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Structural correlates of race-specific drug sales arrests over time

University of Florida Institutional Repository
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PAGE 1

STRUCTURAL CORRELATES OF RACE -SPECIFIC DRUG SALES ARRESTS OVER TIME: ARREST TRAJ ECTORIES FROM 1980-2001 By SCOTT RICHARD MAGGARD A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Scott R. Maggard

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Sean Allen McCluskey This dissertation is dedicated to the best fr iend I could ever ask fo r, Sean McCluskey. A sociologist at heart, it was Sean who firs t convinced me to change my major from psychology to sociology. Sean had the unca nny ability to see the world from what seemed to be an infinite number of perspe ctives. I learned a great deal about life from Sean and I hope that he learned as much from me as I did from him. I will never have another friend like Sean; however I am forever grateful for th e years that I was able to know him and share so much with him.

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iv ACKNOWLEDGMENTS First and foremost I thank Karen Parker, my mentor, chair, and friend. I first met Karen as I began a teaching assistantshi p for research methods and teaching the laboratory sessions for her course. Karen al so introduced me to the theoretical and methodological approaches discussed thr oughout this research. Her expertise and willingness to share that expertise are second to none. My approach to research in general is much attributed to the knowledge I gained through working closely with her. I am also grateful to Aaron Griffin for his willingness to share her time, especially these past few months. Coupled with Karen, Lonn Lanza Kaduce, Ron Akers, Alex Piquero, and Joe Spillane comprised what I would argue to be the most solid and well-balanced committee one could ask for. Lonn was always there to provide practical advice, whether needed within the academy or elsewhere. From Ron Akers I learned much about both theory and the assessment of theories. Alex provided me thodological expertise, and seeing his use of these techniques in the life-course arena led to the idea to apply th ese methods to drug arrests in the first place. Finally, Joe provide d not only his never ending insight into the world of drug use and drug policy, but also added much needed humor and reflection on life in general. The remainder of the faculty and staff in the Department of Criminology, Law, and Society all deserve thanks for their willingne ss to share their know ledge and experiences

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v with their students. Whether writing the diss ertation to obtaining employment, they were always approachable and willing to help. I thank Bob Jones, who is the architect of the PROC TRAJ software plug-in that was utilized in this research. I am grat eful for the many hours Bob has sacrificed answering emails and assisting me to better understand and use the PROC TRAJ software. Bobs support for a software program for which he is not paid is unprecedented. Craig Boylstein and Kim Raymond deserve mu ch credit for alleviating stress and providing much needed humor when needed. Cr aig was one of the first people I met in the sociology program after moving to Gain esville in 1999, and we immediately became close friends. I thank my parents for putting up with what sometimes seemed to them my everlasting journey to remain a student as l ong as possible. They will likely get as much satisfaction from the fact that I am finally exiting school as anyone. I would not have been able to do this without them. The many fellow graduate students with w hom I became friends at the University of Florida I also thank. John Reitzel, and Jennifer Matheny both provided much needed humor and entertainment th roughout graduate school. Finally I thank my wife, A llison Chappell. Without Allison I likely would not be finishing this document. She is my life, a nd her selflessness which made this possible I will be forever grateful for. Allison has been there day and night, through rough times and bright, pushing me along in order to complete this task.

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vi TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES...........................................................................................................ix ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 Introduction...................................................................................................................1 Background............................................................................................................1 Present Study.........................................................................................................3 Specific Aims........................................................................................................6 Significance...........................................................................................................7 Layout of the Dissertation.....................................................................................9 2 THEORETICAL PERSPECTIVES............................................................................10 Introduction.................................................................................................................10 Social Disorganization................................................................................................11 Concentrated Urban Disadvantage.............................................................................18 Unemployment and th e Urban Underclass..........................................................18 Poverty and Segregation......................................................................................22 Racial Threat: Blalocks Power Threat Hypothesis....................................................27 Conclusion..................................................................................................................35 3 REVIEW OF THE LITERATURE............................................................................38 Introduction.................................................................................................................38 Race, Drugs and Crime: A Historical Perspective......................................................38 Race, Drugs and Crime: The New Era.......................................................................44 Hypotheses and Expectations.....................................................................................49 Social Disorganization and Drug Sales Arrests...................................................51 Concentrated Urban Disadvant age and Drug Sales Arrests................................52 Racial Threat and Drug Sales Arrests.................................................................53

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vii Conclusion...........................................................................................................53 4 DATA AND METHODS...........................................................................................55 Introduction.................................................................................................................55 Data Sources...............................................................................................................55 Dependent Variable.............................................................................................56 Independent Variables.........................................................................................58 Methodology...............................................................................................................63 Statistical Procedures...........................................................................................63 Risk Factors.........................................................................................................67 5 DESCRIPTIVE STATISTICS....................................................................................69 Introduction.................................................................................................................69 Descriptive Statistics..................................................................................................69 Independent Variables.........................................................................................69 Dependent Variable.............................................................................................73 Drug Sales Arrests Trajectories..................................................................................74 Conclusion..................................................................................................................78 6 MULTIVARIATE RESULTS....................................................................................85 Introduction.................................................................................................................85 White Drug Sales Arrests...........................................................................................86 Accounting for Change: 1980-1990....................................................................86 Accounting for Change: 1990-2000....................................................................89 Black Drug Sales Arrests............................................................................................90 Accounting for Change: 1980-1990....................................................................90 Accounting for Change: 1990-2000....................................................................92 Conclusion..................................................................................................................92 7 CONCLUSION.........................................................................................................100 Discussion and Implications.....................................................................................100 Limitations................................................................................................................103 Future Research........................................................................................................105 Final Thoughts..........................................................................................................106 APPENDIX: SAMPLE CITIES......................................................................................108 REFERENCES................................................................................................................115 BIOGRAPHICAL SKETCH...........................................................................................129

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viii LIST OF TABLES Table page 4-1. Concentrated Disadvantage Index Factor Loadings...................................................63 5-1. Descriptive Statistics (Means with St andard Deviations in Parentheses)..................81 5-2. Mean Percent Changes Across Ti me for Independent Variables...............................82 5-3. Means and (Standard Deviations ) for Drug Sales Arrest Rates.................................82 6-1. Parameter Estimates and (standard e rrors) Modeling Struct ural Changes from 1980-1990 on White Drug Sales Arrests..................................................................96 6-2. Parameter Estimates and (standard e rrors) Modeling Struct ural Changes from 1990-2000 on White Drug Sales Arrests..................................................................97 6-3. Parameter Estimates and (standard e rrors) Modeling Struct ural Changes from 1980-1990 on Black Drug Sales Arrests..................................................................98 6-4. Parameter Estimates and (standard e rrors) Modeling Struct ural Changes from 1990-2000 on Black Drug Sales Arrests..................................................................99 A-1. Trajectory Group Membership for White Drug Sales Arrests.................................108 A-2. Trajectory Group Membership for Black Drug Sales Arrests.................................112

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ix LIST OF FIGURES Figure page 5-1. Trajectories of White Drug Sales Arrests 1980-2001................................................83 5-2. Trajectories of Black Drug Sales Arrests 1980-2001.................................................84

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x Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy STRUCTURAL CORRELATES OF RACE -SPECIFIC DRUG SALES ARRESTS OVER TIME: ARREST TRAJ ECTORIES FROM 1980-2001 By Scott R. Maggard August 2006 Chair: Karen F. Parker Cochair: Lonn Lanza-Kaduce Major Department: Sociology The relationship between community structur e and crime has received a great deal of research attention in criminology over the past two decades. Rising out of the traditions of Shaw and McKay, researchers have documented how structural changes in communities are related to crime rates in thos e areas. While the majority of these studies have focused on property and violent crim es, few studies have investigated the relationship between social st ructure and race-specific dr ug arrests. Moreover, most studies investigating structural correlates of crime have us ed decennial time periods and typically employ change score t echniques, thereby only allowing between-city comparisons, while neglecting within-city comparisons. Employing techniques used to study the life-course of individual offenders over time, this research ai ms to classify the long term behavior of drug sales arrests in large cities as distinct trajectories over time. Assuming that cities behave differently, this research will shed li ght on how structural changes in cities affect changes in arrest trajectories over tim e. Findings support the

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xi hypotheses that cities do in fact behave over time in regards to drug sales arrest from 1980-2001. Moreover they vary significantly by race and while certain cities may have experienced exponential growth in Black drug sa les arrests, other cities witnessed similar (yet less pronounced) growth in White drug sales arrests. Fi ndings also provide support for both social disorganiza tion and concentrated disadva ntage perspectives on urban crime. The increase in concentrated disa dvantage among Blacks and Whites from 19801990 significantly impacted the lik elihood of those cites being in higher drug sales arrest trajectories. Additionally, those cities which experienced dramatic increases in residential mobility from 1980-1990 were more likely to be in higher drug sales arrests trajectories as compared to the lowest. Overall these findi ngs suggest that the st ructural changes in large cities occurring from 1980-1990 had a more significant impact on drug sales arrest rates than the changes occurring from 1990-2000.

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1 CHAPTER 1 INTRODUCTION Introduction The primary focus of this dissertation is to investigate the extent to which structural factors in large urban areas contribute to th e rise in drug sales arrests over time. Many researchers have focused on the individual f actors that lead to drug use and crime. However an alternative approach is to discer n to what extent ecol ogical factors contribute to the distribution of crime in urban areas, and specifically drug sales arrests. As large urban centers have undergone a metamorphosis covering the past several decades, the individual actors within these areas have been subject to cognitive landscapes of crime and a rather dismal outlook for th e future (Sampson and Wilson 1995). Therefore, peering through an ecological or macro lens, it is believed that it is the structure of these urban landscapes that pe rmit crime to permeate the lives of the residents, rather than simply being that th e residents of these areas are somehow more likely to commit crimes on their own. The goal of this dissertati on is to a) assess to what extent large urban areas b ehave over time (using drug sales arrests as a proxy of behavior), and b) determine what structur al factors predict th e likelihood of a city experiencing sharp increases in drug sales arrests over time. Background In the tradition of the Chica go School, structural theori es of crime and delinquency focus on the community to understand crimin al activity. Rather than focusing on the individual, structural theories seek to understand how community structure may either

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2 foster or inhibit delinquent behavior. The cl assic work of Shaw and McKay (1942) laid much of the groundwork which led to our curr ent conceptualization of community social disorganization. Their argument was not that there is a direct relationship between community factors such as economic stat us and delinquency, but that those areas characterized by dire economic circumstances tended to have high rates of population turnover and population heterogeneity. This, in turn, weakened informal social controls within the community resulting in a more favorable environment for delinquency to occur. Additionally, social diso rganization theory also posits that the effects of declining structural conditions in urban areas are invari ant across race. In other words, as family disruption and low economic status begin to prevail in an ur ban area, informal controls break down and crime can result, regardless of race (Bursik 1988; Shaw and McKay 1942). In addition to the social disorganization of urban communities, researchers have posited that deindustrializati on beginning in the 1970s has also contributed to delinquency in urban areas. Wilson (1987) argue s that the decline in manufacturing and semiskilled jobs beginning in the 1970s has de pleted jobs for minorities in those areas. He argues that black joblessness has led to concentrated disadvantage among blacks in urban areas. Others have also argued that racial isolation, j oblessness and racial residential segregation have fostered delinquency in many communities (Massey et al.1987; Massey and Denton 1988, 1993; Wilson 1987). The concentrated disadvantage faced by blacks has led to a decline in marriageable males, a rise in incarceration rates, family disruption, as well as a rise in crime rates (Almgren et al. 1998; Krivo et al. 1998; Pa rker and McCall 1999; Sampson 1987). As the

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3 residents of these communities struggled to survive with the loss of the unskilled and semi-skilled jobs, many relied on the growi ng informal economy of inner cities for income (Johnson et al. 1990). In addition, others have documented the dramatic surge of young people who became involved in the ill icit drug trade as being unemployed and unskilled workers (Fagan 1992). With urban areas already facing social disorganization and concentrated disadvantage after deindustrial ization, it could be argued that the rise in drug use and sales only exacerbated these problems. In fact the war on drugs began an era of targeting the urban and disproportionately black populatio n of Americas cities with specific drug law enforcement tactics aimed at curbing the rising use and sales of crack cocaine beginning in the earl y 1980s (Tonry 1995). Present Study Drug offense arrests in the United States ha ve varied over time for much of the past century. However, recent research has demonstrated that drug arrests experienced an unprecedented rise beginning in the ea rly 1980s throughout much of the 1990s. Blumstein (1995) has noted that the crack epidemic, which began in the 1980s, was directly related to the sharp rise in homici de rates during the same decade. Moreover it began to affect selected urban areas and later spread to othe r areas of the country as well (Blumstein 1995). Additionally researchers have recently presented data that demonstrate the way in which homicide rates changed during this time in different sections of the country (Messner et al. 2005). They found that thirty five of the si xty eight large cities which they examined behaved in the epidemic-like cycle described by others (Blumstein 1995; Blumstein and Rosenfel d 1998; Golub and Johnson 1997; Messner et al. 2005).

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4 While most data that researchers and activists alike rely on are based solely on averages or means, little is known about the within-city variations of arrests over time. That is, with such a wide margin between ci ties with the lowest drug arrest rates and those with the highest (see Mosher 2001; Pa rker and Maggard 2005), these averages may paint a picture that presents a false sense of urgency for the entire country. I contend that it may be that different cities experience diffe rent rates of increases of drug arrests over time. In fact, I argue that some cities ma y experience few problems with drug arrests, while others may experience grea t troubles. Contributing to this issue is that as specific drug epidemics or drug scares arise, they often develop and diminish in different cities at different periods in time, as was observed with crack cocaine (Golub and Johnson 1997). While much of the macro level research on communities and crime has focused on between-city change over different time peri ods, very little is known about within-city changes in criminal offending over time. That is, how do specific cities behave in terms of arrest rates over a long period of time? This is what is known in the life-course literature as a trajectory While studies that utilize data for many cities may investigate how the change in structural conditions contributes to the change in crime rates over time, they fail to decipher which cities cr ime rates changed and why they changed. Until recently, the idea of community careers in crime was very rare (but see Bottoms and Wiles 1986; Schuerman and Kobr in 1986). Only in the past year has research surfaced linking the methods utilized in life-course criminology (to analyze the offending trajectories of individuals) to m acro level analysis of communities and crime (Griffiths and Chavez 2004; Weisburd et al. 2004).

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5 Schuerman and Kobrin (1986) investigated the 20 year histories of Los Angeles neighborhoods from a developmental perspectiv e. They used cross-sectional and time series analysis to discover that neighborhoods co uld be classified as one of three distinct stages: emerging, transitional and enduring. They found that nei ghborhood deterioration precedes rising crimes rates in the early stages but that rising crime rates precede further neighborhood deterioration in the later stages. They conclude that efforts to prevent the rapid escalation of crime in urban areas must be initiated in the emerging stages of the cycle in order to be most successful (Schue rman and Kobrin 1986). In contrast, Bottoms and Wiles (1986) focus their research in Britain, and they argue that the key to understanding the criminal careers in neighbor hoods is housing tenure. They find that key factors include bureaucratic mechanisms and renting versus home ownership (Bottoms and Wiles 1986). Weisburd and his colleagues (2004) an alyzed trajectories of crime in neighborhoods in Seattle over a 14 year pe riod. They used a group based trajectory analysis to uncover distinct developmental tr ends within the city. They conclude that while a citys crime rates may be declining ove r a period of time, certain segments within the city may be rapidly declining or rapi dly increasing at the same time, and may contribute significantly to the overall crime pi cture in the city (Weisburd et al. 2004). Griffiths and Chavez (2004) merge a group based trajectory analysis and exploratory spatial analysis to investigate homicide trends in Chicago communities from 1980-1995. They identified distinct trajectories for total, street gun, and other weapon homicides across 831 census tracts in the city. Key findings included evidence of a weapon substitution effect for violent neighborho ods that are proximate to each other, in

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6 addition to the fact that street gun specific homicides increased in areas bordering the most violent areas of the c ity (Griffiths and Chavez 2004). Specific Aims This research project seeks to examine the criminal careers of cities using racespecific drug arrests over two decades, identify ing structural correlates to help explain this phenomenon. I am focusing on the theoretica l perspectives of so cial disorganization, urban disadvantage and racial threat. This research seeks to address several related research questions. First, I seek to determine if there are identifiable trajectories of race specific drug-sales arrest rates in large ur ban areas over time in order to determine whether the changes in certain cities experien ced different trajectorie s for different races. In addition, I seek to answer the question of whether certain structural characteristics of cities contribute to explaining the likelihood of a given city being in a specific trajectory. That is, if I determine there to be 4 distin ct trajectories, do social disorganization and other structural correlates expl ain the increase in arrests fo r the highest trajectory and not the lowest? Do they help to explain specifi c trajectories for speci fic races? In other words, are cities plagued with the highest incr ease in drug activity during this time period (as measured by drug sales arrests) also the cities which have expe rienced the greatest change in ecological factors? These factors may include the deindustrialization of urban areas, an increase in residential mobility and racial residential segregation, increasing disorganized family structures throug hout the community and the changing ethnic makeup of the area over time. The focus of this research will be race-specific drug-sales arrests in the largest cities in 1980 for the period 1980 through 2001. The justification for utilizing only drugsales arrests as the focus for this research is twofold. First, the arrest data dating back to

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7 the 1980s are limited to only drug sales a rrests. Second, much of the theoretical reasoning driving this research revolves around the vanishing manufacturing and semiskilled jobs documented during this peri od in urban areas (Sampson 1987; Wilson 1987, 1991). This trend has also been documented at the micro level, as the ethnographic work of Elijah Anderson (1999) illustrates. In the Code of the Streets (1999) Anderson writes: Deindustrialization and the growth of th e global economy have led to a steady loss of the unskilled and semiskilled manufact uring jobs that, with mixed results, had sustained the urban working class since the start of the industria l revolutions. At the same time welfare reform has led to a much weakened social safety net. For the most desperate people, ma ny of whom are not effec tively adjusting to these changes . the underground economy of drugs and crime often emerges to pick up the slack (pp. 108) (Emphasis added) Significance The significance of this research is that it will enable me to differentiate those cities that have experienced the greatest increases in drug sales arrests from those that have not, disaggregated by race. Where prior research describes certain structural variables as being significant to crime in urban areas, or to the change in crime in urban areas, this research will enable me to identify cities that have experienced the most dramatic changes in drug arrest rates. This will enab le policy makers to identify specific urban areas that are in more (or le ss) need of state or federal aid programs, community building programs, job training programs, etc. For exampl e, if it is found that the highest trajectory groups for both blacks and whites also see th e largest impact from a decrease in manufacturing jobs, then it can be said that policy makers should prioritize resources to those cities rather than cities th at may not feel such an impact. While few studies have focused on structural correlates of drug arrest rates in large cities (Mosher 2001; Parker and Maggard 2005), structural theories of crime and deviance have been used to help us unders tand many other types of crime from property

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8 crimes to homicides (Bursik and Grasmi ck 1993; Crutchfield 1989; Crutchfield and Pitchford 1997; Krivo and Peterson 1996, 2000; Parker and McCall 1997, 1999; Sampson 1987; Sampson and Wilson 1995). There are several theoretical reasons to assume that areas plagued by social disorganization will witness an increase in drug sales arrests over time. The first is quite obvious; as deindustrialization has accelerated beginning in the 1970s, many individuals watched as their employer literally drifted awa y. It could be argued that this in and of itself may be enough to warrant expecting an increase in drug sales (income generation). However when this economic motivation is coupled with the criminogenic environment that social disorganization and concentrated disadvantage theorists would predict, it makes even more sense logically. That is, if one desperately needs money to support her/his family, and the family resides in an environment with little informal controls, where criminal behavior is somewhat accepte d and there exists a lack of hope for the future, the chances of joining the illegal drug trade should increase. This research aims to merge the met hodologies traditionally used to analyze individual offending rates over time, with those that have been used to analyze structural changes in urban areas. That is, I seek to utilize the TRAJ procedure in SAS to investigate patterns of drug arrest rates over a 22 year period, cr eating clusters or trajectories of cities with similar patterns. In addition, I seek to use the change in structural conditions between decades to de termine their effect on a given trajectory. This research will add to the literature in several ways. First, it will build on the efforts of Griffiths and Chavez (2004) and We isburd et al. (2004) of merging the TRAJ procedure to macro level analysis. It will a dd the ability to analy ze how the change in

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9 certain structural conditions within cities a ffects drug arrest rates over time. It will also enable me to untangle these changes in orde r to identify whether certain cities were impacted more severely by these changes than others. Finally, it will evaluate the social disorganization, urban disadvantag e and racial threat perspectiv es in a new context. This methodology provides a significant departure from traditional methods, in that statistical outliers may be grouped together in their own trajectories and will not have a significant impact on cities that experience few changes in drug activity over time. Layout of the Dissertation The dissertation will consist of 7 chapters Chapter 2 will provide the theoretical background driving the research, including a re view of the empirical support for each theoretical perspective chosen. Chapter 3 will provide a review of the literature regarding drugs and crime as well as present severa l hypotheses to be tested. Chapter 4 will describe the data and statistical procedures utilized for testing the hypotheses. This will include detailed information on the data, as well as background info rmation relating to the TRAJ procedures that will be performed in SAS. Chapter 5 will provide descriptive statistics for both dependent and independe nt variables. It will also include the trajectories of drug sales a rrests over time, providing unique trajectories for White and Black drug sales arrest rates. Chapter 6 will present how the various theoretically drawn explanatory variables interact with the clusters, or traject ories, and highlight how each theoretical perspective may contribute to our understanding of the changes in drug arrests over time. Finally chapter 7 will provide conc luding remarks, policy implications of the current research, and directions for future research.

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10 CHAPTER 2 THEORETICAL PERSPECTIVES Introduction While the Chicago School propelled many of the great symbolic interactionists within sociology, it was also ripe with many of the brightest thinkers in the field in examining the world of sociology through a macr o lens, or ecological approach. It was a shift that took the emphasis away from as king why certain individuals behave the way they do, and instead asked, how do particular geographic areas influe nce rates of crimes to behave the way they do? It was within th e great city of Chicago that what has now become known as Social Disorganization was born. During the early 20th century, metropolitan areas of th e United States were about to embark on a major metamorphosis from sparsely populated farmlands to highly populated industrial centers. Th e transformation not only brou ght greater prosperity and opportunities to more people, but it also we lcomed an influx of immigrants seeking employment and new opportunities. Many of the structural (or ecological) th eories within sociology and criminology today share their roots with the Chicago School tradition. Robert Park and Ernest Burgess studied the social organization and characteri stics of cities. Whether by coincidence or not, they happened to reside in one of th e fastest evolving metropolitan areas in the country, so what better place to examine how these changes occu r over time through a sociological lens (C ullen and Agnew 2003).

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11 Social Disorganization It was in this context that Clifford Sh aw and Henry McKay drew upon the theories of Park and Burgess and their ideas of how a city is like an organism when they conducted their classic study on juve nile delinquency in Chicago, Juvenile Delinquency and Urban Areas (1942). Burgess had noted that as c ities such as Chi cago transform into the industrial meccas that they are today, they do so from the inside out. Burgess documented specific zones within a city, be ginning in the center and expanding out in concentric rings or zones (Burgess 1925). From a citys core (e.g. central business and industrial center), it naturally sprawled outward over time, through this evolution of co ncentric rings or zone s. Just outside the central business district, Burgess noted what he coined the zone in transition. It was here that immigrants frequently first settle d as they arrived to new areas seeking work opportunities and affordable housing. Just outsi de the zone in transition, lied the zone of working mens homes, the residential zone, and the commu ters zone (Burgess 1925). Burgess observed that while most immigran ts new to an urban area first settle within the zone in transition, most families do not stay in the zone for long periods of time. In fact, the concentric zones are labeled as such since there appears to be a natural progression outward for financial and family reasons. Once individuals established stable employment (typically factory/manufacturing jobs), they would typically save their money for a more prosperous life in one of the outer zones. Th erefore, within the transition zone there always ex ists residential mobility both in and out of the area. As new immigrants excitedly move in seeki ng employment and housing, another family

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12 leaves the zone in search of a more relaxed, fa mily oriented life in the outer zones. These outer zones are certainly where most live today, the suburbs or suburbia. Using this logic, Shaw and McKay hypot hesized that these urban centers (e.g. zones in transition) should have the highest concentration of crime and delinquency, due to the ever-changing landscape of the areas. Th ey theorized that the persistent residential mobility and poverty would contribute to these areas being more disorganized compared to areas that were more affluent su ch as the outer zones within a city. In order to test their hypotheses, Shaw and McKay set out to collect data on juvenile delinquents throughout the city of Chicago and map which areas of the city e xperienced the highest rates of delinquent behavior (Shaw and McKay 1942). Shaw and McKay did not believe that any ci ty necessarily had a distinct number of zones (they are rather arbitrary) or that lines existed for the exact measuring of the zones. However, they argued that organizing their research based on five zones, each about 2 miles apart, they could map how the organi zation and changes in these areas affected juvenile delinquency rates over three periods of time. They collected data on recidivism, truancy rates and referrals to juvenile court to map the periods of 1900-1906, 1917-1923, and 1927-1933. Their methodology would enable th em to compare different cohorts of juveniles residing in the same geogra phic areas at different time periods. Their results confirmed much of what they had predicted. In all three-time periods, the central business districts consistently witnessed the highest rates of juvenile delinquency, with the rates dropping signifi cantly for each successive zone moving outward from center. In other words, it was not the individuals who resided in these areas that created high crime rates, but rather the organi zation (or disorganiz ation) that made

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13 these areas ripe for delinquent activity. Recall this is in stark contrast to earlier criminological theories, which purported that the key to understanding individual delinquency lies in individual tr aits as advanced by social scientists such as Cesare Lombroso and E.A. Hooten for example (Ake rs and Sellers 2003; see also Lindesmith and Levin 1937). One criticism of Shaw and McKays asserti ons lies in the fact that they never explicitly described how or why the zone in transition would have the highest concentration of juvenile delinquents (Akers and Sellers 2003; Cullen and Agnew 2003), although it is assumed today that the true ca use is due to the breakdown of informal controls in these disorganized areas. In other words, an area that is disorganized may be characterized as one experienci ng a lack of the informal control mechanisms that closeknit communities provide in or der to deter and control juve nile delinquents. Further, researchers have long argued that individuals living in high delinquency urban areas experience a duality of conduct norms, wh ere their conduct norms are split between those of mainstream society and those of delinquent subcultu res (Kobrin 1951). In addition it has been noted that it is extremely difficult for communities to establish informal control mechanisms when many of its residents are uninterested in communities they hope to leave at the first opportunity (Kornhauser 1978: 78). While Shaw and McKays ideas were widely cited, Bursik (1988) notes that they fell out of favor for many years due partly to the shift to more micro level theories of crime and delinquency such as differential association/social learning and control theories. Furthermore, Bursik notes that while widely cited, it was generally in reference

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14 to the economic composition of communities and their respective rates of delinquency, according to Bursik: Shaw and McKay did not posit a direct relationship between economic status and rates of delinquency. Rather, areas charac terized by economic deprivation tended to high rates of population turnover (the y were abandoned as soon as it was economically feasible) and population he terogeneity (the rapid changes in composition made it very difficult for those communities to mount concerted resistance against the influx of new groups ). These two processes in turn, were assumed to increase the likelihood of social disorganization, a conc ept that is very similar to Park and Burgesss (1924: 766) formulation of social control as the ability of a group to engage in the activity of selfregul ation (Bursik 1988: 520). It was the reintroduction of social disorg anization theory in the 1980s that led to the vast body of research that exists today, a groundwork laid by a select few sociologists and criminologists. While one reason that so cial disorganization was virtually abandoned for several years is surely due to the trendy na ture that often occurs in academic circles regarding research agenda s (see Sampson and Laub 2005), much of it was certainly attributable to the difficulty in undertaking such projects (s ee Kubrin and Weitzer 2003). To truly test Shaw and McKays hypothese s regarding social disorganization, one would need to conduct a multi-year study that to ok into account interviews with residents as well as collect statistics relevant to the area. However, as technology has progressed, sociologists and criminologists have utilized official crim e data and demographic data collected through the United States Census pr ogram to develop proxies, which help us understand social disorganization and how it may be related to an areas criminal activity. Researchers have been able to extend social disorganization theory further than in the past as well as clarify key conceptualizations. Upon the revitalizati on of Social Disorganization theory in the 1980s, studies began to emerge to gauge to what extent Shaw and McKays hypotheses remained valid over time. Upon an extremely fortunate stroke of luck, Bursik and Webb (1982) were

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15 able to obtain much of the original data utilized by Shaw and McKay from the basement file cabinets of the Inst itute for Juvenile Research at the University of Chicago, as well as data the Institute had compiled through 1970 (Bursik and Webb 1982:28). Utilizing this newly discovered archive they were poised to examine the affects of four key variables on the changes in areal delinquency rates in Chicago from the 1940s forward through the 1970s. They measured changes in th e community population, the percentage of foreign-born whites, the percen tage of non-whites, and levels of household density. Their findings indicate that between 1940 and 1950, invasion and succession did not affect the distributional patterns of delinquency within communities (as Shaw and McKay asserted). However, during the later time periods (notably 1950-1960) community change was associated with patterns of areal delinquenc y rates, which is contrary to Shaw and McKays original theses. Rather than stat e that Shaw and McKay were wrong, they argue that Shaw and McKay were writing a nd making their observations within a much different historical context th an the later years studied by Bursik and Webb. In fact, the premise of the Burgess original tho ughts on invasion and succession rested on the assumption that new immigrants would make a natural transition or assimilation into a new area. Instead, during these later times, i mmigration and segrega tion policies changed the dynamics of how an urban area would r eact to the invasion a nd succession of new residents. They conclude that it is the fact that invasion and succession are occurring, as opposed to who is involved in the occurrence of these changes. In ot her words, it matters little of which race is attributed to the invasion/successi on so much as the process itself

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16 (Bursik and Webb 1982; Bursik and Grasmi ck 1993; Sampson 1987, 1995; Sampson and Groves 1989; Sampson et al. 1997). Sampson and others have noted the importance of collective efficacy in maintaining control over youths in high crime areas. They have recognized that the relationship between disorganization and crime rates is not a simplistic/direct relationship, but rather complex. Sampson goes on to argue the social disorganization framework views neighborhoods and communities as a complex system of friendship and acquaintance relationships which are rooted in family life and ongoing socialization processes within these urban areas (Bur sik 1984, 1988; Bursik and Webb 1982; Sampson et al. 1997). Researchers have also noted the reciprocal effects of structur al factors and urban crime. Shihadeh and Steffensmeier (1994) f ound that reciprocal e ffects do exist among measures of family disruption and crime rates in large cities. They note that while family disruption has a positive signi ficant affect on crime rates, violent crime also has a positive significant affect on family disruption. They argue that this is due to both the incarceration of young males as well as th e unwillingness of young women to marry criminally involved young men. Moreover fam ily disruption was found to be among the strongest predictors of juvenile violent crime due to the lack of informal controls that strong family networks within communitie s are believed to provide. Similarly the dramatic increase in incarceration has been argued to decrease social organization and thus stimulate the breakdown of informal c ontrols within urban communities Rose and Clear 1998; Sampson 1987; Shihad eh and Steffensmeier 1994).

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17 More recent research has investigated the relationship between social disorganization within communities and specifi c types of homicides. Kubrin (2003) finds that the percentage of divorced males in an ur ban area is stat istically significant with only general altercation killings, which are homicides that be gan as a general altercation, leading the subjects outside and resulted as homicides. Additionally she found that areas with high residential mobility experienced a greater number of felony homicides as well as it affecting the overall homicide rates in those areas, consistent with disorganization theory. These findings lead us back to the issue of reciprocal e ffects discussed above. Kubrin and Witzer (2003) argue that future studies of social diso rganization need to become more dynamic. For instance they argu e that street killings within a neighborhood may increase residential mobility, while dom estic homicides may have little impact on mobility. Since domestic homicides are between spouses or acquaintances residents are expected to be less fearful than they may be of random or street homicides (such as drug related homicides) (Kubrin 2003; Kubrin and Weitzer 2003). Kubrin and Weitzer (2003) go on to argue that res earchers evaluating social disorganization theory need to improve not only the conceptualiza tion of concepts but also the methods used to test the effects of these structural covariates on crime rates. One suggestion is to borrow methods from the ps ychological literature which is used to identify life course turning points in offending in individuals. While they suggest growth curve modeling techniques, this study utilizes a procedure that is typically used to model individual offending, PROC TRAJ and is described in Chapter four (Kubrin and Weitzer 2003).

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18 Concentrated Urban Disadvantage Unemployment and the Urban Underclass In The Truly Disadvantaged William Julius Wilson outlined a multi-faceted framework describing what he believes has lead to the growth of the urban ghetto in America, and more precisely, what he has termed the underclass. The underclass, he states, are the results of changes have taken place in ghetto neighborhoods, and the groups that have been left behind are collectiv ely different from those that lived in these neighborhoods in earlier y ears (Wilson 1987: 8). He argues that urban Blacks have been especially vulnerable to both geographic and industrial changes in the economy, which ha ve had the devastating effect of creating high proportions of joblessness among Blacks. This is due to the history of discrimination in America, as well as Blacks migration to large metropolises, both of which have resulted in weak labor force attachment. He argues that these are the results of a variety of compounding factors that ha ve contributed to the underc lass. These factors include shifts in the labor market from goods produci ng sectors to service or iented sectors, the polarization of the labor market into lo w salary and high salary jobs, numerous technological innovations, reloca tion of manufactur ing outside large cities, as well as economic recessions (Wilson 1991: 640). All of these factors combined have re sulted in a significant increase in the concentration of poverty in urban areas, as well as increasing bot h the number of poor single parent families and the number of fam ilies depending on welfare. Moreover, since 1970, inner city neighborhoods have experien ced out migration of working and middle class families, thus leaving a concentrat ed amount of poverty behind, with extreme poverty (greater than 40%) incr easing dramatically (Wilson 1987, 1991).

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19 With the working and middle class black families moving out, Wilson contends that critical social buffers have been rem oved from these communities. Past decades had seen the working and middle class families bring stability to inner city neighborhoods, through patronizing local stores and churches, sending their ch ildren to local schools, and providing legitimate and meaningful visions to lower class Blacks of the chances of upward mobility. When all of these factors compound over ti me, Wilson argues that weak labor force attachment is inevitable. He goes on to elaborate by stating: Thus neighborhoods that have few legitimate employment opportunities, inadequate job information networks, and poor schools not only gi ve rise to weak labor force attachment but also raise the like lihood that people will turn to illegal or deviant activities for income, thereby furt her weakening their attachment to the legitimate labor market (Wilson 1991: 651). So in addition to further weakening th ese attachments, crime in the area is heightened as it manifests and perpetuates itself within the community, with residents being influenced by the beliefs, behavior, and social perceptions of other disadvantaged families. He refers to this as concentration effects, or the effects of living in an overwhelmingly impoverished environment (pp.651). Sampson and Wilson (1995) have extended W ilsons original ideas with what they have coined cognitive landscapes. Cognitive landscapes emerge based on the context of any given community. In other words, ever yone has cognitive landscapes, however, those landscapes will differ greatly across populati ons based primarily upon the organization and structure of the community. Those who reside in impoverished areas such as the areas described by Sampson and Wilson will form a system of values that is less likely to condemn drug use and disorder than those of more affluent communities (Sampson and Wilson 1995; see also Kobrin 1951).

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20 Generally speaking, Wilsons arguments arent usually tested alone. That is they are more often incorporated into tests of theo ries such as social disorganization theory. Studies focusing on these concepts have ge nerally supported many of Wilsons claims. Jargowsky and Bane found during the 1970s alone, the number of people living in extreme poverty (>40%) in creased 30 percent and that the proportion of the poor population residing in ghettos significantly va ries by race. They found that in 1980, only 2% of the non-Hispanic poor lived in ghettos, compared to about 21% and 16% of the Black and Hispanic poor, respectively. In addi tion they noted that almost one third of Blacks living in metropolitan areas resided in the ghetto, and 65% of the ghetto poor were Black. There is also evidence that this trend continued through the 1980s as well (Jargowsky and Bane 1991; see al so Nathan and Adams 1989). They also added to Wilsons argument by di scovering that most of this increase was in the Midwest and Northeast alone. In fa ct, only 10 cities accounted for 75% of the rise in ghetto poverty throughout the 1970s. Even more star tling was that nearly one third was accounted for by New York City alone and nearly one half by New York City and Chicago combined (Jargowsky and Bane 1991). Rosenbaum and Popkin placed low income Blacks in suburban areas and rented apartments for them, while placing a control gr oup in apartments in the city. Controlling for personal characteristics such as family background/circumstances, motivation, as well as education after beginning the program, the authors found that those who had been placed in the suburban apartments were sign ificantly more likely to find employment than their inner city placed counterpart s, citing more employment opportunities (Rosenbaum and Popkin 1991).

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21 Testing Wilsons hypothesis, Sampson (1987) notes that the eff ect of black male joblessness on black crime rates is mediated through its effect s on family disruption. That is, as the number of unemployed black males increases, this in turn increases the number of female-headed households in these areas. He concludes that high black crime rates appear to be caused by a combination of structural factors in cluding unemployment, economic deprivation, and family disruption. Sampson also finds support for Wilsons general thesis that structural unemployment is a more important factor in accounting for Black family disruption as compared to White family disruption These findings are consistent with other works as well (Sampson 1987). More specifically, the implications of W ilsons work lie more in community level explanations of crime. For instance, Sampson and his colleagues have found continued support for his ideas regarding the underc lass. In their study of neighborhood violent crime and collective efficacy, it is clear how well Wilsons arguments mesh into this scheme. Similar to Wilsons arguments rega rding concentration of poverty, collective efficacy demonstrates those conceptualizations well. For instance Sampson et al. (1997) argue that low SES and concentrated disadva ntage lead to a decrease in collective efficacy, thereby leading to increased neighborho od crime, which clearly is in line with Wilsons arguments as well as social disorgan ization theory in general (Sampson et al. 1997). Researchers have explored these concepts utilizing indices intended to represent the concentration of poverty among inner city reside nts. They have found that measures such as male joblessness and racial economic in equality have significant affects on urban crime. For example Krivo and Peterson (2000) find that concentrated disadvantage is a

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22 significant predictor of race-specific homicides. However they also note that if blacks and whites held similar economic positions in soci ety and similar levels of disadvantage, the impacts of theoretically relevant measures would have more equal impacts on crime rates for both groups (Krivo and Peterson 2000:557). While much of Wilsons thesis rests on the concept of the changing urban landscape and the increase of jobless male s, some research has shown that the relationship between male jobl essness and crime may be more complex. It has been noted that while male joblessness is an important factor in the c oncentration of poverty in urban areas, racial residential segreg ation may be key to understand ing why this is so, and is addressed in the following section (Krivo et al. 1998). Poverty and Segregation Exacerbating the urban disadvantage felt am ong African-Americans in large cities, Massey and his colleagues argue th at residential racial segreg ation is the primary culprit for the growing ghettos and underclass in America. In their book, American Apartheid Massey and Denton outline the history of segr egation in the United States, addressing each step and offer empirical evidence to back their claims. As Wilson notes, poverty rates grew rapidly during the 1970s, but more importantly, poverty became more geographically concentrated (Wilson 1987; Bane and Jargowsky 1988; Massey and Eggers 1990). Much of how Massey views Wilsons c ontentions can be summed up by the following quote from Massey: I agree with Wilsons main argumentthat poverty concentration has increased in U.S. cities, with pernicious consequences for minorities. I disagree, however, with his hypothesis that this transformation was brought about by the exodus of middleclass minority members from the ghetto and with his argument that industrial restructuring, in and of itself, was re sponsible for concentr ating urban poverty.

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23 While these processes may have exacerba ted poverty concentration, neither was necessary for its creation. In the absen ce of racial segregation, the economic dislocation of the 1970s woul d not have produced concen trated poverty or led to the emergence of a socially and spatia lly isolated undercla ss (Massey 1990: 330). The three primary reasons that Massey di sagrees with Wilsons claims include, 1) racial segregation in urban areas remain hi gh and show little sign of declining, 2) as education and income go up, the degree of bl ack segregation does not decline, and 3) although the degree of segreg ation between affluent Blacks and poor Blacks has increased slightly during the 1970s, it is still lower than the difference between the affluent and poor of other minority groups (Massey and Eggers 1990). They go on to suggest that instead of being caused by the departure of middleclass blacks from the ghetto, however, these deve lopments are explained statistically by a strong interaction betwee n the level of segregation and ch anges in the structure of the income distribution. They go on to suggest that groups that expe rience high rates of poverty in addition to high rates of segrega tion will experience the greatest levels of disadvantage. Furthermore, concentration of poverty rose the most in urban areas that suffered economic downturn and suffered high leve ls of racial segregation-such as New York and Chicago (Massey 1990; Massey and Denton 1993). Massey and his colleagues use hypothetical ci ties to illustrate how segregation works with other factors. They argue th at when racial segregation is imposed, some Whites are better off, yet all Blacks are worse off. In additi on, the problems inherent with segregation compound as the level of segregation increases, thus as segregation increases, so does the concentration of poverty as the area that minorities occupy becomes smaller and smaller. Likewise as the concentrati on of poverty grows for the minority population, it declines for the White population (Massey 1990).

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24 Additionally, Massey and Denton argue that when class segregation is included in the equation, the effects are compounded furt her. That is, when one considers the differential impact of both race and class se gregation, it heightens the impoverishment felt by Blacks and actually improves the qualities for Whites. So, when racial segregation exists in a class-segregated society, any ec onomic shock (loss of manufacturing jobs, etc.) has a profound impact on that group a nd in turn increases not only the overall poverty rate, but also the concentration of poverty. This is essen tially in line with Wilsons claims, notwithstanding the segreg ation quotient of course (Massey 1990; Massey and Denton 1993). In speaking of support for Wilsons contenti ons, it is hard to se parate them from Massey et al.s arguments. They are so inte rtwined that both are extremely valuable contributions, and both receive a great deal of rigorous empirical testing. Typically, neither Wilsons nor Massey et al.s arguments are tested alone. That is, they are more often incorporated into tests of social di sorganization theory and general structural analyses of crime. Although many of the examples put fort h by Massey et al. (1990, 1993) involve hypothetical data, other researchers have f ound support for their conceptualization of racial segregation. Massey and his colleague s measure segregation using the index of dissimilarity, which represents the proportion of the minority population that would have to change residence locations in order to achieve an even settlement pattern in any given geographical area. This conceptualization ha s successfully been used in subsequent macro-level analyses of crime for some time (Krivo et al. 1998; Massey 1990; Massey and Denton 1993).

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25 In his research on infant mortality rates in U.S. cities, LaVeist found that residential segregation was the strongest predictor of Bl ack infant mortality rates. Furthermore he noted that while racial segr egation increases mortality am ong Blacks, it decreases it among Whites, providing further support for the claim that segreg ation not only hurts Blacks but also benefits Whites (LaVeist 1989). Other researchers have noted a dynamic re lationship between racial segregation, Black socioeconomic status (SES), and di scrimination. They found that rising racial segregation was positively correlated to heightened differences in Black-White occupational differences, which as a resu lt increased the levels of Black-White segregation via negative correlations to Bl ack income. Moreover, the decreasing Black SES led to an increase in housing market discrimination, thus resulting in more segregation. Obviously this is a complex rela tionship, but it clear ly demonstrates the power of this conceptual mode l of how racial segregation influences as well as is influenced by many forces (Galster and Keeney 1988). Several researchers have used racial se gregation to study other topics such as housing preferences and educational disadvant ages. Supporting Massey et al.s claim that segregation leads not only to disadvantages for Blacks but also specific advantages to Whites, Roscigno (1998) finds that racial segreg ation is significant in predicting students academic achievement. His results indicate that attending a Black segregated school (>75% Black) is indicative of performi ng more poorly, while attending a White segregated school (>95% White) is associ ated with better academic performance (Roscigno 1998).

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26 Racial segregation has also proved to be a significant predic tor of homicides. Peterson and Krivo examined African-American homicide victimization for many large U.S. cities, and the impact of segregation on the homicide rates. They found that with the exception of intra family homicides, racial se gregation was a significant predictor of both acquaintance and stranger homicide cases. Th is coincides with the idea that as segregation increases, social isolation also increases, which may explain the lack of segregation predicting intr a family homicide rates (Peterson and Krivo 1993). While both the concentrated disadvantage and social disorganization perspectives view their constructs as bei ng racially invariant in thei r impacts on crime rates (see Sampson and Wilson 1995), thus far results have been mixed as to whether this is the case. Shiahadeh and Ousey (1998) have noted th at while a lack of low skilled jobs affects both black and white homicides, other measures such as the percentage of renters or the prevalence of high school dropouts may only affect black or white homicide rates exclusively. Therefore, segregation may provi de insight into why certain structural measures are significant predictors of black but not white crime rates (or vice versa). Krivo and Peterson (1996) have shown that while both Blacks and Whites experience extreme disadvantage, violen ce rates in predominantly Bl ack neighborhoods remains to be significantly higher than for White neighborhoods (Krivo and Peterson 1996; Shihadeh and Ousey 1998). Providing further support, Krivo et al. (1998) have noted that not only does segregation exacerbate the disadvantage experi enced by Blacks in urban areas, it also increases opportunities for socioeconomic st atus and employment among Whites. They note that disadvantaged Blacks e xperience significantly more isolation from other groups,

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27 thus increasing the likelihood th at their interactio n and contact is with other similarly disadvantaged residents. Furthermore, they fi nd that jobless males are less likely to have contact with people in high status occupatio ns, which operates to curtail the upward mobility we may expect providing residents are exposed to diverse role models and experiences (Krivo et al. 1998: 74). Similarly, Parker and Pruitt found support for racial segregation having a positive influence on Black homicides. In terestingly, they find that th is is the case only in the West, and not in the South. They attribute this to early migration pa tterns, as well as the West being known for having less economic in equality, poverty, and social isolation (Parker and Pruitt 2000). Racial Threat: Blalocks Power Threat Hypothesis In his 1967 book Toward a Theory of Minority-Group Relations Hubert Blalock, introduced a concept he referred to as pow er threat hypothesis, which he argued may help social scientists better understand how a nd when different forms of social control are used by the majority against the minority. Ov er the past three decades, his propositions have received much attention, however the results are mixed as to whether empirical support exists. Generally, Blalock hypothesi zes that as the size of a minority population grows, those in power will sense a threat to their power. Upon the arrival of such a threat, he argues that those in power will implement speci fic mechanisms of social control in order to curtail the ensuing threat (Blalock 1967). Specifically, Blalock argues for two distinct, uniquely behaving hypotheses surrounding power threat. The firs t is the political threat hypothesis, which posits that as the Black population increases over time, t hose in power (Whites) will perceive some

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28 threat to their political stat ure and therefore utilize formal social control mechanisms (arrest and punishment) to main tain their power. He goes on to argue that threats of this nature appear in the form of a positive cu rvilinear relationship between an increasing minority population size and increased discrimi nation in response to power threat, with an increasing slope (Blalock 1967). The second hypothesis Blalock introduces is that of the economic threat hypothesis. He states that as competiti on over economic resources, such as job availability, increases between Blacks and White s, Whites will again utilize social control mechanisms (arrest and punishment) to the ad vantage of those in power. He posits that there will be a positive curvilinear relati onship between discrimination resulting from economic competition, and the slop e will decrease (Blalock 1967). Blalock also recognized the difficulty th at his assertions posed in terms of measurement and conceptual ization. He states that: Empirically determined relationships s hould then be a composite of these two different forms. In many instances, such compositions will be approximately linear in nature, so that adequate tests will requi re one to locate relatively pure instances in which motives can be linked to behavior in a one-to-one fashion (Blalock 1967: 145). Clearly, the level of abstr action in his ideas creates a challenge in not only measuring these concepts, but conceptualizi ng them to a specific problem or scenario. His propositions have received s upport in explaining a wide va riety of issues related to race and crime. These include lynching (Reed 1972; Corzine et al. 1983, 1989), arrest and incarceration rates (Liska and Chamli n 1984; Liska 1992; Myers 1990; Crawford, Chiricos, and Kleck 1998; Parker et al. 2005), as well as inte rracial killing (Jacobs and Wood 1999).

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29 John Reed applied Blalocks ideas to l ynching rates in Mississippi, and measured the proportion of the Black population to asse ss its impact on the rates. Reed found support for the thesis in that the more c oncentrated the Black population, the higher the lynching rate (considered info rmal social control). Howeve r, his study would later come to be criticized for its limited scope and l ack of controlling for ex ternal factors (Reed 1972; Corzine et al. 1983). A similar study was performed to furthe r Reeds findings two decades later. Corzine, Creech, and Corzine (1983) also us ed lynching rates in the South to test Blalocks hypothesis. Using data obtain ed from the NAACP on lynching rates from 1889-1931, in the eleven confederate states, Corzin e et al. set out to unravel the entangled relationship between the impact that percent Bl ack had shown in past research to have on crime in the South. They used Reeds l ynching index, which he introduced in his assessment in 1972 (Corzine, Creech, and Corzine 1983). Their findings offer limited support to Blaloc ks thesis, however they also illustrate that percent Black effects were highly correlated with the sp ecific geographic area. They then separated two sub-regions of deep Sout h and bordering South, finding that the power threat explanation fits better in the deep South regi on as compared to the bordering South (Corzine, Creech, and Corzine 1983). As promising as these two studies were for the prospects of the power threat hypothesis, they would both come under hea vy scrutiny in the late 1980s. With the publication of a criticism of the two studies by Tolnay, Beck, and Massey, a heated exchange occurred regarding many of the c onceptual and measurement issues of both studies. Tolnay et al. (1989) re-examined the data used in the Corzine et al. (1983) study.

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30 They pointed to several discrepancies which existed in their data and measurements as well as their analysis and subsequent inte rpretations (Tolnay, Beck and Massey 1989). Their criticisms covered several areas, but perhaps their greatest concern was with the deletion by Corzine et al. of counties with less than 5% total Black population, and the inclusion of counties with greater than 80% Black of the total population. They argue that three counties in particular, with high concentrations of Black s in the population and a unique structure of lynching during this time skew the results presented by Corzine et al. They offer an analysis based on approximate data that closely resembled that used by the Corzine et al. study (from the same da ta bank at NAACP), and they report to find substantially different results. In addition to these data shortcomings, they point to the inconsistency and inaccuracy of the NAACP da ta, in that they found several instances where the wrong case was recorded, etc. (Tolnay, Beck and Massey 1989). In addition to these criticisms, they also take issue with R eeds lynching index. Relating back to the three outlying counties mentioned above, they argue that these counties exacerbate Reeds index and can pot entially inflate the findings that percent Black is a significant predictor of lynching rates in the South. This is due in part to the fact that two of the counties th at were outliers in the Corzin e et al. study, also resided in the data utilized by Reed in his orig inal study (Tolnay, B eck, and Massey 1989). Additionally, they argu e that Reeds index doesnt re present anything that a rate should. That is it (admittedly by Reed) doesnt represent the rate of lynching compared to the population, but rather it repres ents the rate of lynching, as compared to the expected rate of lynching. This, they contend, inevitab ly causes Reeds index to automatically

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31 assume the slope predicted by Blalock, and that it is unable to diffe rentiate between two scenarios (Tolnay, Beck, and Massey 1989). These criticisms prompted commentary fr om Corzine et al., Reed, as well as Hubert Blalock. The response by Creech et al. to claims of truncating data by eliminating certain counties, while keeping others with no conceptual or theoretical justification, is simply that they had theo retical justifications for doing so due to the scope of the power threat hypothesis. Th at is, the power threat hypothesis is not a general theory of crime, criminal behavior, or disc rimination. Instead it is a fairly narrow and specific hypothesis predicting when discrimination is most likely to increase based on the proportion of the minority population (Cr eech, Corzine, and Huff-Corzine 1989). In response to the critics claims that the Reed index was inappropriate, Creech et al. admit that the index is not perfect (what i ndex is?), and that they had considered others at the time and settled on Reeds as it best captured the measure for testing Blalocks hypothesis. They argue that the alternative m easure provided by Tolnay et al. ignores the racial composition of the county, thus making it difficult to discuss any implications in terms of power threat hypot hesis (Creech et al. 1989). John Reeds response is much less deta iled. He does however declare his disappointment with the data collected by the NAACP. However, in his defense of the data, he argues, and rightly so, that data to test Blalocks hypothe ses rely on areas with heavy concentrations of Blacks where lynchi ng had occurred. If the data that were available had not been used, there simply w ould not have been a test of the hypothesis, because due to the nature of the hypothesis, Mississippi data was crucial to even having the opportunity to apply Blalock to lynching rates in the South. He also points out a

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32 quotation by Blalock regarding his original hypotheses and the difficulty in testing them. He states: inadequate data, the infreque ncy of lynching, and certain methodological difficulties have prevented [us] from obtaining definitive results (Reed 1989). The response by Blalock is largely met hodological, yet brief. He offers many suggestions if choosing differe nt denominators for creating ratios and measures of such items. He ends his short reply with saying th at: In this instance we are involved with a cross-level analysis problem, where the theory refers to the micro-level (perceived threats and loosely coordinated lynchi ng behaviors), whereas data ha ve been aggregated at the macro-level. This presents a problem similar to that of the ecological fallacy, he goes on to state that when testing these ideas further, caution must be taken to carefully construct a theoretical model that clearl y specifies the assumptions in order to select a suitable denominator for creating ratios (Blalock 1989:633). Crawford, Chiricos, and Kleck use racial threat as a means to better understand sentencing under Floridas habitual offende r laws, based on race. They conceptualize racial threat as threatening to mainstream Am erica as well as political elites. They argue that racial threat has become increas ingly prevalent, especially with the media frenzies depicting juvenile violence as ghetto pathologies as well as the images of crack cocaine potentially spreading to previ ously safe places (C rawford, Chiricos, and Kleck 1998). They utilize data from Florida during the period of 1992-1993, involving sentencing of habitual offenders. The data file consisted of 9,690 habitual eligible offenders to determine to what extent race contributed to being sentenced as such. In

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33 order to single out race effects, they cont rolled for demographic characteristics of the offender, legal attributes, as well as th e county in which they were sentenced. One limitation that they state up front is th eir lack of data on race of victim, which is used in much of the racial threat literature. The most in teresting finding perhaps in light of racial threat is that of drug offenders. They argue that a racial threat argument may help to explain the strong effect of race in the habitualization of of fenders. Nearly all of these were cocaine offenders, which is prec isely what the media frenzy in the 1980s focused on almost exclusively regarding drugs and crime. These findings were similar for both drug possession as well as dealing/trafficking offenses (Crawford et al. 1998). An interesting paradox arises in their rese arch relating to raci al threat. Where the race variables showed the most strength were also the areas that w ould traditionally be viewed as low in racial threat. That is, th ey were in communities below the median in percent Black, racial income in equality, and so on. This seem s at odds with racial threat on the one hand, yet on the other, it may contribut e to our understandi ng of racial threat at the same time. While traditionally it was argu ed that racial threat occurred primarily in the areas of highest concentra tion, it could be that these area s deemed safe and free of crime and drugs, are even more punitive (especi ally considering the images in the media portraying crack coming to a neighbor hood near you) (Crawford et al. 1998). Eitle, DAlessio, and Stolzenberg (2002) propose three separate hypotheses. They indicate a need to look at polit ical racial threat, economic r acial threat, as well as Black crime racial threat. Using NIBRS data from South Carolina, they measure political racial threat as the ratio of Black to White vot ers during the 1992 and 1994 elections in South Carolina, economic threat as White to Black unemployment, and Black crime threat as

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34 the influence of Black on White crime on Bl ack arrest levels, where Black on White crime is the percent of violent felonies reported to the police where the offender was Black and the victim was White (Eitle et al. 2002). Controlling for other contextu al factors such as White/B lack divorce ratios, violent crimes in area, and population density, they found no support for either political threat nor the economic threat hypotheses. Howe ver, they do find strong support for Black crime threat. Furthermore, in counties with a higher percentage of Black on White crime, Blacks are significantly more likely to be arre sted. This finding was robust, even in the model containing all of the contro l variables (Eitle et al. 2002). The power threat hypothesis has evol ved substantially since its early conceptualizations by Blalock. Since its earl iest tests by Reed and Corzine et al., the perspective has been improved upon greatl y. Many taking note of the methodological issues associated with the early studies, clea rly has had some impact. Eitle et al. stated that their goals were to better synthesize th e conceptualizations as to limit confusion, since its often referred in several manners. Through the use of NIBRS data, Eitle and colleagues may have discovered a unique and accurate way of measuring and testing the threat hypothesis, without distorting or having to stretch too far the m easurement issues that have surrounded this approach in past studies (see also Stolzenbe rg et al. 2004). Clearly, having data on race of both offenders, as well as victims, provide s a dynamic way of con ceptualizing racial threat. The Crawford et al. st udy offers another unique directi on in that those areas that perceive great racial threat may in fact be those with the least c oncentration of Blacks,

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35 and of crime in general (e.g., middle class subu rbs, where the images of crack babies still persist). More recent studies of raci al threat have attempted to move beyond the myopic measures of percent black of the population. More recent tests of Blalocks hypotheses us other measures such as racial inequality and black immigration patters as proxies of racial threat (Stolzenberg et al. 2004; Parker et al. 2005). Findings indicate that contrary to Blalocks hypothesis, percent of the population that is black and racial ine quality were found to have a negative aff ect on black arrest rates. On e explanation that has been asserted is the benign-neglect hypothesi s by Liska and Chamlin (1984). They argue that in areas with a large bl ack population, crime is more likely to be intra-racial. As a result, there is less pressure on police to control crime because non-white victims are less likely to report crime or, even when they do report crime, police may allocate fewer resources to resolve the offense (Liska and Chamlin 1984). Conclusion In light of the preceding discussion of the prevailing macro-level theoretical perspectives on crime and delinquency, the ways in which these theories have been and will continue to be tested has not been consiste nt to say the least. For the purposes of this study, it is better to explicitly state that I am attempting to account for the rise in racespecific drug sales arrest rates over time, us ing structural factors of large urban areas. That is, I am not necessarily setting out to ex plicitly test social disorganization theory. To do so would require conducting large scal e interviews, surveys and focus groups in large urban areas to truly assess to what exte nt these urban areas are able to exercise informal controls over its residents.

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36 Rather, what I am attempting to do is to choose theoretically relevant structural predictors as proxies for the larger concepts of social disorganization and other ecological theories of crime. This is important as ma ny of the frequently utilized measures of structural factors as they relate to crime and delinquency are shared across theoretical paradigms. For example Pratt a nd Cullen (2005) point out that: Researchers have used measures of un employment as proxies of guardianship (routine activity theory) and economic hardship (economic/resource deprivation theory), as an indicator in the brea kdown in the viability of community control/socialization (social disorganizati on theory), and even as a precursor to frustration-induced anger (anomie/strai n theory) (Pratt a nd Cullen 2005: 430-431). My reasoning for choosing th ese three theories specifica lly is twofold. First an analysis such as this has not yet been conduc ted, so it adds a signifi cant contribution to the research examining these factors on changing drug arre st rates over time. Second these three theories not only allow for race-s pecific changes over time, but as Pratt and Cullen note they are among the most consistently significant predictors of crime in urban areas. As Messner et al. (2005) have suggested, the changes that cities in regards to both drug arrests as well as homicides has changed significantly over the past several decades. Additionally Levitt (2001) suggests that th e declining crime rate during the 1990s is partially attributable to the decline in crack markets in large cities. While these approaches note the importance of accounting fo r changes over time, Levitt in particular considers drug crimes to be an independent (explanatory) variable of crime rates. Therefore the changes in ur ban areas during this same time period creates a dynamic relationship where the changes in theoretically relevant macro-level indicators may be related to the changes in dr ug sales arrest rates over time (Levitt 2004; Messner et al. 2005).

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37 This is especially true for African-A mericans in large urban areas. As Wilson (1987) has argued that black families have b een especially impacted by these industrial shifts. The theories selected for this study allow not only for change in general, but especially allow for modeling change for bl acks specifically, across multiple decades for multiple cities. While the discrepancies in Black and White crime rates have been well documented in past research, no other crime doe s this difference stand out more so than for drug crimes.

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38 CHAPTER 3 REVIEW OF THE LITERATURE Introduction While variations of the structural th eories presented above have received considerable empirical testing, little attention has been pa id to addressing structural explanations of drug offenses. These theories ha ve all been used to help us explain both violent and property crimes (C rutchfield and Pitchford 1997; Liska et al. 1998; Krivo and Peterson 1996, 1998, 2000; Parker and McCall 1997, 1999; Sampson 1987 Shihadeh and Ousey 1998), and have only recently began to garner the attention of researchers interested in drug crimes in large urban areas (see Mosher 2001; Parker and Maggard 2005). This chapter seeks to offer a historical overview of drug use and the laws created to control their use in the United States. It is important to understand the historical connections between drug use, public policy and crime in order to better apprehend why our country is in the position th at it is today. Finally, I will offer a brief discussion of the merging of the theories and how tests of each theoretical construct are not mutually exclusive from the other theories as well as provide several hypot heses to be tested. Race, Drugs and Crime: A Historical Perspective Any discussion of drugs and crime needs to account for the historical landscape that surrounds drug use in this county as well as the way that we have chosen to deal with these issues. We must ask ourse lves, how did we get here? Here referring to the fact that in 2004 there were an estimated 1, 745, 712 drug violation arrests in the United States (U.S. Department of Justice 2004). While ordi nances and legislation dates back more

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39 than 100 years, we continue to find ourselves arresting millions of individuals year after year. We must also take into account the roles that the me dia, doctors, minority groups, as well as the pharmaceutical industry play in this expensive game that is illicit drug use. Drug use in this country is best understood beginning in the 1800s. Opiates were used in medicine in the early years and while addictive, offered medical uses that suited a variety of needs. It was the discovery of he roin during this period that prompted doctors to believe they had found an iteration of opi ates that was nonaddi ctive and yielded few side effects. This belief lasted only about five years, before it became apparent that heroin did in fact possess the same addictive proper ties of other opiates (Conrad and Schneider 1980). Heroin and opiates were later ch aracterized as recreational drugs, yet pharmaceutical companies continued including opiates in a variety of medications, despite the risks of addiction, among others. Although the drug industry continued to use opiates in these formulations, the press wa s reluctant to critic ize the pharmaceutical companies for fear of losing advertising dolla rs and future revenues. Instead, the media portrayed users of opiates in a negative manner, since cr iticizing these companies had financial risks; the media began to portray us ers of opiates in a derogatory manner, for powerless groups could be criticized without such caution (Tieman 1981: 242). The power of the media may never be quite as transparent as is realized while peering through the sociologica l lens at American drug us e. During this same period cocaine use was beginning to be attributed to African Americans in particular. The media presented an image of co caine-crazed black men who upon the ingestion of cocaine experienced greatly increased sex drives and de sires. These desires were said to increase their likelihood of raping white women, which instilled fear among citizens. Further, it

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40 was reported that as African Americans used cocaine, they experienced greater visual clarity, which significantly increased their mark smanship with firearms. Perhaps the most outrageous media reports rest on the asser tions that cocaine made black men less vulnerable to .32 caliber bullets. While this seems outrageous t oday, police agencies actually began switching to .38 caliber handguns to combat the dangerous cocaine crazed black men (Musto 1973: 7-8; McBride 1981: 105-124; Smith 1986). While these derogatory portrayals of African Americans committing crimes while under the influence of cocaine are preposterous, more utilitarian uses of the drug were also circulated in the media. For instance, the Medical News reported that its use in New Orleans made it possible for workers to work seventy hour sh ifts loading and unloading steamboats, that otherwise may not have been able to be accomplished without the use of stimulants (Spillane 2000). African Americans were not the only minority group to cat ch the attention of the media and the government. During the late 1800s Chinese immigrants, many of whom were working on the railroad systems found employment in California among other locales. Their opium smoking behavior led to an 1875 ordinance passed in San Francisco aimed at the un-American habits of the y ellow devils (Brech er 1972: 42-44). These negative portrayals are not unlike other dange rous classes such as prostitutes and vagrants (see Adler 1986 and 1992). This stigma emerged to stick not only to minorities but also to whites that had adopted opium-smoking habits (Lindesmith 1968: 215). These casual opium users were described as gamblers, thieves and prosti tutes, who were not legitimately receiving medicine from doctors. These images of casua l drug use eventually led to the increasing

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41 restrictions placed on opiates wh ich coincided with the 1) rise in patriotism in America, 2) an increasing fear of minorities and 3) what had become an estimated addict population of 250,000 (Musto 1973: 5, 33). This was a critical time in American hi story. While we wished to decrease opium use among residents and immigrants alike, our immigration doors were growing as we expanded and acquired new territories. It was important for the United States to remain vigilant in its abhorrence for drug use with the Chinese government and it was quickly realized that we would not be able to persua de other nations to curb drug use if we did not enact specific legislation at hom e (Duster 1970: 14; Musto 1973: 36). As one of the first measures to control drug use at the federal level, the Harrison Act, passed by Congress in 1914 paved the way for the future of drug control in the United States (Musto 1973; Spillane 2004) While supported by many, the Harrison Act was not free of criticisms by supporters and cr itics alike, including then Speaker of the House Oscar W. Underwood who had shep herded the Harrison Act through the house the previous week, described the prohibit ion amendment as a tyrannous scheme to establish virtue and morality by law (M usto 1973: 67). Sentiment such as Mr. Underwoods notwithstanding, the next four decades would see attention focused on enforcement and drug users were characterized as antisocial or ps ychopathical addicts who chose addiction that normal people would never choose (Tieman 1981). This trend continued for much of the co ming century with the next major event being the formation of the Federal Bur eau of Narcotics (what is now the Drug Enforcement Administration) in 1930 and the appointment of its over-zealous leader Harry J. Anslinger. Anslinger abhorred drug use and demanded that his agents focus on

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42 street peddlers of drugs in American ci ties (Tieman 1981; Carroll 2004). Anslinger was able to persuade citizens of the United Stat es that drug sellers and users alike were criminals, and should be treated as suc h, and from the 1930s forward, drug use was strictly a law enforcement issue versus a public health issue. Anslinger and his subordinates were able to perpetuate the idea of strong linkages between drug use and violent crime through tel ling fabricated horror stories (Carroll 2004). While Anslinger directed most of the Fe deral Bureau of Narcotics attention on hard drugs (opiates and cocaine), he paid little attention to marihuana and did not consider it a serious problem, at least not one that required th e intervention of the federal government (Carroll 2004). Anslinger even te stified to Congress that marihuana did not cause addiction and could be handled at the local level if problems persisted with the substance. However Anslingers true politic al identity would emerge as rumors began circulating on Capitol Hill that a new bill could dissolve the FBN and shift enforcement of drug laws to other government agencies At this point, marihuana was the obvious vehicle he needed to instill hysteria and leve rage the idea that marihuana was a menace that needed to be dealt with and this ev entually led to the Ma rihuana Tax Act of 1937. Anslinger even began referring to marihuana as hashish (an extract from female marihuana flowers), since it seemed to be so closely associated with the word assassin (Musto 1973; Carroll 2004). Anslingers role in shaping federal dr ug control policy would remain steady for many years to come (Tieman 1981; Carroll 2004). While little scientific evidence was presented linking marihuana use and drug use to criminal behavior, Anslinger offered many commonsensical accounts by which addict s would resort to crime (Galliher and

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43 Walker 1977:375). Until the end of the 1940s, addicts remained re latively uninvolved in criminal behavior, as costs fo r drugs were low and many were able to use physicians for their personal n eeds (ODonnell 1969). Perhaps the line in the sand drawn to make clear that drug use would be a criminal justice issue versus a public health issue occurred through the Boggs Amendment of 1951 and the subsequent Narco tic Control Act in 1956. These laws would establish mandatory minimum sentences for drug offenses and were meant to emphasize the seriousness of such offenses and how law enforcement would address them. Anslinger was able to re-emphasize his beliefs that addicts were merely parasites as he stated: The person is generally a criminal or on th e road to criminality before he becomes addicted. Once addicted he has the greates t reason in the world for continuing his life of crime (Anslinger and Tompkins 1953:170). It was not monetary reasons that Anslinger ar gued would lead to the criminal career, but rather that using drugs would erode the moral fiber of individuals in general. This pattern of connecting drug use to criminal behavior continues to this day (Tieman 1981). Anslingers tactic of dem onizing marihuana to pave th e way toward criminalizing its users was carried forward to future eras and specific drugs over time. Indeed the media would also play an important role in shaping the legislation that led to the drug scares of LSD in the 1960s, PCP in the 1970s, cr ack in the 1980s, and MDMA or Ecstasy in the 1990s and 2000 (Goode 2005). Were many of the horror stories true regarding the toxicity of drugs such as PCP? Sure they were. PCP is a dangerous drug, but what the media can contribute to is the misrepresentat ion of the extent of a drugs use. As with other hard drugs, most users are not casua l marihuana users one day who decide to

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44 become hard-core drug users the next, but rath er they have already become enveloped in a hard-core drug scene to begin with. Race, Drugs and Crime: The New Era While the focus of my research is on drug sales arrests from 1980-2001, it was important to point out how the shifts and os cillations in Americas responses to drug use have affected the very arrest rates th at I am investigating. With each of the aforementioned drug scares, mass hysteria and over reaction has ac companied virtually each one (Reinarman and Levine 1997). The ons et of crack in American society was a major event, and politicians were able to use this drug scare as an avenue to create specific laws aimed at controlling its use, not unlike the Harris on Act in 1914 and other drug legislation discussed above. In 1982, President Ronald Reagan officially declared a War On Drugs (Sacher 1997). Shortly after this, Congress enacted the Sentencing Reform Act of 1984 (SRA), which reportedly had the sole intent of es tablishing strict guidelines for federal sentencing. This was an effort to promote fairness and uniformity in federal cases by establishing mandatory minimum sentences for specific offenses. Following the 1984 SRA bill, the Anti Drug Abuse Acts of 1986 and 1988 substantially altered the way in which we would come to treat drug offenders brought to federal cour t on charges of both possession and trafficking of cocai ne (Sacher 1997; Zedeck 2000). During the period that I am focusing on, cr ack cocaine is the most salient drug related event that occurred which resulted in unprecedented drug arrests in the United States. Drug arrests rose from 18,521 to 88,641 respectively from 1980 to 1988 in New York City alone (Belenko et al. 1991; Ne w York City Police Department 1989). The importance of studying arrest rates disaggreg ated by race are clear as Blumstein (1993)

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45 notes that although White arrest rates remained relatively stable from the 1970s through much of the 1980s, non-white drug arrests steadily climbed from 1980-1985, growing exponentially at a rate of 15%-20% per ye ar until peaking in 1989 (Blumstein 1993). A similar portrait was seen at the nationa l level where the number of drug arrests in 1980 was just under 600,000 and by 1990 had risen to roughly 1.1 million total drug arrests (representing nearly a 92% increase over a 10 year period). More striking however are the racial shifts that occurred during this period. In 1980 African-Americans made up about 25% of all drug arrests in the United St ates and by 1990 this figure had increased to 41% of all drug arrests (Goode 2005; U. S. Department of Justice 1996). While it has been argued that the rising drug arrests during th e 1980s was directly attributable to crack cocaine, it is importan t to look at who was using crack cocaine and the potential size of those markets. The de mographic characteristics of crack users sharply differ from their powder cocaine-sn orting counterparts. T hose who abuse crack cocaine, much like daily heroin users, are li kely to be urban poor. Moreover, they are likely to have been hard-core drug users in th e past, for most crack users do not start off with crack cocaine. Rather, they are either ha rd-core heroin addicts or cocaine abusers, looking for a better, more convenient high. It is important to note that even during its most popular year, only a small percentage of those who used cocaine, used crack (Golub and Johnson 1996; Reinarman and Levine 1997). While crack cocaine is primarily prepared and distributed by African Americans, it is also used and abused by Whites at even higher numbers. Although data do suggest that a higher proportion of the Bl ack population use the drug when compared to Whites. However, though Whites make up the majority of crack users, Blacks continue to make

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46 up more than 80 percent of those convicted for distribution of the drug (Gray 1998; Zedeck 2000). Each time a relatively unknown drug gains popularity, there are undoubtedly outrageous exaggerations of its use and it becomes the next scary drug of the year (Akers 1990). Typically thes e reactions are also accompanied to knee jerk hypotheses regarding the new drug and its relationship to violence and crime in general (Goode 2004). In fact, one could certainly argue that as new drugs begin to ga rner the attention of the mass media, their very role is socially constructing how the drug will come to be viewed (Hartman and Golub 1999). The role of language and the mass media in shaping attitudes toward both people as well as inanimate objects is often underestimated (see also Berger and Luckman 1966). One of the more prominent theories in re gards to drugs and crime/violence is Paul Goldsteins tri-partite frame work. His conceptual model of the relationship between drugs and crime/violence consists of three mechanisms by which drugs and crime are related. First, the psychopharmacological he argues is crime a nd violence that results from the actual physical effects of the drug on the user. For example, taking a certain drug may cause a person to lack judgment which may lead to crime and/or violence. What he calls the economic compulsive mechanism is when individuals must commit crimes in order to finance expensive drug habits. Finally, systemic crime/violence is the result of any number of circumstances rela ted to drug use and drug markets. For example the black markets that drugs are typically pedd led in are often territo rial and controlled by gangs which may lead to violence between rival gangs. In another example offered by

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47 Goldstein and his colleagues, individuals may punish someone who sells them bogus drugs in order to teach them a lesson (Goldstein 1985; Goldstein et al. 1991). Goldstein et al. tested these concepts a nd found that alcohol was the substance most correlated to the psychopharmacological model. In terms of the systemic model, heroin markets were the most likely culprit in determining crime and violence for the areas in which they studied. Finally cocaine was the strongest substance related to the economic compulsive mechanism, however the numbers we re quite small. Clearly, using the reasoning that was used to crea te the new laws aimed at crack cocaine, one would expect crack and/or cocaine in general to dominate a ll three categories in research such as this (Goldstein et al. 1991). History has shown us that officials ofte n overreact to the introduction of new drugs, especially when they are primarily associat ed with minorities. However, in line with Goldsteins ideas, Blumstein (1995) has argued that drug ma rkets such as the crack cocaine market greatly contributed to the diffu sion of firearms into the hands of youth. It is further argued that the volatil ity of crack markets, coupled with the lower inhibitions of inner city youths would l ead to the increase of gun cr imes and homicides within American cities. While not immediate, Blum stein argued that as crack markets spread across the country, a short la g would occur and we would find that homicides and gun related violence would follow in those urban areas (Blumstein 1995). In support of Blumsteins hypotheses, cr ack and cocaine have been linked to different types of crime in large cities. Ba umer (1994) found that cocaine use by arrestees was a significant predictor of robbery rates and a more modest predictor of homicide rates in large cities, even wh ile controlling for structural characteristics such as poverty

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48 and divorce rates. Of course whether the co rrelation is due to the dynamics of the drug market or the drug itself is unclear. Sim ilarly Ousey and Lee (2004) found that changing drug markets are linked to the rise in both White and Black homicide rates in large cities. However the relationship for is quite larger fo r Blacks than Whites, with structural factors such as inequality having a much stronger im pact on Black homicide rates than for White homicide rates (Baumer 1994; Ousey and Lee 2004). In another study, Cork (1999) also found support for Blumsteins theory. More specifically he found that the relationship between crack mark ets and their linkage to the availability of guns is quite realistic. While the relationship is dynamic and complex, he also notes that the increase s in gun related homicides committed by juveniles, typically occurred shortly after the emer gence of crack markets in thos e areas. Also worth noting is the fact that while slightly older homicide offenders diffused in a similar manner, they were not as pronounced as with younger perpetra tors, as youth have been said to lack the empathy or concern for strangers as olde r individuals may (Cornell 1993; Cork 1999). These studies are good examples of the way in which researchers have approached the problem of drugs in the li terature. It has been more common to use crack and cocaine, or even heroin or alcohol as explanatory variables in determ ining crime rates in general. That is, little research has focused solely on explaining the rates of drug arrests as much as they have focused on property and violent arrest rates. Given the structural changes across cities in America, as well as the increas e in drug arrests, clea rly this is worthy of further research (see Mosher 2001; Parker and Maggard 2005). Much of the research examining the crime drop that occurred across America in the 1990s has also noted the important role that cr ack cocaine markets in particular played in

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49 this decline (Levitt 2004 ). This is interesting because wh ile we do not want to unjustly punish the behavior of one racial group compared to others, we also have an obligation to protect the citizens in urban ar eas where much of these crim es are centered. It is these reasons that we expect not only for drug ar rests to have risen so sharply from 1980-2001, but also expect Blacks to have incurred much more dramatic rises proportionally compared to Whites. Hypotheses and Expectations Having reviewed prior theoretical research as well as review ing the historical aspects of drug use and arrests, I have gene rated several theoretical ly relevant hypotheses and expectations in this study. The first se t of hypotheses surrounds the trends in race specific drug arrests over time while the sec ond set of hypotheses rela tes specifically to the structural factors discussed earlier and how they may be di rectly related to the trends in drug arrests that appear in the data. As noted earlier drug arrests have seen ma ny fluctuations over the past century. However no era has reflected as much ch ange as the period beginning around 1980 and extending to this day in some respects. The implications for race are even more exaggerated, as African-Americans have been disproportionately target ed and arrested as a result (Tonry 1995). Prior research on drug arre sts trends have tradit ionally been at the decennial level in regards to changes over time Researchers have neglected to attempt to group similarly behaving cities over time, as they relate to racially disaggregated drug arrests. Therefore by applying the TRAJ proce dure to such data, it is possible to model whether particular cities a nd regions behaved as expected over these time periods. As Blumstein and Rosenfeld (1998) have noted, th e changes in homicide rates have differed for coastal cities versus cities that are more inland, which th ey argue is consistent with

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50 the patterns Golub and Johnson (1997) found to surround the crack epidemic (Blumstein and Rosenfeld 1998; Golub and Johnson 1997). Gi ven this past research on homicides and the crack epidemic specifically, I draw th e following two hypotheses in regards to the expected trajectories for drug sales arrests over time: H1: I hypothesize that race spec ific drug sales arrest rate s will significantly vary over time, forming identifiable trajectories distinguishing cities which experienced many drug arrests from those that did not. H2: The witnessed trajectories will differ significantly by race, indicating that black drug sales arrests accelerated at a much greater pace from 1980-2001 than did white drug sales arrests. These expectations arise from reviewing past work examining the relationship between a citys structural characteristics and drug arrests over time. For example, Mosher (2001) notes that the ri se in drug arrest rates across cities is profound. This effect is exacerbated by using the mean examine ch anges over time based on a pool of cities that differ greatly in terms of arrests and structural charac teristics. Parker and Maggard (2005) report that black to tal drug arrests rose by about 211% (versus 106% increase among whites) from 1980-1990 with similar patterns witnessed for possession arrests, yet for drug sales arrests the raci al gap narrowed with a 236% increase in black drug sales arrests versus 193% increase for whites (Parker and Maggard 2005). Moshers finding coupled with the differences noted by Parker and Maggard lead one to believe that it is feasible that the mean increas es witnessed in past research could have been inflated by outliers, especially for drug sales arrests. In other words, most cities may not have experienced epidemics of drug sales and use during key years, yet a few of these cities may have experienced unprecedented pr oblems surrounding drugs and crime which resulted in highly skewed descriptive statis tics. This is especially plausible given Jargowsky and Banes findings that only 10% of the cities they studied accounted for

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51 75% of the rise in ghetto poverty thr oughout the 1970s, indicating a few radically changing cities may paint much of the pi cture of the nation for any given topic (Jargowsky and Bane 1991). The second set of hypotheses stem from theo retically drawn expe ctations of the structural conditions of urban areas and their im pact on fitting into specific trajectories as mentioned above. Since I draw upon multiple st ructural theories of urban crime in constructing these hypotheses, some are not necessarily theory specific. However the directions that I predict clearly rest on particular theories and their expected relationships to urban crime. For example, Pratt and Culle n (2005) note that the expected relationship between unemployment and crime may be positiv e direction for social disorganization or anomie based theories, while predicting an inverse relationship under the umbrella of routine activities theory (Pratt and Cullen 2005). Social Disorganization and Drug Sales Arrests Based on Shaw and McKays original hypothese s as well as past research, I expect the rising disorganization of ur ban areas to result in both an increase in both white and black drug sales arrests over time (attributable to the racially inva riant predictions of social disorganization theory). Recall that so cial disorganizations theory posits that the disorganization of an area results in a brea kdown in informal control mechanisms. This in turn is expected to result in higher crime rates, regardless of who resides in an area. It is this racially invariant nature of social disorganiz ation theory that leads to the following two hypotheses: H3: Cities that experience an increase in family disruption (via the percentage of divorced males) for both blacks and white s will increase the likelihood of those cities belonging to a higher arrest traj ectory as opposed to the lowest arrest trajectory for drug sales arrests.

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52 H4: Those cities that experience increases in residential mobility will increase the likelihood of those cities belonging to a hi gher arrest trajectory as opposed to the lowest arrest trajectory for drug sale s arrests (invariant across race). Concentrated Urban Disadvant age and Drug Sales Arrests Wilson (1987) argues that the deindustriali zation of large urba n areas has had an unprecedented affect on the lives of African-Ame rican residents in those cities. Massey and Denton have extended these ideas to include the role that racial segregation has in concentrating poverty in partic ular areas. Research has show n that this is the case in many instances for both violent and property crimes. However there is good reason to believe that the decreasing avai lability of low-skilled jobs w ould lead to an increase in drug sales arrests, especia lly for African-Americans. El ijah Andersons ethnographic research on urban areas does an excellent j ob describing how the illicit drug trade and poverty are intertwined in a very complex way. Anderson writes: In the impoverished inner-c ity neighborhood, the drug trade is everywhere, and it becomes ever more difficult to separate the drug culture from the experience of poverty. The neighborhood is sprinkled with crack dens located in abandoned buildings or in someones home. On co rner after corner, young men peddle drugs the way a newsboy peddles papers. As Anderson illustrates the difficulty in se parating urban poverty and the illicit drug trade, the urban disadvantage literature presented above l eads me to several hypotheses. With black neighborhoods becoming increasing ly segregated and the concentration of poverty increasing, it should be no surprise that some individuals we re coerced, so to speak to sell illicit drugs. The lure of hi gh profits, designer clothi ng, and the ability to support ones family proved too tempting to pa ss by. Additionally the open-air nature of crack markets left inner city drug sellers espe cially visible to law enforcement agencies. These factors lead to the following two hypotheses:

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53 H5: An increase in residential racial segregation (measured as the Index of Dissimilarity) will increase the likelihood of belonging to a higher arrest trajectory as opposed to the lowest arrest traj ectory for black drug sales arrests. H6: An increase in concentrated disadva ntage (e.g., poverty, income inequality, male joblessness, and percent children not living with both parent s) will increase the probability of cities bel onging to a higher arrest traj ectory versus the lowest arrest trajectory for both Black and White drug sales arrests. Racial Threat and Drug Sales Arrests Hubert Blalocks power threat hypothesis st ates that as the minority population size grows, those in the dominant group will begin to feel threatened and be more likely to utilize formal social control mechanisms (e.g. arrests). Moreover as those in power begin to utilize formal controls mo re liberally, arrest rates for the minority group should rise over time. Drawing both on Blalocks ideas, I am left with the following hypothesis: H7: Cities that experience a significan t rise in the proportion of the population identifying themselves as black, will be mo re likely to belong to a higher trajectory group versus the lowest tr ajectory group for Black dr ug sales arrest rates. Conclusion These variables are also among the list of st rong structural predic tors that Pratt and Cullen argue are necessary for any study seekin g to link macro-level structural conditions of urban areas to crime rates. In their meta-ana lysis, they discovered th at out of thirty-one commonly used measures of structural covari ates of crime, many of those listed above remain in the top ten, especially consider ing both the strength and stability of the predictors across various studies (Pratt a nd Cullen 2005). Additionally as Pratt and Cullen have noted, some of these concepts are not theory specific. In fact one could argue that there is a movement away from te sting specific theories and instead grouping commonly used structural covariates to ga uge the overall importance of structural landscape of urban areas on cr ime rates (see Parker 2004).

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54 Having presented several hypotheses for te sting, the next several chapters will intend to shed light on the importance of not onl y being able to identify trajectories of drug arrests over time, but also which structural covariates ma y predict being in a higher versus lower trajectory. Chapte r four will present the data used to test these hypotheses with the remaining chapters devoted to presenting the results of the analyses.

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55 CHAPTER 4 DATA AND METHODS Introduction As discussed in prior chapte rs, structural theories have been used to explain many types of crimes. Drawing upon the most wi dely and rigorously tested measures, I describe these measures in the following sections. Following the meta-analyses of Pratt and Cullen (2005), I have selected what have proven to be the most reliable and theoretically relevant structural predictors over time. In addition I have chosen predictors that Pratt and Cullen argue have had fe w (if any) serious methodological issues throughout their empirical hist ory (Pratt and Cullen 2005). Since this research utilizes city-level analyses, all m easures are operationalized as aggregates at the city level. Both depe ndent and independent variables have been constructed to represent the bounda ries of American cities. This is consistent with past research linking structural conditions to urban crime. Data Sources The unit of analysis is U.S. cities, be ginning with those with a population greater than 100,000 residents in 1980. While some deba te has occurred regarding the best unit of analysis (see Messner 1982; Bailey 1984; Messner and Golden 1992) for studying structural covariates of crime rates, the city makes the most sense for a study of this kind. First as Chamlin (1989) notes, aggregating police data across jurisdictio ns to the Standard Metropolitan Statistical Area (SMSA) increases th e risk of inflating these statistics (Liska 1989). Second, using city level data allows us to investigate the concentration effects

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56 noted by Wilson (1987), where he contends that the effects of these structural factors were due to the changes in the spatial a nd industrial structure of such areas (Wilson 1987). Finally Land and his coll eagues (1990) argue that it makes little difference which unit of analysis is selected (SMSA, City, et c.), providing proper st atistical techniques are employed to address the research question (Land et al. 1990). This sampling strategy generates 179 cities in 1980 and these cities were the basis for selecting cities for the entire sample in order to establish trajectories for a nested sample of cities. There are five major sources of data for this research. For the dependent variables, the data are Uniform Crime Repor t arrest counts obtained from Chilton and Weber (2000). The second and third ma jor data sources are the 1980, 1990, and 2000 Census of Population and Housing; Both the Characteristics of the Population and the Social and Economic Characteristics volumes (U.S. Bureau of the Census 1983; 1994; 2004). The fourth data sour ce consists of the Bureau of Justice Statistics Census of State Adult Correctional Facilities (1979) and the Census of State and Federal Adult Correctional Facilities (1990 and 2000). Finally, the Uniform Crime Report serves as the source of information on the number of poli ce officers in each city for 1980, 1990, and 2000. These data sources are essential to this research because they provide comparable indictors during the time peri ods of interest (1980-2001). Dependent Variable This study utilizes race-sp ecific drug-sales arrests ba sed on continuous years from 1980-2001. I acknowledge the problem of missing da ta for one or more months of data per year across the dependent variables. In t hose situations in which data were reported for at least 9 or more months, mean substitutions were imputed to correct for missing data on the remaining months. Arrest data for those cities reporting less than 9 months of

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57 arrest statistics (i.e., 8 months or less) we re considered missing. I also constructed a dummy variable to indicate whether this mean substitution was performed in order to determine whether it had significant impact on the data and results. The dependent variable is computed as the race-specifi c drug-sales arrest ra te (arrests per 100,000 population) from 1980-2001. This was calculated as follows: 000 100 on icPopulati RaceSpecif sArrests icDrugSale RaceSpecif rrestRate DrugSalesA In addition to missing data from cities that failed to report all twelve months of each year, it was also found that not all 179 c ities reported drug sales arrests for each consecutive year. It was decide d that for those cities missing 3 or less years of data, mean substitutions would be generated. Consequent ly I constructed a dummy variable for the cities/years in which the mean substitutions we re performed in order to determine if these imputations made any significant impact on the data. Neither these nor the month weighted dummy variables mentioned above pr oved to be significant, confirming the integrity of the data. Although mean substitutions were performe d as described above, attrition is inevitable when attempting to use data fo r 22 consecutive years for so many different cities. After all missing data was disposed of and the mean substitutions were complete, a final sample of 132 cities was retained as the final city list. This list is contained in Appendix A. It is important to acknowledge an on -going debate surrounding the use of drug arrest measures. There are two perspectives to considerone being that a measure of drug arrest rates reflects enforcement patterns or official responses to crime (see Mosher 2001; Quinney 1979) and the other is that meas ures of drug arrest rates indicate actual

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58 drug behavior (Cohen, Felson and Land 1980; Ro senfeld and Decker 1999), particularly at the city level (Rosenfeld 1986) It is not my intention in this dissertation to attempt to decipher the true meaning of these measures. However I believe that these measures of drug arrest rates provide a us eful indicator of the amount/ volume of race-specific drug activity in a given area and thus can serve to gauge (at l east the official accountability of) drug activity in urban cities. It is my contention that no matter what these numbers may actually mean if a city experiences a dramatically increasing trajectory of race specific drug-sales arrests, this should indicate that the city has se rious problems that must be addressed (whether it be over-zealous law enforcement or increasing numbers of drug dealers/users). Furthermore, in light of alternative indicators (e.g., DUF/ADAM, DAWN and/or medical examiners data) of drug activit y, prior research has reported high internal reliability among data sources and that these data sources yield similar estimates of drug use when compared to drug arrests for cities (Baumer 1994; Baumer et al. 1998; Rosenfeld and Decker 1993; Warner and Coom er 2003). Given that th e alternative data sources are limited in sample size, drug ar rests are a good proxy of drug activities across multiple large urban cities over time. Independent Variables I utilize a number of race-specific measur es to serve as proxies of the larger theoretical constructs of so cial disorganization, urban di sadvantage and racial threat theories, as well as their corresponding change scores in order to assess their impact on drug sales arrests over time. These include race-specific measures of poverty, income inequality via the GINI index, racial reside ntial segregation, the percentage of jobless males age 16+, and the percentage of childre n under the age of 18 not living with both parents.

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59 Poverty is defined as the percentage of black (white) persons living below the poverty level and is calculated as follows: 100 # Population ine owPovertyL PersonsBel Poverty The numerator represents the race specific number of persons living below the poverty rate and the denominator represents the total population of each race, respectively. Income Inequality is measured using the Gini Index of Income Concentration and is calculated as described by Coulter (1989:38) I calculate the Gini coefficients for 1980, 1990 and 2000 using race specific family income levels, as reported by the census. This measure represents the proportion of the populat ion within different income categories. The index ranges from 0 to 1, with 0 repres enting perfect equality and 1 representing perfect inequality (Coulter 1989). Racial residential segregation is measured by the Index of Dissimilarity (D) As described by Duncan and Duncan (1955) and Massey and Denton (1988) the index represents the percenta ge of either population that woul d have to relocate in order for a given area to achieve equal distributions (e .g. no segregation). The formula is expressed as: 100 2 1 W w B b Dj j where jb and jw are the number of blacks and whites in census tract j, and B and W are the number of blacks and whites in the city of interest. The measure of male joblessness is co mputed as the number of employed males age 16+ in a racial group divided by the total number of males in that racial group that are 16 years of age and older, and multiplied by 100 The product is then subtracted from one

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60 to represent the percentage of males in each racial group not employed. This is expressed as: 100 16 # 16 # 1 YearsofAge Males Employed Males ployed MalesNotEm Percentage The final measure of urban disadvantage is the percentage of children under the age of 18 that are not living with both pare nts. This measure is expressed as: 100 18 # 18 # der ChildrenUn ents ithBothPar NotLivingw der ChildrenUn thParents sNotWithBo PercentKid Two measures of social disorganization in cities are conceptu alized as family disruption and residential mobility. The first measure of family disruption is the racespecific percentage of divorced males within the male population aged 15 and older. This measures is expressed as: 100 15 # # InAge Males les DivorcedMa Divorce Residential mobility is operationalized as the percentage of residents that reported they were not living in the same residence for the previous five years and is expressed as: 100 5 # Re ation TotalPopul Years mePlace LivingInSa PersonsNot obility sidentialM The measure of racial threat represents the percentage of the population identifying themselves as black. This measure is expressed as follows: 100 Re # ation TotalPopul ck nitfiedBla sidentsIde ck PercentBla Several control variables are utilized in this research. Based on their meta-analysis of structural covariates of crime rates, Pra tt and Cullen note that failing to control for

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61 constructs such as the incarceration rate can lead to unreliable results (Pratt and Cullen 2005). The first is percentage of the population with Hispanic origins and is expressed as: 100 Re # ation TotalPopul panic nitfiedHis sidentsIde panic PercentHis The second control variable is the police presence rate (number of police officers per 100,000 residents) an d is expressed as: 000 100 # Pr ation TotalPopul eOfficers SwornPolic esence Police The incarceration rate refers to the numb er of inmates in a given state per 100,000 residents) which is consistent with past research. Some researchers emphasize the importance in lagging incarceration measures when attempting to explain crime. For instance, the importance of lagging arises when attempting to explain current crime rates with measures of the incarcerated population. This can be problematic since incarceration data reflect the period through December 31 of the year the data represents. Given this, lagging allows researchers to determine the effect of the incarceration rate in 1979 on crime rates in 1980, for example. Since I am using the change in the incarceration rate from one decade to the next as a control meas ure, lagging this measure makes little sense (see Levitt 1996 and 2001). The rate is expressed as: 000 100 Pr # te ationOfSta TotalPopul ison InmatesIn ionRate Incarcerat In addition to the above mentioned contro l variables I have constructed several dummy variables which represent regions that cities belong to. For instance it has been found that cities in regions such as the South, Northeast, and West may experience differential rates of crimes, so it is important to consider this as well.

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62 As past research has noted, several of these structural covariates are highly correlated with one another which results in multicollinearity (see Land et al. 1990), so in order to deal with these i ssues I utilize principal com ponents analysis, with varimax rotation, in order to construct indices whic h are representative of the theoretical constructs they are meant to measure, while maintaining the integrity of the data by eliminating the partialing fallacy (Gordon 1967) often experienced when analyzing aggregate data (Gordon 1967; Land et al. 1990). The indices or component scores are calculated by multiplying the raw variables with the weights obtained in the principa l components analysis, and summing them together to form one measure. This techni que offer advantages over other techniques such as scales or estimates. This results in a summary of the information contained in the raw variables while avoiding many of the a ssumptions that scales or estimates may contain (Kim and Mueller 1985). Race specific indices for concentrated disadvantage are constructed, and their corresponding component analyses are pres ented below in Table 4-1. While some research has included racial re sidential segregation in similar indices, I have chosen to preserve the segregation index in its original form in order to assess what affects the change in segregation has on race specific drug sale s arrests over time. This is consistent with past research (Krivo et al. 1998; Krivo and Peterson 2000). While the indices are race-specific, they are also calculated for each of the three periods 1980, 1990, and 2000. By calculating the i ndices for each period it is possible to generate change scores for not only the rema ining independent variables in the models,

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63 but also this index in order to assess what role the change in concentrated disadvantage had on drug sales arrests specifically. Table 4-1. Concentrated Disadva ntage Index Factor Loadings. 1980 1990 2000 White Black White Black White Black GINI Index .820 .698 .690 .880 .617 .871 % Below Poverty .915 .898 .906 .932 .925 .926 % Jobless Males .643 .706 .791 .766 .819 .772 % Kids One Parent .763 .867 .757 .649 .798 .752 Median Income -.865 -.894 -.820 -.932 -.813 -.928 Eigenvalue 3.26 3.34 3.17 3.52 3.21 3.64 % Variance 54.3% 55.7% 52.8% 58.7% 53.4% 60.7% N=132 To further address the issue of multicollinearity and the partialing fallacy discussed above, basic regression diagnostics were performed. I utilize the widely accepted variance inflation factor, within OLS regression in order to determine to what extent the standard errors of regression coefficients ar e inflated due to the possibility they share variance with other predictor variables in the model. None of the variance inflation factors in my diagnostics exceed four, whic h is a widely accepted cutoff (Fisher and Mason 1981: 109; Messner and Golden 1992; Sa mpson 1987), therefore I feel confident that my indices are unique and collinearity with other predictor variab les is not an issue. Methodology Statistical Procedures Statistical procedures for this study (P ROC TRAJ) draw upon methods that have been developed to analyze the developmental trajectories of individua ls over time. More specifically, this procedure is based on a se mi-parametric, group based modeling strategy (Nagin 1999; Jones et al. 2001). The theory underlying this strategy relies on the framework introduced primarily by Daniel Nagi n and his colleagues (see Bushway et.al.

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64 2001; Nagin and Land 1993; Land, McCall and Nagin 1996; Roeder, Lynch, and Nagin 1999; Nagin and Tremblay 1999; Nagin 1999). Essentially this technique relies on mixture models for modeling unobserved hete rogeneity within a given population and generating offending (or arrest ra te) trajectories over time. This method allows researchers to analy ze longitudinal data over long periods of time to identify distinct groups or clusters of offending (or arrest rates as used in this study) over time. That is, it estimates the proba bility of belonging to a particular group as follows: K k k ky f p y f1) ( ) ( estimating the probability ( pk ) of belonging to group ( k) based on ( k), drug sales arrest rates over time. This enables us to predict the likelihood of belonging to group k while also allowing certain parameters (risk factor s) to vary across all groups or trajectories, which can be used to explain membership is each group respectively. This technique is especially helpful in making sense of more complex issues, such as offending patterns over time (Nagin and Tremblay 2005). The method assumes that in any given popul ation within longitudinal data, there exists a mixture of groups, or trajectories. In order to determine the number of groups that best fit my data, I will rely on the Bayesian Information Criteria (B IC) since conventional likelihood tests are unable to determine whet her a more complex model (more groups) is statistically superior to a simpler model (f ewer groups) (DUnger et al. 1998). Typically, researchers seem to find that four or five groups are su fficient, however, Weisburd and his colleagues (2004) determined that with th eir Seattle community level data, nineteen groups or trajectories were appr opriate. However it is importa nt to note that Weisburd et

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65 al. observed an unusual number of data point s over an extended period of time and the estimation of their model took many hours for the model to converge (Weisburd et al. 2004). A recent debate has emerged surroundi ng the reification of such groups, and it should be pointed out that the goal is to identify the simplest model that best delineates each group (Nagin and Tremblay 2005). The technique that I employ consists of a SAS plug in, downloadable from its author, Bobby L. Jones. Jones developed the programming and commands in order to allow SAS to estimate developmental trajectories while studying with Daniel Nagin at Carnegie Melon University. Since its incepti on, Jones downloadable plug-in has made it possible for a broad range of researchers to utilize a sta tistical procedure previously utilized by select statisticians. As a result of this expanded availability, there has been a sharp increase in the use of these techniques in the analysis of l ongitudinal data. Alex Piquero reports that more than 50 academic peer-reviewed articles have been published recently as a result of the availability and in creased discussion of these techniques, across a range of disciplines from psyc hology to criminology (Piquero 2004). The specific model allows choice in whet her data require censored normal model (CNORM), zero-inflated Poisson (ZIP), a nd Bernoulli (LOGISTIC) distributions of longitudinal data (Jones et al. 2001). I am utilizing the zero-inflated Poisson (ZIP) model in order to compensate for the highly skewed nature of drug arrest rates across cities over time as well as those that may have a high proportion of zero values. This decision is based on my own analysis of the distributions of the dependent variab le (arrest rates from 1980-2001), as well as close consultation with Bobby Jones, the softwares author and statistician. The ZIP model takes the following form:

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66 exp 1 1 ) Pr(00 ij y ijk ijk y ijk y ijk ijk i i i i iy e w W k C y Yij ij ij ijk Nagin and Tremblay (2005) have recently addressed concerns of what could be interpreted as misuse or misinterpretation of the results of trajectory analysis. They caution that researchers should recognize the uti lity of the technique as a tool to simplify complex data, however they must avoid the temp tation to treat the resulting trajectories as gospel. Suppose Atlanta is found to be within trajectory six, the highe st drug sales arrest trajectory, caution must be emphasized to avoi d literal interpretation of these suggestions. That is, because Atlanta may be included in tr ajectory six along with several other cities, it is highly likely that none of the cities w ill display the exact trajectory as the group, if analyzed individually (Nagin and Trembl ay 2005). However it is a useful tool nonetheless as it allows researchers to group somewhat like offenders (cities) over time based on their cumulative behavior over time. Be cause of this, it is beneficial to have the most complete set of longitudinal data possi ble, for having more cases over longer time periods logically allows more accurate and smooth groupings. In a rejoinder to the afor ementioned article above, Sa mpson and Laub (2005) argue that TRAJ may in fact be yet another me thodological fad. They argue that much like LSREL and HLM dominated studi es for some time, TRAJ may run the risk of becoming a bandwagon that researchers are boarding wh ile ignoring the risks involved. Nagin and Tremblays (2005) response to their rejoinde r seems to make clear their intentions in addressing common myths about PROC TRAJ in general. I agree with Nagin and Tremblay in that TRAJ is a useful tool fo r simplifying complex data (such as longitudinal offending rates), in order to better grasp the pattern of offending that may occur.

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67 Sampson and Laubs concerns of abuses of su ch a technique are based on assumptions of what might occur, based on what has happened in the past (as with LSREL and HLM), and may be a bit premature (Nagin a nd Tremblay 2005; Sampson and Laub 2005). Risk Factors In addition to the ability of this proce dures ability to help researchers identify developmental trajectories over time, it also provides the ability to incorporate certain risk factors in predicting group membership. That is, by incorporat ing risk factors, researchers are able to perform multivariat e analyses using time stable independent variables, in order to discern how each cova riate affects the probability of belonging to a particular group. Risk factors that are believed to a ffect group membership based on drug sales arrests are change scores linked to the structural covariates outlined above. In other words, assume there exist six distinct trajec tories of drug sales a rrests for both whites and blacks over time for the sample of 132 cities. Does the change in social disorganization/urban disadvantage indicators affect membership in each group? Will increasing amounts of deindustrialization and urban disadvantage of urban areas predict membership in the highest trajectory of drug sales arrests versus the lowest? There are several ways to model change over time, and disagreement still exist as to which method is best suited for longitudi nal analyses (Allison 1990; Firebaugh & Beck 1994; Hausman, Hall, & Griliches 1984; Kessl er & Greenberg 1981). Researchers have relied on several methods to model change over time using quantitative data. These include the change score method, also called the gain score or difference score method, the cross lag method and the residual change method. However a strong case has been made that the change score method is supe rior, therefore I rely on this method for

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68 calculating the change in race specific measures in order to determine their relationship to drug arrest trajectories. Change scores are calculated as the percentage change between decades and is represented as: 1 / 1 2 e ValueAtTim e ValueAtTim e ValueAtTim Change For this study risk factors are based on the independent variables described earlier in this chapter. More specifically I will use the changes in the independent variables across decades to assess to what extent they predict membership in any given trajectory. I will calculate change scores as described above to model the change from 1980 to 1990 and 1990 to 2000. For the purposes of the PR OC TRAJ software, these measures are time stable covariates and their effect s on group membership are modeled with a generalized logit function as follows: k l i l l i k k i i iz z z Z k C1) 0 exp( ) 0 exp( ) Pr( In the following chapters several analyses are presented. First, descriptive statistics of both the dependent and independent variab les are presented and discussed in Chapter five. This includes discussion of the struct ural change over time experienced by large cities in the sample and discussion of the ba sic trajectory groups. Chapter six will present the multivariate results using the above measur es to determine to what extent (if any) these macro-level structural co variates have on predicting the life-course or trajectory membership of race specific drug sales arrests over time.

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69 CHAPTER 5 DESCRIPTIVE STATISTICS Introduction As described in Chapter 4, the final samp le consisted of 132 cities. In order to assure nested models across time these citie s are the bases of all analysis contained throughout this research. Following are discussi ons of both the descriptive statistics of the independent and dependent variables as well as the resulting drug sales arrest trajectories. Chapter 6 will focus on the multivariate models used to model structural change in large cities and its effects on race-specific dr ug sales arrests over time. Descriptive Statistics Independent Variables Descriptive statistics for all independent measures are presented in Table 5-1 and Table 5-2. Referring to Table 5-1, racial differences within cities become clear, across all three decades. For example the median annual income for White families was over $6000 more than for Black families in 1980. Mo reover, Blacks average just over 65% the median family income of Whites, as presented by the ratio of Black to White median family income in Table 5-1. This trend continues in 1990 and 2000. In 1990 the median family income for Blacks is about $13,500 less than for Whites, and the ratio of Black to White income drops slightly compared to 1980. In 2000, the gap widens while Blacks average almost $19,000 less median fam ily income compared to Whites. The percentage of families living below the poverty line changed only slightly from 1980 to 1990, with Blacks remaining relativ ely unchanged and Whites increasing nearly

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70 a half percent. In 2000 the percentage of Whites living below poverty increases another 1 percent while Blacks living below poverty decr eased by about 1 percent. So while the income gap in terms of median family inco me was increasing from 1980, the percentage of families living below poverty began to decrea se for Blacks. This however still leaves the proportion of Blacks livi ng in poverty more than double the rate for Whites. The percentage of unemployed males aged 16 and older change d little from 1980 to 1990. However from 1990 to 2000 the percenta ge increased about 3% for both Whites and Blacks alike. In 2000 about 33% of White males aged 16 and over were not employed while about 43% of Blacks males fell into this category. This increase maintained the 10% difference in regards to the number of unemployed Black males in large cities compared to Whites. Recall from Chapter four that I construc ted a concentrated di sadvantage index and its corresponding change scores to both addr ess the issue of multicollinearity as well as capture the race specific ch anges in concentrated disadvantage across decades. The change scores are presented in Table 5-2. Referring to this table we find that Whites experienced an increase of about 74% in th e concentrated disadvantage index from 19801990, compared to an 85% increase among Black s for the same period. The changes for the period 1990-2000 are more modest, yet st ill denote increases for both races. During this period disadvantage increased about 37% for Whites and around 40% for Blacks. Referring to Table 5-1, in 1980 the percenta ge of white divorced males age 15 and older was around 7% and by 1990 around 9% of White males age 15 and older were divorced. As seen in Table 5-2, the mean ch ange was about a 28% increase from 1980 to 1990. During the period from 1990 to 2000, the m ean percentage of divorced white males

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71 remained relatively unchanged, rising from 9% in 1990 to 9.9% in 2000. However as seen in Table 5-2 the mean change score represents an increase of about 13.7%. For Blacks, the mean percentage of divor ced males age 15 and older increased from 8.46% in 1980 to 10.5% in 1990, with the m ean change score being 27.2%. This change is seen again with the percentage of divor ced Black males rising from 10.5% in 1990 to 11.25% in 2000 and a mean change scor e representing about a 13% increase. Racial differences again become apparent referring to Table 5-1 and the measure of the percentage of children unde r the age of 18 not living with both parents. In 1980 about 23% of White children did not live with bot h parents as compared to almost 53% of Black children. In 1990 the percentage increas es to 26.5% for White children while rising to about 62% for Blacks. The steady in crease seen between 1980 and 1990 for both Whites and Blacks did not occur for the pe riod between 1990 and 2000. In 2000, with roughly 26% of Whites not living with both parents, virtually no change occurred across the decade. There was however a slight decreas e in the percentage of Black children not living with both parents as this measure declin es to about 60%. Despite the slight decline, proportionally there were still more than twi ce the percentage of Black children versus White children not living with both pa rents in 2000 (60.43% versus 26.36%). In Table 5-1 and Table 5-2 da ta on racial residential segr egation are also presented. Recall the Index of Dissimilarity is a meas ure of the evenness of the distribution of groups across census tracts. The measure ranges form 0 to 100, with 0 being perfectly integrated and 100 being completely segregated If a city has a segregation score of 60, this would indicate that about 60% of one racial group would ha ve to change locations in order to achieve equal distributions of racial groups acr oss racial groups.

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72 The mean score for Black-White in 1980 acro ss all cities in th e sample was 55.34. This is considered a moderately high score, indicating at least 55% of either Blacks or Whites would need to change residences in order to be more evenly distributed across census tracts. In 1990 the mean drops to 48.61, and as seen in Table 5-2 the mean change score is -13%, representing a mean decrease of about 13%. In 2000 the mean score again declines to a mean of 44.85. The mean change score indicates a decrease of about -6.5%, so again across decades the means continue to decline in regards to residential segregation, indicating that overall cities are becoming less segregated as we progress into the 21st century. It is important to note however that scores of 30 or so indicate low levels of segregation, so the means discussed here continue to repr esent moderate levels of segregation despite the declin ing levels from 1980 through 2000. In regards to racial threat the percentage of the popul ation identifying themselves as Black is presented in Table 5-1 and Table 5-2. In 1980 the percentage of the population that was Black was about 16%. By 1990 this number increased to about 17.26%, with a mean change score indicati ng an increase of about 33%. For 2000 the percentage again increased to about 19%, and a mean change of about 21%. As discussed earlier several control variables are utilized in this research. Again, these measures are depicted in Table 5-1 with mean change scores in Table 5-2. The first control measure is the percentage of the popul ation that is Hispanic. In 1980 the mean percentage of the population identifying themselves as Hi spanic was about 11%. By 1990 this number increased to almost 15% with a mean change score representing a 42% increase across cities. In 2000 the figure increased once again to 20.5%, indicating a mean change of about 76.5% from 1990.

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73 The second control measure is the police ra te. Recall this measure represents the number of sworn police officers per 100,000 re sidents within cities In 1980 the mean was about 189 police officers per 100,000 reside nts. In 1990, this measure increased to about 194 per 100,000, with a mean change of almost 3%. By 2000 the mean police rate was about 209 police officers per 100,000 reside nts, with a mean change of 8% from 1990. Finally the incarceration rate represents the final control measure. While this measure is a state level measure, control ling for the widely p ublicized exponential increase in incarceration is important to any study attempting to account for the changes in crime rates over time. Like the police rate discussed above, this measure represents the number of incarcerated indivi duals in a given state per 100,000 residents. In 1980 the mean incarceration rate was about 142 pris oners per 100,000 residents. By 1990 this number increased to about 505 inmates per 100, 000 residents across cities. The dramatic increase from 1980 to 1990 is represented by a mean change score of a 277% increase in the incarceration rate. In 2000 the incarceration rate gr ew once again to about 764 inmates per 100,000 residents. This represen ted a mean change from 1990 to 2000 of about 53%. While these increases are drama tic, they are not a ll that surprising, considering the impact of the crack epidemic on perceptions of get tough policies for drug crimes. Dependent Variable Means and standard deviations for the de pendent variable, race -specific drug sales arrest rates, are presented in Table 5-3. Upon first glance at the table, several things jump out immediately. The first is the racial discre pancies in drug sales arrest rates. Recall this

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74 measure is a rate indicating the number of r ace-specific arrests pe r 100,000 residents of that same race within a city. In 1980 the mean rate for Whites was about 48.5 arrests per 100,000 White residents. For Blacks this number was a bout 134 arrests per 100,000 Black residents. While this study aims to account for the differe ntial changes in drug arrests across races, it is important to note the signi ficance of this first data it em. Looking at the means just presented, it becomes apparent that racial discrepancies did not simply begin in 1980. However as looking further down Table 5-3 reveals the discrepancies climb exponentially over time. The other item that jumps out immediately from the table is that the drug sales arrest rates for both Whites and Blacks appear s to have peaked in 1989, as has been argued to be the peak of the crack epidemic in most cities across th e United States (Golub and Johnson 1997). In 1989 White drug sales a rrests peaked at about 157 arrests per 100,000 White residents while Black arrest s peaked at about 689 arrests per 100,000 Black residents. While the rates decline for both Whites a nd Blacks over the 22 year time period studied here, clearly they never return to where the began, and in 2001 they are still significantly higher than 1980. In 2001 the mean White rate was about 115 arrests per 100,000 White residents while the mean Black drug arrest rate was about 447 arrests per 100,000 Black residents. Drug Sales Arrests Trajectories Having presented the descriptive statistics for drug sales arrests in Table 5-3, the remaining section of this chapter is reserv ed for presenting the trajectories of racespecific drug arrests over time. Recall the previous discussion of the PROC TRAJ

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75 procedure in Chapter 4. Figure 5-1 presents the trajectories for White drug sales arrest rates and Figure 5-2 contains trajectories for Black drug sales arrest rates. These trajectories are examples of how cities beha ve, in regards to dr ug arrest rates over a period of 22 years (1980-2001). Referring to Figure 5-1, the bottom trajecto ry represents about 45% of the sample or 59 cities. Note that this trajectory exhi bited very little changes over time, and also serves as the reference group for the multivar iate analyses forthcoming. Group two began slightly higher than group one, remaining relatively flat until doubling from about 1988 to 1990, reaching a peak average of just over 100 drug sales arre sts per 100,000 White residents, and then tapering off but remain ing significantly higher through the remainder of the study period. Group three comprised almost 17% of the sample or 22 cities. Group three begins the study period averaging roughly 50 drug sa les arrests per 100,000 White residents. Around 1984, group three rapidly escalates to its peak in 1989, averaging around 239 drug sales arrests per 100,000 White residents. Like group two, group three also peaked around 1989 and slowly tapered off, however like group two, group th ree also remained significantly higher throughout the study period compared to where it began averaging just under 200 arrests per 100, 000 White residents in 2001. Finally group four is the hi ghest trajectory group in the sample for White drug sales arrests. Group four makes up about 12% of th e sample or 16 cities. As is apparent in Figure 6-1, group four clearly stands out fr om 1980 onward. At the beginning of the study period, group four cities averaged ove r 100 arrests per 100,000 White residents, then began a rapid progression to peak in 1989 at around 450 White dr ug sales arrests per

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76 100,000 White residents. While tapering off after its peak in 1989, group four still averaged over 350 drug sales arrests per 100, 000 White residents in 2001, so while these cities declined somewhat after 1989, they continued to report unprecedented White drug sales arrest rates though 2001. Overall, we find that White drug sales arre sts peaked in 1989. This is consistent with past research documenting the peak of the crack epidemic in this country. It is also worth noting once again that nearly one half of the sample (59 cities) did not experience the kind of rise in drug sales arrests over th e 22-year period that one might expect by simply examining means and average of all citi es over time. This finding in and of itself provides support for using a methodological a pproach such as PROC TRAJ in order to distinguish cities that experienced little or no problems with drug arrests from cities that experienced major problems. The BIC score was used for determining the proper number of trajectory groups. The BIC score for the f our group model was significantly lower than the three group model by 760.107, indicating that four groups is the best fit. The next section will provide a brief discussion of Black drug sales arrest trajectories as presented in Figure 5-2. Fina lly the chapter will c onclude by revisiting hypotheses one and two put forth in Chapter 3 as they relate to the existence and behavior of drug sales arrest trajectories. The second trajectory represents Black dr ug sales arrests for the same sample of 132 cities and is presented in Figure 5-2. The first thing that jumps out looking at Figure 5-2 is that the maximum average rate on th e left hand scale exceeds 2000. That is, the highest trajectory averaged over 2000 Bl ack drug sales arrests per 100,000 Black residents at its peak in those cities. Recall the White maximum above appears extreme,

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77 but even the highest trajector y group peaks at just under 500 White drug sales arrests per 100,000 White residents. This in itial finding alone sheds some light on the importance of disaggregating crime rates by race and utiliz ing race specific indica tors while attempting to account for crime rates in large cities. About 25% of the cities in the Black mode l remained in the lo west trajectory (34 cities), compared to about 44% (59 cities) for the White models. This provides further evidence for the fact that more cities expe rience at least some changes in drug sales arrests for Blacks compared to Whites. The lowe st trajectory for Blacks reaches a peak of about 100 drug sales arrests per 100,000 Black resi dents, then tapers slightly and remains flat to end in 2001 averaging about 76 drug sales arrests per 100, 000 Black residents. The second trajectory in Fi gure 5-2 begins around the same levels as trajectory one but steadily rises to peak in 1995, averaging just unde r 400 Black drug sales arrests per 100,000 Black residents. After 1995 it began to decline slightly and in 2001 averaged around 250 Black drug sales arrests per 100,000 Bl ack residents. This group comprised about 38% of the sample or 50 cities. The third trajectory comprises almost 29% of the sample or 38 cities. Trajectory group three begins slightly higher than the first two groups in 1980 but climbs more rapidly, peaking around 1989. At its peak th is group averaged about 1100 Black drug sales per 100,000 Black residents. While declin ing after its peak in 1989, trajectory group three tapers off slightly, still averagi ng over 600 Black drug sales arrests per 100,000 Black residents through the 1990s and into 2001. These 38 cities alone continued to average more drug sales arrests per 100,000 Black residents than even the 16 cities comprising the highest White trajec tory group, at its peak in 1989

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78 Finally, looking at trajecto ry group four in Figure 5-2, it becomes apparent that some cities experienced astronomical increases in Black drug sales arrests over time. Group four made up only about 7.6% of the samp le or 10 cities. These 10 cities began the period above the other three groups, averag ing about 360 Black drug sales arrests per 100,000 Black resident sin 1980. From 1986 to 1989 these averages leapt from 600 to more than 2000 Black drug sales arrests pe r 100,000 Black residents. While this rise declined slightly after peaking in 1989, group four continues to mark unprecedented Black drug sales arrest rates throughout the study period. In 2001 group four cities, on average, made 1800 Black drug sales arrests per 100,000 Black residents. Both groups three and four stand out not only because they rise so rapidly until peaking in 1989 but also because both groups end in 2001 still averaging about 660 and nearly 1800 Black drug sales arrests per 100,00 Black residents, respectively. These two groups alone comprise around 35% of the sample or 48 cities, and continued to average higher Black drug sales arrest ra tes than even the highest White group at its peak in 1989. As with the White models discussed a bove, the BIC score was used to assess model fit. The BIC for the four group mode l was 257.43 less than the three group BIC, indicating that the four trajectory group model is the best fit. Conclusion Having now both presented trajectory mode ls for both White and Black drug sales arrests over time, recall that several relate d hypotheses were put forth in Chapter 3. Addressing Hypothesis 1, the ev idence presented in Figure 51 and Figure 5-2 provides clear and convincing evidence that cities do in fact behave over time in regards to race specific drug sales arrests across large cities. Additionally it was shown that nearly half

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79 and one fourth the sample, experienced little changes in either White or Black drug sales arrest rates over time, respectively. Hypothesis 2 presented the argument that tr ajectories would vary significantly by race, with Black drug sales arrest rates rising more rapidly as compared to Whites. Again, referring back to Figures 5-1 and 5-2, it is apparent that cities did in fact differ significantly by race, with Black drug sales arre st rates rising more sharply as compared to White drug sales arrest rates. Moreover this provides evidence that certain cities experienced significant differences in drug ar rests by race. That is, the highest arrest trajectory for White drug sales arrest rates c onsisted of 16 cities, while the highest group among Black rates contained only 10 cities. However these 10 cities outperformed the highest White arrest trajectory by as much as four times the average number of arrests per White (Black) residents. Appendix A contains a list of all cities in the sample as well as their corresponding group membership for both the White and Black trajectory models. Comparing the lists presented in Appendix A, we can see that six of the ten cities that fall in group four in the Black trajectories are also in group four in the White Trajectories. Two are in California (San Francisco and Bakersfield), two are in New Jersey (Newark and Jersey City), and the remaining two are Louisville, Kentuc ky and Allentown Pennsylvania. Perhaps Louisville, Kentucky is the most surprising of the six, whereas the other five cities may be expected to have had drug problems as th e crack epidemic was shown to have began on the East and West coats as well as the Northeast in general. Finally, having presented the trajectories for both White and Black drug sales arrest rates, Chapter 6 will provide results of th e multivariate models in order to determine

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80 whether changes in key structural covariates over time, affect the probability that a city may be in a given trajectory group vers us being in the lowest trajectory.

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81Table 5-1. Descriptive Statistics (Means w ith Standard Deviations in Parentheses). 1980 1990 2000 Race-Specific Measures White Black White Black White Black Median Family Income $21,450 (3402) $14,784 (4243) $39,576 (8675) $26,071 (8959) $55,084 (13947) $36,109 (11411) Percent Families Below Poverty 9.87% (3.62) 24.10% (8.42) 10.46% (4.35) 24.56% (9.43) 11.55% (4.67) 23.53% (8.95 GINI Index for Family Income .35 (.027) .39 (.051) .41 (.034) .43 (.049) .39 (.033) .43 (.042) Percent Jobless Males 16+ 30.53% (14.27) 39.8% (13.99) 30.22% (6.86) 40.25% (11.29) 33.39% (6.85) 43.28% (10.10) Percent Divorced Males 15+ 7.09% (1.82) 8.46% (2.19) 8.89% (1.98) 10.5% (4.80) 9.9% (2.02) 11.25% (3.47 Percent Kids Not Living with Both parents 23.40% (4.58) 52.56% (11.85) 26.53% (5.60) 62.38% (12.16) 26.36% (5.99) 60.43% (12.22) Non Race-Specific Measures Index of DissimilarityRacial Segregation 55.34 (18.54) 48.61 (18.47) 44.85 (17.02) Residential Mobility 47.04% (27.88) 57.26% (6.70) 51.92% (5.56) Percent Black 16.04% (16.13) 17.26 (16.88) 18.99 (18.01) Percent Hispanic 10.70% (12.30) 14.83% (15.53) 20.52% (18.29) Police Rate 189.20 (73.89) 194.45 (79.14) 208.74 (88.69) Incarceration Rate 142.9 (47.37) 505.11 (113.08) 764.51 (213.43) N=132

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82 Table 5-2. Mean Percent Changes Acro ss Time for Independent Variables. 1980-1990 1990-2000 Race-Specific Measures White Black White Black Concentrated Disadvantage Index + 74.3% + 85.2% + 37.6% + 40.4% Percent Divorced Males 15+ + 28.1% + 27.2% + 13.8% + 13.2% Non Race-Specific Measures Index of DissimilarityRacial Segregation 13% 6.5% Residential Mobility + 58% 9% Percent Black + 33% + 21.6% Control Variables Percent Hispanic + 42.7% + 76.5% Police Rate + 2.7% + 8% Incarceration Rate +277% + 53.4% Population Change + 16.6% + 12.1% N=132 Table 5-3. Means and (Standard Deviat ions) for Drug Sales Arrest Rates. Year White Black 1980 48.52 (59.57) 134.43 (192.88) 1981 54.34 (71.86) 151.39 (209.65) 1982 54.59 (57.76) 157.38 (172.56) 1983 61.41 (62.02) 180.29 (235.38) 1984 56.01 (60.09) 163.24 (173.94) 1985 72.80 (78.07) 190.81 (217.04) 1986 76.85 (87.47) 233.59 (275.14) 1987 85.91 (103.27) 324.51 (348.30) 1988 96.28 (115.24) 447.95 (568.93) 1989 145.14 (157.63) 668.89 (762.28) 1990 138.66 (150.62) 559.33 (615.19) 1991 130.31 (145.39) 573.36 (589.09) 1992 130.05 (146.61) 528.04 (524.98) 1993 127.02 (141.63) 494.83 (469.62) 1994 130.95 (144.12) 533.59 (593.21) 1995 131.43 (143.15) 491.04 (525.39) 1996 123.47 (142.85) 480.46 (517.96) 1997 127.34 (136.67) 467.22 (512.27) 1998 130.08 (147.09) 504.03 (559.44) 1999 120.88 (124.91) 479.01 (553.86) 2000 118.16 (132.75) 447.66 (558.64) 2001 115.73 (127.59) 446.63 (567.00) N=132

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83 White Drug Sales Arrest Rates Vs. Time0 50 100 150 200 250 300 350 400 450 500 8082848688909294969800YearRate Figure 5-1. Trajectories of Wh ite Drug Sales Arrests 1980-2001.

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84 Black Drug Sales Arrest Rates vs. Time0 500 1000 1500 2000 2500 8082848688909294969800 YearRate Figure 5-2. Trajectori es of Black Drug Sales Arrests 1980-2001.

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85 CHAPTER 6 MULTIVARIATE RESULTS Introduction Having presented descriptive statistics for all variables in Chapter 5, this chapter is devoted to the multivariate analyses of tr ajectory groups for both White and Black drug sales arrests. Specifically these analyses are ai med at identifying what, if any, effects the changes in the structural characteristics of large cities from 1980 to 1990 and 1990 to 2000 had on the probability of group membership in developmental trajectories. In other words if a given city experienced a rapid e xponential growth in drug sales arrests, were particular structural covariat es responsible in distinguish ing the higher trajectory groups versus the lower groups, as hypothesized in Chapter 3? In order to test these hypotheses, the PROC TRAJ procedure allows the introduction of risk factors into the trajectory models, as discussed in Chapter 4. With the introduction of risk factors, the procedure th en estimates a logit model for each trajectory group, allowing each risk factor to predict the probability of group membership in each respective trajectory versus the lowest trajectory (reference group). The parameter estimates presented below can be interprete d as the natural log of the odds ratio of belonging to a given group versus the refere nce group, for each risk factor (Jones and Nagin 2006). It is also important note th at one of the nicest things about these models (in my opinion), is that they allow tr ajectory groups to vary across the risk factors. In other words it is not simply the values of each dependent variable contributing to group

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86 membership, but once risk factors are in troduced, the groups may vary based on the influence of those risk factors. This is im portant since some critics have argued that researchers overstate claims that a given i ndividual (or city in this case) actually belongs to a particular trajectory group. However as Nagin and Tremblay point out, trajectory group membership is not static, and the flui dity of the model specifications allowing groups to vary based on both dependent variable values as well as risk factors makes the technique that much more useful in ma king sense out of otherwise very complex longitudinal data (Nagin and Tremblay 2005). White Drug Sales Arrests Recall the White drug sales arrest trajecto ries presented in Chapter 5. The model fitting specification comparing BIC scores of more complex models compared to simpler models, showed that the four-group trajectory was the best fit for th e arrest rates for 132 cities, including risk factors. I have established that trends in city drug sales arrest rates not only differ over time but by race as well, supporting hypotheses 1and 2 from Chapter 3. Recognizing that cities did in fact behave differently for White drug sales arrest rates raises the question of wh ether changes in the structural factors of those cities had any affect on their behavior. The next several sections provide multivariate results examining the effects of the structural changes from 1980-1990 and 1990-2000 independently in order to determine whether the changes from one decade to the next were more salient than the other. Accounting for Change: 1980-1990 The first multivariate model examines the impact of the change in key structural covariates from 1980-1990 and their affects on gr oup membership in the four trajectory

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87 groups discussed above. Tables provides para meter estimates for each risk factor, with each said parameter representing the natural log of the odds ra tio of belonging to a given trajectory versus belonging to the lowest trajectory (gr oup 1). Each group in the table represents a separate logit model comparing that group to the reference group (group 1). Table 6-1 provides parameter estimates fo r the full model representing the changes in structural conditions from 1980-1990 and their corresponding affects on group membership. Beginning with traj ectory group two note the signi ficance of the increase in concentrated disadvantage among Whites on the probability of cities being in group two versus group one. Additionally, an increase in the index of dissim ilarity (segregation) decreases the probabili ty of group two membership, comp ared to group one. The control measure police presence loses significance on ce the theoretical measures are introduced into the model. Regarding the probability of being in arrest trajectory three, again the increase in concentrated disadvantage among Whites is a significant predictor of membership in group three versus group one. Mo reover the increase in residen tial mobility in these cities from 1980-1990 increases the likelihood of being in group three versus group one, controlling for other factors. C ontrol variables that are signi ficant for model three show that cities being in both the South and Nort heast were less likely to be in group three versus groups one. Finally, looking at trajectory group four, the change in concentrated disadvantage among Whites is no longer signif icant. However those cities th at experienced an increase in the percentage of divorced males from 1980-1990 were less likely to be in trajectory group four versus group one. As with the model three results, an increase in residential

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88 mobility from 1980-1990 significantly increases the probability of cities being in trajectory group four versus group one. Furt hermore, as a cities black population increases proportionally, the likelihood of bei ng in trajectory group f our versus group one for White drug sales arrests decreases. The only statistically significant cont rol variable for trajectory group four probabilities is the change in the incarceration rate from 1980-1990. This is consistent with arguments of other researchers (see Le vitt 2004). The change in incarceration was also among the most significant changing inde pendent measures. Recall from Chapter 5 that from 1980-1990 the mean change in th e number of prisoners per 100,000 residents across states was 277%. In the models below, the changes from 1980-1990 in both residential mobility and concentrated disadvantage among Whites were significant predic tors of cities being in a higher arrest trajectory versus the lowest. The mean change in concentrated disadvantage was an increase of about 74% and the mean change in residential mobility from 19801990 was about 58% across the 132 cities in the sample. It appears that the changi ng landscape across cities fr om 1980-1990 for constructs such as the concentrated di sadvantage among Whites and the pe rcentage of residents not living in the same home as five years prio r could cause those areas to become more conducive to drug selling. While these cha nges are only from 1980-1990, the next section will address the changes from 1990-2000, again for White drug sales arrests. Following the changes from 1990-2000 for White drug sales a rrests, the same format will be used to address the changes in Black drug sa les arrests for the same periods.

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89 Accounting for Change: 1990-2000 Table 6-2 represents the full model, w ith parameter estimates indicating the probability that structural changes from 19902000 had a significant affect on trajectory group membership. Beginning with group two, onl y two measures are significant, both control variables. The rise in the inca rceration rate from 1990-2000 decreased the likelihood of a city falling in trajectory gr oup two versus group one. Also, those cities in the Northeast were less likely to be in tr ajectory group two versus group one during this period, controlling for all other factors. Group three models show that the rise in concentrated disadvantage among Whites decreased the probability of cities being in trajectory group three versus group one. Additionally, as the proportion of a citys Black populati on increased, its likelihood of being in trajectory group thr ee versus group one decreased. Finally, cities in the South during this period, were less likely to be in trajectory group three versus group one, controlling for all other measures. Similar to the model for group three, the li kelihood of a city being in trajectory group four versus group one decreased as concentrated disadvantage among Whites increased from 1990-2000. Also like group thre e, as the proportion of the population identifying themselves as Black increased from 1990-2000, the likelihood of those cities being in trajectory group four versus group one decreased. The remaining measures were found to be insignificant. Thes e results suggest that the structural changes from 19801990 had more of an impact on cities arrest rates for drug sales arrests, at least among Whites.

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90 Black Drug Sales Arrests Recall the Black drug sales arrest trajectorie s presented in Chapter 5. Much like the trajectory for White drug sales arrests, it wa s found that the four-group trajectory was the best fit for Black drug sales arrest rates for 132 cities, including risk factors. Also, as discussed earlier in Chapter 5 I demonstrated that the trajectory models for White and Black drug sales arrest rates over time pr ovided support for hypotheses 1and 2 from Chapter 3. It was found that cities did in fact behave differently for Black drug sales arrest rates, as they did for White drug sales arre st rates. However the trajectories differed significantly in two ways. First, while near ly half of the 132 city sample fell into trajectory group one for White drug sales arrest s, only about 25% of the sample is in trajectory group one in terms of Black drug sale s. Second, the range of the arrest rates varied dramatically across race, with Black drug sales arrests rates peaking upwards of 2000 arrests per 100,000 Black residents. The next several sections provide multivariate results examining the effects of the structural changes from 1980-1990 and 1990-2000 independently in order to determine whether the changes from one decade to the next were more salient than the other. Accounting for Change: 1980-1990 It is especially interesting to consider the changes in Black drug sales arrests during this period, 1980-2001. While we have seen that the increases in White drug sales arrests were dramatic, the trajectories presented in Chapter 5 also showed that Black drug sales arrest rates escalated and remained much higher, proportionally compared to Whites. Besides the trajectories presented in Chapter 5, also of interest is revisiting the means of drug sales arrest rates by year in Ta ble 5-3. Simply looking at these means, they

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91 peaked in 1989, as do the trajectories fo r the higher groups. However the mean Black drug sales arrest rate in 1989 was about 668 arrests per 100,000 Black residents. Obviously seeing that the trajectories disp lay a unique group of cities which averaged more than 2000 arrests per 100,000 Black residents suggests that something is different about those cities. The following sections attemp t to determine if those differences lie in the structural changes that occurred over time. Now turning to Table 6-3, parameter estimates for all measures representing changes from 1990-2000 are included for each trajectory group. For group two, the only significant variable is that of residential mobility. That is, cities experiencing increases in the percentage of residents between 1980-1990 reporting that th ey resided in a different home five years prior, were more likely to fall into trajectory group two versus group one. Looking at trajectory group thr ee in Table 6-3 we find that the rise in residential mobility between 1980-1990 increases the like lihood of cities belonging to trajectory group three as compared to traj ectory group one. We also see th at cities in the South are less likely to fall in traj ectory group three as compar ed to trajectory group one. Finally, in the highest trajectory, group f our, several measures stand out. First the rise in concentrated disadvantage among Bl acks from 1980-1990 signi ficantly increases the probability of cities bei ng in trajectory group four ve rsus group one, controlling for other factors. Moreover, residential stabil ity remains significant in distinguishing trajectory group four from group one. The estim ate for residential mobility indicates a more robust relationship for trajectory group four than ei ther group two or three.

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92 Accounting for Change: 1990-2000 Recall that some of the structural changes among Whites from 1990 to 2000 were significant predictors of trajectory group memb ership for White drug sales arrests. These time period specific changes are presented in Table 6-4 below. Unlik e the changes in the White models discussed earlier, we find that none of the changes in st ructural conditions among Blacks from 1990-2000 are statistically significant in determining trajectory group membership for Black drug sales arrests. Table 6-4 presents the full model cap turing the changes from 1990-2000 on Black drug sales arrests. These results (or lack thereof) s uggest that at least for Black drug sales arrests, perhaps it was the drastic changes that occurre d between 1980 and 1990 in large urban areas that contributed to trajectory group membership. It also suggests that the more modest changes occurring between 1990 an d 2000 contributed little to the behavior of these cities in regards to Black drug sales arrest rates. Several hypotheses were put forth in Chapte r 3 and these are to be discussed in the following concluding section of this chapter. The final chapter, Chapter 7 will include a discussion of the findings presented throughout Chapter 6. It will also address the significance of this research, limitations of this research, as well as directions for future research on this topic. Conclusion Recall from Chapter 3 that several theoretically draw n hypotheses were argued in regards to the changing structural conditions in large cities and race specific drug arrests over time. While I have addressed hypotheses 1 and 2 in regards to the existence and behavior of trajectories in general, I now turn to the substantive hypotheses from that chapter.

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93 Hypothesis 3 stated that an increase in fa mily disruption (via the percentage of divorced males) would increase the probability of those cities falling in the higher arrest trajectory versus the lowest trajectory, i nvariant across race. This hypothesis is not supported by the data presented in this chap ter. Specifically the only model for which the change in the percenta ge of divorced males wa s statistically significant was in Table 6-2. However the direction is not in the expected direction. Instead it shows that as the percentage of divorced White males increased, those cities were less likely to fall in trajectory group four versus group one. Hypothesis 4 stated that an increase in residential mobility (via the percentage of residents not living in the same home as five years prior) would increase the likelihood of those cities being in a highe r trajectory versus the lowest trajectory. This hypothesis is supported by the data in Tables 6-1 and 63. For Whites the increase in residential mobility increased the likelihood for those cities to be in both trajectory group three and four, as compared to group one. Whereas for Bl acks, this increase was significant across all three trajectory groups vers us group one, with the eff ects on group four membership being the strongest. However these findings we re only applicable to the changes that occurred between 1980-1990, with the change s from 1990-2000 not being statistically significant. Hypothesis 5 predicted that an increase in the index of dissimilarity (segregation) would increase the likelihood of cities being in a higher trajectory versus the lowest trajectory for Black drug sales arrests. This hypothesis is not supported based on the findings presented above. The only model in which this measure was found to be significant is in Table 6-1. Here it was found that cities in which the segregation index

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94 increased were less likely to be in traject ory group two versus group one for White drug sales arrests only. While the hypothesis specifi cally stated that Blacks would experience an increase as a result of segregation, it has b een argued in past research that segregation may actually benefit Whites. In this regard it could be argued that an increase in segregation kept cities out of trajectory group two versus group one for White drug sales arrest rates. Hypothesis 6 stated that an increase among concentrated disadvantage among both Whites and Blacks would increase the probability of those ci ties falling in a higher versus the lowest arrest trajectory. This hypothesis receives partial support, as shown in Tables 6-1 and 6-3. More specifi cally, the increase in concentrated disadvantage from 19801990 increased the probability of cities bel onging to trajectory group two and three versus group one for White drug sales arrest s. Moreover the increase in concentrated disadvantage among Blacks from 1980-1990 si gnificantly increased the likelihood of being in trajectory group four as compared to group one for Blacks dr ug sales arrest rates. However the changes from 1990-2000 shown in Tabl e 6-2 suggest that an increase during this period decreased the probability of being in either trajectory three or four versus trajectory one for White drug sales arrests. The increases in concentrated disadva ntage for both Whites and Blacks were significantly greater for the period 1980-1990 as compared to 1990-2000. This change in statistical direction could signal that the initi al increases experienced from 1980-1990 saturated the affects that it may have on drug sales arrests. In ot her words, by 1990, many of these cities may have expe rienced such high levels of concentrated disadvantage for

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95 both Whites and Blacks, that small percentage increases would not impact their trajectory memberships. Finally, hypothesis 7 argued th at as a citys Black populat ion grows, this in turn would increase those cities probability of being in a higher trajectory group versus the lowest for Black drug sales arrests. As seen in Tables 6-1 and 6-2, this measure was only statistically significant for the White models However the direction was negative. In other words as the proportion of the popul ation identifying themselves as Black increased, those cities were less likely to be in the higher arrest trajectories versus the lowest for White drug sales arrests only. Chapter 7 will continue discussion of these findings as well as their implications for policy and future research. Also I will discuss the limitations of the current research and what similar future research should consider.

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96Table 6-1. Parameter Estimates and (standard errors) Modeli ng Structural Changes from 19801990 on White Drug Sales Arrests. Group 1Lowest (Reference) Group 2 Group 3 Group 4--Highest Concentrated Disadvantage White Disadvantage Index Change ---3.82 (1.89) ** 7.04 (2.20) ** -5.89 (5.21) Racial Segregation Change ----4.79 (2.75) -1.03 (4.01) 6.96 (7.86) Social Disorganization Percent Divorced White Males Change ----2.29 (1.76) -1.78 (2.13) -5.34 (2.83)* Residential Mobility Change ---.41 (.60) 2.75 (.91) ** 2.60 (1.21) Racial Threat Percent Black Change ----.13 (.34) -.04 (.75) -6.11 (3.52)* Control Variables Population Change ----.98 (1.75) 2.62 (2.33) .19 (4.42) Police Rate Change ---2.18 (2.76) -2.36 (3.02) -.07 (4.99) Incarceration Rate Change ---.48 (.39) .61 (.42) 3.47 (1.26) ** Percent Hispanic Change ----.02 (.94) 1.41 (1.19) -1.87 (2.02) South ----1.13 (.86) -2.48 (1.29) 1.35 (1.95) Northeast ----2.43 (1.49) -3.33 (1.51) ** .86 (1.83) West ---.03 (.74) -.34 (.92) .07 (1.52) Constant ----4.03 (2.12) -9.32 (2.81) ** -7.10 (4.07) BIC Score = -9800.53 N=132; *=p< .10; **= p< .05

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97Table 6-2. Parameter Estimates and (standard errors) Modeli ng Structural Changes from 19902000 on White Drug Sales Arrests. Group 1Lowest (Reference) Group 2 Group 3 Group 4--Highest Concentrated Disadvantage White Disadvantage Index Change ----1.60 (3.45) -6.93 (4.00) -9.17 (4.42) ** Racial Segregation Change ----.89 (2.20) -3.14 (3.23) -4.23 (4.03) Social Disorganization Percent Divorced White Males Change ---.48 (.89) -4.32 (3.27) -3.95 (3.46) Residential Mobility Change ----7.19 (6.69) -14.71 (9.09) -8.12 (10.43) Racial Threat Percent Black Change ----.77 (.67) -3.46 (1.67) ** -5.28 (2.29) ** Control Variables Population Change ----2.08 (2.34) 3.53 (3.15) -1.64 (4.07) Police Rate Change ----.79 (1. 65) .01 (2.14) 1.08 (2.03) Incarceration Rate Change ----4.54 (1.56) ** 1.91 (1.61) -1.37 (1.80) Percent Hispanic Change ----.27 (.29) -.35 (.78) -.19 (.64) South ---.62 (.94) -4.22 (1.57) ** -1.47 (1.62) Northeast ----2.73 (1.39) -.25 (1.44) .75 (1.42) West ---.07 (.76) -1.19 (.93) -1.16 (1.24) Constant ---2.57 (2.04) 1.07 (2.42) 3.44 (2.69) BIC Score = -9806.56 N=132; *=p< .10; **= p< .05

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98Table 6-3. Parameter Estimates and (standard errors) Modeli ng Structural Changes from 19801990 on Black Drug Sales Arrests. Group 1Lowest (Reference) Group 2 Group 3 Group 4--Highest Concentrated Disadvantage Black Disadvantage Index Change ----.41 (.77) -.36 (.83) 3.53 (1.87) Racial Segregation Change ----.16 (2.31) -1.16 (2.86) -8.92 (9.38) Social Disorganization Percent Divorced Black Males Change ----1.73 (1.31) -2.11 (1.39) .18 (.94) Residential Mobility Change ---1.16(.57) ** 1.21 (.63) 5.64 (1.92) ** Racial Threat Percent Black Change ----.09 (.31) -1.05 (.71) -5.12 (3.18) Control Variables Population Change ----1.86 (1.58) -.08 (1.74) -4.38 (4.32) Police Rate Change ----2.34 (2.60) -.89 (2.09) -3.03 (4.44) Incarceration Rate Change ---.46 (.37) .08 (.39) .64 (.63) Percent Hispanic Change ---1.08 (.94) .98 (1.07) 2.76 (1.86) South ----.09 (.30) -2.33 (1.16) ** -1.41 (1.65) Northeast ----.90 (1.31) -.71 (1.29) -3.59 (2.28) West ----.64 (.81) -1.12 (.82) -.22 (1.68) Constant ----.33 (1.56) .42 (1.71) -10.90 (4.58) ** BIC Score = -6377.56 N=132; *=p< .10; **= p< .05

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99Table 6-4. Parameter Estimates and (standard errors) Modeli ng Structural Changes from 19902000 on Black Drug Sales Arrests. Group 1Lowest (Reference) Group 2 Group 3 Group 4--Highest Concentrated Disadvantage Black Disadvantage Index Change ---.49 (1.42) 1.98 (1.56) -1.35 (2.59) Racial Segregation Change ---1.69 (2.09) -2.68 (2.54) -1.65 (4.11) Social Disorganization Percent Divorced Black Males Change ----.02 (.77) .32 (.84) -1.01 (1.72) Residential Mobility Change ---3.31 (5.45) -5.48 (6.62) 10.86 (9.82) Racial Threat Percent Black Change ----.68 (.63) -.71 (.61) -1.26 (1.39) Control Variables Population Change ----1.55 (1.76) -3.43 (2.38) -3.32 (3.85) Police Rate Change ---.59 (1. 51) .56 (1.64) .55 (2.34) Incarceration Rate Change ----.27 (1.14) -1.16 (1.37) -.95(1.74) Percent Hispanic Change ---.03 (.22) -.02 (.37) -.31 (.70) South ----.46 (.85) -1.77 (1.09) -.92 (1.36) Northeast ----.25 (1.09) .52 (1.11) .23 (1.41) West ----.70 (.74) -.76 (.74) -1.55 (1.39) Constant ---1.47 (1.11) .26 (1.39) 1.95 (1.93) BIC Score = -6396.63 N=132; *=p< .10; **= p< .05

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100 CHAPTER 7 CONCLUSION Discussion and Implications This research began with the goal being to further clarify why some urban areas are more conducive to drug arrests than others, or at least appear to be by having higher arrest rates. The ecological approach in so ciology and criminology has been evolving for many decades and will likely continue to do so It is my belief that not all large urban areas necessarily experience the same degr ee of crime problems, as was shown by the unique trajectories for race-specific drug sales arrest rates. Additionally they may not all experience the same levels of structural cond itions such as social disorganization and concentrated disadvantage. Moreover it is imperative to acknowledge the prevalence of crack markets during the period of study chosen for this research. Unlike other, well organized drug markets, crack markets are notoriously disorganized and inherently violent. So while much of the focus of this research centers on the social disorganization of cities and neighborhoods, clearly the disorganization of these very drug markets c ontributed to the exponential growth in drug arrests in certain cities. Hi storically, more organized drug markets have typically been able to op erate under the radar of law enforcement much more effectively than the open-air disorganized cr ack markets that grew throughout the 1980s and 1990s. The combination of the open-air na ture of the crack markets as well as the steady recruitment of juvenile s to peddle the drug, only ex acerbated the violence and problems in these areas (Blumstein 1995).

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101 Based on the results presented above, both social disorganization and concentrated disadvantage measures were driving the unprecedented increases in drug arrests throughout the 1980s and into the 1990s. It is also interest ing to note that the changes occurring from 19980-1990 were much more signi ficant in driving these arrest rates. Changes that occurred throughout the 1980s in urban areas left many cities at a saturation point in regards to poverty, disadvantage, a nd disorganized communities. I believe that I have shown how met hodologies typically used to analyze longitudinal behavior of indivi duals can be useful in analyzing crime rates at the macrolevel. The ability to single out cities that experience significant crime problems over time from those that did not is invaluable, especially from a policy viewpoint. For example if it is simply argued that large cities with high proportions of disadvantaged families have higher crime rates, what are policy makers to do with that information? Is it the most useful informati on available? There are many large cities in the United States, and if not all of them need the same amount of assistance (read funding), how are policy makers to know how to best distribute funds at budget time? By applying the PROC TRAJ proce dure to city level race-spe cific drug sales arrests, I believe I have demonstrated the usefulness of such techniques at the macro level, as others have suggested researchers move in this directi on (see Kubrin 2003). The ability to not only see visually how c ities behave over time, but to be able to determine how specific structural changes over time contribute to that behavior has been needed for some time. In fact, the ability to si ngle out these cities and identify them (as in Appendix A), allows even further investiga tion of why certain factors may or may not relate to a citys developmental traj ectory in terms of arrest rates.

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102 Take for example the finding from Table 62 that increases in the proportion of the population that was Black from 1990-2000 decr eased the likelihood of cities being in high trajectories versus the lowest trajectory for White drug sales arrests. At first this seems counterintuitive, and makes little sense, especially from a racial threat perspective. However being able to single these cities out enables us to make sense of these findings. Recall from Chapter 5 that the mean change in the proportion of the Black population across cities was about 22% fr om 1990-2000. Singling out traj ectory group four cities (N=16), we can discern that the mean increase across these 16 cities was only about a 4% increase. In other words the percentage of the population that was Black was already high in these cities. The mean pe rcentage of the population that was Black in these 16 cities was about 10% higher than the mean for the remaining 116 cities. Looking at the findings for the Black drug sa les models similar conclusions can be made. Recall in the Black models, trajectory gr oup four consisted of 10 cities. So if one wonders why the percentage change in the Black population does not affect group membership, the answers are similar, as well as the contributions of other factors. The 10 cities in group four already had about a 10% higher proportion of Black residents compared to the remainder of the 122 cities in the sample. Also recall the strong relationship of re sidential mobility (one of Shaw and McKays key arguments) on trajectory group me mbership, but especially group four for Black drug sales arrests. Referring back to Table 5-2 recall that the mean percentage change in residential m obility across all cities from 1980-1990 was about 58%, a substantial increase. Why would this increa se have such a strong impact on group four membership? When I single out only these cities I find that the mean change in

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103 residential mobility among these 10 cities is an increase of 123%, compared to a mean increase of only 52% for the remaining 122 cities. Similarly, again looking back to Chapte r 5 and Table 5-2, the mean change in Black concentrated disadvantage from 19801990 was about 85%. However this measure was shown to only be significant in predicti ng trajectory group f our membership. Once again looking at only those 10 cities, the mean increase in concentrated disadvantage among Blacks was more than 102%, compared to about 83% for the remaining 122 cities. I believe it is the ability to answer ques tions such as these that demonstrates the advantages of using developmental trajecto ry methodologies in examining macro level crime statistics. The ability to identify sim ilarly behaving cities and look at the changing landscapes of each group with regards to st ructural conditions over time, may prove to assist ecological theories to evolve yet even further. Limitations As with any research, creation limitations must be addressed. First the sample included only 132 cities. This is due in part to several reasons. One reason is attributed to the requirements of the PROC TRAJ pro cedure of having continuous data over long periods of time. Some cities simply do not report to the Federal Bureau of Investigation on a regular basis. Along these lines there are several key cities that are not included in the sample that, ideally, would have been. Some of th ese are Chicago, Miami, New Orleans, and Birmingham. Drug activity and drug related crime were significant in each of these cities, especially during the 1980s. These two cities were excluded simply because both Illinois and Florida cities went through a period of non-reporting to the Uniform Crime Reporting program. While some mean substituti ons were used in the analysis, I did not

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104 want to impute many years of estimate of drug sa les arrests simply to be able to use these two cities. Furthermore, I feel fairly c onfident in stating that none of these cities would have been in trajectory group one in regards to Wh ite or Black drug sale s arrests during this period. In fact, they both would likely have b een in either trajectory group three or four, but that is simply a guess. In addition to the exclusion of specific citi es, it is also important to point out that cities in California are over-represented in the data. While this may demonstrate the consistent reporting of Califor nia cities to the Uniform Crime Reporting program, it may also bias the sample to the West coast and California in particular. Another limitation is relying on arrest data in general. As I discussed earlier, drug sales arrests are not necessarily a measure of eith er police activity or drug sales activity in a given city. However I think it makes sense to assume that a rapid increase in drug sales arrests is most likely not due to either just police behavior or just an increase in drug activity, but rather some of each. I think it is fa ir to say the crack epidemic that occurred in the mid 1980s, certainly raised the concern for illegal drug activit ies in urban areas. The violence associated with these open-air markets shocked communities and the reaction of the police in many cases was si mply that, a reaction to an increase in complaints by resident s about drug sales a nd use in their neighborhoods. This is not to suggest that socially construc ted epidemics did not result fr om these issues. Clearly, the media and politicians have played a vital ro le in shaping views towards illicit drugs, especially in regards to race for many d ecades and will likely continue to do so.

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105 Looking at the arrest trajectorie s in this research, I believ e it would be irresponsible to argue that African-Americans began using and selling drugs at a rate that dwarfed that of Whites. So while the arrest figures clearl y represent this notion, my opinion remains that drug activity likely increased across th e board for both Whites and Blacks. However getting caught (hence ending up in this sample of arrest statistics) may have had more to do with who you were and where you were than what you were doing. Future Research While I only examined race-specific drug sa les arrests in this research, future research should apply these techniques to other crimes as well. For example separating, violent crimes from property crimes may be of interest in identifying specific cities that may have experienced problems with each at different periods of time. Likewise homicide researchers are beginning to adopt these methodologies to make better sense out of the changing structures of cities and their impacts on rates of homicides over time. Additionally as these newly developed me thodologies evolve, I anticipate that creative researchers will implement new ways of analyzing such data. One approach may be to use methods such as PROC TRAJ to identify similarly behaving cities, and after separating the groups, apply diffe rent statistical t echniques to model changes over time. This may prove difficult with small sample sizes but as datasets become larger and larger, the feasibility of such an approach may become a reality. Additionally, it may prove useful to use th ese methods to reduce large samples of data in order to identify problematic areas Once identified, the areas in the higher trajectory groups could be used as a selected sample to pursue further data collection beyond that of simply relying on census measures and arrests rates. It may be possible to test Shaw and McKay and other research ers ideas more t horoughly, conducting

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106 community surveys as well as implementing ethnographic research t echniques to better understand the organizations of these areas. Final Thoughts While much attention has been given to examining the factors that contribute to an areas crime rate, it is importa nt to note what may be done to curb crime, drug use, and violence in urban areas Sampson (1995) notes: . a community-level perspective on crime does have constructive implications for public policy. The reason is that many of the community level correlates of crime (e.g., residential instability, concen tration of poverty and family disruption, high density public housing projects, a ttenuation of soci al networks) are determined, both directly and indirectly, by the policy decisions of public officials (p.81). We know that some of these issues are dir ectly related to crim e in urban areas as seen in this and past research. However at some point it seems that we as academics (and perhaps more importantly citizens) must get this message to the very people Sampson refers to, the public officials (Sampson 1995). This is why I believe having the ability to single out problematic areas is of great importance to public officials because as we have seen by past legislation (i.e. crack laws), public officials do not always consider all of the factors prior to making policy. Politics has become less about improving our cities and neighbor hoods and more about improving the career of those making the decisions. In order to truly address th ese issues, we need the coope ration of all fields and the joint cooperation of public health and crimin al justice organizations. This has been argued in regards to violence prevention (see Spivak et al. 1995). They argue that cooperation must occur at all levels of gove rnment and public service agencies. Further they argue that while public health may fo cus on the prevention of violence, criminal

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107 justice focuses on the reaction to violence afte r the fact. With agencies working together, they argue that more can be accomplished (Spivak et al. 1995) This however is said to be an overs implification (see Darnell Hawkins 1995). Hawkins goes on to argue that while the prevention model has been effective in some campaigns (curbing death due to tobacco use, au to accidents, etc.), th is approach virtually ignores the issues of race, class, and other forms of inequality, which impedes the very groups of individuals in most need from obtaining it (Hawkins 1995) This all seems to come back to culture and more specifically the culture of large urban areas. There is a divide between mainstream society an d that of the disadvantaged urban area that the former does not want to be bothered with the problems of the latter. Whether due to unwillingness to pay higher taxes, or not wa nting to provide support for welfare queens, people in ge neral and especially policy makers must wake up. At the end of the day, when you are driving thr ough an area that is not your neighborhood and you witness several youth fighting, do you stop and intervene? If it is your own neighborhood do you stop and intervene? If we ar ent willing to stop and intervene in the youths quarrel how can we convi nce those who do live there to be willing to intervene?

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108 APPENDIX A SAMPLE CITIES Table A-1. Trajectory Group Member ship for White Drug Sales Arrests Abilene, Texas 1 Albany, New York 1 Alexandria, Virginia 1 Amarillo, Texas 1 Arlington, Texas 1 Austin, Texas 1 Baton Rouge, Louisiana 1 Boise, Idaho 1 Buffalo, New York 1 Chesapeake, Virginia 1 Columbus, Ohio 1 Corpus Christi, Texas 1 Dallas, Texas 1 Dayton, Ohio 1 Denver, Colorado 1 Durham, North Carolina 1 El Paso, Texas 1 Elizabeth, New Jersey 1 Erie, Pennsylvania 1 Eugene, Oregon 1 Evansville, Indiana 1 Fort Wayne, Indiana 1 Fort Worth, Texas 1 Glendale, Arizona 1 Independence, Missouri 1 Indianapolis, Indiana 1 Irving, Texas 1 Knoxville, Tennessee 1 Lakewood, Colorado 1 Lincoln, Nebraska 1 Little Rock, Arkansas 1 Lubbock, Texas 1 Mesa, Arizona 1 Mesquite, Texas 1 Newport News, Virginia 1 Norfolk, Virginia 1 Oklahoma City, Oklahoma 1

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109 Table A-1 Continued. Omaha, Nebraska 1 Pasadena, Texas 1 Plano, Texas 1 Portland, Oregon 1 Portsmouth, Virginia city 1 Raleigh, North Carolina 1 Richmond, Virginia 1 Salem, Oregon 1 Savannah, Georgia 1 Scottsdale, Arizona 1 Shreveport, Louisiana 1 Simi Valley, California 1 Springfield, Missouri 1 St. Louis, Missouri 1 Stamford, Connecticut 1 Syracuse, New York 1 Thousand Oaks, California 1 Virginia Beach, Virginia 1 Waco, Texas 1 Warren, Michigan 1 Winston-Salem, North Carolina 1 Yonkers, New York 1 Atlanta, Georgia 2 Beaumont, Texas 2 Berkeley, California 2 Charlotte, North Carolina 2 Columbus, Georgia 2 Escondido, California 2 Flint, Michigan 2 Fremont, California 2 Fullerton, California 2 Garland, Texas 2 Glendale, California 2 Grand Rapids, Michigan 2 Hampton, Virginia 2 Hayward, California 2 Honolulu, Hawaii 2 Huntington Beach, California 2 Irvine, California 2 Kansas City, Missouri 2 Macon, Georgia 2 Modesto, California 2 Oceanside, California 2 Orange, California 2

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110 Table A-1 Continued. Oxnard, California 2 Pasadena, California 2 Reno, Nevada 2 Rochester, New York 2 Santa Rosa, California 2 Spokane, Washington 2 Sunnyvale, California 2 Tacoma, Washington 2 Toledo, Ohio 2 Torrance, California 2 Tucson, Arizona 2 Tulsa, Oklahoma 2 Vallejo, California 2 Anaheim, California 3 Ann Arbor, Michigan 3 Baltimore, Maryland 3 Chula Vista, California 3 Fresno, California 3 Garden Grove, California 3 Long Beach, California 3 New Haven, Connecticut 3 Oakland, California 3 Ontario, California 3 Phoenix, Arizona 3 Pomona, California 3 Providence, Rhode Island 3 Riverside, California 3 Sacramento, California 3 Salinas, California 3 Salt Lake City, Utah 3 San Antonio, Texas 3 San Bernardino, California 3 San Diego, California 3 San Jose, California 3 Waterbury, Connecticut 3 Allentown, Pennsylvania 4 Bakersfield, California 4 Detroit, Michigan 4 El Monte, California 4 Inglewood, California 4 Jersey City, New Jersey 4 Los Angeles, California 4 Louisville, Kentucky 4 New York, New York 4

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111 Table A-1 Continued. Newark, New Jersey 4 Paterson, New Jersey 4 Philadelphia, Pennsylvania 4 San Francisco, California 4 Santa Ana, California 4 Springfield, Massachusetts 4 Stockton, California 4

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112 Table A-2. Trajectory Group Member ship for Black Drug Sales Arrests Abilene, Texas 1 Amarillo, Texas 1 Arlington, Texas 1 Boise. Idaho 1 Buffalo, New York 1 Chesapeake, Virginia 1 Chula Vista, California 1 Corpus Christi, Texas 1 Dayton, Ohio 1 El Paso, Texas 1 Elizabeth, New Jersey 1 Eugene, Oregon 1 Evansville, Indiana 1 Fremont, California 1 Fullerton, California 1 Glendale, Arizona 1 Huntington Beach, California 1 Independence, Missouri 1 Irvine, California 1 Irving, Texas 1 Macon, Georgia 1 Mesa, Arizona 1 Mesquite, Texas 1 Norfolk, Virginia 1 Orange, California 1 Plano, Texas 1 Salem, Oregon 1 Savannah, Georgia 1 Scottsdale, Arizona 1 Simi Valley, California 1 Springfield, Missouri 1 Thousand Oaks, California 1 Waco, Texas 1 Winston-Salem, North Carolina 1 Albany, New York 2 Anaheim, California 2 Austin, Texas 2 Baton Rouge, Louisiana 2 Beaumont, Texas 2 Columbus, Ohio 2 Dallas, Texas 2 Denver, Colorado 2 Durham, North Carolina 2 Erie, Pennsylvania 2

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113 Table A-2 Continued. Escondido, California 2 Fort Wayne, Indiana 2 Fort Worth, Texas 2 Garden Grove, California 2 Garland, Texas 2 Glendale, California 2 Hampton, Virginia 2 Hayward, California 2 Indianapolis, Indiana 2 Inglewood, California 2 Knoxville, Tennessee 2 Lakewood, Colorado 2 Lincoln, Nebraska 2 Little Rock, Arkansas 2 Long Beach, California 2 Lubbock, Texas 2 Newport News, Virginia 2 Oklahoma City, Oklahoma 2 Omaha, Nebraska 2 Oxnard, California 2 Pasadena, Texas 2 Portland, Oregon 2 Portsmouth, Virginia 2 Raleigh, North Carolina 2 Richmond, Virginia 2 Rochester, New York 2 Salinas, California 2 San Antonio, Texas 2 Santa Ana, California 2 Santa Rosa, California 2 Shreveport, Louisiana 2 St. Louis, Missouri 2 Sunnyvale, California 2 Syracuse, New York 2 Tacoma, Washington 2 Torrance, California 2 Tucson, Arizona 2 Tulsa, Oklahoma 2 Virginia Beach, Virginia 2 Yonkers, New York 2 Alexandria, Virginia 3 Ann Arbor, Michigan 3 Atlanta, Georgia 3 Berkeley, California 3

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114 Table A-2 Continued. Charlotte, North Carolina 3 Columbus, Georgia 3 Detroit, Michigan 3 El Monte, California 3 Flint, Michigan 3 Fresno, California 3 Grand Rapids, Michigan 3 Honolulu, Hawaii 3 Kansas City, Missouri 3 Los Angeles, California 3 Modesto, California 3 New Haven, Connecticut 3 New York, New York 3 Oceanside, California 3 Ontario, California 3 Pasadena, California 3 Paterson, New Jersey 3 Philadelphia, Pennsylvania 3 Phoenix, Arizona 3 Pomona, California 3 Providence, Rhode Island 3 Reno, Nevada 3 Riverside, California 3 Sacramento, California 3 Salt Lake City, Utah 3 San Bernardino, California 3 San Diego, California 3 San Jose, California 3 Springfield, Massachusetts 3 Stamford, Connecticut 3 Stockton, California 3 Toledo, Ohio 3 Vallejo, California 3 Warren, Michigan 3 Allentown, Pennsylvania 4 Bakersfield, California 4 Baltimore, Maryland 4 Jersey City, New Jersey 4 Louisville, Kentucky 4 Newark, New Jersey 4 Oakland, California 4 San Francisco, California 4 Spokane, Washington 4 Waterbury, Connecticut 4

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115 REFERENCES Adler, Jeffrey S. 1986. Vagging the Demons and Scoundrels: Vagrancy and the Growth of St. Louis, 1830-1861. Journal of Urban History 13(1): 3-30. Adler, Jeffrey S. 1992. Streetwalkers, Degraded Outcasts, and Good-For-Nothing Huzzies: Women and the Dangerous Cl ass in Antebellum St. Louis. Journal of Social History. 25: 737-755. Akers, Ronald L. 1990. Scary Drug of the Y ear: Myths and Realitie s in the Changing Drug Problem. Paper presented at the m eetings of the American Society of Criminology, Baltimore. Akers, Ronald L., and Christine Sellers. 2004. Criminological Theo ries: Introduction, Evaluation and Application. Los Angeles: Roxbury. Allison, Paul D. 1990 "Change Scores as Dependent Variables in Regression Analysis." in Clifford Clogg (ed.), Sociological Methodology Oxford: Basil Blackwell. Almgren, Gunnar, Avery Guest, George Inmerwahr, and Michael Spittel. 1998. Joblessness, Family Disruption, a nd Violent Death in Chicago, 1970-1990. Social Forces 76: 1465-1493. Anderson, Elijah. 1990. Streetwise: Race, Class and Change in an Urban Community. Chicago: University of Chicago Press. Anderson, Elijah. 1999. Code of the Streets New York, NY: W.W. Norton and Company, Inc. Austin, Roy L., and Mark D. Allen 2000. R acial Disparity in Arrest Rates as An Explanation of Racial Disparity in Commitment to Pennsylvania Prisons Journal of Research in Crime and Delinquency. 37(2): 200-220. Bailey, W.C. 1984. Poverty, Inequality, And City Homicide Rates Some Not So Unexpected Findings. Criminology 22: 531-550. Bane, Mary Jo, and Paul A. Jargowsky. 1988. Urban Poverty Areas: Basic Questions Concerning Prevalence, Growth, and Dyna mics Paper prepared for the committee on National Urban Policy, National Acad emy of Sciences, Washington, D.C. Baumer, Eric P. 1994. Poverty, Crack and Crime: A Cross-City Analysis Journal of Research in Crime and Delinquency. 31: 311-327.

PAGE 127

116 Baumer, Eric, Janet L. Lauritsen, Richard Rosenfeld, and Richard Wright. 1998. The Influence of Crack Cocaine on Robbery, Bu rglary, and Homicide Rates: A Crosscity, Longitudinal Analysis. Journal of Research in Crime and Delinquency. 35(3): 316-340. Belenko, Steven, Jeffrey Fagan and Ko-Lin Chin. 1991. Criminal Justice Responses to Crack. Journal of Research in Crime and Delinquency. 28(1): 55-74. Berger, Peter L., and Thomas Luckman. 1966. The Social Construction of Reality New York: Anchor Books. Blalock, Hubert M. 1967. Toward a Theory of Minority-Group Relations Capricorn Books. Blalock, Hubert M. 1989. Percen t Black and Lynching Revisited. Social Forces. 67 (3): 626-630. Blau, J., and P. M. Blau. 1982. "The Cost of Inequality: Metropo litan Structure and Violent Crime." American Sociological Review. 47: 114-29. Blumstein, Alfred 1993. Making Rationality RelevantThe American Society of Criminology Presidential Address. Criminology. 31: 1-16. Blumstein, Alfred. 1995. Youth Violence, Guns, and the Illicit-Drug Industry Journal of Criminal Law and Criminology. 86: 10-36. Blumstein, Alfred, and Richard Rosenfeld. 1998. Explaining Recent Trends in U.S. Homicide Rates Journal of Criminal Law and Criminology 88: 1175-1216. Blumstein, Alfred, and Joel Wallman. 2000. The Crime Drop in America Cambridge University Press. Boggess, Scott, and John Bound. 1997. Did Criminal Activity Increase During the 1980s? Comparisons Across Data Sources. Social Science Quarterly 78(3): 725739. Bottoms, Anthony E., and Paul Wiles. 1986. Housing Tenure and Residential Community Crime Careers in Britain in Communities and Crime Reiss, Albert J. Jr., & Tonry, Michael [Eds], Chicago: University of Chicago Press, 1986, pp 101162. Brecher, E.M., and the Editors of Conume r Reports. 1972. Licit and Illicit Drugs. Consumers Union: New York. Brown, Craig M., and Barbara D. Warner 1992. Immigrants, Urban Politics, and Policing in 1900. American Sociological Review 57, no. 3 293-305.

PAGE 128

117 Bureau of Justice Statistics. 1996. Correctional Population in the U.S., 1994. Executive Summary. Washington, D.C. Burgess, Ernest W. 1925. The Growth of a City: An Introduction to a Research Project. In Robert E. Park and Ernest W. Burgess (eds.), The City Chicago: University of Chicago Press. Bursik, Robert J. Jr. (1988). Social Di sorganization and Theories of Crime and Delinquency: Problems and Prospects. Criminology 26: 519-551. Bursik, Robert. J., Jr., and J. Webb. 1982. Community Change and Patterns of Delinquency. American Journal of Sociology 88(1): 24-42. Bursik, Robert J., Jr., and Harold Grasmick. 1993. Neighborhoods and Crime: The Dimensions of Effective Community Control. New York: Lexington Books. Bushway Shawn D., Alex R. Piquero, Lisa M Broidy, Elizabeth Cauffman, Paul Mazerolle An Empirical Framework fo r Studying Desistance as a Process. Criminology. 39: 491-416. Carroll, Rebecca. 2004. Under the Influence: Harry Anslingers Role in Shaping Americas Drug Policy, in Federal Drug Control: The Evolution of Policy and Practice, Jonathon Erlen and Joseph F. Spillane, eds., pp.61-99 New York: The Haworth Press. Chamlin, Mitchell B. 1989. "A Macro Social Analysis of the Change in Robbery and Homicide Rates: Controlling for Static and Dynamic Effects." Sociological Focus 22: 275-286. Chilton, Roland, and Dee Weber. Uniform Crime Reporting Program [United States]: Arrests By Age, Sex, And Race For Poli ce Agencies In Metropolitan Statistical Areas, 1960-2001 [Computer file]. 2nd ICPSR version. Amherst, MA: University of Massachusetts [producer], 2001. Ann Ar bor, MI: Inter-university Consortium for Political and Social Rese arch [distributor], 2001. Cohen, Lawrence E., Marcus Felson, and Ke nneth C. Land 1980. Property Crime Rates in the United States: A Macrodynamic Analysis, 1947-1977; With Ex Ante Forecasts for the Mid-1980s. American Journal of Sociology. 86(1): 90-118. Conrad, Peter, and Joseph W. Schneider. 1980. Deviance and Medica lization: From Badness to Sickness Toronto: CV Mosby Co. Cork, Daniel. 1999. Examining Space-Time Inte raction in City-Level Homicide Data: Crack Markets and the Diffusi on of Guns Among Youth. Journal of Quantitative Criminology. 15(4): 379-406.

PAGE 129

118 Corzine, Jay, James Creech, and Lin Huff -Corzine. 1983. Black Concentration and Lynchings in the South: Testing Bl alocks Power Threat Hypothesis. Social Forces 61: 774-796. Crawford, Charles, Ted Chiricos and Ga ry Kleck. 1998. Race, Racial threat, and Sentencing of Habitual Offenders. Criminology 36: 481-511. Creech, James, Jay Corzine, and Lin Huff-C orzine. 1989. Theory Testing and Lynching: Another Look at the Powe r Threat Hypothesis. Social Forces 67: 626-630. Crutchfield, Robert D. 1989. Labor Stratification and Violent Crime. Social Forces. 68: 489-512. Crutchfield, Robert D., and Susan Pitchford. 1997. Work and Crime: The Effects of Labor Stratification. Social Forces. 76: 93-118. Cullen, Francis T., and Robert Agnew. 2003. Criminological Theory: Past to Present Los Angeles: Roxbury. DUnger, Amy V., Kenneth C. Land, and Pa tricia L. McCall. 1998. How Many Latent Classes of Delinquent/Criminal careers? Results from Mixed Poisson Regression Analyses of the London, Philadelphi a, and Racine Cohorts Studies. American Journal of Sociology 103: 1593-1630. Duster, Troy. 1970. The Legislation of Morality: Law, Drugs and Moral Judgment New York: The Free Press. Eitle, David, Stewart J. DAlessio, and Lisa Stolzenberg. 2002. Racial Threat and Social Control: A Test of the Political, Economic, and Threat of Black Crime Hypotheses. Social Forces 81(2): 557-576. Erlen, Jonathon, and Joseph F. Spillane. 2004. Federal Drug Control: The Evolution of Policy and Practice New York: The Haworth Press. Fagan, Jeffrey. 1992. "Drug Selling and Licit Income in Distressed Neighborhoods: The Economic Lives of Street-Level Drug User s and Sellers.". In V. Harrell and G.E. Peterson (Eds), Drugs, crime and social isolati on: Barriers to urban opportunity (pp. 99-146). Washington, D.C.: Urban Institute. Fagan, Jeffrey, and Ko-lin Chin 1991. Soc ial Processes of Initiation into Crack. Journal of Drug Issues 21(2): 313-343. Farley, Reynolds, and W.H. Frey. 1994. Cha nges in the Segregation of Whites from Blacks During the 1980s: Small Steps Towa rd a More Integrated Society. American Sociological Review. 59: 23-45. Firebaugh, Glenn, and Frank D. Beck. 1994. Does Economic Growth Benefit the Masses? American Sociological Review. 59(5): 631-653.

PAGE 130

119 Fisher, Joseph C., and Robert L. Mason 1981. The Analysis of Multicollinear Data in Criminology. Pp. 99-125 in Methods in Quant itative Criminology edited by James Alan Fox. New York: Academic Press. Fowles, Richard, and Mary Merva. 1996. Wage Inequality and criminal activity: An extreme bounds analysis for the United States, 1975-1990. Criminology 34: 163182. Galliher, John F., and Allynn Walker. 1977. The Puzzle of the Social Origins of the Marihuana Tax Act of 1937. Social Problems 24(3): 367-376. Galster,George C., and W. Mark Keeney. 1988. Race, Residence, Discrimination, and Economic Opportunity: Modeling the Ne xus of Urban Racial Phenomena. Urban Affairs Quarterly 24: 87-117. Goldin, Claudia. 1990. Understanding the Gender Gap New York: Oxford University Press. Goldstein, Paul J., Patricia A. Bellucci, Barry J. Spunt, and Thomas Miller. 1991. Volume of Cocaine Use and Violen ce: A Comparison Between Men and Women. Journal of Drug Issues 21(2): 345-367. Golub, Andrew Lang, and Bruce D. Johnson. 1997. Crack's Decline: Some Surprises Across U.S. Cities, Research in Brief, U.S. Department of Justice, Office of Justice Programs, National Institute of Justice. Goode, Erich. 2005. Drugs in American Society 6th Ed. New York: McGraw-Hill. Gordon, Robert A. 1968. Issues in Multiple Regression. American Journal of Sociology 73(5): 592-616. Griffiths, Elizabeth, and Jorge M. Chav ez. 2004. Communities, Street Guns and Homicide Trajectories in Chicago, 1980-1995: Mergi ng Methods for Examining Homicide Trends Across Space and Time. Criminology 42: 941978. Hartman, D. M., and A. Golub. 1999. The Social Construction of the Crack Epidemic in the Print Media. Journal of Psychoactive Drugs 31: 423-434. Hausman, Jerry, Bronwyn H. Hall, and Zvi Griliches. 1984. Econometric Models for Count Data With an Application to the Patents-R & D relationship. Econometrica. 52(4): 909-938. Hawkins, Darnell F. 1995. Comments on A Comprehensive Approach to Violence Prevention: Public Health and Crim inal Justice in Partnership in Crime, Communities, and Public Policy edited by Lawrence B. Joseph. Chicago: University of Chicago Press. Inciardi, James A. 1981. The Drugs-Crime Connection Beverly Hills: Sage.

PAGE 131

120 Jackson, Pamela Irving, and Leo Carroll. 1981. Race and the War on Crime: The Sociopolitical Determinants of Municipa l Police Expenditures in 90 Non-Southern U.S. Cities. American Sociological Review 46: 290-305. Jacobs, David, and Katherine Wood. 1999. Interracial Confli ct and Interracial Homicide: Do Political and Economic Rivalries Explain White Killings of Blacks or Black Killings of Whites? American Journal of Sociology 105: 157-190. Jargowsky, Paul A., and Mary Jo Bane. 1991. Ghetto Poverty in the United States, 1970-1980, in The Urban Underclass edited by C. Jencks and P.E. Peterson. Washington D.C.: The Brookings Institution. Johnson, Bruce D., Terry Williams, K. Dei, and Harry Sanabria. 1990. "Drug Abuse in the Inner City: Impact of Hard Dr ugs Users and the Community." In Drugs and Crime edited by Michael Tonry and James Q. Wilson (pp. 313371). Chicago: University of Chicago Press. Jones, Bobby L., Daniel S. Nagin and Kath ryn Roeder. 2001. A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories. Sociological Methods and Research 29: 374-393. Jones, Bobby L., and Daniel S. Nagin. 2006. Advances in Group-Based Trajectory Modeling and a SAS Procedure for Estimating Them. Unpublished Manuscript Kasarda, John. D. 1983. "Entry-Level Jobs, Mobility, and Urban Minority Unemployment." Urban Affairs Quarterly. 19: 21-40. _____. 1989. Urban Industrial Transition and the Underclass. The Annals of the American Academy of Political and Social Sciences 501: 26-47. _____. 1992. The Severely Distressed in Economically Transforming Cities. Pp. 4597 in Drugs, Crime, and Social Isolation: Barriers to Urban Opportunity, edited by Adele V. Harrell and George E. Peterson. The Urban Institute. Kessler, Ronald C., and David F. Greenberg. 1981. Linear Panel Analysis: Models of Quantitative Change New York: Academic Press. Kobrin, Solomon. 1951. The Conflict of Values in Delinquency Areas. American Sociological Review 16(5): 653-661. Kornhauser, R. 1978. Social Sources of Delinquency: An Appraisal of Analytic Models Chicago: Univ. of Chicago Press. Krivo, Lauren J., and Ruth D. Peterson. 1996. Extremely Disadvantaged Neighborhoods and Urban Crime. Social Forces. 75: 619-650.

PAGE 132

121 Krivo, Lauren J., and Ruth D. Peterson. 2000. The Structural C ontext of Homicide: Accounting for Racial Differences in Process. American Sociological Review. 65: 547-559. Krivo, Lauren J., Ruth D. Peterson, Helen Rizzo, and John R. Reynolds. 1998. Race, Segregation, and the Concentra tion of Disadvantage: 1980-1990. Social Problems. 45(1): 61-80. Kubrin, Charis E. 2003. Struc tural Covariates of Homici de Rates: Does Type of Homicide Matter? Journal of Research in Crime and Delinquency. 40(2):139-170. Kubrin, Charis E., and Ronald Weitzer. 2003. N ew Directions in So cial Disorganization Theory. Journal of Research in Crime and Delinquency. 40(4): 374-402. Kubrin, Charis E., and Ronald Weitzer. 2003. Retaliatory Homi cide: Concentrated Disadvantage and Neighborhood Culture. Social Problems. 50(2): 157-180. Land, Kenneth C., Patricia L. McCall and Daniel S. Nagin. 1996. A Comparison of Poisson, Negative Binomial, and Semi parametric Mixed Poisson Regression Models with Empirical Applications to Criminal Careers Data. Sociological Methods and Research 24: 387-440. Land, Kenneth C., Patricia L. McCall, and La rry E. Cohen. 1990. Structural Covariates of Homicide Rates: Are there any Inva riances across Time and Social Space? American Journal of Sociology 95: 922-963. LaVeist, Thomas A. 1989. Linking Residentia l Segregation and Infant Mortality on U.S. Cities Sociology and Social Research 73: 90-94. Levitt, Steven D. 2001. Alternative Stra tegies for Identifying the Link Between Unemployment and Crime. Journal of Quantitative Criminology 17: 377-390. Levitt, Steven D. 2004. Understanding Why Cr ime Fell in the 1990s: Four Factors that Explain the Decline and Six that Do Not. Journal of Economic Perspectives. 18(1): 163-190. Lindesmith, Alfred R. 1968. Addiction and Opiates. Chicago: Mokken, RJ and Lewis Co. Lindesmith, Alfred, and Yale Levin. 1937. The Lombrosian Myth in Criminology. American Journal of Sociology 42: 653-671. Liska, Allen E., and Mitchell B. Chamlin. 1984. Social Structures and Crime Control Among Macrosocial Units. American Journal of Sociology. 101: 388-395. Liska, Allen E. 1992. Social Threat and Social Control Albany, N.Y.: SUNY Press.

PAGE 133

122 Liska, Allen E., Joseph Lawrence and Mich ael Benson. 1981. Perspectives on Social Order: the Capacity of Social Control. American Journal of Sociology. 87: 413426. Liska, Allen E., John Logan and Paul Bella ir. 1998. Race and Violent Crime in the Suburbs. American Sociological Review. 63: 27-38. Lo, Celia. 2003. An Application of Social C onflict Theory to Arrestees Use of Cocaine and Opiates. Journal of Drug Issues 33(1): 237-266. Lynch, James, and William Sabol. 1997. Did Getting Tougher on Crime Pay? Crime Policy Report. Washington, D.C.: The Urban Institute State Policy Center. Maddala, G. S. 1983. Limited-Dependent and Qualitativ e Variables in Econometrics New York: Cambridge University Press. Maher, Lisa, and Kathleen Daly. 1996. Women in the Street-Level Drug Economy:Continuity or Change? Criminology. 34: 465-491. Massey, Douglas S. 1990.American Aparth eid: Segregation and the Making of the Underclass American Journal of Sociology 96 (2): 329-357 Massey, Douglas S., Gretchen A. Condran, a nd Nancy A. Denton. 1987. The Effect of Residential Segregation on Black So cial and Economic Well-Being. Social Forces 66: 29-56. Massey, Douglas S., and Nancy A. Denton. 1988. The Dimensions of Residential Segregation. Social Forces. 67: 281-315. Massey, Douglas.S., and Mitchell. L. Egge rs. 1990. "The Ecology of Inequality: Minorities and the Concentra tion of Poverty, 1970-1980." American Journal of Sociology 95(5): 1153-1188. Massey, Douglas S., and Nancy A. Denton. 1993. American Apartheid: Segregation and the Making of the Underclass Cambridge, Massachusetts: Harvard University Press. Massey, Douglas A., Andrew B. Gross, and Kumiko Shibuya. 1994. Migration, Segregation, and the Geographic Concentration of Poverty. American Sociological Review. 59: 425-445. McBride, Duane C. 1981. Drugs and Violence., in The Drugs-Crime Connection James A. Inciardi, ed. Sage: Beverly Hills. McCall, Leslie. 2000. Gender, the Labor Market, and the Educational Wage Gap. American Sociological Review 65(2): 234-255.

PAGE 134

123 Messner, Steven F. 1982. Poverty, Inequal ity, and the Urban Homicide Rate: Some Unexpected Findings. Criminology 20:103-114. Messner, Steven F., and Reid M. Golden 1992. Racial Inequa lity and Racially Disaggregated Homicide Rates: An A ssessment of Alternative Theoretical Explanations. Criminology. 30: 421-447 Messner, Steven F., Glenn D. Deane, Luc Anselin and Benjamin Pearson-Nelson. 2005. Locating the Vanguard in Rising and Falli ng Homicide Rates Across U.S. Cities. Criminology 43: 661-696. Mosher, Clayton 2001. Predicting Drug Arrest Rates: Conflict and Social Disorganization Perspectives. Crime and Delinquency 47(1): 84-104. Musto, David. 1973. The American Disease: Orig ins of Narcotic Control New York: Oxford University Press. Myers, Martha A. 1990. Black Threat a nd Incarceration in Postbellum Georgia. Social Forces 69(2): 373-393. Nagin. Daniel S. 1999. Analyzing Devel opmental Trajectories: A Semi-Parametric Approach. Psychological Methods. 2: 139-157. Nagin, Daniel S., and Kenneth C. Land. 1993. Age, Criminal Careers, and Population Heterogeneity: Specifica tion and Estimation of a Nonparametric, Mixed Poisson Model., Criminology. 31: 327-362. Nagin, Daniel S., and Richard Tremblay 1999. Trajectories of Boys Physical Aggression, opposition, and Hyperactivity on the Path to Physically Violent and Nonviolent Juvenile Delinquency, Child Development 70: 1181-1196. _____.2005. Developmental Trajectory Groups: F act or a Useful Statistical Fiction? Criminology. 43: 873-904. _____.2005. From Seduction to Passion: A Response to Sampson and Laub. Criminology. 43: 915-918. Nathan, Richard P., and Charles F. Adam s. 1989. Four Perspectives of Urban Hardship. Political Science Quarterly. 104: 483-503. New York City Police Department. 1989. St atistical Report: Complaints and Arrests 1988. New York: Office of Manage ment Analysis and Planning. Osgood, D. Wayne. 2000. Poisson-Based Regr ession Analysis of Aggregated Crime Rates. Journal of Quantitative Criminology. 16: 21-44. Osgood, D. Wayne, & Chambers, Jeff M. ( 2000). Social Disorganization Outside the Metropolis: An analysis of rural youth violence. Criminology 38: 81-116.

PAGE 135

124 Ousey, Graham C., and Mathew R. Lee. 2004. Investigating the Connections Between Race, Illicit Drug Markets, and Lethal Violence, 1984-1997. Journal of Research in Crime and Delinquency 41(4): 352-383 Parker, Karen F. 2004. Industrial shift, Po larized Labor Markets and Urban Violence: Modeling the Dynamics Between the Econom ic Transformation and Disaggregated Homicide. Criminology 42: 619-646. Parker, Karen F., and Matthew V. Pruitt. 2000. Why the West Was One: Explaining the Similarities in Race-Specific Ho micides in the West and South. Social Forces 78(4): 1483-1508 Parker, Karen F., and Patricia L. McCall. 1997. Adding Another Piece to the InequalityHomicide Puzzle: The impact of Structur al Inequality on Racially Disaggregated Homicide Rates. Homicide Studies. 1: 35-60. Parker, Karen F., and Patricia L. McCall. 1999. Structural Conditions and Racial Homicide Patterns: A Look at the Multip le Disadvantages in Urban Areas. Criminology. 37: 447-478. Parker, Karen F., and Scott R. Maggard. 2005. Structural Theories and Race-Specific Drug Arrests: What Structural Factors A ccount for the Rise in Race-Specific Drug Arrests Over Time? Crime and Delinquency 51: 521-547. Parker, Karen F., Brian J. Stults and Stephe n K. Rice. 2005. Racial Threat, Concentrated Disadvantage and Social Control: Cons idering the Macro-Level Sources of Variation in Arrests. Criminology. 43(4): 1111-1134. Paternoster, Raymond, Robert Brame, Paul Mazerolle and Alex Piquero. 1998. Using the Correct Statistical Test for the E quality of Regression Coefficients. Criminology 36: 589-866. Peterson, Ruth D., and Lauren J. Krivo. 1993. Racial Segregation and Black Urban Homicide. Social Forces 71(4): 1001-1026. Piquero, Alex. 2004. What Have We Learned About the Natural History of Criminal Offending from Longitudinal Studies? Paper presented at the National Institute of Justice, Washington D.C. Piquero, Alex, and Paul Mazerolle. 2001. Life-Course Criminology: Contemporary and Classic Readings Canada: Wadsworth. Pratt, Travis C., and Francis T. Cullen 2005. Assessing Macro-Le vel Predictors of Theories of Crime: A Meta-Analysis. in Crime and Justice: A Review of Research edited by Michael Tonry. Chicago: University of Chicago Press Quinney, Richard 1979. Criminology New York: Little, Brown.

PAGE 136

125 Reed, John Shelton. 1972. Percent Black and Lynching: A Test of Blalocks Theory. Social Forces 50: 356-360. Reed, John Shelton. 1989. Comme nt on Tolnay, Beck, and Massey. Social Forces 67(3): 626-630. Reinerman, Craig, and Harry G. Levine. 1989. Crack in Context: Politics and Media in Americas Latest Drug Scare. Contemporary Drug Problems. 16: 537-577. Roeder, Kathryn, Kevin G. Lynch and Daniel S. Nagin. 1999. Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology. Journal of the American Statistical Association 94: 766-776. Roscigno, Vincent J. 1998. Race and the Re production of Educational Disadvantage. Social Forces. 76(3): 1033-1060. Rose, Dina, and Todd Clear. 1998. Incarceration Social Capital and Crime: Implications for Social Disorganization Theory. Criminology. 36: 441-479. Rosenbaum, James E., and Susan J. Popkin. 1991. Employment and Earnings of LowIncome Blacks Who Move to Middle-Class Suburbs. in The Urban Underclass edited by C. Jencks and P.E. Pete rson. Washington D.C.: The Brookings Institution. Rosenfeld, Richard 1986. Urban Crime Rates: Effects of Inequality, Welfare Dependency, Region, and Race. In James M. Byrne and Robert J. Sampson (Eds.), The Social Ecology of Crime (pp. 116-130). New York: Springer-Verlag. Rosenfeld, Richard, and Scott.H. Deck er. 1993. Discrepant Values, Correlated Measures: Cross-City Comparisons of Self -Report and Urine Tests of Cocaine Use Among Arrestees. Journal of Criminal Justice. 21: 223-230. _____.1999. Are Arrest Statistics a Valid Measure of Illici t Drug Use? The Relationship Between Criminal Justice and Public H ealth Indicators of Cocaine, Heroin, and Marijuana Use. Justice Quarterly. 16(1): 685-699. Sacher, Andrew N. 1997. Inequities of the Drug War: Legislative Discrimination on the Cocaine Battlefield. Cardozo Law Review 19, No. 3. 1149-1200. Sampson, Robert J. 1987. Urban Black Viol ence: The Effect of Male Black Joblessness and Family Disruption. American Journal of Sociology. 93(2): 348-382. Sampson, Robert J. 1995. Comments on C rime and Communities: Prevalence, Impact and Programs., in Crime, Communities, and Public Policy edited by Lawrence B. Joseph. University of Chicago. Sampson, Robert J. 2002. Transcending Tr adition: New Directions in Community Research, Chicago Style. Criminology. 40: 213-230.

PAGE 137

126 Sampson, Robert. J., and Byron W. Groves. 1989. Community Structure and Crime: Testing Social-Disorganization Theory. American Journal of Sociology 94(4): 774-802. Sampson, Robert. J., Stephen W. Raudenbus h, and Felton Earls. 1997. Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy. Science 277: 918924. Sampson, Robert J., and John H. Laub. 2005. Seductions of Method: Rejoinder to Nagin and Tremblays Developmental Trajector y Groups: Fact or Useful Statistical Fiction? Criminology. 43(4): 905-914 Sampson, Robert J., and William Julius Wilson 1995. Toward a Theory of Race, Crime, and Urban Inequality. in Crime and Inequality Hagan, John, & Peterson, Ruth D. [Eds], Stanford, CA: Stanford U Press, 1995, pp 37-54. Schuerman, Leo, and Solomon Kobrin. 1986. Community Careers in Crime. in Communities and Crime Reiss, Albert J. Jr., & Tonry, Michael [Eds], Chicago: University of Chicago Press, 1986, pp 67-100. Shaw, Clifford, and Henry D. McKay. [1942] 1969. Juvenile Delinquency and Urban Areas. Chicago: University of Chicago Press. Shihadeh, Edward S., and Darrell J. Steffensmeier. 1994. Economic Inequality, Family Disruption, and Urban Black Vi olence: Cities as Unites of Stratification and Social Control. Social Forces. 73: 729-751. Shihadeh, Edward S., and Micheal O. Maume. 1997. Segregation and crime: The Relationship Between Black Centrali zation and Urban Black Homicide. Homicide Studies. 1: 254-280. Shihadeh, Edward S., and Graham Ousey. 1998. Industrial Restruct uring and Violence: The Link between Entry-Level Jobs, Econom ic Deprivation and Black and White Homicide. Social Forces. 77: 185-206. Smith, David E. 1986. Cocaine-Alcohol A buse: Epidemiological, Diagnostic, and Treatment Considerations. Journal of Psychoactive Drugs 18(2): 117-129. Smith, Shelley A., and Marta Tienda. 1987. The Doubly Disadvant aged: Women of the U.S. Labor Force. In Working Women edited by Ann Stromberg and Shirley Harkess. Second edition. Palo Alto California: Mayfield Publishers. Spillane, Joseph F. 2004. The Road to the Harrison Narcotics Act: Drugs and Their Control, 1875-1918, in Federal Drug Control: The Evolution of Policy and Practice, edited by Jonathon Erlen and Joseph F. Spillane, pp.61-99 New York: The Haworth Press.

PAGE 138

127 Spivak, Howard, Deborah Prothrow-Stith, a nd Mark Moore. 1995. A Comprehensive Approach to Violence Prevention: Public Health and Criminal Justice in Partnership in Crime, Communities, and Public Policy edited by Lawrence B. Joseph. Chicago: University of Chicago Press. STATA. 2003. Cross-Sectional Time-Ser ies Reference Manual. STATA Press. Stolzenberg, Lisa, Stewart J. DAlessio, a nd David Eitle 2004. A Multi-Level Test of Racial Threat Theory. Criminology 43: 673-698. Tienda, Marta, and D.T. Lii. 1987. M inority Concentration and Earnings Inequality:Blacks, Hispanics, and Asians Compared. American Journal of Sociology. 93: 141-165. Tieman, Cheryl R. 1981. From Victims to Criminals to Victims: A Review of the Issues. In The Drugs-Crime Connection James A. Inciardi, ed. Sage: Beverly Hills. Tolnay, Stewart E., E.M. Beck, and Jame s L. Massey. 1989. Black Lynchings: The Power Threat Hypothesis Revisited. Social Forces 67(3): 626-630. Tolnay, Stewart E., E.M. Beck, and James L. Massey. 1989. The Power Threat Hypothesis and Black Lynching: Wither the Evidence? Social Forces 67(3): 626-630. Tonry, Michael H. 1995. Malign Neglect: Race, Crime, and Punishment in America New York: Oxford University Press. Trebach, Arnold. 1982. The Heroin Solution New Haven, CT: Yale University Press. United States Department of Justice. Federal Bureau of Investigation. Crime in the United States Uniform Crime Reports. 1996. Washington D.C. United States Department of Justice. Federal Bureau of Investigation. Crime in the United States Uniform Crime Reports. 2004. Washington D.C. Wadsworth, Tim, and Charis E. Kubrin. 2004. Structural Factors and Interracial Homicide: A New Examination of the Causal Process. Criminology. 42: 647-672. Wacquant, Loic J.D., and William Julius Wilson 1989. "The Costs of Racial and Class Exclusion In the Inner City." Annals of the American Academy of Political and Social Science 501: 8-25. Warner, Barbara D., and Brandi Wils on Coomer. 2003. Neighborhood Drug Arrest Rates: Are They a Meaningful Indicator of Drug Activity? A Research Note. Journal of Research in Crime and Delinquency 40(2): 123-138.

PAGE 139

128 Weisburd, David, Shawn Bushway, C ynthia Lum, and Sue-Ming Yang. 2004. Trajectories of Crime at Places: A Longitudinal Study of Street Segments in the City of Seattle Criminology 42: 283-321. Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass and Public Policy Chicago: The University of Chicago Press. _____.1991. Another Look at the Truly Disadvantaged. Political Science Quarterly 106 (4): 639-656. _____.1996. When Work Disappears: The World of the New Urban Poor. New York: Alfred A. Knopf. Wilson, F., and L. Wu. 1993. A Comparative An alysis of the Labor Force Activities of Ethnic Populations, Proceedings of the A nnual Research Conference of the U.S. Bureau of the Census, U.S. Govt Printing Office, Washington, DC. Zedeck, Morris S. 2000. Cocaine: Sentencing and Bad Chemistry. Judicature 84(2) 2.86-91. Zimmer, Lynn. 1987. Operation Pressure Point An Occasional Paper of the Center for Crime and Justice, New York University School of Law.

PAGE 140

129 BIOGRAPHICAL SKETCH Scott Richard Maggard received his Bachelor of Arts degree from the University of Central Florida in 1997. He enrolled in the sociology graduate program at the University of Florida in 1999 and received his Master of Arts in sociology in 2001. Upon completing the Master of Arts degree he remained at th e University of Florida to pursue his doctorate degree in sociology with an em phasis in criminology and devian ce. His research interests continue to be substance use, drug policy, and ecological perspectives of crime and deviance. He is currently employed as a Cour t Research Associate at the National Center for State Courts in Williamsburg, Virginia.


Permanent Link: http://ufdc.ufl.edu/UFE0015603/00001

Material Information

Title: Structural correlates of race-specific drug sales arrests over time : arrest trajectories from 1980 to 2001
Physical Description: Mixed Material
Language: English
Creator: Maggard, Scott Richard ( Dissertant )
Parker, Karen ( Thesis advisor )
Kaduce, Lonn Lanza ( Thesis advisor )
Akers, Ron ( Reviewer )
Piquero, Alex ( Reviewer )
Spillane, Joe ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Sociology Thesis, Ph.D.
Dissertations, Academic -- UF -- Sociology
Genre: bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract: The relationship between community structure and crime has received a great deal of research attention in criminology over the past two decades. Rising out of the traditions of Shaw and McKay, researchers have documented how structural changes in communities are related to crime rates in those areas. While the majority of these studies have focused on property and violent crimes, few studies have investigated the relationship between social structure and race-specific drug arrests. Moreover, most studies investigating structural correlates of crime have used decennial time periods and typically employ change score techniques, thereby only allowing between-city comparisons, while neglecting within-city comparisons. Employing techniques used to study the life-course of individual offenders over time, this research aims to classify the long term behavior of drug sales arrests in large cities as distinct trajectories over time. Assuming that cities behave differently, this research will shed light on how structural changes in cities affect changes in arrest trajectories over time. Findings support the hypotheses that cities do in fact behave over time in regards to drug sales arrest from 1980-2001. Moreover they vary significantly by race and while certain cities may have experienced exponential growth in Black drug sales arrests, other cities witnessed similar (yet less pronounced) growth in White drug sales arrests. Findings also provide support for both social disorganization and concentrated disadvantage perspectives on urban crime. The increase in concentrated disadvantage among Blacks and Whites from 1980- 1990 significantly impacted the likelihood of those cites being in higher drug sales arrest trajectories. Additionally, those cities which experienced dramatic increases in residential mobility from 1980-1990 were more likely to be in higher drug sales arrests trajectories as compared to the lowest. Overall these findings suggest that the structural changes in large cities occurring from 1980-1990 had a more significant impact on drug sales arrest rates than the changes occurring from 1990-2000.
Thesis: Thesis (Ph.D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi 129 p.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0015603:00001

Permanent Link: http://ufdc.ufl.edu/UFE0015603/00001

Material Information

Title: Structural correlates of race-specific drug sales arrests over time : arrest trajectories from 1980 to 2001
Physical Description: Mixed Material
Language: English
Creator: Maggard, Scott Richard ( Dissertant )
Parker, Karen ( Thesis advisor )
Kaduce, Lonn Lanza ( Thesis advisor )
Akers, Ron ( Reviewer )
Piquero, Alex ( Reviewer )
Spillane, Joe ( Reviewer )
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2006
Copyright Date: 2006

Subjects

Subjects / Keywords: Sociology Thesis, Ph.D.
Dissertations, Academic -- UF -- Sociology
Genre: bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

Notes

Abstract: The relationship between community structure and crime has received a great deal of research attention in criminology over the past two decades. Rising out of the traditions of Shaw and McKay, researchers have documented how structural changes in communities are related to crime rates in those areas. While the majority of these studies have focused on property and violent crimes, few studies have investigated the relationship between social structure and race-specific drug arrests. Moreover, most studies investigating structural correlates of crime have used decennial time periods and typically employ change score techniques, thereby only allowing between-city comparisons, while neglecting within-city comparisons. Employing techniques used to study the life-course of individual offenders over time, this research aims to classify the long term behavior of drug sales arrests in large cities as distinct trajectories over time. Assuming that cities behave differently, this research will shed light on how structural changes in cities affect changes in arrest trajectories over time. Findings support the hypotheses that cities do in fact behave over time in regards to drug sales arrest from 1980-2001. Moreover they vary significantly by race and while certain cities may have experienced exponential growth in Black drug sales arrests, other cities witnessed similar (yet less pronounced) growth in White drug sales arrests. Findings also provide support for both social disorganization and concentrated disadvantage perspectives on urban crime. The increase in concentrated disadvantage among Blacks and Whites from 1980- 1990 significantly impacted the likelihood of those cites being in higher drug sales arrest trajectories. Additionally, those cities which experienced dramatic increases in residential mobility from 1980-1990 were more likely to be in higher drug sales arrests trajectories as compared to the lowest. Overall these findings suggest that the structural changes in large cities occurring from 1980-1990 had a more significant impact on drug sales arrest rates than the changes occurring from 1990-2000.
Thesis: Thesis (Ph.D.)--University of Florida, 2006.
Bibliography: Includes bibliographical references.
General Note: Vita.
General Note: Document formatted into pages; contains xi 129 p.
General Note: Title from title page of document.

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0015603:00001


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STRUCTURAL CORRELATES OF RACE-SPECIFIC DRUG SALES ARRESTS
OVER TIME: ARREST TRAJECTORIES FROM 1980-2001














By

SCOTT RICHARD MAGGARD


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006
































Copyright 2006

by

Scott R. Maggard




























Sean Allen McCluskey

This dissertation is dedicated to the best friend I could ever ask for, Sean McCluskey. A
sociologist at heart, it was Sean who first convinced me to change my maj or from
psychology to sociology. Sean had the uncanny ability to see the world from what
seemed to be an infinite number of perspectives. I learned a great deal about life from
Sean and I hope that he learned as much from me as I did from him. I will never have
another friend like Sean; however I am forever grateful for the years that I was able to
know him and share so much with him.
















ACKNOWLEDGMENTS

First and foremost I thank Karen Parker, my mentor, chair, and friend. I first met

Karen as I began a teaching assistantship for research methods and teaching the

laboratory sessions for her course. Karen also introduced me to the theoretical and

methodological approaches discussed throughout this research. Her expertise and

willingness to share that expertise are second to none. My approach to research in general

is much attributed to the knowledge I gained through working closely with her. I am also

grateful to Aaron Griffin for his willingness to share her time, especially these past few

months.

Coupled with Karen, Lonn Lanza Kaduce, Ron Akers, Alex Piquero, and Joe

Spillane comprised what I would argue to be the most solid and well-balanced committee

one could ask for. Lonn was always there to provide practical advice, whether needed

within the academy or elsewhere. From Ron Akers I learned much about both theory and

the assessment of theories. Alex provided methodological expertise, and seeing his use of

these techniques in the life-course arena led to the idea to apply these methods to drug

arrests in the first place. Finally, Joe provided not only his never ending insight into the

world of drug use and drug policy, but also added much needed humor and reflection on

life in general.

The remainder of the faculty and staff in the Department of Criminology, Law, and

Society all deserve thanks for their willingness to share their knowledge and experiences









with their students. Whether writing the dissertation to obtaining employment, they were

always approachable and willing to help.

I thank Bob Jones, who is the architect of the PROC TRAJ software plug-in that

was utilized in this research. I am grateful for the many hours Bob has sacrificed

answering emails and assisting me to better understand and use the PROC TRAJ

software. Bob's support for a software program for which he is not paid is unprecedented.

Craig Boylstein and Kim Raymond deserve much credit for alleviating stress and

providing much needed humor when needed. Craig was one of the first people I met in

the sociology program after moving to Gainesville in 1999, and we immediately became

close friends.

I thank my parents for putting up with what sometimes seemed to them my

everlasting j ourney to remain a student as long as possible. They will likely get as much

satisfaction from the fact that I am finally exiting school as anyone. I would not have

been able to do this without them.

The many fellow graduate students with whom I became friends at the University

of Florida I also thank. John Reitzel, and Jennifer Matheny both provided much needed

humor and entertainment throughout graduate school.

Finally I thank my wife, Allison Chappell. Without Allison I likely would not be

finishing this document. She is my life, and her selflessness which made this possible I

will be forever grateful for. Allison has been there day and night, through rough times

and bright, pushing me along in order to complete this task.



















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ................. ................. viii............


LIST OF FIGURES .............. .................... ix


AB S TRAC T ......_ ................. ............_........x


CHAPTER



1 INTRODUCTION ................. ...............1.......... ......


Introducti on ................. ...............1.................
Back ground ................. ...............1.................
Present Study ................. ...............3.......... ......
Specific Aim s .............. ...............6.....
Significance .................. ...............7.................
Layout of the Dissertation ................ ...............9................

2 THEORETICAL PERSPECTIVES............... ..............1


Introducti on ................. ...............10.................
Social Disorganization ................. ...............11........... ....
Concentrated Urban Disadvantage ................. ...............18........... ....
Unemployment and the Urban Underclass ................. .............................18
Poverty and Segregation ................. ............ ... ................22....
Racial Threat: Blalock's Power Threat Hypothesis ................. ........................27
Conclusion ................ ...............35.................


3 REVIEW OF THE LITERATURE .............. ...............38....


Introducti on ................ ... ....... .... ... .. ...............38...

Race, Drugs and Crime: A Historical Perspective............... ..............3
Race, Drugs and Crime: The New Era .............. ...............44....
Hypotheses and Expectations .................. ........ .............4
Social Disorganization and Drug Sales Arrests ................. .......................51
Concentrated Urban Disadvantage and Drug Sales Arrests .............. .... ........._..52
Racial Threat and Drug Sales Arrests .............. ...............53....












Conclusion............... ...............5


4 DATA AND METHODS .............. ...............55....


Introducti on ................. ...............55.................
Data Sources .............. ...............55...

Dependent Variable ................. ...............56.......... ......
Independent Variables ................ ...............58.................
Methodology ................. ...............63.................
Statistical Procedures............... ...............6
Ri sk F actor s ................. ...............67.......... ......


5 DESCRIPTIVE STATISTICS............... ...............6


Introducti on ................. ...............69.................

Descriptive Statistics .............. ...............69....

Independent Variables ................ ...............69.................
Dependent Variable ................. ...............73.................
Drug Sales Arrests Traj ectories ................. ....__. ....__. ...........7
Conclusion ........._.__........_. ...............78....


6 MULTIVARIATE RESULTS ............. .....__ ...............85..


Introducti on .............. ......_ ...............85...

White Drug Sales Arrests .............. ....... ...............86.
Accounting for Change: 1980-1990 .............. ...............86....
Accounting for Change: 1990-2000 .............. ...............89....
Black Drug Sales Arrests .............. .........__ ...............90..
Accounting for Change: 1980-1990 .............. ...............90....
Accounting for Change: 1990-2000 .............. ...............92....
Conclusion ............. ...... ...............92...


7 CONCLU SION................ ..............10


Discussion and Implications ............. .....__ ...............100..
Lim stations ............. ...... ._ ...............103..
Future Research .............. ...............105....

Final Thoughts ............. ...... ._ ...............106..


APPENDIX: SAMPLE CITIES .............. ...............108....


REFERENCES ............. ...... ...............115...


BIOGRAPHICAL SKETCH ............. ...... ...............129...

















LIST OF TABLES


Table pg

4-1. Concentrated Disadvantage Index Factor Loadings. ................. .................6

5-1. Descriptive Statistics (Means with Standard Deviations in Parentheses). .................81

5-2. Mean Percent Changes Across Time for Independent Variables. .............. ...............82

5-3. Means and (Standard Deviations) for Drug Sales Arrest Rates. ............. .................82

6-1. Parameter Estimates and (standard errors) Modeling Structural Changes from
1980-1990 on White Drug Sales Arrests. ................. .....___............. .....9

6-2. Parameter Estimates and (standard errors) Modeling Structural Changes from
1990-2000 on White Drug Sales Arrests. ................. .....___............. ......9

6-3. Parameter Estimates and (standard errors) Modeling Structural Changes from
1980-1990 on Black Drug Sales Arrests. ................ .....___.............. .....9

6-4. Parameter Estimates and (standard errors) Modeling Structural Changes from
1990-2000 on Black Drug Sales Arrests. ................ .....___.............. ......9

A- 1. Traj ectory Group Memb ershi p for White Drug S al es Arre sts ................. ...............10 8

A-2. Traj ectory Group Membership for Black Drug Sales Arrests ................. ...............1 12

















LIST OF FIGURES


Figure pg

5-1. Traj ectories of White Drug Sales Arrests 1980-2001 ......._____ .... ....__ ...........83

5-2. Traj ectories of Black Drug Sales Arrests 1980-2001 ........._.__.... ...__ ............84
















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

STRUCTURAL CORRELATES OF RACE-SPECIFIC DRUG SALES ARRESTS
OVER TIME: ARREST TRAJECTORIES FROM 1980-2001

By

Scott R. Maggard

August 2006

Chair: Karen F. Parker
Cochair: Lonn Lanza-Kaduce
Major Department: Sociology

The relationship between community structure and crime has received a great deal

of research attention in criminology over the past two decades. Rising out of the

traditions of Shaw and McKay, researchers have documented how structural changes in

communities are related to crime rates in those areas. While the maj ority of these studies

have focused on property and violent crimes, few studies have investigated the

relationship between social structure and race-specific drug arrests. Moreover, most

studies investigating structural correlates of crime have used decennial time periods and

typically employ change score techniques, thereby only allowing between-city

comparisons, while neglecting aI ithrin-city comparisons. Employing techniques used to

study the life-course of individual offenders over time, this research aims to classify the

long term behavior of drug sales arrests in large cities as distinct traj ectories over time.

Assuming that cities behave differently, this research will shed light on how structural

changes in cities affect changes in arrest traj ectories over time. Findings support the










hypotheses that cities do in fact behave over time in regards to drug sales arrest from

1980-2001. Moreover they vary significantly by race and while certain cities may have

experienced exponential growth in Black drug sales arrests, other cities witnessed similar

(yet less pronounced) growth in White drug sales arrests. Findings also provide support

for both social disorganization and concentrated disadvantage perspectives on urban

crime. The increase in concentrated disadvantage among Blacks and Whites from 1980-

1990 significantly impacted the likelihood of those cites being in higher drug sales arrest

traj ectories. Additionally, those cities which experienced dramatic increases in residential

mobility from 1980-1990 were more likely to be in higher drug sales arrests trajectories

as compared to the lowest. Overall these findings suggest that the structural changes in

large cities occurring from 1980-1990 had a more significant impact on drug sales arrest

rates than the changes occurring from 1990-2000.















CHAPTER 1
INTTRODUCTION

Introduction

The primary focus of this dissertation is to investigate the extent to which structural

factors in large urban areas contribute to the rise in drug sales arrests over time. Many

researchers have focused on the individual factors that lead to drug use and crime.

However an alternative approach is to discern to what extent ecological factors contribute

to the distribution of crime in urban areas, and specifically drug sales arrests. As large

urban centers have undergone a metamorphosis covering the past several decades, the

individual actors within these areas have been subj ect to "cognitive landscapes" of crime

and a rather dismal outlook for the future (Sampson and Wilson 1995).

Therefore, peering through an ecological or "macro" lens, it is believed that it is the

structure of these urban landscapes that permit crime to permeate the lives of the

residents, rather than simply being that the residents of these areas are somehow more

likely to commit crimes on their own. The goal of this dissertation is to a) assess to what

extent large urban areas "behave" over time (using drug sales arrests as a proxy of

behavior), and b) determine what structural factors predict the likelihood of a city

experiencing sharp increases in drug sales arrests over time.

Background

In the tradition of the Chicago School, structural theories of crime and delinquency

focus on the community to understand criminal activity. Rather than focusing on the

individual, structural theories seek to understand how community structure may either









foster or inhibit delinquent behavior. The classic work of Shaw and McKay (1942) laid

much of the groundwork which led to our current conceptualization of community social

disorganization. Their argument was not that there is a direct relationship between

community factors such as economic status and delinquency, but that those areas

characterized by dire economic circumstances tended to have high rates of population

turnover and population heterogeneity. This, in turn, weakened informal social controls

within the community resulting in a more favorable environment for delinquency to

occur. Additionally, social disorganization theory also posits that the effects of declining

structural conditions in urban areas are invariant across race. In other words, as family

disruption and low economic status begin to prevail in an urban area, informal controls

break down and crime can result, regardless of race (Bursik 1988; Shaw and McKay

1942).

In addition to the social disorganization of urban communities, researchers have

posited that deindustrialization beginning in the 1970's has also contributed to

delinquency in urban areas. Wilson (1987) argues that the decline in manufacturing and

semiskilled j obs beginning in the 1970's has depleted j obs for minorities in those areas.

He argues that black j oblessness has led to concentrated disadvantage among blacks in

urban areas. Others have also argued that racial isolation, joblessness and racial

residential segregation have fostered delinquency in many communities (Massey et

al.1987; Massey and Denton 1988, 1993; Wilson 1987).

The concentrated disadvantage faced by blacks has led to a decline in marriageable

males, a rise in incarceration rates, family disruption, as well as a rise in crime rates

(Almgren et al. 1998; Krivo et al. 1998; Parker and McCall 1999; Sampson 1987). As the









residents of these communities struggled to survive with the loss of the unskilled and

semi-skilled jobs, many relied on the growing informal economy of inner cities for

income (Johnson et al. 1990). In addition, others have documented the dramatic surge of

young people who became involved in the illicit drug trade as being unemployed and

unskilled workers (Fagan 1992).

With urban areas already facing social disorganization and concentrated

disadvantage after deindustrialization, it could be argued that the rise in drug use and

sales only exacerbated these problems. In fact, the war on drugs began an era of targeting

the urban and disproportionately black population of America' s cities with specific drug

law enforcement tactics aimed at curbing the rising use and sales of crack cocaine

beginning in the early 1980's (Tonry 1995).

Present Study

Drug offense arrests in the United States have varied over time for much of the past

century. However, recent research has demonstrated that drug arrests experienced an

unprecedented rise beginning in the early 1980's throughout much of the 1990's.

Blumstein (1995) has noted that the crack epidemic, which began in the 1980's, was

directly related to the sharp rise in homicide rates during the same decade. Moreover it

began to affect selected urban areas and later spread to other areas of the country as well

(Blumstein 1995). Additionally researchers have recently presented data that demonstrate

the way in which homicide rates changed during this time in different sections of the

country (Messner et al. 2005). They found that thirty five of the sixty eight large cities

which they examined behaved in the "epidemic-like cycle" described by others

(Blumstein 1995; Blumstein and Rosenfeld 1998; Golub and Johnson 1997; Messner et

al. 2005).









While most data that researchers and activists alike rely on are based solely on

averages or means, little is known about the 11 ithrin-city variations of arrests over time.

That is, with such a wide margin between cities with the lowest drug arrest rates and

those with the highest (see Mosher 2001; Parker and Maggard 2005), these averages may

paint a picture that presents a false sense of urgency for the entire country. I contend that

it may be that different cities experience different rates of increases of drug arrests over

time. In fact, I argue that some cities may experience few problems with drug arrests,

while others may experience great troubles. Contributing to this issue is that as specific

drug "epidemics" or drug "scares" arise, they often develop and diminish in different

cities at different periods in time, as was observed with crack cocaine (Golub and

Johnson 1997).

While much of the macro level research on communities and crime has focused on

between-city change over different time periods, very little is known about ~I ithrin-city

changes in criminal offending over time. That is, how do specific cities behave in terms

of arrest rates over a long period of time? This is what is known in the life-course

literature as a trajectory. While studies that utilize data for many cities may investigate

how the change in structural conditions contributes to the change in crime rates over

time, they fail to decipher which cities' crime rates changed and why they changed.

Until recently, the idea of "community careers in crime" was very rare (but see

Bottoms and Wiles 1986; Schuerman and Kobrin 1986). Only in the past year has

research surfaced linking the methods utilized in life-course criminology (to analyze the

offending traj ectories of individuals) to macro level analysis of communities and crime

(Griffiths and Chavez 2004; Weisburd et al. 2004).









Schuerman and Kobrin (1986) investigated the 20 year histories of Los Angeles

neighborhoods from a developmental perspective. They used cross-sectional and time

series analysis to discover that neighborhoods could be classified as one of three distinct

stages: emerging, transitional and enduring. They found that neighborhood deterioration

precedes rising crimes rates in the early stages but that rising crime rates precede further

neighborhood deterioration in the later stages. They conclude that efforts to prevent the

rapid escalation of crime in urban areas must be initiated in the emerging stages of the

cycle in order to be most successful (Schuerman and Kobrin 1986). In contrast, Bottoms

and Wiles (1986) focus their research in Britain, and they argue that the key to

understanding the criminal careers in neighborhoods is housing tenure. They find that key

factors include bureaucratic mechanisms and renting versus home ownership (Bottoms

and Wiles 1986).

Weisburd and his colleagues (2004) analyzed traj ectories of crime in

neighborhoods in Seattle over a 14 year period. They used a group based traj ectory

analysis to uncover distinct developmental trends within the city. They conclude that

while a city's crime rates may be declining over a period of time, certain segments within

the city may be rapidly declining or rapidly increasing at the same time, and may

contribute significantly to the overall crime picture in the city (Weisburd et al. 2004).

Griffiths and Chavez (2004) merge a group based traj ectory analysis and

exploratory spatial analysis to investigate homicide trends in Chicago communities from

1980-1995. They identified distinct traj ectories for total, street gun, and other weapon

homicides across 831 census tracts in the city. Key findings included evidence of a

weapon substitution effect for violent neighborhoods that are proximate to each other, in









addition to the fact that street gun specific homicides increased in areas bordering the

most violent areas of the city (Griffiths and Chavez 2004).

Specific Aims

This research proj ect seeks to examine the criminal careers of cities using race-

specific drug arrests over two decades, identifying structural correlates to help explain

this phenomenon. I am focusing on the theoretical perspectives of social disorganization,

urban disadvantage and racial threat. This research seeks to address several related

research questions. First, I seek to determine if there are identifiable traj ectories of race

specific drug-sales arrest rates in large urban areas over time in order to determine

whether the changes in certain cities experienced different traj ectories for different races.

In addition, I seek to answer the question of whether certain structural characteristics of

cities contribute to explaining the likelihood of a given city being in a specific traj ectory.

That is, if I determine there to be 4 distinct traj ectories, do social disorganization and

other structural correlates explain the increase in arrests for the highest traj ectory and not

the lowest? Do they help to explain specific traj ectories for specific races? In other

words, are cities plagued with the highest increase in drug activity during this time period

(as measured by drug sales arrests) also the cities which have experienced the greatest

change in ecological factors? These factors may include the deindustrialization of urban

areas, an increase in residential mobility and racial residential segregation, increasing

disorganized family structures throughout the community and the changing ethnic

makeup of the area over time.

The focus of this research will be race-specific drug-sales arrests in the largest

cities in 1980 for the period 1980 through 2001. The justification for utilizing only drug-

sales arrests as the focus for this research is twofold. First, the arrest data dating back to









the 1980's are limited to only drug sales arrests. Second, much of the theoretical

reasoning driving this research revolves around the vanishing manufacturing and semi-

skilled jobs documented during this period in urban areas (Sampson 1987; Wilson 1987,

1991). This trend has also been documented at the micro level, as the ethnographic work

of Elij ah Anderson (1999) illustrates. In the Code of the Streets (1999) Anderson writes:

Deindustrialization and the growth of the global economy have led to a steady loss
of the unskilled and semiskilled manufacturing j obs that, with mixed results, had
sustained the urban working class since the start of the industrial revolutions. At the
same time "welfare reform" has led to a much weakened social safety net. For the
most desperate people, many of whom are not effectively adjusting to these
changes .. the underground economy of drugs and crime often emerges to pick up
the slack. (pp. 108) (Emphasis added)

Significance

The significance of this research is that it will enable me to differentiate those cities

that have experienced the greatest increases in drug sales arrests from those that have not,

disaggregated by race. Where prior research describes certain structural variables as

being significant to crime in urban areas, or to the change in crime in urban areas, this

research will enable me to identify cities that have experienced the most dramatic

changes in drug arrest rates. This will enable policy makers to identify specific urban

areas that are in more (or less) need of state or federal aid programs, community building

programs, j ob training programs, etc. For example, if it is found that the highest traj ectory

groups for both blacks and whites also see the largest impact from a decrease in

manufacturing jobs, then it can be said that policy makers should prioritize resources to

those cities rather than cities that may not feel such an impact.

While few studies have focused on structural correlates of drug arrest rates in large

cities (Mosher 2001; Parker and Maggard 2005), structural theories of crime and

deviance have been used to help us understand many other types of crime from property









crimes to homicides (Bursik and Grasmick 1993; Crutchfield 1989; Crutchfield and

Pitchford 1997; Krivo and Peterson 1996, 2000; Parker and McCall 1997, 1999; Sampson

1987; Sampson and Wilson 1995).

There are several theoretical reasons to assume that areas plagued by social

disorganization will witness an increase in drug sales arrests over time. The first is quite

obvious; as deindustrialization has accelerated beginning in the 1970's, many individuals

watched as their employer literally drifted away. It could be argued that this in and of

itself may be enough to warrant expecting an increase in drug sales (income generation).

However when this economic motivation is coupled with the criminogenic environment

that social disorganization and concentrated disadvantage theorists would predict, it

makes even more sense logically. That is, if one desperately needs money to support

her/his family, and the family resides in an environment with little informal controls,

where criminal behavior is somewhat accepted and there exists a lack of hope for the

future, the chances of joining the illegal drug trade should increase.

This research aims to merge the methodologies traditionally used to analyze

individual offending rates over time, with those that have been used to analyze structural

changes in urban areas. That is, I seek to utilize the TRAJ procedure in SAS to

investigate patterns of drug arrest rates over a 22 year period, creating clusters or

traj ectories of cities with similar patterns. In addition, I seek to use the change in

structural conditions between decades to determine their effect on a given trajectory.

This research will add to the literature in several ways. First, it will build on the

efforts of Griffiths and Chavez (2004) and Weisburd et al. (2004) of merging the TRAJ

procedure to macro level analysis. It will add the ability to analyze how the change in









certain structural conditions within cities affects drug arrest rates over time. It will also

enable me to untangle these changes in order to identify whether certain cities were

impacted more severely by these changes than others. Finally, it will evaluate the social

disorganization, urban disadvantage and racial threat perspectives in a new context. This

methodology provides a significant departure from traditional methods, in that statistical

outliers may be grouped together in their own traj ectories and will not have a significant

impact on cities that experience few changes in drug activity over time.

Layout of the Dissertation

The dissertation will consist of 7 chapters. Chapter 2 will provide the theoretical

background driving the research, including a review of the empirical support for each

theoretical perspective chosen. Chapter 3 will provide a review of the literature regarding

drugs and crime as well as present several hypotheses to be tested. Chapter 4 will

describe the data and statistical procedures utilized for testing the hypotheses. This will

include detailed information on the data, as well as background information relating to

the TRAJ procedures that will be performed in SAS. Chapter 5 will provide descriptive

statistics for both dependent and independent variables. It will also include the

traj ectories of drug sales arrests over time, providing unique traj ectories for White and

Black drug sales arrest rates. Chapter 6 will present how the various theoretically drawn

explanatory variables interact with the clusters, or traj ectories, and highlight how each

theoretical perspective may contribute to our understanding of the changes in drug arrests

over time. Finally chapter 7 will provide concluding remarks, policy implications of the

current research, and directions for future research.















CHAPTER 2
THEORETICAL PERSPECTIVES

Introduction

While the Chicago School propelled many of the great symbolic interactionists

within sociology, it was also ripe with many of the brightest thinkers in the field in

examining the world of sociology through a macro lens, or ecological approach. It was a

shift that took the emphasis away from asking why certain individuals behave the way

they do, and instead asked, how do particular geographic areas influence rates of crimes

to behave the way they do? It was within the great city of Chicago that what has now

become known as Social Disorganization was born.

During the early 20th century, metropolitan areas of the United States were about to

embark on a maj or metamorphosis from sparsely populated farmlands to highly

populated industrial centers. The transformation not only brought greater prosperity and

opportunities to more people, but it also welcomed an influx of immigrants seeking

employment and new opportunities.

Many of the structural (or ecological) theories within sociology and criminology

today share their roots with the Chicago School tradition. Robert Park and Ernest Burgess

studied the social organization and characteristics of cities. Whether by coincidence or

not, they happened to reside in one of the fastest evolving metropolitan areas in the

country, so what better place to examine how these changes occur over time through a

sociological lens (Cullen and Agnew 2003).









Social Disorganization

It was in this context that Clifford Shaw and Henry McKay drew upon the theories

of Park and Burgess and their ideas of how a city is like an organism when they

conducted their classic study on juvenile delinquency in Chicago, Juvenile Delinquency

and Grban Area~s (1942). Burgess had noted that as cities such as Chicago transform into

the industrial meccas that they are today, they do so from the inside out. Burgess

documented specific "zones" within a city, beginning in the center and expanding out in

concentric rings or zones (Burgess 1925).

From a city's core (e.g. central business and industrial center), it naturally sprawled

outward over time, through this evolution of concentric rings or zones. Just outside the

central business district, Burgess noted what he coined the "zone in transition". It was

here that immigrants frequently first settled as they arrived to new areas seeking work

opportunities and affordable housing. Just outside the "zone in transition", lied the "zone

of working men' s homes", the "residential zone", and the "commuters' zone" (Burgess

1925).

Burgess observed that while most immigrants new to an urban area first settle

within the "zone in transition", most families do not stay in the zone for long periods of

time. In fact, the concentric zones are labeled as such since there appears to be a natural

progression outward for financial and family reasons. Once individuals established stable

employment (typically factory/manufacturing j obs), they would typically save their

money for a more prosperous life in one of the outer zones. Therefore, within the

transition zone there always exists residential mobility both in and out of the area. As

new immigrants excitedly move in seeking employment and housing, another family









leaves the zone in search of a more relaxed, family oriented life in the outer zones. These

outer zones are certainly where most live today, the suburbs or suburbia.

Using this logic, Shaw and McKay hypothesized that these urban centers (e.g.

zones in transition) should have the highest concentration of crime and delinquency, due

to the ever-changing landscape of the areas. They theorized that the persistent residential

mobility and poverty would contribute to these areas being more "disorganized"

compared to areas that were more affluent such as the outer zones within a city. In order

to test their hypotheses, Shaw and McKay set out to collect data on juvenile delinquents

throughout the city of Chicago and map which areas of the city experienced the highest

rates of delinquent behavior (Shaw and McKay 1942).

Shaw and McKay did not believe that any city necessarily had a distinct number of

zones (they are rather arbitrary), or that lines existed for the exact measuring of the zones.

However, they argued that organizing their research based on five zones, each about 2

miles apart, they could map how the organization and changes in these areas affected

juvenile delinquency rates over three periods of time. They collected data on recidivism,

truancy rates and referrals to juvenile court to map the periods of 1900-1906, 1917-1923,

and 1927-1933. Their methodology would enable them to compare different cohorts of

juveniles residing in the same geographic areas at different time periods.

Their results confirmed much of what they had predicted. In all three-time periods,

the central business districts consistently witnessed the highest rates of juvenile

delinquency, with the rates dropping significantly for each successive zone moving

outward from center. In other words, it was not the individuals who resided in these areas

that created high crime rates, but rather the organization (or disorganization) that made









these areas ripe for delinquent activity. Recall this is in stark contrast to earlier

criminological theories, which purported that the key to understanding individual

delinquency lies in individual traits as advanced by social scientists such as Cesare

Lombroso and E.A. Hooten for example (Akers and Sellers 2003; see also Lindesmith

and Levin 1937).

One criticism of Shaw and McKay's assertions lies in the fact that they never

explicitly described how or why the zone in transition would have the highest

concentration of juvenile delinquents (Akers and Sellers 2003; Cullen and Agnew 2003),

although it is assumed today that the true cause is due to the breakdown of informal

controls in these disorganized areas. In other words, an area that is disorganized may be

characterized as one experiencing a lack of the informal control mechanisms that close-

knit communities provide in order to deter and control juvenile delinquents. Further,

researchers have long argued that individuals living in high delinquency urban areas

experience a "duality of conduct norms", where their conduct norms are split between

those of mainstream society and those of delinquent subcultures (Kobrin 1951). In

addition it has been noted that it is extremely difficult for communities to establish

informal control mechanisms when many of its residents are "uninterested in

communities they hope to leave at the first opportunity" (Kornhauser 1978: 78).

While Shaw and McKay's ideas were widely cited, Bursik (1988) notes that they

fell out of favor for many years due partly to the shift to more micro level theories of

crime and delinquency such as differential association/social learning and control

theories. Furthermore, Bursik notes that while widely cited, it was generally in reference









to the economic composition of communities and their respective rates of delinquency,

according to Bursik:

Shaw and McKay did not posit a direct relationship between economic status and
rates of delinquency. Rather, areas characterized by economic deprivation tended to
high rates of population turnover (they were abandoned as soon as it was
economically feasible) and population heterogeneity (the rapid changes in
composition made it very difficult for those communities to mount concerted
resistance against the influx of new groups). These two processes in turn, were
assumed to increase the likelihood of social disorganization, a concept that is very
similar to Park and Burgess' s (1924: 766) formulation of social control as the
ability of a group to engage in the activity of self- regulation" (Bursik 1988: 520).

It was the reintroduction of social disorganization theory in the 1980's that led to

the vast body of research that exists today, a groundwork laid by a select few sociologists

and criminologists. While one reason that social disorganization was virtually abandoned

for several years is surely due to the trendy nature that often occurs in academic circles

regarding research agendas (see Sampson and Laub 2005), much of it was certainly

attributable to the difficulty in undertaking such projects (see Kubrin and Weitzer 2003).

To truly test Shaw and McKay's hypotheses regarding social disorganization, one

would need to conduct a multi-year study that took into account interviews with residents

as well as collect statistics relevant to the area. However, as technology has progressed,

sociologists and criminologists have utilized official crime data and demographic data

collected through the United States Census program to develop proxies, which help us

understand social disorganization and how it may be related to an area' s criminal activity.

Researchers have been able to extend social disorganization theory further than in the

past as well as clarify key conceptualizations.

Upon the revitalization of Social Disorganization theory in the 1980's, studies

began to emerge to gauge to what extent Shaw and McKay's hypotheses remained valid

over time. Upon an "extremely fortunate stroke of luck", Bursik and Webb (1982) were









able to obtain much of the original data utilized by Shaw and McKay from the basement

file cabinets of the Institute for Juvenile Research at the University of Chicago, as well as

data the Institute had compiled through 1970 (Bursik and Webb 1982:28). Utilizing this

newly discovered archive they were poised to examine the affects of four key variables

on the changes in "areal delinquency rates" in Chicago from the 1940's forward through

the 1970's. They measured changes in the community population, the percentage of

foreign-born whites, the percentage of non-whites, and levels of household density. Their

findings indicate that between 1940 and 1950, invasion and succession did not affect the

distributional patterns of delinquency within communities (as Shaw and McKay

asserted) .

However, during the later time periods (notably 1950-1960) community change

wa~s associated with patterns of "areal delinquency rates", which is contrary to Shaw and

McKay's original theses. Rather than state that Shaw and McKay were "wrong", they

argue that Shaw and McKay were writing and making their observations within a much

different historical context than the later years studied by Bursik and Webb. In fact, the

premise of the Burgess' original thoughts on invasion and succession rested on the

assumption that new immigrants would make a natural transition or assimilation into a

new area. Instead, during these later times, immigration and segregation policies changed

the dynamics of how an urban area would react to the invasion and succession of new

residents. They conclude that it is the fact that invasion and succession are occurring, as

opposed to who is involved in the occurrence of these changes. In other words, it matters

little of which race is attributed to the invasion/succession so much as the process itself










(Bursik and Webb 1982; Bursik and Grasmick 1993; Sampson 1987, 1995; Sampson and

Groves 1989; Sampson et al. 1997).

Sampson and others have noted the importance of "collective efficacy" in

maintaining control over youths in high crime areas. They have recognized that the

relationship between disorganization and crime rates is not a simplistic/direct

relationship, but rather complex. Sampson goes on to argue the social disorganization

framework views neighborhoods and communities as a complex system of friendship and

acquaintance relationships which are rooted in family life and ongoing socialization

processes within these urban areas (Bursik 1984, 1988; Bursik and Webb 1982; Sampson

et al. 1997).

Researchers have also noted the reciprocal effects of structural factors and urban

crime. Shihadeh and Steffensmeier (1994) found that reciprocal effects do exist among

measures of family disruption and crime rates in large cities. They note that while family

disruption has a positive significant affect on crime rates, violent crime also has a

positive significant affect on family disruption. They argue that this is due to both the

incarceration of young males as well as the unwillingness of young women to marry

criminally involved young men. Moreover family disruption was found to be among the

strongest predictors of juvenile violent crime due to the lack of informal controls that

strong family networks within communities are believed to provide. Similarly the

dramatic increase in incarceration has been argued to decrease social organization and

thus stimulate the breakdown of informal controls within urban communities Rose and

Clear 1998; Sampson 1987; Shihadeh and Steffensmeier 1994).









More recent research has investigated the relationship between social

disorganization within communities and specific types of homicides. Kubrin (2003) Einds

that the percentage of divorced males in an urban area is statistically significant with only

"general altercation killings", which are homicides that began as a general altercation,

leading the subj ects outside and resulted as homicides. Additionally she found that areas

with high residential mobility experienced a greater number of felony homicides as well

as it affecting the overall homicide rates in those areas, consistent with disorganization

theory. These findings lead us back to the issue of reciprocal effects discussed above.

Kubrin and Witzer (2003) argue that future studies of social disorganization need to

become more dynamic. For instance they argue that street killings within a neighborhood

may increase residential mobility, while domestic homicides may have little impact on

mobility. Since domestic homicides are between spouses or acquaintances residents are

expected to be less fearful than they may be of random or street homicides (such as drug

related homicides) (Kubrin 2003; Kubrin and Weitzer 2003).

Kubrin and Weitzer (2003) go on to argue that researchers evaluating social

disorganization theory need to improve not only the conceptualization of concepts but

also the methods used to test the effects of these structural covariates on crime rates. One

suggestion is to borrow methods from the psychological literature which is used to

identify life course turning points in offending in individuals. While they suggest growth

curve modeling techniques, this study utilizes a procedure that is typically used to model

individual offending, PROC TRAJ, and is described in Chapter four (Kubrin and Weitzer

2003).









Concentrated Urban Disadvantage

Unemployment and the Urban Underclass

In The Truly Disadvantaged, William Julius Wilson outlined a multi-faceted

framework describing what he believes has lead to the growth of the urban ghetto in

America, and more precisely, what he has termed the underclass. The underclass, he

states, are the results of "...changes have taken place in ghetto neighborhoods, and the

groups that have been left behind are collectively different from those that lived in these

neighborhoods in earlier years" (Wilson 1987: 8).

He argues that urban Blacks have been especially vulnerable to both geographic

and industrial changes in the economy, which have had the devastating effect of creating

high proportions of joblessness among Blacks. This is due to the history of discrimination

in America, as well as Blacks' migration to large metropolises, both of which have

resulted in weak labor force attachment. He argues that these are the results of a variety

of compounding factors that have contributed to the underclass. These factors include

shifts in the labor market from goods producing sectors to service oriented sectors, the

polarization of the labor market into low salary and high salary j obs, numerous

technological innovations, relocation of manufacturing outside large cities, as well as

economic recessions (Wilson 1991: 640).

All of these factors combined have resulted in a significant increase in the

concentration of poverty in urban areas, as well as increasing both the number of poor

single parent families and the number of families depending on welfare. Moreover, since

1970, inner city neighborhoods have experienced out migration of working and middle

class families, thus leaving a concentrated amount of poverty behind, with "extreme

poverty" (greater than 40%) increasing dramatically (Wilson 1987, 1991).









With the working and middle class black families moving out, Wilson contends

that critical social buffers have been removed from these communities. Past decades had

seen the working and middle class families bring stability to inner city neighborhoods,

through patronizing local stores and churches, sending their children to local schools, and

providing legitimate and meaningful visions to lower class Blacks of the chances of

upward mobility.

When all of these factors compound over time, Wilson argues that weak labor force

attachment is inevitable. He goes on to elaborate by stating:

Thus neighborhoods that have few legitimate employment opportunities,
inadequate j ob information networks, and poor schools not only give rise to weak
labor force attachment but also raise the likelihood that people will turn to illegal or
deviant activities for income, thereby further weakening their attachment to the
legitimate labor market (Wilson 1991: 651).

So in addition to further weakening these attachments, crime in the area is

heightened as it manifests and perpetuates itself within the community, with residents

being influenced by the beliefs, behavior, and social perceptions of other disadvantaged

families. He refers to this as "concentration effects", or the "effects of living in an

overwhelmingly impoverished environment" (pp.651).

Sampson and Wilson (1995) have extended Wilson's original ideas with what they

have coined "cognitive landscapes". Cognitive landscapes emerge based on the context of

any given community. In other words, everyone has cognitive landscapes, however, those

landscapes will differ greatly across populations based primarily upon the organization

and structure of the community. Those who reside in impoverished areas such as the

areas described by Sampson and Wilson will form a system of values that is less likely to

condemn drug use and disorder than those of more affluent communities (Sampson and

Wilson 1995; see also Kobrin 1951).









Generally speaking, Wilson's arguments aren't usually tested alone. That is they

are more often incorporated into tests of theories such as social disorganization theory.

Studies focusing on these concepts have generally supported many of Wilson' s claims.

Jargowsky and Bane found during the 1970's alone, the number of people living in

"extreme poverty" (>40%) increased 30 percent and that the proportion of the poor

population residing in ghettos significantly varies by race. They found that in 1980, only

2% of the non-Hispanic poor lived in ghettos, compared to about 21% and 16% of the

Black and Hispanic poor, respectively. In addition they noted that almost one third of

Blacks living in metropolitan areas resided in the ghetto, and 65% of the ghetto poor were

Black. There is also evidence that this trend continued through the 1980's as well

(Jargowsky and Bane 1991; see also Nathan and Adams 1989).

They also added to Wilson' s argument by discovering that most of this increase

was in the Midwest and Northeast alone. In fact, only 10 cities accounted for 75% of the

rise in ghetto poverty throughout the 1970's. Even more startling was that nearly one

third was accounted for by New York City alone, and nearly one half by New York City

and Chicago combined (Jargowsky and Bane 1991).

Rosenbaum and Popkin placed low income Blacks in suburban areas and rented

apartments for them, while placing a control group in apartments in the city. Controlling

for personal characteristics such as family background/circumstances, motivation, as well

as education after beginning the program, the authors found that those who had been

placed in the suburban apartments were significantly more likely to find employment

than their inner city placed counterparts, citing more employment opportunities

(Rosenbaum and Popkin 1991).









Testing Wilson' s hypothesis, Sampson (1987) notes that the effect of black male

joblessness on black crime rates is mediated through its effects on family disruption. That

is, as the number of unemployed black males increases, this in turn increases the number

of female-headed households in these areas. He concludes that high black crime rates

appear to be caused by a combination of structural factors including unemployment,

economic deprivation, and family disruption. Sampson also finds support for Wilson' s

general thesis that structural unemployment is a more important factor in accounting for

Black family disruption as compared to White family disruption. These findings are

consistent with other works as well (Sampson 1987).

More specifically, the implications of Wilson' s work lie more in community level

explanations of crime. For instance, Sampson and his colleagues have found continued

support for his ideas regarding the underclass. In their study of neighborhood violent

crime and collective efficacy, it is clear how well Wilson's arguments mesh into this

scheme. Similar to Wilson's arguments regarding concentration of poverty, collective

efficacy demonstrates those conceptualizations well. For instance Sampson et al. (1997)

argue that low SES and concentrated disadvantage lead to a decrease in collective

efficacy, thereby leading to increased neighborhood crime, which clearly is in line with

Wilson's arguments as well as social disorganization theory in general (Sampson et al.

1997).

Researchers have explored these concepts utilizing indices intended to represent the

concentration of poverty among inner city residents. They have found that measures such

as male joblessness and racial economic inequality have significant affects on urban

crime. For example Krivo and Peterson (2000) find that concentrated disadvantage is a










significant predictor of race-specific homicides. However they also note that if blacks and

whites held similar economic positions in society and similar levels of disadvantage, the

impacts of theoretically relevant measures would have more equal impacts on crime rates

for both groups (Krivo and Peterson 2000:557).

While much of Wilson's thesis rests on the concept of the changing urban

landscape and the increase of j obless males, some research has shown that the

relationship between male j oblessness and crime may be more complex. It has been noted

that while male j oblessness is an important factor in the concentration of poverty in urban

areas, racial residential segregation may be key to understanding why this is so, and is

addressed in the following section (Krivo et al. 1998).

Poverty and Segregation

Exacerbating the urban disadvantage felt among African-Americans in large cities,

Massey and his colleagues argue that residential racial segregation is the primary culprit

for the growing ghettos and underclass in America. In their book, American Apartheid,

Massey and Denton outline the history of segregation in the United States, addressing

each step and offer empirical evidence to back their claims. As Wilson notes, poverty

rates grew rapidly during the 1970's, but more importantly, poverty became more

geographically concentrated (Wilson 1987; Bane and Jargowsky 1988; Massey and

Eggers 1990).

Much of how Massey views Wilson's contentions can be summed up by the

following quote from Massey:

I agree with Wilson's main argument- that poverty concentration has increased in
U.S. cities, with pernicious consequences for minorities. I disagree, however, with
his hypothesis that this transformation was brought about by the exodus of middle-
class minority members from the ghetto and with his argument that industrial
restructuring, in and of itself, was responsible for concentrating urban poverty.









While these processes may have exacerbated poverty concentration, neither was
necessary for its creation. In the absence of racial segregation, the economic
dislocation of the 1970's would not have produced concentrated poverty or led to
the emergence of a socially and spatially isolated underclass (Massey 1990: 330).

The three primary reasons that Massey disagrees with Wilson's claims include, 1)

racial segregation in urban areas remain high and show little sign of declining, 2) as

education and income go up, the degree of black segregation does not decline, and 3)

although the degree of segregation between affluent Blacks and poor Blacks has

increased slightly during the 1970's, it is still lower than the difference between the

affluent and poor of other minority groups (Massey and Eggers 1990).

They go on to suggest that "instead of being caused by the departure of middle-

class blacks from the ghetto, however, these developments are explained statistically by a

strong interaction between the level of segregation and changes in the structure of the

income distribution". They go on to suggest that groups that experience high rates of

poverty in addition to high rates of segregation will experience the greatest levels of

disadvantage. Furthermore, concentration of poverty rose the most in urban areas that

suffered economic downturn and suffered high levels of racial segregation-such as New

York and Chicago (Massey 1990; Massey and Denton 1993).

Massey and his colleagues use hypothetical cities to illustrate how segregation

works with other factors. They argue that when racial segregation is imposed, some

Whites are better off, yet all Blacks are worse off. In addition, the problems inherent with

segregation compound as the level of segregation increases, thus as segregation increases,

so does the concentration of poverty as the area that minorities occupy becomes smaller

and smaller. Likewise as the concentration of poverty grows for the minority population,

it declines for the White population (Massey 1990).









Additionally, Massey and Denton argue that when class segregation is included in

the equation, the effects are compounded further. That is, when one considers the

differential impact of both race and class segregation, it heightens the impoverishment

felt by Blacks and actually improves the qualities for Whites. So, when racial segregation

exists in a class-segregated society, any economic "shock" (loss of manufacturing j obs,

etc.) has a profound impact on that group and in turn increases not only the overall

poverty rate, but also the concentration of poverty. This is essentially in line with

Wilson' s claims, notwithstanding the segregation quotient of course (Massey 1990;

Massey and Denton 1993).

In speaking of support for Wilson' s contentions, it is hard to separate them from

Massey et al.'s arguments. They are so intertwined that both are extremely valuable

contributions, and both receive a great deal of rigorous empirical testing. Typically,

neither Wilson's nor Massey et al.'s arguments are tested alone. That is, they are more

often incorporated into tests of social disorganization theory and general structural

analyses of crime.

Although many of the examples put forth by Massey et al. (1990, 1993) involve

hypothetical data, other researchers have found support for their conceptualization of

racial segregation. Massey and his colleagues measure segregation using the index of

dissimilarity, which represents the proportion of the minority population that would have

to change residence locations in order to achieve an even settlement pattern in any given

geographical area. This conceptualization has successfully been used in subsequent

macro-level analyses of crime for some time (Krivo et al. 1998; Massey 1990; Massey

and Denton 1993).









In his research on infant mortality rates in U.S. cities, LaVeist found that residential

segregation was the strongest predictor of Black infant mortality rates. Furthermore he

noted that while racial segregation increases mortality among Blacks, it decreases it

among Whites, providing further support for the claim that segregation not only hurts

Blacks but also benefits Whites (LaVeist 1989).

Other researchers have noted a dynamic relationship between racial segregation,

Black socioeconomic status (SES), and discrimination. They found that rising racial

segregation was positively correlated to heightened differences in Black-White

occupational differences, which as a result increased the levels of Black-White

segregation via negative correlations to Black income. Moreover, the decreasing Black

SES led to an increase in housing market discrimination, thus resulting in more

segregation. Obviously this is a complex relationship, but it clearly demonstrates the

power of this conceptual model of how racial segregation influences as well as is

influenced by many forces (Galster and Keeney 1988).

Several researchers have used racial segregation to study other topics such as

housing preferences and educational disadvantages. Supporting Massey et al.'s claim that

segregation leads not only to disadvantages for Blacks but also specific advantages to

Whites, Roscigno (1998) finds that racial segregation is significant in predicting students'

academic achievement. His results indicate that attending a Black segregated school

(>75% Black) is indicative of performing more poorly, while attending a White

segregated school (>95% White) is associated with better academic performance

(Roscigno 1998).









Racial segregation has also proved to be a significant predictor of homicides.

Peterson and Krivo examined African-American homicide victimization for many large

U.S. cities, and the impact of segregation on the homicide rates. They found that with the

exception of intra family homicides, racial segregation was a significant predictor of both

acquaintance and stranger homicide cases. This coincides with the idea that as

segregation increases, social isolation also increases, which may explain the lack of

segregation predicting intra family homicide rates (Peterson and Krivo 1993).

While both the concentrated disadvantage and social disorganization perspectives

view their constructs as being racially invariant in their impacts on crime rates (see

Sampson and Wilson 1995), thus far results have been mixed as to whether this is the

case. Shiahadeh and Ousey (1998) have noted that while a lack of low skilled jobs affects

both black and white homicides, other measures such as the percentage of renters or the

prevalence of high school dropouts may only affect black or white homicide rates

exclusively. Therefore, segregation may provide insight into why certain structural

measures are significant predictors of black but not white crime rates (or vice versa).

Krivo and Peterson (1996) have shown that while both Blacks and Whites experience

extreme disadvantage, violence rates in predominantly Black neighborhoods remains to

be significantly higher than for White neighborhoods (Krivo and Peterson 1996;

Shihadeh and Ousey 1998).

Providing further support, Krivo et al. (1998) have noted that not only does

segregation exacerbate the disadvantage experienced by Blacks in urban areas, it also

increases opportunities for socioeconomic status and employment among Whites. They

note that disadvantaged Blacks experience significantly more isolation from other groups,









thus increasing the likelihood that their interaction and contact is with other similarly

disadvantaged residents. Furthermore, they find that "j obless males are less likely to have

contact with people in high status occupations", which operates to curtail the upward

mobility we may expect providing residents are exposed to diverse role models and

experiences (Krivo et al. 1998: 74).

Similarly, Parker and Pruitt found support for racial segregation having a positive

influence on Black homicides. Interestingly, they find that this is the case only in the

West, and not in the South. They attribute this to early migration patterns, as well as the

West being known for having less economic inequality, poverty, and social isolation

(Parker and Pruitt 2000).

Racial Threat: Blalock's Power Threat Hypothesis

In his 1967 book Toward' a 7Jheory of2~inority-Group Relations, Hubert Blalock,

introduced a concept he referred to as "power threat" hypothesis, which he argued may

help social scientists better understand how and when different forms of social control are

used by the maj ority against the minority. Over the past three decades, his propositions

have received much attention, however the results are mixed as to whether empirical

support exists.

Generally, Blalock hypothesizes that as the size of a minority population grows,

those in power will sense a threat to their power. Upon the arrival of such a threat, he

argues that those in power will implement specific mechanisms of social control in order

to curtail the ensuing threat (Blalock 1967).

Specifically, Blalock argues for two distinct, uniquely behaving hypotheses

surrounding power threat. The first is the "political threat" hypothesis, which posits that

as the Black population increases over time, those in power (Whites) will perceive some









threat to their political stature and therefore utilize formal social control mechanisms

(arrest and punishment) to maintain their power. He goes on to argue that threats of this

nature appear in the form of a positive curvilinear relationship between an increasing

minority population size and increased discrimination in response to power threat, with

an increasing slope (Blalock 1967).

The second hypothesis Blalock introduces is that of the "economic threat"

hypothesis. He states that as competition over economic resources, such as job

availability, increases between Blacks and Whites, Whites will again utilize social control

mechanisms (arrest and punishment) to the advantage of those in power. He posits that

there will be a positive curvilinear relationship between discrimination resulting from

economic competition, and the slope will decrease (Blalock 1967).

Blalock also recognized the difficulty that his assertions posed in terms of

measurement and conceptualization. He states that:

Empirically determined relationships should then be a composite of these two
different forms. In many instances, such compositions will be approximately linear
in nature, so that adequate tests will require one to locate relatively "pure" instances
in which motives can be linked to behavior in a one-to-one fashion (Blalock 1967:
145).

Clearly, the level of abstraction in his ideas creates a challenge in not only

measuring these concepts, but conceptualizing them to a specific problem or scenario.

His propositions have received support in explaining a wide variety of issues related to

race and crime. These include lynching (Reed 1972; Corzine et al. 1983, 1989), arrest and

incarceration rates (Liska and Chamlin 1984; Liska 1992; Myers 1990; Crawford,

Chiricos, and Kleck 1998; Parker et al. 2005), as well as interracial killing (Jacobs and

Wood 1999).









John Reed applied Blalock' s ideas to lynching rates in Mississippi, and measured

the proportion of the Black population to assess its impact on the rates. Reed found

support for the thesis in that the more concentrated the Black population, the higher the

lynching rate (considered informal social control). However, his study would later come

to be criticized for its limited scope and lack of controlling for external factors (Reed

1972; Corzine et al. 1983).

A similar study was performed to further Reed's findings two decades later.

Corzine, Creech, and Corzine (1983) also used lynching rates in the South to test

Blalock' s hypothesis. Using data obtained from the NAACP on lynching rates from

1889-1931, in the eleven confederate states, Corzine et al. set out to unravel the entangled

relationship between the impact that percent Black had shown in past research to have on

crime in the South. They used Reed's lynching index, which he introduced in his

assessment in 1972 (Corzine, Creech, and Corzine 1983).

Their Eindings offer limited support to Blalock' s thesis, however they also illustrate

that percent Black effects were highly correlated with the specific geographic area. They

then separated two sub-regions of "deep South" and "bordering South", finding that the

power threat explanation fits better in the deep South region as compared to the bordering

South (Corzine, Creech, and Corzine 1983).

As promising as these two studies were for the prospects of the "power threat"

hypothesis, they would both come under heavy scrutiny in the late 1980's. With the

publication of a criticism of the two studies by Tolnay, Beck, and Massey, a heated

exchange occurred regarding many of the conceptual and measurement issues of both

studies. Tolnay et al. (1989) re-examined the data used in the Corzine et al. (1983) study.









They pointed to several discrepancies which existed in their data and measurements as

well as their analysis and subsequent interpretations (Tolnay, Beck and Massey 1989).

Their criticisms covered several areas, but perhaps their greatest concern was with

the deletion by Corzine et al. of counties with less than 5% total Black population, and

the inclusion of counties with greater than 80% Black of the total population. They argue

that three counties in particular, with high concentrations of Blacks in the population and

a unique structure of lynching during this time skew the results presented by Corzine et

al. They offer an analysis based on approximate data that closely resembled that used by

the Corzine et al. study (from the same data bank at NAACP), and they report to find

substantially different results. In addition to these data shortcomings, they point to the

inconsistency and inaccuracy of the NAACP data, in that they found several instances

where the wrong case was recorded, etc. (Tolnay, Beck and Massey 1989).

In addition to these criticisms, they also take issue with Reed's lynching index.

Relating back to the three outlying counties mentioned above, they argue that these

counties exacerbate Reed's index and can potentially inflate the findings that percent

Black is a significant predictor of lynching rates in the South. This is due in part to the

fact that two of the counties that were outliers in the Corzine et al. study, also resided in

the data utilized by Reed in his original study (Tolnay, Beck, and Massey 1989).

Additionally, they argue that Reed' s index doesn't represent anything that a rate

should. That is it (admittedly by Reed) doesn't represent the rate of lynching compared to

the population, but rather it represents the rate of lynching, as compared to the expected

rate of lynching. This, they contend, inevitably causes Reed's index to automatically









assume the slope predicted by Blalock, and that it is unable to differentiate between two

scenarios (Tolnay, Beck, and Massey 1989).

These criticisms prompted commentary from Corzine et al., Reed, as well as

Hubert Blalock. The response by Creech et al. to claims of "trncating data" by

eliminating certain counties, while keeping others with no conceptual or theoretical

justification, is simply that they had theoretical justifications for doing so due to the

scope of the power threat hypothesis. That is, the power threat hypothesis is not a general

theory of crime, criminal behavior, or discrimination. Instead it is a fairly narrow and

specific hypothesis predicting when discrimination is most likely to increase based on the

proportion of the minority population (Creech, Corzine, and Huff-Corzine 1989).

In response to the critics' claims that the Reed index was inappropriate, Creech et

al. admit that the index is not perfect (what index is?), and that they had considered others

at the time and settled on Reed's as it best captured the measure for testing Blalock' s

hypothesis. They argue that the alternative measure provided by Tolnay et al. ignores the

racial composition of the county, thus making it difficult to discuss any implications in

terms of power threat hypothesis (Creech et al. 1989).

John Reed's response is much less detailed. He does however declare his

disappointment with the data collected by the NAACP. However, in his defense of the

data, he argues, and rightly so, that data to test Blalock' s hypotheses rely on areas with

heavy concentrations of Blacks where lynching had occurred. If the data that were

available had not been used, there simply would not have been a test of the hypothesis,

because due to the nature of the hypothesis, Mississippi data was crucial to even having

the opportunity to apply Blalock to lynching rates in the South. He also points out a









quotation by Blalock regarding his original hypotheses and the difficulty in testing them.

He states: "inadequate data, the infrequency of lynching, and certain methodological

difficulties have prevented [us] from obtaining definitive results" (Reed 1989).

The response by Blalock is largely methodological, yet brief. He offers many

suggestions if choosing different denominators for creating ratios and measures of such

items. He ends his short reply with saying that: "In this instance we are involved with a

cross-level analysis problem, where the theory refers to the micro-level (perceived threats

and loosely coordinated lynching behaviors), whereas data have been aggregated at the

macro-level." This presents a problem similar to that of the ecological fallacy, he goes on

to state that when testing these ideas further, caution must be taken to carefully construct

a theoretical model that clearly specifies the assumptions in order to select a suitable

denominator for creating ratios (Blalock 1989:633).

Crawford, Chiricos, and Kleck use "racial threat" as a means to better understand

sentencing under Florida' s habitual offender laws, based on race. They conceptualize

racial threat as threatening to "mainstream America" as well as "political elites". They

argue that racial threat has become increasingly prevalent, especially with the media

frenzies depicting juvenile violence as "ghetto pathologies" as well as the images of

crack cocaine potentially spreading to previously "safe places" (Crawford, Chiricos, and

Kleck 1998).

They utilize data from Florida during the period of 1992-1993, involving

sentencing of "habitual" offenders. The data file consisted of 9,690 "habitual eligible"

offenders to determine to what extent race contributed to being sentenced as such. In









order to single out race effects, they controlled for demographic characteristics of the

offender, legal attributes, as well as the county in which they were sentenced.

One limitation that they state up front is their lack of data on race of victim, which

is used in much of the racial threat literature. The most interesting finding perhaps in light

of racial threat is that of drug offenders. They argue that a racial threat argument may

help to explain the strong effect of race in the habitualization of offenders. Nearly all of

these were cocaine offenders, which is precisely what the media frenzy in the 1980's

focused on almost exclusively regarding drugs and crime. These findings were similar for

both drug possession as well as dealing/trafficking offenses (Crawford et al. 1998).

An interesting paradox arises in their research relating to racial threat. Where the

race variables showed the most strength were also the areas that would traditionally be

viewed as low in racial threat. That is, they were in communities below the median in

percent Black, racial income inequality, and so on. This seems at odds with racial threat

on the one hand, yet on the other, it may contribute to our understanding of racial threat

at the same time. While traditionally it was argued that racial threat occurred primarily in

the areas of highest concentration, it could be that these areas deemed "safe" and free of

crime and drugs, are even more punitive (especially considering the images in the media

portraying crack coming to a neighborhood near you) (Crawford et al. 1998).

Eitle, D'Alessio, and Stolzenberg (2002) propose three separate hypotheses. They

indicate a need to look at political racial threat, economic racial threat, as well as Black

crime racial threat. Using NIBRS data from South Carolina, they measure political racial

threat as the ratio of Black to White voters during the 1992 and 1994 elections in South

Carolina, economic threat as White to Black unemployment, and Black crime threat as









the influence of Black on White crime on Black arrest levels, where Black on White

crime is the percent of violent felonies reported to the police where the offender was

Black and the victim was White (Eitle et al. 2002).

Controlling for other contextual factors such as White/Black divorce ratios, violent

crimes in area, and population density, they found no support for either political threat

nor the economic threat hypotheses. However, they do Eind strong support for Black

crime threat. Furthermore, in counties with a higher percentage of Black on White crime,

Blacks are significantly more likely to be arrested. This finding was robust, even in the

model containing all of the control variables (Eitle et al. 2002).

The power threat hypothesis has evolved substantially since its early

conceptualizations by Blalock. Since its earliest tests by Reed and Corzine et al., the

perspective has been improved upon greatly. Many taking note of the methodological

issues associated with the early studies, clearly has had some impact. Eitle et al. stated

that their goals were to better synthesize the conceptualizations as to limit confusion,

since it' s often referred in several manners.

Through the use of NBRS data, Eitle and colleagues may have discovered a

unique and accurate way of measuring and testing the threat hypothesis, without

distorting or having to stretch too far the measurement issues that have surrounded this

approach in past studies (see also Stolzenberg et al. 2004). Clearly, having data on race of

both offenders, as well as victims, provides a dynamic way of conceptualizing racial

threat. The Crawford et al. study offers another unique direction in that those areas that

perceive great racial threat may in fact be those with the least concentration of Blacks,










and of crime in general (e.g., middle class suburbs, where the images of "crack babies"

still persist).

More recent studies of racial threat have attempted to move beyond the myopic

measures of percent black of the population. More recent tests of Blalock' s hypotheses us

other measures such as racial inequality and black immigration patters as proxies of racial

threat (Stolzenberg et al. 2004; Parker et al. 2005). Findings indicate that contrary to

Blalock' s hypothesis, percent of the population that is black and racial inequality were

found to have a negative affect on black arrest rates. One explanation that has been

asserted is the "benign-neglect hypothesis" by Liska and Chamlin (1984). They argue

that in areas with a large black population, crime is more likely to be intra-racial. As a

result, there is less pressure on police to control crime because non-white victims are less

likely to report crime or, even when they do report crime, police may allocate fewer

resources to resolve the offense (Liska and Chamlin 1984).

Conclusion

In light of the preceding discussion of the prevailing macro-level theoretical

perspectives on crime and delinquency, the ways in which these theories have been and

will continue to be tested has not been consistent to say the least. For the purposes of this

study, it is better to explicitly state that I am attempting to account for the rise in race-

specific drug sales arrest rates over time, using structural factors of large urban areas.

That is, I am not necessarily setting out to explicitly "test" social disorganization theory.

To do so would require conducting large scale interviews, surveys and focus groups in

large urban areas to truly assess to what extent these urban areas are able to exercise

informal controls over its residents.









Rather, what I am attempting to do is to choose theoretically relevant structural

predictors as proxies for the larger concepts of social disorganization and other ecological

theories of crime. This is important as many of the frequently utilized measures of

structural factors as they relate to crime and delinquency are "shared" across theoretical

paradigms. For example Pratt and Cullen (2005) point out that:

Researchers have used measures of unemployment as proxies of guardianship
(routine activity theory) and economic hardship (economic/resource deprivation
theory), as an indicator in the breakdown in the viability of community
control/socialization (social disorganization theory), and even as a precursor to
frustration-induced anger (anomie/strain theory) (Pratt and Cullen 2005: 430-431).

My reasoning for choosing these three theories specifically is twofold. First an

analysis such as this has not yet been conducted, so it adds a significant contribution to

the research examining these factors on changing drug arrest rates over time. Second

these three theories not only allow for race-specific changes over time, but as Pratt and

Cullen note they are among the most consistently significant predictors of crime in urban

areas. As Messner et al. (2005) have suggested, the changes that cities in regards to both

drug arrests as well as homicides has changed significantly over the past several decades.

Additionally Levitt (2001) suggests that the declining crime rate during the 1990's is

partially attributable to the decline in crack markets in large cities. While these

approaches note the importance of accounting for changes over time, Levitt in particular

considers drug crimes to be an independent (explanatory) variable of crime rates.

Therefore the changes in urban areas during this same time period creates a dynamic

relationship where the changes in theoretically relevant macro-level indicators may be

related to the changes in drug sales arrest rates over time (Levitt 2004; Messner et al.

2005).









This is especially true for African-Americans in large urban areas. As Wilson

(1987) has argued that black families have been especially impacted by these industrial

shifts. The theories selected for this study allow not only for change in general, but

especially allow for modeling change for blacks specifically, across multiple decades for

multiple cities. While the discrepancies in Black and White crime rates have been well

documented in past research, no other crime does this difference stand out more so than

for drug crimes.















CHAPTER 3
REVIEW OF THE LITERATURE

Introduction

While variations of the structural theories presented above have received

considerable empirical testing, little attention has been paid to addressing structural

explanations of drug offenses. These theories have all been used to help us explain both

violent and property crimes (Crutchfield and Pitchford 1997; Liska et al. 1998; Krivo and

Peterson 1996, 1998, 2000; Parker and McCall 1997, 1999; Sampson 1987 Shihadeh and

Ousey 1998), and have only recently began to garner the attention of researchers

interested in drug crimes in large urban areas (see Mosher 2001; Parker and Maggard

2005). This chapter seeks to offer a historical overview of drug use and the laws created

to control their use in the United States. It is important to understand the historical

connections between drug use, public policy and crime in order to better apprehend why

our country is in the position that it is today. Finally, I will offer a brief discussion of the

merging of the theories and how tests of each theoretical construct are not mutually

exclusive from the other theories as well as provide several hypotheses to be tested.

Race, Drugs and Crime: A Historical Perspective

Any discussion of drugs and crime needs to account for the historical landscape

that surrounds drug use in this county as well as the way that we have chosen to deal with

these issues. We must ask ourselves, how did we get here? Here referring to the fact that

in 2004 there were an estimated 1, 745, 712 drug violation arrests in the United States

(U. S. Department of Justice 2004). While ordinances and legislation dates back more









than 100 years, we continue to find ourselves arresting millions of individuals year after

year. We must also take into account the roles that the media, doctors, minority groups,

as well as the pharmaceutical industry play in this expensive game that is illicit drug use.

Drug use in this country is best understood beginning in the 1800's. Opiates were

used in medicine in the early years and while addictive, offered medical uses that suited a

variety of needs. It was the discovery of heroin during this period that prompted doctors

to believe they had found an iteration of opiates that was nonaddictive and yielded few

side effects. This belief lasted only about five years, before it became apparent that heroin

did in fact possess the same addictive properties of other opiates (Conrad and Schneider

1980). Heroin and opiates were later characterized as recreational drugs, yet

pharmaceutical companies continued including opiates in a variety of medications,

despite the risks of addiction, among others. Although the drug industry continued to use

opiates in these formulations, the press was reluctant to criticize the pharmaceutical

companies for fear of losing advertising dollars and future revenues. Instead, the media

portrayed users of opiates in a negative manner, since criticizing these companies had

financial risks; the media began to portray users of opiates in a derogatory manner, for

"powerless groups could be criticized without such caution" (Tieman 1981: 242).

The power of the media may never be quite as transparent as is realized while

peering through the sociological lens at American drug use. During this same period

cocaine use was beginning to be attributed to African Americans in particular. The media

presented an image of "cocaine-crazed" black men who upon the ingestion of cocaine

experienced greatly increased sex drives and desires. These desires were said to increase

their likelihood of raping white women, which instilled fear among citizens. Further, it









was reported that as African Americans used cocaine, they experienced greater visual

clarity, which significantly increased their marksmanship with firearms. Perhaps the most

outrageous media reports rest on the assertions that cocaine made black men less

vulnerable to .32 caliber bullets. While this seems outrageous today, police agencies

actually began switching to .38 caliber handguns to combat the dangerous cocaine crazed

black men (Musto 1973: 7-8; McBride 1981: 105-124; Smith 1986). While these

derogatory portrayals of African Americans committing crimes while under the influence

of cocaine are preposterous, more utilitarian uses of the drug were also circulated in the

media. For instance, the M~edical News reported that its use in New Orleans made it

possible for workers to work seventy hour shifts loading and unloading steamboats, that

otherwise may not have been able to be accomplished without the use of stimulants

(Spillane 2000).

African Americans were not the only minority group to catch the attention of the

media and the government. During the late 1800's Chinese immigrants, many of whom

were working on the railroad systems found employment in California among other

locales. Their opium smoking behavior led to an 1875 ordinance passed in San Francisco

"aimed at the un-American habits of the 'yellow devils'" (Brecher 1972: 42-44). These

negative portrayals are not unlike other "dangerous classes" such as prostitutes and

vagrants (see Adler 1986 and 1992).

This stigma emerged to stick not only to minorities but also to whites that had

adopted opium-smoking habits (Lindesmith 1968: 215). These casual opium users were

described as "gamblers, thieves and prostitutes", who were not legitimately receiving

medicine from doctors. These images of casual drug use eventually led to the increasing









restrictions placed on opiates which coincided with the 1) rise in patriotism in America,

2) an increasing fear of minorities and 3) what had become an estimated addict

population of 250,000 (Musto 1973: 5, 33).

This was a critical time in American history. While we wished to decrease opium

use among residents and immigrants alike, our immigration doors were growing as we

expanded and acquired new territories. It was important for the United States to remain

vigilant in its abhorrence for drug use with the Chinese government and it was quickly

realized that we would not be able to persuade other nations to curb drug use if we did

not enact specific legislation at home (Duster 1970: 14; Musto 1973: 36).

As one of the first measures to control drug use at the federal level, the Harrison

Act, passed by Congress in 1914 paved the way for the future of drug control in the

United States (Musto 1973; Spillane 2004). While supported by many, the Harrison Act

was not free of criticisms by supporters and critics alike, including then Speaker of the

House Oscar W. Underwood "...who had shepherded the Harrison Act through the house

the previous week, described the prohibition amendment as a 'tyrannous scheme to

establish virtue and morality by law"' (Musto 1973: 67). Sentiment such as Mr.

Underwood's notwithstanding, the next four decades would see attention focused on

enforcement and drug users were characterized as antisocial or psychopathical addicts

who chose addiction that "normal" people would never choose (Tieman 1981).

This trend continued for much of the coming century with the next maj or event

being the formation of the Federal Bureau of Narcotics (what is now the Drug

Enforcement Administration) in 1930 and the appointment of its over-zealous leader

Harry J. Anslinger. Anslinger abhorred drug use and demanded that his agents focus on









street peddlers of drugs in American cities (Tieman 1981; Carroll 2004). Anslinger was

able to persuade citizens of the United States that drug sellers and users alike were

criminals, and should be treated as such, and from the 1930's forward, drug use was

strictly a law enforcement issue versus a public health issue. Anslinger and his

subordinates were able to perpetuate the idea of strong linkages between drug use and

violent crime through telling fabricated "horror stories" (Carroll 2004).

While Anslinger directed most of the Federal Bureau of Narcotics' attention on

"hard drugs" (opiates and cocaine), he paid little attention to marihuana and did not

consider it a serious problem, at least not one that required the intervention of the federal

government (Carroll 2004). Anslinger even testified to Congress that marihuana did not

cause addiction and could be handled at the local level if problems persisted with the

substance. However Anslinger' s true political identity would emerge as rumors began

circulating on Capitol Hill that a new bill could dissolve the FBN and shift enforcement

of drug laws to other government agencies. At this point, marihuana was the obvious

vehicle he needed to instill hysteria and leverage the idea that marihuana was a "menace"

that needed to be dealt with and this eventually led to the Marihuana Tax Act of 1937.

Anslinger even began referring to marihuana as hashish (an extract from female

marihuana flowers), since it seemed to be so closely associated with the word assassin

(Musto 1973; Carroll 2004).

Anslinger' s role in shaping federal drug control policy would remain steady for

many years to come (Tieman 1981; Carroll 2004). While little scientific evidence was

presented linking marihuana use and drug use to criminal behavior, Anslinger offered

many "commonsensical" accounts by which addicts would resort to crime (Galliher and









Walker 1977:375). Until the end of the 1940's, addicts remained relatively uninvolved in

criminal behavior, as costs for drugs were low and many were able to use physicians for

their personal needs (O'Donnell 1969).

Perhaps the "line in the sand" drawn to make clear that drug use would be a

criminal justice issue versus a public health issue occurred through the Boggs

Amendment of 1951 and the subsequent Narcotic Control Act in 1956. These laws would

establish mandatory minimum sentences for drug offenses and were meant to emphasize

the seriousness of such offenses and how law enforcement would address them.

Anslinger was able to re-emphasize his beliefs that addicts were merely "parasites" as he

stated:

The person is generally a criminal or on the road to criminality before he becomes
addicted. Once addicted he has the greatest reason in the world for continuing his
life of crime (Anslinger and Tompkins 1953:170).

It was not monetary reasons that Anslinger argued would lead to the criminal career, but

rather that using drugs would erode the "moral fiber" of individuals in general. This

pattern of connecting drug use to criminal behavior continues to this day (Tieman 1981).

Anslinger' s tactic of demonizing marihuana to pave the way toward criminalizing

its users was carried forward to future eras and specific drugs over time. Indeed the media

would also play an important role in shaping the legislation that led to the "drug scares"

of LSD in the 1960's, PCP in the 1970's, crack in the 1980's, and MDMA or Ecstasy in

the 1990's and 2000 (Goode 2005). Were many of the horror stories true regarding the

toxicity of drugs such as PCP? Sure they were. PCP is a dangerous drug, but what the

media can contribute to is the misrepresentation of the extent of a drug' s use. As with

other "hard drugs", most users are not casual marihuana users one day who decide to









become hard-core drug users the next, but rather they have already become enveloped in

a hard-core drug scene to begin with.

Race, Drugs and Crime: The New Era

While the focus of my research is on drug sales arrests from 1980-2001, it was

important to point out how the shifts and oscillations in America's responses to drug use

have affected the very arrest rates that I am investigating. With each of the

aforementioned "drug scares", mass hysteria and over reaction has accompanied virtually

each one (Reinarman and Levine 1997). The onset of crack in American society was a

maj or event, and politicians were able to use this "drug scare" as an avenue to create

specific laws aimed at controlling its use, not unlike the Harrison Act in 1914 and other

drug legislation discussed above.

In 1982, President Ronald Reagan officially declared a "War On Drugs" (Sacher

1997). Shortly after this, Congress enacted the Sentencing Reform Act of 1984 (SRA),

which reportedly had the sole intent of establishing strict guidelines for federal

sentencing. This was an effort to promote fairness and uniformity in federal cases by

establishing mandatory minimum sentences for specific offenses. Following the 1984

SRA bill, the Anti Drug Abuse Acts of 1986 and 1988 substantially altered the way in

which we would come to treat drug offenders brought to federal court on charges of both

possession and trafficking of cocaine (Sacher 1997; Zedeck 2000).

During the period that I am focusing on, crack cocaine is the most salient drug

related event that occurred which resulted in unprecedented drug arrests in the United

States. Drug arrests rose from 18,521 to 88,641 respectively from 1980 to 1988 in New

York City alone (Belenko et al. 1991; New York City Police Department 1989). The

importance of studying arrest rates disaggregated by race are clear as Blumstein (1993)









notes that although White arrest rates remained relatively stable from the 1970's through

much of the 1980's, non-white drug arrests steadily climbed from 1980-1985, growing

exponentially at a rate of 15%-20% per year until peaking in 1989 (Blumstein 1993).

A similar portrait was seen at the national level where the number of drug arrests

in 1980 was just under 600,000 and by 1990 had risen to roughly 1.1 million total drug

arrests (representing nearly a 92% increase over a 10 year period). More striking however

are the racial shifts that occurred during this period. In 1980 African-Americans made up

about 25% of all drug arrests in the United States and by 1990 this figure had increased to

41% of all drug arrests (Goode 2005; U. S. Department of Justice 1996).

While it has been argued that the rising drug arrests during the 1980's was directly

attributable to crack cocaine, it is important to look at who was using crack cocaine and

the potential size of those markets. The demographic characteristics of crack users

sharply differ from their powder cocaine-snorting counterparts. Those who abuse crack

cocaine, much like daily heroin users, are likely to be urban poor. Moreover, they are

likely to have been hard-core drug users in the past, for most crack users do not start off

with crack cocaine. Rather, they are either hard-core heroin addicts or cocaine abusers,

looking for a better, more convenient high. It is important to note that even during its

most popular year, only a small percentage of those who used cocaine, used crack (Golub

and Johnson 1996; Reinarman and Levine 1997).

While crack cocaine is primarily prepared and distributed by African Americans, it

is also used and abused by Whites at even higher numbers. Although data do suggest that

a higher proportion of the Black population use the drug when compared to Whites.

However, though Whites make up the maj ority of crack users, Blacks continue to make









up more than 80 percent of those convicted for distribution of the drug (Gray 1998;

Zedeck 2000).

Each time a relatively unknown drug gains popularity, there are undoubtedly

outrageous exaggerations of its use and it becomes the next "scary drug of the year"

(Akers 1990). Typically these reactions are also accompanied to knee jerk hypotheses

regarding the new drug and its relationship to violence and crime in general (Goode

2004). In fact, one could certainly argue that as new drugs begin to garner the attention of

the mass media, their very role is "socially constructing" how the drug will come to be

viewed (Hartman and Golub 1999). The role of language and the mass media in shaping

attitudes toward both people as well as inanimate obj ects is often underestimated (see

also Berger and Luckman 1966).

One of the more prominent theories in regards to drugs and crime/violence is Paul

Goldstein' s tri-partite framework. His conceptual model of the relationship between

drugs and crime/violence consists of three mechanisms by which drugs and crime are

related. First, the psychopharnacological he argues is crime and violence that results

from the actual physical effects of the drug on the user. For example, taking a certain

drug may cause a person to lack judgment which may lead to crime and/or violence.

What he calls the economic compulsive mechanism is when individuals must commit

crimes in order to finance expensive drug habits. Finally, systemic crime/violence is the

result of any number of circumstances related to drug use and drug markets. For example

the black markets that drugs are typically peddled in are often territorial and controlled by

gangs which may lead to violence between rival gangs. In another example offered by









Goldstein and his colleagues, individuals may "punish" someone who sells them "bogus"

drugs in order to teach them a lesson (Goldstein 1985; Goldstein et al. 1991).

Goldstein et al. tested these concepts and found that alcohol was the substance most

correlated to the psychopharnacological model. In terms of the systemic model, heroin

markets were the most likely culprit in determining crime and violence for the areas in

which they studied. Finally cocaine was the strongest substance related to the economic

compulsive mechanism, however the numbers were quite small. Clearly, using the

reasoning that was used to create the new laws aimed at crack cocaine, one would expect

crack and/or cocaine in general to dominate all three categories in research such as this

(Goldstein et al. 1991).

History has shown us that officials often overreact to the introduction of new drugs,

especially when they are primarily associated with minorities. However, in line with

Goldstein's ideas, Blumstein (1995) has argued that drug markets such as the crack

cocaine market greatly contributed to the diffusion of firearms into the hands of youth. It

is further argued that the volatility of crack markets, coupled with the lower inhibitions of

inner city youths would lead to the increase of gun crimes and homicides within

American cities. While not immediate, Blumstein argued that as crack markets spread

across the country, a short lag would occur and we would Eind that homicides and gun

related violence would follow in those urban areas (Blumstein 1995).

In support of Blumstein's hypotheses, crack and cocaine have been linked to

different types of crime in large cities. Baumer (1994) found that cocaine use by arrestees

was a significant predictor of robbery rates and a more modest predictor of homicide

rates in large cities, even while controlling for structural characteristics such as poverty









and divorce rates. Of course whether the correlation is due to the dynamics of the drug

market or the drug itself is unclear. Similarly Ousey and Lee (2004) found that changing

drug markets are linked to the rise in both White and Black homicide rates in large cities.

However the relationship for is quite larger for Blacks than Whites, with structural factors

such as inequality having a much stronger impact on Black homicide rates than for White

homicide rates (Baumer 1994; Ousey and Lee 2004).

In another study, Cork (1999) also found support for Blumstein' s theory. More

specifically he found that the relationship between crack markets and their linkage to the

availability of guns is quite realistic. While the relationship is dynamic and complex, he

also notes that the increases in gun related homicides committed by juveniles, typically

occurred shortly after the emergence of crack markets in those areas. Also worth noting is

the fact that while slightly older homicide offenders diffused in a similar manner, they

were not as pronounced as with younger perpetrators, as youth have been said to lack the

empathy or concern for strangers as older individuals may (Cornell 1993; Cork 1999).

These studies are good examples of the way in which researchers have approached

the problem of drugs in the literature. It has been more common to use crack and cocaine,

or even heroin or alcohol as explanatory variables in determining crime rates in general.

That is, little research has focused solely on explaining the rates of drug arrests as much

as they have focused on property and violent arrest rates. Given the structural changes

across cities in America, as well as the increase in drug arrests, clearly this is worthy of

further research (see Mosher 2001; Parker and Maggard 2005).

Much of the research examining the crime drop that occurred across America in the

1990's has also noted the important role that crack cocaine markets in particular played in









this decline (Levitt 2004). This is interesting because while we do not want to unjustly

punish the behavior of one racial group compared to others, we also have an obligation to

protect the citizens in urban areas where much of these crimes are centered. It is these

reasons that we expect not only for drug arrests to have risen so sharply from 1980-2001,

but also expect Blacks to have incurred much more dramatic rises proportionally

compared to Whites.

Hypotheses and Expectations

Having reviewed prior theoretical research as well as reviewing the historical

aspects of drug use and arrests, I have generated several theoretically relevant hypotheses

and expectations in this study. The first set of hypotheses surrounds the trends in race

specific drug arrests over time while the second set of hypotheses relates specifically to

the structural factors discussed earlier and how they may be directly related to the trends

in drug arrests that appear in the data.

As noted earlier drug arrests have seen many fluctuations over the past century.

However no era has reflected as much change as the period beginning around 1980 and

extending to this day in some respects. The implications for race are even more

exaggerated, as African-Americans have been disproportionately targeted and arrested as

a result (Tonry 1995). Prior research on drug arrests trends have traditionally been at the

decennial level in regards to changes over time. Researchers have neglected to attempt to

group similarly behaving cities over time, as they relate to racially disaggregated drug

arrests. Therefore by applying the TRAJ procedure to such data, it is possible to model

whether particular cities and regions behaved as expected over these time periods. As

Blumstein and Rosenfeld (1998) have noted, the changes in homicide rates have differed

for "coastal cities" versus cities that are more inland, which they argue is consistent with









the patterns Golub and Johnson (1997) found to surround the crack epidemic (Blumstein

and Rosenfeld 1998; Golub and Johnson 1997). Given this past research on homicides

and the crack epidemic specifically, I draw the following two hypotheses in regards to the

expected traj ectories for drug sales arrests over time:

H1: I hypothesize that race specific drug sales arrest rates will significantly vary
over time, forming identifiable traj ectories distinguishing cities which experienced
many drug arrests from those that did not.

H2: The witnessed traj ectories will differ significantly by race, indicating that black
drug sales arrests accelerated at a much greater pace from 1980-2001 than did
white drug sales arrests.

These expectations arise from reviewing past work examining the relationship

between a city's structural characteristics and drug arrests over time. For example,

Mosher (2001) notes that the rise in drug arrest rates across cities is profound. This effect

is exacerbated by using the mean examine changes over time based on a pool of cities

that differ greatly in terms of arrests and structural characteristics. Parker and Maggard

(2005) report that black total drug arrests rose by about 211% (versus 106% increase

among whites) from 1980-1990 with similar patterns witnessed for possession arrests, yet

for drug sales arrests the racial gap narrowed with a 236% increase in black drug sales

arrests versus 193% increase for whites (Parker and Maggard 2005). Mosher' s finding

coupled with the differences noted by Parker and Maggard lead one to believe that it is

feasible that the mean increases witnessed in past research could have been inflated by

outliers, especially for drug sales arrests. In other words, most cities may not have

experienced "epidemics" of drug sales and use during key years, yet a few of these cities

may have experienced unprecedented problems surrounding drugs and crime which

resulted in highly skewed descriptive statistics. This is especially plausible given

Jargowsky and Bane' s findings that only 10% of the cities they studied accounted for









75% of the rise in ghetto poverty throughout the 1970's, indicating a few radically

changing cities may paint much of the picture of the nation for any given topic

(Jargowsky and Bane 1991).

The second set of hypotheses stem from theoretically drawn expectations of the

structural conditions of urban areas and their impact on fitting into specific traj ectories as

mentioned above. Since I draw upon multiple structural theories of urban crime in

constructing these hypotheses, some are not necessarily "theory specific". However the

directions that I predict clearly rest on particular theories and their expected relationships

to urban crime. For example, Pratt and Cullen (2005) note that the expected relationship

between unemployment and crime may be positive direction for social disorganization or

anomie based theories, while predicting an inverse relationship under the umbrella of

routine activities theory (Pratt and Cullen 2005).

Social Disorganization and Drug Sales Arrests

Based on Shaw and McKay's original hypotheses as well as past research, I expect

the rising disorganization of urban areas to result in both an increase in both white and

black drug sales arrests over time (attributable to the racially invariant predictions of

social disorganization theory). Recall that social disorganizations theory posits that the

disorganization of an area results in a breakdown in informal control mechanisms. This in

turn is expected to result in higher crime rates, regardless of who resides in an area. It is

this racially invariant nature of social disorganization theory that leads to the following

two hypotheses:

H3: Cities that experience an increase in family disruption (via the percentage of
divorced males) for both blacks and whites will increase the likelihood of those
cities belonging to a higher arrest traj ectory as opposed to the lowest arrest
traj ectory for drug sales arrests.









H4: Those cities that experience increases in residential mobility will increase the
likelihood of those cities belonging to a higher arrest traj ectory as opposed to the
lowest arrest trajectory for drug sales arrests (invariant across race).

Concentrated Urban Disadvantage and Drug Sales Arrests

Wilson (1987) argues that the deindustrialization of large urban areas has had an

unprecedented affect on the lives of African-American residents in those cities. Massey

and Denton have extended these ideas to include the role that racial segregation has in

concentrating poverty in particular areas. Research has shown that this is the case in

many instances for both violent and property crimes. However there is good reason to

believe that the decreasing availability of low-skilled jobs would lead to an increase in

drug sales arrests, especially for African-Americans. Elij ah Anderson' s ethnographic

research on urban areas does an excellent j ob describing how the illicit drug trade and

poverty are intertwined in a very complex way. Anderson writes:

In the impoverished inner-city neighborhood, the drug trade is everywhere, and it
becomes ever more difficult to separate the drug culture from the experience of
poverty. The neighborhood is sprinkled with crack dens located in abandoned
buildings or in someone's home. On corner after corner, young men peddle drugs
the way a newsboy peddles papers.

As Anderson illustrates the difficulty in separating urban poverty and the illicit drug

trade, the urban disadvantage literature presented above leads me to several hypotheses.

With black neighborhoods becoming increasingly segregated and the concentration of

poverty increasing, it should be no surprise that some individuals were coerced, so to

speak to sell illicit drugs. The lure of high profits, designer clothing, and the ability to

support one's family proved too tempting to pass by. Additionally the open-air nature of

crack markets left inner city drug sellers especially visible to law enforcement agencies.

These factors lead to the following two hypotheses:









HS: An increase in residential racial segregation (measured as the Index of
Dissimilarity) will increase the likelihood of belonging to a higher arrest traj ectory
as opposed to the lowest arrest traj ectory for black drug sales arrests.

H6: An increase in concentrated disadvantage (e.g., poverty, income inequality,
male j oblessness, and percent children not living with both parents) will increase
the probability of cities belonging to a higher arrest traj ectory versus the lowest
arrest traj ectory for both Black and White drug sales arrests.

Racial Threat and Drug Sales Arrests

Hubert Blalock' s power threat hypothesis states that as the minority population size

grows, those in the dominant group will begin to feel threatened and be more likely to

utilize formal social control mechanisms (e.g. arrests). Moreover as those in power begin

to utilize formal controls more liberally, arrest rates for the minority group should rise

over time. Drawing both on Blalock' s ideas, I am left with the following hypothesis:

H17: Cities that experience a significant rise in the proportion of the population
identifying themselves as black, will be more likely to belong to a higher traj ectory
group versus the lowest traj ectory group for Black drug sales arrest rates.

Conclusion

These variables are also among the list of strong structural predictors that Pratt and

Cullen argue are necessary for any study seeking to link macro-level structural conditions

of urban areas to crime rates. In their meta-analysis, they discovered that out of thirty-one

commonly used measures of structural covariates of crime, many of those listed above

remain in the "top ten", especially considering both the strength and stability of the

predictors across various studies (Pratt and Cullen 2005). Additionally as Pratt and

Cullen have noted, some of these concepts are not theory specific. In fact one could argue

that there is a movement away from testing specific theories and instead grouping

commonly used structural covariates to gauge the overall importance of structural

landscape of urban areas on crime rates (see Parker 2004).









Having presented several hypotheses for testing, the next several chapters will

intend to shed light on the importance of not only being able to identify traj ectories of

drug arrests over time, but also which structural covariates may predict being in a higher

versus lower traj ectory. Chapter four will present the data used to test these hypotheses

with the remaining chapters devoted to presenting the results of the analyses.















CHAPTER 4
DATA AND METHODS

Introduction

As discussed in prior chapters, structural theories have been used to explain many

types of crimes. Drawing upon the most widely and rigorously tested measures, I

describe these measures in the following sections. Following the meta-analyses of Pratt

and Cullen (2005), I have selected what have proven to be the most reliable and

theoretically relevant structural predictors over time. In addition I have chosen predictors

that Pratt and Cullen argue have had few (if any) serious methodological issues

throughout their empirical history (Pratt and Cullen 2005).

Since this research utilizes city-level analyses, all measures are operationalized as

aggregates at the city level. Both dependent and independent variables have been

constructed to represent the boundaries of American cities. This is consistent with past

research linking structural conditions to urban crime.

Data Sources

The unit of analysis is U.S. cities, beginning with those with a population greater

than 100,000 residents in 1980. While some debate has occurred regarding the best unit

of analysis (see Messner 1982; Bailey 1984; Messner and Golden 1992) for studying

structural covariates of crime rates, the city makes the most sense for a study of this kind.

First as Chamlin (1989) notes, aggregating police data across jurisdictions to the Standard

Metropolitan Statistical Area (SMSA) increases the risk of inflating these statistics (Liska

1989). Second, using city level data allows us to investigate the concentration effects









noted by Wilson (1987), where he contends that the effects of these structural factors

were due to the changes in the spatial and industrial structure of such areas (Wilson

1987). Finally Land and his colleagues (1990) argue that it makes little difference which

unit of analysis is selected (SMSA, City, etc.), providing proper statistical techniques are

employed to address the research question (Land et al. 1990).

This sampling strategy generates 179 cities in 1980 and these cities were the basis

for selecting cities for the entire sample in order to establish traj ectories for a nested

sample of cities. There are five maj or sources of data for this research. For the dependent

variables, the data are Uniform Crime Report arrest counts obtained from Chilton and

Weber (2000). The second and third major data sources are the 1980, 1990, and 2000

Census ofPopulation and Housing; Both the Characteristics of the Population and the

Social andEconomic Characteristics volumes (U.S. Bureau of the Census 1983; 1994;

2004). The fourth data source consists of the Bureau of Justice Statistics' Census of State

Adult Correctional Facilities ( 1979) and the Census of State and Federal Adult

Correctional Facilities (1990 and 2000). Finally, the Uniform Crime Report serves as the

source of information on the number of police officers in each city for 1980, 1990, and

2000. These data sources are essential to this research because they provide comparable

indicators during the time periods of interest (1980-2001 ).

Dependent Variable

This study utilizes race-specific drug-sales arrests based on continuous years from

1980-2001. I acknowledge the problem of missing data for one or more months of data

per year across the dependent variables. In those situations in which data were reported

for at least 9 or more months, mean substitutions were imputed to correct for missing data

on the remaining months. Arrest data for those cities reporting less than 9 months of









arrest statistics (i.e., 8 months or less) were considered missing. I also constructed a

dummy variable to indicate whether this mean substitution was performed in order to

determine whether it had significant impact on the data and results. The dependent

variable is computed as the race-specifie drug-sales arrest rate (arrests per 100,000

population) from 1980-2001. This was calculated as follows:

~ ~,,,,,= i RaceSpe cificDrugSalesA arrests "0,0
RaceSpecificPopulation

In addition to missing data from cities that failed to report all twelve months of

each year, it was also found that not all 179 cities reported drug sales arrests for each

consecutive year. It was decided that for those cities missing 3 or less years of data, mean

substitutions would be generated. Consequently I constructed a dummy variable for the

cities/years in which the mean substitutions were performed in order to determine if these

imputations made any significant impact on the data. Neither these nor the month

weighted dummy variables mentioned above proved to be significant, confirming the

integrity of the data.

Although mean substitutions were performed as described above, attrition is

inevitable when attempting to use data for 22 consecutive years for so many different

cities. After all missing data was disposed of and the mean substitutions were complete, a

Einal sample of 132 cities was retained as the final city list. This list is contained in

Appendix A.

It is important to acknowledge an on-going debate surrounding the use of drug

arrest measures. There are two perspectives to consider--one being that a measure of

drug arrest rates reflects enforcement patterns or official responses to crime (see Mosher

2001; Quinney 1979) and the other is that measures of drug arrest rates indicate actual










drug behavior (Cohen, Felson and Land 1980; Rosenfeld and Decker 1999), particularly

at the city level (Rosenfeld 1986). It is not my intention in this dissertation to attempt to

decipher the true meaning of these measures. However I believe that these measures of

drug arrest rates provide a useful indicator of the amount/volume of race-specific drug

activity in a given area and thus can serve to gauge (at least the official accountability of)

drug activity in urban cities. It is my contention that no matter what these numbers may

actually mean, if a city experiences a dramatically increasing traj ectory of race specific

drug-sales arrests, this should indicate that the city has serious problems that must be

addressed (whether it be over-zealous law enforcement or increasing numbers of drug

dealers/users). Furthermore, in light of alternative indicators (e.g., DUF/ADAM, DAWN

and/or medical examiner' s data) of drug activity, prior research has reported high internal

reliability among data sources and that these data sources yield similar estimates of drug

use when compared to drug arrests for cities (Baumer 1994; Baumer et al. 1998;

Rosenfeld and Decker 1993; Warner and Coomer 2003). Given that the alternative data

sources are limited in sample size, drug arrests are a good proxy of drug activities across

multiple large urban cities over time.

Independent Variables

I utilize a number of race-specific measures to serve as proxies of the larger

theoretical constructs of social disorganization, urban disadvantage and racial threat

theories, as well as their corresponding change scores in order to assess their impact on

drug sales arrests over time. These include race-specific measures of poverty, income

inequality via the GINI index, racial residential segregation, the percentage of jobless

males age 16+, and the percentage of children under the age of 18 not living with both

parents .









Poverty is defined as the percentage of black (white) persons living below the

poverty level and is calculated as follows:

Pove2~ =# PersonsBe lowPover2ln~pltyon ine


The numerator represents the race specific number of persons living below the poverty

rate and the denominator represents the total population of each race, respectively.

Income Inequality is measured using the Gini Index of Income Concentration and

is calculated as described by Coulter (1989:38). I calculate the Gini coefficients for 1980,

1990 and 2000 using race specific family income levels, as reported by the census. This

measure represents the proportion of the population within different income categories.

The index ranges from 0 to 1, with 0 representing perfect equality and 1 representing

perfect inequality (Coulter 1989).

Racial residential segregation is measured by the Index ofDissimilarity (D). As

described by Duncan and Duncan (1955) and Massey and Denton (1988) the index

represents the percentage of either population that would have to relocate in order for a

given area to achieve equal distributions (e.g. no segregation). The formula is expressed

as:


D = -~~ *100


where b, and w~ are the number of blacks and whites in census tract j, and B and W are

the number of blacks and whites in the city of interest.

The measure of male joblessness is computed as the number of employed males

age 16+ in a racial group divided by the total number of males in that racial group that are

16 years of age and older, and multiplied by 100. The product is then subtracted from one









to represent the percentage of males in each racial group not employed. This is expressed

as:

# Malesl 6 + Employed
Percentage2\ a~lesNotEmployed = 1-: *2ls 2Ge~sfg: 100


The final measure of urban disadvantage is the percentage of children under the age

of 18 that are not living with both parents. This measure is expressed as:


PercentKiu'fldsotithiothraents # Children Underl 8Nodl ir ing rS\itilrothParen ts 100


Two measures of social disorganization in cities are conceptualized as family

disruption and residential mobility. The first measure of family disruption is the race-

specific percentage of divorced males within the male population aged 15 and older. This

measures is expressed as:


~- ; r#Malesl 5 +InAge

Residential mobility is operationalized as the percentage of residents that reported

they were not living in the same residence for the previous five years and is expressed as:

# PersonsNotLivinglnSa~mePlace 5YearsoQoplt


The measure of racial threat represents the percentage of the population identifying

themselves as black. This measure is expressed as follows:


Percen lack # Re sidentsldenitfiedBI~c~Q~plack 100

Several control variables are utilized in this research. Based on their meta-analysis

of structural covariates of crime rates, Pratt and Cullen note that failing to control for









constructs such as the incarceration rate can lead to unreliable results (Pratt and Cullen

2005). The first is percentage of the population with Hispanic origins and is expressed as:

Pecet~sp.i # Re sidentsldenitfiedHisp anicoQopltn


The second control variable is the police presence rate (number of police officers

per 100,000 residents) and is expressed as:


,~,,,,;(Swrn~~c~f# eSwornPoplticOfficers ,00


The incarceration rate refers to the number of inmates in a given state per 100,000

residents) which is consistent with past research. Some researchers emphasize the

importance in lagging incarceration measures when attempting to explain crime. For

instance, the importance of lagging arises when attempting to explain current crime rates

with measures of the incarcerated population. This can be problematic since incarceration

data reflect the period through December 3 1 of the year the data represents. Given this,

lagging allows researchers to determine the effect of the incarceration rate in 1979 on

crime rates in 1980, for example. Since I am using the change in the incarceration rate

from one decade to the next as a control measure, lagging this measure makes little sense

(see Levitt 1996 and 2001). The rate is expressed as:


..,,,,,,,=(Tt~ Topu~talopulationO/State 0

In addition to the above mentioned control variables I have constructed several

"dummy" variables which represent regions that cities belong to. For instance it has been

found that cities in regions such as the South, Northeast, and West may experience

differential rates of crimes, so it is important to consider this as well.










As past research has noted, several of these structural covariates are highly

correlated with one another which results in multicollinearity (see Land et al. 1990), so in

order to deal with these issues I utilize principal components analysis, with varimax

rotation, in order to construct indices which are representative of the theoretical

constructs they are meant to measure, while maintaining the integrity of the data by

eliminating the partialing fallacy (Gordon 1967) often experienced when analyzing

aggregate data (Gordon 1967; Land et al. 1990).

The indices or component scores are calculated by multiplying the raw variables

with the weights obtained in the principal components analysis, and summing them

together to form one measure. This technique offer advantages over other techniques

such as scales or estimates. This results in a summary of the information contained in the

raw variables while avoiding many of the assumptions that scales or estimates may

contain (Kim and Mueller 1985).

Race specific indices for concentrated disadvantage are constructed, and their

corresponding component analyses are presented below in Table 4-1. While some

research has included racial residential segregation in similar indices, I have chosen to

preserve the segregation index in its original form in order to assess what affects the

change in segregation has on race specific drug sales arrests over time. This is consistent

with past research (Krivo et al. 1998; Krivo and Peterson 2000).

While the indices are race-specific, they are also calculated for each of the three

periods 1980, 1990, and 2000. By calculating the indices for each period it is possible to

generate change scores for not only the remaining independent variables in the models,










but also this index in order to assess what role the change in concentrated disadvantage

had on drug sales arrests specifically.

Table 4-1. Concentrated Disadvantage Index Factor Loadings.
1980 1990 2000
White Black White Black White Black
GINI Index .820 .698 .690 .880 .617 .871
% Below Poverty .915 .898 .906 .932 .925 .926
% Jobless Males .643 .706 .791 .766 .819 .772
% Kids One Parent .763 .867 .757 .649 .798 .752
Median Income -.865 -.894 -.820 -.932 -.813 -.928

Eigenvalue 3.26 3.34 3.17 3.52 3.21 3.64
% Variance 54.3% 55.7% 52.8% 58.7% 53.4% 60.7%
N=132

To further address the issue of multicollinearity and the partialing fallacy discussed

above, basic regression diagnostics were performed. I utilize the widely accepted

variance inflation factor, within OLS regression in order to determine to what extent the

standard errors of regression coefficients are inflated due to the possibility they share

variance with other predictor variables in the model. None of the variance inflation

factors in my diagnostics exceed four, which is a widely accepted cutoff (Fisher and

Mason 1981: 109; Messner and Golden 1992; Sampson 1987), therefore I feel confident

that my indices are unique and collinearity with other predictor variables is not an issue.

Methodology

Statistical Procedures

Statistical procedures for this study (PROC TRAJ) draw upon methods that have

been developed to analyze the developmental traj ectories of individuals over time. More

specifically, this procedure is based on a semi-parametric, group based modeling strategy

(Nagin 1999; Jones et al. 2001). The theory underlying this strategy relies on the

framework introduced primarily by Daniel Nagin and his colleagues (see Bushway et.al.









2001; Nagin and Land 1993; Land, McCall and Nagin 1996; Roeder, Lynch, and Nagin

1999; Nagin and Tremblay 1999; Nagin 1999). Essentially this technique relies on

mixture models for modeling unobserved heterogeneity within a given population and

generating offending (or arrest rate) traj ectories over time.

This method allows researchers to analyze longitudinal data over long periods of

time to identify distinct groups or clusters of offending (or arrest rates as used in this

study) over time. That is, it estimates the probability of belonging to a particular group as

follows:





estimating the probability (pk) of belonging to group (k) based on ( Lk), drug sales arrest

rates over time. This enables us to predict the likelihood of belonging to group k, while

also allowing certain parameters (risk factors) to vary across all groups or traj ectories,

which can be used to explain membership is each group respectively. This technique is

especially helpful in making sense of more complex issues, such as offending patterns

over time (Nagin and Tremblay 2005).

The method assumes that in any given population within longitudinal data, there

exists a mixture of groups, or traj ectories. In order to determine the number of groups that

best fit my data, I will rely on the Bayesian Information Criteria (BIC) since conventional

likelihood tests are unable to determine whether a more complex model (more groups) is

statistically superior to a simpler model (fewer groups) (D'Unger et al. 1998). Typically,

researchers seem to find that four or five groups are sufficient, however, Weisburd and

his colleagues (2004) determined that with their Seattle community level data, nineteen

groups or traj ectories were appropriate. However it is important to note that Weisburd et









al. observed an unusual number of data points over an extended period of time and the

estimation of their model took many hours for the model to converge (Weisburd et al.

2004). A recent debate has emerged surrounding the reification of such groups, and it

should be pointed out that the goal is to identify the simplest model that best delineates

each group (Nagin and Tremblay 2005).

The technique that I employ consists of a SAS plug in, downloadable from its

author, Bobby L. Jones. Jones developed the programming and commands in order to

allow SAS to estimate developmental traj ectories while studying with Daniel Nagin at

Carnegie Melon University. Since its inception, Jones' downloadable plug-in has made it

possible for a broad range of researchers to utilize a statistical procedure previously

utilized by select statisticians. As a result of this expanded availability, there has been a

sharp increase in the use of these techniques in the analysis of longitudinal data. Alex

Piquero reports that more than 50 academic peer-reviewed articles have been published

recently as a result of the availability and increased discussion of these techniques, across

a range of disciplines from psychology to criminology (Piquero 2004).

The specific model allows choice in whether data require censored normal model

(CNORM), zero-inflated Poisson (ZIP), and Bernoulli (LOGISTIC) distributions of

longitudinal data (Jones et al. 2001). I am utilizing the zero-inflated Poisson (ZIP) model

in order to compensate for the highly skewed nature of drug arrest rates across cities over

time as well as those that may have a high proportion of zero values. This decision is

based on my own analysis of the distributions of the dependent variable (arrest rates from

1980-2001), as well as close consultation with Bobby Jones, the software' s author and

statistician. The ZIP model takes the following form:











Pr(r; = y, C, = k, W =I w,)= ] pifk,)~]nl~ gk~l yk gkO~- fk)
v, =o v,,> v. Fit


Nagin and Tremblay (2005) have recently addressed concerns of what could be

interpreted as misuse or misinterpretation of the results of traj ectory analysis. They

caution that researchers should recognize the utility of the technique as a tool to simplify

complex data, however they must avoid the temptation to treat the resulting traj ectories as

gospel. Suppose Atlanta is found to be within traj ectory six, the highest drug sales arrest

traj ectory, caution must be emphasized to avoid literal interpretation of these suggestions.

That is, because Atlanta may be included in traj ectory six along with several other cities,

it is highly likely that none of the cities will display the exact traj ectory as the group, if

analyzed individually (Nagin and Tremblay 2005). However it is a useful tool

nonetheless as it allows researchers to group somewhat like offenders (cities) over time

based on their cumulative behavior over time. Because of this, it is beneficial to have the

most complete set of longitudinal data possible, for having more cases over longer time

periods logically allows more accurate and smooth groupings.

In a rej oinder to the aforementioned article above, Sampson and Laub (2005) argue

that TRAJ may in fact be yet another methodological fad. They argue that much like

LSREL and HLM dominated studies for some time, TRAJ may run the risk of becoming

a bandwagon that researchers are boarding while ignoring the risks involved. Nagin and

Tremblay's (2005) response to their rej oinder seems to make clear their intentions in

addressing common myths about PROC TRAJ in general. I agree with Nagin and

Tremblay in that TRAJ is a useful tool for simplifying complex data (such as longitudinal

offending rates), in order to better grasp the pattern of offending that may occur.










Sampson and Laub's concerns of abuses of such a technique are based on assumptions of

what might occur, based on what has happened in the past (as with LSREL and HLM),

and may be a bit premature (Nagin and Tremblay 2005; Sampson and Laub 2005).

Risk Factors

In addition to the ability of this procedure' s ability to help researchers identify

developmental traj ectories over time, it also provides the ability to incorporate certain

risk factors in predicting group membership. That is, by incorporating risk factors,

researchers are able to perform multivariate analyses using time stable independent

variables, in order to discern how each covariate affects the probability of belonging to a

particular group.

Risk factors that are believed to affect group membership based on drug sales

arrests are change scores linked to the structural covariates outlined above. In other

words, assume there exist six distinct traj ectories of drug sales arrests for both whites and

blacks over time for the sample of 132 cities. Does the change in social

disorganization/urban disadvantage indicators affect membership in each group? Will

increasing amounts of deindustrialization and urban disadvantage of urban areas predict

membership in the highest traj ectory of drug sales arrests versus the lowest?

There are several ways to model change over time, and disagreement still exist as

to which method is best suited for longitudinal analyses (Allison 1990; Firebaugh & Beck

1994; Hausman, Hall, & Griliches 1984; Kessler & Greenberg 1981). Researchers have

relied on several methods to model change over time using quantitative data. These

include the change score method, also called the gain score or difference score method,

the cross lag method and the residual change method. However a strong case has been

made that the change score method is superior, therefore I rely on this method for









calculating the change in race specific measures in order to determine their relationship to

drug arrest traj ectories. Change scores are calculated as the percentage change between

decades and is represented as:

Chang~e = [(VlurzeAtTime2 -Va~lueAtTimel)]/Va~lueAtTimel

For this study risk factors are based on the independent variables described earlier

in this chapter. More specifically I will use the changes in the independent variables

across decades to assess to what extent they predict membership in any given traj ectory. I

will calculate change scores as described above to model the change from 1980 to 1990

and 1990 to 2000. For the purposes of the PROC TRAJ software, these measures are

"time stable covariates" and their effects on group membership are modeled with a

generalized logit function as follows:


Pr(C1= kl Z = z2)= kx(,+ ~l
~exp(0; +7;'z )


In the following chapters several analyses are presented. First, descriptive statistics

of both the dependent and independent variables are presented and discussed in Chapter

five. This includes discussion of the structural change over time experienced by large

cities in the sample and discussion of the basic traj ectory groups. Chapter six will present

the multivariate results using the above measures to determine to what extent (if any)

these macro-level structural covariates have on predicting the life-course, or traj ectory

membership of race specific drug sales arrests over time.















CHAPTER 5
DESCRIPTIVE STATISTICS

Introduction

As described in Chapter 4, the final sample consisted of 132 cities. In order to

assure nested models across time these cities are the bases of all analysis contained

throughout this research. Following are discussions of both the descriptive statistics of

the independent and dependent variables as well as the resulting drug sales arrest

traj ectories. Chapter 6 will focus on the multivariate models used to model structural

change in large cities and its effects on race-specific drug sales arrests over time.

Descriptive Statistics

Independent Variables

Descriptive statistics for all independent measures are presented in Table 5-1 and

Table 5-2. Referring to Table 5-1, racial differences within cities become clear, across all

three decades. For example the median annual income for White families was over $6000

more than for Black families in 1980. Moreover, Blacks average just over 65% the

median family income of Whites, as presented by the ratio of Black to White median

family income in Table 5-1. This trend continues in 1990 and 2000. In 1990 the median

family income for Blacks is about $13,500 less than for Whites, and the ratio of Black to

White income drops slightly compared to 1980. In 2000, the gap widens while Blacks

average almost $19,000 less median family income compared to Whites.

The percentage of families living below the poverty line changed only slightly from

1980 to 1990, with Blacks remaining relatively unchanged and Whites increasing nearly









a half percent. In 2000 the percentage of Whites living below poverty increases another 1

percent while Blacks living below poverty decreased by about 1 percent. So while the

income gap in terms of median family income was increasing from 1980, the percentage

of families living below poverty began to decrease for Blacks. This however still leaves

the proportion of Blacks living in poverty more than double the rate for Whites.

The percentage of unemployed males aged 16 and older changed little from 1980 to

1990. However from 1990 to 2000 the percentage increased about 3% for both Whites

and Blacks alike. In 2000 about 33% of White males aged 16 and over were not

employed while about 43% of Blacks males fell into this category. This increase

maintained the 10% difference in regards to the number of unemployed Black males in

large cities compared to Whites.

Recall from Chapter four that I constructed a concentrated disadvantage index and

its corresponding change scores to both address the issue of multicollinearity as well as

capture the race specific changes in concentrated disadvantage across decades. The

change scores are presented in Table 5-2. Referring to this table we find that Whites

experienced an increase of about 74% in the concentrated disadvantage index from 1980-

1990, compared to an 85% increase among Blacks for the same period. The changes for

the period 1990-2000 are more modest, yet still denote increases for both races. During

this period disadvantage increased about 37% for Whites and around 40% for Blacks.

Referring to Table 5-1, in 1980 the percentage of white divorced males age 15 and

older was around 7% and by 1990 around 9% of White males age 15 and older were

divorced. As seen in Table 5-2, the mean change was about a 28% increase from 1980 to

1990. During the period from 1990 to 2000, the mean percentage of divorced white males









remained relatively unchanged, rising from 9% in 1990 to 9.9% in 2000. However as

seen in Table 5-2 the mean change score represents an increase of about 13.7%.

For Blacks, the mean percentage of divorced males age 15 and older increased from

8.46% in 1980 to 10.5% in 1990, with the mean change score being 27.2%. This change

is seen again with the percentage of divorced Black males rising from 10.5% in 1990 to

11.25% in 2000 and a mean change score representing about a 13% increase.

Racial differences again become apparent referring to Table 5-1 and the measure of

the percentage of children under the age of 18 not living with both parents. In 1980 about

23% of White children did not live with both parents as compared to almost 53% of

Black children. In 1990 the percentage increases to 26.5% for White children while rising

to about 62% for Blacks. The steady increase seen between 1980 and 1990 for both

Whites and Blacks did not occur for the period between 1990 and 2000. In 2000, with

roughly 26% of Whites not living with both parents, virtually no change occurred across

the decade. There was however a slight decrease in the percentage of Black children not

living with both parents as this measure declines to about 60%. Despite the slight decline,

proportionally there were still more than twice the percentage of Black children versus

White children not living with both parents in 2000 (60.43% versus 26.36%).

In Table 5-1 and Table 5-2 data on racial residential segregation are also presented.

Recall the Index of Dissimilarity is a measure of the evenness of the distribution of

groups across census tracts. The measure ranges form 0 to 100, with 0 being perfectly

integrated and 100 being completely segregated. If a city has a segregation score of 60,

this would indicate that about 60% of one racial group would have to change locations in

order to achieve equal distributions of racial groups across racial groups.









The mean score for Black-White in 1980 across all cities in the sample was 55.34.

This is considered a moderately high score, indicating at least 55% of either Blacks or

Whites would need to change residences in order to be more evenly distributed across

census tracts. In 1990 the mean drops to 48.61, and as seen in Table 5-2 the mean change

score is -13%, representing a mean decrease of about 13%. In 2000 the mean score again

declines to a mean of 44.85. The mean change score indicates a decrease of about -6.5%,

so again across decades the means continue to decline in regards to residential

segregation, indicating that overall cities are becoming less segregated as we progress

into the 21st century. It is important to note however that scores of 30 or so indicate low

levels of segregation, so the means discussed here continue to represent moderate levels

of segregation despite the declining levels from 1980 through 2000.

In regards to racial threat, the percentage of the population identifying themselves

as Black is presented in Table 5-1 and Table 5-2. In 1980 the percentage of the

population that was Black was about 16%. By 1990 this number increased to about

17.26%, with a mean change score indicating an increase of about 33%. For 2000 the

percentage again increased to about 19%, and a mean change of about 21%.

As discussed earlier several control variables are utilized in this research. Again,

these measures are depicted in Table 5-1 with mean change scores in Table 5-2. The first

control measure is the percentage of the population that is Hispanic. In 1980 the mean

percentage of the population identifying themselves as Hispanic was about 1 1%. By 1990

this number increased to almost 15% with a mean change score representing a 42%

increase across cities. In 2000 the figure increased once again to 20.5%, indicating a

mean change of about 76.5% from 1990.










The second control measure is the police rate. Recall this measure represents the

number of sworn police officers per 100,000 residents within cities. In 1980 the mean

was about 189 police officers per 100,000 residents. In 1990, this measure increased to

about 194 per 100,000, with a mean change of almost 3%. By 2000 the mean police rate

was about 209 police officers per 100,000 residents, with a mean change of 8% from

1990.

Finally the incarceration rate represents the Einal control measure. While this

measure is a state level measure, controlling for the widely publicized exponential

increase in incarceration is important to any study attempting to account for the changes

in crime rates over time. Like the police rate discussed above, this measure represents the

number of incarcerated individuals in a given state per 100,000 residents. In 1980 the

mean incarceration rate was about 142 prisoners per 100,000 residents. By 1990 this

number increased to about 505 inmates per 100,000 residents across cities. The dramatic

increase from 1980 to 1990 is represented by a mean change score of a 277% increase in

the incarceration rate. In 2000 the incarceration rate grew once again to about 764

inmates per 100,000 residents. This represented a mean change from 1990 to 2000 of

about 53%. While these increases are dramatic, they are not all that surprising,

considering the impact of the crack epidemic on perceptions of "get tough" policies for

drug crimes.

Dependent Variable

Means and standard deviations for the dependent variable, race-specifie drug sales

arrest rates, are presented in Table 5-3. Upon first glance at the table, several things jump

out immediately. The first is the racial discrepancies in drug sales arrest rates. Recall this









measure is a rate indicating the number of race-specific arrests per 100,000 residents of

that same race within a city.

In 1980 the mean rate for Whites was about 48.5 arrests per 100,000 White

residents. For Blacks this number was about 134 arrests per 100,000 Black residents.

While this study aims to account for the differential changes in drug arrests across races,

it is important to note the significance of this first data item. Looking at the means just

presented, it becomes apparent that racial discrepancies did not simply begin in 1980.

However as looking further down Table 5-3 reveals the discrepancies climb

exponentially over time.

The other item that jumps out immediately from the table is that the drug sales

arrest rates for both Whites and Blacks appears to have peaked in 1989, as has been

argued to be the peak of the crack epidemic in most cities across the United States (Golub

and Johnson 1997). In 1989 White drug sales arrests peaked at about 157 arrests per

100,000 White residents while Black arrests peaked at about 689 arrests per 100,000

Black residents.

While the rates decline for both Whites and Blacks over the 22 year time period

studied here, clearly they never return to where the began, and in 2001 they are still

significantly higher than 1980. In 2001 the mean White rate was about 115 arrests per

100,000 White residents while the mean Black drug arrest rate was about 447 arrests per

100,000 Black residents.

Drug Sales Arrests Trajectories

Having presented the descriptive statistics for drug sales arrests in Table 5-3, the

remaining section of this chapter is reserved for presenting the traj ectories of race-

specifie drug arrests over time. Recall the previous discussion of the PROC TRAJ









procedure in Chapter 4. Figure 5-1 presents the traj ectories for White drug sales arrest

rates and Figure 5-2 contains traj ectories for Black drug sales arrest rates. These

traj ectories are examples of how cities behave, in regards to drug arrest rates over a

period of 22 years (1980-2001).

Referring to Figure 5-1, the bottom trajectory represents about 45% of the sample

or 59 cities. Note that this traj ectory exhibited very little changes over time, and also

serves as the reference group for the multivariate analyses forthcoming. Group two began

slightly higher than group one, remaining relatively flat until doubling from about 1988

to 1990, reaching a peak average of just over 100 drug sales arrests per 100,000 White

residents, and then tapering off but remaining significantly higher through the remainder

of the study period.

Group three comprised almost 17% of the sample or 22 cities. Group three begins

the study period averaging roughly 50 drug sales arrests per 100,000 White residents.

Around 1984, group three rapidly escalates to its peak in 1989, averaging around 239

drug sales arrests per 100,000 White residents. Like group two, group three also peaked

around 1989 and slowly tapered off, however like group two, group three also remained

significantly higher throughout the study period compared to where it began averaging

just under 200 arrests per 100,000 White residents in 2001.

Finally group four is the highest traj ectory group in the sample for White drug sales

arrests. Group four makes up about 12% of the sample or 16 cities. As is apparent in

Figure 6-1, group four clearly stands out from 1980 onward. At the beginning of the

study period, group four cities averaged over 100 arrests per 100,000 White residents,

then began a rapid progression to peak in 1989 at around 450 White drug sales arrests per









100,000 White residents. While tapering off after its peak in 1989, group four still

averaged over 350 drug sales arrests per 100,000 White residents in 2001, so while these

cities declined somewhat after 1989, they continued to report unprecedented White drug

sales arrest rates though 2001.

Overall, we find that White drug sales arrests peaked in 1989. This is consistent

with past research documenting the peak of the crack epidemic in this country. It is also

worth noting once again that nearly one half of the sample (59 cities) did not experience

the kind of rise in drug sales arrests over the 22-year period that one might expect by

simply examining means and average of all cities over time. This finding in and of itself

provides support for using a methodological approach such as PROC TRAJ in order to

distinguish cities that experienced little or no problems with drug arrests from cities that

experienced maj or problems. The BIC score was used for determining the proper number

of traj ectory groups. The BIC score for the four group model was significantly lower than

the three group model by 760. 107, indicating that four groups is the best fit.

The next section will provide a brief discussion of Black drug sales arrest

traj ectories as presented in Figure 5-2. Finally the chapter will conclude by revisiting

hypotheses one and two put forth in Chapter 3 as they relate to the existence and behavior

of drug sales arrest traj ectories.

The second traj ectory represents Black drug sales arrests for the same sample of

132 cities and is presented in Figure 5-2. The first thing that jumps out looking at Figure

5-2 is that the maximum average rate on the left hand scale exceeds 2000. That is, the

highest trajectory averaged over 2000 Black drug sales arrests per 100,000 Black

residents at its peak in those cities. Recall the White maximum above appears extreme,









but even the highest traj ectory group peaks at just under 500 White drug sales arrests per

100,000 White residents. This initial finding alone sheds some light on the importance of

disaggregating crime rates by race and utilizing race specific indicators while attempting

to account for crime rates in large cities.

About 25% of the cities in the Black model remained in the lowest traj ectory (34

cities), compared to about 44% (59 cities) for the White models. This provides further

evidence for the fact that more cities experience at least some changes in drug sales

arrests for Blacks compared to Whites. The lowest traj ectory for Blacks reaches a peak of

about 100 drug sales arrests per 100,000 Black residents, then tapers slightly and remains

flat to end in 2001 averaging about 76 drug sales arrests per 100,000 Black residents.

The second traj ectory in Figure 5-2 begins around the same levels as traj ectory

one but steadily rises to peak in 1995, averaging just under 400 Black drug sales arrests

per 100,000 Black residents. After 1995 it began to decline slightly and in 2001 averaged

around 250 Black drug sales arrests per 100,000 Black residents. This group comprised

about 3 8% of the sample or 50 cities.

The third traj ectory comprises almost 29% of the sample or 3 8 cities. Traj ectory

group three begins slightly higher than the first two groups in 1980 but climbs more

rapidly, peaking around 1989. At its peak this group averaged about 1100 Black drug

sales per 100,000 Black residents. While declining after its peak in 1989, traj ectory group

three tapers off slightly, still averaging over 600 Black drug sales arrests per 100,000

Black residents through the 1990's and into 2001. These 38 cities alone continued to

average more drug sales arrests per 100,000 Black residents than even the 16 cities

comprising the highest White traj ectory group, at its peak in 1989









Finally, looking at traj ectory group four in Figure 5-2, it becomes apparent that

some cities experienced astronomical increases in Black drug sales arrests over time.

Group four made up only about 7.6% of the sample or 10 cities. These 10 cities began the

period above the other three groups, averaging about 360 Black drug sales arrests per

100,000 Black resident sin 1980. From 1986 to 1989 these averages leapt from 600 to

more than 2000 Black drug sales arrests per 100,000 Black residents. While this rise

declined slightly after peaking in 1989, group four continues to mark unprecedented

Black drug sales arrest rates throughout the study period. In 2001 group four cities, on

average, made 1800 Black drug sales arrests per 100,000 Black residents.

Both groups three and four stand out not only because they rise so rapidly until

peaking in 1989 but also because both groups end in 2001 still averaging about 660 and

nearly 1800 Black drug sales arrests per 100,00 Black residents, respectively. These two

groups alone comprise around 3 5% of the sample, or 48 cities, and continued to average

higher Black drug sales arrest rates than even the highest White group at its peak in 1989.

As with the White models discussed above, the BIC score was used to assess

model fit. The BIC for the four group model was 257.43 less than the three group BIC,

indicating that the four traj ectory group model is the best fit.

Conclusion

Having now both presented traj ectory models for both White and Black drug sales

arrests over time, recall that several related hypotheses were put forth in Chapter 3.

Addressing Hypothesis 1, the evidence presented in Figure 5-1 and Figure 5-2 provides

clear and convincing evidence that cities do in fact 'behave' over time in regards to race

specific drug sales arrests across large cities. Additionally it was shown that nearly half









and one fourth the sample, experienced little changes in either White or Black drug sales

arrest rates over time, respectively.

Hypothesis 2 presented the argument that traj ectories would vary significantly by

race, with Black drug sales arrest rates rising more rapidly as compared to Whites. Again,

referring back to Figures 5-1 and 5-2, it is apparent that cities did in fact differ

significantly by race, with Black drug sales arrest rates rising more sharply as compared

to White drug sales arrest rates. Moreover this provides evidence that certain cities

experienced significant differences in drug arrests by race. That is, the highest arrest

traj ectory for White drug sales arrest rates consisted of 16 cities, while the highest group

among Black rates contained only 10 cities. However these 10 cities outperformed the

highest White arrest traj ectory by as much as four times the average number of arrests per

White (Black) residents.

Appendix A contains a list of all cities in the sample as well as their corresponding

group membership for both the White and Black traj ectory models. Comparing the lists

presented in Appendix A, we can see that six of the ten cities that fall in group four in the

Black traj ectories are also in group four in the White Traj ectories. Two are in California

(San Francisco and Bakersfield), two are in New Jersey (Newark and Jersey City), and

the remaining two are Louisville, Kentucky and Allentown Pennsylvania. Perhaps

Louisville, Kentucky is the most surprising of the six, whereas the other five cities may

be expected to have had drug problems as the crack epidemic was shown to have began

on the East and West coats as well as the Northeast in general.

Finally, having presented the traj ectories for both White and Black drug sales arrest

rates, Chapter 6 will provide results of the multivariate models in order to determine






80


whether changes in key structural covariates over time, affect the probability that a city

may be in a given traj ectory group versus being in the lowest traj ectory.










Table 5-1. Descriptive Statistics (Means with Standard Deviations in Parentheses).
1980 1990 2000
Race-Specific White Black White Black White Black
Measures
Median Family Income $21,450 $14,784 $39,576 $26,071 $55,084 $36,109
(3402) (4243) (8675) (8959) (13947) (11411)
Percent Families 9.87% 24.10% 10.46% 24.56% 11.55% 23.53%
Below Poverty (3.62) (8.42) (4.35) (9.43) (4.67) (8.95
GINI Index for Family .35 .39 .41 .43 .39 .43
Income (.027) (.051) (.034) (.049) (.033) (.042)
Percent Jobless Males 30.53% 39.8% 30.22% 40.25% 33.39% 43.28%
16+ (14.27) (13.99) (6.86) (11.29) (6.85) (10.10)
Percent Divorced 7.09% 8.46% 8.89% 10.5% 9.9% 11.25%
Males 15+ (1.82) (2.19) (1.98) (4.80) (2.02) (3.47
Percent Kids Not 23.40% 52.56% 26.53% 62.38% 26.36% 60.43%
Living with Both (4.58) (11.85) (5.60) (12.16) (5.99) (12.22) o
parents
Non Race-Specific
Measures
Index of Dissimilarity- 55.34 48.61 44.85
Racial Segregation (18.54) (18.47) (17.02)
Residential Mobility 47.04% 57.26% 51.92%
(27.88) (6.70) (5.56)
Percent Black 16.04% 17.26 18.99
(16.13) (16.88) (18.01)
Percent Hispanic 10.70% 14.83% 20.52%
(12.30) (15.53) (18.29)
Police Rate 189.20 194.45 208.74
(73.89) (79.14) (88.69)
Incarceration Rate 142.9 505.11 764.51
(47.37) (113.08) (213.43)
N=132















Concentrated Disadvantage
Index
Percent Divorced Males 15+
Non Race-Specific Measures
Index of Dissimilarity-
Racial Segregation
Residential Mobility
Percent Black
Control Variables
Percent Hispanic
Police Rate
Incarceration Rate
Population Change
N= 132


Table 5-3. Means and (Standard Deviations) for Drug Sales Arrest Rates.
Year White Black

1980 48.52 (59.57 134.43 (192.88)
1981 54.34 (71.86) 151.39 (209.65)
1982 54.59 (57.76) 157.38 (172.56)
1983 61.41 (62.02) 180.29 (235.38)
1984 56.01 (60.09) 163.24 (173.94)
1985 72.80 (78.07) 190.81 (217.04)
1986 76.85 (87.47) 233.59 (275.14)
1987 85.91 (103.27) 324.51 (348.30)
1988 96.28 (115.24) 447.95 (568.93)
1989 145.14 (157.63) 668.89 (762.28)
1990 138.66 (150.62) 559.33 (615.19)
1991 130.31 (145.39) 573.36 (589.09)
1992 130.05 (146.61) 528.04 (524.98)
1993 127.02 (141.63) 494.83 (469.62)
1994 130.95 (144.12) 533.59 (593.21)
1995 131.43 (143.15) 491.04 (525.39)
1996 123.47 (142.85) 480.46 (517.96)
1997 127.34 (136.67) 467.22 (512.27)
1998 130.08 (147.09) 504.03 (559.44)
1999 120.88 (124.91) 479.01 (553.86)
2000 118.16 (132.75) 447.66 (558.64)
2001 115.73 (127.59) 446.63 (567.00)
N= 132


Table 5-2. Mean Percent Changes Across Time for Independent Variables.
1980-1990 1990-200
Race-Specific Measures White Black White


0O
Black
+ 40.4%

+ 13.2%


+ 74.3%

+ 28.1%


+ 85.2%

+ 27.2%


+ 37.6%

+ 13.8%


- 13%

+ 58%
+ 33%


- 6.5%

9%
+ 21.6%


+ 76.5%
+ 8%
+ 53.4%
+ 12.1%


+ 42.7%
+ 2.7%
+277%
+ 16.6%

















White Drug Sales Arrest Rates Vs. Time


500


450


400 -1





300


250


200


150


100 L
50 .---.'





80 82 84 86 88 90 92 94 96 98 00

Year


Figure 5-1. Traj ectories of White Drug Sales Arrests 1980-2001.
















Black Drug Sales Arrest Rates vs. Time


2500





2000





1500


1000





500





0


82 84 86 88 90 92 94 96 98 00
Year


Figure 5-2. Trajectories of Black Drug Sales Arrests 1980-2001.















CHAPTER 6
MULTIVARIATE RESULTS

Introduction

Having presented descriptive statistics for all variables in Chapter 5, this chapter is

devoted to the multivariate analyses of traj ectory groups for both White and Black drug

sales arrests. Specifically these analyses are aimed at identifying what, if any, effects the

changes in the structural characteristics of large cities from 1980 to 1990 and 1990 to

2000 had on the probability of group membership in developmental traj ectories. In other

words if a given city experienced a rapid exponential growth in drug sales arrests, were

particular structural covariates responsible in distinguishing the higher trajectory groups

versus the lower groups, as hypothesized in Chapter 3?

In order to test these hypotheses, the PROC TRAJ procedure allows the

introduction of risk factors into the traj ectory models, as discussed in Chapter 4. With the

introduction of risk factors, the procedure then estimates a logit model for each traj ectory

group, allowing each risk factor to predict the probability of group membership in each

respective traj ectory versus the lowest traj ectory (reference group). The parameter

estimates presented below can be interpreted as the natural log of the odds ratio of

belonging to a given group versus the reference group, for each risk factor (Jones and

Nagin 2006).

It is also important note that one of the nicest things about these models (in my

opinion), is that they allow traj ectory groups to vary across the risk factors. In other

words it is not simply the values of each dependent variable contributing to group









membership, but once risk factors are introduced, the groups may vary based on the

influence of those risk factors. This is important since some critics have argued that

researchers overstate claims that a given individual (or city in this case) actually belongs

to a particular traj ectory group. However as Nagin and Tremblay point out, traj ectory

group membership is not static, and the fluidity of the model specifications allowing

groups to vary based on both dependent variable values as well as risk factors makes the

technique that much more useful in making sense out of otherwise very complex

longitudinal data (Nagin and Tremblay 2005).

White Drug Sales Arrests

Recall the White drug sales arrest traj ectories presented in Chapter 5. The model

fitting specification comparing BIC scores of more complex models compared to simpler

models, showed that the four-group traj ectory was the best fit for the arrest rates for 132

cities, including risk factors. I have established that trends in city drug sales arrest rates

not only differ over time but by race as well, supporting hypotheses land 2 from Chapter



Recognizing that cities did in fact behave differently for White drug sales arrest

rates raises the question of whether changes in the structural factors of those cities had

any affect on their behavior. The next several sections provide multivariate results

examining the effects of the structural changes from 1980-1990 and 1990-2000

independently in order to determine whether the changes from one decade to the next

were more salient than the other.

Accounting for Change: 1980-1990

The first multivariate model examines the impact of the change in key structural

covariates from 1980-1990 and their affects on group membership in the four traj ectory










groups discussed above. Tables provides parameter estimates for each risk factor, with

each said parameter representing the natural log of the odds ratio of belonging to a given

traj ectory versus belonging to the lowest traj ectory (group 1). Each group in the table

represents a separate logit model comparing that group to the reference group (group 1).

Table 6-1 provides parameter estimates for the full model representing the changes

in structural conditions from 1980-1990 and their corresponding affects on group

membership. Beginning with traj ectory group two note the significance of the increase in

concentrated disadvantage among Whites on the probability of cities being in group two

versus group one. Additionally, an increase in the index of dissimilarity (segregation)

decreases the probability of group two membership, compared to group one. The control

measure police presence loses significance once the theoretical measures are introduced

into the model.

Regarding the probability of being in arrest traj ectory three, again the increase in

concentrated disadvantage among Whites is a significant predictor of membership in

group three versus group one. Moreover the increase in residential mobility in these cities

from 1980-1990 increases the likelihood of being in group three versus group one,

controlling for other factors. Control variables that are significant for model three show

that cities being in both the South and Northeast were less likely to be in group three

versus groups one.

Finally, looking at traj ectory group four, the change in concentrated disadvantage

among Whites is no longer significant. However those cities that experienced an increase

in the percentage of divorced males from 1980-1990 were less likely to be in traj ectory

group four versus group one. As with the model three results, an increase in residential









mobility from 1980-1990 significantly increases the probability of cities being in

traj ectory group four versus group one. Furthermore, as a cities' black population

increases proportionally, the likelihood of being in traj ectory group four versus group one

for White drug sales arrests decreases.

The only statistically significant control variable for trajectory group four

probabilities is the change in the incarceration rate from 1980-1990. This is consistent

with arguments of other researchers (see Levitt 2004). The change in incarceration was

also among the most significant changing independent measures. Recall from Chapter 5

that from 1980-1990 the mean change in the number of prisoners per 100,000 residents

across states was 277%.

In the models below, the changes from 1980-1990 in both residential mobility and

concentrated disadvantage among Whites were significant predictors of cities being in a

higher arrest traj ectory versus the lowest. The mean change in concentrated disadvantage

was an increase of about 74% and the mean change in residential mobility from 1980-

1990 was about 58% across the 132 cities in the sample.

It appears that the changing landscape across cities from 1980-1990 for constructs

such as the concentrated disadvantage among Whites and the percentage of residents not

living in the same home as Hyve years prior could cause those areas to become more

conducive to drug selling. While these changes are only from 1980-1990, the next section

will address the changes from 1990-2000, again for White drug sales arrests. Following

the changes from 1990-2000 for White drug sales arrests, the same format will be used to

address the changes in Black drug sales arrests for the same periods.









Accounting for Change: 1990-2000

Table 6-2 represents the full model, with parameter estimates indicating the

probability that structural changes from 1990-2000 had a significant affect on traj ectory

group membership. Beginning with group two, only two measures are significant, both

control variables. The rise in the incarceration rate from 1990-2000 decreased the

likelihood of a city falling in traj ectory group two versus group one. Also, those cities in

the Northeast were less likely to be in traj ectory group two versus group one during this

period, controlling for all other factors.

Group three models show that the rise in concentrated disadvantage among Whites

decreased the probability of cities being in traj ectory group three versus group one.

Additionally, as the proportion of a city's Black population increased, its likelihood of

being in traj ectory group three versus group one decreased. Finally, cities in the South

during this period, were less likely to be in traj ectory group three versus group one,

controlling for all other measures.

Similar to the model for group three, the likelihood of a city being in traj ectory

group four versus group one decreased as concentrated disadvantage among Whites

increased from 1990-2000. Also like group three, as the proportion of the population

identifying themselves as Black increased from 1990-2000, the likelihood of those cities

being in traj ectory group four versus group one decreased. The remaining measures were

found to be insignificant. These results suggest that the structural changes from 1980-

1990 had more of an impact on cities' arrest rates for drug sales arrests, at least among

Whites.