Cleaner Streets and Safer Neighborhoods

UNIVERSITY OF CALIFORNIA
RIVERSIDE
Cleaner Streets and Safer Neighborhoods: Testing the Broken Windows Thesis in
Redlands, California
A Dissertation submitted in partial satisfaction
of the requirements for the degree of
Doctor of Philosophy
in
Sociology
by
Michael Timothy Matthews
August 2004
Dissertation Committee:
Dr. Austin Turk, Chairperson
Dr. Augustine Kposowa
Dr. Edgar Butler

Copyright by
2004

The Dissertation of Michael Timothy Matthews is approved:
__________________________________________________________
__________________________________________________________
__________________________________________________________
Committee Chairperson
University of California, Riverside

iv
ACKNOWLEDGEMENTS
No dissertation is ever completed without the help and assistance of many
individuals, and this one is certainly no exception. It would be impossible to name
everyone who has helped me along the way, but I want to express my deepest gratitude to
those individuals whose help, both direct and indirect, made this dissertation possible.
First, I wish to thank the Redlands Police Department, especially Chief James
Bueermann, Deputy Chief Clete Hyman, Sheila Harbert, and everyone in the
department’s Community Analysis Unit. Back when I was struggling to find a
dissertation topic, I saw Chief Bueermann give a presentation on the kinds of things his
department was doing to strengthen the relationship between law enforcement and the
community and, of course, to prevent crime. I knew then that both he and his department
were combining research, technology, and the latest policing methods in an innovative
way. I was especially impressed with his willingness to let me—an outsider—examine
how effective the city’s programs had been in reducing crime. The department gave me
access to a rich source of data that, as far as I can tell, is unmatched by other departments.
Sheila Harbert of the Community Analysis Unit, who had to field most of my questions,
was extremely helpful and very patient. Thank you, Sheila!
I would also like to thank all of the members of my dissertation committee, Dr.
Austin Turk, Dr. Augustine Kposowa, and Dr. Edgar Butler, all of whom provided me
with assistance and support throughout. Dr. Turk helped me from start to finish on this
dissertation—from the development of the proposal to the final product—and his
comments and advice were invaluable. When I encountered obstacles and delays (and

v
there were many), he encouraged me to stay focused on the most important goal—
finishing. I have been very fortunate to have him as Chair of my dissertation committee.
I am also deeply indebted to Dr. Augustine Kposowa not only for his comments
on various drafts of this dissertation but also for helping me make key decisions
concerning my research methodology. It is safe to say, I could not have done this
dissertation without his help. I spent several long afternoons in his office discussing this
dissertation with him, but he was always patient with me.
Dr. Edgar Butler was the one I turned to when I first began mulling over ideas for
dissertation topics. My years working as his research assistant taught me the value of
conducting evaluation research, and I wouldn’t have ever thought of tackling this topic if
I hadn’t spent time under his tutelage. His comments and advice early on and throughout
the writing of this dissertation have been indispensable.
Also, I wish to thank Dr. Ruth-Ellen Grimes for her support and advice, especially
for her encouragement when I started falling behind. She inspired me to work harder.
Finally, I would like to extend a special thanks to my parents and girlfriend for
their love and support throughout the long and often stressful process of writing a
dissertation. I feel blessed. I know they were all probably wondering why it was taking
so long to finish. They are, no doubt, as happy as I am to see it finally completed.

vi
ABSTRACT OF THE DISSERTATION
Cleaner Streets and Safer Neighborhoods: Testing the Broken Windows Thesis in
Redlands, California
by
Doctor of Philosophy, Graduate Program in Sociology
University of California, Riverside, August 2004
Dr. Austin Turk, Chairperson
Many programs designed to reduce crime and disorder are based on the
assumption, predicated on the broken windows thesis, that relatively minor offenses (e.g.,
graffiti, panhandling, litter, etc.) invite crime. Using longitudinal data taken from the
U.S. Census and the Redlands Police Department, this study aimed to (1) evaluate a
neighborhood improvement initiative; and (2) test two assumptions of the broken
windows thesis—namely, that increases in neighborhood disorder levels lead to increases
in crime and that social disorganization facilitates disorder. Results obtained using
regression analysis suggested that the Redlands’ neighborhood improvement initiative
significantly reduced violent and property crime, but not disorder. Consistent with the
broken windows thesis, furthermore, levels of disorder in 1990 were associated with
increases in violent and property crime between 1990 and 2000; but for property crime,
this relationship disappeared after controlling for initial levels of property crime. Finally,
measures of neighborhood structure, particularly disadvantage and stability, were
significant but not necessarily powerful predictors of disorder. The implications of this
study are discussed and recommendations for future research are offered.

vii
TABLE OF CONTENTS
CHAPTER I...…………………………………………………………………….. 1
Introduction 1
What Is Disorder? 6
Research Design in Brief 8
Site Selection 10
Unit of Analysis 11
Data 11
Design and Analysis 11
The Organization of this Study 12
CHAPTER II……………………………………………………………………... 14
Broken Windows and Neighborhood Disorder 14
Conceptual Development of the Broken Windows Thesis 16
Empirical Support for the Broken Windows Thesis 20
Summing Up 33
CHAPTER III…………………………………………………………………... 35
The Neighborhood Context of Crime and Disorder 35
Understanding Neighborhood Differences: Communities or Individuals? 37
Social Disorganization Theory 38
The Systemic Model of Social Control 40
Related Perspectives 44
Measuring Neighborhood Structure 48
Social Disorganization Theory and Broken Windows 54
CHAPTER IV…………………………………………………………………... 56
Improving Neighborhoods in Redlands, California 56
The City of Redlands, California 57
The Redlands Police Department 59
Crime Prevention in Redlands 61
Neighborhood Improvement 63

viii
CHAPTER V…………………………………………………………………... 67
Research Methodology 67
Data Sources 68
Crime and Disorder Data 68
Intervention Data 69
Census Data 69
Design and Analysis 71
Missing and Excluded Cases 71
Operationalizing Concepts 73
Crime 73
Disorder 76
Neighborhood Structure 79
Interventions 83
Analysis Plan 84
Limitations 87
Conclusion 89
CHAPTER VI…………………………………………………………………... 90
Research Findings 90
Evaluating Redlands’ Neighborhood Improvement Initiative 91
Bivariate Analysis 91
Regression Analysis 95
Testing the Broken Windows Thesis 106
Bivariate Analysis 107
Regression Analysis 110
The Disorder-Crime Relationship 111
Predicting Disorder 114
Summing Up 117
CHAPTER VII…………………………………………………………………... 119
Discussion and Conclusion 119
Evaluating Neighborhood Improvement Programs 119
Testing the Broken Windows Thesis 122
Limitations of This Study 124
Implications of This Study 126
Suggestions for Future Research 128
Conclusion 130
REFERENCES…………………………………………………………………… 132

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LIST OF TABLES
TABLES
4.1 Percent in Poverty by Race in Redlands 58
4.2 Racial and Ethnic Distribution in Redlands 59
4.3 Risk-Focused Prevention in Redlands 62
5.1 Neighborhood Crime and Disorder Levels in 1990 and 2000 74
5.2 Offenses Included as Measures of Disorder 77
5.3 Neighborhood Conditions in Redlands in1990 and 2000 82
6.1 Correlation Matrix of Variables: Impact of Neighborhood
Improvement Programs 94
6.2 Regressions of Violent Crime Change (1990-2000) on Intervention
Level, Neighborhood Structure, and Prior Level of Crime 99
6.3 Regressions of Property Crime Change (1990-2000) on Intervention
6.4 Regressions of Disorder Change (1990-2000) on Intervention
6.5 Regressions of Violent Crime Change (1990-2000) on Intervention
Duration, Neighborhood Structure, and Prior Level of Crime 103
6.6 Regressions of Property Crime Change (1990-2000) on Intervention
6.7 Regressions of Disorder Change (1990-2000) on Intervention
6.8 Correlation Matrix of Variables: Testing the Broken Windows
Thesis 109
6.9 Regressions of Violent Crime Change (1990-2000) on Disorder,
Neighborhood Structure, and Prior Level of Crime 111

x
6.10 Regressions of Property Crime Change (1990-2000) on Disorder,
Neighborhood Structure, and Prior Level of Crime 113
6.11 Regressions of Neighborhood Disorder Rate in 2000 on Neighborhood
Structure and Prior Level of Disorder 115
6.12 Regressions of Neighborhood Disorder Change on Neighborhood
Structure and Prior Level of Disorder 116

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LIST OF FIGURES
FIGURES
2.1 Wilson and Kelling’s Broken Windows Thesis 16
2.2 Al Hunter’s Thesis 18
2.3 Skogan’s Spiral of Decay 20
2.4 Skogan’s Analysis of Neighborhood Satisfaction 29
2.5 Skogan’s Analysis of Neighborhood Disorder 31
3.1 Shaw and McKay’s Social Disorganization Model 40
3.2 Bursik and Grasmick’s Systemic Model of Crime 43
3.3 Neighborhood Context of Crime and Disorder over Time 55
5.1 Map Comparing Reconciled BG Boundaries to Redlands City 72
5.2 Change in the Violent Crime Rate (per 1,000) from 1990 to 2000 75
5.3 Change in the Property Crime Rate (per 1,000) from 1990 to 2000 76
5.4 Change in the Disorder Rate (per 1,000) from 1990 to 2000 78

1
CHAPTER I
Introduction
Travel through nearly any city, and you will no doubt observe a sometimes subtle
but often striking change as you go from one neighborhood to another. In some
neighborhoods, for instance, you may notice that the streets are clean and litter-free, the
homes are maintained with pride, and the yards are neatly manicured. In others, you may
feel somewhat uneasy as you pass graffiti-covered walls, run-down or boarded-up
buildings, empty lots, and littered streets. Your reaction may be based only on the most
obvious and superficial of cues, but you can’t help feeling that these neighborhoods are
unsafe. Residents in these neighborhoods, no doubt, experience a similar uneasiness, a
similar fear and apprehension, one that not only influences their attachment to their
neighborhood but also their interactions with other residents.
When residents feel unsafe in their own neighborhood, the long-term
consequences can be devastating. Made popular by James Q. Wilson and George
Kelling’s (1982) article in the Atlantic Monthly, the broken windows thesis asserts that
widespread fear among residents can trigger a gradual but relentless cycle of disorder,
crime, and neighborhood decline. Urban decay and widespread public disorder invite
more disorder and may, according to thesis, generate higher levels of crime.
For this reason, relatively minor offenses—loitering, public drinking, and loud
noise, for example—are increasingly regarded as serious problems. Hoping it will yield a
long-term reduction in crime, a number of police departments have adopted a strategy

2
focused on reducing blight and restoring public order. This strategy, often called broken-
windows policing, praised by law enforcement officials, policy-makers, and the media,
has been credited with improving neighborhoods and reducing crime (Bratton, 1998;
Cullen, 1997; DiIulio, 1996; Jones, 1997; Rosen, 2000).
Of course, there is no shortage of ideas about how best to prevent crime, and
many theories and rationales have been used to justify this or that approach. These
approaches, while all sharing the same goal of reducing crime, tend to disagree over how
this can and should be achieved. Some crime prevention initiatives focus on reducing
opportunities for crime, for example, through target-hardening measures (e.g., CCTV,
locks, increased security); some on using the criminal justice system to selectively
incarcerate the most serious and habitual offenders; and still others on increasing
economic opportunity and reducing conditions believed to cause criminal behavior (see
Lab, 1997). The current popularity of broken-windows policing as a crime prevention
method is, in part, due to the perception that some (or all) of these methods are either
impractical or ineffective, or both.
But there is probably more to its appeal than this. Broken-windows policing
reflects an intuitive, almost common sense notion about why crime emerges and persists
in certain neighborhoods and about what triggers the public’s fear of crime. It is based
on the idea that neighborhoods replete with broken windows, abandoned cars, graffiti,
and panhandlers signal to residents and visitors alike that an area is uncontrolled and
unprotected. Such neighborhoods are commonly looked upon as “dangerous places”,
areas where it is unsafe for “good people” to be out alone, especially at night. And this

3
perception, if widely held, will motivate many to withdraw from community life all
together or seek residence elsewhere. More importantly, these neighborhoods become
magnets for crime, as offenders, believing that the risk of detection is low, become
attracted to these areas. If the broken windows thesis is true, then maintaining order and
reducing blight should go a long way toward keeping crime rates down and halting (if not
reversing) neighborhood decline. This, at least, has been the hope.
Policing disorder also has tremendous public appeal—who, after all, doesn’t want
to live in a safe neighborhood? We already know vandalism, graffiti, abandoned homes,
and other neighborhood disorders are associated with fear and negatively impact
residents’ perceptions of their neighborhood (e.g., Ferraro, 1995; LaGrange et al., 1992;
Perkins et al., 1992; Skogan, 1986; Taylor et al., 1985; Taylor, 1996). There is also
reason to believe that high levels of disorder can damage the local economy, particularly
the housing market and local business (Fisher, 1991; Skogan, 1990: 77-84; Taub, Taylor,
and Dunham, 1984). Add to these the promised long-term benefit of preventing crime,
and broken-windows policing becomes nearly irresistible.
Beyond its broad appeal, though, broken-windows policing complements a
relatively recent shift to community policing. Although more of a general policing
orientation than a systematic strategy, proponents of community policing generally argue
that traditional policing methods fail to address the underlying causes of—and, therefore,
the most effective preventive solutions to—crime and disorder. Community policing
stresses the need for crime prevention, which is made more effective when law
enforcement and local residents work together to identify and solve neighborhood

4
problems (Goldstein, 1990, 1993; Lab, 1997; 170-174: Rosenbaum, 1988). According to
Bratton (1999), Chief of Police for New York City during the city’s experiment with
order-maintenance policing, police departments have become complacent, too satisfied
with merely reacting to crime and doing too little to prevent it, except by whatever
deterrent effect police presence provides.
Community policing can take many forms, and in practice, police departments
often mix-and-match strategies to suit their needs (Crank, 1994; see also Mastrofski,
Worden, Snipes, 1995). Still, a couple of general approaches have emerged. One
approach, called community building, for instance, attempts to strengthen a community’s
capacity to control crime and improve law enforcement’s relationship with a community,
particularly with African American and Hispanic communities (Crank, 1994;
Rosenbaum, 1988; Skogan, 1990; Trojanowicz and Bucqueroux, 1990: Ch. 5). Problem-
oriented policing, another common approach, assumes that communities, rather than the
police, need to define neighborhood problems, an approach requiring that police officers
work closely with residents (Lab, 1997: 172-173; Spelman and Eck, 1987). Proponents
of problem-oriented policing argue that strictly enforcing criminal codes is not the best
way to reduce crime. Instead, police are encouraged to respond to residents’ concerns
and to address the underlying causes of crime (Lab, 1997; Mastrofski, Worden, Snipes,
1995: 541; Spelman and Eck, 1987).
Broken-windows policing is another variant of community policing, one
increasingly incorporated by law enforcement, either alone or as part of a larger policing
strategy (Buerger, 1994; Green and Taylor, 1988; Green and McLaughlin, 1993; Pate,

5
1986). Much like community policing in general, broken-windows policing has been
implemented in a number of ways, with many police departments choosing either to
focus on aggressively enforcing public-order crimes or on reducing blight and developing
communities. In New York City, for example, the police department’s “zero-tolerance”
policy focused heavily on public-order offenses, like public urination, panhandling, and
vagrancy. Along with a reduction in crime, New York City, also witnessed an increase in
charges of police abuse (see Greene, 1999). Other cities, like Oakland, adopted broken-
windows policing, but focused instead on reducing grime and urban blight.
So, what of its effectiveness? Is broken-windows policing a viable crime
prevention strategy? While proponents have been eager to declare it a success (e.g.,
Bratton, 1998; Cullen, 1997; DiIulio, 1996; Jones, 1997; Kelling and Coles, 1996; Rosen,
2000), others have been more skeptical (e.g., Fagan, Zimring, and Kim, 1998; Harcourt,
1998, 2001; Roberts, 1999; Sampson and Raudenbush, 1999). For the most part, though,
little empirical evidence can be marshaled to defend either position. Evaluations of
broken-windows policing initiatives are relatively scarce, and tests of the underlying
assumptions of these initiatives—assumptions derived from the broken windows thesis—
have not yielded anything decisive.1
What we are left with, then, are two related issues:
(1) a crime prevention strategy that has not been fully evaluated; and (2) a hypothesis
about the relationship between neighborhood disorder and crime that has not been
1
Skogan (1990: 93-124) describes the impact of two disorder-reduction and crime prevention programs
implemented in Houston, Texas and Newark, New Jersey. Although carried out differently, both programs
appeared to affect residents’ perceptions of crime and disorder. But neither evaluation used crime and
disorder rates to evaluate the effectiveness of the programs and, instead, relied on residents’ perceptions
(gathered in interviews or surveys). Additionally, the evaluations were conducted over a relatively short
time (around one year), so they were unable to determine whether program effects were short-lived.

6
completely tested. This dissertation will address both of these issues. At the center of
these issues is the concept of neighborhood disorder.
What Is Disorder?
Neighborhood disorders are often divided into two categories—physical disorders
or social disorders. Physical disorders typically refer to structural, exterior, and
environmental conditions that suggest negligence and decay—for example, abandoned
buildings, graffiti, or litter. Social disorders, on the other hand, typically refer to specific
events or actions, such as public drinking, panhandling, and noisy neighbors. Physical
disorders are visual signs of urban decay; social disorders involve people, often strangers,
whose behavior is perceived as threatening (Sampson and Raudenbush, 1999: 603-604).
Classifying neighborhood disorders into these two categories, it turns out, is a
little tricky. The problem is, in practice, assigning a disorder the label of “social” or
“physical” sometimes makes little sense. First, there is considerable overlap between
measures of social and physical disorder. Graffiti, noise, and vandalism, for instance, can
be thought of as examples of both physical and social disorder—physical disorder
because they reflect “the physical look and sound of a neighborhood”, social disorder
because they are often associated with the actions of individuals (Ross and Mirowsky,
1999: 423). Only a few indicators, such as dilapidated or abandoned buildings, appear to
fall consistently into only one category (Ross and Mirowsky, 1999).2
Given this, it may
not be practical or even necessary to distinguish between physical and social disorders.
Second, it is not clear whether certain crimes, even if they are non-violent offenses,
2
In fact, Ross and Mirowsky (1999) point out that Skogan (1990: 4, 51) himself classified vandalism as an
example of a social disorder in one instance and as a physical disorder in another.

7
should be used as measures of disorder. For example, drug possession and prostitution,
both of which may be properly considered crimes, are regularly classified as disorders.
When examining the disorder-crime relationship, then, researchers run the risk of making
tautological explanations by using crime to measure changes in other types of crime.
Since this would make explanations largely meaningless, some have argued that drug
possession and prostitution should not be used as measures of disorder (Harcourt, 2001:
68; Sampson and Raudenbush, 1999: 608-609).
If measuring disorder is challenging, then defining the concept is even more so.
The concept of “disorder” is, in reality, a nebulous one. Many definitions are of the I’ll-
know-it-when-I-see-it variety. For instance, Skogan (1990: 4), whose work on
neighborhood disorders is one of the most frequently cited, initially avoided a definition,
stating that disorder is defined by the reactions it elicits in people—a definition that is
hardly illuminating. Later, Skogan (1999: 43) elaborated, maintaining that disorder
violates widely shared and agreed upon norms about public behavior, many of which are
not codified into law. “These norms”, Skogan wrote, “prescribe how people should
behave in relation to their neighbors or while passing through the community” (1999:
43). Similarly, Lewis and Salem (1986: XIV) noted that disorders reflect the “erosion of
commonly accepted standards and values”.
It is just this idea—that neighborhood disorder violates shared values—that has
made the concept the target of sharp criticism, especially when these norms are
aggressively enforced and elevate the prankster, the vagrant, and the drunk to the status
of “serious criminal”. Some have suggested that the norm of order is really a middle-

8
class norm, that policing disorder is nothing more than a clever way of imposing middle-
class standards on groups of people who do not consider disorder particularly bothersome
(Harcourt 2001; Lewis and Salem, 1986: 10).3
Another problem with the concept of disorder is that its meaning is shaped by
how others, particularly law enforcement, respond to it. Harcourt (2001) notes:
“The term ‘disorder’ is the locus of the problem. What we are referring to when
we talk about ‘disorder’ in this context are certain minor acts that we have come
to view as ‘disorderly’ mostly because of police and punitive strategies—such as
the quality-of-life initiative and, long before it, the disciplinary practices of
orderliness—that shape the way we judge others and experience the world. We
have come to identify certain things (graffiti, litter, panhandling, turnstile
jumping, public urination) and not others (paying workers under the table, minor
tax evasion, fraud, and police brutality) as “disorderly” and somehow connected
to crime, in large part because of the social practices that surround us. But the
concept of ‘disorder’ is not natural. Nor do these ingredients of ‘disorder’ have a
fixed meaning. …The meaning of these various acts is contextual and itself
constructed” (p. 243).
Harcourt reminds us that, even if people agree about what is considered disorderly
conduct, the meaning this conduct has (i.e., it leads to more serious crime, it warrants
increased police attention) is shaped and formed largely by the actions (or inactions) of
law enforcement.
Research Design in Brief
Few published studies have examined whether or not increasing levels of disorder
produce increasing levels of serious crime, much less evaluated police and community
3
Recognizing this as a potential criticism, Skogan (1990: 3-9) argued that bias is not as serious a problem
as one might think because residents within a neighborhood tend to agree about what constitutes disorder,
in many cases considering it as serious as crime (e.g., Hope and Hough, 1988: 34-36; Lewis and Salem,
1986; Perkins and Taylor, 1996; Skogan, 1987; Skogan, 1990).

9
efforts to reduce neighborhood disorder. This dissertation will tackle both of these issues
by addressing two related research questions.
1. Did a neighborhood improvement initiative reduce levels of crime and disorder in
targeted areas?
To answer this question, I evaluated Redlands’ neighborhood improvement
initiative, a variant of broken-windows policing. The effectiveness of broken-windows
policing strategies are largely without empirical support, which considering their
popularity, is unusual. Is broken-windows policing effective in preventing all types of
crime, or are some offenses more responsive than others? Equally important is the extent
to which reductions in crime and disorder are influenced by other factors, such as the
poverty level or the unemployment rate.
2. Are the assumptions of the broken windows thesis defensible?
To answer this question, I addressed two assumptions of the broken windows
thesis: (1) that disorder leads to increases in crime, particularly serious crime and (2) that
neighborhood structure is an important predictor of later disorder. A key assumption of
the broken windows thesis is that increases in levels of neighborhood disorder precede
increases in serious crime. Research conducted on this hypothesis so far has been
inconclusive (see; Skogan, 1990: 73-75; Taylor, 2001; then see Harcourt, 2001; Sampson
and Raudenbush, 1999; Taylor and Gottfredson, 1985). The thesis also assumes that the
relationship between neighborhood disorder and crime unfolds gradually (see Skogan,
1990; and see Taylor, 2001: 101-103 for a discussion), but with few exceptions (see
Taylor, 2001), research has not examined the temporal aspect of the thesis. Recent

10
versions of the thesis also assume that neighborhood structure makes the emergence of
neighborhood disorder more likely. Any test of the broken windows thesis, therefore,
needs to examine how neighborhood structure affects disorder.
Site Selection
To answer these questions, I relied on data obtained from the police department in
Redlands, California, a suburb sixty miles East of Los Angeles, where for the last five
years the Redlands Neighborhood Improvement Team (RNIT) has been administering a
neighborhood improvement program. My decision to use data from Redlands was based
on two factors: (1) The police department has maintained a comprehensive crime
database for 15 years, complete with the crime’s location; and (2) The police department
and city organizations have developed an innovative way of reducing neighborhood
disorder, one that not only targets the visual signs of urban decay but also their presumed
cause, neighborhood instability. Together, these features allowed me to attend to issues
that, largely due to data limitations, have been neglected in previous research.
In addition to reducing visual signs of disorder, the Redlands neighborhood
improvement initiative (1) offers loans to low and middle-income renters, rental property
owners, and homeowners for rehabilitation projects (Multi-Family Residential
Rehabilitation Program and Great Neighborhoods Program); (2) offers financial
assistance to low and middle income families toward the purchase of their first home in
exchange for 50-100 hours of community service; and (3) provides educational seminars
to property owners and managers to help them maintain their property and reduce crime
problems in apartment and condominium complexes (Crime-Free Multi-Housing).

11
Unit of Analysis
All of the data used in this dissertation were measured at the neighborhood level,
with census block groups used to identify neighborhoods within the city. A census block
group (BG) normally contains about 1,200 residents and consists of several street blocks.
In 1990, only 43 BGs were entirely within Redlands city limits.
Data
Generally, neighborhood disorders are measured by obtaining residents’
perceptions of crime and disorder using surveys, interviews, or on-site observations (e.g.,
Hope and Hough, 1988; Lewis and Maxfield, 1980; Perkins, 1990; Sampson and
Raudenbush, 1999; Skogan, 1990; Skogan and Maxfield, 1981; Taylor, 1996; Taylor et.
al., 1981, 1985). Police data are used less frequently, if at all, to measure neighborhood
disorder (see Stephens, 1999: 59 for a discussion). Difficulties associated with accessing
police data, problems and potential biases accompanying the use of official records, or
deficiencies in police department reporting practices are all likely reasons.
Using Redlands Police Department’s comprehensive crime-reporting system, I
was able to measure levels of crime and disorder from 1989 to 2001 and identify where
and when the city implemented its neighborhood improvement projects. All of this
information was linked with data from the 1990 and the 2000 censuses.
Design and Analysis
This study evaluates the effectiveness of Redlands’ neighborhood improvement
program and tests some of the most important assumptions of the broken windows thesis.
To evaluate the effectiveness of Redlands’ neighborhood improvement program, I

12
examined crime and disorder levels before and after the program began using panel
regression (see Kposowa, 1993: 9-10). I compared 1990 and 2000 levels of crime and
disorder for each neighborhood, controlling for neighborhood structure. To examine the
validity of the broken windows thesis, I tested the hypothesis that increasing levels of
neighborhood disorder predict increasing levels of serious crime. Levels of
neighborhood disorder in 1990 were used to predict changes in crime from 1990 to 2000.
Additionally, neighborhood structure was used to predict neighborhood disorder.
The Organization of This Study
In this first chapter, I have provided a cursory review of the issues surrounding
the broken windows thesis, listed the general research questions this study addresses, and
briefly summarized this study’s methodology. Each of these topics (and more) will be
addressed more thoroughly in subsequent chapters.
In Chapter II, I review the main assumptions of the broken windows thesis, trace
the development of the thesis, and summarize research on neighborhood disorder. As
will become apparent, the broken windows thesis has not been adequately tested. What’s
more, few studies have evaluated programs designed to reduce disorder.
In Chapter III, I examine the alleged cause of neighborhood disorder, social
disorganization. The goal of this chapter is to address how neighborhood disorders and,
ultimately, crime are related to measures of neighborhood structure. After exploring the
development of social disorganization theory, I review research testing the theory, paying
particularly close attention to measures of social disorganization.

13
In Chapter IV, I provide a brief history and description of Redlands, California,
the site of this study. In this chapter, I describe how the Redlands Police Department has
approached crime prevention and community policing. As such, this chapter describes
the context within which the Redlands’ neighborhood improvement initiative was
implemented.
In Chapter V, I describe the methodology I used to carry out this study. This
chapter explains how the data were assembled, how key variables were defined, and how
the analysis was conducted. This chapter also addresses the data and methodological
limitations of this study.
In Chapter VI, I present my findings. In this chapter, I summarize findings
related to my evaluation of the Redlands’ neighborhood improvement initiative and my
examination of the broken windows thesis.
In Chapter VII, the final chapter, I summarize my major findings and discuss their
implications. I also address this study’s strengths and weaknesses, propose areas for
future research, and discuss how my findings can inform future crime prevention
programs.

14
CHAPTER II
Broken Windows and Neighborhood Disorder
The fundamental (and most controversial) assertion of the broken windows thesis
is that neighborhood disorder and crime, particularly serious crime, are causally related.
Disorder, in effect, breeds crime. The implication is that safe and peaceful
neighborhoods can become dangerous and crime-ridden if urban blight, physical decay,
and other public nuisances are ignored.
The process, we are told, begins with relatively minor offenses. First, litter is
thrown on lawns, streets, and sidewalks, goes unnoticed, and is never cleaned up. Then,
more and more strangers, who appear to be “up to no good”, begin frequenting the
neighborhood; and graffiti, once only seen in other neighborhoods or downtown, now
mars nearby shops, homes, and walls. Frustrated, many long-time residents decide it is
time to move. For those that remain, the neighborhood is not what it used to be: It is
unfamiliar, unpredictable, unfriendly. In time, the decline becomes more rapid:
Panhandlers beg on street corners, at bus stations, and in parking lots; and drug-dealers
and street gangs become more visible. Emboldened by the apparent lack of resident
control, criminals gravitate toward the neighborhood, while residents, fearing for their
safety, retreat to the comfort of their homes.
This progression, while neither immediate nor inevitable, was depicted by James
Q. Wilson and George L. Kelling in their 1982 article, “Broken Windows”, published in
The Atlantic Monthly. This article, in which Wilson and Kelling recounted their

15
observation of the Newark Police Department’s quality-of-life initiative, continues to
inspire many police departments and policymakers nationwide.
According to Wilson and Kelling (1982), Newark residents supported the police
department’s quality-of-life initiative for a very simple reason: People value public order.
They argued that, for most people, being bothered by disorderly, unpredictable people—
vagrants, panhandlers, or squeegee-men, for example—evokes nearly as much concern,
anxiety, and fear as crime does.
The long-term effects of neighborhood disorder, Wilson and Kelling (1982)
believed, could be disastrous. Citing an experiment conducted by Stanford psychologist
Philip Zimbardo, in which “abandoned” cars were vandalized only after damage became
visible, Wilson and Kelling warned: “Untended property becomes fair game for people
out for fun or plunder, and even for people who ordinarily would not dream of doing such
things and who would probably consider themselves law-abiding” (p. 30-31). The
problem is not that a window gets broken or that graffiti mars a few walls—these things
can happen even in the best of neighborhoods; it’s that the window is not fixed, the
graffiti is not removed, and no one seems to care. Signs of disorder are persistent
reminders that “something is not right” in the neighborhood. If they can, residents may
choose to move, or maybe visitors will simply avoid the bad part of town. But those who
cannot afford to move and those who must frequent these areas are likely to feel uneasy
doing so. Fear will motivate many to withdraw from community life or avoid the
neighborhood completely. As they do, local offenders and petty criminals will find
comfort in the fact that no one seems to be watching. As depicted in Figure 2.1, Wilson

16
and Kelling predicted a gradual escalation from disorder and fear to neighborhood
instability and serious crime.
Figure 2.1. Wilson and Kelling’s Broken Window Thesis4
Conceptual Development of the Broken Windows Thesis5
Even before Wilson and Kelling’s article linking neighborhood disorder and fear
appeared, most studies found that fear of crime, as it was typically measured, did not
reflect one’s risk of being a victim of crime.6
Several studies, for example, demonstrated
that fear of crime tends to be highest among those least likely to be victimized (Cook and
Skogan, 1984; Dubow et al., 1979). Why should this be the case?
The answer, it seems, has to do with how people assess their risk of personal
victimization—that is, what factors are considered in calculating risk. When respondents
were asked, for instance, whether there was an area in their neighborhood where they
4
This figure was adapted from Taylor (2000:48).
5
The broken windows thesis was introduced in Wilson and Kelling’s (1982) article and is most associated
with their work. For the sake of simplicity and because of its familiarity, I will use the term “broken
windows” to refer to all research examining the origin and impact of neighborhood disorders. Others prefer
the term incivilities or the incivilities thesis.
6
According to Ferraro (1995), fear of crime is defined as an affective state reflecting concern about
personal safety and victimization. As such, it is considered distinct from perceptions of risk, which are
cognitive assessments about the probability of being a victim of crime (LaGrange and Ferraro, 1989). Fear
of crime is an emotional and physiological response; risk perception is a cognitive assessment. Many
measures intended to measure fear of crime were really measuring risk perception.
Signs of
disorder
Serious
offenders
move into
the area
More
residential
flight, more
fear
“No one
seems to
care”; more
petty crimes,
more disorder
Residents
move out,
withdraw,
fear
increases

17
would not feel safe going out at night alone—the most common measure of crime fear,
by the way—residents were answering based on their impression of their neighborhood,
which, in turn, was based on nearby signs of disorder (Garofalo and Laub, 1978; Wilson,
1975). Environmental cues, then, appear to be at least as important as crime in
determining residents’ perception of safety.
With this, researchers suggested that a connection between crime, fear of crime,
and neighborhood disorder existed but did not articulate how they were related. In 1978,
at a conference for the American Society of Criminology, Al Hunter proposed a model
(see Figure 2.2) that attempted to do this. The primary variable to be explained in
Hunter’s model was fear. Working backwards from this, he hypothesized that fear is
caused by crime and by “signs of incivility” (e.g., graffiti, litter, public drunkenness) and
that crime and incivility are reciprocally related, with neither preceding the other. But
Hunter hypothesized that the most common and strongest pathway to fear was through
signs of incivility, since incivilities are more prevalent and more likely to be encountered
by residents than crime. Hunter’s point here was that people attach meaning and attribute
causes to signs of incivility in their environment.
Hunter argued that “disorder” simultaneously causes both crime and signs of
incivility, but it is unclear what he meant by “disorder”. According to Taylor (1999), he
was probably referring either to social disorganization—the community’s inability to
control behavior and work toward common goals (Bursik, 1988)—or to neighborhood
characteristics, such as poverty or minority concentration, that are typically associated
with high crime rates (Baldwin and Bottoms, 1976; Harries, 1980). Hunter could have

18
been more specific here, but he was clearly willing to incorporate contextual and
environmental factors to explain fear.
In 1982, when Wilson and Kelling published “Broken Windows”, the outcome
(dependent) variable shifted from fear of crime to crime. They hypothesized a causal
connection between neighborhood disorder and crime, positing that increases in
neighborhood disorder precede increases in neighborhood crime. Recall that they
envisioned a gradual escalation from disorder to crime, with residents’ fear of crime an
intermediate step in this process.
Reflecting both Hunter’s (1978) insights about the importance of neighborhood
structure and Wilson and Kelling’s (1982) hypothesis about the relationship between
disorder and crime, Wesley Skogan (1990) changed the primary outcome of interest yet
again—this time to neighborhood decline.7
7
It is probably no coincidence that this shift occurred just as interest in social disorganization theory began
to rise (see, for example, Bursik, 1986, 1988; Sampson, 1987a; Sampson and Groves 1989; Stark, 1987).
Fear of
Crime
Signs of
Incivility
Disorder
Crime
Figure 2.2. Al Hunter’s Thesis
Indicates a more
common pathway
Adapted from Taylor (1999:67)

19
Figure 2.3 illustrates Skogan’s conceptual model. According to Skogan,
neighborhood decline is characterized by high rates of crime, neighborhood
dissatisfaction, and changes in neighborhood conditions—all of which are caused by
increasing levels of neighborhood disorder or, to use the term Skogan favored,
incivilities. Skogan recognized that disorders weaken housing markets, threaten local
businesses, and disrupt residents’ ability to mobilize resources to stop a “spiral of decay”.
Residents do not want to live in a run-down area, so many residents will simply move.
Those who do not are most likely dissatisfied with where they live (1990: 77-84).
Making matters worse, potential investors—prospective homeowners, entrepreneurs, and
banks—are also increasingly likely to look elsewhere to invest.
As community satisfaction wanes, social networks in the community, so essential
to preventing crime, begin to erode. Mistrust, apathy, and fear may taint residents’
interactions and discourage them from cooperating to prevent crime. In a disorderly
neighborhood, Skogan noted, individuals might feel helpless and lack the necessary
motivation and interest to initiate neighborhood change (1990: 66-72).
Skogan also argued that certain neighborhood conditions, such as poverty,
residential turnover, and racial composition, make the emergence of disorder more
likely.8
In doing so, Skogan linked neighborhood disorder with social disorganization
theory. However, Skogan did not believe that neighborhood conditions were directly
8
Note that the terms “neighborhood conditions”, “neighborhood context”, and “neighborhood structure”
have all been used interchangeably to refer to neighborhood-levels features, such as poverty and racial
composition. For the most part, I will refer to such measures as “neighborhood structure”.

20
related to crime; their impact was indirect and entirely mediated by neighborhood
disorder (see Figure 2.3).9
Empirical Support for the Broken Windows Thesis
With Skogan (1990), research on neighborhood disorders shifted in several
important ways (Taylor, 1999: 71-72; 2001: 94). First, researchers injected a longitudinal
element into the study of neighborhood disorder. Early research on neighborhood
disorder only asserted that fear and disorder were related; it did not describe how this
connection emerged or developed. Beginning with Wilson and Kelling’s (1982) broken
windows thesis, a gradual escalation from disorder to crime was envisioned. Conceiving
of the relationship in this way alters how the thesis ought to be tested, but as will soon
become evident, most empirical studies have simply ignored the temporal dimension of
9
In an earlier formulation, Skogan (1986) also included “random” shocks—forces arising outside the
neighborhood (e.g., national economic downturn)—as an explanatory factor. In later descriptions of his
thesis, he makes no mention of them, perhaps because they are too difficult to measure.
Neighborhood
Conditions
1. Poverty
2. Instability
3. Racial
Composition
Neighborhood
Disorders
Crime
Dissatisfaction/
Changes in
Neighborhood
Structure
Figure 2.3. Skogan’s Spiral of Decay
Adapted from Taylor (1999:71)

21
the thesis. Second, the level of analysis was shifted from individuals to neighborhoods.
What started out as an interest in accounting for individual differences in crime fear—a
largely psychological focus—was replaced by an interest in understanding how
neighborhood dynamics generate disorder and, in turn, how disorder influences
neighborhood social structure and crime. Related to this was yet another modification: an
emphasis on accounting for and explaining differences in the prevalence of disorder.
Especially in Skogan’s (1990) model (see Figure 2.3), we see that neighborhood
conditions, like poverty and racial composition, provide the context in which
neighborhood disorders are expected to arise.
Empirical research on the broken windows thesis, overall, has not kept pace with
these conceptual advances. Here we examine the empirical evidence for several key
relationships: (1) the impact of disorder on crime; (2) the impact of disorder and crime on
residents; (3) the impact of disorder and crime on neighborhood decline; and (4) the
impact of neighborhood structure on disorder and crime.
1. The Impact of Disorder on Crime
Perhaps the most controversial hypothesis of the broken windows thesis is the
presumption that disorder and crime are causally related, with rising levels of
neighborhood disorder preceding higher levels of crime. Obviously, this implies that the
impact of disorders on crime should be investigated over time, yet most of the empirical
research on this issue has been cross-sectional (i.e., studied at one point in time).
Research on this relationship—both cross-sectional and longitudinal—indicate that the
disorder-crime relationship is complex.

22
Cross-sectional studies that examine the disorder-crime relationship typically
proceed by correlating levels of disorder (measured either by residents’ perceptions of
disorder or by on-site assessments of physical disorder) with levels of crime. The
expectation is that high levels of perceived or observed disorder will explain and predict
high levels of crime. For example, Perkins et al. (1992) showed that neighborhoods
(defined as street blocks) where residents perceived more disorders also had more crime.
Taylor and Covington’s (1993) study similarly demonstrated a relationship between
observed neighborhood deterioration and residents’ belief that unsupervised teens were a
problem.
Probably the most recognized cross-sectional test of the disorder-crime
relationship was reported in Wesley Skogan’s (1990) Disorder and Decline.10
Skogan
assembled and merged data from five separate studies completed between 1977 and 1983
and included data on crime, disorder, and neighborhood structure (e.g., racial
composition). Responses to telephone interviews with residents from six cities—Atlanta,
Chicago, Houston, Newark, Philadelphia, and San Francisco—were used to measure
robbery victimization. Disorder was measured by asking respondents to rate (on a scale
from 1 to 3) how serious problems were in their neighborhood (see Skogan 1988a: 6-8).
Ultimately, five indicators of social disorder (i.e., loitering, drug use and sale, vandalism,
10
Note that Skogan (1990) examined several issues other than the relationship between disorder and crime,
including the relationship between disorder and fear and the relationship between disorder and community
satisfaction.

23
gang activity, public drinking, and street harassment) and three measures of physical
disorder (i.e., vandalism, dilapidation and abandonment, and litter) were selected.11
To examine the disorder-crime relationship, Skogan used the level of disorder to
predict the rate of robbery victimization, and then repeated the analysis controlling for
neighborhood levels of poverty, residential stability, and racial composition. He found
that levels of robbery victimization were positively related (+.80) to levels of disorder in
30 of the 40 areas (1990: 73), as were perceptions of crime seriousness (+.82). That is, as
perceived levels of disorder increased among respondents, so, too, did reports of robbery
victimization. Although weaker, this relationship remained significant even after
controlling for neighborhood levels of poverty, stability, and racial composition (+.54).
He concluded that neighborhood structure has an important impact on crime, but its effect
is mostly mediated by disorder (1990: 75).
Not all cross-sectional studies have found the disorder-crime relationship to hold
up so well. Two recent empirical studies are especially noteworthy. The first is
Harcourt’s (2001) replication of Skogan’s (1990) study. Harcourt objected to Skogan’s
methodology, particularly Skogan’s handling of missing cases, his failure to adjust for the
disproportional influence of Newark, and his decision to include drug offenses as a
measure of disorder. Using Skogan’s dataset, Harcourt removed drug offenses and
included other measures—for example, residents’ perception that adult movie houses and
bookstores were a neighborhood problem; residents’ perception that dogs were a
neighborhood problem; and residents’ perception that garbage was not disposed of
11
Skogan cross-listed vandalism, initially referring to it as an example of social disorder and later as an
example of physical disorder.

24
properly in their neighborhood. To minimize bias introduced by missing data, Harcourt
also standardized missing values in Skogan’s dataset, something that Skogan had not
done. Finally, he excluded Newark from the analysis because it disproportionately
influenced the results.
After making these adjustments, Harcourt found that, contrary to Skogan’s
findings, there was no statistically significant relationship between disorder and crime
when levels of poverty, stability, and racial composition were controlled. Harcourt
initially found that the relationship between disorder and robbery persisted even after
controlling for poverty, stability, and race—but only when data from Newark were
included. When Newark was eliminated, this relationship vanished.
A second cross-sectional study challenging the alleged relationship between
disorder and crime was conducted by Sampson and Raudenbush (1999). They argued
that crime and disorder may have a common origin in structural disadvantage and
attenuated collective efficacy, the latter defined as “cohesion among residents combined
with shared expectations for the social control of public space” (1999: 603). In a study of
Chicago neighborhoods, they found that the disorder-crime relationship persisted only for
robbery when neighborhood characteristics were considered. They also found that
concentrated disadvantage was the single most important predictor of disorder.
According to Sampson and Raudenbush, when structural factors and collective efficacy
were included in the model, neighborhoods with high levels of disorder did not
necessarily have higher crime rates than those with low levels of disorder.

25
Far fewer studies have attempted to examine the relationship between disorder
and crime over time. Skogan (1987), for instance, found that neighborhoods with drug
problems previously had high levels of perceived disorders. In a similar study, Skogan
and Lurigio (1992) confirmed this, showing that prior levels of perceived social and
physical disorder predicted later levels of drug crime. Harrell and Gouvis (1994), in
another longitudinal study, tried to verify Skogan’s thesis using data from census tracts in
Cleveland and Washington D.C. The indicators they used, however, were not really
measures of disorder, but measures of certain types of crime (e.g., arson rates). Limited
to census data, they demonstrated only that some crime rates helped predict changes in
other types of crime rates.
Taylor’s (2001) Breaking Away from Broken Windows was the first study to test
the broken windows thesis over time. Taylor examined crime and disorder data for
Baltimore neighborhoods for 1970, 1980, and 1990 to determine if assessed or perceived
disorders were related to changes in crime. To measure disorder, he conducted on-site
assessments and phone interviews with residents of 66 Baltimore neighborhoods in 1981
and in 1994. To measure crime, he obtained crime data (Part I crimes) from the
Baltimore Police Department. Taylor found that, over time, assessed disorders
(determined by on-site observations) predicted future changes only in homicide.
Perceived levels of social and physical disorder (determined by interviews with residents)
successfully predicted future levels of rape and assault, respectively. The results were in
the expected direction but were inconsistent and contingent upon the method used to
measure disorders.

26
Also troubling for the broken windows thesis was that disorder did not predict
changes in robbery rates, contradicting Skogan’s (1990) major finding that disorder levels
predicted robbery rates. Furthermore, only one variable—racial composition (measured
as the percentage African American)—was consistently related to changes in crime,
regardless of the way in which disorder was measured. Confronted with these results,
Taylor concluded: “For no crime do the results show an independent impact of incivilities
regardless of type of indicator…That the predicted impacts emerge is encouraging for the
theory; that the impacts are not consistent across different and presumably comparable
indicators is worrisome” (p. 190).
2. The Impact of Disorder and Crime on Residents
One of the most consistent findings is that an individual’s fear of crime and
perception of safety are related to neighborhood disorder (Covington and Taylor, 1991;
Lewis and Maxfield, 1980; Rountree and Land 1996a, 1996b; Taylor, 1997). Put simply,
those who perceive more neighborhood disorder are more afraid of crime. Residents who
perceive more disorder than others also tend to be more fearful of their neighbors
(Taylor, 1997). Interestingly, though, strong local ties appear to buffer disorder’s fear-
inducing effect: Residents who have a better relationship with their neighbors (more local
ties) are impacted less by the disorders they perceive than residents with fewer or weaker
local ties (House et al., 1988; Ross and Jang, 1996).
Neighborhood disorder is important not only because it affects residents’
perceptions and attitudes but also because it affects neighborhood relationships and
community life. Over time, widespread fear may motivate residents to avoid public

27
places, retreat indoors, and mistrust their neighbors. Fear may motivate many residents
to move, and this, in turn, can generate neighborhood instability and further weaken local
networks of control. Rising crime levels may spark similar changes (see Goodstein and
Shotland, 1982; Skogan, 1986, 1991; Sampson and Wooldredge, 1986). Together, fear of
crime and community dissatisfaction “move the model”. Without either, there is little
reason to anticipate any of the outcomes predicted by the broken window thesis.12
3. The Impact of Disorder and Crime on Neighborhood Decline
Fear of crime, low community satisfaction, and declining levels of community
social control are typically viewed as intermediary steps along the path to neighborhood
decline. First, high levels of disorder and crime stimulate fear and neighborhood
dissatisfaction (Kasl and Harburg, 1972; Droettboom et al., 1971; Hope and Hough,
1988; Taylor, Schumaker, and Gottfredson, 1985). Next, fear of crime and
dissatisfaction motivate many to withdraw from community life and avoid their
neighbors, weakening levels of community social control. At the same time, fear of
crime and dissatisfaction motivate residents to move and discourage new investment—
both of which create instability (see Skogan and Maxfield, 1981). Then, long-term
instability triggers changes in racial composition and in the spatial concentration of
economic disadvantage (see Liska and Bellair, 1995; Wilson, 1987). Finally, instability
and other neighborhood conditions heighten levels of disorder and crime and hasten
neighborhood decline.
12
These outcomes are not inevitable, of course. A certain degree of fear may, in fact, encourage residents
to participate in a neighborhood watch group, volunteer for a citizen patrol, or simply become more
protective of their neighborhood (e.g. by looking out for suspicious behavior or by offering to watch a
neighbor’s house while they are away). As fear levels increase, however, proactive responses may become
less likely.

28
Skogan (1990) examined residents’ intention to move and found that levels of
disorder had a strong negative effect (-0.58) on residents’ satisfaction with their
neighborhood but a relatively weak positive impact on residents’ intention to move
(+0.16). Residents’ satisfaction with their neighborhood had the strongest impact (-0.66)
on intent to move. As shown in Figure 2.4, the impact of disorder on residents’
satisfaction, however, was twice as strong as that of robbery victimization, and robbery
victimization and levels of disorder had roughly the same impact on intent to move. The
total effect of disorder on residents’ intent to move, while not as large as that of
satisfaction, was still considerable. Skogan concluded that disorder negatively impacts
the housing market by triggering housing instability, even if that impact mostly operates
indirectly.
Care should be taken, though, when assessing the significance of these findings.
Skogan’s (1990) research was not a direct test of the disorder-crime relationship. To be
able to conclude that disorder leads to neighborhood decline on the basis of Skogan’s
research, one has to make two assumptions: that intended behavior will translate into
actual behavior (i.e., those who said they will move actually do) and that residential
instability will produce sizeable changes in neighborhood structure. Neither of these
assumptions is necessarily unreasonable, but a longitudinal test employing similar
measures seems needed.

29
Figure 2.4. Skogan’s (1990: 83) Analysis of Neighborhood Satisfaction
Direct Effects Indirect Effects Total Effects
Disorder +0.16 +0.38 +0.54
Robbery +0.19 +0.16 +0.35
Satisfaction -0.66 --- -0.66
Taylor’s (2001) study of neighborhood decline is just such a test. By collecting
longitudinal data on disorders, crime, and neighborhood characteristics, Taylor was able
to observe how neighborhood conditions (neighborhood structure) changed over time and
how well disorder predicted decline relative to other variables. Using both assessed and
perceived levels of disorder measured in 1981 and 1982, he concentrated on three
indicators of neighborhood structure—stability (measured by the proportion of
homeowners and one-unit dwellings in the neighborhood), economic disadvantage
(measured by the vacancy rate and poverty rate), and status (measured by the proportion
having completed some college and the relative house value). He also included measures
of racial composition (measured as percent African American), average neighborhood
house value, and the percentage of owner-occupied units.
Robbery Victims
Satisfaction
Other Factors
Disorder
Intent to Move
Other Factors
-0.24
+0.19
+0.43
-0.66
-0.58
+0.16
+0.67

30
Taylor found that disorders might not be as important in triggering neighborhood
decline as originally believed. First, contrary to the broken windows thesis, he found that
the influence of disorder on decline was inconsistent across his three measures of
neighborhood structure. Changes in the level of neighborhood disorders, for instance, did
not predict changes in neighborhood status, but they were able to predict changes in the
proportion of vacancies in the neighborhood. Even so, his results depended on whether
disorders were measured using on-site assessments or resident perceptions. Second,
instability was not predicted by any measure of disorder—on-site assessments or
perceived disorders—or by any other variable in Taylor’s model, for that matter. Taylor
reasoned that stability buffers the impact of disorder, with higher levels of stability
protecting against later disadvantage. Third, according to Taylor’s model, race, stability,
and status were more important predictors of neighborhood decline than disorder. Racial
composition, for example, predicted changes only in neighborhood status and
disadvantage. Taylor suggested that neighborhood structure, overall, was a better
predictor of neighborhood decline than disorder.
4. The Impact of Neighborhood Structure on Disorder and Crime
Having examined how well disorder and crime predict neighborhood decline, let
us now look at the relationship in reverse: how well neighborhood structure predicts
future levels of disorder and crime. Disorder, according to Skogan (1990), has its origin
in neighborhood structure; therefore, variations in crime and disorder should be explained
by variations in neighborhood structure.

31
Skogan (1990) approached the issue by examining the spatial concentration of
disorder. Skogan focused on several variables to help explain differences in levels of
disorder between areas—neighborhood stability, poverty, and racial composition. His
path model is reproduced in Figure 2.5 (adapted from Skogan, 1990: 60), where we see
that, in terms of total effects, racial composition (measured as percent minority)
accounted for most of the variability in disorder, followed closely by poverty and
instability. Additionally, the percentage of minorities in a neighborhood had a modest
direct impact on disorder and a strong relationship with poverty. In all, nearly two-thirds
of disorder was explained by structural factors in Skogan’s study.
Figure 2.5. Skogan’s (1990: 60) Analysis of Neighborhood Disorder
Direct Effects Indirect Effects Total Effects
% Minority +0.28 +0.22 +0.50
Poverty +0.46 --- +0.46
Instability +0.43 --- +0.43
Sampson and Raudenbush (1999) argued that both crime and disorder stem from
the same structural factors—concentrated economic disadvantage, residential stability,
population density, and mixed land-use. They reasoned that if the broken windows thesis
Poverty
% Minority
Other Factors
Instability
Disorder
Other Factors
+0.47
+0.46
+0.57+0.88
+0.43
+0.16
+0.28

32
is accurate and disorder directly causes crime, then the effect of these structural variables
on crime should be almost entirely mediated by disorder. If, on the other hand, crime and
disorder arise from the same causal forces, then the relationship would be spurious.
Sampson and Raudenbush (1999) proceeded to test a conceptual model borrowed
from social disorganization theory, whereby structural factors either facilitate or impede
effective crime prevention. Both economic disadvantage and residential stability, they
asserted, undermine collective efficacy, which is necessary to prevent high rates of crime
and disorder. Sampson and Raudenbush observed signs of disorder between a sample of
face blocks in 196 Chicago neighborhoods, interviewed roughly 3,800 residents about
their relationship with their neighbors and their history of crime victimization, and
associated all of this with police crime data and census data. They discovered that
poverty was the single most important factor in explaining the level of disorder; disorder
was high where poverty and immigrant populations were concentrated; and
neighborhoods with mixed land-use—that is, where residential, commercial, and
industrial land were concentrated in the same area—tended to have higher levels of
disorder. They found that higher levels of collective efficacy, moreover, were associated
with lower levels of social and physical disorder, once again supporting the notion that
strong social ties and neighborhood networks protect neighborhoods from rising levels of
crime and disorder.
Taylor’s (2001) longitudinal examination of disorder’s causes confirmed that
structural variables, particularly those relating to economic disadvantage, helped predict
where disorder will appear. Neighborhood economic status (measured by relative house

33
value) consistently predicted later shifts in disorder. Taylor also showed that
neighborhood stability was a strong predictor of certain types of disorder, most notably
graffiti and changes in perceived disorder; racial composition (measured as the percent
African American) had little impact on the development of disorders; and initial
neighborhood crime levels had a relatively modest lagged impact on disorders.
Summing Up
Beginning with a focus on the causes and consequences of people’s fear of crime,
research on neighborhood disorder has expanded and developed, as researchers have
readily linked the broken windows thesis with social disorganization theory.
Consequently, the thesis not only posits a disorder-crime relationship, but it also now
posits a reciprocal relationship between disorder and neighborhood decline.
Additionally, we have seen that, beginning with Wilson and Kelling’s (1982) article on
broken windows, the neighborhood transition from disorder to crime develops over time
and that Taylor’s (2001) study is one of the few to test the longitudinal aspect of thesis.
When the broken windows thesis is put to the test, the results are, frankly,
disappointing. I suppose one could argue that only a few studies have really attempted to
test the thesis as it is now understood, yet even the most recent longitudinal research on
the topic (i.e., Taylor 2001) suggests that we still don’t know much: The relationship
between disorder and crime appears to be neither as consistent nor as straightforward as
originally thought. While Taylor (2001) found some evidence for a causal relationship
between disorder and crime, his results depended on how disorder was measured (i.e.,
survey versus on-site assessment) and what type of disorder was predicted (i.e., social or

34
physical). Skogan (1990) only examined the relationship between levels of perceived
disorder and robbery victimization, for which he found the two to be related; but upon
further investigation even that relationship disappeared when controlling for
neighborhood structure (Harcourt, 2001)
At the same time, other research poses an additional challenge to the thesis:
Neighborhood structure may be more important than disorder in accounting for
differences in crime (Harcourt, 2001; Sampson and Raudenbush, 1999; Taylor, 2001).
Neighborhood structure appears to be a “problem” for the broken windows thesis,
consistently mitigating, if not eliminating, the impact of disorder on crime and
neighborhood decline.
Given the close relationship between neighborhood disorders and neighborhood
structure, and the influence neighborhood structure appears to have on the validity of the
broken windows thesis, it is worth examining in more detail. Much of the literature on
the broken window thesis draws directly from social disorganization theory, so it is to
this topic that we now turn.

35
CHAPTER III
The Neighborhood Context of Crime and Disorder
In the previous chapter, I established that, according to the broken windows
thesis, higher levels of disorder precede higher levels of crime. If this is true, then police
crackdowns on loitering, graffiti, panhandling, and similar offenses should eventually
yield long-term reductions in serious crime.13
In the short-term, as the neighborhood
visibly improves, residents’ commitment to their neighborhood may be restored and fear
of crime reduced. I also pointed out that tests of the broken windows thesis began to
incorporate neighborhood structure as an important variable, with most versions of the
thesis now hypothesizing that certain neighborhood conditions—poverty and instability,
for example—give rise to disorder.
This brings us to the present chapter, where I will examine the connection
between neighborhood context and disorder more closely, for up to this point, I have only
addressed the issue in passing. Broken windows are repaired quickly in some
neighborhoods; in others, they are neglected. Graffiti is dutifully reported and removed
in some neighborhoods; in others, it flourishes. How can these differences be explained?
Many scholars have tried to answer this question by examining differences in
neighborhood structure. Consistent with the ecological approach typified by early
criminologists like Clifford Shaw and Henry McKay (1942), most researchers have
13
It is not at all clear, by the way, whether or not broken-windows policing is equally effective in all
neighborhoods, regardless of crime level. That is, it is not hypothesized or known whether broken-windows
policing would be as effective in a neighborhood with initially high levels of crime as one with initially low
or moderate levels of crime.

36
argued that certain neighborhood conditions— for example, a neighborhood’s poverty
level and racial composition—influence residents’ ability to control crime. When
residents are unable to realize the common goal of neighborhood safety (or any other
common goal, for that matter), social disorganization prevails (Sampson and Groves,
1989).
According to social disorganization theory, common values and goals are
established and maintained through several sets of networks: relationships within
families; relationships between residents; relationships between residents and local
institutions; and relationships between residents and institutions outside the
neighborhood, especially government agencies. Neighborhood conditions are thought to
influence how strong and broad these networks eventually become. Social
disorganization theory asserts that when networks are weak, social control is weak; and
when social control is weak, levels of crime and disorder are likely to rise.
Explaining disorder by pointing to neighborhood structure, while intuitively
appealing, raises questions about the validity of the broken windows thesis. As we saw
in the previous chapter, empirical research suggests that neighborhood structure is at least
as good a predictor of crime as disorder (Harcourt, 2001; Sampson and Raudenbush,
1999; Taylor, 2001). If true, then many of the underlying assumptions of the broken
windows thesis are inaccurate. Even worse, if neighborhood structure is more important
than disorder in explaining crime, is disorder even relevant?
Disorder probably does have an effect on crime, yet there is also sufficient reason
to believe that the disorder-crime relationship may exist only for certain offenses and that

37
neighborhood structure plays a role not only in explaining the disorder-crime relationship
but also in explaining why some neighborhoods, and not others, consistently have high
rates of crime and disorder.
In the pages that follow, I address the issue of neighborhood context, or
neighborhood structure, suggesting that conditions like poverty and residential instability
are central to understanding the disorder-crime relationship. To do this, I begin by
drawing attention to the difference between examining neighborhood-level predictors and
individual-level predictors of crime. I then trace the development of social
disorganization theory. Additionally, and perhaps more importantly, I identify key
neighborhood-level factors linked to social disorganization. I conclude by returning to
the relationship between neighborhood structure and the broken windows.
Understanding Neighborhood Differences: Communities or Individuals?
As an ecological approach to crime, social disorganization theory does not
attempt to explain individual motivations for crime, which are either taken as given
and/or thought to be shaped by neighborhood structure. Rather, it attempts to explain
why some neighborhoods are more crime-ridden than others. Ecological units (usually
neighborhoods) are thought to affect crime in ways not reducible to individual-level
attributes or motivations—which is to say that social disorganization theory favors
structural, not psychological explanations of crime.
Since individuals make up and reside in neighborhoods, researchers interested in
explaining why neighborhoods have different levels of crime confront a troublesome
question: How can the effects of neighborhood-level factors be disentangled from

38
individual-level ones? Perhaps crime-prone individuals are selectively aggregated into
some neighborhoods, and measures of neighborhood structure are nothing more than the
aggregation of these individuals (see Kornhauser, 1978: 114). If so, then any purported
relationship between neighborhood structure and crime is an illusion, one completely
accounted for by individual-level factors.
Contemporary social disorganization theory, while not denying that individual-
level factors are important in understanding and explaining crime, maintains that
structural forces are equally important, if not more so. When it comes to explaining why
crime varies, social disorganization theory begins by examining differences in
neighborhood structure and hypothesizes that the relationship between neighborhood
structure and crime is primarily indirect. Neighborhood conditions like poverty are
believed to help generate social processes that make crime more likely.
Social Disorganization Theory
Social disorganization theory, the dominant criminological perspective prior to
World War II, was largely abandoned by the 1970’s and 1980’s and replaced social
psychological explanations of criminality, particularly those dealing with criminal
dispositions. Prevailing wisdom held that social disorganization theory had little
explanatory value because, as Arnold and Brungardt (1983: 113) put it, “it [social
disorganization] is not even a necessary condition of criminality, let alone a sufficient
one.” Yet, with the publication of a number of theoretical and empirical works in the
mid-1980s and early 1990s, many researchers expressed a renewed interest in social
disorganization theory (e.g., see Bursik, 1988; Bursik and Grasmick, 1993a; Byrne and

39
Sampson, 1986; Kornhauser, 1978; Reiss and Tonry, 1986; Sampson and Groves, 1989;
Stark, 1987).
Clifford Shaw and Henry McKay’s (1942) research at the University of Chicago
exemplifies the ecological approach to crime, and is the starting point for contemporary
social disorganization theory. When Shaw and McKay associated housing, welfare, and
census data with the residences of youth referred to juvenile courts, they reached two
important conclusions: (1) the highest rates of delinquency were found nearest the inner
city, or central business district (CBD), but steadily declined as the distance from the
CBD increased; and (2) inner city areas retained high levels of delinquency, crime, and
disorder (e.g., high incidence of mental health problems, drug use, etc.) despite
substantial changes in population composition. These findings suggested that crime and
delinquency rates were not solely attributable to population composition and individual-
level explanations; neighborhood structure mattered, and it could be used to help explain
why high crime rates seem to persist in some neighborhoods.
Shaw and McKay’s (1942) findings validated earlier studies that depicted cities as
divided into concentric zones—distinct areas (based on land use, population turnover,
and poverty) that radiate outward like ripples in a pond (Burgess, 1925). The zone in
transition, the ring surrounding the CBD, was characterized by high rates of population
turnover, as people moved away to find better housing; high levels of ethnic
heterogeneity, since immigrants tended to settle near industry and in areas with
affordable housing; and high levels of poverty. Combined, these elements disrupt
networks of social control. Residents’ inability to supervise their youth and agree on

40
proper standards of conduct, it was believed, would ultimately lead to high rates of crime
and delinquency. This model of social disorganization theory is presented in Figure 3.1.
Figure 3.1. Shaw and McKay’s Social Disorganization Model14
The Systemic Model of Social Control
Current thinking on social disorganization focuses less on neighborhood structural
characteristics and more on social networks, the latter of which are believed to be
essential for exercising social control and, therefore, for controlling crime (Sampson,
1987, 1988, 1991; Sampson and Groves, 1989; Bursik and Grasmick, 1993a, 1995).15
According to the systemic model of social control, a neighborhood’s capacity to control
crime is a function of the strength and breadth of kinship and friendship networks,
networks that are created and sustained through socialization and rooted in family life
14
Adapted from Bursik and Grasmick (1995: 110)
15
Part of this shift was due to the difficulty the original model had in describing high crime and
delinquency rates in relatively stable neighborhoods (see Whyte, 1955; Bursik and Grasmick, 1995:111).
Economic
Deprivation
Residential
Turnover
Ethnic
Heterogeneity
Regulatory
Capacity Crime

41
(see Berry and Kasarda, 1977: 56; Kasarda and Janowitz, 1974 329; Sampson and
Groves, 1989: 777).
These networks represent relationships between family and friends (primary
relations) and neighbors (secondary relations), and are the “glue” that binds residents
together. Neighborhood networks, which are conditioned by neighborhood structure,
facilitate the supervision of youth and the protection and surveillance of property. As
Reiss observed (1986: 15), “the basic causal argument is that certain kinds of community
structure either weaken forms of social control that induce conformity to law-abiding
norms or generate control that inhibit conformity.” The importance of neighborhood
structure on crime, then, rests on how conditions like poverty, mobility, and
heterogeneity influence residents’ ability to establish and maintain informal ties.
Recognizing that social control can operate in different ways and within different
networks, systemic theorists have identified three levels of social control: the private, the
parochial, and the public (Bursik and Grasmick, 1993a: 16-18; Hunter, 1985). At the
private level, intimate, primary groups—for example, family members or close friends—
exercise social control through the “allocation or threatened withdrawal of sentiment;
social support; and mutual esteem” (Bursik and Grasmick, 1993a: 16). Networks of
private control are responsible for transmitting expectations of appropriate behavior. Of
course, measuring the strength and breadth of these networks is difficult, so many
researchers have tried to do so indirectly—for instance, by asking residents to identify
how many friends they have in the neighborhood (see Sampson and Groves, 1989) or by
describing family structure patterns in the neighborhood (see Sampson, 1986).

42
At the parochial level, residents are connected to each other through local
institutions, such as schools, churches, or watch groups. The networks that develop in
these organizations are believed to enhance residents’ ability to mobilize and cooperate to
prevent crime. Consistent with this idea is research indicating that higher levels of
participation in local organizations are associated with lower levels of crime (Sampson
and Groves, 1989; Simcha-Fagan, 1986).
At the public level of control, residents are connected to agencies outside the
neighborhood (e.g., government agencies) (Bursik and Grasmick, 1993a, 1995). By
providing goods and services for the neighborhood, these external agencies strengthen a
neighborhood’s ability to control crime and disorder: Crime control agencies and local
governments help improve community conditions; financial and housing agencies make
critical development decisions influencing an area’s economic vitality; and municipal
service agencies assist in maintaining an area’s physical appearance and desirability.
Figure 3.2 illustrates these arguments in more detail. Thus far, theoretical
development has outpaced empirical investigation, for only a few studies have examined
the intervening processes illustrated in this model (see Simcha-Fagan, 1986; Sampson
and Groves, 1989).
In a recent study using Taylor’s Crime Changes data, Snell (2001) tested Bursik
and Grasmick’s model of neighborhood social control in over sixty Baltimore
neighborhoods. Following Bursik and Grasmick’s model, Snell tested whether the
impact of neighborhood structure on crime was mediated by several intervening
variables—disorder; family and friendship networks; neighborhood interaction and

43
mutual trust; and informal control. While his findings pointed to the relative importance
of disorder in understanding crime rate changes, Snell’s research also suggested that
neighborhood structure had direct effects on crime, with instability having a relatively
large impact on crime. Low socio-economic status also exhibited a significant direct
effect on neighborhood crime rates. The intervening variables were ineffective in
explaining either of these relationships.
Figure 3.2. Bursik and Grasmick’s (1993a: 39) Systemic Model of Crime
These findings suggest that neighborhood structure may be more important in
explaining variations in neighborhood crime rates than many of the hypothesized
Socio-Economic
Composition
Racial/Ethnic
Heterogeneity
Residential
Stability
Primary
Relational
Networks
Solicitation
of External
Resources
Secondary
Relational
Networks
Exercise of
Parochial
Control
Effective
Socialization
Exercise of
Private
Control
Exercise of
Public
Control
Crime
Rate

44
intervening variables. Or, perhaps measures of the intervening processes were
inadequate.
Related Perspectives
The relationship between social disorganization theory and the broken windows
thesis has been made explicit in research on neighborhood disorder, but other theoretical
perspectives are also relevant. Two in particular are worth mentioning: environmental
design/defensible space and opportunity theory.
Like the broken windows thesis, these perspectives assume that the physical
environment affects residents and, ultimately, crime. And like social disorganization
theory, they maintain that surveillance—which occurs when individuals monitor,
supervise, and respond to activities in their neighborhood—is essential to preventing
crime. The difference between these perspectives is one of emphasis.
Environmental Design and Defensible Space. In The Death and Life of Great American
Cities, long considered a classic in the field of urban planning, Jane Jacobs (1961)
charged that misguided city planning encouraged a variety of urban ills. One of her
biggest criticisms was that poor planning made cities unsafe and inhospitable. She
believed that a city’s physical environment affects its rhythm, its vibrancy, and its
character. Changing that environment, she believed, could increase public safety by
influencing how people relate to their environment.
Her commentary illustrated her concern for safety and crime prevention (see
Mumford, 1971). In the city, strangers are everywhere and make the environment
unpredictable and unfamiliar. This, combined with the effect of poor urban planning, is

45
likely to make residents feel uncomfortable and afraid. When residents are afraid, they
are more likely to stay inside, venturing outside the safety of their home only when
necessary. According to Jacobs, people need to be on the streets, watching and
observing, and able to discern residents from transients, regulars from strangers. This,
according to Jacobs, is what makes crime prevention possible. She urged urban planners
to consider how the urban environment affects its inhabitants:
“Even residents who live near each other are strangers, and must be, because of
the sheer number of people in small geographic compass. The bedrock attribute
of a successful city district is that a person must feel safe and secure between all
these strangers. He must not feel automatically menaced by them. A city district
that fails in this respect also does badly in other ways and lays up for itself, and
for its city at large, mountains on mountains of trouble” (p. 300).
Over a decade later, Oscar Newman (1972), following Jacobs’ lead, argued that
flaws in urban design and layout could facilitate criminal activity by obstructing
surveillance. For Newman, urban design should create “defensible space”, an area that
signals to residents and visitors alike that it is owned, cared for, and protected.
In practice, reducing blight and creating defensible space are similar. Creating
defensible space involves controlling access to areas (e.g., fences, gates, etc.); increasing
surveillance and, with it, the likelihood that potential offenders will be observed; and
strengthening cohesion among residents. These principles became key elements in Crime
Prevention through Environmental Design (CPTED), a crime prevention technique that
stresses manipulating the environment (usually to structures and landscapes) as a means
of reducing crime (see Jeffrey, 1971; Poyner, 1983).
Theories of Criminal Opportunity. Opportunity theories are increasingly used to link
neighborhood structure to crime (Miethe and Meier, 1994; Moriarty and Williams, 1996;

46
Rountree et al., 1994; Smith et al., 2000). In general, these theories—routine activity and
lifestyle theories, and research on the geographical and spatial distribution of crime—
attempt to explain the convergence of offenders, targets, and victims in time and space
(Brantingham and Brantingham, 1984, 1991; Cohen, 1981; Cohen and Felson, 1979;
Cohen et al., 1981; Felson, 1998; Felson and Cohen, 1980; Hindelang et al., 1978).
Of the research traditions classified as “opportunity theory”, routine activities
theory is the most prominent.16
Both social disorganization and opportunity theory call
attention to the spatial distribution of crime, but routine activities theory, at least in its
more recent adaptations, highlights the situational aspects of crime.
According to routine activities theory, three main factors make criminal events
more probable: the presence of a likely offender; the presence of a suitable target; and the
absence of a capable guardian.17
A likely offender is anyone who might commit a crime,
but the issue of what motivates the offender—why the individual might commit a
crime—is either not addressed or considered “given” and explained by other theories. A
suitable target refers to any person or object (e.g., a house, store, etc.) that is likely to be
16
The lifestyle perspective has also been used to examine victimization risk (Hindelang et al., 1978). In
short, it argues that the likelihood of falling victim to crime is a function of one’s exposure to high-risk
situations. Going out late at night in a “bad” neighborhood or working at a convenience store after
midnight increases one’s risk of victimization. The lifestyle perspective complements routine activity
theory by explaining those factors that make one a suitable target. I will only focus on routine activities
theory, however.
17
To accommodate a micro-level analysis and to wed routine activities theory to social control theories of
crime, Felson (1986, 1998) has added two other components to a situational analysis of crime: the handled
offender and the intimate handler. The intimate handler refers to someone who can exert enough influence
(i.e., emotional and psychological control) on the potential offender to prevent the crime. At the micro-
level, the handled offender takes the place of the likely offender, referring to the individual’s susceptibility
to informal social control. Felson’s notion of the handled offender was derived from Hirschi’s (1969)
theory of social control, which argues, in part, that the choice not to engage in law-breaking behavior
results from the individual’s emotional bond and commitment to societal norms.

47
attacked by the offender. As Felson (1998) describes, the likelihood of becoming a target
is influenced by how valuable the target is perceived to be, how much effort the offender
perceives is needed to attack the target, how visible the target is to the offender, and how
accessible the target is to the offender. A capable guardian refers to anyone (e.g., a
policeman, security guard, or a stranger) or anything (e.g., window locks, guard dogs,
etc.) that reminds the potential offender that s/he may be seen and/or stopped from
committing the crime. Capable guardians, that is, increase the “costs” of committing a
crime.
Tying the above to the systemic model and to research on disorder, opportunity
theories view neighborhood structural characteristics (e.g., poverty and stability),
disorder, and informal social control as influencing the supply of motivated offenders,
suitable targets, capable guardians, and intimate handlers. In short, structure provides the
context in which criminal events occur. Felson and Cohen’s (1980) research, for
instance, demonstrated that family structure (e.g., single versus married) influenced the
supply of offenders as well as the supply of opportunities. Supervision and monitoring of
community activities, important crime prevention activities in the systemic model, are
reflected in opportunity theory as guardianship. Effective guardianship not only requires
a willingness to intervene but also the ability to recognize suspicious people and activity,
to recognize that something is “not quite right”. Naturally, this is more difficult when
areas have high resident turnover and when residents do not know their neighbors.

48
Measuring Neighborhood Structure
Drawing from urban ecologists, social disorganization theory asserts that
neighborhood structural variables weaken social control, drive out law-abiding citizens,
and attract deviant or crime-prone individuals (Stark, 1987). Over the years, research on
social disorganization has adopted an ever-wider array of variables, a few of which are
worth mentioning here (see Sampson 1995 for a review).
1. Poverty/Inequality. While the relationship between economic deprivation and crime
was central in Shaw and McKay’s model of social disorganization, Warner (1999) claims
that it is less important in the systemic model of social disorganization, which tends to
emphasize other factors, like housing density, mobility, and family structure. Measures
of poverty are often combined with other variables—for example, median home values,
and occupational status—to measure the abstract construct of socio-economic
disadvantage.
Whether poverty has a significant and independent impact on crime at the
neighborhood-level is contested. Some scholars suggest a direct relationship between
poverty levels and crime (Block, 1979; Bursik and Grasmick, 1993b; Curry and Spergel
1988), while others insist that this relationship is either weak (Messner and Tardiff, 1986;
Sampson, 1985, 1986) or is conditioned by population mobility (Smith and Jarjoura,
1988). For example, the effect of poverty on violent victimization all but vanishes when
other factors, such as racial composition and divorce rates, are considered (Sampson,
1986). Research also indicates that low socio-economic status (SES) is related to low
rates of participation in formal and voluntary organizations (Tomeh, 1973: 97).

49
2. Residential Instability/Community Change. Residential instability results when the
population of a neighborhood changes in a relatively short time. When new neighbors
come and go, residents are unlikely to form strong and lasting relationships with one
another. In stable neighborhoods, parents will often take on the responsibility of
supervising children other than their own, but this is less likely to be the case in unstable
neighborhoods (Sampson 1986, 1987b). Residential instability also makes it difficult for
residents to distinguish visitors from neighbors—both may be perceived as strangers.
This has important implications for crime prevention because people are unlikely to
intercede in criminal events involving people they barely know (Greenberg et al., 1982).
Cross-sectional studies indicate that higher levels of population mobility and
higher levels of community change are related to higher violent crime rates (Block, 1979;
Sampson, 1985; Sampson and Wooldredge, 1986; Snell, 2001), even when other
neighborhood-level correlates of crime are controlled (see Sampson, 1985; 1986; Snell,
2001). A handful of studies have examined the instability-crime relationship over time
(Bursik and Webb, 1982; Heitgard and Bursik, 1987; Taylor and Covington, 1988).
The relationship between instability and crime may also interact with other
variables, particularly poverty (Rose and McCain, 1990; Schuerman and Kobrin, 1986;
Taylor and Covington, 1988). In their test of Shaw and McKay’s model, for instance,
Smith and Jarjoura (1988) interviewed 200 people in 57 neighborhoods and found that
rates of residential mobility interacted with a neighborhood’s poverty level such that
mobility rates affected violent victimization in low-income neighborhoods, but not in
affluent ones.

50
3. Heterogeneity/Racial Composition. Ethnic and racial heterogeneity—that is, diversity
in ethnic and racial composition— is assumed to make establishing networks of social
control more difficult. Presumably, culture and value differences are the source of this
difficulty, although this assumption is rarely stated outright. Some research, however,
supports the assumption that living close to others with different cultural backgrounds
and mannerisms arouses fear (Covington and Taylor, 1991; Merry, 1981; Moeller, 1989;
Ortega and Myles, 1987; Parker and Ray, 1990). If so, then high levels of racial and
ethnic diversity should make crime control more difficult, since it would be difficult to
get residents to cooperate with their neighbors.
Even a cursory review of the social disorganization literature will reveal that,
although ethnic heterogeneity is consistently reported as a primary structural variable of
interest, it is rarely utilized in empirical research. Racial or ethnic composition (usually
measured by variables like “percent African American” or “percent non-White”) is much
more common.
Part of the reason for this may be due the relative ease with which the former can
be obtained, particularly from census data. Measuring heterogeneity, on the other hand,
requires additional computations and a probability-based measure of interracial contact.
Smith and Jarjoura (1988), in one of the few studies to employ heterogeneity as a
measure, calculated the probability that any two randomly selected members of a
neighborhood would be of a different racial and ethnic group and found that this measure
partially explained differences in neighborhood delinquency rates. This effect
disappeared, however, when family structure was controlled.

51
Another reason that measures of racial composition are preferred over
heterogeneity may be due to the number of studies that have confirmed the former’s
significance. Even Shaw and McKay (1942) mostly referred to population composition
because they found that delinquency rates tended to be higher in predominantly African
American and immigrant neighborhoods than in areas of maximum diversity—more than
double the rate, in fact. And studies following Shaw and McKay have confirmed that
neighborhood rates of violence are consistently found in neighborhoods with higher
levels of racial concentration, particularly percent African American (Beasley and
Antunes, 1974; Mladenka and Hill, 1976; Messner and Tardiff, 1986; Sampson, 1985;
Roncek et al., 1981; Smith and Jarjoura, 1988). More important perhaps, is that the effect
of ethnic heterogeneity on crime seems modest when compared to poverty. In short,
these findings suggest that crime rates tend to be highest in poor, homogenous
neighborhoods, not in heterogeneous ones (McNulty, 1995; Warner and Pierce, 1993;
Warner and Rountree, 1997).
This last point raises another issue regarding the relative importance of
heterogeneity and racial composition: Since some studies have shown that the effects of
racial composition and heterogeneity disappear when other variables like family structure
or socio-economic status are considered, the independent effect of neighborhood racial
composition on crime may be more apparent than real (see Block, 1979; Curry and
Spergal, 1988; Messner and Tardiff, 1986; Sampson, 1985; Smith and Jarjoura, 1988).
The extent to which race—whether it is measured as racial composition or ethnic
heterogeneity—can help explain neighborhood crime patterns is likely to be the subject

52
of continued debate. As long as multiple measures of racial structure are used, and as
long as some research continues to find that the effect of racial/ethnic composition is
weakened or “explained away” by other measures, the issue is likely to remain
unresolved.
4. Housing/Population Density. Housing density and population density are occasionally
used as measures of neighborhood structure. Of particular interest are land area,
population size, concentration of multi-unit dwellings, and household size. Research
suggests that as the number of multi-unit houses and multiplex dwellings increases, so,
too, does the level of violent crime (Roncek, 1981; Schuerman and Kobrin, 1986). It is
assumed that density increases anonymity, weakening control, regardless of population
composition. Additionally, when population density is high, it is difficult for residents to
distinguish neighbors from strangers.
5. Family Structure. Family disruption, often measured by the divorce rate or the rate of
female-headed households with children, is a key variable in recent versions of social
disorganization theory (see Bursik and Grasmick, 1993; Sampson, 1986, 1987a 1987b;
Sampson and Groves, 1989; Sullivan, 1989; Taylor et al., 1984). The argument being
made is not that children of single-parent households are the ones turning to crime—that
may or may not be true—but that networks of informal social control are weakened when
fewer adults and guardians are available. Additional research has found that controlling
for family structure makes the relationship between race and crime, at least among
African Americans, insignificant (see Messner and Tardiff, 1986; Smith and Jarjoura,

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