine use among arrestees. Journal
of Criminal Justice, 21, 223–230.

Rosenfeld, R., & Fornango, R. (2014). The impact of police stops on precinct robbery and
burglary rates in New York City, 2003–2010. Justice Quarterly, 31, 96–122.

Rosenfeld, R., &Messner, S. F. (2009). The crime drop in comparative perspective: The im-
pact of the economy and imprisonment rates on American and European burglary
rates. British Journal of Sociology, 60, 445–471.

Rytina, N. (2009). Estimates of the legal permanent resident population in 2008. October
2009. US DEPARTMENT of Homeland Security, Office of Immigration Statistics.

Sampson, R. J. (2008). Rethinking immigration and crime. Contexts, 7(1), 28–33.
Sampson, R. J. (2013). The place of context: A theory and strategy for criminology’s hard

problems. Criminology, 51(1), 1–31.
Sampson, R. J., & Wilson, W. J. (1995). Toward a theory of race, crime, and urban inequal-

ity. In J. Hagan, & R. D. Peterson (Eds.), Crime and inequality. Stanford: Stanford Uni-
versity Press.

Spelman, W. (2006). The limited importance of prison expansion. In A. Blumstein, & J.
Wallman (Eds.), The crime drop in America. Cambridge University Press.

Spelman, W. (2009). Crime, cash, and limited options: Explaining the prison boom.
Criminology and Public Policy, 8(1), 29–77.

Steffensmeier, D., Feldmeyer, B., Harris, C., & Ulmer, J. (2011). Reassessing trends in black
violent crime, 1980-2008: Sorting out the “Hispanic effect” in Uniform Crime Reports
arrests, National Crime Victimization Survey offender estimates, and U.S. prisoner
counts. Criminology, 49(1), 197–251.

Stowell, J., Messner, S. F., McGeever, K., & Raffalovic, L. (2009). Immigration and the recent
violent crime drop in the United States: Cross-sectional time-series analysis of metro-
politan areas. Criminology, 47(3), 889–928.

Strom, K. J., & MacDonald, J. M. (2007). The influence of social and economic disadvantage
on racial patterns in youth homicide over time. Homicide Studies, 11, 50–69.

Stults, B. J. (2010). Determinants of Chicago neighborhood homicide trends, 1965–1995.
Homicide Studies, 14, 244–267.

U.S. Bureau of the Census (1983). Census of population and housing, 1980 [United States]:
Summary tape file 1A [computer file].Washington, D.C.: U.S. Department of Commerce,
Bureau of the Census, 1982.

U.S. Bureau of the Census (1994). Census of population and housing, 1990 [United States]:
Summary tape file 1A [computer file].Washington, D.C.: U.S. Department of Commerce,
Bureau of the Census, 1991.

U.S. Bureau of the Census (2003). Census of population and housing, 2000 [United States]:
Summary tape file 1A [computer file].Washington, D.C.: U.S. Department of Commerce,
Bureau of the Census, 2001.

Unnever, J. D., & Gabbidon, S. L. (2011). A theory of African American offending: Race, rac-
ism, & crime. New York: Routledge.

Unnever, J., Barnes, J. C., & Cullen, F. T. (2016). The racial invariance thesis revisited: Test-
ing an African American theory of offending. Journal of Contemporary Criminal Justice,
32, 7–26.

Wadsworth, T. (2010). Is immigration responsible for the crime drop? An assessment of
the influence of immigration on changes in violent crime between 1990 and 2000.
Social Science Quarterly, 91, 531–553.

Warner, B. D., & Coomer, B. W. (2003). Neighborhood drug arrest rates: Are they a mean-
ingful indicator of drug activity? A research note. Journal of Research in Crime and
Delinquency, 40(2), 123–138.

Warren, J. R., Luo, L., Halpern-Manners, A., Raymo, J. M., & Palloni, A. (2015). Do different
methods for modeling age-graded trajectories yield consistent and valid results?
American Journal of Sociology, 120(6), 1809–1856.

Weisburd, D., Bushway, S., Lum, C., & Yang, S. (2004). Crime trajectories at places: A lon-
gitudinal study of street segments in the city of Seattle. Criminology, 42, 283–322.

Weisburd, D., Telep, C., & Lawton, B. (2014). Could innovations in policing have contrib-
uted to the New York City crime drop even in a period of declining police strength?
The case of stop, question, and frisk as a hot spots policing strategy. Justice Quarterly,
31(1), 129–153.

Western, B. (2006). Punishment and inequality in America. New York: NY. Russell: Sage.
Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public pol-

icy. Chicago: University of Chicago Press.
Xie, M., Lauritsen, J., & Heimer, K. (2012). Intimate partner violence in US metropolitan

areas: The contextual effects of police and social services. Criminology, 50(4),
961–992.

Zimring, F. E. (2007). The great American crime decline. Oxford: Oxford University Press.
Zimring, F. E. (2011). The city that became safe: New York’s lessons for urban crime and its

control. Oxford: Oxford University Press.

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0255

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0260

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0265

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0270

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0275

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0280

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0285

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0290

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0295

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0300

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0305

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0310

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0315

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0320

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0325

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0330

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0340

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0345

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0355

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0360

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0365

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0370

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0375

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0380

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0385

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0390

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0395

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0400

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0405

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0410

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0415

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0425

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0430

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0435

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0440

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0445

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0450

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0455

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0460

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0465

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0470

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0475

http://refhub.elsevier.com/S0047-2352(16)30048-4/rf0480

Temporal changes in racial violence, 1980 to 2006: A latent trajectory approach

1. Introduction
2. Racial violence and macrostructural conditions
3. Crime control and changing drug sale patterns
4. Data and methods
4.1. Independent variables
5. Results
5.1. Step one: latent class trajectory analysis
5.2. Step two: multilevel time series analysis
6. Conclusion
References

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