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Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

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Crime, Neighborhood, and Public Housing

Garth Davies

LFB Scholarly Publishing LLC New York 2006

Copyright © 2006 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data Davies, Garth, 1967Crime, neighborhood, and public housing / Garth Davies. p. cm. -- (Criminal justice recent scholarship) Includes bibliographical references and index. ISBN 1-59332-144-9 (alk. paper) 1. Crime--Sociological aspects. 2. Public housing--Social aspects. 3. Social ecology. 4. Spatial behavior. I. Title. II. Series: Criminal justice (LFB Scholarly Publishing LLC) HV6177.D38 2006 364.2'56--dc22 2006014616

ISBN 1-59332-144-9 Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

Table of Contents

CHAPTER 1

Introduction: Crime Diffusion in Public Housing Neighborhoods

1

CHAPTER 2

Crime and Public Housing

7

CHAPTER 3

Public Housing Neighborhoods and the Social Ecology of Crime

27

Informal Social Control, Crime, and Public Housing

41

CHAPTER 5

Crime Diffusion as a Sociostructural Process

59

CHAPTER 6

Research Design and Analytic Framework

87

CHAPTER 7

Spatial Patterns and the Diffusion of Crime

107

CHAPTER 8

Validation and Diagnostics

133

CHAPTER 9

Summary: Crime and Public Housing in Context

155

CHAPTER 4

APPENDICES

165

NOTES

171

REFERENCES

173

INDEX

191

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Acknowledgements

A book such as this is made possible only with the guidance and support of a great many colleagues, friends, and family members, to whom I am both grateful and indebted. Thank you to Jeff Fagan and Ray Corrado, mentors in the truest sense. I value our ongoing collaborations and the opportunity to continue growing and learning. Thanks also to Elin Waring and Bill Glackman, who shared all that I could absorb. The co-habitants of the cell block otherwise known as Talbott were a fantastic sounding board and reality check, especially Silvina Ituarte, without whom I’d have gone hungry, and Justin Ready, without whom I’d have gone homeless. Finally, I wish to thank my Mom and Dad, who continued to believe in me even when I wavered, and my brother Grant, who continues to ground me. This book is dedicated to my wife Debbie and daughter Leila, for giving my life and work meaning.

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CHAPTER 1

Introduction: Crime Diffusion in Public Housing Neighborhoods

Public Housing Public housing has not always been associated with the tangle of social pathologies that currently plague these areas. It is only in recent years that public housing has become conflated with a variety of social ills, including serious crime. Owing perhaps to its more benevolent origins, criminologists have tended to overlook the unique sociopolitical context of public housing. When it has been studied at all, it has normally been as merely a spatial entity – public housing as a backdrop against which a variety of criminal and deviant behaviors is played out. Conspicuously absent from most of this research is a consideration of social factors in the genesis and spread of crime in these areas. If public housing does occupy a unique place in the urban landscape, there is reason to believe that this “anomalous” character is relevant in accounting for social phenomena occurring therein. Since Newman’s (1972) work on “defensible space,” the structural characteristics of public housing have been scrutinized at length. In contrast, much of the social context of public housing, particularly as it relates to crime, remains shrouded in mystery. One of the principal objectives of this study is to begin to develop a more social understanding of crime in and around public housing; more specifically, the intent here is to present a socially-based perspective on the diffusion of crime in public housing neighborhoods or communities. The first, tentative step towards what will ultimately be referred to as an “informal social control” theory of crime diffusion commences with the argument that public housing projects, both in their structural design and sociodemographic make-up, constitute “neighborhoods.” These public housing neighborhoods may be characterized by social 1

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factors that are implicated in crime rate discrepancies across projects; that is, differences in the level of crime between one development and the next are, at least in part, attributable to disparities in social composition and context. Public housing neighborhoods do not, however, exist in a vacuum. While they sometimes display a distinctive physical appearance, they are nonetheless an integral part of their surrounding environment. On one hand, neighborhoods adjacent to public housing are likely to be affected by its proximity. On the other hand, influence in these areas it not unidirectional; there are reciprocal relationships at work such that public housing is similarly affected by its immediate neighbors. The nonrecursive nature of influence in these larger communities has important implications for the diffusion of social phenomena within. Thus, crime rates in and around public housing are also contingent upon transmission effects operating between neighboring areas. To understand the dynamics at play, it is necessary to first derive a theoretical framework and then to examine the concept of diffusion more closely.

Social Ecology and Informal Social Control After a period of relative dormancy, social ecological perspectives are enjoying something of a renaissance in criminology. Drawing on Shaw and McKay’s social disorganization theory, researchers are reexamining the relationship between macrostructural factors and neighborhood crime rates. One variant of the new social ecology perspectives, referred to as informal social control theory, suggests that certain social factors differentially affect a neighborhood’s ability to regulate itself. In other words, the social context plays a significant role in determining a neighborhood’s collective capacity to control the conduct of its residents, including their criminal activity. Informal social control identifies a variety of considerations that are relevant in this regard, including population mobility, segregation, inequality, and family structure. Neighborhoods that demonstrate higher levels of informal social control are more likely to have correspondingly lower crime rates. Conversely, deficiencies in informal social control increase the probability that an area will experience comparatively elevated levels of crime. As neighborhoods in their own right, public housing developments may be categorized along a continuum of informal social control.

Introduction: Crime Diffusion in Public Housing Neighborhoods

3

Contrary to the prevailing folk wisdom, projects are not unitary social constructs; different projects demonstrate distinctions in their social control capabilities. Extrapolating further, the current study hypothesizes that these differences will have a substantive impact on the abilities of public housing neighborhoods to resist the influx of crime from adjacent areas. Developments with strong, stable levels of informal social control will be less susceptible to crime from without, while neighborhoods characterized by attenuated informal social control will show greater propensities toward the inward diffusion of crime. In the same way, the outward influence of public housing to proximal areas will also be mediated by informal controls in those areas. Crime originating in public housing is less likely to diffuse into nearby areas marked by more established informal controls, but is more likely to spread into surrounding areas where informal controls are lacking.

Diffusion To understand the dynamics at play, it is necessary to examine the concept of diffusion more closely. Spatial diffusion is the process by which behaviors or characteristics of the landscape change as a result of what happens earlier somewhere else (Morrill, Gaile and Thrall, 1988:7). A simpler definition identifies phenomenon and spread as the key elements of diffusion. First, some phenomenon, such as crime, is somehow brought into existence. The genesis of the phenomenon that may diffuse may be important, but it is often difficult to explain and identify. Of greater substantive interest here is spatial movement, as the phenomenon spreads beyond its origins and, in doing so, alters the character of other places. Thus, spatial diffusion may be usefully operationalized as the spread of a phenomenon, over space and time, from limited origins. While the use of “diffusion” in this dissertation has thus far remained undifferentiated, there are, in practice, several “types” of diffusion. In the most general sense, diffusion may be of the expansion or relocation variety (Gould, 1969). With expansion diffusion, the total number of people or places who “know about” or have been affected by the original phenomenon increases. Conversely, relocation diffusion involves phenomenon migration only; the overall number of “adopters” remains unchanged. It is at this most general level that diffusion is

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most often considered. At least in criminology, distinctions are rarely made between the contagious and hierarchical forms of diffusion. Contagious diffusion, or contagion, is drawn from epidemiology and is strongly influenced by the frictional effect of distance. Because contagion is conceptualized as a smooth and continuous spread, it is normally reserved for the diffusion of physical phenomena, such as the spread of a city into its rural hinterland (Morrill et al., 1988). Because of spatial distances and patterns of interaction, human phenomena are more likely to be characterized as hierarchical in nature. With hierarchical diffusion, the phenomenon in question “jumps” across the landscape. As human interaction tends to be concentrated in urban centers, it is common for some diffusion to proceed from city to city. Moreover, this process may be stochastic; for example, a phenomenon need not necessarily jump from one city to the next closest urban area. In contrast to some other social phenomena, crime is contextually specific in both the social and physical sense. Findings from the geography of crime routinely confirm that individuals tend to be lazy, preferring crime targets in concentrated areas near to their places of work or residence. Moreover, the criminally disposed appear to be more comfortable operating in their limited “awareness” and “activity” spaces (Brantingham and Brantingham, 1984). That crime is a highly localized and patterned phenomenon is also implicitly consistent with other “criminology of place” approaches (see Chapter 3). Taken together, these perspectives suggest that if crime diffuses, it is unlikely to spread very far. If the image of a wave spreading from a rock falling in the water provides the idealized vision of contagion, the diffusion of crime will not be “perfectly contagious.” However, the contagion metaphor is still more accurate than hierarchical diffusion. Thus, for this research, “diffusion” and “contagion” will be used interchangeably to denote the spread of crime between neighborhoods. Once a phenomenon such as crime is somehow brought into existence, its subsequent transmission is, to various degrees, a function of “barriers.” Of primary interest here, barriers are those things that get in the way of, slow down, or alter the process of diffusion. There are several varieties of barriers, including those that are physical, cultural, linguistic, religious, political, and psychological (Gould, 1969). This research is guided by the supposition that barriers may also be social in nature. If this is the case, specifying the nature of relevant social barriers assumes a significance greater than that evidenced in previous analyses of crime and public housing. In the parlance of diffusion,

Introduction: Crime Diffusion in Public Housing Neighborhoods

5

various levels of informal social control evidenced between neighborhoods act as differentially effective barriers to the diffusion of crime: higher levels of informal social control operate as barriers to contagion, while the low levels allow crime to spread more freely. In this way, the divergent concepts of neighborhood, informal social control, contagion, and barriers may be synthesized into a comprehensive theory of crime diffusion in and around public housing. This study is presented in nine parts. By way of introduction, Chapter 2 reviews the current state of criminological knowledge as it pertains to crime and public housing. The results are less than encouraging; mythology has more or less been held out has fact, such that much of what passes as “the way things are” in public housing is premised on weak or nonexistent evidence. In truth, comparatively little is known about the specifics of any presumed association between developments and crime. While public housing almost invariably portrayed as crimeridden and dangerous, data supportive of this contention are in conspicuously short supply. There are very few reliable statistics relating to the incidence and prevalence of crime in these areas, and inquiry into the etiological bases of crime is equally as barren. There has been some attention afforded to the structural characteristics of public housing, but theorizing about the potential importance of its unique social context has been minimal. The groundwork for, and facets of, an informal social control theory of public housing crime diffusion are developed in Chapters 3 and 4. The former chapter provides the basis for the consideration of public housing projects as social neighborhoods and sketches a rough “social ecology” approach to the analysis of contextual effects premised on Shaw and McKay’s social disorganization theory. From this foundation, Chapter 4 specifies more precisely the systemic bases of social control in urban communities and the elements of an informal social control theory. Chapter 4 concludes that the theoretical elements have special saliency in the context of public housing. Chapter 5 is the diffusion chapter. It sets the basic conceptual framework of diffusion, and reviews its limited applications in the criminological literature. Ultimately, the goal of Chapter 5 is to develop a more macro-level social understanding of diffusion than has traditionally been advanced. Chapter 6 articulates the research methodology for this study, including background on the spatial autocorrelation and generalized

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estimating equation techniques that inform the bulk of the analyses. It identifies a series of hypotheses, as well as the dependent and independent variables that will be used to test the hypotheses. As the examination of diffusion effects is not straightforward, this chapter also specifies, in some detail, two diffusion models: the outward and inward diffusion models. The results of the study are presented in Chapters 7 and 8. Chapter 7 reveals the spatial autocorrelation and diffusion findings, while Chapter 8 assesses the validity of the models. Finally, Chapter 9 provides a summary of the results and discusses their policy implications.

CHAPTER 2

Crime and Public Housing

The Popular Perception of Public Housing Crime In the criminological literature, few things are taken on faith as much as the “problem” of crime in public housing. That public housing projects are rife with serious crime is more or less accepted as fact, despite a discernible paucity of empirical evidence supporting such a contention. As underresearched as public housing has been generally, no particular area has been neglected more than the gathering of very basic statistical information on crime levels and rates. Although there has been a resurgent interest in public housing recently, basic questions concerning the nature, incidence, and prevalence of their supposed crime problems have yet to be satisfactorily answered. On one level, the dearth of valid and reliable measures specific to crime in public housing raises important methodological and statistical questions, not least of which relates to the use of crime as a dependent variable. But the lack of quality data has also contributed to an equally pressing substantive issue. Public housing suffers from a substantial image problem, an important element of which concerns the link that has been fostered in the public imagination between public housing and crime (Farley, 1982). The stereotypical perception that public housing is riddled with crime has contributed significantly to the near universal acceptance that public housing is almost per se a poor place to live, and to widespread resistance to public housing routinely encountered in the US (Roncek, Bell, and Francik, 1981). As public housing increasingly comes under popular and analytic scrutiny, a better understanding of the public housing - crime nexus will undoubtedly be required.

7

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Crime, Neighborhood, and Public Housing

The Nature, Incidence, and Prevalence of Public Housing Crime As there have been few attempts to systematically measure crime in public housing, there is a glaring absence of accurate information on the relationship between the two. Holzman and Piper (1995:1) note very simply that “valid statistics on the level of crime in public housing do not exist.” The research that is available on the volume and types of crime in public housing predominantly dates back to the 1970s and 80s. In addition to being dated, this body of research is characterized by conflicting findings that may not be particularly relevant today. While Brill and Associates (1975, 1976, 1977a, 1977b) suggested that public housing crime rates were elevated in comparison to both the larger cities they were located in (Baltimore, Boston, Los Angeles and Washington) and the US as a whole, their results were only partially supported by Roncek et al. (1981), who found that the statistically significant effect of public housing on violent crime rates in Cleveland and San Diego was diminished once they controlled for other neighborhood characteristics. In contrast to both studies, Farley (1982) concluded that the incidence of crime in multiblock areas containing public housing developments in St. Louis was not higher than, but rather generally comparable to, citywide figures. The most current research does little to clarify the muddied waters. Whereas Harrell and Gouvis (1994) argue that the presence of public housing does not uniformly contribute to increased crime rates for their larger census tracts in Washington and Cleveland, Dunworth and Saiger (1993) maintain that violent and drug-related criminal activity are significantly greater for a sample of projects in Los Angeles, Phoenix, and Washington. That the existing research has produced contradictory findings over twenty years is not entirely surprising, given that it has consistently been plagued by a variety of shortcomings. There are a number of difficulties and obstructions pertinent to obtaining accurate public housing crime statistics. Public housing is usually only part of a much larger administrative unit (Holzman, 1996). Consequently, data that carefully distinguishes public housing from its surrounding areas are often unavailable. Farley (1982), for example, was forced to infer public housing crime rates from nearby blocks because the police department in St. Louis did not keep project specific statistics. Because of the unique difficulties posed by public housing, no standardized method for collecting or analyzing crime data has been adopted. Lacking standardized tools for validly and reliably collecting

Crime and Public Housing

9

information with respect to crime rates in public housing, criminologists continue to utilize a variety of disparate analytic strategies and techniques. As a consequence, the analyses produced thus far are of limited comparative value. The propensity to use aggregate measures has similarly contributed to confusion regarding crime in public housing. The general practice is to refer to a crime rate, without specifying exactly what kinds of activities are included and how it is comprised. Focusing on particular types of crime may provide a skewed or incomplete view of what is actually going on in these areas. For example, Dunworth and Saiger (1993) note that, while the comparative rate of drug and violent offenses in their sample developments is elevated, the level of property crimes in Los Angeles and Washington projects is less than those for the larger city. The practice of considering public housing collectively also presents potential analytic pitfalls, in that it may mask variation that is important in accounting for differences across the public housing universe. Fagan and Davies (2000) provide evidence indicating significant diversity in the rates of violent crime for public housing projects in New York City, and Dunworth and Saiger (1993) suggest that there are pronounced differences in offense rates among public housing developments Los Angeles, Phoenix, and Washington. Thus, while there may, in fact, be some truth to the concept of “problem projects,” it is inaccurate and unfair to paint the entire public housing universe with the same broad strokes. In a similar vein, the selection of research cites also serves to present a biased view of crime in public housing. First, the bulk of existing information tends to focus on conditions in large public housing authorities in older central cities (Holzman and Piper, 1995). This is problematic insofar as relatively few (less than 5%) of the public authorities in the US may be accurately characterized as large. Rather, almost 50% of public housing authorities maintain less than 100 units, while nearly 90% of authorities have less than 500 units. Given that these smaller public housing authorities cumulatively account for more than 40% of total units, the ability to generalize from the sparse crime data that has been generated seems tenuous. Second, samples are often very small and unsystematic. In fact, many PHAs are probably chosen specifically because they exhibited higher crime levels, or because they had been imbued with reputations as having significant crime problems (Dunworth and Saiger, 1993). What is presently known, in effect, amounts to only snapshots of a relatively few densely populated localities (Holzman, 1996). At best, there is

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Crime, Neighborhood, and Public Housing

considerable question as to how representative our picture of public housing crime rates is; more likely, it fails to adequately reflect or portray the complexity of public housing reality. The lacunae in the criminological research concerning crime in smaller public housing authorities further extends to developments themselves. Coincidental with the advent of defensible space theory (Newman, 1972) has been the tendency toward prejudicial selection bias favoring the examination of very large public housing projects. As was the case with public housing authorities, this predilection for studying the largest developments is highly suspect and unwarranted. The most predominant form of public housing architecture is not high rises, and few projects are composed solely of buildings of this type. Moreover, there is a geographic concentration of high rise architecture, which tends to large be confined to the urban Northeast and large Midwestern cities (Holzman, 1996). Conversely, close to half of public housing is comprised entirely of row houses, low-rise apartments, and/or single-family dwellings. In contrast to the prevailing research wisdom, public housing in many areas is largely indistinguishable from surrounding homes. The recent resurgence in public housing research has as yet accomplished very little in the way of dampening the tendency toward studying large developments in large public housing authorities in older, central cities. Conditions endemic to the largest high rises almost certainly are distinct from those experienced in other areas, further buttressing the impression that we remain woefully misinformed about what really is happening in public housing. The temporal contexts of available public housing and crime research is also worthy of some critical mention. To date, said research has been restricted to cross-sectional analyses that fail to capture the dynamic and continually evolving nature of crime, public housing, and any correlation that may exist between the two. In addressing the social ecology of crime more generally, Bursik and Webb (1982) and Schuerman and Kobrin (1986) argue quite convincingly that communities undergo changes over the course of time with respect to their “careers” in crime, some of which may be quite dramatic. Public housing developments, as constituent elements, are inevitably affected by the changing fortunes of their larger neighborhoods. However, longitudinal methods that are more amenable to capturing variations are all but absent from public housing analyses. The type, incidence, and prevalence of crime in public housing is not immutable, and only by recognizing shifting trends and emergent patterns can we begin to

Crime and Public Housing

11

approach a more critical understanding of the fundamental nature of the public housing universe.

The Correlates and Causes of Public Housing Crime The earliest pronunciations on the potential importance of public housing characteristics may be attributed to activist Elizabeth Wood, who contended that the physical design features of developments contribute to the absence of a sense of community and to the tenants’ lack of social control by minimizing both communication and informal gathering. Although not specifically predicated on public housing per se, Jacobs (1961) introduced the concept of natural surveillance, or “eyes on the street,” as a central theoretical tenet. Simply put, Jacobs states that surveillance deters crime by increasing the offender’s risk of detection and apprehension. Underdeveloped as a theoretical framework, Wood’s and Jacob’s work nonetheless provided the foundation for more sophisticated conceptualizations. As well, these initial pieces clearly promoted a research agenda that emphasized the structural attributes of public housing as central criminogenic considerations. Interest in public housing crime specifically may be traced back to Newman’s seminal work on defensible space theory. Informed by Wood and Jacobs, Newman Emphasized the key role of criminal opportunities: “The physical environment exerts a direct influence on crime settings by delineating territories, reducing or increasing accessibility by the creation or elimination of boundaries and circulation networks, and by facilitating surveillance by the citizenry and the police.” Defensible space theory posits that the physical design of public housing exerts a powerful influence on both crime and the fear of crime within the development (Newman, 1972). In particular, defensible space suggests that certain design features of public housing discourage residents from extending their sphere of influence (territoriality) beyond the more immediate confines of the individual apartments to adjacent semi-private or public areas. After the publication of Defensible Spaces (Newman, 1972), the efficacy of design factors in alleviating public housing crime was seen to hinge on their ability to encourage “territoriality” and the creation of “defensible space,” as well as promote the impact of “access control” on social cohesion. Newman’s early work concluded that building size was the key physical attribute in account for varying levels of both

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crime and fear of crime across developments in New York City. As important as this specific finding was, however, it could not match the larger significance provided by Newman’s more general focus on physical features. Until only recently, design considerations have dominated theorizing on crime in public housing. In their 1978 report Crime in Public Housing, Rouse and Rubenstein set out to review for the US Department of Housing and Urban Development (HUD) what had been learned about the relationship between crime and the physical environment of public housing in the more than 15 years that had passed since the landmark works of Wood and Jacobs. The findings were not encouraging. Even at that early time, the report recognized that the literature describing or measuring the extent of crime in public housing milieus was not sufficiently developed. It seems that a further 25 years has failed to appreciably improve the situation. Strongly influenced by defensible space theory, the report afforded considerable weight to the role of physical characteristics in public housing crime. It confirmed the following factors as the six most commonly mentioned in the literature: • Lack of surveillance. • Lack of adequate locks, door and window frames, alarms, etc. • Lack of access control. • Lack of clearly defined areas and physical facilities. • Conflicting uses of public housing grounds. • Lack of adequate circulation patterns and transportation services and facilities. At the same time, the reported noted “a recent trend” recognizing that physical design only partially accounted for public housing problems, that social factors might also be implicated. While the state-of-the-art knowledge concerning the role of social features in public housing crime was characterized as limited, the reported nonetheless identified six emerging themes: • Lack of social organization, social cohesion, and informal social control. • Lack of proprietary interests and territoriality among residents.

Crime and Public Housing

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• Lack of adequately trained, culturally sensitive security personnel. • Lack of social service programs to address social service problems of residents. • Lack of supervision and organized activity for youth. • Lack of employment opportunities for residents. Ultimately, the report highlighted corresponding difficulties relating to relative criminogenic factors, positing that the literature neither pinpoints the extent to which each of the factors actually contributes to the crime problem nor suggests which of the relevant physical and social factors are comparatively more or less important (Rouse and Rubenstein, 1978: vi). The physical and social correlates subsequently identified by the report are informative, particularly in light of the fact that, as with crime measures, there have been few substantive advances in the interceding 25 years. Physical factors continued to be preeminent in explanations, but they were no longer regarded as exclusively responsible for public housing crime. For example, while Rainwater’s original examinations of the Pruitt-Igoe Project in St. Louis were primarily concerned with physical design, subsequent work suggested a combination of social factors that contributed significantly to crime and related problems (Rainwater, 1970). In a similar vein, Newman himself retreated from an almost wholly structural approach, embracing instead one that recognized the importance of social elements. Following Defensible Space, he maintained that although building size was a key explanatory variable in the public housing crime equation, the social characteristics of the resident population were stronger predictors of crime rate than design factors. Later, he would suggest that the precise factors proposed to mediate the relationship between design and crime – especially building size – were not actually specified or measured in his earlier conception (Newman and Franck, 1982). In the wake of Defensible Space, Newman set out to expand the scope of his New York City findings and address critics who contented, among other things, that Newman ignored the social factors that might influence crime and fear of crime. Specifically, Newman (with Franck) developed a more complex, revised causal model that included both physical and social characteristics as independent variables and specified a series of intervening variables posited to link these characteristics with personal crime rates in public housing. Drawing

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Crime, Neighborhood, and Public Housing

upon a larger sample, Newman and Franck found that the direct effect of building size on personal crime was small, but also, that the total indirect effect of building size, mediated through both residents’ use and residents’ control of space, was significant. In response to these findings, Newman and Franck suggested a more qualified set of conclusions regarding the significance of physical factors. While the importance of structural features has not been substantially diminished, they are increasingly being supplemented by social factors such as income levels, welfare, and social interaction. The burgeoning importance of social factors was perhaps captured most eloquently by Brill: The vulnerability to crime of many public housing projects, particularly large projects, does not stem just from design and equipment deficiencies . . . The problem of security in public housing also stems from the weak social structure of the residents, the absence of supporting groups, and a lack of interpersonal trust – all factors that inhibit people from protecting and helping each other (1975:47). Unfortunately, more than 25 years has failed to produce substantial empirical support for the proposition that social factors are related to public housing crime. It is not that evidence has been contradictory, but rather, that very little evidence exists one way or the other. As with incidence and prevalence of crime, evidence of social structure in public housing, and of the link between social structure and crime in these developments, tends to be assumed or taken for granted. Newman’s work in the early 1980s, his attempts to articulate multifactor models and specify mediating links between supposed causal factors and public housing crime, still stands as a significant precedent for how to attempt this sort of research. Unfortunately, attempts to proceed according to Newman’s revised position are rare.

Fear of Crime in Public Housing In discussions of public housing problems, the fear of crime is almost invariably intertwined with crime itself. Fear of crime is routinely lumped in with a laundry list of other detrimental effects and social ills symbolic of public housing, and is generally cited as one of the most

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serious issues confronting public housing tenants. Given the negative publicity and stereotypes that have come to characterize public housing, it is hardly surprising that the fear of crime is at least as pervasive as the actual threat of crime in particular projects. Even more than with crime, however, there is a paucity of empirical evidence relating to fear in public housing. Despite the overwhelming importance that fear of crime may play in the dynamic of life in public housing, comparatively little is known about the levels or causes of fear in these areas. Because studies consistently show rates of fear in excess of actually probability of victimization, fear of crime must be addressed independent of crime itself (Skogan, 1990). Factors other than actual victimization must be accounting for fear. Like crime, the fear of crime has been implicated as an important element in the downward spiral of public housing neighborhoods. Elevated levels of fear produce a variety of negative effects that further exacerbate neighborhood problems. In particular, fear undermines the cohesiveness of the neighborhood by encouraging avoidance behaviors (Conklin, 1971). There is a strong, negative relationship between fear and requisite behaviors that promote social contact, such as participation, responsibility, and territoriality. At its most extreme, avoidance behaviors are realized as residential turnover: public housing tenants move out of fear-inducing neighborhoods whenever possible. High levels of turnover are historically related to social disorganization, and in turn crime. Yet despite the necessary conceptual differentiation of crime and fear of crime, too often the two have not remained analytically distinct. Rather, research has tended to examine both simultaneously. Only recently has fear of crime become a subject worthy of study in its own right. As was the case with crime, studies on the fear of crime have been powerfully influenced by Newman’s pioneering work on defensible space. Newman theorized that the fear of crime was predicated on the same factors as crime, namely the physical features of public housing that resulted in diminished surveillance, control, territoriality, and responsibility. Consequently, Newman initiated a practice of considering crime and fear of crime in tandem. Whereas Newman’s later research led to more qualified pronunciations of the effects of structural attributes, that same research confirmed that the direct relationship between building size and fear of crime was significant (Newman and Franck, 1982). But, the strength of his multifactorial approach notwithstanding, the substitution of fear of crime for crime as an independent variable ignores the potentially unique nature of fear.

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Crime, Neighborhood, and Public Housing

Crime and fear continue to be considered in concert more often than not, but a divergent research tangent rooted in the unique nature of fear alone has also developed. Just as little is known about the levels of fear because it is assumed or taken for granted, so too is there an absence of knowledge regarding the causes or correlates of fear. Several theories have been advanced to account for exaggerated levels of fear in the context of public housing. The earliest explanations of fear not solely contingent upon physical factors suggested (or at least implied) that the incidence of fear could be attributed to the atypical compositional facets of public housing. More precisely, fear could be expected to be more pronounced in public housing because it tends to be disproportionately inhabited by those groups of people that have traditionally been found to be a greatest fear of criminal victimization: women and the elderly. From this premise, a series of articles focusing particularly on the elderly emerged. Several studies found that fear of crime for the elderly frequently surpassed that of younger residents (Lawton, 1975; Normoyle et al., 1981). Subsequent studies by Lawton and his associates reported that older tenants were less like to fear crime when they were segregated from young families (Lawton and Yaffe, 1980; Teaff, Lawton, Nahemow, and Carlson, 1978). These studies suggested that the particular vulnerability of the elderly and their inability to cope with the activities of youth in the public housing domain contributed directly to their feelings of fear. In contrast, Normoyle (1987) maintained that Lawton failed to recognize a potentially confounding aspect of public housing residential practice; that is, that the practice of segregating older tenants is considerably more prevalent in sites where the proportion of elderly is already quite high. In other words, she suggested and provided evidence that it is the relative size of the elderly population, as opposed to segregation per se, that determines fear of crime among public housing’s elderly residents. In an attempt to evaluate this finding in light of Newman’s defensible space model, Normoyle and Foley (1988) discovered that fear of crime was actually lower among elderly tenants living in high-rises, but that withinbuilding segregation tended to produce assessments ranking local crime problems as most serious. Normoyle and Foley were unable to reconcile these contradictory results, and research bearing directly on elderly public housing tenants’ fear of crime has been more or less abandoned in recent years. While fear of crime has developed into a distinct substantive interest, the saliency of its premises within public housing remains

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largely unknown. Research in this area is almost universally afflicted with the same methodological drawback: the paucity of sound comparative bases. This principal limitation can be traced back to Newman, and has been perpetuated in subsequent efforts. There is actually no work that compares fear of crime within public housing to other residential settings or urban areas. Are public housing residents more fearful of crime than individuals living in the surrounding neighborhood? Are they more fearful than individuals in other neighborhoods? As yet, answers to these seemingly central questions have not been forthcoming. The closest available approximation is Burby and Rohe’s (1989) study on deconcentration and its affect on fear of crime. Comparing tenants from four inner-city projects and four more suburban developments, Burby and Rohe noted that the fear of crime was significantly lower in the decentralized public housing areas. But these results still do not speak to the comparability of public housing neighborhoods and those areas without public housing. There is some evidence that the causal mechanisms of public housing fear may not vary dramatically from other settings. Rohe and Burby (1988:717-718) have produced qualified support for three explanatory models derived from the general fear of crime literature, suggesting that “fear of crime in public housing is influenced by essentially the same factors that account for fear of crime in larger contexts.” However, as these results were garnered from a sample of public housing in Durham, North Carolina, their representativeness is unknown. In short, while much is made of the fear of crime in public housing, little empirical evidence exists. Levels of fear normally are not measured in any systematic way. Rather, fear is assumed from stereotypical notions of what it means to live in the projects. Information regarding the causes of crime fear is similarly sketchy, especially when considered across all public housing tenants and not just seniors. Finally, work in this area has been largely decontextualized by the failure of researchers to establish a comparative basis. As both the physical and social features of public housing differ from typical residential neighborhoods, it is important to know how this contrast influences the fear of crime dynamic in these disparate environments.

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Prevention Policies and Programs in Public Housing The relative absence of sound information on the incidence, prevalence, and correlates of crime and fear of crime in public housing has in no way dampened efforts directed towards rectifying or alleviating these and other problems. On the contrary, the pace of proposed policies, programs, and strategies to combat crime in public housing has intensified in recent years, during which time next to nothing new about the crime problem itself has been ascertained. Researchers and practitioners continue to draw heavily on the physical and social theories highlighted previously to fashion a multitude of potential responses to the crime problem, while further recognizing that governments, housing authorities, and the neighborhoods themselves must also be included in comprehensive crime reduction and prevention plans. In attempting to discern the correlates of crime in public housing, Rouse and Rubenstein (1978) were among the first to recognize that governmental and managerial policies could also be variables in the public housing/crime equation. On one hand, Rouse and Rubenstein suggested that governments generally might be chastised for failing to provide adequate and stable funding for crime prevention programs. For example, the lament that levels and effectiveness of security personnel in public housing were inadequate was common. On the other hand, they also allowed that a variety of Public Housing Authority (PHA) regulations may serve as barriers to the creation and maintenance of effective security programs. PHAs were cited for their inability to effectively coordinate preventive efforts with local law enforcement agencies. At the policy level, issues such as tenant eviction (for anti-social behavior) and screening (for entry purposes) had been less than successfully addressed. In a discussion that predated the one above, there were also strong reservations expressed regarding the mixing of young families and the elderly. Again, these issues are noteworthy given that they remain prevalent in crime prevention strategies today. As influential as Newman’s theses regarding crime and the fear of crime have been, his most enduring legacy has arguably been his impact on prevention strategies and techniques. In the context of public housing, Newman identified a variety of physical features that made developments more attractive to offenders in terms of ease of committing crimes and ease of avoiding detection or arrest. These features included the massive size of buildings; multiple points of entry

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and egress; the location of public housing in high crime areas; and the low-cost appearance of the public housing, which contributed to the stigma surrounding “the projects.” It follows logically from these premises that the undesirable consequences of crime and fear of crime might best be avoided through careful design or modifications to existing physical structures. Jeffrey (1971) is credited with coining the phrase that has come to embody Newman’s philosophy toward addressing the crime problem in public housing: crime prevention through environmental design (CPTED). Rejecting the dominant deterrence- or rehabilitation-based paradigms of the day, Jeffrey instead suggested that changes to the physical environment could independently prevent crimes through the reduction of criminal opportunities. CPTED, and the closely related approach of situational crime prevention (SCP), remain at forefront of prevention efforts. The eventual prominence of Newman’s influence was belied by a wave of criticism that initially met his work. Detractors warned of the dangers of “environmental determinism”: because it neglected important social causes such as poverty, unemployment and racism, defensible space theory represented an oversimplification of the crime problem. Some chastised Newman for his uncritical adoption and application of Ardrey’s (1966) principle of territoriality, while others charged that his implicit model of the public housing offender - the predator from outside – ignored the reality that much crime was actually perpetrated by residents (Mawby, 1977). More empiricallybased reviews concluded that the relationship between physical context and crime was, in fact, weaker that Newman had claimed (Murray, 1995), and introduced displacement as a concept that would come to be the bane of CPTED efforts. Although Newman’s ideas did lead to a moratorium on the construction of high rise blocks, CPTED remained largely ignored by the US government. The 1980s saw a wave of penal conservatism, during which government policy was less inclined toward prevention and greater priority was afforded increasing the severity of punishment and the building of new prisons. This heightened conservatism, in conjunction with discouraging empirical results, culminated in a loss of federal support for CPTED (Clarke and Sorensen, 1998a). The renewal of government interest in and support for CPTED did not come until the early 1990s, when the human and economic costs of wholly repressive policies coupled with a lack of evidence pertaining to their efficacy ushered in a call for alternative crime solutions, including CPTED (Clarke and Sorensen, 1998a). At the same time,

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public housing crime problems were becoming even more pronounced, as juvenile gang activity, gun crime, and the drug epidemic added urgency to the search for sound prevention strategies (Holzman, 1996; Keyes, 1992). Finally, both of these trends were commensurate with a revolution of thought among previously skeptical social scientists who were increasingly prepared to concede that the causes of crime did not reside exclusively in social and psychological factors. Since Defensible Space, accumulated evidence suggests a significant correlation between situational opportunities and crime, and that such opportunities or inducements may be reduced or eliminated through environmental changes (Weisburd, 1997). Still, little of the available evidence has been drawn from public housing, where assessments of CPTED have been less than positive (Bannister, 1991; Schneider and Pearcey, 1996). Several of the lessons learned regarding the application of CPTED in public housing have been summarized by Clarke and Sorensen (1998a). First, direct measures aimed at reducing unauthorized access, such as street closures, walled perimeters, and guarded entrances, are sometimes more effective in curtailing crime than indirect means predicated on improving territoriality and surveillance. This suggests that the concept of territoriality is perhaps too narrow a platform upon which to develop physical changes. Second, defensible space is not a universal prescription or package that may be applied uniformly across intervention sites. At the very least, developments vary in terms of their size and the nature of their crime problems, and these differences necessitate divergent responses. Thus, strategies must be “carefully tailored to analysis of the specific site, wider neighborhood setting, and actual crime problems.” Third, crime in public housing cannot simply be attributed to “outside predators.” Insofar as tenants themselves commit antisocial acts, public housing may generate crime in adjacent neighborhoods, as opposed to “serving principally to attract criminals from surrounding neighborhoods to developments.” Finally, too much should not be expected of design modifications alone. Comprehensive security plans are considerably more complicated and require more than simply commissioning an architect to apply defensible space measures. These initiatives remain central to preventing crime and fear of crime in public housing, albeit in a somewhat modified form. In light of the conceptual shortcomings of defensible space theory in its original form, Clarke and Sorensen (1998b) have proposed augmenting CPTED with principles of situational crime prevention (SCP). But even in this modified form, environmentally-based approaches are in and of themselves not

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enough. Rather, enlightened management, sound policing, and close consultation with residents are all elements of successful crime prevention programs. This multifaceted approach currently comprises the paradigm for addressing public housing crime problems. An emerging supplement to SCP is the promise of community policing. Recognizing that that police play a pivotal role in controlling crime, but that traditional policing techniques such as random patrols have been ineffective, comprehensive efforts are increasingly incorporating some element of community policing. In Washington, community policing is credited with significant crime reductions in notoriously crime-ridden public housing communities, including as 38% drop crime in and around Hendley Homes. Moreover, increased levels of trust and cooperation between residents and the police assisted in cultivating valuable new sources of information for the police (United States National Institute of Justice, 1992). Community policing efforts have also fostered improved police-tenant relations in St. Paul. Unfortunately, community policing was less successful in lowering tenants’ fear of crime (Brandel and Witt, 1993). Despite this less sanguine result, the growing popularity of community policing generally will likely continue to influence public housing policing for the foreseeable future. Perhaps the most important change in crime prevention strategy has been the advent of public input and participation. More or less ignored until very recently, public housing residents are recognized now as the linchpin of crime reduction initiatives. In North Carolina, efforts are explicitly predicated on the premise that crimes can be prevented when residents of public housing work in partnership with law enforcement agencies and the community at large (Martin, 1993). Virginia similarly advertises the need for cooperation between PHAs, law enforcement authorities, and neighborhood residents (Wells, Everton, and Wright, 1992). With a greater stake in their neighborhoods than either the PHAs or police, public housing tenants can resist or subvert otherwise well-intentioned strategies. Residents are in the best position to assess the specific nature of problems within their developments, and thus are an integral part of the crime prevention process. Public housing crime problems are complex, and no one group or agency can be expected to address them. Only through collaboration are efforts likely to make a dent in the growing menace of crime in these neighborhoods.

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While the “lack of information with regard to crime and public housing” refrain is by now all too familiar, it is most striking in the context of crime prevention programs and evaluation. First, at the “front end” of the process, strategies to curb public housing crime have tended not to be informed by empirical evidence. Second, at the “back end” of the process, prevention efforts often have not been subject to rigorous evaluations. As few evaluations of CPTED or SCP measures have been conducted in public housing, their relative value in this specific context is largely unknown. Data concerning other anti-crime efforts is sparser still. Thus, the lacunae in public housing information is twice compounded, beginning with the very conceptualization of programs, and continuing through their implementation and ultimate evaluation. Awareness of the evaluation shortfall has grown incrementally, but addressing the problem faces several difficulties. In particular, architects, public housing managers, and the police generally lack the requisite skills to conduct systematic program evaluations (Clarke and Sorensen, 1998b). As well, few criminologists with the necessary evaluation skills have a detailed understanding of public housing. In practice, information relevant to reducing or preventing crime in public housing will advance only at the rate that the knowledge and skill of these experts are reconciled. Conceptually, it is important that crime is not divorced from other social pathologies evident in public housing. Policies and programs are less likely to be successful if they fail to address the full context of public housing. Context and Public Housing Crime: Prelude to a Social Ecology Theory of Diffusion The growing complexity of our theoretical understanding has analogy in the way public housing environs have come to be conceptualized with greater finesse. Having previously regarded public housing as “merely a platform upon which a variety of very nasty social pathologies happen to be played out” (Holzman, 1996: 361), criminologists have increasingly come to discern and appreciate its unique social, economic, and political nature and context. In short, the uniqueness of public housing, and its consequent relationship with its immediate surroundings, are important considerations in the analysis of crime in these areas. Many of the public housing crime studies

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reviewed earlier contain allusions to the larger neighborhood or community context, implying that there is some sort of connection between the two. In some cases, this may have been more of a methodological artifact reflecting need to use “the larger area” for the purpose of generating crime rate approximations or proxies. But the counting of events and creation of rates is only part of the story. On a variety of social, economic, and political levels, public housing projects constitute an integral part of the larger neighborhoods and communities within which they are located. This intimate linkage makes the separate consideration of public housing and surrounding neighborhood effects every bit as arduous as the estimation of crime rates. It has already been noted that the status of public housing as a subunit in larger administrative entities presents special difficulties in the collection of development-specific crime data. Related technical difficulties, such as the uncertain assignment of events to particular localities and the misidentification of perpetrators as public housing residents, similarly add to the onerous task of rate computation. The reality of public housing, however, is even more complex than this discussion has suggested. Beyond the basic issues of counting and categorizing criminal events, researchers are only beginning to appreciate the nuances and nature of the processes that produce the events. Public housing is not randomly distributed across neighborhoods. On the contrary, the specific location of projects is culmination of clearly discernible patterns of historical segregation. Marcuse (1995) asserts that a wide variety of competing forces have influenced the locational assignment of public housing throughout its history. Given the public perception of public housing as a generator of crime and other social pathologies, communities with the requisite social, economic, and political resources have generally attempted (with success) to oppose the allocation of developments to their areas. Both Hirsch (1983) and Bursik (1989) provide compelling evidence that projects tend instead to be located in areas where the resources necessary to resist public housing are lacking. Not surprisingly then, public housing is most often situated in neighborhoods already characterized by marked levels of poverty, deteriorating housing stocks, and high crime rates. While public housing may raise crime rates in its adjacent neighborhood, these “bad neighborhoods” may experience exaggerated levels of crime as a consequence of their own population and housing compositions, independent of the effects of public housing (Roncek et. al, 1981). Alternatively, the direction of

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influence may be reversed: neighborhood compositional effects may affect public housing crime rates. If, as the statistics suggest, public housing predominantly features women, children, and the elderly as residents, in other words those groups that traditionally have the lowest rates of criminal offending, it is certainly reasonable to ask who exactly is committing the crimes in and around public housing. Even taking into account the debate over “official” versus “non-official” residents, there is some reason to suspect that it may be people who are attracted to public housing from the surrounding area that are perpetrating offenses that are subsequently attributed to the project. Finally, the spillover effect between public housing and neighborhood crime could be reciprocal, running in both directions. Fagan and Davies (2000) demonstrate simultaneous inward and outward diffusion effects in New York City, where the sociostructural characteristics of both public housing projects and their larger neighborhoods impact upon each other’s crime rates. At the same time, the compositional peculiarities shared by public housing and its surrounding communities serve as barriers to better understanding public housing crime rates in a comparative context. This confounding takes place on two levels. First, the unique concentration of certain socioeconomic factors in neighborhoods containing public housing makes it impossible to juxtapose, for example, projects in “good” areas against those in “bad” areas. Massey and Kanaiaupuni (1993) have shown that public housing projects in Chicago are targeted to poor, African American neighborhoods, and that the presence of public housing in turn served to intensify the concentration of poverty in these neighborhoods. Bickford and Massey (1991) found that African Americans and whites are highly segregated across housing developments within large US cities. The principal caveat of comparative research, that of “all else being equal” doesn’t apply here, because all else is almost never equal. Second, the shortage of analytically relevant controls further makes it difficult to make accurate or meaningful citywide comparison. The physical and social features of public housing differ so considerably from those found in typical residential neighborhoods that contrasting areas with and without public housing is risky at best, and may be entirely inappropriate. Much work remains to be done with regard to the untangling of these contaminating effects. While some studies have identified these larger contextual issues, very few have attempted any sort of systematic assessment. Those that have have uncovered a public housing reality

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infinitely more complicated than that illustrated by previous research. Specifically, controlling for the socioeconomic and housing attributes of the larger community may significantly reduce the criminogenic effects of public housing. Roncek et al. (1981) found that neighborhood factors such as population size, density, the proportion of primary to individual households, and racial composition were all significantly more important predictors of area crime rates than is public housing. Improving both the accuracy of crime data and theorizing about potential correlates clearly requires research designs that recognize and incorporate the intricacies of the public housing universe, an important facet of which is wrapped up in its relationship to the environment that sustains it.

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CHAPTER 3

Public Housing Neighborhoods and the Social Ecology of Crime

Given that information concerning public housing crime generally and diffusion more specifically is seriously lacking, it is hardly surprising that the existing literature provides little in the way of theoretical guidance concerning the relationship between the two. As little as we know about more concrete things such as actual incidence and prevalence, we know considerably less about the factors that might influence these rates. However, the existing literature does provide some clues as to where one might begin to construct a public housing specific theory pertaining to the diffusion of crime. At the broadest level, two factors consistently seem to be invoked in connection with both crime and the fear of crime: social and structural/ environmental. In recent years, the latter has achieved some dominance over the former. While structural theories and research have continued to develop, the social side of the equation has, beyond mere descriptives, been largely ignored. Supporters of CPTED and physical structure-based approach themselves suggest that more favorable evaluations of CPTED do not mean that social factors are any less important in explaining crime than has traditionally been assumed. Rather, they suggest that both social and environmental factors are important in accounting for crime, and that preventive policies must acknowledge the contribution of both. But the legacy of Newman has been hard to overcome. Theorizing about public housing crime began at a structural level, and it has largely stayed this way. In reviewing Rouse and Rubenstein’s report from some 25 years ago, one is immediately struck by how little formal theorizing about the role of social factors has advanced in the interceding years. But the context provided by social compositional factors is not passive. Rather, it 27

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exerts an active influence on behaviors and events in those areas. While in no way denying or attenuating the role of structural factors, the position taken here is that the contagious diffusion of violence requires explicit recognition of the interplay between public housing and neighborhood at the social level. More specifically, this research represents an attempt to integrate public housing contexts and the current resurgence of theories relating to the social ecology of crime.

Public Housing and Neighborhood On the structural level, public housing, especially large developments, constitute bounded “neighborhoods” in their own right. Even within urban neighborhoods that have themselves been segregated and isolated by processes such as slum clearance and urban renewal, public housing structures are unique, at least insofar as they are physically distinguishable from immediately adjacent areas. What is less evident is the degree to which projects constitute distinct social neighborhoods. For example, Tienda (1991) has questioned whether the social networks and patterns of interaction characteristic of public housing are sufficiently developed to warrant their identification as neighborhoods in the “social” sense. In response, recent studies provide evidence suggesting that public housing projects do vary according to social organization and, moreover, that the extent of differentiation has definite implications for corresponding rates of crime. Williams and Kornblum (1990) highlight variations in social disorganization in explaining crime rate contrasts across four public housing developments in New York City, further noting that this “social dimension” effect is independent of building design and associated structural factors. Ongoing research in New York City suggests that differences in levels of social organization and control may even be evident between buildings within the same project (Saegert and Winkel, 1997). The argument that public housing developments are analytically relevant social neighborhoods is further bolstered by research suggesting that public housing also presents variegated social contexts. Public housing, almost by definition, involves the concentration of poor people in relatively static environments (Dumanovsky, 1999). Sampson and Wilson’s (1995) contention that public housing serves as an institution for the isolation of African American families by race and class implies that public housing, at least compositionally, might

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be expected to be fairly homogeneous, so much so that distinctions between projects might be rendered irrelevant. Massey (Bickford and Massey, 1991; Massey and Kanaiaupuni, 1993) has argued quite convincingly that public housing areas have served as a vehicle to increase both the concentration of poverty and racial segregation. Still, it would be erroneous to conclude that public housing is decidedly lacking in heterogeneity. Despite factors tending to operate toward this “objective,” public housing nonetheless continues to provide a wide assortment of social contexts. Moreover, these differences in social context, like differences in social organization, have been found to influence crime rate differences across developments (Fagan and Davies, 2000). If we accept that public housing developments are neighborhoods in some meaningful sense, then we can consider whether there are specific neighborhood effects that impact crime rates. When applied to public housing, factors often used to measure neighborhood effects, such as female-headed households, unemployment, welfare dependency, and youth population, show substantial variation between developments. This allows for an evaluation of the impact of this type of ‘neighborhood effect’ within public housing developments – whether differences in the social organization of projects are related to differences in crime rates between projects. But while the relationship between public housing and surrounding environs would appear to be paramount in explaining the contagion or diffusion of violence into and out of public housing, theories situating public housing within the “criminology of place” more generally have been virtually absent.

The Criminology of Place The past decade has seen a renewed theoretical interest in the correlation between crime and “place;” in particular, in explaining differential rates across areas. This renewed interest, however, has not been confined to a single explanatory framework or trajectory. Instead, several semi-autonomous tacks are identifiable in the criminological literature. One trajectory that has gained significant credence is organized around the premise that “criminal events” represent the culmination of a series of decision points or the convergence of several requisite elements that are influenced by environmental contexts. For example, Brantingham and Brantingham suggest that the criminal event is comprised of four elements: the law, offender, target, and place.

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Their theory of place, referred to as environmental criminology, proposes the following (Brantingham and Brantingham, 1978, 1993): • individuals exist who are motivated to commit specific offenses. • given criminal motivation, the actual commission of an offense is the end result of a multistaged decision process in which an offender seeks out and identifies a target within the general environment. • the “activity spaces” and “awareness spaces” that comprise the environment emit cues about its physical, spatial, cultural, legal and psychological characteristics. • the motivated individual uses cues from the environment to locate and identify targets or victims. Thus, the environment, or place, provides the structural backcloth against which criminal events may be played out. But place may similarly establish what has been dubbed an “activity backcloth” as well. Cohen and Felson’s (1979) variant on criminal events maintains that the vast majority of criminal acts require the convergence in time and space of a likely offender; a suitable target; and the absence of a capable guardian.1 Premised on Hawley’s theory of human ecology, the routine activities approach contends that the probability of this convergence is contingent upon social structure. By implication, different areas, characterized by disparate social structures, will produce varying opportunities for convergence and crime. That the spatial (and temporal) structure of routine activities (i.e. the timing of work, school and leisure activities) plays an important role in determining the susceptibility of particular places to criminal events is underscored by hot spot theories. Most police calls for service come from especially dangerous locations or “hot spots” (Spelman, 1995). Sherman and Buerger (1989) found that crime is heavily concentrated in a small number of places. For example, only 3% of places in Minneapolis produced 50% of calls to which police responded. Disaggregated data show that this pattern of concentration also holds for subtypes of crimes, such as drugs (Green, 1995; Weisburd and Green, 1995), burglary (Johnson, Bowers, and Hirschfield, 1997), breaking and entering (Queensland Criminal Justice Commission, 1997) and alcohol-related crimes (Block and Block,

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1995). The intensive concentration of crime in repeat locations further buttresses the importance of place in criminological theorizing. Finally, an emphasis on place is also evident in Skogan’s (1990) work on the relationship between “incivilities” and crime. Skogan argues that disorder, both social and physical, acts as an instrument of destabilization and decline. Disorders initiate an iterative process that results in community change and what is referred to as the “spiral of decay.” Ultimately, disorder adversely affects a community’s ability to exercise effective control. Disorder breeds fear and demoralization, but more importantly, may have the further contagious effect of breeding more and more serious crime. Skogan’s perspective is of particular interest here, insofar as disorder and its various manifestations are held to signal a breakdown of the social order in a particular locality. While conceptualized quite generally here, the idea of social order may be analyzed more specifically. Quite apart from the perspectives noted above, the resurgence of the criminology of place has also prompted a resurrection of the social ecology of crime.

The Social Ecology of Crime Theoretical Background Although substantively very different from its predecessor theories, the social ecology of crime has enjoyed a renaissance of late. The theoretical framework for classical ecological studies of crime and delinquency was originally proposed by Robert Park in 1916. After witnessing conflict and adjustment between waves of incoming immigrants, Park was impressed how similar their “struggle for existence” was to the Darwinian conception, and how, over time, each community seemed to progress toward a state of economic and social equilibrium based on competitive cooperation among the various immigrant groups (Berry and Kasarda, 1977). He believed that, just as in plant and animal communities, order emerged in human communities through the operation of “natural” or unplanned processes such as competition, dominance, succession, and segregation. This approach viewed the human community as a dynamic, adaptive system in which competition served as the primary organizing agent. It was the spontaneous operation of market forces, not conscious design, which dictated how individuals and groups would carve out both residential and functional niches. From this premise, Park, along with Burgess,

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articulated his theory of urban growth, now known to generations of undergraduate students as concentric zones. Park and Burgess suggested that cities were characterized by five concentric zones, emanating out from the center of the city. Zone I was the central business and industrial district; Zone II was the zone in transition or slum areas, in the throes of change from residence to business and industry; Zone III was the zone of workingmen's homes; Zone IV was the residential zone; and Zone V was the outer commuter zone, beyond city limits. In a growing city, zones continue to expand, with each zone invading the next. Park and Burgess posited that the process of change starts with the concentration of industry and commerce. The presence of industrial/commercial districts affects the desirability of adjacent residential areas, such that people move out of “invaded” residential areas as soon as possible. Further, this differentiation on the basis of physical characteristics is co-ordinate with segregation of the population along economic and racial/ethnic lines. However, this segregation is not permanent: as immigrants prosper, they move out. Their places are then taken by newer immigrant groups. Heavily influenced by the human ecologists in general, and by Park and Burgess in particular, Shaw and McKay laid the foundation for the subsequent area studies conducted by the Chicago School. Their classic work, originally published in 1929, presented two important findings: first, the relative distribution of delinquency rates remained fairly stable among Chicago's neighborhoods between 1900 and 1933 despite dramatic changes in the ethnic and racial composition of these neighborhoods; and second, delinquency rates were negatively correlated with distance from the central business district, or alternatively, delinquency rates were negatively correlated with the economic composition of local communities. This laid the groundwork for their social disorganization approach. Shaw and McKay concluded that patterns of neighborhood delinquency were related to the same ecological processes (invasion and succession, residential mobility, and the concentration of economic and occupational groups into particular neighborhoods in the city) that gave rise to the socioeconomic structure of urban areas (Bursik and Grasmick, 1993a). Park and Burgess had already suggested that areas characterized by economic deprivation tended to have high rates of population turnover because they were undesirable residential areas. This rapid compositional change made it difficult for these communities to mount resistance against the influx of potentially threatening groups. Therefore, poor neighborhoods also

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tended to be characterized by racial and ethnic heterogeneity. Shaw and McKay argued that these characteristics made it difficult for these neighborhoods to achieve the common goals of its residents. Drawing on Thomas and Znaniecki (1920), they called this situation social disorganization. In short, Shaw and McKay did not posit a direct relationship between community economic status and community delinquency rates. Rather, areas characterized by economic deprivation tended to have high rates of population turnover and heterogeneity. These two processes in turn were assumed to increase the likelihood of social disorganization.

Criticisms of Shaw and McKay Given the impact of their work, it is hardly surprising that Shaw and McKay have been the subject of a wide variety of criticisms. Several of the most general points of contention have been summarized by Bursik (1988). First is the issue of levels of analysis and the disciplinary shift in emphasis that resulted from the publication of Delinquent Areas. With the publication of his now classic work, Robinson’s (1950) warning regarding the problematic nature of making individual-level inferences on the basis of aggregate data had a devastating effect on the development of social disorganization. However, Robinson did not say that aggregate level data is inappropriate for the investigation of all theoretical issues. Still, his caution became a taboo against the use of aggregate data (Borgatta and Jackson, 1980). Many researchers used Robinson to conclude that ecological models of crime were fairly meaningless (Baldwin, 1979). This sentiment resulted in an important shift in the orientation of spatial research from models emphasizing social disorganization to “opportunity models” of crime. According to Stark (1987), poor neighborhoods disappeared, to be replaced by individual kinds with various levels of family income. Most criminological theories became more social-psychological than sociological (see Erickson and Jensen, 1977; Johnstone, 1978). More recently, Short (1985) has highlighted the dangers of confusing the levels of analysis implied by various theoretical models. Macrosociological models, such as social disorganization, refer to properties of groups. That is, they assume that there are important community-level dynamics related to crime that are not simple aggregations of individual motivational processes. However, some dismiss the social disorganization perspective simply because its

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findings do not (and usually cannot) lead to predictions concerning individual behavior. Such criticisms are based on an inappropriate standard of evaluation, but are understandable given an important trend in the orientation of criminological research. Still, although the traditional emphasis on group dynamics and organization has given way to a concern with the sources of individual motivation, the group orientation of the social disorganization perspective is not in itself a valid basis for criticism. The second major criticism leveled at Shaw and McKay relates to the measurement of their key concept: social disorganization. In particular, confusion has been generated by the fact that Shaw and McKay sometimes did not clearly differentiate the presumed outcome of social disorganization (i.e. increased delinquency rates) from disorganization itself. This led to a tendency to equate social disorganization with the phenomena it was intended to explain, an interpretation clearly not intended by Shaw and McKay. Recently, attempts to clarify have been made by defining social disorganization in terms of the capacity of a neighborhood to regulate itself through formal and informal processes of social control. A more damaging critique of Shaw and McKay concerns the measurement of delinquency. As early as 1936, Robinson criticized Shaw and McKay’s use of official records, arguing that systematic biases existed in the juvenile justice system that gave rise to the differences among local community areas and that the “actual” distribution of delinquency was more evenly dispersed throughout the city. However, to date the degree to which the relative distribution of neighborhood crime rates is an artifact of police decision-making practices has not been extensively examined due to the limited availability of the appropriate data. Despite the possibility that systematic bias in police records may distort the actual spatial distribution of crime, social disorganization is still an important determinant of this distribution. Finally, Shaw and McKay have been criticized for the normative assumptions of social disorganization. The definition of social disorganization implies that the notion of consensus is a central component of the model. But, given the dynamics of power, the political ramifications of crime control, and the relativistic nature of criminal definitions, this may be appear to be insensitive to the realities of political and social life. A much more subtle normative assumption is imbedded in the Shaw and McKay model that is even more problematic: they assumed that the ecological distribution and

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movement of populations within urban areas reflect the “natural market of housing demand.” In reality, social disorganization can be manipulated by a wide variety of nonmarket forces. Skogan (1986), for example, has identified key factors of change with sources outside the local community that can affect neighborhood stability: disinvestment, demolition and construction; demagoguery; and deindustrialization.

Post Shaw & McKay Ecological Research In response to these criticisms, several authors have reformulated and reconceptualized Shaw and McKay’s approach to discerning community and neighborhood effects. Notwithstanding the methodological critiques of Jonassen (1949) and Robinson (1950), the most important “neighborhood effects” research immediately following Shaw and McKay was the work of Bernard Lander. Using 1940 census data, Lander (1954) tried to predict census tract delinquency in Baltimore. He found that only two of seven variables, percent nonwhite and percent homes that were owner occupied, were independent correlates of delinquency rate. Because these two variables went together in his factor analysis, Lander interpreted his results as supporting an “anomie,” or social instability, explanation of delinquency. Moreover, he concluded that other predictors, indicators of underlying socioeconomic status, were not fundamentally related to delinquency. The almost shocking nature of Lander’s results, which ran contrary to much statistical evidence and intuition regarding the importance of socioeconomic status, prompted a series of replications. In a more or less direct attempt at replication, Bordua (1958) used 1950 census data from Detroit and Baltimore. He cautiously concluded that, although the empirical findings were not exactly the same, Lander’s findings were essentially confirmed. However, Bordua further commented that the anomie concept as specified by Lander largely served to obscure the analysis of the causes of delinquency. The utility of the term anomie was further called into question by Chilton’s (1964) study of Baltimore, Detroit and Indianapolis. Chilton asserted that the factorial results for all three cities were equivocal with respect to the hypothesis that delinquency is closely related to a condition of anomie. Finally, Rosen and Turner (1967) concluded that Lander’s statements about the “fundamental” relationship of “anomie” and delinquency were not warranted in light of the non-orthogonal rotation procedure utilized in

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his factor analysis. In a methodological point that has largely been overlooked, Rosen and Turner contended that “community effects” research should employ methods that are more sensitive to the discovery of what they refer to as “unexpected interactions.” Ultimately, none of these early studies really advanced knowledge about community effects and delinquency beyond the work of Shaw and McKay. The Lander controversy was largely put to bed by an insightful piece by Robert Gordon (1967), who suggested that, once the methodological and statistical inadequacies of previous research were taken into account, the association between delinquency and socioeconomic status is quite unambiguously strong. Gordon’s essay was particularly important for his presentation regarding the dangers of “atheoretical partialling.” Lander’s erroneous definition of social disorganization as delinquency led him to try to identify other related community characteristics that should also be considered to represent social disorganization. Until Gordon, there seemed to be no limit as to the potential community-level variables researchers considered important. Gordon’s paper served as an initial attempt to reorient “community effects” research. Specifically, his discussion about the differential sensitivity of census measures to the lower ends of their distributions aptly demonstrated that the “kitchen sink” approach to specifying ecological models was entirely inappropriate, and that some level of empirical sophistication was required before ecological studies could progress. Gordon concluded that, barring surprising new data, there should no longer be any question about the relationship between ecological variables and delinquency. Unfortunately for ecological studies, a general paradigmatic shift was occurring in criminological theory, one in which individual level explanations such as control theory were pushing more structurally-based theories out of the limelight. When the ecological approach began to resurface, the nature of community effects being investigated had changed. Whereas ecological studies originally located the social structural base of delinquency at the level of the local community or social area, researchers had come to think of social location more in terms of society-wide status than as a phenomenon linked to the economic conditions of local areas, and correspondingly emphasized lower-class culture or lower-class social status in their formulations. This shift in emphasis revealed the increasing influence of social-psychological concepts in the explanation of delinquency; it also demonstrated the introduction of survey methodology into the field. In the decades following Shaw and

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McKay, most of the important developments in delinquency theory in some way introduced social-psychological mechanisms as pivotal concepts of the explanatory apparatus: i.e. Sutherland’s (1947) excess of definitions favorable to violations of the law; Cohen’s (1955) reaction formation; and Cloward and Ohlin’s (1960) strain. And while each of these theories explicitly acknowledged that the formulations were meant to account for delinquency in the slum areas of large cities, the impact of their ideas on the field was to divert attention away from environmental or community determinants of delinquency. Subsequent formulations of the impact of social position on delinquency increasingly located effects at the level of the status position of the individual or family rather than in the status characteristics of a neighborhood, community, or social area (Johnstone, 1978). This trend was reinforced by the increasing use of sample surveys. This methodology has had an individualizing effect on concepts of social location: when interviewing individuals it is easier and probably more valid to ask about their own economic circumstances than about their impressions of economic conditions in the surrounding neighborhood or community. The net impact of these trends was to divert attention away from the community as the generating context of deviant behavior. The Return of Social Ecology: Compositional and Contextual Effects The resurgence of social ecology coincided with, and was invigorated by, researchers and theorists articulating more sophisticated perspectives on the operation of areal effects. A key emergent debate suggested that variations in crime rates across social areas might be generated by either a contextual or a compositional mechanism. In keeping with the focus on individuals, the compositional proposition contents that differences in crime rates in different areas are a result of the aggregate characteristics of the individuals who inhabit the areas, such as might occur if a community recruits crime-prone people. Wilson and Herrnstein (1985), for example, comment that a neighborhood may have more crime because conditions there cause it or because certain kinds of neighborhoods attract persons predisposed to criminality. In contrast, the contextual proposition suggests that the social organization of an area influences the individuals who inhabit it, such as might occur if a community loses control over its inhabitants. This subtler understanding of the potential effects of neighborhoods

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provided the genesis of renewed theorizing that once again elevated the community as a central analytic construct. In a “comeback” paper of sorts, Johnstone (1978) referred to the above trends as examples of a kind of “individualistic fallacy,” a frame of reference which ignores the possibility that actors who share the same ascribed status may behave differently in different types of social environments. Johnstone argued that the two dimensions of social location (individual and community) could only be understood in relation to each other, and that the impact of social position on delinquency was basically a contextual one. His investigation centered on the intersection of the actor's status position and the status characteristics of the surrounding community on patterns of delinquent expression. Johnstone’s results suggested that a relative deprivation explanation showed the best fit with all of his measures of delinquency, except violence. In doing so, Johnstone initiated a continuing research trajectory that focuses on the integration of individual and contextual effects. Johnstone asserted that straight areal status models were inadequate for explaining delinquency. He concluded that contextual interpretations seemed to account for several varieties of delinquency which did not fit well with conventional stratification (family) or ecological frames of reference. The situation of being a “have not” in a community of “haves” represents a context which can produce high levels of utilitarian delinquency, as well as contranormative behavior which may be aimed primarily at middle-class authority. The criminogenic nature of “proximity” was also identified by Block (1979), who argued that neighborhoods where poor and middle class families live in close proximity are likely to have higher crime rates than other communities. The complexity of ecological effects implied by Johnstone’s work was subsequently taken up by more theoretically and methodologically vigorous research. In one of the earliest attempts to address emerging issues in neighborhood research, Roncek et al. (1981) suggested that areal differences in crime rates and victimization potential were contingent upon the interaction of social composition and the features of particular residential environments. They maintained that previous research had neglected the effects of several aspects of urban organization, including household composition, features of the housing environment other than overcrowding and density, and the interactions between the characteristics of the residents and housing environments. Cities are not random collections of individuals, but are structured organizations of people and environments. The structure of this

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organization ultimately affects the amount of crime in different areas by altering levels of social control. Thus, individual and organizational dynamics are actually complementary components of any comprehensive theory of crime, and a fuller understanding of criminological phenomenon requires the incorporation of both (Reiss, 1986). Although the growing distinction between compositional and contextual effects served to assist in reasserting the relevance of neighborhood effects, by the early 80s it had become apparent that the division was increasingly problematic. In a move away from the misleading dichotomy, Simcha-Fagan and Schwartz (1986) suggested that the analysis of contextual effects necessarily included consideration of both community-level characteristics and individuallevel compositional characteristics. However, few researchers have taken up the methodological challenge of combining both levels of analysis in explaining crime and deviance (Bursik and Grasmick, 1993a), and those that have have not proceeded a uniform manner and have failed to produce consistent results. On one hand, Simcha-Fagan and Schwartz took a comparative approach that assessed the relative contribution of factors across levels. They found that while the amount of variance uniquely explained by community-level factors was attenuated when individual-level compositional factors were controlled for, community effects nonetheless remained significant. Gottfredson and Taylor (1986) were successful in increasing the predictive power of their recidivism models through the inclusion of environmental characteristics, but the improvements were principally the result of the interaction of environmental and offender characteristics. In contrast, Gottfredson, McNeil and Gottfredson (1991) were unable to demonstrate significant social area effects in relation to individual delinquency. Thus, while studies seem overall to suggest that the neighborhood does have a significant effect on the probability of criminal behavior that is independent of the effects that can be attributed to the personal attributes of the residents of the community, “the magnitude of this effect relative to that which can be attributed to individual-level processes is not entirely clear and cannot yet be resolved on the basis of existing research” (Bursik and Grasmick, 1993a:29). Without denying the importance of individual-level factors, the social ecology approach to crime and delinquency clearly tries to avoid overreliance on compositional or “kinds of people” approaches to criminality, maintaining instead that neighborhoods are more than

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aggregates of individuals (Tienda, 1991). Since the mid-1980s, variations in crime rates between places, plus continuing debates concerning the growth, consolidation and perpetuation of the urban underclass, have focused research on the coincidence of urban poverty, race and crime. Consequently, social ecological orientations have attempted to specify more precisely the nature of relevant contextual effects. While much of this research owes a theoretical tip of the hat to Shaw and McKay, considerable effort has been expended on attempts to refine social disorganization theory, and make it more applicable to contemporary urban contexts. Where Shaw and McKay focused on poverty, heterogeneity, and mobility as elements of social disorganization, consideration is now afforded racial segregation, economic inequality, economic segregation, and family structure as they relate to and help inform a theory of informal social control.

CHAPTER 4

Informal Social Control, Crime Diffusion, and Public Housing

To this point, several arguments have been advanced concerning the nature of the relationship between public housing, its surrounding environs, and crime. First, there is little reliable information on the incidence and prevalence of crime in public housing. Given this paucity of data, it is not surprising that few attempts have been made to develop a formal etiology of public housing crime and its diffusion. If, as posited in the preceding chapters, public housing projects are neighborhoods in some meaningful sense, then the social ecology approach to understanding crime might have some utility in these areas. In particular, elements derived from Shaw and McKay’s original work on social disorganization provide the basis for a theory of informal social control in and around public housing. This chapter considers the substantive form of an informal social control theory of public housing crime. Social control, defined as an area’s ability to regulate itself, is conceptually consistent with the tenets of social disorganization. However, perhaps owing to the legacy of Shaw and McKay and the desire to be more contemporary, the term social disorganization has fallen out of favor. Instead, en vogue reference is made to levels of social integration, organization, and control. Many of the traditional ideas remain intact and direct tests of Shaw and McKay continue (Smith and Jarjoura, 1988; Sampson and Groves, 1989), but in light of criticisms cited in Chapter 2, it has become commonplace for researchers to distance themselves from the perceived difficulties of past. Moreover, new considerations such family dynamics have been added to enhance the original social disorganization model. For these 41

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reasons, informal social control must be considered on its own merits, as distinct from its (albeit very influential) predecessor.

The Bases of Social Control in Urban Communities The attempts at reconceptualizing and reoperationalizing social disorganization theory that emerged in the mid- to late-80s were deeply indebted to antecedent studies that identified and detailed a more social understanding of communities and neighborhoods. Originally, urban sociology was less than optimistic about the social control potential of neighborhoods. Capturing the zeitgeist of the 1930s, Wirth (1938) painted a bleak picture of urban areas predominantly characterized by impersonal, superficial, and transitory interactions. Wirth maintained that the segmentation of everyday life that necessarily accompanied the population growth, density, and heterogeneity of urban areas resulted in the development of secondary relationships. However, Wirth’s approach came to be challenged by more systematic approaches to urban organization. The assault on the “anomic urban dweller” began with Janowitz’s (1951) contention that “urban community” means more than the dense aggregate of human settlement, that the emphasis on impersonality and disorganization characteristic of earlier approaches fails to accurately describe urban social organization. Rather, the “local urban community appears to be a complex of social interactions which tends to identify a local elite and local institutionalized patterns for controlling social change” (Janowitz, 1967:200). However, Janowitz was careful to temper his approach with the proviso that individual attachments are necessarily bounded in the amount of social and psychological investment they represent, resulting in communities of “limited liability” (211). Later, Janowitz’s criticism of Wirth formed the basis for a more systemic model of community attachment, wherein . . . community organization is treated as an essential aspect of mass society. It has ecological, institutional, and normative dimensions. The local community is viewed as a complex system of friendship and kinship networks and formal and informal associational ties rooted in family life and on-going socialization processes. At the same time it is fashioned by the large-scale institution of mass society. It is a generic structure of mass society, whose form, content and effectiveness varies

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widely and whose defects and disarticulations reflect the social problems of the contemporary period (Kasarda and Janowitz, 1974:330). The development of more systemic conceptualizations of community informed more sophisticated theorizing concerning the areal basis of social control. This was particularly evident in a series of ethnographic studies conducted in the 1960s and 70s in “ghetto” or “slum” areas. What appeared to Shaw and McKay as social disorganization was recast more as differential social organization. “Ghetto” communities were not so much disorganized as organized in distinct, nontraditional ways that demonstrated a unique and internally consistent structure and logic. The trouble with the slum district, some say, is that it is a disorganized community. In the case of Cornerville such a diagnosis is extremely misleading . . . Cornerville’s problem is not lack of organization but failure of its own social organization to mesh with the structure of the society around it (Whyte, 1955: 272-273). Following Whyte, researchers attempted to specify more precisely the nature of “ghetto” social organization. Compared to more “mainstream” or traditional communities, “ghetto” areas were identified as being more “personalistic” in nature. More than most social worlds, perhaps, the streetcorner world takes its shape and color from the structure and character of the face-to-face relationships of the people who live in it. Unlike other areas in our society, where a large portion of the individual’s energies, concerns and time are invested in selfimprovement, career and job development, family and community activities, religious and cultural pursuits, or even in broad, impersonal social and political issues, these resources in the streetcorner world are almost entirely given over to the construction and maintenance of personal relationships (Liebow, 1967:161). Still, Liebow was wary of attributing social order to these “personal communities. ” Rather, he maintained that the “effortless sociability”

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characteristic of streetcorner relationships contributed to the intrinsically transient quality of daily life in these areas. Liebow implied that neighborhoods were poorly equipped to exercise social control and that control was instead largely confined to immediate personal networks. Conversely, while Anderson (1978) concurred with the essential precariousness of the social order of the streetcorner, he afforded greater efficacy to long-term social investments in “extended primary groups.” Instead of regarding neighborhoods as essentially amalgamations of autonomous personal communities, Anderson argued that “groups,” though distinct, continuously renegotiate their places within the social order through social interaction. Individuals are thus seen acting collectively, interpreting and defining one another; they make distinctions between and among those with whom they share this social space. They are seen fitting themselves in with one another’s expectations and collective lines of action, each one informed by a sense of what actions are allowed and not allowed to different kinds of people in varying sets of circumstances (Anderson, 1978:4). Despite the heterogeneity of “ghetto” areas, residents are bound together, if by nothing else, then by their common experiences. But the “community” moniker is normally reserved for geographically identifiable areas that also manifest indigenous social institutions. Insofar as “slums” are bereft of such institutions, they are considered to be only partial communities. There is, however, a shared consciousness in these areas that transcends their lack of social-structural coherence. Proximity should never be confused with social awareness, of course, but it does tend to produce both conduct that references that of other members of the community and a “shared perspective toward the ghetto condition.” A set of conventional understands of ghetto life is developed, and it is generally recognized to be the property of the entire community. This alone gives the ghetto dwellers a kind of Durkheimian solidarity. Since they realize that their common perspective is not shared with the world outside their community, it also marks them off from the surrounding society in their self-definition. This contributes to making the ghetto in some ways a united community. If it does not have

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overarching institutions of control which are its own, at least it has an overarching perspective (Hannerz, 1969:139). The idea of highly personalistic relations, then, is not anathema to community and the exercise of social control therein; certain neighborhoods clearly lack indigenous institutions, but they have compensated by adopting more flexible approaches to conduct norms and values. “Organization” is not universal, and the level of organization in any given community must be evaluated according to its own standards. Neighborhoods still cling to conventional morality and established codes of conduct to the extent that it is feasible to do so. To the extent that it is not, conventional norms are not rejected but differentially emphasized or suspended for widely understood reasons. Taken out of context, many area social arrangements may seem an illusory denial of the beliefs and values of the wider society. But seen in more holistic terms, residents are bent on ordering local relations where the beliefs and evaluations of the wider society do not provide adequate guidelines for conduct (Suttles, 1968:4). Thus, in order to better understand the role of the community in social control, more differentiated models of community are required (Suttles, 1972). Instead of dichotomizing the existence or nonexistence of “community,” it is more appropriated to determine what elements of “community” are present, and to what degree (Hunter, 1974). Public housing neighborhoods, like others, vary in the degree to which they are proficient in sustaining viable levels of informal social control. Moreover, these variations are hypothesized to influence not only public housing crime, but also patterns of crime diffusion into and out of adjacent areas. But if, as a systemic theory of organization would suggest, neighborhoods are capable of acting as agencies of social control (Bursik and Grasmick, 1993a), what specifically accounts for the relative abilities of disparate neighborhoods to do so? While mobility continues to be a key element of informal social control, racial segregation, economic inequality, economic segregation, and family structure have acquired increasing currency in the contemporary context of urban communities.

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Informal Social Control Mobility Of Shaw and McKay’s original primary concepts, mobility has changed the least conceptually. Shaw and McKay argued that areas characterized by economic deprivation tended toward high rates of residential turnover because they were undesirable areas; the consequent mobility or transience itself led to heterogeneity, adversely affecting community solidarity and its ability to act collectively. Kasarda and Janowitz (1974) similarly afford considerable currency to residential attachment, suggesting that length of residence is the key exogenous factor influencing local community attachment, behavior, and attitudes. Mobility in the form of rapid population turnover diminishes a neighborhood’s ability to exercise internal control insofar as the development of primary relationships is impeded when local networks are in a perpetual state of flux (Berry and Kasarda, 1977). Moreover, the deterioration of friendship and kinship bonds and formal and informal associational ties assists in explaining the paucity of control institutions in these areas. Rapid population turnover can impede the emergence of strong conventional neighborhood organizations and institutions which might encourage obedience with the law (Kapsis, 1978). In communities where many residents are disinterested and hope to leave at the first available opportunity, internal control institutions may prove very difficult to establish (Kornhauser, 1978). An integrated social system is critical to internal control in that it provides a high degree of consensus in norms, values, and goals; cohesiveness, or social solidarity; and a sense of belonging among persons living in the community. Crutchfield et al. (1982) suggest that geographic mobility is detrimental to internal control through its effects on the integrity of the social system. Given the expenditure of energy necessary to maintain special contacts over significant distances, there is an inverse relationship between the population mobility and the degree of permanence or depth of feeling that characterizes social relationships in a community. Moreover, elevated levels of mobility are likely to enhance feelings of anonymity in an area, contributing to an overall impression that individuals are more apt to be ignored or escape notice by both the authorities and local residents. While population mobility is an important exogenous variable in the breakdown of social control, it may also operate as endogenous

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component of a process that Stark (1987) refers to as deviance amplification. That is, the relationship between mobility and internal control is not necessarily unidirectional, but rather, may be reciprocal in nature. Stark identifies an iterative process through which density, poverty, mixed usage, transience and dilapidation lead, in turn, to moral cynicism among residents, increased motivations to and opportunities for crime, and diminished social control. As social control deteriorates, deviant individuals and activities are attracted to a neighborhood. Consequently, the least deviant residents are driven out of the area, and further reductions in social control are experienced. Thus, while mobility is deleterious in the absolute sense of instability (proportion of people who maintain strong relational ties within the community), it also exerts a relative influence on social control in terms of the potential contrast between the people entering a neighborhood and the people leaving the same area. Racial Segregation As parts of a larger complex, it only makes sense that the composite elements of Shaw and McKay’s social disorganization are theoretically consistent with one another. There is, for example, a degree of conceptual symmetry between mobility and heterogeneity: higher levels of mobility increase the probability that neighbors will have diverse characteristics, thereby inhibiting the creation of new friendship and relationship networks (Angell, 1974). In this way, heterogeneity, like mobility, is negatively related to social integration. In contrast to mobility, which results in instability and limits the depth of affective relational networks, neighborhood heterogeneity in restricts the breadth of such networks (Merry, 1981). However, heterogeneity may be becoming less and less relevant in the contemporary urban context. There is considerable empirical evidence suggesting that urban neighborhoods may be increasingly characterized by racial segregation (Massey and Denton, 1987; Massey and Hajnal, 1995). This is particularly true of public housing developments, the majority of which are predominantly or solely populated by minority residents. Without denigrating the conceptual utility of heterogeneity, then, it would appear that sociostructural changes occurring since Shaw and McKay require a change in theoretical focus in the direction of segregation. One of the weaknesses of social disorganization in its original form was the implicit assumption it made regarding the distribution of residential opportunities. Specifically, Shaw and McKay’s supposition

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that the distribution and movement of populations within an urban area reflected the “natural” market of housing demand seriously neglected a variety of political determinants of neighborhood residential makeup (Bursik, 1989). For example, Hirsch (1983) has convincingly argued that the contemporary or “second” ghetto is not simply a legacy of the past, but rather, is a dynamic institution that is continuously being renewed, reinforced, and reshaped through political decision-making processes. In Chicago, whites responded to residential succession in the 1940s and 50s through violence or through the application of political power in the guise of “urban planning.” When and where it was possible, the later was the response of choice: only in communities lacking sufficient political and legal alternatives did residents resort to violence to maintain the status quo. This legacy of discrete domination has continued more or less unabated, as inherently political decisions continue to shape the residential landscape of urban neighborhoods. If, as a political economy perspective would surmise, the value of a neighborhood is shaped by its connection to the larger commodity system, the crux of peoples’ problems in poor and segregated areas is that they are often seen as “damaging” to exchange values (Logan and Molotch, 1987). Ghetto neighborhoods are particularly unable to defend themselves from outside influences, their special vulnerability stemming from residents’ low standing in the larger systems of economic and political power. Accordingly, urban structures may be seen as a mirror of economic and political interests, and the segregation of class and race as largely a function of the underlying orders of power (Bartelt et al., 1987). This has been evident in the case of public housing, which is most often placed in the most unstable neighborhoods and those areas least able to influence where it will be constructed. Housing is most likely to be constructed in areas characterized by high rates of instability; after controlling for this effect, the composition of the neighborhood adds no predictive power. Most importantly, market value continues to be a poor predictor of the location of new public housing units . . . (N)eighborhoods characterized by such construction tended to be those least able to organize an effective resistance (Bursik, 1989:115). Since public housing projects are not typically a valued community resource, more stable and well-organized neighborhoods

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are able to operate within existing power structures to have proposed public housing sites relocated. There are several ways in which segregation may directly affect areal crime rates. At the level of formal social control, Smith (1986) maintains that variations in the police use of coercive authority between neighborhoods are at least partially attributable to differences in neighborhood racial composition, noting that the police are more likely to exercise coercive authority toward suspects encountered in nonwhite neighborhoods. There may also be some indication that the lower levels of “social distance” found in highly segregated communities contributes to some forms of crime. If social and physical distance between races reduces opportunity for contact and therefore interracial crime (Blau and Blau, 1977), then it stands to reason that decreases in areal heterogeneity are likely to provide fewer opportunities to associate with unlike persons. So, as interracial crime declines, intraracial crime is more likely to climb. Shihadeh (with Flynn, 1996; with Maume, 1997) provides evidence that inner-city segregation has given rise to unique structural impediments, which are in turn strongly related to violent crime rates among African Americans. But segregation may also have less direct effects. As was the case with mobility, the association between segregation and areal crime rates may be mediated through social control. It’s not only that neighborhoods are or are not racially segregated: the processes that ultimately result in segregation are also of concern, insofar as these processes further disrupt social control in already fragile communities. In the context of public housing, there is evidence that the introduction of public housing into already tenuous areas further exacerbates the instability of these neighborhoods, leading the dramatic reductions in internal control capabilities (Bursik, 1989). This concentration of instability in what may be referred to as “underclass” urban areas is particularly problematic to the degree that it is increasingly infused with race and ethnicity. Urban underclass areas are predominantly inhabited by minorities; very few whites live in conditions proximate to those experiences by African Americans (and Hispanics) in these areas (Peeples and Loeber, 1994). In urban areas, segregation has made ethnicity almost synonymous with the underclass, so that when we talk about the effects of social dislocations on the urban underclass, we are speaking almost completely of African Americans and Hispanics. Neighborhoods with dangerously low levels of social control are not randomly distributed, and through processes of segregation, minority

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residents find themselves disproportionately vulnerable to breakdowns of informal control and its consequences. Economic Deprivation The link between crime and what Shaw and McKay originally specified as “poverty” has become perhaps the most vexing of sociostructural concepts. This is, in no small part, attributable to the growing conflation of race with economic considerations. One of the lasting remnants of social disorganization is the presumed, albeit indirect, relationship between neighborhood crime levels and economic deprivation. Specifically, under the social disorganization framework, the socioeconomic status of a neighborhood corresponds with its crime rate only to the extent that it makes the probability of residential turnover and racial heterogeneity more likely (Bursik and Grasmick, 1993b). However, significant changes in economic conditions, including poverty becoming more spatially concentrated in inner-city neighborhoods (Lynn and McGeary, 1990), have culminated in a more detailed consideration of the nature of economic deprivation. In the main, the debate over the specification of the effects of economic deprivation has tended to revolve around the distinction between absolute and relative deprivation. On one hand, poverty may be operationalized as some standard of living or well-being relative to a comparative baseline. For example, poverty has been measured as the percentage of a population below the poverty line, or the percentage of households with a cumulative income of less than $5000. These more or less objective circumstances are referred to as absolute poverty or deprivation. Absolute poverty may be related to crime because of the dehumanizing nature of the former; the struggle for daily sustenance is held to hinder the development of "civilized" norms and values. An alternative operationalization of poverty emphasizes the degree of economic inequality within local communities. Relative deprivation is based on the dispersion of income throughout the population and contingent on the subjective assessment of economic position and status in comparison with that of others. According to Gurr (1970), relative deprivation corresponds with crime and violence through frustration-aggression mechanisms. In light of the emergent fusion of race and economic circumstance, Blau and Blau (1982) present a theory of inequality that is essentially relative deprivation with a more specific recognition of racially-based economic disparities. The Blaus argue that variations in the rates of

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urban criminal violence largely result from differences in racial inequality in socioeconomic conditions, and that once economic inequalities are controlled for, absolute poverty is no longer a significant influence on these rates. The effect of inequality on crime is held to be indirect, mediated through its negative impact on neighborhood solidarity. Ascriptive socioeconomic inequalities undermine the social integration of a community by creating multiple parallel social differences which widen the separations between ethnic groups and between social classes, and it creates a situation characterized by much social disorganization and prevalent latent animosities (Blau and Blau, 1982:119). Owing perhaps to the overarching precedence currently afforded race, the inequality concept has served to reframe the debate over economic deprivation. In contrast to earlier distinctions, deprivation is more often characterized now as either absolute poverty - referred to simply as poverty – or inequality. Despite voluminous research, the relative efficacy of poverty and inequality remains unclear. For example, the comparative merits of each have been hotly contested in the homicide literature. In defense of inequality, Blau and Golden’s (1986) methodologically improved reexamination confirmed the Blau’s original findings. Rosenfeld (1986) determined that while absolute poverty was associated with homicide, the relationship between inequality and homicide was stronger. As well, Carroll and Jackson (1983) asserted that inequality produced differential opportunities for victimization and consequently higher rates of homicide. Conversely, Messner (1982; 1983) and Sampson (1985) have suggested that inequality is not a critical factor in the explication of homicide rates, and several studies have concluded that the direct effect of absolute poverty is more pronounced. Curry and Spergel (1988) found a relationship between gang-related homicides and poverty levels, while Taylor and Covington (1988) confirmed this result for homicides more generally. A variety of reasons have been advanced to account for researchers’ inability to resolve the debate over the most appropriate specification of economic deprivation. On a theoretical level, Wilson’s (1987) thesis concerning the flight of affluent families from inner-city neighborhood predicts that inequality should feature less prominently

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in the explanation of homicide as the level of inequality within these urban areas diminishes over time. With the geographic concentration of the urban underclass, inequality becomes less relevant. As well, there is some concern that the global or between-race conception of inequality that has been the standard since the Blaus fails to adequately capture the nature of relative deprivation. Because the perception of inequality depends on comparisons made with an appropriate “frame of reference” or “comparative reference group,” Harer and Steffensmeier argue that “a within-race measure of inequality is more consistent with relative deprivation theory and with the implied micro-to-macro linkage that underlies the expected relationship between economic inequality and rates of violent crime” (1992:1037). African Americans do not necessarily use whites as referents for feelings about themselves; as African American neighborhoods become increasingly more segregated, “significant others” are more and more likely to be African American and fewer comparisons are likely to be made with white society. Disagreement over the precise effects of economic deprivation is also maintained through a variety of unresolved methodological issues. First, neighborhoods have been defined in several ways, presenting significant operational problems relating to the level of analysis (Patterson, 1991). The level of aggregation of neighborhood (block group, census tract, SMSA, etc.) can dramatically affect research results. By way of example, both Bailey (1984) and Messner (1982) have voiced concern about whether widely employed measures of community - SMSAs - provide “relevant frames of reference in the assessment of economic well-being.” Second, inconsistency has also been evident in various measures used as indicators of both poverty and inequality, resulting in rather low levels of comparability between studies. As well, few of the studies on the economic covariates of homicide have addressed the multicollinearity problems identified by Land et al. (1990). Finally, numerous concerns have been raised regarding the dependent variables in this body of research. O’Brien (1983) has pointed out the difficulties of using UCRs in crossjurisdictional contexts. Perhaps more importantly, in light of the position taken by Harer and Steffensmeier above, homicides have generally been undifferentiated. Williams and Flewelling (1988) have noted the importance of disaggregating homicide more generally, while Sampson (1985) has illustrated the necessity of disaggregating homicide by race as well.

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In order to specify economic deprivation properly, it is important that it not be removed from racial segregation. In urban underclass areas, the key is not poverty or inequality per se, but economic marginalization and social isolation, and the relationship between the two. It is not just poverty or inequality that must be considered, but racially specific poverty and inequality. Furthermore, “deprivation” is not merely economic; it is social as well. Taylor and Covington (1988), for example, suggest that the increasing social isolation of the urban underclass leads to a heightened sense of relative deprivation, which in turn increases the likelihood of violent crimes. Thus, in addition to their independent effects, racial segregation and economic deprivation must also be appraised in conjunction with one another. Economic Segregation The proposition that economic marginality and racial oppression can have a determinative effect on urban underclass neighborhoods is not novel; almost 30 years ago, Rainwater (1970) contended that they were the two primary forces to be indicted for the creation and maintenance of the “lower-class Negro community.” He suggested that underclass African Americans were economically marginal because they were effectively barred from securing a rewarding place in the economic structure of the city, and that racial oppression exacerbated relative deprivation in almost every aspect of their lives (1970:370). Despite these early words of warning, however, it was not until the publication of Wilson’s The Truly Disadvantaged in 1987 that the “economic segregation” of inner-city African Americans was brought into sharper relief. As noted earlier, inner-city neighborhoods once exhibited notable features of social organization, including “a sense of community, positive neighborhood identification, and explicit norms and sanctions against aberrant behavior,” (Wilson, 1987:3). Starting in the 1960s, however, increasing rates of social dislocation touched off significant changes in the organizational capacities of these areas; by the 80s, these changes had become catastrophic. In today’s inner-cities, racial inequality has become a shorthand way of signifying a tangle of innercity pathologies such joblessness, teenage pregnancies, out-of-wedlock birth, female-headed families, welfare dependency, and serious crime that are unevenly distributed by race. Wilson posits that these dislocations, which are especially relevant in large public housing

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developments, are the result of pervasive and deleterious economic and social transformations. First, Wilson (1987:39) suggests that urban minorities have been particularly vulnerable to structural economic changes, including the shift from goods-producing to service-producing industries; the increasing polarization of labor market into low-wage and high-wage sectors; technological innovation; and the relocation of manufacturing industries out of the central cities. A primary consequence of these structural transformations is a mismatch between the jobs that are available in cities (especially northern metropolises) and the educational requirements of those jobs. Since the 70s, Northern cities have simultaneously experienced the greatest decline of jobs in the lowereducational-requisite industries and significant increases in minority residents who are seldom employed in the highgrowth industries (Wilson, 1987:41). This mismatch is implicated in higher rates of unemployment and joblessness among central city African Americans; so too, however, is the general economic slowdown evidenced in the 80s. Manufacturing industries, traditionally an important source of African American employment, have been particularly hard hit by the slack economy. Finally, the employment mismatch and general economic malaise have been reinforced by demographic shifts – such as the increasing laborforce participation of women and the coming of age of baby boomers – which have produced a “labor surplus environment” that is notably hostile to individuals with low job skill and educational levels. In addition to economic changes, inner-city neighborhoods have similarly experienced grave social transformations. While there has been a massively disproportionate increase in African Americans living extreme-poverty areas (in comparison to whites), the nature and class structure of these poverty areas has also changed (Wilson, 1987). The large out-migration of nonpoor African American middle and working classes has left behind a much more highly concentrated poverty population, and deprived these areas of an important “social buffer” and source of mainstream role models (Wilson, 1987). Basic institutions such as churches and schools cannot remain viable if their base of economically stable families deteriorates, and the lack of interaction with stably-employed persons challenges the

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meaningfulness of employment and education. Wilson suggests that the most appropriate term to capture the difference between earlier periods and today in the inner city is concentration effects. The disproportionate concentration of the most disadvantaged segments of the urban Black population has created a social milieu that is significantly different from the environment that existed in these communities several decades ago (Wilson, 1987:58). Contrary to the media’s emphasis on the crystallization of a ghetto culture of poverty, then, the key theoretical concept is social isolation. Culture is relevant only to the extent that it is a response to structural constraints. In concert with the economic mismatch and residential segregation noted above, the concentration effects of social isolation culminate in what Jargowsky (1996) generically refers to as economic segregation. The social and economic marginalization of largely African American, inner-city neighborhoods has had a devastating effect on local social control capabilities (Wacquant and Wilson, 1989). This lack on control may, in turn, produce persistently elevated rates of crime and deviance, further exacerbating the loss of control. Macrosocial patterns of residential inequality give rise to the social isolation and ecological concentration of the truly disadvantaged, which in turn leads to structural barriers and cultural adaptations that undermine social organization and hence the control of crime (Sampson and Wilson, 1995:37). In response to changing economic and social contexts, then, the heterogeneity and poverty elements of the original social disorganization theory have been significantly altered. But a more comprehensive approach to informal social control must also draw on aspects of social ecology not originally envisioned by Shaw and McKay. An important facet of contemporary urban underclass areas not reflected in the 1930s and 40s is the catastrophic fragmentation of family structures and the subsequent effect of this attenuation on crime.

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Family Structure The dominance of social and economic isolation has tended to shift attention away from family structure as a source of variation in crime rates. However, given that stable family structures provide the cornerstone of the systemic theory of social organization, family dissolution will have negative consequences for community social bonds. If Kasarda and Janowitz (1974) are correct in their assertion that local community is a complex system of networks “rooted in the family life and ongoing socialization processes,” a widespread breakdown in family ties may well lead to the constricting of social bonds, community sentiment, and organizational participation (Sampson, 1987). High levels of marital and family disruption thus decrease formal social controls to the extent that they impede community participation. But Sampson (1985) argues that a more important consequence of family dissolution is the attenuation of informal controls. First, family disruption is related to informal social control through mechanisms of surveillance. In areas with cohesive family structures, parents often assume responsibility for monitoring not just for their own children but for other youths in the community as well (Sampson, 1986). Adults are more likely to keep an eye on children in integrated neighborhoods, and the whole community eyes strangers carefully (Skogan and Maxfield, 1981). But in neighborhoods where family structures have been fractured, surveillance is more problematic. Single parents have less time and energy to devote to the monitoring of children’s activities, and, bereft of significant interpersonal contract, people are less willing to intervene in the affairs of others. This “willingness to intervene” is closely related to guardianship, a second concept that links family instability and informal control. Neighborhoods populated by intact families have better developed networks of relationships, such that residents are prepared to intercede on the part of one another. Conversely, neighborhoods destabilized by family dissolution typically exhibit less of this guardianship-type behavior. In their “routine activities” approach, Cohen and Felson (1979) argue that guardianship is a principal element in the crime equation, and that criminal victimization is more probable in circumstances where capable guardians are lacking. Thus, through their deleterious effect on guardianship, the disruption of family structures contributes to increased opportunities for crime and victimization. Sampson (1985, 1986) and Sampson and Groves (1985) have demonstrated that neighborhood family structure has significant and

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substantively important influences on both crime rates and levels of personal criminal victimization even when the effects of powerful theoretical variables such as race, poverty, inequality, mobility, and density are controlled for. Sampson (1986) argues that the significance of family structure does not negate the importance of considerations such as race or inequality, but rather, that inequality, race, and family disorganization are causally linked. For example, the effects of social isolation and economic segregation in African American communities may be mediated through pronounced family disruptions in these areas.

Informal Social Control in Public Housing The elements of the informal social control theory of crime illustrated above have special saliency in public housing. In fact, public housing generally has come to epitomize the hallmark traits of neighborhoods deficient in informal social control. Segregation, while not endemic only to public housing, is even more pervasive in these neighborhoods than in inner-city areas generally. Evidence of segregation across housing developments nationally has been confirmed in New York City (Dumanovsky, 1999). Concerning segregation, Bickford and Massey posit that “public housing represents a federally funded, physically permanent institution for the isolation of African American families by race and class, and must therefore be considered an important structural constraint on ecological areas of residence” (1991:1035). Moreover, the segregation of pubic housing has physical aspects as well; developments tend to be located in disadvantaged areas that offer little in the way of services, and that are literally “cut-off” from major centers of employment because of physical distance. In a similar vein, living in the projects has become de facto shorthand for economic deprivation; in New York, eligibility criteria alone virtually guarantee a residential population that is extremely disadvantaged, even in comparison with inner-city neighborhoods more generally. Mean income levels are exceeding low in housing projects, and welfare receipts are very high. With regard to economic segregation, Sampson and Wilson (1995) suggest that there have been negative consequences arising from deliberate policy decisions to concentrate minorities and the poor in public housing; specifically, the cumulative result of economic segregation is such that, given the same objective socioeconomic status, minorities face very different environments in which to live, work, and raise their children. Finally,

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public housing has also become almost synonymous with the breakdown of family structure. Like segregation and inequality, some of the highest rates of family disruption are found in public housing. NYCHA statistics indicate that almost 50% of families in public housing are headed by a single parent. The one facet of public housing in New York City that seems, at first blush, to contradict the elements of informal social control is residential tenure. Because of very long waiting lists, public housing is a very hot commodity. Not surprisingly, individuals and families show extreme reluctance to leave once they have secured what has become increasingly rare accommodations. As a result, public housing is not marked by rapid population turnover; quite the opposite, the average tenure in New York is a remarkable 17 years. However, the potentially positive effects of this durability have been attenuated in two significant ways. First, extended residency has not fostered sociability in projects in the same manner that it has in other neighborhoods. It seems that the concentration of social pathologies has reduced the benefits of residential stability. Second, and perhaps more important, the location of public housing in arguably the most deprived areas of the city also serves to militate against tenure. Because of their unseemly reputations, public housing neighborhoods make attractive targets and are subject to invasion from a wide spectrum of unsavory characters drawn from adjacent areas. Despite low rates of population turnover, anonymity is still prevalent and residents remain less likely to engage in supervisory behavior.

CHAPTER 5

Crime Diffusion as a Sociostructural Process

Even if public housing and its surrounding environs are intimately connected, as the preceding chapters posit, theoretical explication about the nature of the diffusion process is still required. Intuitively, the mechanics of diffusion are most easily understood as operating at an individual level. Not surprisingly, diffusion studies have traditionally been tilted in the direction of more micro-level explanations. However, this conception of diffusion is limited insofar as it disregards the aggregate context of human behavior. Any adequate model of the diffusion of social behavior, including crime and violence, must necessarily address both levels. Without denying or denigrating the significance of individual level processes, the intention here is to focus instead on the macrostructural aspects of diffusion.

Foundations of Diffusion The word diffuse is derived from diffundere, Latin for “to spread over.” Phenomenologically, it has come to refer to the spread in space, or to the acceptance in a social system, over time, of some specific term or pattern (Kwasnicki and Kwasnicki, 1996). Perhaps the earliest social science applications of diffusion were theories of social imitation, such as that advanced by Gabriel Tarde. Tarde argued generally that inventions diffuse through processes of imitation, and more specifically that social change results from microlevel penetration through the imitation of beliefs, desires, and motives. With the formulation of his ‘Laws of Imitation,” Tarde secured a place as “one of the founding fathers of diffusion research” (Kinnunen, 1996). Tarde’s orientation 59

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was buttressed by more contemporary work, including Ryan and Gross’s seminal study of the spread of hybrid corn innovation in Iowa (1943) Their inquiry established the basic paradigm for early diffusion research, including the categorization of adopters into early, early majority, late majority, and laggard. Owing to this pioneering work, and perhaps because the spread of ‘innovations’ lends itself so nicely to discussions of diffusion, much of the literature has been dedicated to the diffusion of innovations; other phenomena are most often discussed within this broad framework, with changes made on an ad hoc basis. Innovations became entrenched as the home territory of diffusion with Rogers’ Diffusion of Innovation (with Shoemaker, 1962; 1995). This treatise spawned the “classical diffusion model,” the elements of which implicitly emphasized the saliency of individual level factors and fostered a micro-level theoretical and methodological bias in diffusion research. Rogers synopsizes diffusion as “the process by which an innovation is communicated through certain channels over time among the members of a social system (1995: 10).” Most of these elements are straightforward. An innovation, for example, is simply an idea, practice, or object that is perceived as new by an individual (1995: 11). Note that the idea, practice, or object in questions need not actually be novel: after sufficient time has elapsed, rediscovery is perceptually the same as original use. All innovations are not created equal; rather, they may be distinguished according to their relative advantage, compatibility, complexity, trialability, and observability. These dimensions, in turn, influence an innovation’s differential attractiveness for potential diffusion. Given the existence of an innovation, the essence of diffusion is the exchange of information facilitated by communication channels. For diffusion to be realized, those individuals with knowledge about or experience with the innovation must find a way to connect with those still lacking in knowledge or experience. Communication channels are conduits, the means by which messages are transmitted. Mass media channels are the most obvious, and ubiquitous, communication portals, arguably constituting the most efficient means for disseminating knowledge or awareness of an innovation. However, the element of uncertainty inherent in innovations suggests that interpersonal channels are more effective in persuading acceptance. In evaluating the feasibility of an innovation, people are more likely to rely on the subjective assessment of someone who has already adopted the innovation than on media hype or even scientific pronouncements. The

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centrality of interpersonal networks in the diffusion process underscores its highly social nature. The time facet highlights that diffusion is indeed a process. Successful innovation must proceed through a series of stages, of which knowledge and persuasion are only the beginning. At some point, an individual must decide whether to adopt or reject the innovation. If adoption is chosen, it is followed by both implementation and confirmation. Reminiscent of Ryan and Gross’s adopter categories, the element of time similarly recognizes that individuals progress through the process at varying rates. Rogers, however, utilizes a slightly modified scheme that divides adopters into innovators, early adopters, early majority, later majority, and laggards. Finally, at the systemic level, the significance of time is reflected in the disparate rates of adoption across different innovations, or for the same innovation across different social systems. Social systems, broadly defined as “a set of interrelated units that are engaged in joint problem-solving to accomplish a common goal” (1995: 23), comprise the boundaries within which diffusion takes place. Several aspects of a social system impinge upon the diffusion process. Social systems are, for example, populated by different opinion leaders and change agents. As innovators are rarely in a position to influence people on a large scale, the spread of information and advice is often contingent upon those who are in a more central communicative position. The roles of opinion leaders (exercising more informal authority) and change agents (functioning more at the agency or institutional level) are especially pertinent in early stages of the diffusion process, before critical mass is reached. Social systems also provide normative underpinnings, established patterns of conduct which classify behaviors on a continuum ranging from encouraged to tolerated to prohibited. But from a macro-theoretical perspective, social systems are primarily noteworthy insofar as they structure social relationships and communication. Unfortunately, the sociostructural component of diffusion has, in comparison to the other dimensions, been neglected. While the subordinate status of broader contextual considerations has doubtlessly resulted from the pursuit of a more individualistic agenda, structural approaches have also been hampered by theoretical and methodological complexity as well. It is a rather tricky business to untangle the effects of a system’s structure on diffusion, independent from the effects

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Most macro research on diffusion presumes, although often fails to acknowledge, social influence processes operating at the micro level (Rosero-Bixby and Casterline, 1993).

Diffusion in Criminology As the stranglehold of (technical) innovations has been relaxed, substantive interest in the diffusion of social phenomena has concomitantly mushroomed. The diverse universe of processes surveyed through the lens of diffusion ranges from the epidemiological spread of AIDS (Wallace et al. 1993, 1994, 1997; McCoy et al, 1996; Loytonen, 1994; Drucker, 1990) to family planning and fertility (Rosero-Bixby and Casterline, 1994). Religious studies have invoked diffusion in conjunction with: the “Southernization” of religion in the US (Shibley, 1991) and support for the “Moral Majority” (Tamney and Johnson, 1988); Zen Buddhism (Finney, 1991); non-conventional religions (Melton, 1993); the ordaining of women (Chaves, 1996) and patterns of church adherence (Blau, Land and Redding, 1992). Cultural studies have examined the diffusion of fashion (Ragone, 1996) and sports (Callede, 1993; Guttmann, 1991), as well on the broader impact of diffusion on culture itself (Carley, 1995). In the realm of political science, explanations for events as disparate as joining the Nazi Party (Ault and Brustein, 1998) and transitions to democracy (Wejnert, 1993) have invoked diffusion, as have more specific policy studies concerning the reform of abortion regulation prior to Roe (Mooney and Lee, 1995) and state-level amendments to AFDC eligibility criteria (Soule and Zylan, 1997). Diffusion-based research on other areas of the world has included the emergence of the lesbian and gay movement in Argentina (Brown, 1997), the interplay of protest and repression during the Iranian revolution (Rasler, 1996), coups in Sub-Saharan Africa (Lutz, 1989), industrialization and economic growth in Belgium (DeBrabander, 1985), waves of coal mining strikes in France (Conell and Cohn, 1995), and the dissemination of democratic values in postunification Germany (Rohrschneider, 1996). The spread of the streaking fad (Aguirre, Quarantelli, and Mendoza, 1988) and the shantytown protest movement on US college campuses (Soule, 1997),

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as well as the gentrification of New York’s East Village (Smith et al., 1989) all share a thread of diffusion in common. Even the attribution of names in medieval English rural society is held to have diffused (Postles, 1995). Despite its variegated use, diffusion had also generated relatively little interest within criminology. While the idea that crime and violence are candidates for diffusion is hardly original, only recently has it been stalked with vigor. After Meier’s (1968) suggestion that the epidemic of violence was the “last great plague of the cities,” Fischer (1978) posited that the “cultural phenomenon” of violent crime was spreading to rural areas more than 20 years ago (see also Smith and Huff, 1982). In a similar vein, Stahura and Huff (1986) maintained that the diffusion of “transitional populations” helped to account for the spillover of urban crime into suburban areas. The assumption of crime’s inevitable spread has not gone unchallenged. Zevitz and Takata (1992), for example, concluded that, contrary to popular conception, gangs in regions surrounding Chicago were not the result of sprawl. Even though they shared a cultural affinity with their metropolitan analog, regional gangs were the products of local factors. In the main, however, the evidence produced by these early studies confirmed that crime spreads (see Mehay, 1977; Fabrikant, 1979; Hakim et al., 1979). Unfortunately, the theoretical foundation for this diffusion has been conspicuously underdeveloped or altogether absent. More current criminological diffusion studies have tended to remain rather undifferentiated. Allusions to the “diffusion” of given phenomena are not uncommon, but this loose semantic usage is not often supported by systematic analysis. Where research or theorizing touches on diffusion at all, the focus is usually on the outcome of the alleged diffusion and not on the process of diffusion itself. For example, the finding that marijuana use, once largely confined to marginalized subcultures, has become prevalent among a much broader social spectrum (particularly the middle class), is characterized as diffusion. Hathaway (1997) suggests that the diffusion of drug usage has resulted in increasing tolerance by law enforcers and the general public alike, without explaining the process itself or even verifying that the spread of drugs was actually the outcome of an identifiable diffusion process. In similar ways, Crank (1994) posits that the diffusion of community policing reflects “an institutional process aimed at restoring legitimacy to the police,” while Merzagora (1990) argues that the diffusion of organized crime stems not from “pathology-based” factors but from congruence in the ideologies of

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organized crime and its “host societies.” Both perspectives are admittedly intriguing, but they nonetheless demonstrate a limited view of “diffusion.” The point here is not to critique the theoretical bases of these works, especially given that none is specifically focused on diffusion. Rather, these cases are merely illustrative of how the indiscriminate use of “diffusion” has become all too commonplace. The spread or expansion of drug use, organized crime, or community policing is not, in and of itself, proof of diffusion in a meaningful sense. At the very least, some attempt must be made to identify the process or processes through which diffusion is hypothesized to occur. The approaches cited above were deficient insofar as they lacked such processural specifications. However, process “theorizing” is, in turn, only an intermediate step in adequately assessing diffusion. Even where research has advanced explanations for the diffusion of phenomena, credible empirical evidence is too often absent. Again, drug market research is telling. Rengert (1996) contends that, owing to the outward diffusion of use and trafficking caused by drug profits, popular drug enforcement policies premised on containing the drug trade in certain “declining” neighborhoods are inherently ineffective. In other words, the outward spread of the drug trade is a consequence of its profitability. This echoes Gross and Hakim’s (1982) argument that newer opportunities for wealth accumulation drive the spread of property crime to peripheral areas. Unfortunately, as intuitively appealing as these assertions may be, neither Rengert nor Gross and Hakim provide much in the way of support: a potential diffusion process is articulated, but its efficacy remains unknown. Criminological research has, in short, predominantly focused on establishing a) that diffusion occurs, or b) why diffusion occurs, in the sense of broad contextual interpretations. In need of further development is a more dynamic treatment, one that more precisely specifies the mechanism of crime diffusion. Criminology is hardly alone in this regard, as diffusion research generally suffers from dissatisfaction about its theoretical base. A decade ago, Ausubel (1991) suggested that, despite considerable empirical support, we did not feel as though we explained the phenomenon well, nor understood its mechanisms. This situation has changed some in the interceding period, but the steps currently taken continue to be best described as tentative. To be sure, progress has been realized. But more can and must be done.

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While interest in the spread of crime to suburban and rural areas was piqued in the late 1970s, criminologists were, in the main, still attending to what has been dubbed the distance decay of crime gradients from the Central Business District (Brantingham and Brantingham, 1984), or the gravity model (Smith, 1976). Empirical evidence suggested that crime overwhelmingly transpired within a very short distance of the perpetrator’s residence. Not unlike patterns of shopping and recreation, individuals demonstrate a pronounced preference for committing crimes close to home. Even today, this finding is largely uncontroverted. Distance is inversely related to expected returns on the grounds that: • the longer the distance, the less familiar the criminal is with crime opportunities, escape possibilities, and police practices. • because communities located further away from the central city are usually homogeneous in their socioeconomic characteristics, the criminal, a stranger in that locale, becomes more noticeable. • explicit transportation costs have a deterring effect for all types of journeys (Deutsch et al., 1984: 451). Without making specific reference to rational choice theory, Deutsch et al. essentially advanced a perspective couched in familiar terms. Succinctly put, criminals “face diminishing productivity the farther they operate from the CBD (Deutsch et al., 1984: 457).” Taking for granted that criminals are primarily consigned to urban cores, it logically followed that these areas would be most adversely affected by crime. Even accepting evidence of diffusion, crime rates, as ever, continued to concentrate in central cities and decline toward the periphery. So, what happened? If the spread of crime is constrained as a function of distance, how did diffusion ever become a noteworthy concern? While the manifestation of distance decay would, at least initially, seem antithetical to diffusion, mechanisms explicating the migration of crime to suburban and rural settings were premised on changes to both residential locality and geographic mobility. Simply put, criminals’ residences eventually dispersed outward, in concert with increased spatial mobility (Lenz, 1986). Together, these changes greatly enhanced criminals’ sphere of accessibility and catalyzed broader diffusion, such that adjacency to central cities such as

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Philadelphia (Gross and Hakim, 1982) or Chicago (Brown, 1982) promoted higher crime rates than those experienced in less convenient localities. Yet accessibility is not evaluated apart from other considerations. For instance, all things (such as distance, ease of entry and egress) being equal, criminals are more inclined to ply their trade in areas already hurt by high crime rates (Deutsch et al., 1984). An extension of the rationality posited above, this preference reflects an assessment of apprehension potential. But it also suggests that purely spatial dynamics are tempered by external factors, and hints at interplay between the geographic and sociostructural facets of diffusion.

Guns, Gangs, and Drugs: the Diffusion of Criminal Violence While compelling, these studies were limited by their analytic scope. Specifically, they overwhelmingly focused on property crime. With the epidemic of violence that began in the mid-80s and peaked in the early90s, attention turned from burglary, theft, and larceny to assaultive behaviors and homicide. This changing substantive context has been accompanied by a conceptual shift regarding the operational mechanism of diffusion, as the movement of criminals is eclipsed by the dispersion of the phenomenon itself. The nature of violence mandates a new approach, one that affords less weight to physical mobility and more to communicative and cultural elements. At the same time, the proposed scale of diffusion is also being reconsidered, as urban-suburban, urban-rural, and interregional research is supplemented by intra-city studies. This new brand of inquiry has produced a relatively consistent theoretical framework predicated on the nexus of gangs, guns, and drugs, but equivocal empirical results. Crack Perhaps the most popular explanation for the explosion of violence that plagued the late 80s is the advent of crack and its attendant problems. From its inception, crack was treated differently than other drugs. In New York, compared to the enforcement efforts against powdered cocaine just a few years earlier (1983 and 1984), cases involving the possession and sale of crack were more often changed and processed as felonies, more apt to entail pre-trial detention, more prone to be transferred to State supreme court, and more likely to culminate in a jail or prison sentence (Belenko et al., 1991). That dispositional

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disparities were manifest even controlling for arrest charge severity and prior criminal record highlighted the widespread perception that crack posed dangers in excess of those associated with previous drug phenomena. Akin to previous drug epidemics, crack was immediately vilified as the “worst of all,” the most perilous of all drug threats. Despite the paucity of evidence available at the time, crack was rapidly and irrevocably wed to assumptions concerning increases in violent crime (Goldstein et al., 1989). Because crack was so propitious an issue in the prevailing conservative political climate, politicians and the media ignored or misrepresented information, choosing instead to proffer drug war propaganda. Reinarman and Levine (1989) argue that all of the hype surrounding what they deem the crack scare essentially served a scapegoating function, as crack came to dominate discourse relating to almost all social ills, including those that had existed well before its appearance. We use the term “drug scare” to designate periods when antidrug crusades have achieved great prominence and legitimacy … (D)uring drug scares all kinds of social problems are blamed on one chemical substance or another. Drug scares have typically linked a scapegoated drug with a subordinate group – especially working-class immigrants, racial or ethnic minorities, and youth. This latest drug scare has tied cocaine, and especially its derivative “crack,” with inner-city Black and Hispanic young people (Reinarman and Levine, 1989: 537-8). Typical of drug scares, response policies were driven not by empirical corroboration, but by media portrayals, political considerations, and public pressure (Fagan, 1990). But what of the reality? Inclinations to infer a generalized correlation between crack and violence to the contrary, the reality of any such connection would presumably be subtler than this. Goldstein (1985), for example, proposed a tripartite scheme that has become the archetype for gauging drug-related violence. Reflecting the long held supposition that doing drugs makes people excitable, irrational, and more violent, pharmacological effects are those associated with drug use. In contrast, economic compulsive violence refers to the crimes that users engage in to sustain their expensive addictions. The relatively low cost of crack is offset by short duration of the “rush,” which must be constantly replenished. Finally, systemic violence is that attributable

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to the drug distribution system. Whether drug use is implicated in violence is open to dispute, but the etiological contribution of systemic factors is not: predation is intrinsic to crack market participation. There is constant exposure to violence. Transactions are highly vulnerable to exploitation. Duplicity on the part of customers and sellers is so common as to be institutionalized. Overall, instability reigns, and predatory arrangements thrive between actors at all levels (Jacobs, 1999: 43). Political rhetoric and public perceptions aside, support for pharmacological violence is sparse. With the notable exception of alcohol, virtually no research has concluded that drugs account for a substantial proportion of drug-related violence. In a study of adolescent drug users in Miami, Inciardi (1990) reported that only 5.4% had participated in pharmacological violence. Some studies have affirmed drug-induced violence, but they have been marred by methodological weaknesses ranging from small, specialized samples to a lack of relevant control variables. When adequate control are introduced, direct relationships between drug use and violence are generally attenuated or eliminated completely (Collins, 1990; see also Johnson et al., 1995). Economic compulsive violence, on the other hand, has tended to garner more support. Individuals addicted to costly drugs such as cocaine and heroin occasionally resort to violent crime, typically robbery, to generate money (Ball et al., 1981; Chaiken and Chaiken, 1982; Johnson et al., 1985). Although considerably less developed, the literature on crack seems to bare this out. The Inciardi (1990) study cited above found that 59.1% of the adolescent drug users had engaged in robberies, and that the majority did so for the purpose of purchasing more drugs. However, Inciardi also revealed that, in the overall venue of financing drug activities, robbery paled in comparison to shoplifting, stolen goods offenses, and burglary. Drug users do commit violent crime to feed their habits, but they are significantly more likely to resort to nonviolent property crime. Of much more genuine concern has been systemic violence linked to the crack trade. Although available evidence asserts that the natural history of crack use differed little from that of previous drug epidemics, selling patterns for crack were distinct (Fagan and Chin, 1989). Prior to crack, organized crime groups and networks controlled the drug trade. This domination, in turn, produced a reasonably stable marketplace. Violence existed, but its indiscriminant use by individuals

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was discouraged in the strongest terms. Instead, it was normally sanctioned only on behalf of the group, reserved as an instrument of internal control. The crack manufacturing process, cheaper and more efficient than that of freebase cocaine, helped loose the reigns of this sovereignty. Crack was marketed at a low unit cost in a rock or pebble form that was concealed and ingested. Its crystalline appearance conveyed an image of purity. The ingenious production and marketing strategy for crack gave it the appearance of a cheaper (albeit shorter) “high” from a purer form of cocaine (Fagan and Chin, 1990: 10). The introduction of this new, highly popular product created unprecedented levels of demand. The expansion of the drug economy eventually outstripped the capacity of established distribution networks and engendered new opportunities for street-level drug selling for new groups and individuals. Start-up costs were removed as an impediment, as entry-levels roles now required only modest capital investment (Fagan, 1992). But all of this entrepreneurial spirit was purchased at the cost of stability. With the decentralization of drug markets, peaceful enterprise quickly devolved into normative violence (Hamid, 1990; Goldstein et al., 1989). Competition between rival drug sellers led to defensive clashes over territory. Transactions became more tense and unpredictable: by carrying both drugs and cash, sellers were also prime robbery targets. Deregulation similarly served to embolden employees, thereby escalating violence as a means of disciplining the rank and file. Given the dangers of the crack trade, it was more appealing to those individuals willing to be exposed to risk and use violence. This selfselection process more or less ensured the perpetuation of violence. Gangs According to popular mythology, the violent nature of the industry itself was exacerbated by the involvement of gangs. Government and law enforcement, spurred on by the media, almost universally subscribed to the belief that gangs had centrally positioned themselves in the crack market. When the press and police first alerted the public to the crack phenomenon in 1984, anecdotal evidence was employed to illustrate that the crack market was the dominion of African American street gangs. Within a short period of time, crack had reached

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“epidemic” proportions, and anecdotes had become accepted as facts confirmed by various federal agencies (Klein and Maxson, 1994). … California is home to one of the most dangerous and menacing developments in drug trafficking, the large scale organized street gang … The Los Angeles gangs are radiating out from the areas where they originated … One of the most frightening aspects of California street gangs is their willingness to direct their violence at each other, at the police, at members of the public – at anyone who stands in the way of their operations (Attorney General of the United States, 1989: 33-35). Reciprocally, the infusion of crack was blamed for fueling the imperialistic spread of gangs (Taylor, 1990). Widespread though these claim were, they were largely rejected by scholarly research. There was virtually no empirical support for characterizations tying street gangs to systematic drug trafficking. In reviewing what they call “the hyperbole of gangs and crack,” Klein and Maxson (1994) refuted the pillars of gang-drug mythology. Gangs, with their haphazard organizational structures and low levels of cohesiveness, were far from “tailor-made” for crack distribution; they were poorly suited to “control” street-level distribution. Gangs are undeniably aggressive, but their violence has always been mostly expressive, as opposed to the instrumental violence emblematic of the drug trade. The two are not readily or easily interchangeable. Gangs did not exported their crack structures across the country, and even law enforcement has come to seriously question the existence of “instrumental” or “drug” gangs. None of this denies that individual gang members were immersed in drug trafficking. It may even be that certain elements within a gang were devoted more toward drug enterprises. But the portrayal of monolithic gang dominance was clearly problematic. Crack thrived without gangs in New York (Fagan and Chin, 1989) Miami (Inciardi, 1990), and Detroit (Mieczkowski, 1990). Rather, the stereotype of gangs and drugs is yet another example of how concern, bordering on alarm, over a social problem encourages misconstrued public rhetoric and can create a moral panic (Moore, 1990). The intractability of the drug problem and the failure to win the “war on drugs” in the US fostered the need for bogeymen, enemies who could be seen to be

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being brought under control and neutralized (Collins, 1990). Gangs were obviously an expedient focal point for politicians, who didn’t risk alienating any significant constituencies, and law enforcement officials, who were able to argue for large budget increases. However, the tendency of the media, politicians, and law enforcement to mix gangs and drug distribution into one large “ball of wax” to the contrary, these phenomena largely proceeded independently of each other (Spergel, 1995). Guns If the relationship between crack and gangs was more spurious than sustainable, the same could not be said about the impact of guns. Just as there was little doubt that the introduction of crack increased rates of violence, so too was gang violence taken for granted. Street gangs may not have been pivotal players in the emergent crack business, but their violent fingerprints were nonetheless unmistakable. And while crack distribution injected new motives for drug-related violence, gang violence remained spontaneous and impulsive, responsive to immediate threats to self or status. Why then the proliferation of violence, especially lethal violence? For that matter, the same question may be asked of crack. Even stipulating that crack distribution was more dangerous than previous drug enterprises does not account for why it was more lethal. The common denominator, the explanation for escalating drug and gang violence, is guns. As an explication of lethality, the greater availability of guns coupled with enhanced “firepower” transcended both gangs and drugs. At the risk of offending the NRA and descending into the quagmire that is gun control discourse, the balance of evidence strongly indicates that the primary factor in the increase in youth homicides in the mid- to late-80s was greater access to handguns by youth. The juvenile age bracket is significant because the dramatic rise in overall homicide rates is wholly attributable to this group: during the period in question, the number of homicides committed by older offenders actually declined. Thus, discussions of gun violence invariably turn on younger offenders, as they were disproportionately affected by increases in homicide. Blumstein and Cork (1996) identified the drug market recruitment of juveniles as the probable cause of emergent homicide trends. As demonstrated earlier, the dangerous nature of drug enterprise essentially makes weapons, specifically guns, a prerequisite for doing business. The drug trade

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precipitated neighborhood arms races, as other youth in connected networks (i.e. going to the same schools and living on the same streets as the dealers) felt compelled to follow suit. Whether the guns were acquired for protection or as a status symbol, their presence elevated the “stakes of the game,” as confrontational situations escalated into homicide. Lethal violence was facilitated by the seeming recklessness with which males teenagers, never known for peaceful dispute resolution, appeared ready to use guns (Blumstein and Cork, 1996). Operating as a parallel process, the convergence of gangs and guns further compounded the spiral of violence. Ultimately, the rising tide of violence could not be reduced to the effects of crack, or gangs, or even to guns. Rather, it was the symbiosis of guns with both gangs and the drug trade that drove up homicide rates. Crack markets and youth gangs contributed both directly, through the behaviors of their participants, and indirectly, by serving as key conduits for the diffusion of guns (Cohen and Tita, 1999). The ascendance of the trade brought with it guns. These guns diffused to gangs, which dispersed guns further. And so it went. But it was not the mere availability guns; firepower too was implicated in the spike in violence. It is hardly a stretch to assert that violence levels increase concomitant with access to lethal technologies such as semi-automatic weapons and handguns larger than .32 caliber (Koper, 1997). As gangs have been able to secure more sophisticated and powerful weaponry, the intensity and extent of violence has increased dramatically … (A)lmost every sophisticated weapon is accessible to gang members. They can legally purchase many sophisticated weapons (particularly semiautomatic guns that can easily be made fully automatic) at any gun store. Those weapons that are not available through retail outlets can be purchased on the lucrative black market in guns (Jankowski, 1991: 172). Depending on the nature of the technology in question, the rise in violence can be catastrophic, as evinced by the Los Angeles gang wars in 1987-88. ATF records revealed that the black market had come to include military firearms and the TEC-9 assault gun (Stewart and Alexander, 1989), and there was no reason to presume that their acquisition was restricted to gangs. More guns with greater firepower were attainable for gang member and nongang youth alike (Fagan,

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1996). As if to underscore the importance of guns in the violence equation, their diminished use was cited as a material factor when youth homicides inevitably dropped in the mid-90s (Cohen and Tita, 1999).

Social (Dis)organization and the Diffusion of Violence As compelling as the crack-guns-gangs triumvirate is as explanation for the diffusion of violence, it feels incomplete just the same. More precisely, it seems spatially and temporally wanting. That crack was in some respects novel, and quantitatively different than previous drug epidemics, does not convincingly account for why communities had such a difficult time adjusting to this threat: they had, after all, successfully absorbed prior incursions from drug enterprises. Why did crack remain immune from assimilation? And what of gangs? Gangs have been present in the US for over 200 years, well before Thrasher introduced them as a criminological curiosity, and have always been associated with some discernible level of violence. Did the gun availability really tip the scales enough to precipitate the surge in violence? The role of guns, while substantial, was surely not determinative. Easier access to guns does not inexorably lead to profligate bloodshed. Absent from our nascent theory thus far is a consideration of context: the diffusion of violence necessarily implicates broader social, political, and economic forces. New York, like other metropolitan areas in the mid-80s, was in the depths of a restructuring that had devastated neighborhoods both economically and socially. The effects of this destabilization were concentrated in inner cities and other historically neglected communities. In terms of employment, these areas witnessed the decimation of opportunities in the legitimate economy throughout the 70s. Jobs, particularly in manufacturing, migrated to the suburbs and other areas of the country. Nonwhite residents were excluded from a constricting labor market on a massive scale, as the loss of blue-collar and clerical jobs primarily deprived African-Americans of traditional avenues for financial sustenance and social mobility (Fagan, 1992). Increasingly, people in these communities were forced to depend on unregulated labor markets for employment and income (Kasarda, 1992). Given that drug dealing has always been a vital part of the illicit

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economy, it naturally followed that, as illegal endeavors became more indispensable to community life, so too did drug enterprises. These, then, were the circumstances into which crack emerged. With the intensification of poverty and social disorganization, crack became not only the most lucrative employment available in inner cities neighborhoods, but one of the few existing job opportunities period. That drug markets were a primary source of income and status was demonstrated in Chicago (Padilla, 1992), Detroit (Taylor, 1990), East Los Angeles (Moore, 1992), Milwaukee (Hagedorn, 1994) and Philadelphia, (Anderson, 1990). By assuming such an integral economic position, crack inverted what had been a traditional pattern of integration. Until the 1980s, drug market were eventually integrated into neighborhood cultures (Fagan, 1992). Hamid (1990), for example, has detailed how marijuana markets were gradually incorporated into the culture of Caribbean immigrant communities in Brooklyn and Harlem. In contrast, the crack trade proved resistant to assimilation, instead consuming local economies and providing new opportunities in neighborhoods where much legitimate economic activity had all but ceased. When drug markets are subsumed within their communities, sporadic violence does not threaten their stability and is more easily contained. But when drug enterprises are the predominant vehicle for earnings, the mediating effect of neighborhood structures is severely undermined. The abilities of neighborhoods to regulate residents’ behavior were further compromised by parallel damage to local systems of social control. As predicted by Stark’s (1987) theory of deviance amplification, inner-city areas were caught in a cycle that produced problematic levels of isolation. As legitimate jobs and opportunities migrated, so too did the middle-class residents who had served both as anchors in the neighborhood and bridges to the “outside world.” With their flight, communities experienced a dramatic loss of role models and connections to work. This hemorrhaging of social capital (Coleman, 1988) hastened the inflow of drugs and installed drug sellers as role models for local youth. Economic segregation, tainted with racial overtones, cultivated a concentration of poor minority people in inner cities. Residents became increasingly isolated, with predictably deleterious consequences for neighborhoods’ capacity to “shape the lives of their residents.”

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When social norms and values develop in a homogeneously poor context, void of material and social inducements from conventional norms, the ties of the poor to the social contract are attenuated, and deviance is a logical, and perhaps inevitable, adaptation (Fagan, 1999: 161). Physically, the insular nature of inner cities was reinforced by the depletion of housing stock and retreat of basic commercial services. Wallace (1991) evinced the “hollowing out” of these areas, an expanding pattern of “community meltdown” that decayed minority neighborhoods and seriously disrupted mechanisms of social control. At work in these communities were mutually reinforcing feedback processes of physical disintegration and forced displacement which shred personal, domestic, and community networks, thereby further eroding social organization. Together, physical and social isolation effectively blocked out external influences, cultivating instead norms and values that were skewed and distorted by poverty and inequality (Fagan, 1999). With few conventional processes to counter it, crack markets become institutionalized. Not coincidentally, this same dearth of economic and social cohesiveness similarly aided and abetted the entrenchment of street gangs. In terms of economics, just as crack selling had become a prominent feature of the informal trade in inner cities, so too had gangs insinuated themselves into illicit labor markets. In the perception of some gang members, crime became interchangeable with legal work (Sullivan, 1989). Gangs were able to compete with, and in some instances even replace, the vanishing unskilled labor market as a principal cache of economic opportunity. Moreover, as the availability of licit work contracted, “normal” gang career trajectories were upset. Whereas members had traditionally evolved out of gang life and assumed conventional jobs, these exit pathways were either absent or unattractive (Fagan, 1999). The continued attenuation of legitimate prospects enticed members into protracted affiliations with the gangs, solidifying members’ allegiance to both the gangs and illegal means of employment. The coeval symmetry of street gangs and drug markets extended to social organization, as the lacuna in neighborhood controls also had implications for the fortification of gangs. In structurally decimated areas, where long-standing sources of socialization and regulation had

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been profoundly constricted, gang members, like drug dealers, effectively functioned as de facto authorities. In neighborhoods where gangs were active and influential participants in community life, their influence quickly expanded to fill this void. Their influence as the dominant informal social control and socialization force outweighed the influences of schools, the licit economy, and legal institutions (Fagan, 1999: 157). The answer, then, to why street gangs and crack distribution escalated violence so precipitously in the mid- and late-1980s is found in the socioeconomic isolation and the devastation of bases of informal social control that characterized inner-city neighborhoods. The diffusion of crime and violence was grounded in collective conditions, specifically intensifying neighborhood deterioration, economic disadvantage, and social disorganization. Bereft of their traditional regulatory mechanisms, these communities were unable to stem the diffusion of gangs, drugs, and guns. With the dissolution of informal social control, they essentially fell under the aegis of gang members and drug sellers, and the violence intrinsic to these groups spread unabated. In a perverse way, violence, once subject to controls, was transformed into a means of control. But rather than assisting the community, the violence only prompted deeper disintegration.

Wither Individual Mechanisms? This chapter began with the contention that appropriate models of social diffusion must necessarily incorporate both individual and structural level considerations. Owing to the relative neglect and underdevelopment of the latter, the preceding discussion has primarily attempted to sketch out the macrostructural facets of diffusion and emphasize the centrality of social disorganization in the diffusion of crime and violence. But individual level mechanisms of diffusion are by no means straightforward in the criminological context; consequently, some elucidation of micro-level processes is similarly required.

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Social Networks To begin, the specification of diffusion mechanisms requires that a distinction be made between external and internal sources of diffusion. External sources, most notably the mass media, account for diffusion into a population. It is difficult to overestimate the influence of the media on the introduction, dissemination, amplification, editing, and metamorphosis of information. This is especially true for crime, which is both newsworthy and a key constituent of popular culture. However, their power notwithstanding, media messages and portrayals are less convincing as explanations of diffusion within a population. Diffusion studies have traditionally assumed the superiority of face-to-face interactions in persuading others to adopt (and therefore spread) particular ideas. In this regard, internal sources are more salient. Internal diffusion processes operate via information transmitted across channels of interpersonal communication, or social networks. Conceptually, interaction networks bridge analytic levels, linking the individual and the structural. As a consequence of this pivotal role, there is no shortage of speculation about the precise operation of social networks as conduits of diffusion. Classic diffusion studies took as their starting point the supposition that influence flows along the lines of close social relations. According to this cohesion perspective, frequent interactions facilitates and promotes considerable exchange of information about the character, motivations, and effects of diffusing practices (Strang and Soule, 1998). Adopting new ideas or assimilating new information entails risk, to the extent that inherent limitations on direct experiential knowledge compromise calculations of costs and benefits. People manage this uncertainty by drawing on others to define a socially acceptable interpretation of the risk (Burt, 1987). Social contagion is, by this logic, an outgrowth of physically and socially proximate individuals using one another to cope with ambiguity. The seminal example of social contagion predicated on cohesion is Coleman, Katz, and Menzel’s Medical Innovation, a study of how tetracycline, then an unfamiliar antibiotic, found acceptance among a select group of Midwest physicians in the mid-1950s. Coleman et al. concluded that informal professional discussions between physicians produced the social diffusion of tetracycline. Confronted with the need to make a decision in an ambiguous situation – a situation that does not speak for itself – people turn to each other for cues as to the structure of the situation.

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Crime, Neighborhood, and Public Housing When a new drug appears, doctors who are in close interaction with their colleagues will similarly interpret for one another the new stimulus that has presented itself, and will arrive at some shared way of looking at it (1966:118119).

Implicit in the cohesion approach is the notion that problem solving advances principally within a cooperative, mutualistic framework. Intuitively appealing, this orientation may nonetheless underestimate the extent to which social relations may be characterized by rivalry and conflict. In addressing this deficit, structural equivalence highlights competition over collaboration. Both cohesion and structural equivalence are contingent upon proximity, but whereas the former regards “nearness” or “closeness” as corresponding to social similarity, the latter views it more in terms of relational patterns. “Structurally equivalent people occupy the same position in the social structure and so are proximate to the extent that they have the same patterns of relations with occupants of other positions” [emphasis added] (Burt, 1987:1291). From this perspective, the key question is not, “Who are my friends and colleagues?” but rather is, “Who forms my most significant frame of reference?” Individuals of higher, lower, or different statuses are irrelevant in terms of this evaluative scheme, as structurally equivalent actors only attend carefully to one another; they pay closest attention to those possessed of the same status, because they cannot afford to fall behind. Once the occupants of [an individual’s] status begin adopting, [he or she] is expected to follow suit rapidly in order to avoid the embarrassment of being the last to espouse a belief or practice that has become a recognized feature or occupying [his or her] status (Burt, 1987:1294). In contrast to the collegiality proffered by cohesion, structural equivalence portrays social contagion as emanating from a patterned, high-stakes game of “keeping up with the Joneses.” Both cohesion and structural equivalence explanations represent variations on a theme, founded as they are on the practical importance of strong ties. The two differ in the extent to which these ties are fostered by cooperation or competition. But, suppose that the strength of interpersonal connections is not the key construct. Granovetter (1973), in particular, has posited that new information is more likely to

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travel farther via weak ties. Groups of actors with strong interpersonal relationships have little new to report to one another; for the most part, they tend to have access to the same pool of information. Still, even the “tightest” of social circles tend to include less invested members, those on the periphery who might best be characterized as exhibiting weak ties to the group. Viewed in a positive light as “casual acquaintances” or more negatively as “hangers-on” or “wannabes,” these more marginal relations serve as bridges that facilitate communication across groups that would otherwise remain disconnected. This, in turn, allows ideas and information to traverse greater social distances, to more readily diffuse. The concept of “Connectors” who span an array of social climes is a crucial feature of the Law of the Few, one of Gladwell’s three rules of epidemics (2000). The Law of the Few maintains that certain people play invaluable roles in the spread of ideas. Gladwell variously dubs these individuals Connectors, Mavens, and Salesmen. Consider first Connectors. History recollects the ride of Paul Revere but fails to recall the efforts of William Dawes, a fellow revolutionary who undertook precisely the same urgent errand, because Revere was infinitely more successful in galvanizing people into action. Why? Paul Revere was a prototypical Connector. He was, for example, gregarious and intensely social. When he died, his funeral was attended, in the words of one contemporary newspaper account, by ‘troops of people.’ He was a fisherman and a hunter, a cardplayer and a theatre-lover, a frequenter of pubs and a successful businessman. He was active in the local Masonic Lodge and was member of several select social clubs. He was also a doer, a man blessed . . . with an ‘uncanny genius for being at the center of events’” (Gladwell, 2000:56). Revere’s success was attributable to his ability to activate a vast series of social networks. Connectors are indispensable to the diffusion process, insofar as they enable the spread of ideas from limited origins. Connectors are, however, only one component of the Few. Mavens and Salesmen also perform vitals services. The word Mavens is Yiddish, referring to one who accumulates knowledge (Gladwell, 2000). Mavens are those comparatively rare persons who pay careful attention to the details and minutiae that tend to escape the majority of

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marketplace “consumers.” Moreover, Mavens are willing to share their wealth of knowledge with others, for no other reason than their own largesse. But while Mavens are founts of ideas, they are not natural persuaders. Motivated by the genuine urge to be of assistance, Mavens’ stock-in-trade is information, which is shared and traded like any other commodity. Mavens are less inclined to try to convince or to create preference. This task falls instead to Salesmen. The dynamics underlying persuasion are not entirely clear. Like pornography, we know it when we see it, even if what “it” is defies unambiguous definition. Salesmen have it: on the cutting-edge of the “fashionable,” they are arbiters providing the cues that allow people to choose between various alternatives. In short, Gladwell’s Law of the Few contends that social contagion is driven by a handful of pivotal individuals. Finally, consideration must be afforded to social networks where sociability is premised not on social ties or dominant figures but on geographic propinquity. Diffusion research has consistently found that spatially proximate actors influence each other. No distinctive logic can be deduced to explain this result; indeed, spatial proximity appears to encourage a wide variety of interaction and influence. At a minimum, “nearness” would seem to increase the probability of mutual awareness and interdependence, particularly within bounded areas which may promote high levels of interaction and a common sense of identity (Strang and Soule, 1998). In this case, the basis for social contagion is merely physical immediacy. Diffusion arises as a consequence of individuals occupying the same physical, and therefore social, space. Unsurprisingly, none of the social network approaches outlined has established dominance. As noted above, each has been found to be credible in certain circumstances. By the same token, each displays a more restricted utility in other respects. Cohesion, for example, may assume an unwarranted degree of homogeneity in social networks, and leaves unanswered the question of how ideas migrate beyond the boundary constraints of relatively small cliques. Structural equivalence may prompt social contagion, but it may just as easily spur differentiation as “competitors” strive to distinguish themselves. Far from being content to merely “keep up with the Joneses,” the Smiths are likely to attempt to distance themselves to whatever degree possible (and in doing so, curtail the further spread of an idea in its present incarnation). Weak ties may be advantageous vis-a-vis passing on information, but the influence or persuasiveness of secondary associations is apt to be less pronounced than that characteristic of

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more intimate relationships. The Law of the Few overreaches by imbuing contagion with excessive “cult of personality,” by surmising that certain central figures are indispensable to the process. They are not: propitious to be sure, they are not absolutely requisite. With regard to the contagion of crime specifically, definitive determinations concerning the most appropriate functional form of social networks still are not possible. As research related to the microlevel transmission of crime and violence is still in its infancy, closing down any avenue of inquiry would be premature. Currently, each of the social network approaches appears to be prima facie valid for facilitating the interpersonal spread of crime, and resolving the mechanisms of crime contagion is beyond the macrostructural scope of this dissertation. Given that public housing is conceptualized here as both a social and geographic entity, the generalized applicability of the spatial proximity approach is probably most defensible. It lacks theoretical precision, but is sufficiently flexible so as not to constrict the development of more rigorous orientations or prevent the emergence of a more informed understanding of crime diffusion. Crime Memes Thus far, the notion of crime contagion has been addressed uncritically, as merely one form of diffusion (see Chapter 1). But contagion also carries with it more vivid imagery, concepts linked to its roots in infectious disease epidemiology such as risk, exposure, infection, susceptibility, resistance, carriers, vectors, vaccination, inoculation, and epidemics. And therein lies a potential problem: to treat crime as something that is literally contagious would be both wrong-headed and dangerous. Even equating crime with disease on a metaphorical level carries substantial risk of misunderstanding. Still, the language of infectious disease epidemiology is otherwise well suited to the task at hand. While crime is not a pathogen, and there is no germ theory of crime, it is nevertheless possible that the idea of crime, or ideas about crime, may be infectious. There is a germ theory of ideas (Lynch, 1996:155). Borrowing liberally from the field of memetics, this dissertation asserts that thoughts and beliefs about crime are virulent and are disseminated in ways analogous to those of a virus. Memetics is the study of memes and their social and cultural effects. Like Kuhn’s paradigms, memes are notoriously difficult to define, made all the more so by subtle distinctions that accompany their usage in various disciplines such as biology, psychology, and cognitive

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sciences. The word itself was coined by biologist Richard Dawkins (1976) in arguing that cultural transmission is analogous to genetic transmission. In essence, Dawkins identified the cultural equivalent of the gene: memes are a unit of cultural transmission, or a unit or imitation. If the gene is the basic building block of biological life, then the meme is the basic building block of culture. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation (Dawkins, 1989:192). Simply stated, memes are actively contagious, self-propagating ideas. Just as self-propagation is a core trait of biological viruses, so too is it an essential facet of memes: it produces the diffusion of ideas. Lynch refers to this process of self-reproduction and circulation as thought contagion. “Like a software virus in computer network or a physical virus in a city, thought contagions proliferate by effectively “programming” for their own transmission” (Lynch, 1996:2). In a similar vein, Brodie (1996) suggests that a meme is a unit of information whose existence in one mind influences events such that more copies of itself get created in the minds of others: memes are viruses of the mind. It is misleading to equate the diffusion of crime with viral infection, but there does exist an epidemiology of criminogenic ideas, of crime memes. What are crime memes exactly? And what it is about them that encourages their spread? Brodie (1996) surmises that there are three classes of memes. Distinction memes are the knives used to slice up, categorize, and label the world around us; association memes are responsible for our attitudes about everything in life; and strategy memes correspond to our beliefs about which causes will produce which effects. All three are relevant to various aspects of activities that fall under the rubric of “crime.” Distinction memes, for instance, are clearly evident in the selection of crime targets. One of the oldest established criminological “facts” suggests that crime is not a randomly occurring phenomenon, that spatial patterning reflects the cumulative effect of an identifiable site selection process. By which criteria do motivated individuals distinguish between their assorted options? Divergent targets (be they individuals, structures, or geographic areas)

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emit cues about their physical, spatial, cultural, legal and psychological characteristics. Motivated individuals use these cues to select targets in time and space. Over time, these cues coalesce to form a template, a conglomeration of cues if you will, of what constitutes a “good” target (Brantingham and Brantingham, 1978). Put another way, there are distinction memes that separate “good” from “bad” targets. What are some examples of distinction memes? Take residential burglary: “Out of all of the potential homes, how does the burglar chose?” Minimal considerations would include the absence of dogs; high bushes or trees, to obscure the burglar’s activities; and proximity to a major road, for ease of egress. As well, given that family residential areas are usually more deserted during the day, this is probably the best time frame for this particular activity. As commercial establishments are more likely to be vacant in the evening, the difference between residential and commercial burglary is the difference, literally, between night and day. Robbery offers another series of distinction memes. Why are some businesses, such as gas stations and convenience stores, at greater risk of robbery than others? That these enterprises still deal in cash and can be easily surveilled is surely pertinent, as is the fact they are more likely to be open later in the evening, when there are fewer people around. Distinction memes are even at play in interpersonal crimes such as muggings. All things being equal, would-be muggers perceive certain individuals (i.e. the elderly) as being “safer” targets than others (i.e. 300 lb. football players). Association memes and strategy memes capture the distinction between expressive and instrumental crimes respectively. Association memes produce attitudes by linking two or more idea in our minds. Brodie (1996) uses the example how the smell of creosote reminds him of his childhood at the Boston waterfront. In a similar fashion, expressive crimes can link particular behaviors to an expected emotional response. With joyriding or vandalism, the goal may not be the illicit taking of a car or the destruction of property so much as the coincidental feelings of excitement, exhilaration, and catharsis (Presdee, 2000; McCullough et al., 1990; Goldstein, 1996). A strategy, on the other hand, is a kind of “floating rule-of-thumb that tells you what to do when you come across an applicable situation in order to achieve some desired result” (Brodie, 1996:43). Strategies are ways of behaving that produce desired (or avoid undesired) effects. To avoid accidents and citations, drivers use numerous strategies, such as stopping at red lights; traveling counterclockwise around traffic circles;

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and slowing down in the presence of police officers. Likewise, many crimes, particularly of (but not limited to) the economic variety, are instrumental, undertaken with the expectation that the activity will culminate in some benefit or reward: robbery, prostitution, and corporate crime readily come to mind. It is important to note that association and strategy memes do not separate crimes into mutually exclusive and exhaustive categories. Many crimes, such as arson (Canter and Fritzon, 1998) and homicide (Miethe and Drass, 1999), can be either affectional or purposive, depending on the circumstances. Even crime types that appear to be predominantly expressive may on occasion be instrumental, and vice versa. For example, while vandalism most often reflects psychological motivations, it can also be an instrument of intimidation wielded against homosexuals and racial minorities (Haacke, 1997). Drug usage is also routinely emotive, but the altered consciousness sought after by artists and writers is more instrumental in nature. What matters is not whether a particular crime is expressive or instrumental, but that differences in the motivations to crime may be accounted for by differences in meme-types. Once they are established as “successful,” memes tend to become stable and self-reinforcing. They have “infected” their hosts, who then transmit this particular “cultural knowledge” to others. The issues of “why certain crime memes spread” and “what constitutes a successful meme” are essentially one and the same, intertwined as they are; a good meme is one that is contagious, and a meme that diffuses is a good meme. That a meme is “good” does not imply that it is a good idea, in the sense that it is beneficial. Nazism proved to be a highly effective meme, but no one would mistake it as a good idea. Good memes are ideas or beliefs that easily travel through a population. But this criterion for pronouncing a meme good begs the more fundamental question of, “What is it that makes one meme more contagious than another?” Lynch (1996) has proposed seven means by which memes exercise comparative advantage, several of which have direct relevance for crime memes. First, preservational beliefs are ideas that influence their hosts to remain hosts by keeping them from adopting new memes. A meme emphasizing gang affiliation and allegiance above all else decreases the likelihood that anti-gang proselytizing will be effective in attenuating gang membership. Memes that demonstrate preservational advantage effectively immunize their hosts against countervailing memes. In areas where pro-gang memes

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have infected a majority, anti-gang forces face an increasingly “resistant strain” of individuals unreceptive to their message. Perceived cogency or cognitive advantage also affects a meme’s virulence. Ideas that simply “make sense” are among the most disposed to spreading. For individuals residing are poor areas with restricted access to legitimate jobs with decent wages, drug selling as an avenue to financial independence doubtlessly seems eminently reasonable. In areas where physical jeopardy is commonplace, the defense afforded by gang affiliation similarly “makes sense.” Cognitive advantage is closely related to motivational advantage. People adopt or retain ideas because they have some motive for doing so; specifically, they expect to be “better off” as hosts than as nonhosts (Lynch, 1996). Motivational advantage is clearly in evidence for crimes predicated on material gain, but violent crime memes may also profit from motivational propagation. In situations where status and/or power can be garnered through displays of aggression, pro-violence memes have motivational advantage. The idea that “good” memes are contagious accords well with Gladwell’s (2000) second rule of epidemics, the Stickiness Factor. Gladwell’s first rule, the Law of the Few, has already been summarized as “the messenger matters.” But the content of the message is equally as meaningful; to be successful, the idea conveyed must have the trait of stickiness. Stickiness here relates to adherence or bonding, a gluelike attribute. Just as a successful meme need not be a good idea, stickiness need not have anything to do with the inherent quality of the idea. Rather more important is whether the idea is memorable enough to spur someone to action. One can theorize, then, that good memes are sticky ideas that remain with people for a long time. And what of Gladwell’s third rule of epidemics? The Power of Context avers that contagion “is sensitive to the conditions and circumstances of the times and places in which they occur” (Gladwell, 2000:139). This dissertation is unable to assess the first or second of Gladwell’s laws. Because of it macrostructural design, specifying the individual mechanisms of contagion must remain speculative. The third law, the Power of Context, is the guiding precept of this study.

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CHAPTER 6

Research Design and Analytic Framework

The unique contextual features of public housing articulated in previous chapters have given rise to a variety of research issues. More specifically, the peculiar historical and political-economic contexts within which public housing was developed have produced a range of substantive methodological difficulties that must be surmounted before a more informed dialogue on the nature of crime and violence in these areas may be sustained. Fagan et al. (1998) have identified a series of research domains that often are not adequately addressed or even explored in the existing criminological literature on public housing. Among the principal design limitations expressly identified is the dearth of information regarding the diffusion of crime and violence in and around public housing developments. The success of efforts aimed at unraveling diffusion effects will be predicated, at least in part, on their ability to appropriately respond to these concerns. Only very recently has the diffusion of criminogenic effects in and around public housing become a primary design consideration for researchers. The relationship of public housing to surrounding areas reflects a further dimension of the nested status of public housing. While individual residents are nested within buildings and developments, buildings and developments are further nested within neighborhoods, which may themselves exert criminogenic influences on people, buildings, and other places within developments. For example, drug transactions in public housing often involve nonresidents, persons whose transit into and out of projects creates expansive areas where crime may occur. Neighborhoods encompassing projects are characteristically commercial or low density residential areas in which social control is diminished in comparison to the projects themselves, where tenants may exert stronger influence 87

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(Saegert and Winkel, 1997). Largely inhabited by vulnerable populations and an abundance of potential victims, developments provide an attractive target environment to criminals from nearby areas. In contrast to diffusion into public housing, crime may also spread outward, from projects to adjacent neighborhoods. Public housing residents may be less likely to victimize other inhabitants for fear of being recognized; criminally inclined residents may instead commit illegalities in nearby areas where social control is diminished. Evidence gleaned from the few scattered studies that have addressed the diffusion of violence in and around public housing remains inconclusive. Many of the studies that demonstrate higher rates of violence within public housing compared to the overall city rate are of questionable validity, given the incomparability of areas; public housing areas are substantively different than areas devoid of public housing. Using a more consistent frame of reference, Roncek et al. (1981) found that, compared to blocks outside public housing, blocks within public housing were characterized by higher rates of violence. Conversely, Harrell and Gouvis (1994) noted that the presence of public housing demonstrated inconsistent effects in predicting subsequent neighborhood crime rates. Using weighted least squares procedures to approximate diffusion effects, Fagan and Davies (2000) concluded that there was evidence of outward diffusion for some violent crimes (robbery, homicide), but that reciprocal diffusion was evident only for assault. The equivocal nature of these findings suggests that much work remains to be done in the specification of diffusion effects both into and out of public housing.

Public Housing and Neighborhoods The New York City Housing Authority (NYCHA) listed 335 public housing projects in 1996, 255 of which were suitable for analysis. Of the 80 that were removed from consideration: 25 were “amalgamations,” such as Red Hook I & II, Queensbridge North & South, Throggs Neck & Addition, and Millbrook & Extension; 18 were “scattered-sites,” projects that are administered as single units but which comprise more than one physical location; and 37 were “consolidations,” groups of projects administered as single entities. Ideally, the units of analysis for this study would be the public housing projects. Unfortunately, not enough is known about their socioeconomic composition. NYCHA publishes basic demographic

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information for each project annually, but the data are inadequate for testing the informal social control hypotheses. In particular, not enough is known about poverty and inequality in public housing. The NYCHA data do not include measures such as the proportion of household living under the poverty level. They provide only average gross income, which is insufficiently detailed to compute inequality measures such as Gini coefficients. The NYCHA data are also missing many requisite labor market indicators. They offer no information on educational background or employment skills. Fundamental economic variables such as employment rates and labor force participation rates are similarly lacking. None of this is intended as an indictment of the data. Some of the factors used in this study could be reproduced or approximated. Again, there just is not enough to adequately estimate all of the various facets of informal social control. As a result of these limitations, the actual unit of analysis is the block group or groups where the public housing is located. In terms of their geographic size, block groups are well suited to serve as public housing proxies. There are 5875 block groups in New York City, 309 (or 5%) of which contain public housing sites. On average, block groups are 0.044 square miles in area, containing about 1277 persons. Typically, three to four block groups comprise a census tract (the mean is actually 3.87 per tract). Block groups offer a wide range of demographic, social, and economic indicators. In the majority of cases, public housing constitutes all or most of a block group. Census tracts, on the other hand, would have been too large. Public housing neighborhoods are similarly composed of block groups. Each neighborhood was created manually, and generally includes every block group that shares a border with the public housing block group. However, consideration was also afforded to structural barriers such as train tracks and major highways. Based on the assumption that movement across these impediments is highly limited, it seemed unlikely that these areas would be affected by the diffusion of a social process such as crime. Thus, areas separated from public housing by these barriers have been excluded: they do not form part of the neighborhood. Given the hypothesized role of informal social control, block groups bereft of social composition, primarily large parks and sprawling industrial areas, have also been removed.

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Spatial Autocorrelation Diffusion is essentially spatial dependence with an added temporal element. Before attempting to model a higher-order process such as diffusion, it is useful (not to mention necessary) to first explore the extent to which crime has a fundamental spatial dimension. Spatial autocorrelation analysis tests whether the observed value of a variable, such as crime, at one locality is independent of the values of the variable at neighboring localities. If dependence exists, the variable is said to exhibit spatial autocorrelation. Spatial autocorrelation measures not only the level of interdependence between the variables, but also the nature and strength of that interdependence. It has been argued that the first law of geographic analysis is that everything is related to everything else, but near things are more related than distant things (Tobler, 1970). Only recently, however, have spatial relationships been incorporated into the social sciences in any substantively significant way. Crime, like most other social phenomena, tends not to occur randomly, but rather, demonstrates identifiable spatial patterning. Spatial autocorrelation is concerned with the degree to which events in a given area are similar (or dissimilar) to events in nearby locations (as opposed to more distal places) and is said to exist “whenever a variable exhibits a regular pattern over space in which its values at a set of locations depend on values of the same variable at other locations (Odland, 1988:7).” Detecting and controlling for spatial autocorrelation is essential for any areal research, because its presence violates the independence assumption of traditional analytic methods. Spatial autocorrelation statistics comprise a variety of techniques used to analyze patterns of association. Selecting an appropriate technique depends first on the geographic resolution of the data. The crime data analyzed here are based on addresses. These addresses constitute specific points, the clustering of which could be evaluated using point descriptors (see Moser, 1987). Because we rarely have detailed information about precise points, however, they are not very useful in analyzing social behavior; the clustering of points says very little about the nature of the underlying process driving the association. In contrast, aggregating points into polygonal areas allows for the consideration of covariates related to the areas themselves (i.e. poverty, demographics, etc.). To investigate spatial autocorrelation in New York City, crime counts have been aggregated into census tracts which, while not perfect, are more socially meaningful units of analysis.

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The second consideration of note concerns the selection of a spatial weight matrix. Spatial autocorrelation is the comparison of given values to nearby values, but there are a variety of ways to operationalize and quantify what is meant by “nearby.” The simplest definition of “neighborhood” is through a binary connectivity matrix: any two polygons that are immediately adjacent to one another are given a value of 1, while all others receive a 0. A mathematically helpful derivative is what Lee and Wong (2000) refer to as stochastic or row standardized weights, where each adjacent neighbor is assigned a proportionate fractional value summing to one. For example, if a polygon has five neighbors, each would have an assigned value of 0.20. While these measures have many useful properties, they are limited insofar as they treat all of polygon’s neighbors (1s) and nonneighbors (0s) as essentially the same, regardless of their size and shape. In short, they are premised on a restrictive notion of proximity. A more flexible weighting scheme involves using the centroid distance, or the distance between the geographic center of the polygons.2 Finally, although centroids are the easiest points of reference for calculating distance, other measures are also available. The most common of these is nearest parts distance, which is pretty much as it sounds. Rather than utilizing the geographic center of the polygon, it computes distance from the closest points of contact. Because the geographic study of crime is still in its adolescence, the theoretical rationale for selecting among the various options remains underdeveloped. Exploratory in nature, few compelling justifications can yet be offered to advocate one weighting scheme over another. Consequently, all four of the formulations are investigated here. Analytic Techniques Once the resolution and weights matrix issues have been addressed, the final technical consideration involves the selection of specific spatial autocorrelation techniques. Two global measures of spatial autocorrelation are Moran’s I and Geary’s C. Moran's I works by comparing the value at any one location with the value at all other locations. Moran's I requires an intensity value for a crime point (i.e. the weight matrix). This point is then assigned an intensity value, in this case the count of crimes within that tract. The Moran's I result varies between -1 and +1. Values closer to +1 indicate high degrees of clustering of similar values, either high (positive) or low (negative). Conversely, values closer to -1 demonstrate dispersion, where areas

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with high values are surrounding by neighbors with low values, and vice versa. Directional indications notwithstanding, the actual value of Moran’s I doesn’t convey the relative strength of dependence. Analogous to chi-square analysis and other measures of association, the observed Moran’s I must be compared to its expected value.3 This comparison (scaled by the standard error of the Moran’s I) produces a z-score, with its attendant distribution: z-scores greater than 2 and less than –2 are considered to be significantly different from random. Geary’s C diverges from Moran’s I insofar as its cross-product compares deviations between neighboring values directly, instead of comparing the deviation of each from the mean (Lee and Wong, 2000). Owing to its distinct calculation, Geary’s C always has an expected value of 1 and ranges from 0 to 2, with 0 indicative of perfect positive spatial autocorrelation and 2 suggesting perfect negative spatial autocorrelation. As with Moran’s I, the observed value is differenced from the expected value. Because of its counterintuitive interpretation, negative z-scores mean positive spatial autocorrelation and positive zscores suggest negative spatial autocorrelation. Moran’s I tends to be the more popular of the two measures, doubtlessly due in no small part to its more straightforward interpretation. Still, the available literature offers little guidance concerning which of these two global techniques ought to be favored. While both Moran’s I and Geary’s C are widely accepted measures of assessing spatial autocorrelation globally, their ability to distinguish between different types of clustering spatial patterns is restricted (Anselin, 1992). These patterns, the convergence of high or low values respectively, are known colloquially as “hot spots” and “cold spots.” Although these represent processes that are substantively very different, the global spatial autocorrelation procedures outlined thus far are unable to tell them apart. To Moran’s I and Geary’s C, concentrations of either high or low values look identical. Fortunately, an alternative means of identifying the precise nature of spatial patterning is available in the form of the general G-statistic. The Gstatistic combines a distance parameter and a binary weights matrix: areal units contained within the boundary of the distance parameter receive a value of 1, while those outside are scored 0. Similar to the other techniques, G produces a z-score based on observed and expected values (again, scaled by variance of G).4 Large positive values are indicative of hot spots, and large negative values denote cold spots.

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Although the G-statistic is more attuned to the specific nature of spatial clustering, it is still a global measure. It functions as a summary of hot and cold spots over the entire area in question. In some circumstances, this is insufficient. The need for greater precision informs local indicators of spatial autocorrelation (LISA), local versions of Moran’s I and Geary’s C which allow for z-scores to be attached to particular spatial units, such as census tracts or block groups (Anselin, 1995). Whereas the global measures are equipped to detect either spatial autocorrelation (Moran’s I and Geary’s C) or spatial concentration (G-statistic) generally, LISA facilitates identification of hot (or cold) spots at the tract or block group level. All of these procedures have merit, in that each answers different but equally valid questions. What, if any, evidence is there of spatial autocorrelation of crime in New York City? If it exists, what is the nature of the autocorrelation? Is New York City marked with hot spots? Or cold spots? More important for the purposes of this study, what of public housing? To what extent do spatial concentrations converge in public housing? In public housing neighborhoods? Resolving all of these questions will, in the next chapter, require the full array of analytic techniques.

Models: Outward and Inward Diffusion While there may be reason to believe that diffusion processes operate between public housing and neighborhood violence, the evidence thus far remains inconclusive. Further, even if the existence of diffusion could be assumed, its direction surely could not. Thus, two general models of diffusion are analyzed in this study. The first model, the outward diffusion model, posits that criminal violence spreads out from public housing to adjacent block groups referred to as the surrounding neighborhood. In this model, then, the prevalence of crime and violence in a given public housing block group at time t exerts a significant influence over the prevalence of crime and violence in the adjacent neighborhood at time t+1. In other words, a one year lagged effect is hypothesized. Lagged positive covariation is anticipated whereby increases or decreases in the levels of crime and violence in public housing are reflected in concomitant increases or decreases in the amount of crime and violence in surrounding neighborhoods. It is entirely possible, however, that the contagion effect of violence is, in fact, reversed. The inward diffusion model asserts that the level of

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crime in an immediate public housing area at time t is at least partially contingent upon the level of crime in its broader community at time t-1. In addition to corresponding lagged crime counts, both the outward and inward diffusion models incorporate relevant sociostructural features of neighborhoods as key explanatory constructs. Thus, for the full outward diffusion model, the sum of crime in the surrounding neighborhood is a function of the relevant characteristics of the area as well as the amount of crime in the neighborhood. In contrast, the full inward diffusion model suggests that both relevant public housing area features and the number of crimes in the neighborhood predict the level of crime in public housing areas. These factors are presumed to play a significant, independent role in the prediction of crime and violence, but one that will be attenuated once controls for relevant neighborhood factors have been introduced. Third, outward diffusion models also include a control for spatial lag. Generically, spatial lag is a weighted average of the values in locations neighboring each observation. More precisely, if an observation on a variable x at location i is represented by xi, then its spatial lag the sum of the product of each observation in the data set with its corresponding weight from the ith row of the spatial weights matrix (Anselin, 1992). Here, spatial lag is the row-standardized5 sum of crime counts in adjacent block groups. Spatial lag is used to account for the exogenous effect of crime in block groups lying just beyond the immediate public housing neighborhood. The outward diffusion model surmises that crime and violence flow from public housing out to the neighborhood. But the neighborhood itself is further bounded by another layer of block groups which could be termed the second order neighborhood, and, as such, is potentially subject to the influence of crime levels in these areas as well. Spatial lag controls for this external effect. In contrast, the inward diffusion models contain no such control, because there are no second order neighborhoods in these models. The only neighborhood per se is the immediate neighborhood, which is already presumed to condition the internal public housing area. The final two modeling considerations are time and population size. Time enters the process in two ways. First, time is an independent variable that may or may not effect the crime counts. It answers the question: “Does the overall mean number of crimes change significantly across years in study period.” Second, the longitudinal (as opposed to mean) effects of the other independent variables are gauged via time interaction terms. Time effects are straightforward when the

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phenomenon in question progresses in a reasonably linear fashion, but are more complicated when the distribution across time is nonlinear. The descriptive findings in Chapter 7 illustrate both patterns: total assaults for New York City decrease steadily from 1990, but both homicides and drug arrests show much more varied trends over time. To better approximate these distributions, time in these models is subject to quadratic transformations. For homicide, time is actually the inverse of time, centered at 1990 (the midpoint of the time series and roughly the peak of the homicide explosion). Drug arrests follow a more bimodal distribution that is best fit with a sine transformation of time. Population size (logged) is included in the diffusion models as a control variable. Osgood (2000) points out that there are serious drawbacks to the traditional criminological approach of investigating crime rates with ordinary least squares regression, and shows how adding the natural logarithm of the size of the population at risk (in this case the block group population) to count models is more appropriate for aggregate analyses. The inclusion of the logged population term to a basic count model essentially changes the analysis from counts to rates of events per capita. Osgood maintains that count-based models standardized for at-risk population in this way are more precise and avoid the problems that hinder conventional OLS regression analyses of crime rates (see next section for more details).

Model Summaries Outward Diffusion Neighborhood Crimetime t = Public Housing Crimetime t-1 + Sociostructural Factors + Spatial Lag + Time + Population (Logged) Inward Diffusion Public Housing Crimetime t = Neighborhood Crimetime t-1 + Sociostructural Factors + Time + Population (Logged)

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General Linear Models and Generalized Estimating Equations Criminological research has traditionally used ordinary least squares (OLS) regression to analyze aggregate crime rates. But, for a variety of reasons, the OLS approach is unsatisfactory when rates must be calculated from small numbers of events and small populations. The “rate problem” arises because crimes are discrete events. Regardless of population size, the rate will still correspond to integer counts of crimes. In other words, appearances aside, crime rates are not truly continuous. The practical application of rate transformations suggest how skewed a distribution may become. “For instance, in a county of 200,000 individuals, every additional crime increase the crime rate by half an arrest per 100,000, while in a neighborhood of 5000 each crime corresponds to 20 crimes per 100,000” (Osgood, 2000:22). If the population sizes of the aggregate units are relatively large compared to the average arrest rate, then the computed rates would approximate a continuous distribution and OLS techniques would be appropriate. Osgood suggests that denominators of several hundred thousand would suffice for most measures of crime. In contrast, in situations where populations number only in the thousands (remember that the average population block group population is 1277), the discrete nature of the crime counts is an unavoidable concern: a single homicide or assault could correspond to a high crime rate. In these circumstances, crime rates derived from small crime counts present two major problems for OLS analysis (Osgood, 2000). First, the central OLS assumption pertaining to the homogeneity of error variance is violated where population sizes differ across aggregate units. Crime rates based on small populations will tend to produce larger errors in prediction than will rates based on larger populations. Second, since the lowest possible crime count is zero, there is effective censoring at zero. As populations decrease, progressively larger percentages of the cases will have offense rates of zero, thus biasing regression coefficients. As result of these problems, models based on the counts themselves, as opposed to rates, have increasingly gained currency in the aggregate analysis of crime. Count techniques, however, carry their own pitfalls. As noted earlier, counts are discrete, not continuous, and therefore cannot be assumed to be normally distributed any more than rates can. To the extent that the assumptions of the normal distribution are violated, the data are not amendable to standard linear modeling techniques. The general linear models (GLM) approach has been

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developed as an extension of traditional linear models that allows the mean of a population to depend on a linear predictor through a nonlinear link function and allows the response probability distribution to be any member of an exponential family of distributions (Nelder and Wedderburn, 1972). Many other useful statistical models can be formulated as GLMs by the selection of an appropriate link function and response probability distribution (McCullagh and Nelder, 1989). The type of data used in this study, count data, is normally modeled using either the Poisson or negative binomial distribution. The Poisson distribution assumes that the chance of an event (such as crime) occurring is randomly distributed: all study units have an equal chance of experiencing one, two or more events. Where this assumption holds, the variance and mean of the data are necessarily equal. This supposition that the mean and variance are equal is quite restrictive, however, as counts of events are often overdispersed or underdispersed. Osgood (2000) argues that, for a number of reasons, overdispersion is ubiquitous in analyses of crime data. The negative binomial distribution is a generalization of Poisson that does not assume randomness. Instead, it allows for the possibility of “proneness,” the potential that certain areas, for instance, have a greater chance of having more criminal events than would be expected at random. The negative binomial distribution is particularly well suited for relatively rare events, such as homicide, for which the likelihood of overdispersion increases. And where the variance and the mean of the data are equal, the negative binomial and Poisson distributions converge perfectly. For these reasons, all of the models in Chapter 7 are run with negative binomial distributions. The link function most commonly associated with these distributions is the “log” link, so this specification is used here. The diffusion modeling process proposed here presents one final twist, one final complication to be untangled. Thus far, the explication of general linear models has not touched upon the obstacles presented by longitudinal designs. The analysis of correlated data arising from repeated measurements when the measurements are assumed to be multivariate normal has been studied extensively. However, the normality assumption is not always be reasonable; for example, different methodologies must be used in the data analysis when the responses are discrete and correlated, as they are here. When data are collected on the same units across successive points in time, these repeated observations are correlated over time. If this correlation is not taken into account then the standard errors of the parameter estimates

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will not be valid and hypothesis testing results will be non-replicable. Repeated measures can be incorporated into the GLMs framework via Generalized Estimating Equations (GEEs). GEEs provide a practical method with reasonable statistical efficiency to analyze such data (Liang and Zeger, 1986). As in GLMs, users specify a distribution family and a link function to transform the expected values. But GEE models additionally allow for repeated or dependent observations and the ability to specify the within-group correlation structure for the panels. Any number of correlation structures may be used. Here, theoretical considerations and preliminary empirical testing dictated that all models be estimated with exchangeable (equivalent to compound symmetry covariance structure) correlation matrices. There are several ways to estimate the parameters for generalized linear models. Models may be fit through the method of least squares or by means of maximum likelihood estimation. The parameters for longitudinal generalized linear models analyzed in this study are obtained via quasi-likelihood GEEs in SAS using GENMOD. In practice, GENMOD comprises two components: the modeling statement, which sets out the dependent variable, independent variables, probability distribution, and link function; and the repeated measures statement, which specifies the subject as well as the type of working correlation matrix.

Dependent Variables Homicides The homicide statistics used in this study are drawn from Center for Disease Control Vital Statistics for the years 1985 through 1996 inclusive. The Vital Statistics data are advantageous in that their collection is not imbued with the legal connotations normally attributed to official police or court statistics. Unfortunately, they are also characterized by a notable methodological limitation; that is, they are geographically coded only at the block group and census tract levels. Thus, it is not possible to allocate events or cases specifically to public housing. Again, this is one of the justifications for characterizing entire block group(s) as public housing areas. In the outward diffusion model, the dependent variable is the aggregate homicide count for the surrounding neighborhood. The dependent variable for the inward

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diffusion model is the level of homicide for the public housing block group. Injurious Assaults In addition to the methodological quandary alluded to earlier, homicides also present something of a conceptual difficulty. While homicide is the most serious manifestation of violence, it is a comparatively rare occurrence. Moreover, as a distinct literature could attest to, homicide may be atypical in the causal sense as well. For these reasons, it seems prudent at attempt a broader validation of diffusion approach advocated here. To this end, a second dependent variable is required. The Statewide Planning and Research Cooperative System (SPARCS) provides a measure of assaults that are result in injury but that are nonlethal (referred to hereinafter as assaults). SPARCS assaults are more generic than homicides and are therefore less open to charges that their “uniqueness” might bias the results in some way. The drawback to the SPARCS data is that they are available for a more abbreviated time frame, for the years 1990 to 1996 only. Still, this is more than enough for adequate repeated measures analyses. As with homicide, two versions of assault are to be used as dependent variables, each corresponding to the outward or inward model respectively. Drug Arrests While homicides and assaults both represent facets of the violence that is perceived to be endemic to public housing areas, the final crime measure, drug arrests, taps another problem of some repute: drug markets. This measure, derived from the Department of Criminal Justice Statistics, is a 10% sample of drug arrests and available for 1985 to 1996. Similar to the other measures, the drug arrest data were obtained without addresses, and as such had to be aggregated. Again, each of the diffusion models requires its own version of the drug arrests variables. Further Alternative Specifications In addition, these three principal measures can be further divided or combined to allow for more detailed hypothesis testing. First, the Vital Statistics can be disaggregated by weapon type, thus permitting a comparison of gun and nongun violence. This is a distinction worth exploring, as much of the debate concerning the rise and subsequent fall of criminal violence in New York City in the late 80s and early 90s

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has revolved around the extent to which gun violence was implicated in that process. Second, homicide and assault can be combined to form a more general “lethal violence” index. Finally, drug arrests may be divided by drug type. Given that marijuana is perceived by many, including law enforcement and the courts, as being quite different from narcotics such as cocaine and heroin, this distinction may influence underlying patterns of association and movement. These alternative model specifications are intended to maximize the validity of the results and enhance the robustness of the findings.

Independent Variables As outlined in the discussion of diffusion models, each of the dependent variables identified above also serves an independent variable in the appropriate reciprocal model. For example, homicide count, the dependent variable in the inward diffusion model, is also an independent variable in the outward contagion model (lagged by one year, of course). Conversely, neighborhood assault level, the dependent variable in the outward diffusion model, is, when lagged by one year, an independent variable in the inward model. In the diffusion results presented in Chapter 7, these independent versions are referenced as “PH Block Group(s) Diffusion Effect” for the outward models and “Neighborhood Diffusion Effect” for the inward models. In addition to the diffusion variables, data reflecting the informal social control capacities of neighborhoods are also incorporated into the diffusion models. As argued throughout this study, the risk associated with violent crime such as homicide and assault, as well as that associated with drug activities, is not constant across neighborhoods. Rather, it varies according to the social, structural, and economic characteristics and dynamics of social control in these areas (Morenoff, Sampson, and Raudenbush, 2001). Originally, 19 variables from the 1990 US Census were selected to operationalize neighborhood risk and susceptibility. These measures included social and economic indicators that reflected the theorizing about place and violent crime presented in earlier chapters, theories that incorporate the structural deficits of social areas, but also the dynamic processes of social control in these areas (see Appendix A.1 for a list of the original variables). However, preliminary analyses suggested significant correlation between many of the items. As well, there was considerable

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conceptual overlap between the variables. Consequently, using an approach heavily informed by Wilson (1987), the variables were categorized into seven separate dimensions that reflected the social, economic, and structural theoretical domains of neighborhood risk. The dimensions included poverty, racial residential segregation, labor market participation, immigration, housing structure, and two measures of social control, supervision and anonymity. a. Poverty This factor comprises three indicators: (1) percentage of households with incomes below the poverty level; (2) percentage of household receiving public assistance; and (3) a Gini coefficient to measure household income inequality at the block group level. b. Segregation Segregation is composed of two variables: (1) percentage of the population that is nonwhite, and (2) a racial fragmentation index to characterize population heterogeneity within block groups.6 c. Labor Market This factor, a combination of labor market participation and human capital, is composed of four measures: (1) employment rate; (2) labor force participation rate;7 (3) percentage employed in professional or managerial jobs; and (4) the proportion of the adult population over 25 that has completed a high school education. d. Immigration This factor has two facets: (1) linguistic isolation,8 and (2) percentage of households with foreign-born heads. e. Housing Structure The dimensions of housing structure are: (1) vacancy rate; (2) percentage of housing units that are owner-occupied; and (3) the mean number of persons per room in residential units, as an indication of density and overcrowding.

Table 6.1 Factor Composition, NYC Block Groups, 1990 Factor

Component

Poverty/Inequality % Households Under Poverty Level % Households with Public Assistance Income Gini for Total Hhld Income

Eigenvalue 2.37

% Expl. Variance 78.9

2.43

60.7

1.26

62.9

1.95

64.9

1.02

51.0

1.51

75.5

1.22

40.8

.942 .898 .821

Labor Market/Human Capital % High School Graduates Total - 25+ Employment Skills Managerial, Professional, or Technical Jobs Employment Rate Labor Force Participation Rate

.898 .817

.684 .697

Segregation Racial Fragmentation Index % Nonwhite

.793 .793

Supervision %Youth Population (5-15) % Female Headed Households with Children Under 18 Supervision Ratio (25-64 by 5-24)

.905 .817 -.678

Anonymity Population - 1990 Residential Mobility - Same House as 1985

.714 .714

Immigration Linguistic Isolation Foreign Born

.869 .869

Housing Structure % Occupied Units that are Rentals Density - Mean Persons Per Occupied Room Vacancy Rate

.775 .639 .464

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f. Supervision This attempts to capture the extent of supervision of young people, and includes: (1) the concentration of youth population;9 (2) the percentage of female-headed households with children under 18; and (3) the ratio of youths to adults. g. Anonymity This factor has two aspects: (1) total population size, and (2) residential stability based on length of residential tenure.10 Consistent with the other studies of “neighborhood effects,” the seven composite factors were created using principal component analysis (PCA) to represent the original variables (Morenoff and Sampson, 1997). With factor analysis, PCA is typically used to analyze groups of correlated variables representing one or more common domains. PCA is used to find optimal ways of combining variables into a small number of subsets, while factor analysis may be used to identify the structure underlying such variables and to estimate scores to measure latent factors themselves. Since the underlying structure of the latent factor was of less interest here, PCA was selected. Because the diffusion analyses are conducted at the block group level, the factor scores in Table 6.1 are based on New York City block groups. Descriptive statistics pertaining to the original variables are available in Appendix A.1. Overall, the PCA proved satisfactory. All but one of the factors explained more than 50% of the variance, quite acceptable given the relatively small number of variables that comprises each. Not coincidentally, this factor, housing structure, similarly exhibited the only factor loadings below .600. The generally high factor loadings, or “components,” suggest that each of the compositional variables contributed significantly to it corresponding factor. Although the factoring process succeeded in reducing correlation, it was unable to eliminate it completely (Appendix A.2). The remaining level of correlation, while not excessive, is sufficient to underscore the cumulative interpretation of factor effects advocated earlier.

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Hypotheses The results presented in the following chapters are oriented around the substantive issues addressed above. Spatial Autocorrelation 1. The indicators of crime used in this study, namely counts of homicides, assaults and drug arrests, are spatially patterned. 2.

Each individual indicator of social control and sociostructural disadvantage used in this study will evidence spatial autocorrelation.

3.

The spatial concentration of both crime and sociostructural deficit is more likely to occur in public housing areas than in areas without public housing.

Diffusion Outward Models 4a. The levels of homicide in public housing areas are significant in predicting homicide counts in surrounding neighborhoods in the following time period. 4b. The levels of assault in public housing areas are significant in predicting assault counts in surrounding neighborhoods in the following time period. 4c. The levels of drug arrests in public housing areas are significant in predicting the number of drug arrests in surrounding neighborhoods in the following time period. 5.

The structural and socioeconomic features of these surrounding neighborhoods will exert a substantial influence on the number of homicides, assaults, and drug arrests in these areas.

6a. The diffusion effects of public housing homicides on surrounding neighborhood homicides will be reduced to insignificance when relevant structural and socioeconomic features of the neighborhood are controlled for. 6b. The diffusion effects of public housing assaults on surrounding neighborhood assaults will not longer be significant when relevant

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structural and socioeconomic features of the neighborhood are controlled for. 6c. The diffusion effects of public housing drug arrests on surrounding neighborhood drug arrests will be reduced to insignificance when relevant structural and socioeconomic features of the neighborhood are controlled for. Inward Models 7a. Homicide counts in adjacent neighborhoods have a significant impact on the counts of homicides in public housing in the following year. 7b. The level of assaults in adjacent neighborhoods has a significant influence on the number of assaults in public housing in the following year. 7c. The number of drug arrests in adjacent neighborhoods has a significant effect on the number of drug arrests in public housing in the following year. 8.

The levels of homicide, assault and drug arrests in public housing are contingent upon the socioeconomic characteristic of public housing.

9a. The diffusion effect of neighborhood homicide counts on public housing homicide will be reduced to insignificance by the socioeconomic factors of public housing. 9b. The diffusion effect of neighborhood assaults on public housing assault will be reduced to insignificance by the socioeconomic factors of public housing. 9c. The diffusion effect of neighborhood drug arrests on the number of arrests in public housing will be reduced to insignificance by the socioeconomic factors of public housing.

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CHAPTER 7

Spatial Patterns and the Diffusion of Crime

The Distribution of Crime As alluded to in the previous chapter, the various crime measures employed in this study each demonstrate distinct longitudinal patterns. The time trend for homicides in Appendix B.1, for example, is roughly curvilinear, illustrating the epidemic rise of lethal violence in New York City in the late 1980s and its subsequent fall after 1990 (see Appendices B.2 and B.3 for assault and drug arrest time trends). Because of the abbreviated time frame for which the data are available, the trend line for assaults is more linear. Had the data been available, assaults presumably would have shown escalation resembling that of homicides. In contrast, drug arrests are more varied over time, peaking twice during the study period. For each crime type, the pattern of events was closely mirrored in both public housing and public housing neighborhoods. While carefully avoiding ecological fallacy, this intimates that public housing crime dynamics are probably not completely divorced from external events. It appears, from these preliminary analyses, that what goes on in public housing is a reflection of broader dynamics. It is not implausible, then, that broader dynamics influence events transpiring within public housing.

Spatial Autocorrelation At a fundamental level, diffusion is about the patterning of human activities in both time and space. Before introducing time into the equation, it is important first to establish the spatial foundations of the 107

108

Crime, Neighborhood, and Public Housing

phenomena in question. In this case: “Are there discernible geographical patterns of crime and violence in New York City?” If so, a more precise question would be: “To what extent is the spatial patterning of crime and violence evinced in public housing areas?” The following section attempts to answer these questions. Crime Measures Table 7.1 presents the results of the four global tests of spatial autocorrelation that are premised on varying definitions of connectivity and distance as they relate to “neighbors.”11 The measures are based on within-borough census tracts as the unit of analysis. Overall, the findings point to significant spatial autocorrelation across all measures of crime and violence.12 The generality of the findings hold across all boroughs, with the possible exception of Staten Island, for which indications of spatial autocorrelation are more varied. Leaving aside for the moment the partially anomalous nature of Staten Island, Moran’s I indicates significant spatial dependence for virtually all of the dependent variables, the only blemish being non-gun homicide in the Bronx (as measured by nearest distance). Geary’s C, while also overwhelmingly suggestive of autocorrelation, produces a few more qualifications, most notably with regard to assault in Manhattan and both assault and violence in Queens. The evidence concerning spatial dependence in Staten Island is more equivocal. Using Geary’s C, consistently the more conservative of the tests, none of the crime measures reach statistical significance. In contrast, Moran’s I demonstrates modest contingency, most notably for assault, violence, drug arrests and narcotics only arrests. Several factors may account for Staten Island’s divergence from the other boroughs. First, Staten Island exhibits considerably lower base rates for particular crime categories. The mean number of homicides for Staten Island census tracts, for example, is 30% lower than it is in Queens, the next lowest borough. Assaultive behavior and drug arrests are more comparable between the two, but pale in comparison with the other boroughs, which have at least 50% more of these types of events. The Bronx records almost 70% more assaults than Staten Island, while drug arrests in Manhattan outstrip those on Staten Island by a factor of almost five. A second potential explanation relates to the relative size of Staten Island’s census tracts, which are, in terms of average area, 3.5 to 7 times larger than those in other boroughs. Larger means more heterogeneous, and heterogeneity is generally negatively associated

Spatial Patterns and the Diffusion of Crime

109

with spatial autocorrelation. The anomalous nature of Staten Island’s results, while interesting (in the sub-city dynamic of New York), should not detract from the main point: crime and violence in New York City is not randomly distributed, but rather, are marked by distinctive geographical patterns.13 The importance of distance hinted at in the earlier analysis is confirmed in Table 7.2. Using a different global measure, the general G, brings the issue of distance into better relief. As well, G is specifically designed to address the deficiencies of other global measures; that is, their inability to distinguish between diverse types of spatial clustering patterns.14 In all cases, the strength of spatial concentration diminishes with distance. The strong positive coefficients indicate the presence of hot spots. With few exceptions, New York City boroughs are characterized by hot spots of crime and violence. For the Bronx and Brooklyn, this concentration remains substantial even at 5 miles. In Manhattan and Queens, hot spots continue to at least the two-mile mark, but disappear by five miles. Not surprisingly, Staten Island presents a more uneven picture. Under the nearest distance criterion, only homicide shows evidence of local concentration, and even then only at relatively small distances. Conversely, the centroid distance measures shows hot spots for almost all of the crime indictors, with several extending out for up to one mile. Again, Staten Island’s spoiler status notwithstanding, crime and violence in New York City is exceedingly concentrated in the form of hot spots, irrespective of which assessment measure is used. It is worth mentioning that the concentration of crime and violence is not contingent upon the level of spatial analysis. Although the findings in Tables 7.1 and 7.2 are based on census tracts, the real areal units of interest, the units of analysis for the diffusion models, are block groups. To verify the tract results, supplemental analyses were conducted on those boroughs with fewer than 1000 block groups: the Bronx, Manhattan, and Staten Island. The findings (not shown), buttress and even extend the tract outcomes. The inverse distance principal revealed in Table 7.2, whereby concentration is amplified at proximate distances but tends to dissipate as distances increase, is affirmed. Block groups are smaller, and are therefore more readily amenable to concentration effects. The block group results concur with those for the tracts, just amplified some. The hot spots prevalent in Table 7.2 are even more apparent. xx

Table 7.1 Global Indicators of Spatial Autocorrelation - Z-Scores for Moran’s I and Geary’s C* Crime Measures - New York City Census Tracts Binary Moran I Geary C Bronx Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Brooklyn Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Manhattan Homicide Gun Homicide Non-Gun Homicide

Stochastic Moran I Geary C

Centroid Distance Moran I Geary C

Nearest Distance Moran I Geary C

11.62 11.74 8.67 11.93 12.22 14.38 14.67 11.70

-6.75 -7.00 -4.43 -6.75 -7.07 -7.48 -7.61 -4.49

11.21 11.34 8.39 11.62 11.88 13.66 27.61 10.96

-8.81 -9.08 -6.12 -8.90 -9.23 -9.95 -3.52 -6.74

22.63 22.10 8.97 24.31 24.70 26.10 13.95 14.19

-3.35 -3.41 -2.39 -3.73 -4.03 -3.11 -10.12 0.08

12.87 12.62 0.61 13.88 14.11 14.74 15.54 8.12

-2.29 -2.44 -0.17 -2.60 -2.84 -2.14 -2.55 0.65

25.34 25.25 18.92 26.03 26.13 22.13 21.56 25.25

-16.80 -16.36 -13.05 -16.60 -16.71 -12.96 -12.58 -12.58

24.69 24.62 18.53 25.29 25.39 22.26 21.74 24.97

-21.00 -20.70 -15.93 -21.09 -21.20 -16.80 -16.28 -17.73

65.42 65.84 46.81 66.41 67.00 51.50 51.97 43.53

-8.66 -8.11 -6.82 -8.39 -8.50 -8.42 -8.54 -3.47

36.84 37.04 26.67 37.61 37.85 26.66 26.93 24.19

-8.53 -8.11 -6.73 -8.19 -8.31 -7.44 -7.57 -3.41

13.50 13.69 11.06

-7.90 -7.54 -6.63

13.85 14.06 11.39

-10.35 -10.03 -8.67

30.25 30.47 23.92

-10.12 -10.12 -6.69

17.99 17.98 14.26

-7.12 -7.34 -4.20

Table 7.1 Continued Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Queens Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Staten Island Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests

6.96 8.04 12.54 12.57 10.88

-1.51 -2.31 -6.49 -6.66 -4.05

7.08 8.17 12.44 12.48 10.61

-2.31 -3.42 -8.76 -8.89 -6.21

13.77 16.46 18.98 18.87 16.46

-1.93 -2.75 -4.40 -4.54 -2.30

8.32 10.08 9.41 9.36 8.13

-0.33 -0.95 -2.77 -2.90 -0.97

13.93 14.18 8.95 5.19 5.76 17.80 17.45 15.78

-6.46 -6.46 -5.45 -0.92 -1.03 -6.56 -5.99 -6.95

15.15 14.72 11.13 6.40 7.08 19.01 18.19 18.89

-9.18 -9.54 -6.80 -1.25 -1.44 -9.64 -8.86 -10.44

20.46 22.88 10.35 4.74 5.82 27.23 19.57 21.32

-4.98 -4.77 -4.70 -0.56 -0.64 -4.59 -3.28 -5.37

13.62 15.50 6.46 2.81 3.51 19.74 26.75 13.76

-4.02 -3.75 -3.99 -0.54 -0.62 -3.88 -3.91 -4.39

1.34 0.61 1.76 3.06 2.93 2.28 2.54 0.08

0.53 0.90 -0.88 -0.59 -0.50 -0.11 -0.26 0.93

1.92 1.19 2.03 3.66 3.56 2.38 2.67 -0.11

-0.25 0.32 -1.32 -1.88 -1.76 -0.88 -1.07 0.69

2.25 0.90 2.56 3.30 3.40 2.52 2.77 -0.07

0.92 1.04 0.70 0.87 0.86 1.07 1.05 1.12

1.12 -0.25 0.67 -0.15 0.14 -0.31 -0.39 -0.55

0.36 0.73 0.41 0.85 0.77 0.81 0.80 1.24

* Under randomization for Moran and Geary; inverse distance for Centroid and Nearest Neighbor

Table 7.2 Global Indicators of Spatial Autocorrelation – Z-Scores for G-Statistic Crime Measures - New York City Census Tracts .5 Mile Bronx Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Brooklyn Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Manhattan Homicide Gun Homicide Non-Gun Homicide

Centroid Distance 1 Mile 2 Miles

5 Miles

.5 Mile

Nearest Distance 1 Mile 2 Miles

5 Miles

13.55 13.80 11.07 12.99 13.26 15.76 16.06 12.75

14.08 14.01 12.31 13.89 14.11 15.21 15.76 10.95

11.08 10.72 10.23 11.06 11.21 11.50 12.25 5.84

4.53 4.47 4.02 4.23 4.34 4.17 4.47 2.46

13.87 13.84 11.91 13.80 13.98 15.73 16.12 12.84

13.33 13.02 12.06 13.43 13.60 14.17 14.77 9.91

10.84 10.37 10.23 10.84 10.96 11.46 12.28 5.36

4.46 4.42 3.93 4.06 4.21 4.22 4.55 2.21

13.55 13.80 11.07 12.99 13.26 15.76 16.06 12.75

14.08 14.01 12.31 13.99 14.11 15.21 15.76 10.95

11.08 10.72 10.23 11.06 11.21 11.50 12.25 5.84

4.53 4.47 4.02 4.23 4.34 4.17 4.47 2.46

28.66 29.45 21.83 29.02 29.00 20.38 20.36 23.89

28.32 29.56 20.90 27.99 28.10 17.91 17.98 22.91

23.42 24.59 17.04 23.01 23.07 13.50 13.26 17.96

10.78 11.31 7.87 10.22 10.34 5.51 4.97 7.96

13.18 13.59 11.39

12.32 12.93 10.48

8.15 8.56 7.44

1.75 1.93 1.94

12.39 13.15 10.10

10.68 11.39 8.83

7.64 8.13 6.74

1.49 1.71 1.62

Table 7.2 Continued Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Queens Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests Staten Island Homicide Gun Homicide Non-Gun Homicide Assault Violence Drug Arrests Narcotics Only Arrests Marijuana Only Arrests

8.39 9.09 12.48 12.55 11.07

8.26 8.83 11.28 11.23 10.56

4.95 5.26 5.13 5.10 4.67

-1.02 -0.97 -1.81 -1.77 -1.91

7.87 8.52 10.45 10.49 9.26

7.11 7.61 8.52 8.45 8.11

4.76 5.02 4.10 4.06 3.75

-1.16 -1.07 -2.09 -2.05 -2.15

6.87 7.45 4.53 1.54 1.92 9.84 10.14 7.76

10.00 10.70 7.10 2.84 3.44 13.76 13.78 10.80

7.58 8.45 4.52 2.47 3.44 10.16 10.13 8.53

-1.64 -1.02 -2.08 -1.67 -1.74 -1.27 -1.16 -1.48

11.61 12.57 7.69 3.65 4.24 16.77 16.63 13.97

10.46 11.63 6.29 2.73 3.35 14.87 14.77 12.14

6.24 7.26 3.28 1.94 2.25 9.21 9.17 7.82

-0.92 -0.30 -1.54 -1.30 -1.35 -1.27 -0.05 -0.49

2.61 1.78 2.67 4.15 4.13 4.13 4.41 0.36

1.76 1.35 1.59 2.57 2.63 2.39 2.64 -0.08

1.44 1.41 0.81 1.42 1.52 1.64 1.79 0.21

0.29 0.73 -0.60 0.90 0.86 1.08 1.32 -0.52

2.23 1.37 2.19 1.50 1.62 1.52 1.62 0.42

0.64 0.39 0.74 0.39 0.42 0.65 0.80 -0.58

0.58 0.99 -0.27 0.09 0.18 0.59 0.75 -0.50

0.28 0.84 -0.77 0.29 0.31 0.23 0.33 -0.38

Table 7.3 Global Indicators of Spatial Autocorrelation - Z-Scores for Moran’s I and Geary’s C* Sociostructural Measures - New York City Census Tracts Binary Moran I Geary C Bronx Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Brooklyn Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Manhattan Poverty Labor Market Segregation

Stochastic Moran I Geary C

Centroid Distance Moran I Geary C

Nearest Distance Moran I Geary C

20.85 20.81 17.15 21.64 5.32 11.65 18.84

-17.12 -14.80 -13.11 -17.03 -1.97 -2.14 -13.77

21.04 21.01 18.99 21.62 5.14 11.57 19.44

-19.37 -17.92 -16.16 -19.31 -2.94 -5.65 -16.45

50.25 46.57 25.47 49.04 3.47 17.00 42.07

-23.68 -16.55 -14.03 -20.61 -2.78 -3.56 -16.64

28.76 27.09 13.60 30.06 0.94 12.07 25.00

-17.80 -13.36 -10.91 -16.59 -0.51 0.81 -14.10

31.76 31.51 32.84 30.82 6.97 28.44 30.17

-27.46 -25.72 -31.37 -27.35 -4.57 -24.89 -24.13

31.72 31.38 33.62 30.89 6.94 28.92 29.88

-29.76 -28.78 -33.01 -29.32 -5.43 -27.49 -27.49

79.47 65.42 82.82 76.95 9.89 38.13 83.82

-28.00 -19.52 -42.91 -31.79 -5.43 -15.50 -20.61

44.57 38.86 43.80 44.25 7.05 21.16 46.30

-23.35 -18.46 -32.67 -27.65 -2.57 -13.69 -18.27

20.79 20.20 20.19

-17.31 -17.46 -16.42

20.72 19.89 19.42

-19.22 -18.93 -18.27

41.26 39.47 29.63

-21.54 -23.22 -14.96

24.82 24.53 16.30

-16.67 -17.78 -10.43

Table 7.3 Continued Supervision Anonymity Immigration Housing Structure Queens Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Staten Island Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure

16.20 7.34 16.81 9.28

-10.53 -2.57 -10.31 -1.84

16.62 9.09 17.65 8.21

-13.67 -5.59 -13.36 -3.62

38.15 9.73 21.96 12.31

-13.89 -6.80 -11.48 -.035

24.02 4.54 16.02 6.96

-11.52 -3.13 -8.22 -0.90

15.92 19.11 30.15 13.49 7.79 31.33 12.61

-9.62 -10.95 -25.85 -3.25 -4.39 -23.18 -0.14

17.31 19.66 29.62 13.34 7.57 30.26 13.12

-12.93 -15.29 -28.08 -5.31 -4.69 -27.54 -1.21

24.38 32.69 41.42 26.51 5.61 78.41 26.99

-9.25 -7.41 -13.22 -3.59 -7.61 -15.85 -2.74

14.45 23.68 26.78 16.86 1.59 47.80 16.61

-7.56 -6.94 -13.24 -3.23 -3.88 -15.20 -2.45

4.90 3.85 9.52 1.18 4.83 6.74 9.69

-4.24 -2.82 -8.43 -1.86 -2.47 -3.44 -1.71

5.41 3.80 9.11 1.19 5.24 6.15 8.69

-5.19 -3.18 -8.67 -1.60 -3.39 -4.20 -3.31

6.10 1.96 11.63 1.26 4.33 7.17 8.95

-1.40 0.51 -5.15 -0.67 -4.00 -2.43 -1.75

1.24 0.20 4.06 0.52 1.86 4.21 4.00

-0.94 0.91 -1.68 -0.86 -2.25 -0.59 0.01

* Under randomization for Moran and Geary; inverse distance for Centroid and Nearest Neighbor

Table 7.4 Global Indicators of Spatial Autocorrelation - Z-Scores for G-Statistic Sociostructural Measures - New York City Census Tracts Centroid Distance .5 Mile 1 Mile 2 Miles 5 Miles Bronx Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Brooklyn Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Manhattan Poverty Labor Market Segregation

.5 Mile

Nearest Distance 1 Mile 2 Miles

5 Miles

26.02 23.55 18.59 25.81 3.72 12.54 24.33

32.98 28.76 20.89 32.48 3.59 13.14 36.07

30.80 26.33 15.59 29.27 0.43 14.75 39.82

11.54 11.40 6.68 9.27 0.89 2.60 17.34

29.65 26.89 20.49 30.19 4.26 16.84 32.00

32.97 28.31 19.40 32.50 3.36 14.29 39.72

31.18 26.59 14.51 29.36 -0.15 14.64 42.92

12.67 11.12 8.37 10.26 0.44 7.22 24.57

38.78 38.56 40.56 38.32 8.09 -34.15 38.83

59.42 55.93 59.17 56.98 9.58 -45.73 66.08

62.62 53.94 69.79 56.34 8.92 -34.65 95.52

18.81 5.84 52.81 29.43 1.11 -3.03 56.28

51.66 49.46 50.99 49.63 7.95 -42.10 54.49

62.34 57.85 61.90 59.00 9.61 -44.65 74.60

59.03 51.82 66.24 53.22 8.01 -31.06 93.15

15.85 5.10 45.33 27.87 2.18 -4.50 46.90

-27.88 18.57 24.47

-43.76 30.14 32.44

-48.63 30.35 27.95

-26.01 15.16 14.66

-35.89 22.81 29.44

-45.86 28.87 31.95

-48.38 28.83 26.67

-25.95 15.08 13.57

Table 7.4 Continued Supervision Anonymity Immigration Housing Structure Queens Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Staten Island Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure

17.59 8.32 19.84 9.16

22.25 8.51 22.08 9.98

25.01 5.78 18.37 3.69

13.69 5.70 12.52 3.48

17.68 8.20 21.46 8.78

20.67 7.81 19.90 7.62

23.61 4.34 17.53 3.02

13.92 6.13 11.89 3.04

2.30 14.66 33.17 11.31 8.04 42.66 8.92

2.38 17.89 51.35 15.31 6.22 61.39 10.67

1.82 12.74 45.54 15.08 5.78 56.97 8.84

0.99 4.38 -4.51 7.64 4.73 37.14 5.73

7.04 22.34 46.84 11.31 7.27 53.26 11.40

6.04 21.14 52.01 18.23 5.53 59.38 10.46

3.83 13.19 35.27 15.52 3.42 49.23 8.97

1.01 4.31 0.76 8.04 5.05 37.79 6.13

-1.41 0.51 1.01 -0.66 -0.91 -1.38 0.54

-1.55 0.20 1.72 -1.28 -3.60 -0.91 3.18

-1.25 -.015 1.14 .033 -4.66 -1.30 3.26

-1.05 0.04 1.18 -1.75 -7.97 -2.02 1.50

1.99 1.00 4.28 -.069 -4.71 -0.92 5.54

1.39 0.67 3.28 -0.84 -5.24 -1.07 4.80

0.90 0.24 2.36 -1.53 -4.78 -1.44 3.60

-0.49 -0.37 1.12 -1.38 -5.78 -1.61 1.77

118

Crime, Neighborhood, and Public Housing

The block group analyses validate those carried out at the tract level, further identifying high degrees of spatial patterning of crime and violence. Finally, year-by-year breakdowns are required to demonstrate that the results attained thus far, based on aggregate data, are not methodological artifacts. Annualized figures (not shown) portray spatial autocorrelation as a fairly consistent process, one not driven by anomalous events. It is ultimately a very robust phenomenon as well: variations in specification across space (census tracts or block groups) and time (annualized or aggregate years) produce little appreciable effect. Crime and violence in New York City are unequivocally spatially patterned, in the form of hot spots. Sociostructural Characteristics And what of the independent variables, the sociostructural factors? Do compositional elements such as poverty similarly evidence underlying order? In a word, yes: New York City tracts characterized by distinct social and economic factors unequivocally cluster together. In fact, the results provided in Table 7.3 may be more compelling than those in Table 7.1. Certainly, there are comparatively fewer “anomalous” cases. Even the outlier that is Staten Island, though it is once again more variegated than the other boroughs, illustrates more stable spatial autocorrelation than it did for the dependent variables. Consistent with Table 7.2, Table 7.4 reflects a familiar pattern of hot spots, the vast majority of which appear to be quite large, radiating out to for distances of at least five miles. But Table 7.4 also produces three “cold spot” findings that merit special attention: immigration in Brooklyn, poverty in Manhattan, and anonymity in Staten Island. In both of these cases, spatial autocorrelation is based not on the concentration of tracts with correspondingly low values, not high values. In Brooklyn, areas of low immigration tend to be found in close proximity to one another, as do areas of low poverty in Manhattan and low anonymity in Staten Island. While the reasons for these aberrations are not immediately clear, they are few enough in number that they do not compromise the integrity of the overall findings. Despite the strong patterning displayed in Table 7.3, Staten Island fails to deliver decisive confirmation of hot spots at the tract level. Here, scale is a crucial consideration. Supplemental analyses conducted at the block group level corroborate spatial patterning and more

Spatial Patterns and the Diffusion of Crime

119

conclusively demonstrate hot spots for labor, segregation, supervision, anonymity, and housing. Only poverty and immigration remain insignificant. The results for Staten Island are still far from unambiguous, but they are more positive than the census tract analysis necessarily suggests.

Spatial Autocorrelation in Public Housing Areas Although there is irrefutable evidence of spatial autocorrelation for both the crime measures and the sociostructural factors, the role of public housing in these processes needs also to be established. How do these hot spots play out in public housing? To what extent are hot spots of crime and socioeconomic distress correlated with public housing environs? According to Table 7.5, public housing areas are inextricably linked to crime and violence. Their relationship to structural disadvantage, however, is more complicated. Table 7.5 is constructed using local indicators of spatial autocorrelation (LISA) to assign a sigma value to each tract: values greater than 2 are indicative of hot spot concentration. Rather than merely substantiating the existence of hot spots, as global techniques do, LISA affords a way of identifying them. Part I of the table takes each of the crime types and apportions its total number of hot spots across public housing and non-public housing tracts. The comparison of proportions is staggering: public housing tracts are up to 5.5 times more likely to be hot spots of particular types of crimes and violence. Take homicide, for example. Only 9.4% (186/1988) of tracts without public housing constitute hot spots of homicide. For public housing tracts, however, the figure rises to 32.0%. In other words, the probability of a public housing tract experiencing concentrated homicide events is 340% greater than it is for non-public housing tracts. For assault, the difference between the tracts is a whopping 550% (6.2% vs. 34.6%). Even at its least severe, for marijuana only arrests, the disparity between public housing and non-public housing tracts is 250%. For every other crime type, the former outstrips that latter by at least a factor of three. The final column of Table 7.5 (headed % Tracts with Public Housing) uses the total number of hot spots as the basis for comparison by contrasting base rate of tracts with public housing (of the total

Table 7.5 Local Indicators of Spatial Autocorrelation Crime Measures Public Housing

Non Public Housing Tracts

Public Housing Tracts

% Tracts with Public Housing*

N

%

N

%

186

9.4

73

32.0

28.2

Gun Homicide

198

10.0

69

30.3

25.8

Non-Gun Homicide

124

6.2

63

27.6

33.7

Part I - Hot Spots Homicide

Assault

124

6.2

79

34.6

38.9

Violence

136

6.8

78

34.2

36.4

Drug Arrests

147

7.4

77

33.8

34.4

Narcotics Only Arrests

143

7.2

76

33.3

34.7

Marijuana Only Arrests

161

8.1

47

20.6

22.6

0 of 3

1711

86.1

121

53.1

6.6

1 of 3

139

7.0

30

13.2

17.8

2 of 3

93

4.7

32

14.0

25.6

3 of 3

45

2.3

45

19.7

50.0

Part II - Extreme Concentration

Table 7.5 Continued Neighborhood

Non Public Housing Neighborhood Tracts

Public Housing Neighborhood Tracts

% Tracts that are PH Neighbors**

N

%

N

%

Homicide

156

8.7

103

24.2

39.8

Gun Homicide

172

9.6

95

22.3

35.6

95

5.3

92

21.6

49.2

Part III - Hot Spots

Non-Gun Homicide Assault

103

5.8

103

24.2

50.0

Violence

111

6.2

103

24.2

48.1

Drug Arrests

125

7.0

99

23.2

44.2

Narcotics Only Arrests

122

6.8

97

22.8

44.3

Marijuana Only Arrests

142

7.9

66

15.5

31.7

0 of 3

1554

86.8

278

65.3

15.2

1 of 3

123

6.9

46

10.8

27.2

2 of 3

78

4.4

47

11.0

37.6

3 of 3

35

2.0

55

12.9

61.1

Part IV - Extreme Concentration

* Compared with base rate of 10.3%. ** Compared with base rate of 19.2%.

Table 7.6 Local Indicators of Spatial Autocorrelation Sociostructural Measures Public Housing

Non Public Housing Tracts

Public Housing Tracts

% Tracts with Public Housing*

N

%

N

%

460 488 631 420 164 486 401

23.1 24.5 31.7 21.1 8.2 24.4 20.2

122 107 57 102 18 44 37

53.5 46.9 25.0 44.7 7.9 19.3 16.2

21.0 18.0 8.3 19.5 9.9 8.3 8.4

553 588 387 386 74

27.8 29.6 19.5 19.4 3.7

56 28 44 84 16

24.6 12.3 19.3 36.8 7.0

9.2 4.5 10.2 17.9 17.8

Part I - Hot Spots

Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Part II - Extreme Concentration

0 of 7 1 of 7 2 of 7 3 or 4 of 7 5, 6 or 7 of 7

Table 7.6 Continued Neighborhood

Non Public Housing Neighborhood Tracts

Public Housing Neighborhood Tracts

% Tracts that are PH Neighbors**

N

%

N

%

384 418 581 345 153 437 347

21.5 23.4 32.5 19.3 8.5 24.4 19.4

198 177 107 177 29 93 91

46.5 41.5 25.1 41.5 6.8 21.8 21.4

34.0 29.7 15.6 33.9 15.9 17.5 20.8

506 544 348 335 57

28.3 30.4 19.4 18.7 3.2

103 72 83 135 33

24.2 16.9 19.5 31.7 7.7

16.9 11.7 19.3 28.7 36.7

Part III - Hot Spots

Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Structure Part IV - Extreme Concentration

0 of 7 1 of 7 2 of 7 3 or 4 of 7 5, 6 or 7 of 7 * Compared with base rate of 10.3%. ** Compared with base rate of 19.2%.

124

Crime, Neighborhood, and Public Housing

number of tracts, 10.3% contain public housing) to analogous tracts with hot spots. That is to say, it answers the question: “What proportion of crime hot spots is comprised of public housing tracts (relative to their overall percentage)?” Again, homicide is illustrative. Almost 30% of all homicide flash points coincide with public housing tracts. Juxtaposed against the base rate of public housing tracts, this is nearly three times the anticipated number. For every crime type, the incidence of hot spots for public housing tracts is two to three times the expected rate, based on the distribution of these tracts throughout the city. Part II of Table 7.5 attempts to assess cumulative effects or “extreme concentration” through the three primary crime types (homicide, assault, and drug arrests). Tracts are denoted as having zero, one, two, or three hot spots, the implication being that the greater the number of hot spots, the more potent the concentration. Looking first at tracts devoid of public housing, it is apparent that an overwhelming number (86%) have no hot spots. The corresponding figure for tracts with public housing is much lower, only 53%. Conversely, as the number of hot spots climbs, the positions are reversed. The ratio of public housing to non-public housing tracts having one hot spot is 2:1, rising to 3:1 for two hot spots. At the farthest end of the spectrum, public housing tracts are eight times more apt to be characterized as intense crime hot spots; roughly one in five public housing areas is marked by these daunting levels of concentration. The last column again reinforces this finding. Of the total areas lacking hot spots, a meager 6.6% are public housing tracts. Conversely, half of the most severe hot spots are found in public housing tract, five times the expected number. Parts III and IV of Table 7.5 repeat the preceding analyses on neighborhoods adjacent to public housing tracts, with complimentary results. Public housing neighborhoods are between two and four times more likely than non-public housing neighborhoods to demonstrate significant concentrations of crime. In addition, a disproportionate number of hot spots fall within these neighborhoods. Hot spots of crime are found in public housing neighborhoods at twice their expected rate.15 Analogous to Part II, the indicators in Part IV point to formidable multi-crime clustering in public housing neighborhoods. The probability of a neighborhood presenting the most extreme concentrations is more than six times greater if it is in the vicinity of public housing. Alternatively, the last column shows that over 60% of

Spatial Patterns and the Diffusion of Crime

125

extreme hot spots are public housing neighborhoods, over three times the expected number. In short, there is significant symmetry between public housing areas and crime. Hot spots of crime and violence are more prevalent in census tracts containing public housing, as well as surrounding public housing neighborhoods. Contrarily, the confluence of adverse sociostructural conditions and public housing is more nebulous. Table 7.6 reveals several variables for which the evidence of convergence is unmistakable, notably poverty, labor market, and supervision. But for the other factors, the preponderance of hot spots between public housing and non-public housing tracts is considerably more balanced. That is to say that public housing tracts are not more disadvantaged vis-à-vis segregation, anonymity, immigration or housing structure; in fact, they appear to be marginally less so. In terms of extreme sociostructural deficit, however, public housing is once again inordinately adversely affected. These areas have a reduced chance of being relatively free of distress (zero or one hot spots) and a higher likelihood of concerted deprivation (three or more hot spots). Precisely the same results hold for public housing neighborhoods. These areas are possessed of some sociostructural hot spots, but the real separation is evident when the amplification effects of multiple deficiencies are accounted for. In response to the earlier query, then, public housing appears to be a focal point for both criminal behavior and socioeconomic distress. The same is true, however, of neighborhoods surrounding public housing. It isn’t simply that public housing suffers from a host of social ills: the immediate context within which it finds itself situated is marked by the same maladies. Spatial autocorrelation statistics, while offering important hints and insights, can barely begin to appraise the complex relationship between public housing and its broader environs. They can do little more to further evaluate or disentangle effects, tasks better suited to more sophisticated, multivariate statistical modeling procedures.

Diffusion Results Notes on Interpretation Modeling crime as function of a) the direct effect of the independent variables; and b) the interaction effect of the independent variables and time allows for the simultaneous estimation of both “mean variation”

126

Crime, Neighborhood, and Public Housing

and “slope variation” respectively. This explains why the diffusion models always include what appear to be two versions of the same variable. In Table 7.7 for instance, there are two entries for “PH Block Group(s) Diffusion Effect”. In the homicide model (just for the sake of orientation), the z-score for the first occurrence of this variable is 2.11, while the second z-score noted is 2.87. The estimate for the first diffusion variable refers to its effect on the mean of the dependent variable “neighborhood homicide.” Put another way, the estimate for the first diffusion variable is used to answer the question: “To what extent is the mean level of homicide in public housing neighborhoods contingent upon average homicide levels in corresponding public housing block groups (lagged by one year)?” If the example were changed to include the first poverty variable, the relevant question would simply be “What is the effect of mean neighborhood poverty on mean neighborhood homicide levels over time?” For purposes of interpretation, the first version of each of the independent variable is referenced by using “mean-state” or “average-state” (i.e. mean-state diffusion). In contrast, the second diffusion parameter (set off by the Time Interactions heading) pertains to its effect on the within-unit variation of neighborhood homicide, where within-unit variation is synonymous with the change over time, or slope, of the dependent variable. The interaction with time addresses the question: “To what extent do lagged public housing block group homicides account for changes in neighborhood homicides over time?” For poverty, the question would be “To what extent does neighborhood poverty influence neighborhood homicide levels over time?” To set these effects apart, they are denoted with “time-interaction” (i.e. time-interaction diffusion), “over time” (i.e. poverty over time), or “slope” (slope diffusion).

Outward Diffusion Models Diffusion Variables As illustrated by Table 7.7, there is sufficient evidence to conclude that homicide diffuses outward, from public housing block groups to their surrounding neighborhoods. For overall homicide, the z-scores for both mean-state diffusion (2.11) and diffusion over time (2.87) are above the threshold for significance (z > |1.96|). This suggests that the level of

Table 7.7 Outward Models - Summary of Z-Values Parameters

Homicide

Assault

-24.04

-17.04

Drug Arrests -21.51

Time Public Housing Block Group(s) Contagion Effect Poverty

-2.08

-3.85

1.56

2.11

3.16

9.00

1.79

-0.55

4.79

Labor Market

-1.58

-4.50

-0.91

2.41

4.70

5.85

Supervision

2.99

-1.81

-2.02

Anonymity

-2.40

-2.18

-1.84

Immigration

-4.44

-7.81

-5.27

Intercept

Segregation

Housing

1.05

3.44

-0.03

10.16

3.69

11.26

2.87

-1.28

2.38

-0.93

0.61

-0.77

Labor Market

-0.61

0.51

-0.73

Segregation

-0.57

-0.63

0.97

Supervision

0.52

0.62

-0.43

Anonymity

0.75

-1.17

1.78

Immigration

-1.30

0.17

-0.13

Housing

0.00

-2.58

0.00

Spatial Lag

1.91

0.48

-2.56

22.75

21.81

24.91

0.56

0.82

0.81

Spatial Lag Time Interactions Public Housing Block Group(s) Contagion Effect Poverty

Population - Logged Model Fit R2

127

128

Crime, Neighborhood, and Public Housing

public housing block group homicide plays an important role in determining relative average-state homicide levels in adjacent neighborhoods as well as changes in neighborhood homicide over time. These diffusion effects persisted even after controlling for neighborhood composition (the factors) and spatial lag. The robustness of mean-state diffusion is noteworthy, given that several of the meanstate factors and the average-state spatial lag variable are also significant. The successive introduction of the factors and spatial lag attenuated the strength of mean-state diffusion slightly, but not entirely (see Appendix C.1). Conversely, that over-time diffusion is not effected by the other time-interactive independent variables is anticipated by the relatively weak coefficients produced by the overtime factors and spatial lag. In what will become a familiar pattern, none of the independent variables, apart from diffusion, is useful in explaining changes in neighborhood homicide. The remaining crime types also evidence strong mean-state diffusion. As with homicide, the outward diffusive effects of assaultive behaviors and drug arrests are constant: the introduction of the factors and the spatial lag variable failed to curb diffusion effects in any of these models (see Appendices C.2 and C.3). The slope diffusion findings, however, are more disparate. Assault does not demonstrate time-interactive diffusion, while drug arrests do diffuse over time. Sociostructural Characteristics and Spatial Lag In general, neighborhood composition is related to average levels of crime and violence in public housing neighborhoods. Table 7.7 shows that, of the seven mean-state sociostructural factors, segregation and immigration are most uniformly influential across the crime models. Their divergent signs are not inconsistent, if both are considered complementary facets of neighborhood heterogeneity. More important, given that some of the factors are not completely orthogonal,16 the specific effects of particular factors are less consequential than their composite impact. On average, each of the factors is significant in more than half of the models, and every factor is significant in at least one of the primary models. Moreover, with the exception of marijuana arrests, each model features at least four significant mean-state factors: alternatively stated, on average, more than half of the factors exert a noteworthy influence on mean neighborhood crime counts. In stark contrast, the factors show a negligible effect on crime trajectories.

Spatial Patterns and the Diffusion of Crime

129

Simply put, with the sole exception of the time-interactive housing variable in the assault model, none of the slope factors is significant in accounting for changes in crime levels over time. Similar to sociostructural composition, geographic proximity is a key predictor of mean neighborhood crime levels: the mean-state spatial lag covariate achieves significance in every model. But unlike the neighborhood factors, spatial lag is also significant in accounting for over-time changes, particularly with respect with drug arrests. As suggested in Chapter 6 and by the spatial autocorrelation results presented earlier in this chapter, the proper specification of diffusion requires controls for influence of spatial patterning. Although the impact of spatial lag on model fit tends not to be large, it is nonetheless above and beyond the effects of the other variables in the models. Appendices C.1 to C.3 indicate that the introduction of spatial lag does not usually induce notable changes in model coefficients. So, while spatial lag merits attention principally for its exogenous effect, it also carries with it the potential to moderate central explanatory variables. Model Fit By social science standards, the model fits for the crime models range from acceptable to quite favorable. While there are no universally accepted criteria upon which R2 may be evaluated, values exceeding 0.75 are normally taken as at quite good. Two of the models, assault (0.82) and drug arrests (0.81), both surpass this benchmark. At over 50%, homicide exhibits a respectable level of explained variance. Without overstating the utility of what is really a proxy measure of explained variance, it appears that the models of outward crime diffusion are at worst serviceable, and at best verging on exemplary. Thus far, the results have essentially confirmed what has already been posited or presumed about public housing projects: that they are criminogenic places from which crime spreads. In epidemiological terms, public housing crime and violence seems to “infect” adjacent neighborhoods. This is especially the case for mean-state crime. The sociostructural characteristics of neighborhoods are also significant elements in determining crime levels, but their effects are insufficient to function as barriers to the emanating contagion. The question still remains, however, as to whether the reciprocal is similarly true: “Does neighborhood violence seep into public housing?”

130

Crime, Neighborhood, and Public Housing

Inward Diffusion Models Diffusion Variables The short answer to the query posed above is yes, but, as is always the case with complex phenomena, the reality of inward diffusion is a good deal more complicated. The most immediately striking features of the diffusion results conveyed in Table 7.8 are their similarity to the outward diffusion findings. First, mean-state diffusion is evident in each of the crime models. As designated by Neighborhood Diffusion Effect, the prevalence of crime in public housing is, at least in part, conditioned by average neighborhood crime levels. Time-interaction diffusion, on the other hand, is more ambiguous. At first glance, only drug arrests evince a significant over-time diffusion effect. Part of this picture is obscured, however, because Table 7.8 only presents final models. Follow-up analyses (not shown) reveal reveal further proof of diffusion, corroboration that implicates the mediating effect of sociostructural characteristics. As hypothesized in Chapter 6, the factors function as barriers to the diffusion of homicide over time. Sociostructural Characteristics Though less pronounced in comparison to the outward models, the mean-state effects of the sociostructural factors in Table 7.8 are still material. Both the homicide and assault models have three significant mean-state factors. Drug arrests show only one salient factor, but it is telling that that factor, poverty, is the only compositional element that is consequential across all three crime models. Three of the other factors, segregation, anonymity, and housing, are not pertinent in any of the models, while labor market and supervision in just one. Without violating the caveat against overstating the importance of any one variable, the findings imply that whereas heterogeneity is of principal relevance to outward diffusion, mean-state inward diffusion depends more on poverty. Akin to the outward models, virtually none of the time-interactive factors is independently substantial. Still, that they attenuate slope

Table 7.8 Inward Models - Summary of Z-Values Parameters

Homicide

Assault

-16.86

-11.22

Drug Arrests -9.17

-0.83

-2.81

0.88

4.72

2.01

10.98

Intercept Time Neighborhood Contagion Effect Poverty

3.10

3.44

3.32

-0.95

-2.11

-1.36

Segregation

1.65

1.03

1.84

Supervision

2.29

0.89

1.17

Labor Market

Anonymity

-1.94

-0.27

-0.56

Immigration

-4.73

-4.12

-0.45

0.58

1.36

1.21

Neighborhood Contagion Effect

1.81

1.84

3.13

Poverty

1.14

-2.49

-0.26

Labor Market

1.54

-0.11

1.48

Segregation

-0.71

-0.44

0.76

Supervision

-0.65

1.09

-0.75

Anonymity

0.58

-0.72

1.65

Immigration

-0.58

-0.72

-0.15

0.28

0.73

1.71

14.93

13.28

9.88

0.36

0.68

0.58

Housing Time Interactions

Housing Population - Logged Model Fit R2

131

132

Crime, Neighborhood, and Public Housing

diffusion in two of the three models underscores their effectively conjoint nature. Whereas none of the constituent factors is individually capable of successfully stemming diffusion over time, cumulatively they carry the potential to withstand the inflow of crime from nearby areas. Put another way, public housing areas are socially and structurally differentiated with respect to their ability to resist external criminogenic influences over time. Model Fit Owing, perhaps, to the reduced number of significant factors, the statistical fits for the inward models are less impressive than where their outward diffusion counterparts. On average, introducing the factors into the outward models prompted an almost 50% (49.4) increase in R2 (0.224 points). The comparable figure for the inward models is less than half, at 23.5% (or 0.097 points). The suitability of the assault (0.68) and drug arrest (0.58) models continues to be at least adequate, but the R2 for homicide (0.36) clearly highlights the need for further model elaboration. Given that, for outward diffusion, the homicide model similarly produced the lowest model fit (by a considerable margin), it is possible that that there is something about this particular phenomenon which makes it more difficult to model accurately. Homicide is, for example, characterized by relatively low base rates that may render it less amenable to forecasting. Alternatively, there may be some unique aspect of homicide that is not reflected here. Homicide, the ultimately personal and intimate crime, may engage micro-level dynamics not captured by the current macrolevel constructions. Either way, more definitive conclusions about the nature of homicide diffusion in and out of public housing, but especially of the inward variety, will require further specification.

The results thus far provide tentative support for most diffusion hypotheses. However, before the results may be deemed conclusive, before their implication may be explored, their validity must be formally established through a variety of testing procedures. As well, there are numerous diagnostics that need to be conducted to ensure that these findings are not artifacts. The requisite validity checks and diagnostic assessments are performed in the following chapter.

CHAPTER 8

Validation and Diagnostics

Validation The Specification of Lag Periods Designing the modeling strategy employed in the preceding chapter required several a priori decisions to be made; specifically, decisions related to the length of the lag term. While the decision to use one year as the lag seemed reasonable, it was prudent to test whether different lag periods would significantly alter the results. Six months (of “halfyears”) and two years were arbitrarily chosen as alternative lag specifications. Each of the tables summarizes any changes in the significance of the coefficient estimates across the various specifications. In each table, one-year lag models were used as the base, and noteworthy changes were recorded in relation to that base. The word “Gain” appears where the one-year model performed substantively better than the alternative approach, while “Drop” represents a corresponding loss of significance for the one-year model.17 For example, Table 8.1 shows that the “PH Block Group(s) Contagion Effect” remained relatively unchanged in all but two of the models. The coefficient for gun homicide, which was insignificant for the one-year lag (outward) model, reached significance in the half-year lag model. This comparatively “worse” performance for the one-year model is thus presented as “Drop”. Conversely, the significance of the coefficient for non-gun homicide in the one-year model, contrasted with its less than significant counterpart in the half-year model, resulted in the “Gain” designation. The same logic of baseline comparison informs the presentation of model fit. A positive (+) change in the R2 statistic indicates that the one-year model had a superior fit to the optional specification, while a negative (-) notation suggests an associated decrease in predicitive power for the one-year model. 133

Table 8.1 Reliability Summary – 1 Year vs. Half-Year Lags Parameters Intercept Time PH BG(s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Time Interactions PH BG (s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Population - Logged Model Fit Change in R2

Homicide

Outward Models

Gun Homicide

Non-Gun Homicide

Drop

Gain Gain

Gain

Assault

Violence

Drug Arrests

Narcotics Arrests

Marijuana Arrests

Gain

Gain

Gain Drop

Drop Gain Gain

Gain

Gain Drop

Drop Gain

+0.14

+0.14

+0.14

+0.06

+0.06

Gain

Gain

+0.07

+0.07

+0.14

Validation and Diagnostics

135

Across the outward models in Table 8.1, the parameter estimates were generally unchanged. Where considerable shifts were evident, they more often favored the one-year specification. This was particularly true for the time interactive contagion effect, which registered gains in each of the drug arrest models as well as the undifferentiated homicide model. In a similar vein, three time interactive spatial lag parameters benefited from the use of one-year lags. In contrast, no variable realized more than one drop. For the remainder of the covariates, distinctions between the one-year and halfyear models, where they existed at all, lacked any discernible pattern. Fully half of the parameters were stable across all models for both specifications. The absence of systematic modifications was likewise absent within each of the various models. Non-gun homicide, the model with the largest number of “same direction” changes, produced an underwhelming three parameter gains. Only one model, marijuana only arrests, produced more than one drop. This was also the only model where the number of drops outstripped the number of gains: in every other instance, the one-year models were characterized by an excess of gains. But if these suggest that the one-year lags were marginally preferable, one area that did illustrate unambiguous advances for these models was model fit. Without exception, the fit statistics were substantially lower for the half-year models. Even marijuana only arrests, with two drop parameters, had an R2 that was 14 points higher for the one-year model. Given the paucity of contradicting evidence, the relative strength of the fit statistics provided tentative support for the one-year models. With even fewer changes, support for the one-year lagged inward models presented in Table 8.2 was also largely predicated on model fit. Across models, only six coefficients experienced movement. Although the time interaction contagion effect was once again the most active parameter, two gains and one drop constituted more modest (not to mention ambiguous) than that witnessed in the outward models. Marijuana only arrests also repeated as the most active model, the only one with more than one change in significance. With a grand total of one more gain than drop, there was little to mediate between the oneyear and half-year models. Only the familiar pattern of elevated model fit convincingly differentiated the two. Reminiscent of Table 8.1, R2

Table 8.2 Reliability Summary – 1 Year vs. Half-Year Lags Parameters Intercept Time Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Time Interactions Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Population - Logged Model Fit Change in R2

Homicide

Gun Homicide

Inward Models Non-Gun Homicide

Assault

Violence

Drug Arrests

Narcotics Arrests

Marijuana Arrests Gain

Gain Drop

Gain

Drop

Drop

Gain

Gain

Drop

+0.12

+0.11

+0.07

+0.10

+0.10

+0.08

+0.05

+0.11

Validation and Diagnostics

137

values for the one-year inward models were, on average, nine points higher than those of their half-year counterparts. At a minimum, these differences tended to offer greater support for the former models. There is little evidence to suggest that the use of half-year lags would have produced better substantive results for either the outward or inward contagion models. Perhaps the most equivocal of the validity analyses are presented in Table 8.3, which compares one-year and two-year lags for the outward models. If these juxtapositions were less than definitive, it was certainly not attributable to a dearth of movement: these were the busiest of the validity results. Although some of the culprits are well known by now, including the time interactive contagion effect (four gains) among the parameters and marijuana only arrests (two gains and three drops) between the models, new players also insinuated themselves into the fray. The direct effect of public housing block group contagion, for example, also gained in four instances, while anonymity experienced considerable movement both with its direct effect and with its time interaction parameter. Marijuana only arrests remained the most dynamic of the models, but total drug arrests, narcotics only arrests, and homicide were also exceptionally lively. But all of this hustle and bustle notwithstanding, no clear pattern of change emerged. Contagion effects performed more positively under the oneyear formulation, whereas the results for anonymity would have been bolstered through the two-year specification. Moreover, the vast majority of coefficients stayed constant. Ultimately, the customary patchwork of seemingly random movement exposed in Tables 8.1 and 8.2 persisted. The difference, however, between those half-year comparisons and these ones was that no aid was forthcoming from model fit. On the contrary, where R2 varied at all (in three models it did not), the divergence was negligible and tended not to favor either lag designation. The worst that can be said of the one-year model is that it did not represent a significant improvement over the two-year variant. If a tie goes to the runner, there is no reason to dismiss the one-year specification. The results for the inward models demonstrated in Table 8.4 inspire similar ambivalence. To be sure, there was slightly less movement in terms of the parameters and a little more disparity in the model fit statistics. But on the whole, the same conclusion could be reached: the one-year and two-year lagged models were virtually indistinguishable from one another.

Table 8.3 Reliability Summary – 1 Year vs. 2 Year Lags Parameters Intercept Time PH BG(s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Time Interactions PH BG(s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Population - Logged Model Fit Change in R2

Homicide

Gun Homicide

Gain Gain

Outward Models Non-Gun Homicide

Assault

Violence

Gain

Gain

Gain

Gain

Gain

Drug Arrests

Narcotics Arrests

Drop

Drop

Marijuana Arrests

Gain

Drop

Drop

Gain

Gain

Drop

Drop

Drop Drop

0

0

+0.02

Gain Drop

Gain Gain

-0.01

-0.02

+0.01

+0.01

0

Table 8.4 Reliability Summary – 1 Year vs. 2 Year Lags Parameters Intercept Time Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Time Interactions Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Population - Logged Model Fit Change in R2

Homicide

Gun Homicide

Inward Models Non-Gun Homicide

Assault

Violence

Gain

Gain

Drug Arrests

Narcotics Arrests

Marijuana Arrests

Drop

Drop

Gain Gain

Gain Drop Gain

Drop

Drop

Drop

Gain

Gain

Gain

Gain

Gain

Drop

-0.01

0

-0.02

+0.02

+0.02

-0.03

-0.05

+0.05

140

Crime, Neighborhood, and Public Housing

Overall, two inferences seem warranted. First, the evidence marshaled in Tables 8.1 and 8.2 supports the primacy of the one-year lagged models over the half-year versions. There was nothing in the parameter estimates to differentiate the two, but the inferior model fits statistics for the latter promoted the one-year models by default. Second, the results of the two-year lagged models were remarkably consistent with the original results. To be sure, differences were noted for some coefficients. But these changes were essentially stochastic. There was no definitive pattern to suggest that either of the alternative specifications would have produced substantively divergent outcomes. At a minimum, the absence of countervailing proof must translate into qualified support for the validity of the one-year specification. The Dependency of Crime Types To facilitate presentation, the contagion models were developed for each crime type (and subtype). On one hand, this seemed a perfectly reasonable approach. It was certainly possible that different dynamics were at play for different types of crimes. Indeed, the results seemed to confirm this. It was not surprising, for example, that drug arrests appeared to be more contagious than homicide. Based on the severity of the underlying behavior, one would expect that less serious transgressions would be more likely candidates for emulation. On the other hand, breaking the analyses down by crime type may have fundamentally misrepresented the complexities of crime. More specifically, the separation of crimes implicitly assumed that crime types were independent of one another. But suppose they were not. It was equally as plausible that the obverse was true, that crime types were intimately related. If they were dependent, that dependence could have altered the results. To test for this contingency, “stacked” models that include all of the crimes at the same time must be run. The results of these dependency tests are presented in tables 8.5 and 8.6. The broad symmetry that emerged in outward models in Table 8.5 indicates that the results in Chapter 7 were not contingent upon peculiarities related to the specification of crime. The only parameter for which change was even remotely noteworthy was anonymity, and even those outcomes were mixed. The initial-state effect of anonymity dropped to insignificance for both the homicide and assault models, but for the latter, the time interaction effect of anonymity rose to

Table 8.5 Validity Summary – Independence vs. Dependence Parameters Intercept Time PH BG(s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Time Interactions PH BG(s) Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Spatial Lag Population - Logged

Homicide Original Model Dependence (t) Model (t) -24.04 -19.58 -2.08 -2.22 2.11 4.42 1.79 1.21 -1.58 -1.69 2.41 2.33 2.99 3.01 -2.40 0.76 -4.44 -4.90 1.05 2.13 10.16 11.02

Outward Model Assault Original Model (t) -17.04 -3.85 3.16 -0.55 -4.50 4.70 -1.81 -2.18 -7.81 3.44 3.69

Dependence Model (t) -19.58 -1.23 2.80 -1.07 -2.99 3.81 -1.24 -0.04 -4.94 4.20 2.52

Drug Arrests Original Model Dependence (t) Model (t) -21.51 -19.58 1.56 1.43 9.00 9.01 4.79 4.27 -0.91 -1.18 5.85 5.96 -2.02 -2.55 -1.84 -1.04 -5.27 -5.89 -0.03 0.58 11.26 13.53

2.87 -0.93 -0.61 -0.57 0.52 0.75 -1.30 0.00 1.91

2.97 -0.90 -0.55 -0.50 0.50 0.76 -1.45 0.02 1.94

-1.28 0.61 0.51 -0.63 0.62 -1.17 0.17 -2.58 0.48

-1.94 1.02 0.83 -1.72 0.36 -1.99 0.50 -3.12 -0.76

2.38 -0.77 -0.73 0.97 -0.43 1.78 -0.13 0.00 -2.56

2.70 -0.80 -0.77 0.98 -0.43 1.86 -0.14 -0.08 -2.25

22.75

18.87

21.81

23.49

24.91

23.05

Table 8.6 Validity Summary – Independence vs. Dependence Parameters Intercept Time Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Time Interactions Neighborhood Contagion Effect Poverty Labor Market Segregation Supervision Anonymity Immigration Housing Population - Logged

Inward Model

Homicide Original Model Dependence (t) Model (t) -16.86 -8.76 -0.83 -0.87 4.72 5.39 3.10 2.71 -0.95 -1.00 1.65 1.24 2.29 2.61 -1.94 1.43 -4.73 -4.78 0.58 -0.33

Assault Original Model (t) -11.22 -2.81 2.01 3.44 -2.11 1.03 0.89 -0.27 -4.12 1.36

Dependence Model (t) -8.76 0.30 -0.33 3.84 -0.77 2.14 -0.51 0.67 -1.83 1.80

Drug Arrests Original Model Dependence (t) Model (t) -9.17 -8.76 0.88 0.95 10.98 11.17 3.32 3.15 -1.36 -1.17 1.84 1.89 1.17 1.19 -0.56 -0.07 -0.45 -0.85 1.21 1.32

1.81

1.71

1.84

2.12

3.13

3.40

1.14 1.54 -0.71 -0.65 0.58 -0.58 0.28 14.93

1.24 1.58 -0.75 -0.70 0.52 -0.56 0.21 7.34

-2.49 -0.11 -0.44 1.09 -0.72 -0.72 0.73 13.28

-3.22 -0.18 -1.92 1.40 -1.53 -0.09 -0.65 10.48

-0.26 1.48 0.76 -0.75 1.65 -0.15 1.71 9.88

-0.26 1.60 0.72 -0.58 1.53 -0.06 1.72 9.43

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significance. With these alterations in its anonymity estimates, in addition to the decrease in the time variable (and, if you like, the “almost” gain for time interactive contagion), the model for assault had the most activity: hardly a stinging indictment of the original model constructions. Homicide showed no change in any of its time interactive parameters, and drug arrests experienced no movement of any kind. That the parameters for the original models and dependence models were generally uniform tends to validate the presentation of crime-specific models, the supposition of independence notwithstanding. Table 8.6 conveys a more muddled picture, if for no other reason than the even greater shifts manifested in the assault model. Analogous to Table 8.5, the inward models were remarkably static. Other than the odd, but not-quite-significant, change in the direction of anonymity, both the homicide and drug arrest models were free of discrepancies. Conversely, the assault models were riddled with inconsistencies, especially for the intercept-related parameters. The most salient of the transformations concerned the initial-state neighborhood contagion effect, which registered as insignificant under the dependence model. This reduction would have been more worrisome were its deleterious impact not offset by the concomitant jump in the time interactive contagious effect. With only the one material blemish, there was no case to be made for abrogating the original results. The findings from the models run under the assumption of dependence were insufficiently divergent to nullify or otherwise vitiate the implicitly independent models. Bear in mind that these analyses were in no way intended as authoritative tests of the “independence-versus-dependence-of-crime thesis.” Rather, the more limited ambition was merely to assess whether the crime-specific modeling strategy was methodologically sustainable and to ensure that the outcomes were not an artifact of design. To this end, the independent approach appears to have been justified, or at least not to have been foolhardy.

Diagnostics Randomness of Residuals Diagnostics refers simply to the process of checking the adequacy of the various models; in particular, the detection of outliers and evaluation of potentially influential observations. Any appropriate

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analytic framework must include a final check of the sufficiency of the selected models. Owing to their unique properties, however, many of the well-known techniques for investigating residuals cannot be applied, without modification, to Generalized Estimating Equations (GEEs) (Hardin and Hilbe, 2002). Thankfully, specific techniques have beenadapted for use with GEEs as their use has become more widespread. Here, diagnostics began with the Wald-Wolfowitz runs test, which can be applied to detect patterns among the residuals (Chang, 2000). The first step in the test is to recode the residual values. If the residual is positive, it is assigned a value of one (+1); negative residuals are scored as negative one (-1). Next, the new sequence of positive and negative values is examined to determine the number of runs that comprise the series (regardless of the length of the runs). For example, the sequence of signs + – + + – – + + + – – + + contains seven runs. After the number of runs has been calculated, equations 8.1 through 8.3 can be used to evaluate the hypothesis that the signs of the residual values are randomly distributed. Assume that T is the number of runs observed in a sequence, np is the total number of positive residual values in the sequence, and nn is the total number of residual values that are negative. Under the random distribution hypothesis, the expected value of T is E(T) =

2 np nn ______ np + nn

+1

(8.1)

the variance of T is V(T) =

2 np nn(2 np nn – np – nn) _____________________ (np + nn)2(np + nn – 1)

(8.2)

T – E(T) _______ V(T)

(8.3)

and the test statistic is Z=

For large samples, the test statistic Z has an approximate standard normal distribution (Chang, 2000). Extreme values of Z indicate that the hypothesis of random residual values should be rejected.

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Table 8.7 Wald-Wolfowitz Diagnostics for Randomness of Residuals Outward Inward W-W Prob. W-W Prob. Final model for: Score (