Maintaining Racial Inequalities through Crime Control

Permanent Link: http://ufdc.ufl.edu/UFE0041240/00001

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Title: Maintaining Racial Inequalities through Crime Control The Relationship between Residential Segregation and Mass Incarceration
Physical Description: 1 online resource (122 p.)
Language: english
Creator: Smith, Justin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009


Subjects / Keywords: incarceration, race, segregation
Criminology, Law and Society -- Dissertations, Academic -- UF
Genre: Criminology, Law, and Society thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: The criminal justice system has dramatically grown in the United States over the past several decades. The use of the prison system to incarcerate has been one of the primary control mechanisms since the early 1970s, immediately after many civil rights changes. The system of mass incarceration has entailed wide and continuous racial disparities which maintain inequality across social institutions such as the economy and political participation ? the institutions in which the civil rights movement sought to secure equality. The current study attempted to examine the association between crime control and racial residential segregation, another major social institution targeted by the civil rights movement. This research links theoretical discussions on race relations, law and crime control, and residential segregation to advance our understanding of the reciprocal relationships among these institutionalized processes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Justin Smith.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Lanza-Kaduce, Lonn M.

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Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041240:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041240/00001

Material Information

Title: Maintaining Racial Inequalities through Crime Control The Relationship between Residential Segregation and Mass Incarceration
Physical Description: 1 online resource (122 p.)
Language: english
Creator: Smith, Justin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009


Subjects / Keywords: incarceration, race, segregation
Criminology, Law and Society -- Dissertations, Academic -- UF
Genre: Criminology, Law, and Society thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation


Abstract: The criminal justice system has dramatically grown in the United States over the past several decades. The use of the prison system to incarcerate has been one of the primary control mechanisms since the early 1970s, immediately after many civil rights changes. The system of mass incarceration has entailed wide and continuous racial disparities which maintain inequality across social institutions such as the economy and political participation ? the institutions in which the civil rights movement sought to secure equality. The current study attempted to examine the association between crime control and racial residential segregation, another major social institution targeted by the civil rights movement. This research links theoretical discussions on race relations, law and crime control, and residential segregation to advance our understanding of the reciprocal relationships among these institutionalized processes.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Justin Smith.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Lanza-Kaduce, Lonn M.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041240:00001

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2 2009 Justin M. Smith


3 To Becky Hayes-Smith and Lori Smith


4 ACKNOWLEDGMENTS There are many people that helped m e get here from friends to family to faculty I want to thank all of you. Thanks first goes to my family To my parents, Lori and Cal Smith, who from the very beginning always made sure I did not sl ip up and settle for less. They gave me many opportunities that many folks are not offered and I am one of the lucky ones. I think I won The Birth Lottery. To my brother a nd sister-in-law, Ryan and Jill Smith thanks. They helped me out in many ways and even let me sleep in th eir basement for awhile during graduate school. To Becky our world of work, study, and ever yday life is all fit in one big bag and we managed to get through this graduate school hurdle (so far). Thanks for all the confidence that you gave me over the last few years. My committee members were very helpful along the way and were open to letting me run with this project. Dr. Lonn Lanza-Kaduce, thanks for reading many drafts and getting this thing organized. Lonns classes thre w a new outlook on many sociologica l issues Ive been thinking about over the years and definitely led to a better project. Dr. James Al gina, thanks for all the help which an external member does not always do for students. Without it, I dont know where I would be right now. Dr. Marian Borg, thanks for talking issues over with me, giving insightful comments, and organizing my thought s more clearly. All the career advice helped too. Dr. Tim Clark, thanks for sticking with this project afte r a move and talking over many of the theoretical arguments I was grappling with. I appreciate all of this and I can say it all made me a stronger student. At Western Michigan University is where my academic career began and where I was first introduced to this thing called sociology. Back in the first few years of college, Dr. Doug Davidson taught many lessons on race, sociological th eory, and inequality. This stuff really got me interested in sociology and has remained with me. Then, after they let me into graduate


5 school, Dr. Rachel Whaley kept confidence in me and led me along the way for two years. We spent a lot of time together as I was beginning to learn what it took to perform research and figure out academic life. With much caring and patience with me during the first couple years of graduate school, Rachels help allowed me to k eep going. Dr. Susan Carlson is one of the best teachers I have had in my life. Her guidance and continued help from a distance is much appreciated. Thank you.


6 TABLE OF CONTENTS page ACKNOWLEDGMENTS ............................................................................................................... 4LIST OF TABLES ...........................................................................................................................8LIST OF FIGURES .........................................................................................................................9ABSTRACT ...................................................................................................................... .............10CHAPTER 1 INTRODUCTION .................................................................................................................. 11Introduction .................................................................................................................. ...........11Functional Analysis Orientation ............................................................................................. 13Specific and Significant Contributions ................................................................................... 15Layout of the Dissertation ......................................................................................................16Reflexive Statement ........................................................................................................... .....162 DEVELOPMENT AND TRENDS IN INCARCERATION .................................................. 17Introduction .................................................................................................................. ...........17Incarceration Trends ...............................................................................................................18Putting the Development of Pr isons in Social Context ..........................................................20Racial Disparities in Imprisonment ........................................................................................ 22Race and Collateral Conseque nces of Crime Control ............................................................ 25Community Effects of Incarceration ......................................................................................283 RACIAL RESIDENTIAL SEGREGATION ......................................................................... 36Introduction .................................................................................................................. ...........36Three Primary Explanations of R acial Residential Segregation ............................................. 38The Influence of Mass Incarceration of Racial Residen tial Segregation ...............................42Previous Research on the Statistical Correla tes of Black-White Racial Segregation ............474 DATA AND METHODS ....................................................................................................... 52Introduction .................................................................................................................. ...........52Data Sources ...........................................................................................................................52Dependent Variable .........................................................................................................55Independent Variables ..................................................................................................... 56Control Variables .............................................................................................................61Limitations of the Incarceration Data .............................................................................. 64


7 5 DESCRIPTIVE STATISTICS ................................................................................................ 68Introduction .................................................................................................................. ...........68Descriptive Statistics ........................................................................................................ ......68Conclusion .................................................................................................................... ..........706 MULTIVARIATE ANALYSIS .............................................................................................75Introduction .................................................................................................................. ...........75Examining Regression Assumptions ...................................................................................... 76Detecting Outliers ............................................................................................................76Multicollinearity and heteroskedasticity ......................................................................... 76Accounting for Change in the Dissimilarity Index from 1990-2000 ...................................... 78Accounting for Change in the Interaction Index from 1990-2000 ......................................... 827 DISCUSSION OF STATISTICAL RESULTS ...................................................................... 878 LOOKING BACK AT METHO DOLOGICAL SHORTCOMINGS ..................................... 91Assessment of the National Correct ions Reporting Program Data ........................................ 91Measuring Housing Changes ..................................................................................................949 DISCUSSION OF THE RECIPROCAL NATURE OF T HE CRIME CONTROLRACIAL INEQUALITY RELATIONSHIP ..........................................................................96Introduction .................................................................................................................. ...........96New Sources of Racial Formation .......................................................................................... 97Use of Criminal Law to Maintain Racial Order .....................................................................99Conclusion .................................................................................................................... ........104APPENDIX A METROPOLITAN AREAS IN MAJOR ANALYSIS ......................................................... 105B MEAN PERCENT MISSING RACE DATA.......................................................................112C CORRELATION BETWEEN PERCENT RAC E MISSING AND BLACK INCARCERATION RATE ..................................................................................................113LIST OF REFERENCES .............................................................................................................114BIOGRAPHICAL INFORMATION ...........................................................................................122


8 LIST OF TABLES Table page 5-1 Annual rate of black prison incarcer ation per 100,000 black residents across m etropolitan areas with 2,500 black residents. .................................................................. 725-2 Estimated linear trends and initia l status of black in carceration rates. .............................725-3 Linear trend from 1990-2000 cat egorized into quartiles. .................................................. 725-4 Descriptive statistics for de pendent and control variables .................................................735-5 Descriptives of dummy variables....................................................................................... 736-1 Zero-order correlations for segrega tion change and incarceration trends. ........................846-2 Regression estimates for change in resi dential segregation (dissimilarity) on three trends in black incarceration rates and contro l variables. ..................................................856-3 Regression estimates for change in resi dential segregation (interaction) on three trends in black incarceration rates and contro l variables. ..................................................868-1 Percentage of cases without county identification per year. .............................................. 95


9 LIST OF FIGURES Figure page 4-1 Distribution of households within on e hypothetical m etropolitan area with high segregation and one with low segregatio n: Dissimilarity index (evenness). .....................664-2 Distribution of households within on e hypothetical metropolitan area with high segregation and one with low segrega tion: Interaction index (exposure). .........................675-1 Trend for black incarceration rate. ..................................................................................... 74


10 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 MAINTAINING RACIAL INEQUALITI ES THROUGH CRI ME CONTROL: THE RELATIONSHIP BETWEEN RESI DENTIAL SEGREGATION AND MASS INCARCERATION By Justin M. Hayes-Smith December 2009 Chair: Lonn Lanza-Kaduce Major: Criminology, Law and Society The criminal justice system has dramatically grown in the United States over the past several decades. The use of th e prison system to incarcerate has been one of the primary control mechanisms since the early 1970s, immediately after many civil rights change s. The system of mass incarceration has entailed wide and continuous racial disparities which maintain inequality across social institutions such as the economy an d political participati on the institutions in which the civil rights movement sought to secure equality. The curren t study attempted to examine the association between crime control a nd racial residential segregation, another major social institution targeted by the civil rights movement. This research links theoretical discussions on race relations, law and crime contro l, and residential segregation to advance our understanding of the reciprocal relationships among these institutionalized processes.


11 CHAPTER 1 INTRODUCTION Introduction The civil rights m ovement brought hope of ma ny new forms of social equality between racial groups in the United States. In the past several decades however the racial disparities in economic opportunities, housing, voti ng participation, health care, and education continue to appall those who are carefully watching. The relia nce on explaining these di sparities in terms of differences in personal responsibility is most common. The view of a color-blind society overshadows the systemic, subtle and accumulating social processes that ma intain stratification between white and black Americans. As sociologists continue to study the durability of racial inequalities we must bear witness to the institu tionalized processes that co ntribute and facilitate their continuation. In the post-civil rights era the criminal justice system, particularly the institution of mass incarceration, has maintained racial inequalities within the United States. By incarcerating a mass population we segregate people into categories of official and unofficial citizens (Western, 2006). The dispropo rtional distribution of current and former inmates between blacks and whites create vast ine qualities in the collateral consequences that follow incarceration (Mauer and Chesney-Lind, 2002). Voting rights are relinquished by many felons essentially eliminati ng them from the pool of potentia l voters (Manza and Uggen, 2007). Inmates are eliminated from being counted in employment figures, in turn disguising a more comprehensive estimate of citizens who are actu ally unemployed. Western (2007) insightfully points out that since incarceration rates are dram atically higher for black Americans compared with white Americans, official measures of inequality are often underestimated due to the elimination of individuals from official measures based on criminal histories. The employment opportunities of not only former in mates but young, black males are substantially diminished due


12 to the stigmatization of actua l or perceived criminal backgrounds (Pager, 2007). Family disruption and community disorganization of bl ack communities are also unevenly affected by concentrated incarceration in disadvantaged places (Clear, 2007). The story this project attemp ts to unfold entails both theoretical propositions and empirical evidence to investigate the extent to which the system of mass incarceration has worked to uphold a system of durable racial inequality. This manuscript begins by examining the trends and development of mass incarceration in the U.S. Next, it covers the existing theoretical and empirical state of knowledge on racial residentia l segregation. A discussion of the relationship between mass incarceration and segregation follows. Currently, there are vast and continuing patte rns of inequality be tween black and white Americans across social, economic, education, and health measures. The institutions in which these clear disparities resonate are not disconnect ed from disparities in the other institutions. Instead, influences go back and forth. For instance, racialized patterns in the health care system influence differences in family relations; di sparate trends in income and wealth affect educational attainment; and shifts in the criminal justice system affect housing patterns. Below are examples of striking figures that i llustrate inequality between races. In 2006, 21.6 % of black families were below po verty while 8 % of white families were below poverty. In 2007, 18 % of black males had a college degree compared to 29.9 % of white males. In 2004, the median net worth of white fa milies was about $140,000 compared to about $25,000 for non-white families. In 2005, the infant mortality rate for blacks wa s 13.7 per one thousand live births while the rate for whites was 5.7. In 2007, the black unemployment rate was 8.3 compared to 4.1 for whites.


13 The figures display some alarming differences between races (U.S. Department of Commerce, 2006). As a former professor of mine once said, if all Americans were in the state of black Americans we would consider it the worst econo mic depression in our countrys history. In the following sections, I will discuss 1) the extent and development of mass incarceration in the United States 2) descrip tions and theoretical unde rstandings of racial residential segregation, 3) an empirical analysis, 4) and theo ry on the lasting relationships between law and race relations Functional Analysis Orientation This dissertation approaches the topic of st udy from a perspective consistent with the history of functional analysis. Overall, the dissertation uses f unctional analysis to understand how and the extent to which th e system of mass incarceration (and the parallel increase in punitive crime control strategies) that began in the 1970s and continues into the present has functioned to uphold racial inequali ties in the U.S. The use of a functionalist analytic approach should not be confused with the functionalist theoretical paradigm. Long ago, Merton (1967) clarified confusions about functi onal analysis. First, he explic itly stated that functionalist analysis was inherently linked to no theoretical paradigm. Instead, it is put into sociological practice to identify functions, bot h latent and manifest, of social mechanisms in order to understand social structures and institutions in society. Second, Merton authoritatively argued that func tions should not be viewed as subjective categories of disposition (i.e. motivations or an ticipated outcomes) but instead he argues that functions should clearly refer to objective outcomes, or both the dysfunctional and functional outcomes of a social institution or cultural practice (Merton, 1967). For example, while crime deterrence is an anticipated outcome or motiv e for punishment it may be only one of several functions, or perhaps not a function at all. Likewise, while economic inequality between groups


14 may not be an explicit motive it may be a latent f unction. Therefore practices that are divisive or lead to social change can in fact be functi ons, along with those that maintain order. Since many scholars have confus ed functionalist analysis with the functionalist theory of law, which emphasizes the notion that law serves the interests of a consensus within society, it has been erroneously inferred that functionalist analysis inherently implies a consensus view of society. In response to this last confusion, Merton clarified that institutions and structures often exist because they function for powerful groups, not for all groups. He stated that simply because structures and cultural practices exist does not make them entirely functional toward maintaining order. Even though a structure or practice emits a function toward social order, functionalism admits that altern atives may exist and should be s ought perhaps those that entail fewer dysfunctions. And herein lays the connection to the current study: an examination of a potential dysfunction of a particul ar style of law administration a nd an effort to seek potential alternatives. This study follows a history of functionalist an alyses that has outlined the consequences of law and social control. Theoretical de velopments by Jeffrey Reiman (2007) serve as particularly similar examples of examining th e consequences of punish ment, specifically the administration of mass incarcerati on in contemporary US. His e xploration of classand racebased inequalities that have accumulated during the tough on crime era draws from a functionalist approach to unders tand the continuation of the prev ailing crime control regime. Inveriarity, Lauderdale and Feld (1 983) illustrate how equality right s laws functioned to maintain capitalist markets following the demise of previous modes of production. Their application of Marxian analysis of law within a functionalist framework shows the reci procal nature between the administration of law and other social institutions.


15 The empirical section of this study focuses on two specific indicators that represent crime control (incarceration rates) and r acial stratification (racial residential segregation). Specifically, the empirical portion includes an examination of the association between incarceration rates and changes in racial residential segr egation. Rather than approach the empirical analysis of this relationship as a test of all the theoretically important infl uences upon segregation, this study focuses on examining the extent of one potenti al function of mass incarceration. To help understand the specific association between incar ceration and segregation, discussions following the empirical section revolve around how crim e control and race re lations are bound in a reciprocal relationship. This dissertation synthesizes theory and research evidence on the 1) maintenance of racial inequalities, 2) conflic t theory of law, 3) and research on unequal outcomes of mass incarceration to co ntribute a theoretica l understanding of ra cial inequalities as a function of crime control in th e US during the latter part of th e twentieth and into the twentyfirst century. Specific and Significant Contributions This dissertation contributes three particular advancements in the sociological study of social control and race relations. First, it provides the first macro-level, empirical analysis of the segregation-incarceration a ssociation. Second, it brings togeth er various theoretical statements on sociology of law, segregation, and race rela tions to understand the diverse and intersecting associations between race a nd crime control. Third, it a dds a methodological advancement toward the macro-level study of in carceration. Specific to this th ird contribution, data from the National Corrections Reporting Prog ram (NCRP) are used to compile the first measurement of prison admission at the metropolitan area-level. Prev ious research has generally used state-level incarceration data, even in studies where the pr imary unit of analysis was a smaller aggregate such as the metropolitan area or city. By contribu ting this methodological tool future research on


16 the macro-level effects of incarceration, an emer ging area of study (Clear 2007), can utilize this more refined measure. Layout of the Dissertation The manuscript first introduces the development and trends of incarceration within the United States. Since this study is primarily concerned with examining the consequences of mass incarceration, it attempts to put the development of this historically unique social process into context. Next, an overview of research and theoretical statements on racial residential segregation follows. An empirical examination of the segregation-incarceration relationship and its results are then presented. Lastly, a theore tical discussion concl udes this dissertation by providing understanding to the complex linkages between crime contro l and race relations. Reflexive Statement I dove in to this project because I was, and continue to be, working toward understanding the continuation of social inequa lities. During my first experiences with sociology and into the present, inequalities based on race, gender, and class have attracte d my attention. The fact that our arbitrary and constructed soci al boundaries are so closely tie d with basic human rights such as housing, health, and justice trouble s me. Part of my responsibility as a sociologist is to use the language of the sociological imag ination to bear witness to th e things that go unseen for many people. Institutionalized racism is one of them. I hope that this project is only the beginning of a long struggle for equality on ma ny fronts. At the very least, it was good practice and sharpened my ability to think theoretically.


17 CHAPTER 2 DEVELOPMENT AND TRENDS IN INCARCERATION Introduction Mass incarceration is happening in the United States. It has been happening here since the m id-1970s and there is little dispute of its existence and continuation. The recent developments in incarceration over the past few decades have been astounding; many statistics have been displayed in reports, books, articles, and media programs to show the extent and characteristics of the system of mass incarcer ation (Garland, 2001; Mauer, 2006; Western, 2006). Scholars, practitioners, and administrators market their opinions and empirical research to showcase their litany of evidence th at speaks to the extraordinary character of our incarceration trend. Garland (2001) insightfully points out that th ere are two distinct characteristics of mass incarceration as a unique phenomenon distinguished from normal patterns of punishment. First, the sheer numbers create a di stinguishable phenomenon. From a historical and comparative perspective the US system has met the criterion. The second feature is that mass incarceration does not only deal with individuals under the supervision of the pr ison system but its effects also breed within entire, spec ific groups. In the U.S. the experience of imprisonment has become normalized among populations of young, urban black males it has become a social institution with long-reaching effects upon a concentrated population. This evidence is important to consider when examining racial inequalities acr oss different social ar enas. In order to understand the effects that incarceration may be exerting, incarceration trends must first be delineated and placed into historical and comparativ e context. In addition, it is also important to detail the extent of racial disparities in incarceration and in crime control generally.


18 Incarceration Trends The num ber of individuals under supervision in prisons and jails across the US has become a well-documented phenomenon in the ge neral media and even more so across the academic community. The well-noted trend displays that during the 50 years preceding 1973 the incarceration rate neve r eclipsed 150 inmates per 100,000 resi dents (Blumstein and Beck, 1999). Analyses show that as of year-end 2007 there we re nearly 2.3 million individuals detained in jails and prisons, over 1.5 million of which in fe deral and state prisons (West and Sabol, 2008). This amounts to 506 inmates per 100,000 residents in the U.S. The increase in incarceration has affected a growing number of U.S. resident s. In 2001, 2,673 persons per 100,000 had ever gone to prison, a significant increase from th e 1,251 persons per 100,000 in 1974 (Bonczar, 2003). Prison growth has led to several consequences that trouble state and federal governments. Prison overcrowding, recidivism, and persistent ra cial disparities are pr imary concerns that prison growth has created. Interestingly, states with high prison populations continue their binge on incarceration. Of the 10 states with the largest prison populati ons in 2000, nine experienced increases in the rate of growth in 2006 (Sabol et al., 2007). Clear ly, the role of the correctional system in social control has become dramatically more substantial. These numbers are the result of an uninterrupted rise in pris on populations since the early 1970s. Coincidentally, as others have pointed out this was th e same time that criminologists were arguing that prison popul ations were stable across tim e (Blumstein and Cohen, 1973). These recent prison trends provide an illustra tion of what is currently happening. But a comparison of present statistics to statistics from earlier points provides an even grimmer outlook on our punitive movement. To describe the salience of this shift, Loury (2008) draws on clear and alarming patterns in the era of mass incarceration. For instance, from 1980 to 2001 the chance of being arrested


19 after a filed complaint was stable it remained slightly under 50%. Ho wever, the likelihood of being incarcerated as a result of that arrest increased from 13% to 28%. In addition, the incarceration rate for non-viol ent offenders in 1997 was triple its rate from 1980, and drug incarceration rates increased eleven-fold. Felon disenfranchisement has increased to 3.9 million individuals six times its figure in 1974 (Uggen and Manza, 2007). Loury adds: As of 2000, thirty-three states had abolished limited parole (up from seventeen in 1980), twenty-four states had introduced thre e-strikes laws (up from zero) and forty states had introduced truth-in-s entencing laws (up from three). The vast majority of these changes occurred in the 1990s, as crime rates fell. (Loury, 2008:9-10) Blumstein and Beck (1999:54) provide some of the most comprehensive analyses of prison growth and state that, other than drug offens es, very little is attr ibutable to changes in offense rates. Instead, the preponderance of the responsibility for prison population growth lies in the sanctioning phase, the conversion of arrests into prisoners and the time they serve in prison. The uniqueness of shifts in drug contro l creates difficulty in unraveling the independent influences of drug offenses on prison population gr owth. That is, it is di fficult to decipher the extent to which actual drug activity was causi ng more arrests (and in turn convictions and sanctioning) or if police decision-making was responsible. Nonetheless, from 1980 to 1996 drug arrests accounted for about one-third of the increase in drug incarceration growth, while the conversion of drug arrests into convictions accounted for approximately two-thirds (Blumstein and Beck, 1999). Looking back at the last thirty years of pris on growth three fairly distinct periods of increases appear (Zimring, 2001). The first wave from 1973 to 1981 was due mostly to an increased commitment of marginal offenders rega rdless of offense type and the revocation of parole. From 1982 to 1992, drug arrests accounted for a significant portion of increases. Finally, since 1993 we have moved from a lock em up approach toward a throw away the key


20 movement characterized by truth in sentencing policies and furt her sentencing polices such as Californias Three Strike s policy (Zimring, 2001). Putting the Development of Prisons in Social Context The developm ent of corrections policy mu st be considered in the contextual characteristics in which broad changes have ta ken place (see Pratt, 2009). The transformations in penal policy and practice that led to such dram atic increases in the utilization of incarceration simply did not occur in a vacuum. What ofte n seems most astounding is the sudden back-peddle away from the rehabilitation id eal that had a strong hold on co rrectional philosophy and practice for several decades and into a reaffirmation of re tributive styles of just ice that were formerly seen as obsolete (Garland, 2001). While only a brief historical overview fo llows, it is useful to place the rapid, dramatic philosophical and prac tical changes into a historical context. In the late nineteenth and early twentiet h century prisons were primarily focused on isolating and reforming the immorality of offenders. In this era, the lack of connection to a moral code undoubtedly one based on a Christian ethic was a major explanation of crime and reform was heavily based on a one size fits all scheme where connection to jobs and religion were central. The ideals of this era shifted larg ely due to the emergence of scientific positivism in the 1920s. In regards to the study of crime, during the first half of the twentie th century there was a focus on finding observable and objective sources of criminal behavior. Researchers sought to find the psychological and sociological influences on human behavior through scientific methods. This was reflected in the prison sy stem through reform policies that stressed the importance of individualized treatment thr ough psychological and medical practices a philosophical and practical standpoint broadl y termed the rehabilitative ideal. The rehabilitation model of justice fl ourished in this context as treat ment was implemented to fix the


21 psychological and social factors that were supposedly pushing people to behave criminally (Rothman 1980). As Tonry (1999) notes, it was quite startling how much attention was paid to the individualized needs of offenders towa rd their correctional rehabilitation. For a variety of reasons, the rehabilitation model was pushed aside and gave way to a conservative model of deterrence and incapac itation (Zimring and Hawkins 1995). There are now numerous explorations and explanations into this unpredicted transition that are important to consider (Currie, 1998; Ga rland, 2001; Simon, 2007). In short, crime control policy and discourse shifted during and in the aftermath of a period of major social changes where racial, ge nder, and class hierarch ies were being broken down. In this atmosphere the criminal justic e system did not go untouched. Politicians from both sides of the aisle stressed the imperativeness of a new approach toward social control and criminal justice. Noting the rising crime rates, conservatives Goldwater and Nixon were two of the first to explicitly state their advocacy for increasing policy based on deterrence and law and order (Parenti, 1999). Liberals also played an in fluential role in the de cline of rehabilitation, albeit a bit inadvertently, by confronting it as another repressive a nd discriminatory form of state control. For instance, prisone r and civil rights groups claimed the discriminatory nature of indeterminate sentencing and claimed it neglected due process (Tonry, 1999). In this social and historical context, t hose from opposing politic al groups reacted with evidence from the social scientific that stated the inadequate natu re of rehabilitation. Martinson (1974) reviewed hundreds of studies on rehabilitation and implied that n othing works in the field. The studies included analyses of educatio n, medical, vocational, and therapeutic treatment and although 48% showed some positive results he offered an overall negative perception of rehab programming. From here the get tough movement took hold and politicians rode the


22 wave. Therefore when Martinson wrote his analysis of rehabilitation in corrections there was a perfect storm for correctional policy change. Further criminological literature has note d the weakness of the Martinson study and the over reliance on its findings (Pal mer, 1978) but most policy discus sion toward prison growth did not reflect admonition from the academic criminologi cal community. In this sense, it is clear that correctional policy as well as criminal justice policy in general has not been founded simply on rational, evidence-based strategies to combat increasing crime and disorder. Indeed, crime rates have been on a significant downward trend wh ile incarceration rates continue to increase (King, Mauer and Young, 2005). While there are many insightful theoretical explanations for this phenomenon (see Garland, 2001), the focus of the cu rrent study lies in the effects of mass incarceration rather than explaining its rise. In order to cl arify the racialized trends in th e institution of mass incarceration the next section details the ra cial disparities that abound in the prison population. Reasonable and guided predictions toward th e collateral outcomes of mass inca rceration are easier to grasp once the range and prevalence of racial dispar ities in the prison population are understood. Racial Disparities in Imprisonment Incarceration is unevenly a pplied across racial groups. Mass incarceration extends substantial social and legal consequences across societal arrangements and continues not only to affect those who are imprisoned but also to ha ve continued, far-reaching impacts on families and communities. One conspicuous sign of an institu tionalization of racial disparities in the penal system is that the disparity in imprisonment be tween whites and blacks is wider than in any other major social arena (Western, 2007). Due to these undeniable patterns of incarceration we must view the penal system as a social instituti on that profoundly impacts entire communities and consider the potential racialized impact it may place upon other instituti ons of society. An


23 overview of racial disparities is warranted in order to offer a contextual discussion of the influence that mass incarceration ha s upon communities and race relations Tonry and Melewski sum up the state of raci al disparities in the correction system adequately: Blacks constituted 12.8 percent of the general population in 2005 but nearly half of prison inmates and 42 percent of Death Row residents. Imprisonment rates for black men were nearly seven times higher than for white men. About a third of young black men aged 20-29 were in prison or ja il or on probation or parole on an average day in 2005. The Bureau of Justice Statistics (BJS) estimated in 2003 that 32 percent of black men born in 2001 will spend some part of their lives in a state or federal prison. That is a substantial underestimat e of the likelihood that black men will spend time behind bars; it does not take acc ount of jail confinement, which is much more common than time in prison. (2008:2) The explanations of racial di sparities in incarceration have been contested. Arguments pointing toward systemic racial discrimination as the primary influence have lasted for decades, while others claim that differences in offendi ng rates between whites and blacks makes most of the difference. In the 1980s, strong evidence established that, while bias and discrimination occur, they are not the primary forces behind raci al disparities in incarc eration (Blumstein, 1982; Langan, 1985). Two qualifications must be c onsidered however when drawing on this conclusion. First, sentencing for violent offenses reflect patterns of actual violent offenses but disparities for less serious offenses are not e xplained as strongly by actual offending patterns. Essentially, there is less bias and discrimination for serious offenses so racial disparities for these crimes are mostly explained by racial differen ces in violent criminal behavior. Second, drug offense rates and drug arrests are not necessarily linked. Therefore, racial disparities in arrests for drugs are largely due to discretionary police behavior (Tonry and Melewski, 2008). Further, the explanatory power of racial differences in crime involvement toward sentencing has been declining. Using 1991 prison and arrest data Blumstein (1993) found that unexplained disparities increased from 20.5% in 1979 to 25% in 1991. Tonry and Melewski (2008)


24 continued this analysis with 2004 data and report ed that unexplained di sparities increased to 38.9%. Although racial disparities have been present in prison rates for decades, the war on drugs significantly widened the gap. In the late 1970s, whites made up about 80% of drug arrestees. Since the late 1980s blacks have come to represent 32 to 40% (Tonry and Melewski, 2008). Often people may consider it reasonable to assume that blacks are more likely to use and traffic drugs, in turn explaini ng this wide gap in drug impris onment. Analyses by Blumstein (1982) and Tonry and Melewski (2 008) report, however, that drug offenses explained the least amount of racial disproportionality for imprisonm ent compared with any other offense. It is evident that rates of drug use and drug traffick ing for blacks and whites do not explain the vast disparities in imprisonment for drugs. Beatty, Patteruti and Ziedenberg (2007) indicate that when patterns of county drug admission rates were compared with those of drug use rates there is little evidence that drug use drives drug imprisonment. One illustrative ex ample is that Macomb County, Michigan and Cook County, Illinois have similar rates of dr ug use but Cook County has an admission rate seven times greater than Macomb County indicating that incarcera tion for drugs offenses is not evenly distributed across places either. In fact, counties wi th higher poverty levels, higher proportions of black residents, a nd those which spend more on policing and the judicial system tend to have higher rates of drug imprisonmen t (Beatty, Patteruti a nd Ziedenberg, 2007). Drug incarceration is obviously affected by the policing and arrest of drug offenses (Blumstein and Beck, 1999). The clearest explan ation of why police arrest blacks for drug crimes and subsequently why black are highly more likely to be imprisoned for drug offenses is that they are easier to arrest. While white drug d ealing is more likely to occur in private spaces


25 and amongst friends and relatives, a lot of black drug dealing specificall y in low-income areas on the other hand happens in th e open-air market and among strangers (Tonry and Melewski, 2008). Another major contributor to continuation of wide racial disparities in incarceration is the increase in sentencing policies for violent and drug crimes. As menti oned above these policies were influential toward prison growth in gene ral and they have particularly impacted the imprisonment of black Americans, in turn aggr avating racial disparit ies. Meanwhile, these policies have done little to slow crime (Tonry and Melewski, 2008). Here we can see how the ironies of punishment abound. The racially unequal crime control strategies perpetuate the disadvantages seen in metr opolitan communities. Meanwhile, the disparate use of police and prisons does not go unseen by citizens; instead it is observed and provides the basis for a loss of legitimacy within contexts of concentrated incarceration (LaFree, 1999; Tyler, 1990). These ironies of punitivenes s likely account for some of the lack of a deterrent effect from tough on crim e strategies over past decades. Overall, the experience of proceeding through th e system from arrest to imprisonment to release entails an array of negative effects. Clearl y, racial disparities exist within arrest rates, court decisions, and imprisonment rates. These di sparities are higher in certain geographic areas and lower in others. From decades of social research we know that people experience lifechanging effects after going thr ough the criminal justice system (e.g. Becker, 1963; Braithwaite, 1990). If these effects are experienced in diffe rent degrees between black and white Americans and across places, then it is plausible that the outcomes of these effect s are also likely to vary. Race and Collateral Consequences of Crime Control Bruce W estern and several colleagues have produced enlightening findings surrounding the effects of mass imprisonment on economic in equalities (Western and Pettit, 2000; 2005).


26 Western states that there are two profound effects th at must be recognized. First, incarceration has created invisible inequalities. Second, inca rceration significantly reduces the life chances after release. Recent eviden ce clearly shows these patterns across measures of economic inequality (Western, 2006; 2007). The following s ection describes how racial inequalities in the economy and other realms such as voting rights are exacerbated and made invisible through mass imprisonment. Western and Beckett (1999) offer a dynamic understanding of incarceration that goes beyond the typical understanding of the control of a surplus supply of labor. They claim that mass incarceration has become a labor market ins titution that has tightened the labor market in the short-run but will make workers more unemp loyable in the long-run. This labor market effect is highly concentrated among black males and under-skilled workers. The dramatic racial differences in future unemployment continue the institutionalized system of racialized labor market control. Interestingly however, these effect s are hidden in official joblessness statistics since incarcerated individuals ar e not included in calculations of unemployment. The racial disparities work to mask vast racial differen ces in unemployment by underestimating joblessness among incarcerated populations. Incarceration has significant negative effects on future employment opportunities by reducing labor wages, employment rates, and annual earnings (Western, 2007). By comparing a group of former inmates with a similar group of men who had not been incarcerated, Western (2006) reported that incarcerati on substantially reduced length of job tenure, especially for African-American and Hispanic workers. Thus black and Hispanic men with a history of incarceration are moving in and out of short-term jobs more fr equently compared with their white counterparts.


27 Wage growth is also affected by incarceration. As men age from 25 to 35, they typically experience steady increases in wages. Incarceration disrupts this trajectory and significantly slows this tr end (Western, 2002). Former inmates are more likely to be pushed into the secondary labor market due to restrictions on employment for many public sector jobs, professional positions, and skilled professions and disruption of social networks where legitimate employment can be found. Therefore, the expe rience of being locked up in prison has ongoing impacts that follow individuals th roughout their life course. The stigma that follows conviction appears to have racialized outcomes as well. Western (2002) shows evidence that wage growth differentials between exand non-inmates were larger for whites compared with blacks. With such a hi gh rate of incarceration for young black males in certain communities, the difference in wages among black men who are ex-inmates and those who are not may be small due to the stigma that is applied to this group as a whole. While individualized effects are experienced by incarce rated whites, the massive scale of imprisonment among young, urban black males propels aggr egate effects among this entire group. Devah Pager (2003; 2007) accelerated the research on the array of effect s that histories of incarceration and criminal records have upon individuals. Her anal yses dive into the heart of sociological and criminological th eory by portraying the effects of labeling citizens as criminal. Through stringent experimental designs her work shows strong evidence of the expectation that individuals with a mark of a criminal reco rd experience exceptionally difficult obstacles in finding jobs. Moreover, black Americans who are given the mark are substantially less likely to acquire employment. The stigmatizing effects th at criminal histories exert even go beyond those individuals the criminal justice system has succe ssfully marked as criminal. This research speaks to the stigmatizing effects of upholding hi storical perceptions of the black, male


28 criminal across communities. Stigmatization that follows young, black men is upheld by mass labeling. Indeed, she found that black males without a criminal record were less likely to find employment compared with white males with a criminal record (Pager, 2007). Along with impact upon employment, the disconnection of felons from political participation is clearly articulat ed by Manza and Uggen (2006) in their recent analysis of felony voting disenfranchisement. The authors show the far-reaching consequences of revoking voting rights from felons. Felon disenfranchiseme nt has sharply increased since 1976 following a decline between 1960 and 1976 that was due to civ il rights era changes. In 1976 there was a population of approximately 1.2 million dise nfranchised; by 2004 the number reached 5.3 million. The state of Florida alone had over 1 million disenfranchised citizens by the end of 2004. The impact of voting disenfranchisement has a specifically greater effect on black Americans. In several states over 20 % of the A frican-American voting ag e population has lost its right to vote (Manza and Uggen 2006). As Manza and Uggen claim, wide-spread disenfranchisement across the US has had debilitating effects upon individual offenders, communities and politic al participation. Community Effects of Incarceration By the m id-1990s criminologists had observed twenty years of in creasing incarceration rates. Meanwhile, dramatic re ductions in crime were not bei ng seen (although by the end of the decade we saw a downward trend). Around this time some criminologists began to offer contributions toward understanding the effect s that concentrated incarceration was extending beyond the individuals being locked up. After re alizing that some communities were beginning to have a significant number of residents impr isoned, particularly young males, researchers and practitioners started to focus on the collatera l consequences mass incarceration was exerting beyond the individual prisoners (e.g. Cl ear, 1996; Hagan and Dinovitzer, 1999).


29 Before the recognition of collateral conse quences of incarceration, its effects were generally studied through an at omistic approach where the thought s and behaviors of individuals committing crime were the object of examination. This line of research ignored the contextual connections between incarceration and larger social arrangements, and in turn, perpetuated the study of individual criminal careers (Clear, 1996). Clear pronounced the advantages of looking at developments in inca rceration as a product of shifting social forces and as a provocation of social change (Clear, 1996:11) a nd specified that a research age nda be developed to analyze the ways incarceration promotes social conditions that foster criminal behavior. This vantage point has accelerated the study of various reciprocal relationships betw een patterns of incarceration and social characteristics of families and communities. Incarceration is often highly c oncentrated in and around cert ain neighborhoods. An often cited statistic shows that about three quarters of the prison population in New York come from seven New York City neighborhoods. Clear an d Rose (Clear, 1996; 2007; Rose and Clear, 1998) have conceptualized how incarceration, especial ly when many individuals experience it in a concentrated place, can have unintended crime-in ducing effects. Clear (2007) illustrates four central points: 1) The extraordinary growth in the U.S. prison system, sustained over 30 years, has had, at best, a small impact on crime. 2) The growth in imprisonment has been concentrated among poor, minority males who live in impoverished neighborhoods. 3) Concentrated incarceration in those impoverished communities has broken families, weakened the social-control capacity of parents, eroded economic strength, soured attitudes toward society, a nd distorted politics; even, af ter reaching a certain level, it has increased rather than decreased crime. 4) Any attempt to overcome the proble ms of crime will have to encompass a combination of sentencing reforms and philosophical realignment. (Clear, 2007: 5-6).


30 According to their coercive mobility perspective, communities that experience low levels of social cohesion and se lf-regulation amongst residents due to concentrated incarceration can potentially experience increased crime and diso rder. Considering the dramatic increases in incarceration, Rose and Clear illustrate the conu ndrum that we face between the crime-inducing properties of incarceration and the rehabilitative ideal. Myers (1980) has referred to the crimebreeding effects of inca rceration as a negative externalit y effect that may neutralize the intended rehabilitative effect. The concentration of incarceration clearly can have a negative effect upon community social capital. Social capital results from an exte nsion of social networks and involvement in other social structures (Put nam, 2000). The loss of impor tant male residents and the concentration of officially sanctioned indi viduals is accompanied by an accumulation of criminal human capital (Myers, 2000:6) which negates forms of positive social capital from environments such as schools. These negative, accumulating effects on social capital that emanate from concentrated involvement in the criminal justice system sensitize us to the differential role incarceration can play betw een minority and non-minority communities (Loury, 2008). In some disadvantaged minority communities the concentration of extracted residents and former inmates links almost everyone to someone who has experienced being under surveillance. Not only does it increase networks with stigmatized individuals but it also decreases the likelihood of gaining positive contacts such as those with steady employment. The ability of community members to obtain connections with people employed in steady work maintains the social capital within a community. When a work force disappears from a community and social networks are desolate, many employers resort to other areas fo r a workforce (Wilson, 1996).


31 Recent research has begun to clarify the effects incarceration has had on communities. Empirical research points toward three primar y unintended consequences of mass incarceration related to the idea that mass incarceration increa ses or at least maintains crime. First, imprisonment worsens socioeconomic inequalities by a) reducing the employability of released offenders b) stigmatizing potential employees from certain areas (Pager, 2007) and c) indirectly resulting in moving urban economic resources to other settings (Clear 1996; Myers, 1983; Pager, 2007). The decisions of potential employers are likely to be touc hed by the concentration former prisoners released into areas as reaffirm ation of stereotypes can stigmatize entire sectors of the city. Potential employees from some ne ighborhoods may be perceive d to be connected to the world of individuals working the informal econo my stereotyped to be a part of the informal networks of violent and drug dealing in neighbo rhoods experiencing concen trated incarceration. Extracting a highly concentrated number of young males from a co mmunity also may injure the local economy. The removal of one or a few me mbers simply may lead to the replacement by another community member, but removing many members can have negative effects upon the local economy. This coercive removal raids lo cal supplies of human capital and leaves a gap in employable residents (Clear, 2007:109). Second, incarceration can destabilize comm unity organization (Nightingale and Watts, 1996; McGahey, 1986). Community or ganization can be conceptuali zed here as the extent to which community members engage with one anothe r, trust in one anothe r, and are able to informally supervise the community. Community organization relies heavily on the economic opportunities for community members and as these opportunities decline community organization is negatively impacted. Crime cont rol strategies can also have an effect on community organization. Some argue that police presence can increase community interactions


32 in disadvantaged communities (Wilson and Kell ing, 1982) while others show evidence that heavy police presence can negatively impact vi ews toward the criminal justice system and political structures in turn limiting involvement among member s of the community (Moore, 1996; Anderson, 1999). Lynch and Sabol (2001) studied the affect of incarceration across Baltimore neighborhoods and found that higher rates of in carceration were correlated with reduced community solidarity, interactions among commun ity members, and involvement in community organizations. However, there was not a significant relationshi p between neighborhood incarceration rates and info rmal social control. Andersons (1999) ethnographic research illustrates some of the dynamics that lead researchers to believe that crime control stra tegies in disadvantage d neighborhoods can have deleterious outcomes on community organization. The significant amount of adults taken out of the neighborhood reduces the supply of mentors th at ease the transition into adulthood. These relationships can be key transitional elements among young men in these communities and without them we expect that crime, sexual pr omiscuity, and educational achievement would be affected. In addition, views surrounding the legitimacy of state authority and political action are affected. The importance of attitudes toward state authority and the criminal justice system is shown in research evidence that finds that people who have a lo w sense of legitimacy toward the justice system are less likely to conform to its standards (Tyler, 1990). In communities where incarceration is highly concentr ated, it becomes somewhat normalized for people growing up there (Rose and Clear 2004). Even further, th e acquisition of street status can follow the survival of being locked up.


33 Third, incarceration is purporte d to have destabilizing effect s upon family structure. That is, through the removal of a high proportion of re sidents mostly males communities are less likely to have two-parent homes and simply ha ve fewer family members to provide community supervision and strong informal ties. This is important to family relations since most male prisoners with families report having spent time with their children most everyday before being arrested and most were living with their childre n before being placed in prison (Lanier, 1993). After fathers are taken away, their children ofte n experience trauma and other psychological issues that resemble effect s of death and divorce (Low enstein, 1986). Although many young fathers who become incarcerated often show patterns of irresponsible behavior and intermittent relations with the mothers of their children, many of them reportedly spend a considerable amount of time with their children. The incarceration of mothers is also becoming an increasing concern. In fact, the growth rate of women prisoners has recently been highe r than that for men prisoners (Blumstein and Beck, 1999). While mothers account for only less that 10% of the prison population, the growth of this population presents challenge s to the criminal justice system and social services agencies. When fathers are incarcerated, about 90% of the time their children fall under the custody of the mother. The childrens custody presents a mu ch different problem when mothers are incarcerated. In these cases, fr iends or relatives typically gain custody rather than the fathers (Raeder, 1995). The increasing number of wome n entering prison introduces life changes and struggles to many other people besi des the individual serving time. By extracting a concentrated number of young a dults, sexual and intimate relations can be changed. The affected sex ratio may change how women and men decide to remain in or refrain from long-term relationships. Thomas and Torrone (2006) performed a study that


34 showed that incarceration rates were associated with higher le vels of sexually transmitted diseases among women and higher rates of childbirth among teenage women. Again, the deleterious effects of incarcera tion on community-level health outcomes are not evenly distributed by race. The extrac tion of males from communities is even more detrimental to black families and communities (Darity and Myers 1984; Lynch and Sabol, 2004). Noting the wide racial disparitie s in incarceration rates, differences in the social impacts across racial groups are important to examine. In terestingly, many effects are manifested quite differently between white and black communities. For instance, Lynch and Sabol (2004) found that in counties with high proportions of black re sidents incarceration is correlated with family disruption and unemployment rates. Piquero and colleagues (2006) examined the e ffects of incarceration across census tracts in New York City. Their research added an exam ination of the race-specific effects of jail and prison admissions on neighborhood median income and human capital. They found African American prison admissions led to lower median income and human capital between 1985 and 1996. Their evidence provides further suggestions th at not only are the ra tes of incarceration racialized but the negative consequences also have more pronounced effects on black neighborhoods. In sum, there are negative consequences when incarceration is concentrated in communities. Family structure, community orga nization, and stable economic opportunities are adversely affected by the extract ion of many community members through the criminal justice system. These effects are particularly expe rienced by already-disadvantaged, urban black communities. Such racialized impacts maintain the cycle of disadvantage within these places. The communities themselves become stigmatized as ones seen as being comprised mostly of


35 crime and disorder ones perceived to be feared and controlled. In thes e ways, the patterns of racial inequality persist between neighborhoods by repressi ng struggles to improve upon community organization and life chances of folks in disadvantaged neighborhoods. To the extant that communities themselves are stigmatized by racialized incarceration patterns, we may expect that segregation patte rns develop. Indeed, this study examines the relationship between incarcerati on and segregation. Therefore, the next section turns to the literature on racial resi dential segregation.


36 CHAPTER 3 RACIAL RESIDENTIAL SEGREGATION INTRODUCTION In the late 1980s and early 1990s scholars revi ved research on the prim acy of residential segregation in urban Americ a through empirical evidence (Wilson, 1987; Massey and Denton, 1993). Most importantly, it called attention to the dire situation of those urban communities that found themselves at the crux of an intersecti on of economic disadvantage and blackness. The focus on places comprised of concentrated, extr eme impoverishment of black residents showed the sociological importance of the convergence of raceand class-based disadvantage and the powerful and haunting forces that situated so many individuals into a world of evident disadvantage. Racial residential segregation remains an impe rative focus for social research. To some degree, describing and explaining the segregation of certain soci al groups has been a part of human inquiry for centuries. The experience of residential segregation between black and white Americans in the twentieth century was a primary issue among many scholars, politicians, artists, and writers and this problem continues into our tw enty-first century contex t. W.E.B. DuBois and Gunnar Myrdal were two of the most prominent sc holars to examine and detail segregation in the U.S. (DuBois, 1899; Myrdal, 1944). Their descrip tions gave us many insights into the dynamics of race and segregation. The social inequalities that arise from spatial housing patterns which they spoke of have lasted through today. Today, however, we find ourselves in a differe nt era where overt racial discrimination is not nearly as common. While overt discriminati on clearly exists, it is often stigmatized and formally controlled. Since the legal breakth roughs of the civil ri ghts era, overt housing discrimination is formally regulated. Unfortuna tely, we still see discrimination and disparate


37 outcomes when it comes to housing and our other major social institutions (Roscigno, Karafin and Tester, 2009). These discriminatory outcomes a nd racialized patterns of social behavior are maintained through exercises of more subtle, covert mechanisms (Bonilla-Silva, 2003). Therefore to understand racial residential segregation we mu st account for the developing institutional arrangements that maintain it in contemporary times; to understand segregation using theoretical understandings of past decades will likely fall short. The criminal justice system is one social institution that we must examine to understand the influences of racial residential segregation patterns across the US. In particular, this study focuses on the institution of mass incarceration and the extent it upholds black-white residential segregation across urban areas of the U.S. While the argument that the criminal justice system works to oppress disadvantaged groups is not a new found claim, the di rect connection between mass incarceration and segregation patterns has yet to be explicated. In our current social and historical context, mass incarceration is a social institution that affects the lives of not only the millions of citizens under surveillance but th e families and communities to which they are connected. The experience of incarceration is no t a corrective or rehabilitating one for these individuals and many of the social groups they identify with and be long to. Instead, it formally disallows future employment opportunities for ex -inmates, stigmatizes entire communities that experience concentrated imprisonment, and disrupts and stigmatizes entire families (Clear, 2007). These collateral effects of mass incarceration are clearly r acialized (Tonry and Melewski, 2008). We observe the racial di sparities throughout st ages of the criminal justice system, especially within incarceration the last stop. In turn, the collatera l consequences of mass incarceration proliferate uneve nly between racial groups impoverished, urban black


38 communities become more stigmatized, lose social and human capital, and in the end remain highly segregated from places where opportunities abound. The complexities of processes that lead to the connection between mass incarceration and racial residential segregation are plentiful, yet recognizable on ce we acknowledge the developing insights from work on theories of racial inequa lity, collateral consequences of incarceration, racial disparities in criminal ju stice, and patterns of segregati on. Here, I will describe current explanations of segregation and de lineate some of the salient social patterns that are involved in the connection between mass incarceration and racial residential segregation. Three Primary Explanations of Racial Residential Segregation Essentially, there are two models that explain racial residential segr egation, one of which has two variants. Perspectives focusing on th e socioeconomic status fall under the spatial assimilation model, and the explanations emphasi zing discrimination and prejudice are included within the variants of the place stratificati on model (Charles, 2006). In the following, I will discuss evidence from studies focusing on these two models. The spatial assimilation model predicts that residential segregation by race results from persistence and severity of soci oeconomic inequalities between r aces. Since blacks, Hispanics, and other minorities have a higher concentratio n in lower-status occupations, earn less, and receive less quality education, segr egation is an outcome of differe nces in statuses and lifestyles (Charles, 2006). Over the past several decades, research findings illustrate complex dynamics of this model. For Asians and Hispanics upward mobility in socioeconom ic status along with generation shifts from foreign-born to native-born result in reduced segregation from whites. For blacks the story is different. Even as blacks gain economic status, it generally does not lead to substantial declines in segregation from whites. Research shows that while gains in income and education are positively associated with resident ial outcomes, blacks suffer the smallest returns


39 compared with other minority groups (Alba, Logan and Stults, 2000). A large reason for the lowered importance of socioeconomic status among blacks is connected to homeownership. Black homeowners are less likely to live among whites, instead living in more segregated and less affluent areas thus paying a penalty compared with homeo wners of other races (Alba et al., 2000). Overall, compared with other groups, blacks fare less well from improvements in socioeconomic status thus signaling the importa nce of race beyond class in constructing social boundaries. The place stratification model understands se gregation as a form of separation that maintains social distance and social positioning for whites and develops from structural processes linked to racial prejudice and discrimination that main tain the status of whites (Bobo and Zubrinsky, 1996; Massey and Denton, 1993). The place stratifica tion model has two variants: traditional prejudice and race prejudice as a sense of group position (Charles, 2006). The traditional prejudice viewpoint predicts a strong association between negative racial stereotypes and neighborhood racial compositi on preferences and points its focus on the importance of individual attitudes about living near racial minorities. This perspective would expect that decreases in overt r acial stereotypes and preferences would clearly lead to reductions in segregation. Since there are clearly individuals with negati ve attitudes toward blacks and other groups, this expecta tion appears reasonable. Perhaps, structural processes beyond trad itional prejudices impact group positioning and drive residential patterns on a larger scale. The second variant of pl ace stratification model stresses the importance of such structural forces and group positioning. Emanating from Blumers (1958) conceptualization of race prejudice as a sense of group positioning, this perspective explains segregation as a form of racial separation th at results in the maintenance of


40 white superiority. The expectation here is the greater the percei ved relative differences, the less desirable out-group members will be as potential members (Charles, 2006: 47; see also Bobo and Zubrinsky, 1996; Charles, 2000a). Further, in times of significant population shifts and immigration, tensions and conflicts arise th at reduce cooperation between groups (Blumer, 1958). Thus, in periods of transformation r ace structures actions and attitudes to uphold entitlements and privilege. In innovative research on residential prefer ences, it has been show n that race clearly makes a difference beyond economic status. In a study of Detroit-area residents, whites showed clear resistance toward integration while blacks favored integration (Farley et al, 1978). In addition, further studies showed th at Latinos and Asians also a ppear to favor integration and substantial coethnic presence with in their neighborhoods. This lin e of research consistently indicates that people of various races order whites as the most desirable out-group (racial group other than ones own) to live amongst and blacks as the least desirable (Charles, 2006). Some have argued that peoples genera l discomfort with living around a significant black population is an indirect race effect, referred to as the race-as-proxy perspec tive. That is, people actually prefer not to live in areas of perceived high crime, lower-qua lity schools, and low property values. However, this racial-pr oxy explanation of residential pr eferences is in the end racial, although more subtle than explicit and less articulate reasoning for preferring one neighborhood over the next (Krysan, 2002:693). As Quillian and Pager clarify: Even if neighborhood evaluations and decisi ons to move are largely determined by nonracial considerations, such as percep tions of neighborhood crime, if these perceptions are themselves influenced by r acial context, then they can no longer be thought of as race-neutral. (2001:721) Krysan, Farley and Couper (2008) detail how race has a direct influence on housing preferences. By controlling obj ective features of neighborhoods such as housing cost, safety,


41 and quality of schools, their experimental design showed that white respondents view neighborhoods with black resident s significantly more negatively compared with the exact same neighborhoods shown with white residents. Thus, race is not simply a proxy for other presumably deciding factors, but instead is a proxy shaped by negativ e stereotypes. The objective traits of a neighborhood ar e not sufficient to overcome raci al stereotypes toward black residents. From this evidence, Krysan and colleagues (2008) claim that negative stereotypes toward areas with integrated populations that in clude more than a few black residents directly influence whites decisions toward a location to settle. Another explanation of the continuance of reside ntial segregation is that it is due largely to housing discrimination. Audit studies over th e past several decades have shown clear and consistent discriminatory practices within th e housing market (Yinger, 1995; Turner and Ross 2005). The presumed driving force in the main tenance of housing discrimination falls upon the prejudice and the more subtle institu tionalized processes that support it. Recent evidence continues to show housing discrimination for blacks compared with their white counterparts. The 2000 Housing Discrimination Study reports mixed results regarding shifts in housing discrimination in the housing sales a nd rental markets, but again shows that there are differences between trea tment of whites compar ed with blacks across several metropolitan areas. In the rental market during 2000, wh ites were favored over blacks 21% of the time. While this was a decrease from 26.4% in 1989 discrimination is far from being eliminated. A 12% drop in the percentage of whites favored over blacks from 29% in 1989 to 17% in 2000 is a promising sign in the sales mark et. However, this recent report interestingly shows that the steering of black potential homebuye rs away from certain geographic spaces has increased (Turner and Ross 2005). Therefore, while we have seen changes in attitudes toward


42 segregation and the mechanisms that maintain it, black and white segregation continues for a variety of complex reasons. The Influence of Mass Incarceration of Racial Residential Segregation There are three general expl anations of racial reside ntial segregation: unequal socioeconomic status, prejudice, and housing di scrimination (Charles, 2006). The trends in incarceration over the past few decades have affected each of them. First, socioeconomic status is presumed to influence segregation based on the assumption that as minorities experience increases in social position they will be more likely to live am ong white neighbors. To the extent that high incarceration rates affect the economic status of predominantly black communities and residents in those communities, incarceration will have an impact on segregation. The experience of incarceration dramatically reduces the chances of individuals finding employment once released from prison or jail, es pecially for black men (see Pager, 2007). Job opportunities following incarceration are lost through both formal and informal processes. There are now several formal barriers to employment for individuals with criminal histories. States legally prohibit those with criminal records to be employed in certain sectors of the labor market. This negative credential placed upon form er inmates disallows employment for many young adults during a period in their life-course where developing an employment career projects them on a path that will largely determine their future economic prospects. The criminal mark makes it predictable for most individuals to remain economically stagnant. Informal stigmatization processes extend to reaffirm negative attitudes and stereotypes toward urban, black communities. In communities where incarceration is seen as an extraordinary experience that only a few residents are familiar with, the economic consequences only diffuse so far. In communities where there is a concentration of individuals with histories of incarceration, employment opportunities stretc h beyond their own individual experiences and


43 upon the neighborhoods and the families they ar e connected to. The concentration of incarceration leads to the stigmatization of othe r individuals from these communities which has negative consequences on their likelihood of ga ining employment and prevents new employers from entering these communities to provide a substantial amount of jobs. Therefore, the community as a whole is affected by high rates of incarceration. Outsiders have little incentive to move into the urban community when no jobs are available and few residents have reason to move away from their intimat e groups if they experience ex treme difficulty finding a job elsewhere anyway. Second, housing preferences toward living in integrated or segregated neighborhoods substantially guide resi dential outcomes. Most importan tly, white prejudice against living among black neighbors at least more than a fe w token black neighbors presumably maintains segregated patterns of residen ce (Charles, 2006; Krysan, Farley and Couper, 2008). To the extent that high incarceration rates affect the attit udes and perceptions s tigmatization toward communities with a heightened density of black residents and residents in those communities, incarceration will have an effect on segregation. To this point, there has been no research th at directly tests the association between community-level incarceration rate s with racial attitudes or housing preferences. But recent work illustrates important related patterns surrounding perceptions of crime and disorder and neighborhood racial composition. It is plausibl e to argue that racially disproportionate incarceration rates will r eaffirm and reproduce racial stereotypes toward young black men and communities where they reside. This proposition re sts on evidence that the dramatic increases in incarceration and crime cont rol in general, alongside the politicization of crime have maintained a culture of fearing crime, especially in black communities (Russell, 1998). Images and stories


44 across local media outlets that showcase the c onviction of high rates of young, minority men are observed by wide audiences, perp etuating stereotypes that exer t influence upon perceptions of crime and attitudes toward certain neighborhoods. A ll of this occurrs, iron ically, while violent crime rates continue to drop, most dramatica lly among young black popula tions (Parker, 2008). Recently, some have indicated that neighbor hoods with high concentrations of black residents are perceived as having high crime rates irrespective of actual crime levels. Quillian and Pager (2001) show that perceptions of high crime neighborhoods are associated with the percentage of young black men in a neighborhood even after actual cr ime rates and other neighborhoods traits are taken in to account. People are likely to attribute neighborhood safety based on its racial composition for two primary r easons. First, racial composition is easily observable in the U.S.; second, stereotypes a ssociating black indivi duals with crime are persistent and commonly known (Quillian and Pa ger, 2001). While this relationship does not clearly indicate a direct connection between inca rceration rates and segreg ation, it sheds light on the continuing connection between views of area s with a concentration of young black men and how residents perceive cr ime in those areas. Similarly, Sampson and Raudenbush (2004) argue that perceptions of neighborhood disorder are not necessarily reflective of actual observed disorder, but instead are guided by social influences outside of actual neighborhood disorder. Through a unique and comprehensive study design carried out in Chicago neighborhood s, they found that racial composition was actually a stronger predictor of pe rceived disorder than observed disorder. Individual residents of all races perceived heightened disorder as the concentration of minority populations and poverty increased. This signals to us that re sidents augment their first-hand observations of neighborhoods with prior beliefs re garding racial stigma rooted in the long American history of


45 racial inequality and conflict (Loury, 2002). The larger issue th is evidence speaks to is that race plays a significant role in the de velopment of perceptions of urba n residential locations because it is intrinsically linked to i ndicate levels of crime and disorder. As Sampson and Raudenbush conclude: Perceptions of disorder thus appear to create a self-fulfilling structural prophecy whereby all actors are likely to disinvest in or move away from black or mixed areas viewed as high in risk for disorder, but in which whites are more sensitive in the first place and consequently more likely to move. In this way, implicit bias in perceptions of disorder may be one of the undera ppreciated causes of continued racial segregation in the United States. (2004:337) As past research has shown, perceptions of cr ime do not clearly align with actual crime rates (Quillian and Pager, 2001). Knowing that pr eferences and attitudes toward neighborhoods are determinants of residential decision-making, especially for those with th e financial wherewithal, and knowing that perceptions of crime guide attitudes, we must consider social arrangements that maintain historical stereotypes. These patterns might suggest that incarceration and the images and stories of convictions maintain historical stereotypes toward black criminality thus reproducing perceptions even more than actual crime. Therefor e, any evidence that suggests incarceration rates influence perceptions of crime may a dd to our understanding of the discrepancy between perceptions of crime and act ual crime rates. In addition, any evidence that black incarceration rates influence segregation pa tterns would imply that perceptions might be affected by unequal incarceration, in turn maintaining white fear of movement into black or integrated communities. Third, housing discrimination stands in the wa y of blacks and other minorities who would like to purchase or rent housing in predominantly white areas (Yinge r, 1995). To the extent that high incarceration rates constrai n the residential options of black potential residents both formally and informally, incarceration will influe nce segregation. In addition, to the extent


46 perceptions of area crim e are influenced by unequal incarcer ation, segregation will be affected due to avoidance and steering of whites away from neighborhoods consis ting of considerable black residents. There is reason to believe that a criminal history can put forth strong constraints on the ability of ex-inmates to find housing. Travis (2002) claims that for many ex-offenders finding housing, alongside employment, is becoming a noticeable difficulty and leading many into homelessness. Much like formal restrictions on employme nt, housing has become more restricted since landlords have become professionalized and trained to perform criminal background checks on potential tena nts (Thacher, 2008). In a recent survey the National MultiHousing Council shows that 80% of its members check criminal histories of tenants (Delgado, 2005). In a study of tenant screening, Thacher (2008) illustrates how actual methods of social control diffuse beyond the criminal justice system and into our institutions such as private housing. He pulls quotations from how-to ma nuals on property management to illustrate recent techniques in the industry: [I]n a discussion of problem tenants, the book advises that an ounce of prevention is worth many pounds of cure, for cures are costly, agonizing, timeconsuming, crisis-oriented, and sometimes downright dangerous to life, limb, and property. (quotation from Robinson, 1997:197) The concentration on tenant criminal history is a recent phenomenon within this literature, thus showing the pervasiveness of the crime control movement. The shift of landlord focus upon safety and controlling risk emanates partially fr om legal transformations that now hold landlords responsible for maintaining safety ( Kline v. 1500 Massachusetts Avenue Apartment Corp, 1970 ) In turn, landlords have become pressured by justice officials to scr een in order to reduce liability


47 and are provided with institutional infrastructure s to facilitate their use of criminal record databases (Thacher, 2008). It is important to note that restrictions on housing for ex-offenders may not lead directly or necessarily to residential segregation by race. Perhaps these restrictions push folks of all races into places that are more racially integrated due to lack of ot her housing options. It is more likely, however, that property managers use thes e procedures to their advantage and use them selectively. In areas of concentrated poverty and racial minorities, pr operty owners may have fewer potential tenants and beco me willing to accept those with lesser credentials. In the end, the majority of potential tenant s who are black, come from im poverished urban areas and who have not been selected for residence are unl ikely to end up living in areas new to them. In addition to formal processes that control th e housing of those with criminal records, as mentioned above, housing discrimination continues to occur largely as a result of informal interactions such as steering pot ential renters and buyers away fr om certain neighborhoods. This seems to occur due to the perceptions of cr ime surrounding an area and the property values associated with it. Therefore, as the system of incarceration continues to associate crime with poor, black populations it will indirectly affect attitudes and perceptions regarding neighborhood safety. Previous Research on the Statistical Correl a tes of Black-White Racial Segregation In analyses similar to the current one, resear chers include several ec ological variables to indicate macro-level patterns expected to influenc e residential segregation. This study accounts for such relevant variables theorized to be asso ciated with segregation. The following provides brief descriptions of the impor tance of these ecological factor s and the operationalization of these variables is discussed further in the next section.


48 One of the primary macro-level explanations of the persistence of racial residential segregation is that large and/or growing black populations are feared by white populations. The racial threat perspective on race relations proposes that larger black populations and the growth of black populations solidify a perceived threat among whites, leading to the maintenance of residential segregation (Blalock, 1967). This hypot hesis is optimized in Detroit, the metropolitan area with the highest level of segregation. Detroi t experienced a major growth of blacks, mostly from southern regions, during the period of labor expansion in the first half of the twentieth century. Subsequently, in the 1960s, fearful whit es secured segregation through suburbanization and hostility toward blacks. Since then the large black population, perc eived as a threat by suburban residents, has remained largely isolated in the central city of Detroit (Farley, Danziger, and Holzer, 2000). The growth of a black middle-class pres ents a complex influence on segregation. Segregation research on other ethnic groups besides blacks and whites shows how economic assimilation leads to residentia l integration for groups such as Asian-Americans. While many have argued that the closing of the black-to-w hite income gap will mirror assimilation by other groups and result in considerable increases in integration, others have documented that blacks continue to be segregated re gardless of socioeconomic status (Massey and Denton, 1988). When studied at the aggregate-level as the curren t study does blacks do not experience the same gains as other minority groups when their socioeconom ic status increases. This is tied to black homeowners as a group being more segregated that black renters (Alba, Logan and Stults, 2000). It should be noted however that at the individual-level, soci oeconomic status does have a significant effect on residential out comes for blacks (see Charles, 2003 ). Overall, the intricacies


49 of the influence of socioeconomic assimilation on the segregation of blacks from whites continue to be explored. The influence of black suburbanization upon se gregation patterns has also been detailed and is related to the discussi on on the socioeconomic assimilation. There is, however, little consensus on its influence. On the one hand, as blacks move into the suburbs it could be expected that they will become more integrated with white residents. While on the other hand, it could be found that black enclaves develop and simply replace old forms of segregation (Logan et al., 2004). Regional differences also account for variat ion in segregation patterns due to their connection with the structure of local governments. Farley and Frey (1 996) note the importance of region by illustrating differences in the histor ical developments of local governments in the Midwest and northeast with local governments in the South. In the northern and Midwestern metropolises, local governments in the suburbs functioned to shie ld white residents who fled central cities with growing black populations. Suburban city offi cials were able to effectively isolate white communities through their own police strategies, zoning regulations, and public schools and growing recognition of being hostile toward blacks. Meanwhile, as metropolises grew in the South the local govern mental authority rested at the county-level. Therefore racial integration through federal mandates occurred in a more pronounced way due to the inability of southern whites to move to a suburb that held an all-white school system. These patterns continue to be reflected in recent studies of segregation. In the We st, there is more variability as old cities have similar histories of the Midwestern and Northeas t cities, while newly developed areas in the West tend to have lower segregation levels due to their annexation of outlying areas (Farley & Frey, 1996).


50 The functional specialization of a metropolitan area influences residential patterns. Functional specialization refers to the economic ba se of a community. If most or a significant portion of a communitys population is employed in a certain type of labor, this can affect where people live and how racial groups are integrated in many realms of social life. In macro-level segregation studies and the current one, functiona l specialization or the core economic base of a community is broken down into several groups: manufacturing, educational, military, retirement and government. Some areas do not ha ve one clear specialized economic base and in this study these are categorized as having no specialization. Government, military and educat ional (e.g. university towns) communities generally have a different level of educationa l attainment and other compositi onal features compared with manufacturing areas (Farley & Frey, 1996). Military and university communities are also different from others since many residents do not choose their place of residence. Also, university towns are unique in their capacity to have unique changes in housing dynamics. First, since educational atta inment and racial attitude s are strongly associated with one another, such places should entail greater levels of racial tole rance. Also, structural forces in a university community create distinguishable patterns. For example, after the University of Florida became integrated black students and black professiona ls new to the area were allowed to live in neighborhoods that had previously excluded ot her blacks (Wineberg, 1983). Research also shows that military communities have the lowest level of segregation while university and government towns have moderate levels of se gregation; while all th ree typically show a significant and negative a ssociation with black-wh ite segregation when compared with areas of other economic bases (Farley & Fr ey, 1996; Logan et al., 2004).


51 Retirement communities have consistently been shown to have a high level of segregation (Farley & Frey, 1996; Lo gan et al., 2004). It is genera lly assumed that retired blacks secure fewer resources to afford the ability to move into retirement communiti es in states such as Florida and Arizona. In addition, th ere is evidence that whites from racially segregated areas in the North who search for retirement homes in such communities tend to look for racially homogenous neighborhoods. Manufacturing in an area has influences on re sidential segregation largely because of the history connected to the pull of southern blacks into Midwes t and Northeast cities such as Detroit, MI, Cleveland, OH, and Flint, MI during the first half of the twentieth century. These manufacturing communities tend to have heightened levels of segregation. Another ecological factor asso ciated with the segregation of blacks is the overall growth of housing across metropolises typically indi cated by the percentage of new housing in a metropolis. Newly developed areas tend to be less segregated (Logan et al., 2004). This tendency is likely connected to two processes: HUD regulations on affirmative marketing of new housing toward minority populations since the 1970s and the lack of local histories of being hostile toward blacks in recently developed areas. The age of the metropolis is associated with segregation. Age is indicated by the period in which the largest city in the metropolitan area surpa ssed a population of 50,000. The time period matters once we consider that places su ch as Boston, New Orlean s, and San Francisco were large metropolises before the turn of the twentieth century while places like Anaheim grew after World War II and Daytona Beach developed after many changes in civil rights and housing regulations.


52 CHAPTER 4 DATA AND METHODS Introduction As discussed in prior sections, the current study will empirically examine the extent to which trends of incarceration are related to patter ns of residential racial segregation. To this point an empirical analysis of the relationship between incarceration ra tes and segregation has yet to be examined by sociological research. The current study se rves as an initial attempt to answer the research questions at hand by bringing together data sources that provide the most valid measures possible. In the following, da ta from the U.S. Census, American Community Project and the National Corrections Reporti ng Program (NCRP) will be discussed. Since this analysis is perf ormed using Metropolitan Statisti cal Areas (MSA), all measures are operationalized at the MSA level. Both th e dependent and independe nt variables represent boundaries of the MSAs across the U.S. This strate gy is consistent with research that examines predictors of segregation patte rns (Iceland, Weinberg and Steinmetz, 2002; Logan et al, 2004). Appendix A shows the list of MSAs included. Data Sources The unit of analysis is the metropolitan area. Since residential segregation occurs at many levels there are several possible units of analysis. Following extant research on segregation, this study uses the metropolitan ar ea because it represents the most reasonable approximation of housing markets and is chosen over other units for a couple of reasons (see Iceland et al, 2002 for detailed discussion). Fi rst, using the census unit of place which represents a town or city is typically considered too small. I ndividuals may move across or into a nearby neighborhood and would be in a different jurisdiction. Second, the consolidated metropolitan statistical area (CMSA) is too larg e. For instance, boundaries of the New York


53 CMSA go from Pennsylvania to Connecticut. Ther efore, this analysis estimates measures for primary metropolitan areas. When measuring racial residential segregation a nother geographic component necessitates a decision among alternatives. Sinc e there are independent estimates of racial characteristics available for occupied households census tabulation blocks, block groups, tracts, places, and counties, segregation could potentially be measured using each (see Iceland et al, 2002). For this research the census tract is used as the unit of an alysis for calculating residential segregation since it is made to represent neighbo rhoods, varies little from census to census, and has been used by most recent research (Ic eland et al, 2002; Logan et al. 2004). Data utilized in this study come from th ree main sources. The segregation data were collected from the American Communities Projec t (ACP). The ACP (2008) used data from the U.S. Bureau of the Census to create segrega tion scores across metropolitan areas from 1990 and 2000 (available at www.s4.brown.edu/cen2000/index.html ). The U.S. Bureau of Census (1992; 2002) Summary Tape File 3 from 1990 and 2000 provides the sec ond source of data. These data offer information on population and housing ch aracteristics such as racial composition, employment status, and educati on. Finally, the National Corrections Reporting Program (NCRP) supplies data on incarceration. The NCRP data give information on offense and offender characteristics and county where the sentence was imposed which allow for aggregation of incarceration data to the metropolitan area. The NCRP data begin in 1983 but few states were involved until the late-1980s. This study uses NCRP data from 1988 to 2000 because this range of years is temporally linked to the change in segregation from 1990 to 2000 the dependent variable and before this time period the numbe r of MSAs with sufficient data did not produce a large enough sample.


54 There are 331 metropolitan areas with segreg ation data for 1990 and 2000 from the ACP. There are some sampling decisions that reduce th e number of metropolitan areas included within this study. First, NCRP data are only availabl e for areas in states that participated. Second, while some metropolitan areas have some data for all these years 1988 to 2000 they do not all include all data since some counties may lie within a state that did not participate for at least one of th e years. In this study, an MSA is excluded if it had three or more years with incomplete data. In other words, if an MSA had complete data for all years from 1988 to 2000 they are included. An MSA was also included if it had only one or two years missing complete data. (This is explained more thoroughly below in the section on measurement of variables). The NCRP data allow for three different trends to be used in analyses. Third, following previous segregation rese arch, this study includes only metropolitan areas with a significant population of black residents. The expected value of our dependent variable index of dissimilarity (see below) is not zero under conditions of random assignment if the population of one race is sm all compared with the number of geographic units used in its calculation (Johnson and Farley 1985; Logan et al. 2004). Therefore, it is not necessary or appropriate to include areas w ith very few black residents at start or end dates. After metropolitan areas with less than 2% black re sidents in 2000 were excluded the number of metropolitan areas was further reduced. The sample size for the trend from 1990 to 2000 was 222, while the sample for models using the 1989 to 2000 and 1988 to 2000 trends were 214 and 213, respectively. This is compared with sim ilar work by Logan and colleagues (2004) that included 255 metropolitan areas all metropolitan areas with si zable black populations in 1980 and 2000.


55 Dependent variable Massey and Denton (1988) show that the measurement of racial residential segregation can occur at five different dimensions: evenne ss, exposure, concentra tion, centralization, and clustering. This study will use tw o of the most common dimensi ons of segregation: evenness and exposure. Figures 4-1 and 4-2 show two hypothetical metropolitan areas to help conceptualize these two dimensions. Evenness refers to the differen tial distribution of two social groups among areal units in a city (Massey and Denton, 1988: 284). Furthermore: Evenness is maximized and segregation mi nimized when all units have the same relative number of minority and majority members as the city as a whole. Conversely, evenness is minimized, and se gregation maximized, when no minority and majority members share a common ar ea of residence. (Massey and Denton, 1988: 285) This study uses the most common measure of eve nness (and segregation in general) the index of dissimilarity. The index of di ssimilarity ranges from 0 (complet e integration) to 1 (complete segregation) and captures the p ercentage of a groups population that would have to change residence for each neighborhood to have the same percent of that group as the metropolitan area overall (Iceland et al, 2002). The index of dissimilarity (D) is generated by: W W B B Dii)2/1( Where: B = the metropolitan black population Bi = the black population of tract i W = the metropolitan white population Wi = the white population of tract i


56 The second measure of segregation is expos ure, which is measured by the interaction index. The interaction index one index of a group termed P* re fers to the extent to which minority members are exposed to members of the majority group as the minority-weighted average of the majority proportion of the population in each areal unit (Icel and et al., 2002). The interaction index (P*) is calculated using the following formula: i iiT Y B B P Where: B = the metropolitan black population Bi = the black population of tract i Yi = the white population of tract i Ti = the total population of tract i In order to analyze change from 1990 to 2000, change scores are created and represent the dependent variables. These are generated for both the index of dissim ilarity and exposure by: D%change = ((D2000 D1990)/D1990) P* %change = ((P*2000 P*1990)/P*1990) Positive change scores for the dissimilarity index indicate increases in segregation and positive change scores for the interaction index indicate decrease s in segregation. Independent variables From the review of literature, several variables are considered to influence patterns of residential racial segregation be tween blacks and whites. This study is primarily interested in extending evidence on the influences of segregati on shifts by examining the extent they are associated with incarceration trends. In a ddition to including meas ures of incarceration, variables that indicate regional ch aracteristics, population size and changes, growth of minority


57 groups, racial differences in economic status, and suburbanization are included within this research design. The measure of attitudes and pr eferences are not availa ble at the metropolitan area level so they are not included in this analysis. The independent variable of most im portance to the current study concerns black incarceration trends Black incarceration trends are expe cted to indicate the extent to which formal and informal mechanisms work to stig matize blacks across metropolitan areas. To the extent stigmatization occurs, incarceration l eads to further housing discrimination, economic disparities, and housing preferen ces that maintain racial residential segregation across metropolitan areas. This study is the first to utilize the NCRP data in this fash ion. Not only is this the first to test the association between incarc eration and segregation using these data, but it is also the first to aggregate the prison data to the metropolitan area. Noting the unique quality and importance of the NCRP data in this analysis, it is importa nt to carefully detail the aggregation and data management process. The NCRP data were aggregated through a multi-stage process to create rates of incarceration at the metropolitan area level, and in the end, bl ack incarceration trend variables. From Inter-University Consortium for Political a nd Social Research (ICPSR) annual NCRP data were gathered from publicly available files. These individual-level data contained information on offender characteristics such as offense type, se x, race, date of birth and offense with longest sentence length. Information that identified the county where the sentence was imposed allow for aggregation to the county-level. Counts of incarceration disaggregated by sex, offense type,


58 and race are then made possible as well.1 For this particular analysis, black incarceration counts were the only cases used however future analyses could disaggregate by se x or offense type. After county-level incarceration counts were created for each year, it was then possible to create counts for metropolitan areas. All count y-level data from 1988 to 2000 were merged into one data file. In this file, each county that part icipated in the NCRP at least for one year was included and annual counts were available for a ll years in which each individual county provided incarceration data to the NCRP. The next step then was to aggregate these county level data to the metropolitan area. Using a crosswalk program provided by MABLE/GEOCORR (Missouri Census Data Center 2009), each county was given its MSA iden tifier a number that identified which MSA it belonged to in 2000.2 Before aggregation to the metropo litan area took place, it was necessary that counties with only complete data for all 13 years between 1988 and 2000 be identified and in turn included in aggregation. If an MSA ha d a county with missing data for a particular year, that year was excluded for that county. In th e end, a MSA was included if it had two or less years of missing data. Therefore, for some metr opolitan areas, the tre nd variable only included eleven years for the 1988-2000 trend, ten years fo r the 1989-2000 trend, and nine years for the 1990 trend.3 This strategy was used to exclude inaccurate counts for the entire MSA due to one or more counties missing data for that year. For instance, if a large county in an MSA was missing for one year, it would cons iderably reduce the total count of incarceration for the MSA. 1 Offenders were sometimes given multiple sentences and were then included as two separate cases within the individual level file. Since this study is concerned with the number of people incarcerated and not the total number of sentences imposed, these duplicate cases were found and deleted prior to aggregation. 2 Some counties FIPS codes the identification number used by the Census and MABLE/GEOCORR changed from 1990 to 2000. A notable example is Miami-Dade County. The NCRP used the older codes; throughout, these were manually changed in SPSS in order to merge the MABLE/GEOCORR file to the NCRP file. 3 This was further reduced for only a few MSAs due to the exclusion of outlying years in the linear trend.


59 It was useful to keep those MSA with two or less years of incomplete data due to the increase in sample size that resulted. In the 1988-2000 tr end there were 197 MSAs with zero years of incomplete, 236 with one or less years of incomple te data, and 255 with two or less years. In the 1989-2000 trend there were 204 MSAs with zero y ears of incomplete data, 236 with one or fewer years of incomplete data, and 258 with tw o or fewer years. In the 1990-2000 trend there were 224 MSAs with zero years of incomplete data, 237 with one or fewer years of incomplete data, and 268 with two or fewer years. Ther efore in the 1988-2000 trend the samples size was increased by 58 by including those with two or less years of incomplete data, in the 1989-2000 trend 54, and in the 1990-2000 trend ther e was an increase of forty-four.4 Another issue in the aggr egation process involved counties whose boundaries straddled metropolitan areas. That is, part of one county may be in one metropolitan area and another part in another metropolitan area. Also, some countie s may be partially within a metropolitan area and partially in a non-metropolitan ar ea. In total, across the US there are 33 such counties and they all fall within the New England region. Here a decision had to made in regards to including these straddling counties or exclude them. The GEOCORR file provides the percentage of a county that falls within a certain MSA; thus we ca n identify those that do not have 100% of their boundaries within a single MSA. The criterion for inclusion into a metropolitan area was that a county had to have 75% or more of its space with in a metropolitan area to be included. This presents missing data issues since a county with 74% of its areal coverage would be excluded from this analysis. Therefore, incarceration data in these metropolitan areas with excluded straddling counties may be underestimated. 4 These numbers reflect the sample before thos e with small black population were excluded.


60 From this aggregation process, racially disaggregated counts of black incarceration admissions were calculated for metropolitan areas. The file containing metropolitan area counts of incarceration admissions was merged with a file of census data that contained population size information. From this merged file black incarceration rates we re generated as: Annual black admission rate = (total black admissions/(total black pop/1000)) After annual rates were calculated for each ye ar included within this study it was possible to generate trend variables for the black incarcer ation rate. In the curr ent study, trend variables are used to indicate the extent to which metropol itan areas have experien ced black incarceration across three time periods: 1988 to 20 00, 1989 to 2000, and 1990 to 2000. Linear trends in black incarceration rates for metropolitan areas were created using procedures outlined by Singer and Willett (2003:2833). Essentially, this consisted of creating slope estimates for change in black incarceration rates across each of the three time periods used in these analyses, and in addition an intercept estimate was produced. Th e primary justification for the use of black incarceration trends as an in dependent variable is that it provides an intuitive representation of change in blac k incarceration. Other change trajectories such as quadratic and cubic were considered but these did not produce an optimal varian ce/covariance structure. In addition, after visually explori ng the trends for individual MS As by plotting them on a graph many trends displayed a relativel y linear pattern, yet there was no clear trend that was common across all or most MSAs. Theref ore, the decision was to use the linear trend to indicate change in black incarceration rates. To estimate the within-MSA regression model for each MSA in the data set I regressed black incarceration rate on time for each individual MSA. In this model, the fitted slope estimates the annual rate of change in the black in carceration rate and is clearly the parameter of


61 most interest as it is the most substantively important independent vari able used in the final regression analysis. This model also produced an intercept which indicates the estimated initial status of black incarceration rate at the first year of the tr end either 1988, 1989 or 1990 since there are three different trends used in this research design. The in itial status variable is included in regression models since the lin ear trend and the initial status were significantly and negatively related this indicates that the lower the incarcera tion rate at the beginni ng of the time period the more it increased across this span. Since some annual black incarceration rates may have an influence on the parameter estimates described above, detection of outliers was also conducted. Outlying years for each metropolitan area were defined as those who showed a standardized DFBeta value of 2.0 or greater. These outlying years were deleted (ten metro areas had a year deleted for the 1990-2000 trend, nine metro areas had a y ear deleted for the 1989-2000 trend, and nine metro areas had a year deleted for the 1988-2000 trend) and subsequent analyses were ran to create new slope and intercept coefficients for MSAs with outlying year s and these coefficients were used in the OLS analysis. Control variables Regional dif ferences in segregation are expected since previous work illustrates such patterns (Logan et al. 2004; Farley and Frey 1994; Krivo and Kaufman 1999; Massey and Gross 1991). In the Midwest and North east segregation has b een more pronounced largely due to local governments. The cities of Midwest and Northe ast states are generally more surrounded by suburbs that have maintained segregation thr ough local policies and sy stematic arrangements during the expansion of suburbi a (Farley, Danziger and Holzer 2000). In order to assess regional differences, dummy variables were crea ted to distinguish four regions: Northeast, Midwest, South, and West (see U.S. Census Bureau, 2000).


62 The size of the black population has shown to influence segregation larger black populations increase the extent of segregation (Logan et al., 2004). Black population size was measured as the percentage of black residents. %Black = (# of black re sidents/total population)*100 In relation, the growth rate of blacks relative to the growth of whites is likely to increase segregation (Logan et al., 2004). The racial threat hypothesis pr edicts that as minority group populations increase forms of social control such as segregation will increase as a mechanism to combat the perceived social threat (Blalock 1967) The growth rate of bl acks less than the growth of whites is generated by: Growth rate of blacks = (%black200 %black1990)(%white2000%white1990) Black suburbanization is also taken into account as ch anges in the rate of black in suburban areas may influence segregation across the metropolitan area in general. This influence appears complex; however, it is uncertain whet her blacks moving into su burban areas leads to increased integration with white suburban resi dents or whether clusters of black suburban enclaves develop to maintain or perhaps increase metropolitan segregation. Changes in black suburbanization were generated as 2000 values less the 1990 values. %Black Suburb Growth = (2000% of blacks in sub 1990% of blacks in sub) Percent of new housing is measured to account for differences in housing development across metropolitan areas. Farley (1996) notes new housing across many metropolitan areas may be increasing integration. As more black reside nts become able to obtain their own home and new housing districts without a hi story of racial hostility deve lop, blacks and whites presumably become more mixed into new neighborhoods. Percent New Housing = (# of houses built since 1990)/total # of houses)


63 The type of housing may matter here too. That is, to what extent is the new housing lowor middle income? The extent to which new housing is subsidized would be an important figure to include. However, data from the Census do not offer this information. The change in black income is also included. Past analyses indicate the economic shifts by black residents influence residential segr egation patterns (Krivo and Kaufman, 1999). Change in black income is measured as the change in the percentage of black families with middle-income from 1990 to 2000. Change in black income = (% of black families with mid-income in 2000/% of black families with mid-income in 2000) Age of metropolis is also included as Logan and colle agues (2004:13) state that in older metropolises, many neighborhoods were established wh en brokers and lenders strictly enforced racist policies, and racial segregation has b een the norm for decades. In younger locations, most of the population growth occurred after the Op en Housing Law prohibi ted discrimination. Many metropolitan areas included in this analysis experienced considerable growth after civil rights laws were passed thus it is important to control for the ag e of the metropolitan area. Following past research the age of a metropolis was determined by the decade in which the largest central city firs t reached 50,000 people. Areas were categorized in one of the following: 1900 and before, 1910 to 1940, 1950 to 1960, or 1970 and after (those that do not include a city of 50,000 people or more were placed into the 197 0 and after group which was also the reference group). Population size is also connected to segregation patte rns. Specifically, evidence suggests that larger metropolises experien ce more segregation, although this relationship is not linear.


64 Here population size is measured as the log of th e total number of reside nts within a metropolitan area in 1990 The functional specialization of a metropolitan area influences the type of people living there, where they live, and varieties of housing available. The economic base that creates and maintains a metropolis varies across areas. For in stance, metropolises that include a state capital or an army base will substantially differ from an area that relies heavily on a manufacturing economic base. In order to be categorized into a functional sp ecialization the metropolitan area had to be one or more standard deviations above the national average for 2000. Metropolitan areas above one standard deviation for more than one specialization were placed into the specialization which they had the higher score. Dummy vari ables were created for each specialization and for those areas that have no sp ecialization (which was the reference group). Functional specialization was categorized as follows: Manufacturing = % of employed worke rs in manufacturing industry in 2000 Government = % of employed workers in lo cal, state or federal government in 2000 Military = % of populati on between 18 and 64 in the armed forces in 2000 Retirement = % of popu lation 65 years of age and older in 2000 Education = % of population 15 or older enrolled in college in 2000 Although it might also seem apparent to control for crime rates, this analysis is primarily concerned with the number of people extracted from the community indicating a measure of stigmatization regardless of the amount of cr ime. Thus, in order to create parsimonious models, it is not equally con cerned about the rates of crim e occurring in these areas. Limitations of the incarceration data One important limitation to the NCRP data is that some cases do not include information of the race of the individual admitted to prison. This clearly has implications since the black


65 admission rate may be overor underestimated. Appendix B shows the average percent of total cases per MSA that did not offer race informa tion. At the maximum 6% of cases were missing race information for one year while at th e minimum .05% of cases were missing race information. Perhaps more importantly, I a ssessed whether missing race information was correlated with black incarceration rate across MSA per year. I ran correlations for each year for MSA black incarceration rate and found that between 1988 and 2000, in three years MSA black incarceration rate and the MSA perc ent of race missing were positivel y and significantly related. In all the other years, there was no significant correlation (see Appendi x C). Therefore, in 1995, 1998 and 1999 the higher the black incarceration rate the higher the percent of race information missing per MSA. Noting this significant relationship in these three years these correlations suggest that the black incarceration rate might be misestimated for many metropolitan areas.


66 Figure 4-1. Distribution of house holds within one hypothetical metropolitan area with high segregation and one with low segregation: Dissimilarity index (evenness). Adopted from Iceland et al. 2002.


67 Figure 4-2. Distribution of house holds within one hypothetical metropolitan area with high segregation and one with low segregatio n: Interaction index (exposure). Adopted from Iceland et al. 2002.


68 CHAPTER 5 DESCRIPTIVE STATISTICS Introduction This chapter provides descriptive statistics of the dependent and independent variables. It also describes data for the indivi dual years used in the NCRP data. In the regression analyses that follow, there were three trends used as the ma in independent variable. Trends from 1990 to 2000, from 1989 to 2000, and from 1988 to 2000 were all used to indicat e changes in black incarceration rates. The amount of MSAs with valid data varies, which leads to differences in sample sizes used across the different models The sample size for the 1990 to 2000 is 222, while the sample size fo r the 1989 to 2000 and 1988 to 2000 trends are 214 and 213, respectively. The descriptive statistics (in Tabl es 5-4 and 5-5) below are calculated using the sample used for the 1990 to 2000 trend, the largest sample. Subsequent analyses showed that no substantial differences for the descriptive statisti cs for the other two samples; therefore they are not shown to guard against redundancy. Descriptive Statistics Table 5-1 shows the mean annual rate of black incarceration acr oss the MSAs with complete data for each year. The rates increase across the time period from 1988 to 2000 with a minimum of 5.85 in 1988 to a maximum of 10.15 in 2000. Figure 5-1 also reflects this trend. These descriptive statistics coincide with other research on incarceration trends and tell us that black incarceration rates were increasing fairly consistently across this time span. Table 5-2 shows the descriptive statistics for each of the thr ee trends and the coinciding initial status variable the intercept of the lin ear trend equation that are independent variables in the regression analyses that follow. The linear trends become steeper as the trend goes back in time. The 1988 to 2000 trend has a mean of .27, while the 1989 to 2000 and the 1990 to 2000


69 trends have means of .22 and .19, respectively. Similar to descrip tive statistics in Table 5-1, the trends show that black incarce ration rates were increasing duri ng these time periods. As the minimum values of the linear trends show, in th is period of increasing black incarceration there were MSAs that experienced decreasing trends as well. Table 5-3 shows that most areas experienced increases in black incarceration rates. That is, the first three quartiles all averaged an increase in black in carceration, with the f ourth quartile of MSAs experiencing an average declining trend. As the descriptive statistics in Table 5-4 indicate changes were occurring from 1990 to 2000 in terms of economic measures, housing, growth rates of black populations, and residential segregation between blacks and wh ites. Referring to the depende nt variable first, on average segregation decreased across these metropolitan ar eas. This is similar to other research on segregation during this time period that used a full sample of all MSAs1 (Logan et al., 2004). The mean dissimilarity index dropped about 4% from 59.17 to 55.18 from 1990 to 2000, showing that areas became more integrated. In a ddition, the change in the interaction index (p*) from 7.59 to 8.73 indicates that whites became less isolated from blacks. The average reduction in segregation across this set of metropolitan areas is important to consider when considering that we might expect increases (or smaller reductions) as black inca rceration increases. Again, the positive changes in the interaction index and negative changes in the dissimilarity index indicate reductions in segregation. The rate of black population ro se 6.7% on average and the percentage of black residents in suburban areas rose by .64%. The initial black population size measured in 1990 ranged from 1 Logan and colleagues (2004) found that the decreases in segregation from 1990 to 2000 were much smaller than those from 1980 to 1990. These statistics show us th at, on average, black incar ceration was increasing while segregation was decreasing. This may lead to the inference that black incarceration and segregation could possibly have a negative association. The regression analyses that follow will allow an estimate of the association within individual units while taking into account other ecological variables.


70 .9% to 45.6%. The change in the percentage of black residents with middle incomes also had a mean increase of .25%, although some areas experien ced a substantial decrea se on this measure. The 1990 population size of this sample ranged from 68,956 to over 8.8 million. These changes have theoretical importance in light of research on segregation which exp ects that growing black populations often lead to increased efforts to maintain segregation. This study includes various dummy variables in order to account for variation in the age of metropolitan areas, the functi onal specialization, and regional differences. In Table 5-5 information regarding the dummy variables is pres ented. As it shows about one-fifth of MSAs developed before the turn of the twentieth century and half developed after 1970 or never accumulated a large central city. Also, over half of the areas do not have an economic functional specialization, while there are between 6 and 9% of metropolit an areas with manufacturing, government, military, retirement, or education sp ecializations. Slightly over half (51%) of MSAs in this sample are in the South, 20% are in the Midwest, 15% in the West, and 14% in the Northeast. Conclusion The descriptive statistics for the dependent variable, independent va riables, and control variables show important details about changes in residential segregation, black incarceration rates, and other metropolitan char acteristics. There is variation in the change in segregation across this sample of metropolitan areas. On av erage most areas experi enced a reduction in black-to-white segregation. In addition, metropolitan areas experi enced varying levels of black incarceration trends. The overall trend (Figure 5-1) for this sample shows that black incarceration rates increased continually from 1988 to 2000. The next chapter will discuss the multivariate results in order to determine whether trends in black incarceration rates are


71 associated with changes in racial residential segregation while contro lling for other ecological variables.


72 Table 5-1. Annual rate of black prison in carceration per 100,000 black residents across metropolitan areas with 2,500 black residents. Year N Rate 1988 211 5.85 1989 202 7.02 1990 214 7.81 1991 214 8.10 1992 222 8.47 1993 222 8.41 1994 222 8.73 1995 222 9.16 1996 222 8.85 1997 214 9.32 1998 222 9.83 1999 200 10.00 2000 201 10.15 Table 5-2. Estimated linear trends and in itial status of black incarceration rates. N Min Max Mean St Dev Estimated initial status 88-00 -0.4630.356.97 4.28 Estimated linear trend 88-00 213 -1.463.610.27 0.64 Estimated initial status 89-00 0.5929.767.55 4.23 Estimated linear trend 89-00 214 -1.693.600.22 0.67 Estimated initial status 90-00 0.5529.247.93 4.14 Estimated linear trend 90-00 222 -2.153.610.19 0.69 Table 5-3. Linear trend from 1990-2 000 categorized into quartiles. Linear trend Min Max Mean 1s t quartile .523.61.8977 2n d quartile .16.52.2923 3r d quartile -.13.16.0217 4t h quartile -2.15-.14-.5507


73 Table 5-4. Descriptive statistics fo r dependent and control variables MinimumMaximum Mean Std. Deviation Dissimilarity change 90-00 -25.529.70-6.79 5.61 Dissimilarity w/b 1990 26.1387.4859.17 12.20 Dissimilarity w/b 2000 23.2184.7255.18 11.96 P star change 90-00 -0.260.950.21 0.18 p star w/b 90 0.8030.937.59 6.19 p star w/b 00 1.1835.028.73 6.78 Black suburbanization 90-00 -11.367.300.64 1.59 Population 1990 68,9568,862,948707,496 1,157,242 Percent black 1990 0.945.6012.74 10.40 Black growth rate 90-00 -3.5123.556.70 3.45 Change in % black with midincome -20.688.470.25 3.97 Percent New Housing 90-00 4.4540.4217.95 6.96 N=222 Table 5-5. Descriptives of dummy variables. Variable Percent of MSAs 1900 and before 21% 1910-1940 29 1950-1960 14 1970 and after 46 Manufacturing 16 Government 9 Military 6 Retire 9 Education 8 No function specialization 51 Northeast 14 Midwest 20 South 51 West 15


74 Figure 5-1. Trend for black incarceration rate. Trend in Black Incarceration Rate0.00 2.00 4.00 6.00 8.00 10.00 12.001 988 1 9 8 9 1 9 9 0 199 1 1992 1993 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 199 8 1 9 9 9 2 0 0 0YearBlack Incarceration Rate Rate per 100,000 Black Residents


75 CHAPTER 6 MULTIVARIATE ANALYSIS Introduction The primary analytic strategy used in this study will be ordinary least squares (OLS) regression. Following similar anal yses of residential segregation using ecological predictor variables at the metropolitan area-level, OLS allows an examination of multiple influences on patterns of segregation. To test the hypothesis that black incarcer ation rates are associated with segregation changes I regress residential segregation change scores on black incarceration trends controlling for the ecological measures described above. Models are weighted by the percentage of the residents that are black, thus making areas with higher percentages of black populations more heavily wei ghted. According to Logan, Stults and Farley: The index of dissimilarity is statistically i ndependent of the relative size of the two groups used in its composition (Zoloth 1976) Thus, indexes of dissimilarity for metropolises with small black populations ma y be compared to those for locations with many blacks. However, if mean i ndexes of black-white segregation are intended to describe the typical extent of segregation for bl acks, these indexes should be weighted by the relative si ze of the black population. (2004: 6) Again, those MSAs with less than 2% black in 2000 were excluded. For instance, Bismarck, ND was excluded because it had only a small number of black residents amounting to less than 1% of its population. Finally, I emphasize that the ex tent to which we can claim causal effects remains limited due to the reciprocal nature of relationships between variables included in models that predict segregation. For instance, there is strong evidence that as racial residential segreg ation increases crime rates increase, which in turn could impact incarceration rates (Peterson and Krivo, 1999; Madden, 2001). The incarceration of large numbers of black residents coul d also lead to the coercive mobility of black families out of areas where gentrification is taking hold. Noting the


76 reciprocal characteristics of th e black incarceration-segregation re lationship, the results of this analysis are to be interpreted as correlational in nature. Examining Regression Assumptions Detecting Outliers In order to exam ine whether there were infl uential outlying cases, distance measures and influence statistics were obtaine d. An examination of leverage measured the difference between each cases value on X relative to the difference be tween the rest of the cases values on X and the mean of X. The analysis of leverage showed no outliers. The studentized residuals were also examined in order to measure the size of th e standardized residuals when removing case i from the denominator of the t-ratio. The assessment of st udentized residuals also revealed no outliers. A Cooks distance measure was also created to find the overall change in the regression coefficients when case i is omitted from the analysis. This an alysis showed no outliers as well. The DFBETA influence statistics measured th e impact of individual cases on the regression results and showed that the slope coefficient for the black incarceration linear trend was affected by two MSAs: Binghamton and Honolulu. Models were ran without these two cases and the coefficient for black incarceration only ch anged by .002 and significant levels were only marginally changed. Due to these rather small ch anges and the substantive interest of including these cases, they were kept in the final analyses. Multicollinearity and Heteroskedasticity In m acro-level research such as this multicol linearity is fairly common. In order to assess the extent of the OLS regression assumption that independent variables are uncorrelated, multicollinearity diagnostics were performed. Collinearity was found to exist among a number of independent variables.


77 The variance inflation factor (VIF) for each independent variable was found in order to examine the extent of variance inflation as a resu lt of multicollinearity. Here, VIF values of 2.50 are problematic (Allison, 1999). In addition, tolerance (TOL) scores were examined to show the unique variance in each independent variable th at is not shared with the other independent variables in the model. Allison (1999) suggest s that tolerance scores of .40 and below are problematic. For the 1990 to 2000 trend mode l (Model 1), the logged popula tion size control variable was excluded from the reported model due to significant levels of multicollinearity caused primarily by the inclusion of logged population si ze logged population had a tolerance score of .299 and the metropolitan age pre1900 dummy variable had a to lerance of .284. Importantly, the inclusion of logged population size reduced the black incarceration trend coefficient to nonsignificance. While collinearity remained am ong the 1990 index of dissimilarity (VIF=2.78; TOL=.36) and percent black (VIF=2.60; TOL=.38) these important cont rol variables were included to ensure appropriate mo del specification. In addition, when these two variables were excluded in a subsequent model, the coefficien t for the black incarceration slope only changed marginally from -.633 to -.681. Similar to Model 1, for the 1989 to 2000 tr end model (Model 2) the logged population size control variable was excluded from the re ported model due to significant levels of multicollinearity caused primarily by the inclus ion of logged population size logged population had a tolerance score of .289 and the metropolitan age pre-1900 dummy variable had a tolerance of .273. Again, the inclusion of logged populati on size reduced the black incarceration trend coefficient to non-significance. While collinearity remained among the 1990 index of dissimilarity (VIF=2.80; TOL=.36), pre-1900 dummy variable (VIF=.38; TOL=2.61) and


78 percent black (VIF=2.62; TOL=.38) these important control variable s were included to ensure appropriate model specification. In addition, when these va riables were excluded in a subsequent model the coefficient for the black incarceration slope only changed marginally. In Model 3, similar outcomes occurred when collin earity was assessed. Therefore, the reported model does not include logged population. While this is an important vari able to think about when examining influences of segregation, the in clusion of the age of metropolitan area is highly correlated to population size the older an area is generally leads to a larger population size.1 Heteroskedasticity tests were performed to examine the assumption of constant error variance. In OLS regression it is important to test the extent to which the size of the residuals increase or decrease systematically with the value of one or more of the independent variables. A Whites test was conducted and results indicate that there was not a significant level of heteroskedasticity (McClendon, 1994:178-195). Accounting for Change in the Di ssimilarity Index from 1990-2000 In Table 6 -1 the results of a zero-order corr elation are presented. For two of the three black incarceration trends there is a positive rela tionship with changes in dissimilarity index. This indicates that as black inca rceration trends increase, dissimilarity index scores also increase, or that segregation between black s and whites increase. This is in the direction that would be expected. However, to examine the relationship fu rther control variables mu st also be taken into account. Models 1 through 3 in Table 6-2 present the unstandardized coefficients and standard errors for the black incarceration trend and control variables. Negative coefficients show that the 1 The results of colliearity are in regards to the model usin g the dissimilarity index. Those for the interaction index had very similar results when collinearity diagnostics were evaluated. The decision to exclude log population was made to guard against severe influence of collinearity.


79 variable was associated with decreases in segreg ation. Model 1 uses the incarceration data from 1990 to 2000, model 2 uses incarceration data from 1989 to 2000, and model 3 uses data from 1988 to 2000. After examining the hypothesis that the black incarceration trend would have a positive association with residential segregation it is clear that this hypothesis is not supported. For models 1 through 3, while controlling for the in itial level of black in carceration and other ecological variables, it was found that as the trend in black in carceration rate accelerates residential segregation between blacks and white s decreases across this sample of metropolitan areas. Recall that positive relationship indicate s more segregation. When examining each model separately, the unstandardized coefficients for the black incarceration trend are always negative and they become larger as the time period incl uded more years. For M odel 1 the unstandardized coefficient equals -.633 (p<.01), for Model 2 it is -.795 (p<.01) and for Model 3 it equals -1.062 (p<.01). These coefficients are in the opposit e direction of the hypothesized relationship, yet they yield interesting results that will be discussed further in th e following chapter. Meanwhile, the results regarding the control variables must be discussed to fully interpret the models. As stated earlier, research suggests that many ecological variables may influence patterns of residential segregation across metropolitan areas. Ther efore many control variables were included in the various models. (Since models 1 through 3 show consistent findings in regards to the coefficients for the control variables and the dependent variable this section will speak directly toward only model 1, unless otherwise stated, to avoid redundancy.) First, the initial status of black incarceration was included and has a negative and significant association (-.318, p<.01 ) with change in segregation. This shows that high initial levels of black incarceration were associated with larger reductions in segregation.


80 Beyond black incarceration trends and its initial status, other variables have associations with segregation change from 1990 to 2000. The percentage of black re sidents (B= .063) was significantly and positively related to segregation change indicating that the more a metropolis is made up of black residents the less integration it experienced dur ing this time period. This is consistent with previous research (Logan et al. 2004). Black suburbanization (B= .285) also showed a significant and positive association. Somewhat surprisingly, as black suburbanization increases, residential segregation also increases. Although some re search suggests that as blacks move to suburbs there will be reductions in segregation, others state that certain black enclaves will simply develop. This finding favors the latter. The increase in blacks with middle income had mixed findings between models. While in Model 1 the association was not significant, Models 2 and 3 indicate there was a positive and significant association between change in the percentage of blacks with middle income and segregation change (B= .112 and B= .120, respectively). Much like the above finding in regards to black suburbanization, this finding suggests that changes in areas of housing and income levels ma y simply not reduce segregation but reproduce clusters of black ne ighborhoods seen in the central city or poorer areas. Similar to past research (Logan et al. 2004), the level of segregation in 1990 has a negative and significant relationship (B= -.045) with change s in segregation. This suggests that there might be a pattern of regression toward the mean. The growth rate of blacks did not have significant association with segregation change in any of the models. As stated above, past research has also suggested that amount of new housing may influence segregation patterns. The results show that the percent of new housing in a metropolitan area is significantly associated (B= -17.613) with a reduc tion in residential segregation. Again this is si milar to earlier research (Loga n et al., 2004). The age of


81 metropolitan areas, the functional specialization and region also show associations with segregation changes. Areas which developed du ring the middle of the twentieth century from 1910 to 1960 experienced significantly fewer reduc tions in segregation compared with newer metropolitan areas. The oldest metropolises those that developed before 1900 also experienced fewer reductions in segregation, but th is association was only statistically significant for model 1. Compared with metropolitan areas with no functional specia lization (the reference category) those with manufacturing and gove rnment based economies, and retirement communities experienced larger reductions in segregation. Meanwhile, areas with larger educational communities and military personnel e xperienced significantly fewer reductions in segregation during this time period. Regional diff erences were also shown. In comparison with the South, Midwestern metropolitan areas experien ced larger reductions in segregation, while there was no significant difference between the S outh (the reference categ ory) and the West or Northeast. This finding is somewhat contra sting with earlier work by Logan and colleagues (2004). Besides this last finding, many of the associations ar e similar in significance and direction when compared with research by Loga n and others (2004) which examined segregation change from 1980 to 2000 as opposed to change from 1990 to 2000 as the current study did. Thus, we can be fairly confident that the results of this analysis which utilized a reduced sample of metropolitan areas are represen tative. The most interesting finding, of course, is the negative association between black incarcer ation trend and segregation change This is the opposite of the hypothesis, but again, leads to interesting methodological and theoretical questi ons that are discussed further in th e next chapter.


82 Accounting for Change in the Interaction Index from 1990-2000 In Table 6 -1 the results of a zero-order corr elation are presented. For two of the three black incarceration trends there is a positive relati onship with changes in interaction index. This indicates that as black incarcerati on trends increase, interaction inde x scores also increase, or that segregation between blacks and whites decrease. This is in the oppos ite direction that was expected. In addition, this si gnals different findings that were found in the zero-order correlations that included the diss imilarity index. This finding s uggests that the two measures of segregation change indicate two different processes. The multivariate results discussed below detail findings when other cont rol variables were included. Table 6-3 presents coefficients and standard errors which are the results of models when the change in the interaction index was regresse d on black incarceration trends and the control variables. These models were included in th is study to examine the extent to which black incarceration trends are associated with segregat ion, and to see if there were different outcomes when another measure of segregation was used. The interaction index m easures the extent to which whites are exposed to black residents across metropolitan ar eas. An increase indicates more integration, thus positive coefficients indi cate that the variable was associated with a decrease in segregation. Similar to the previous models using the dissimila rity index, the results show that increasing black incar ceration trends are significantly associated with decreases in residential segregation. More specifically, in those areas experi encing steeper increases in black incarceration white residents became more exposed to black residents. Once again, this is in direct contrast to expectations and the theoretical importance of this finding will be discussed in the following sections.


83 The models also show similar results in re gards to most of the control variables. For instance, in areas with high percentages of blacks there was an in crease in segregation. Also, the percent of new housing had a sign ificant association with change in segregation. That is, increases in new housing were asso ciated with decreases in segreg ation. In these models with change in exposure as the dependent variable some of the du mmy variables that indicated functional specialization showed consistent finding compared with the previous models that used dissimilarity index. Manufacturing, government, and retirement areas show significantly more integration compared with those with no functional specialization. When examining differences between m odels using the dissimilarity index and interaction index, some of the re lationships changed. The initial status of black incarceration no longer is significantly associated in the interaction index models. This indicates that the black incarceration rates at the begi nning of the period are not rela ted to the extent to which segregation changed from 1990 to 2000. In addi tion, the age of the metropolitan area was significant but in the opposite dire ction as before. Older areas te nded to experience increases in exposure between whites and blacks compared with the younger areas.


84 Table 6-1. Zero-order correlations for segregation change and in carceration trends. Notes: p < .05, ** p < .01 1988-00 1989-00 1990-00 Change in dissimilarity .03 .05* .06** Change in p star .10** .10** .12**


85 Table 6-2. Regression estimates for change in residential segreg ation (dissimilarity) on three trends in black incarceration ra tes and control variables. Model 1 1990-00 Model 2 1989-00 Model 3 1988-00 Variables B SE B SE B SE Black incar trend -.633** .193 -.795** .220 -1.062** .243 Black incar initial status -.318** .029 -.335** .031 -.351** .032 Black growth rate 90-00 .022 .029 .012 .031 .015 .031 Percent Black .063** .011 .068** .012 .065** .012 Black suburbanization .285** .049 .254** .053 .255** .053 Change in mid-inc blacks .054 .034 .112** .035 .120** .036 % new housing 90-00 -17.613** 1.685 -18.931** 1.898 -19.331** 1.902 Dissimilarity 1990 -.045** .011 -.048** .011 -.047** .011 1900 and before .853** .316 .365 .338 .397 .345 1910-1940 .924** .244 1.048** .262 1.035** .263 1950-1960 1.668** .272 1.804** .296 1.785** .297 1970 and after *** *** *** Manufacturing -1.286** .268 -1.822** .282 -1.834 .283 Government -.674* .291 -1.541** .317 -1.526** .318 Military 1.821** .384 1.132** .397 1.097** .398 Retirement -2.685** .449 -2.597** .468 -2.568** .468 Education 1.745** .360 1.150** .370 1.191** .372 No functional specialization *** *** *** Northeast -.226 .393 -.213 .421 -.370 .428 Midwest -1.026** .357 -.958* .378 -1.057** .383 West -.478 .474 -.243 .485 -.269 .488 South *** *** *** Constant 1.074 .883 1.881* .926 1.969* .929 Adjusted R .221 .218 .219 N 222 214 213 Notes: p < .05, ** p < .01


86 Table 6-3. Regression estimates for change in residential segreg ation (interaction) on three trends in black in carceration rate s and control variables. Model 1 1990-00 Model 2 1989-00 Model 3 1988-00 Variables B SE B SE B SE Black incar trend .026** .004 .023** .005 .024* .006 Black incar initial status -.001 .001 -.001 .001 -.002** .001 Black growth rate 90-00 .012** .001 .013** .001 .013** .001 Percent Black -.001* .001 -.001* .001 -.001* .001 Black suburbanization .004** .001 .004** .001 .004** .001 Change in mid-inc blacks .003** .001 .002* .001 .002** .001 % new housing 90-00 .110** .001 .191** .043 .182** .043 Interaction 1990 -.001* .001 -.001 .001 -.002 .001 1900 and before .003 .007 .020** .001 .021** .008 1910-1940 .019** .005 .024** .006 .024** .006 1950-1960 .023** .006 .024** .007 .024** .007 1970 and after *** *** *** Manufacturing .003 .006 .016* .007 .016* .007 Government .037** .007 .045** .007 .045** .007 Military -.005 .008 .009 .009 .008 .009 Retirement .147** .010 .158** .011 .158** .011 Education -.013 .008 -.001 .008 .000 .008 No functional specialization *** *** *** Northeast .070** .009 .074** .010 .071** .010 Midwest .129** .008 .134** .008 .132** .009 West -.027* .011 -.027* .011 -.028* .011 South *** *** *** Constant .050** .012 .017 .014 .020 .014 Adjusted R .394 .399 .398 N 222 214 213 Notes: p < .05, ** p < .01


87 CHAPTER 7 DISCUSSION OF STATISTICAL RESULTS The results described above show that bl ack incarceration trends are negatively associa ted with racial residential segregation while controlling for the initial level of black incarceration and other ec ological variables. That is, the more that black incarceration was increasing during the nineti es the more segregation dropped, or the more integration took hold. However, the zero -order correlation using the dissimilarity index shows a complex relationship betw een incarceration trends and segregation change. Even though increases in segregation were occurr ing alongside increasing black incarceration across metropolitan areas, there we re other factors that affected the nature of this relationship. After reviewing these results, I propose a few potential ways to interpret these interesting findings. At the macro-level, a primary difficulty whic h researchers face is trying to empirically disentangle all the possible social fo rces that lead to certain outcomes. Here, I offer theoretical interpreta tions of the associati ons found in the results above. These interpretations may help iden tify additional variables that should be incorporated into future research. First, I argue that concentrated black in carceration is related to gentrification of urban areas. The gentrification-crime control relationship discussed sporadically across studies (Freeman, 2006), but often goes unreco gnized in criminol ogical research. Gentrification presents a different form of segregation compared with traditional measures of segregation such as those utilized in this st udy. Gentrification occurs when an area in the central city has experienced di sinvestment over a fairly long period of time then experiences a period of reinvestment along with the in-migration of a new group of


88 residents into a low-income neighborhood. Generally, gentrification includes neighborhoods and commercial ar eas that cater to a group of largely white residents with a higher level of affluence than the establ ished minority residents of the neighborhood. Just as black residents may relocate suburba n enclaves, certain groups of whites have shifted to central city locations. The ability of a metropolitan area to gent rify neighborhoods might be seen as the product of the power of a social class to en force its ability to tr ansform neighborhoods to fit its commercial and residential needs (Wacquant, 2008; Sanchez-Geraci, 2009). In order for many urban areas to be gentrified and revitalized i.e. the in-migration of young, mostly white, middle-class residents during the 1990s, strong crime control strategies may have often been a necessary c ondition. According to the popular theory of the Creative Class developed by Richard Florida, many old urban areas have been revitalized by a growth of gentrified neighborhoods that attract th e Creative Class, the new class of people employed in research, art, and technology who have revitalized urban areas by residing in bohemian-friendly areas. Seattle, Portland, and San Francisco make up a few of the exemplar cities in this movement (Florida, 2002). In preliminary investigations, most of these metropolitan areas that attract the creative class have some of the highest black incarceration trends throughout the 1990s. Using the same NCRP dataset, creative cla ss cities Seattle, San Francisco, and Albany had black incarceration rates well above the mean during the 1990s. Meanwhile, metropolitan areas that have failed to develop much urban revitalization during the 1990s, such as Buffalo and Detroit, have well below-average black incarceration rates


89 during this time period. Perhaps, arresting and incarcerating black populations in these areas are necessary conditions for many urban areas to experience a revitalized image. Second, since the association between crime control and re sidential segregation is a reciprocal one, integration coul d be leading to more punitive control than first expected. While the 1980s and 1990s consisted of re sidential integratio n between whites and blacks, there were also dramatic increases in prison admissions. The racial threat theory of race relations expects that when minority groups acquire power and dominant groups perceive a loss of privilege, more severe control and punishment toward minority groups will follow (Blalock, 1967; Hawkins, 1987). The analysis above was unable to tease out temporal ordering to directly examine the e ffect of black incarceration on residential segregation, thus we must also consider the forces that resi dential integration might have on crime control. In light of these findings, future research on the regional or macrolevel influences of incarceration patters should take shifts in segregation of blacks and whites into account. Third, these contradictory findings coul d have occurred due to mismeasurement of key variables. Both the measuremen t of the dependent variable and black incarceration trend indepe ndent variable are in need of fu rther examination. In regards to the dependent variable, change in segreg ation, the measurement of segregation change is measured in the standard and approp riate way. However, upon further thought, the extent to which it reflects the reprod uction of the extremely poor, isolated and concentrated black neighborhoods is not as clear. In his theoretical statements, Wacquant (2009) details the severe marginalization of poor, black urban communities as a result of mass incarceration. The measures of segregation used here simply provide an estimate in


90 regards to the overall racial residential segregation that happens across entire metropolitan areas. More refined measures of the segregation of this marginalized group is likely necessary in order to provide an adequate quantitative analysis of hypotheses that emerge from this theoretical perspectiv e on punishment and society. Future research should attempt to explore how the concentr ation and isolation of such neighborhoods may change as a result of incarceration trends. In addition, the utility of N CRP data to indicate incarce ration estimates needs to be examined further. As stated in earlier sections, there are limitati ons. This initial use of an incarceration measurement at the metr opolitan area-level attempted to push this area of research further and similar to many other new me thodological tool s its validity must be further explored. For instance, the use of a linear trend may create complications due to the evidence that not all MSAs experi enced a linear trend of black incarceration during this period. More discus sion of these issues follows in the next chapter. The empirical results show interesting findings that direct this line of research to further investigate the nature of the rela tionship between residential segregation and incarceration trends. For a l ong time, theoretical arguments have stated the connections between social control and race relations. Currently, we now see an upward trend in incarceration that is over three decades long; meanwhile we experience a complex state of race relations. In order to understand race relations in the US, we must understand its connections with crime control, and to unders tand crime control we must understand race relations.


91 CHAPTER 8 LOOKING BACK AT METHODOLOGICAL SHORTCOMINGS Assessment of the National Corrections Reporting Program Data In Chapter 4 the process of creating the incarceration trend variables was outlined. In addition to testing the theoretical relati onship between segregation and incarceration a primary contribution of this study was to advance the area of research on macrocriminology, particularly research on causes and consequences of incarceration trends across metropolitan areas. In this chapte r, I underline the contri butions, shortcomings, and potential of these data. The newly developed aggregation to th e metropolitan area level is advancement over previous work which typically used stateor county-level measur es of incarceration. Many criminological studies analyze relationshi ps across metro areas since they are often considered a unit of communities that shar e many common experiences. Research has clearly illustrated the importa nce of community-level conseq uences of mass incarceration on family life and poverty (Clear, 2007). Yet, at the macro-level, many hypotheses from this research remain untested. As incarcerat ion measures develop further, they can be used to test relationships be tween incarceration and poverty, fa mily variables (i.e. femaleheaded households), and other housing patterns such as gentrification. For instance, these macro-level data could be disaggregated by gender to examine if there were unique patterns across female incarceration. Also, data can be disaggregate d by offense type to examine whether drug admissions are determin ed by different social factors compared with admissions for violent or property offenses. Another quality of these data is their nati onal representation. While not all states participated and the earlier years of this da taset do not include as many states as later


92 years, most states did participate and we can gain valuable estimates across regions of the United States. At the end of this project, a collection of problematic methodological issues were gathered in regards to the NCRP data on prison admissions. Alt hough the aggregation of these data is a contribution that offers re searchers a new methodological tool, there are some issues that need to be recognized in future studies. As stated in Chapter 4, the unavailability of race information for many cases limits the validit y of our racially disaggregated admissions variables, and importantly these are correlated with black incarceration rates (see Chapter 4). There are other methodol ogical issues that arise in the NCRP that future studies will have to c onsider. Since the NCRP data are little-used in this way, I now offer descriptions of a few pointed issues and suggestions for future research. The analyses above suggest that the hypothe sis that black incarceration increases racial inequalities in hous ing is not supported. Upon fu rther reflection, one of my primary interpretations of the results is that the methodological tools used in this initial analysis need improvement. First, the measur e of change in incarceration is a difficult one to conceptualize. Which time frame of incarceration may have had a larger effect the 1980s, the 1990s, both? Future analyses must explore how incarceration rates during different periods may have associations with changes in segregation. Although the linear trend variable provided a ge neral indication of change, it included weakness largely because the change was not linear for many of the metropolitan areas. For instance, curvilinear trends are seen in many areas of North Carolina which experienced dramatic


93 increases in the early half of the 1990s but then saw severe declines after 1994 following changes in sentencing law. In addition, some prison admission cases do not include information regarding the county of admission which disallows this study to include these cases since aggregation to the MSA is not possible without a county id entifier. As Table 8-1 indicates, in the earlier years from 1988 to 1991 there were aroun d 9% of cases for each year that did not have a county identifier which disallowed aggregation and thus inclusion into this analysis. In the middle of the range of years from 1992 to 1998, the percent of those missing a county identifier substa ntially dropped to around 3.5 pe r year to as low as 1.37. However, in 1999 and 2000 this percentage in creased to around 7% of cases without a county identifier This appears to have th e potential to have serious implications upon the measure of black incarceration trends. Clearly, the trend could be misestimated if the rate of incarceration was artificially low in the ea rly years, fairly accu rate during the middle years, and again was artificially low during the end of the trend. Another limitation is that there are fewer states that provide data in the 1980s when incarceration rates were increasing largel y due to drug control. This study initially examined rates during this earlier period and it was decided that the reduction in sample size was substantial enough to only examine change from 1988 to 2000. This has important theoretical implications since th e imprisonment rates began their upward trend in the 1970s and became increasingly racially disparate in the 1980s. Therefore, perhaps many of the racialized effects of mass incarceration were already experienced prior to the 1990s.


94 Measuring Housing Changes The m easurement of changes in housing patter ns must also be explored further. This study hypothesized that at the metropo litan area-level residential segregation between blacks and whites would increase as black incarceration rates increased. Residential segregation measured by the diss imilarity index and the interaction index was meant to be an indicator of racial inequa lity. While it remains an indicator of racial inequality, it may not be measuring the exact type of housing patte rns that result from permeating crime control that we obser ve within changing urban areas. I offer two suggestions for other indicat ors of residential changes. First, gentrification might also be a part of the equation here One potential reason the hypothesis was not supported coul d be related to the neighbor hood integration that occurs during periods of gentrification. While gent rification also entails social inequalities across racial and ethnic lines, it is likely to be associated w ith lower segregation scores as they are measured in the current study. Recent research has utilized measures of gentrification using data from the Neighbor hood Change Database across metropolitan areas (Sanchez-Geraci, 2009). This database could be a fruitful addition to this line of research. Second, segregation of extremely poor black neighborhoods could also be included. The measures included in this analys is did not take the isolation of poor, black communities into account. The inclusion of economic measures alongside race in order to account for the intersectionality of r ace and class could provide a more nuanced measure of the extremely isolated communities that Wacquant has theorized on. Perhaps the influence of incarceration is unique when analyzing segregation of this specific group.


95 Table 8-1. Percentage of cases withou t county identifica tion per year. Year % 2000 6.50 1999 7.27 1998 2.41 1997 2.58 1996 1.53 1995 1.37 1994 3.55 1993 3.34 1992 3.13 1991 9.03 1990 8.56 1989 9.12 1988 9.83


96 CHAPTER 9 DISCUSSION OF THE RECIPROCAL NATURE OF T HE CRIME CONTROLRACIAL INEQUALITY RELATIONSHIP Introduction The results from the methodological appr oach used above did not support the hypothesis that larger increases in black incarceration rate s were positively associated with racial residential segreg ation. Therefore we must theo retically examine these results and try to explain th e finding that racial integration was actually associated with black incarceration trends. The hypothesis was based primarily on the premise that imprisonment of large numbers of the black population leads to s tigmatizationthrough both informal (i.e. neighborhood perceptions of crime) and formal social mechanisms (i.e. loss of employment right s). In response to the opposit e findings the argument could be made that imprisonment has lessened stigma and allowed for more opportunities among black Americans as a whole. This is di fficult to believe in li ght of research that states otherwise (Pager, 2007; Western, 2006). My argument is that the system of mass incarceration has been influenced by and an influencing force upon patterns of racial in equalities across metropolis es of the U.S. The test of the segregation-incarceration a ssociation at the metropolitan-area level is a difficult one to examine. Some neighborhoods may become more highly black and poor due to crime control while others become gentrified. The findings suggest a complex set of reciprocal inter actions between imprisonment and hous ing patters when accounting for other characteristics of metropolitan areas that must be examined further. Therefore, in future analyses, we must recognize that residential mobility is a likely influential factor in explaining changes in crime control, especi ally upon minority populat ions. In relation, different patterns of residentia l transitions such as gentrifi cation may be manifesting in a


97 different qualitative fashion in comparison to traditional types of segregation all the while maintaining racial stratification albeit in new forms. In the following, I try to place the recipro cal nature of crime control and racial inequalities into perspective. If the results of this study are te ntatively viewed as either 1) suggesting that gentrification may have pl ayed a role in the association between incarceration and segregation or 2) that incarc eration was increasing due to integration, or both of these, then the pro cesses of the reciprocal rela tionship must be explained. Further, contemporary sources of racial form ation must be outlined. Therefore, below I discuss the durable importance of race, reasons why black Americans experienced increased social control, and the consequences of increased social control and punishment. New Sources of Racial Formation In the U.S., race is one basis of stratif ication. Race refers to a concept that signifies and symbolizes sociopolitical conflicts and interests in reference to different types of human bodies (Winant, 2000:172) It is a socially constructed concept, influenced more by mechanisms that shape social perceptions of categorization than connection to genetic or actual biological differences. Omi and Winant (1986:68) claim race must be seen as an unstable and decente red complex of social meanings constantly being transformed by po litical struggles. Many scholars have begun to argue that the significance of race is in decline (Wilson, 1978). The election of Barack Obama provides a clear example of a meaningful event that upholds the saliency of this c onclusion. However, the idiosyncrasy of Obamas election and other individual crysta llizing moments of racial progression do not provide analogous portrayals of r ace relations on the aggregate. Racial inequality is now


98 observed in a subtle, covert form (Bonill a-Silva, 2003; Bobo, Kluegel, and Smith, 1997; Kinder and Sears, 1981). Racial dynamics in the U.S. continue to be constructed through political, economic and social processes. Likewise, U.S. politics have been shaped by the changing racial dynamics. Race is ever ywhere, and although the role of race is often complex and covert, it influences politics, individual identities, and cu lture. It is a fundamental organizing principle across sect ors of society (Omi and Wi nant, 1986). Distinguishable from class relations, we cannot fail to see the discrete and unique fo rces that race exerts upon material, symbolic and emotional resources if we are to understand current patterns of durable racial inequality. An examination of contemporary racial inequality must not focus simply on individual bigotry, personal re sponsibility, or explicit acts of racism. This view is fundamentally flawed, both theoretically and empirically (Brown et al., 2003). Instead, it is more useful to understand durable racial ineq uality as a result of hi storical processes of white accumulation and black disaccumulation (Brown et al, 2003). The history of disaccumulation of social, economic, and cultural wealth usually small deficits that add up over time has resulted in unreasonable outcomes for black communities. One of the clearest examples of disaccumulation is the deindustrialization of urban areas in the U.S. While affecting all races, the economic impact the movement of industry has been more deeply experienced by black Americans and communities that had come to rely on decent work opportunities. The administration of law is another source of disaccumulation that maintains racial divisions. Across the era of mass incarceration, blacks have been


99 disproportionately controlled which has led to many unequal outcomes detailed throughout the literature. Since we current ly experience appalling racial inequalities across our social institutions, it is necessary to understand the trajecto ry of racial ordering and the processes that guide it. Use of Criminal Law to Maintain Racial Order Although the state has been concerned with race since the beginning of Am erican history, during previous eras racial c onflict largely took place through informal mechanisms. Social control of slave upris ings, black population growth, and residential integration was not carried out through democratic processe s but instead these conflicts were confronted with informal control m echanisms. For example, lynchings in the American South were used to control percei ved racial uprisings, miscegenation and other acts that threatened the do minant racial order. Beginning during the post-war era, in the U.S. the state became the central context where racial conflict could be confronted (Kennedy, 1997). Clearl y, the civil rights movement can be labeled an instance of stat e intervention upon racial formation. As the civil rights movement brought anti-discrimination policies, backlash against these reforms soon followed through state action. Ther efore, the state is not merely changed by racial dynamics but it intervenes upon the racial order. The criminal justice system has historica lly been one instituti on used to control black populations following periods of re form (Hawkins, 1987). Manza and Uggen (2007) put forth an appealing description of the formation and application of felon disenfranchisement laws across the U.S. They argue that the history of racial politics has been involved in the development of felon di senfranchisement. Their claims are parallel with contemporary statements about the instit utionalization of processes that maintain

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100 racial stratification. Instit uting this practice appears ra ce-neutral all individuals convicted of felonies must concede to th e same penalties and defenders of felon disenfranchisement herald the color-blindne ss of these provisions. However, after examining its development and its outcomes any explanation of the vastness of felon disenfranchisement must view it as a product of racialized politi cs (Manza and Uggen, 2007). Following a period of significant gains by the civil rights movements, the criminal justice system has been one instit ution that has resisted movements toward equality and its implications stretch well beyond the system itself. That is, the racially disparate outcomes within the criminal ju stice system affect opportunities across other social institutions. Criminal justice policies throughout the post-civil rights era have resisted the egalitarian and anti-discriminati on reforms in other stat e institutions as well as its own. In an era where decision-makers are legall y restricted not to discriminate solely on the basis of race following several civil ri ghts advances, the history of a criminal record has become an even more important negative credential (P ager, 2007). Thus, it is not necessarily discriminatory attitudes or actions due to stereotyping by individuals that creates discrimination. Th e stigma of a criminal record has affected the structure around individuals with such an experi ence, often followed by the unwelcome consequences that accompany the stigma (Link and Phelan, 2001). Obvious forms of conscious discrimination are not necessari ly occurring. The use of observable discriminatory practices has become ideo logically difficult, in turn making more sophisticated forms of discrimination become adopted in place of the antiquated forms.

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101 Here it is a structural discrimination proce ss where the stigma of race and a criminal record interact to uphold stru ctural inequalities observed across many of lifes important outcomes. An important insight included in Page rs work points toward the negative credential that a criminal record imposes. As sociologist Randall Collins claims, within the U.S. credentials have become increasingl y important toward categorizing people into distinct groups based on formal merits (C ollins, 1979). The structural arrangements produce circumstances where official credential s negative or positive determine life outcomes. For instance, to obtain employment we increasingly rely on official positive credentials such as college diplomas or trai ning certificates. In instances of defining people as criminal, individuals acq uire a negative credential. In this sense the criminal record is serving as a legitimate mechanism th at formally categorizes individuals as less worthy or less trustworthy. Stigma from being both a black male and having a criminal re cord clearly has a detrimental impact on chances to get hired, ob tain loans, and other credential-based entitlements. Once we consider the frequency of these types of interactions that entail stigma from a criminal record compounde d by racial stigma, the damaging effects upon the collective of black Americans across the U.S. become compelling. As Pager argues, the effects of stigma go well beyond the dom ain of employment and the categorical exclusion of blacks stretches ac ross a wide range of contexts. The stigma arising from a multitude of individual-level interactions accumulates to further disadvantage for blacks to gain opportunity within ma ny social institutions. As she thoughtfully states, [f]or blacks, everyday life achievements take longer, require more effort, and impose greater

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102 financial and psychic costs (Pager, 2007:149). While th is is not a uniquely new observation, evidence of the exacerbation of inequalities thro ugh mass incarceration becomes a strikingly important contribution. In his theoretical explanation of the tr anscendence of social control beyond the prison walls, Loic Wacquant (2001, 2009) illustrates the connections between the ghetto and the prison in contemporary U.S. they have become a functionally equivalent symbiosis that marginalizes poor bl ack communities through separation and stigmatization. He claims the emerging system of incarceration enforces and perpetuates the socioeconomic marginality of urban blacksit also plays a role in the remaking of race and the rede finition of the citizenry via the production of a racialized public culture of vilification of criminals (Wacquant, 2001:97). Mass incarceration has regulated race and poverty in the post-civil ri ghts era since forms of informal control from past decades were responded to by consti tutional protections and legal initiatives to protect individual rights of minorities. Accord ing to Wacquant, mass incarceration is the fourth peculiar institution to develop to cont rol blacks. Eras of slavery, Jim Crow, and the black ghetto preceded the development of mass incarceration. The movement toward mass incarceration must now be considered a major institutional arrangement within American society. Its effects dissemina te across communities and place stigmatizing labels on the general population of young, poor black males. Many of Wacquants conceptions are para llel to work by Rose and Clear (1998) who argue that mass incarceration has led to community disorganization and social exclusion when it becomes highly concentrated By extracting then releasing a highly concentrated population of incarcerated i ndividuals into areas of concentrated

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103 disadvantage neighborhoods become further st rained by increased fear of crime from within and outside of the neighborhood, detached family relations, and a general sense of social exclusion from institutions such as education and a lack of justice by the legal system. He argues that such hyper-segrega ted areas of extreme disadvantage have reproduced prison atmospheres with high levels of surveillance and a state of constant fear of crime and distrust among citizens. The relationships shown in this study s uggest that there are complex influences between mass incarceration of black populations and resident ial segregation. Increasing incarceration may lead to the m ovement of white residents in to certain parts of central cities. While this shift may indicate integration as measured in this analysis, new forms of segregation may also be materializing. Gentrification, while oftentimes resulting in increased interactions between different groups, is a form of housing inequality one that necessitates disinvestment or disaccumulation for substantial periods of time. Incarcerating large porti ons of a neighborhood is one powerful mechanism of disaccumulation. Overall, Wacquant theorizes that the c onfluence of segregation of the ghetto and mass incarceration work together in functional relationship to uphold ra cial stratification. Neutralization of blacks, both materially and symbolically, thus becomes apparent. Materially, neutralizat ion occurs by disallowing employment, welfare benefits, and educational funding for felony convictions. Symbolically, neutralization occurs by thwarting social mobility by a portion of the black population through the reification of the stigmatization of the poor, black population as criminal and deficient. This is what is meant by institutionalized, covert racism.

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104 Conclusion The racially disparate results of many social policies remain neglected by the general public as a resu lt of the racial stigma generali zed across the black population of the United States. Mass incarceration is one striking example of the administration of social policy that disproportionately affect s poor, black Americans. This study attempted to bring more evidence on the collateral consequences of mass in carceration. It brought together theoretical insights and empirical evidence from various fields of study surrounding race relations and crim inal justice. The results of the empirical analysis entail complex interpretations and future empirical work in this area needs to clarify the dynamic interrelations between crim e control and race relations.

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105 APPENDIX A METROPOLITAN AREAS IN MAJOR ANALYSIS Abilene, TX MSA Akron, OH PMSA Albany, GA MSA Albany-Schenectady-Troy, NY MSA Albuquerque, NM MSA Alexandria, LA MSA Allentown-Bethlehem-Easton, PA MSA Altoona, PA MSA Amarillo, TX MSA Anchorage, AK MSA Ann Arbor, MI PMSA Anniston, AL MSA Appleton-Oshkosh-Neenah, WI MSA Asheville, NC MSA Athens, GA MSA Atlanta, GA MSA Atlantic-Cape May, NJ PMSA Auburn-Opelika, AL MSA Augusta-Aiken, GA-SC MSA Austin-San Marcos, TX MSA Bakersfield, CA MSA Baltimore, MD PMSA Bangor, ME MSA Barnstable-Yarmouth, MA MSA Baton Rouge, LA MSA Beaumont-Port Arthur, TX MSA Bellingham, WA MSA Benton Harbor, MI MSA Bergen-Passaic, NJ PMSA Billings, MT MSA Biloxi-Gulfport-Pascagoula, MS MSA Binghamton, NY MSA Birmingham, AL MSA Bismarck, ND MSA Bloomington, IN MSA Bloomington-Normal, IL MSA Boise City, ID MSA Boston, MA-NH PMSA Boulder-Longmont, CO PMSA Brazoria, TX PMSA Bremerton, WA PMSA Bridgeport, CT PMSA Brockton, MA PMSA Brownsville-Harlingen-San Benito, TX MSA Bryan-College Station, TX MSA Buffalo-Niagara Falls, NY MSA Burlington, VT MSA

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106 Canton-Massillon, OH MSA Casper, WY MSA Cedar Rapids, IA MSA Champaign-Urbana, IL MSA Charleston-North Charleston, SC MSA Charleston, WV MSA Charlotte-Gastonia-Rock Hill, NC-SC MSA Charlottesville, VA MSA Chattanooga, TN-GA MSA Cheyenne, WY MSA Chicago, IL PMSA Chico-Paradise, CA MSA Cincinnati, OH-KY-IN PMSA Clarksville-Hopkinsville, TN-KY MSA Cleveland-Lorain-Elyria, OH PMSA Colorado Springs, CO MSA Columbia, MO MSA Columbia, SC MSA Columbus, GA-AL MSA Columbus, OH MSA Corpus Christi, TX MSA Corvallis, OR MSA Cumberland, MD-WV MSA Dallas, TX PMSA Danbury, CT PMSA Danville, VA MSA Davenport-Moline-Rock Island, IA-IL MSA Dayton-Springfield, OH MSA Daytona Beach, FL MSA Decatur, AL MSA Decatur, IL MSA Denver, CO PMSA Des Moines, IA MSA Detroit, MI PMSA Dothan, AL MSA Dover, DE MSA Dubuque, IA MSA Duluth-Superior, MN-WI MSA Dutchess County, NY PMSA Eau Claire, WI MSA El Paso, TX MSA Elkhart-Goshen, IN MSA Elmira, NY MSA Enid, OK MSA Erie, PA MSA Eugene-Springfield, OR MSA Evansville-Henderson, IN-KY MSA Fargo-Moorhead, ND-MN MSA Fayetteville, NC MSA Fayetteville-Springdale-Rogers, AR MSA

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107 Fitchburg-Leominster, MA PMSA Flagstaff, AZ-UT MSA Flint, MI PMSA Florence, AL MSA Florence, SC MSA Fort Collins-Loveland, CO MSA Fort Lauderdale, FL PMSA Fort Myers-Cape Coral, FL MSA Fort Pierce-Port St. Lucie, FL MSA Fort Smith, AR-OK MSA Fort Walton Beach, FL MSA Fort Wayne, IN MSA Fort Worth-Arlington, TX PMSA Fresno, CA MSA Gadsden, AL MSA Gainesville, FL MSA Galveston-Texas City, TX PMSA Gary, IN PMSA Glens Falls, NY MSA Goldsboro, NC MSA Grand Forks, ND-MN MSA Grand Junction, CO MSA Grand Rapids-Muskegon-Holland, MI MSA Great Falls, MT MSA Greeley, CO PMSA Green Bay, WI MSA Greensboro--Winston-Salem--High Point, NC MSA Greenville, NC MSA Greenville-Spartanburg-Anderson, SC MSA Hagerstown, MD PMSA Hamilton-Middletown, OH PMSA Harrisburg-Lebanon-Carlisle, PA MSA Hartford, CT MSA Hattiesburg, MS MSA Hickory-Morganton-Lenoir, NC MSA Honolulu, HI MSA Houma, LA MSA Houston, TX PMSA Huntington-Ashland, WV-KY-OH MSA Huntsville, AL MSA Indianapolis, IN MSA Iowa City, IA MSA Jackson, MI MSA Jackson, MS MSA Jackson, TN MSA Jacksonville, FL MSA Jacksonville, NC MSA Jamestown, NY MSA Janesville-Beloit, WI MSA Jersey City, NJ PMSA

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108 Johnson City-Kingsport-Bristol, TN-VA MSA Johnstown, PA MSA Jonesboro, AR MSA Joplin, MO MSA Kalamazoo-Battle Creek, MI MSA Kankakee, IL PMSA Kansas City, MO-KS MSA Kenosha, WI PMSA Killeen-Temple, TX MSA Knoxville, TN MSA Kokomo, IN MSA La Crosse, WI-MN MSA Lafayette, LA MSA Lafayette, IN MSA Lake Charles, LA MSA Lakeland-Winter Haven, FL MSA Lancaster, PA MSA Lansing-East Lansing, MI MSA Laredo, TX MSA Las Cruces, NM MSA Las Vegas, NV-AZ MSA Lawrence, KS MSA Lawrence, MA-NH PMSA Lawton, OK MSA Lewiston-Auburn, ME MSA Lexington, KY MSA Lima, OH MSA Lincoln, NE MSA Little Rock-North Little Rock, AR MSA Longview-Marshall, TX MSA Los Angeles-Long Beach, CA PMSA Louisville, KY-IN MSA Lowell, MA-NH PMSA Lubbock, TX MSA Lynchburg, VA MSA Macon, GA MSA Madison, WI MSA Manchester, NH PMSA Mansfield, OH MSA McAllen-Edinburg-Mission, TX MSA Medford-Ashland, OR MSA Melbourne-Titusville-Palm Bay, FL MSA Memphis, TN-AR-MS MSA Merced, CA MSA Miami, FL PMSA Middlesex-Somerset-Hunterdon, NJ PMSA Milwaukee-Waukesha, WI PMSA Minneapolis-St. Paul, MN-WI MSA Missoula, MT MSA Mobile, AL MSA

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109 Modesto, CA MSA Monmouth-Ocean, NJ PMSA Monroe, LA MSA Montgomery, AL MSA Muncie, IN MSA Myrtle Beach, SC MSA Naples, FL MSA Nashua, NH PMSA Nashville, TN MSA Nassau-Suffolk, NY PMSA New Bedford, MA PMSA New Haven-Meriden, CT PMSA New London-Norwich, CT-RI MSA New Orleans, LA MSA New York, NY PMSA Newark, NJ PMSA Newburgh, NY-PA PMSA Norfolk-Virginia Beach-Newport News, VA-NC MSA Oakland, CA PMSA Ocala, FL MSA Odessa-Midland, TX MSA Oklahoma City, OK MSA Olympia, WA PMSA Omaha, NE-IA MSA Orange County, CA PMSA Orlando, FL MSA Owensboro, KY MSA Panama City, FL MSA Parkersburg-Marietta, WV-OH MSA Pensacola, FL MSA Peoria-Pekin, IL MSA Philadelphia, PA-NJ PMSA Phoenix-Mesa, AZ MSA Pine Bluff, AR MSA Pittsburgh, PA MSA Pittsfield, MA MSA Pocatello, ID MSA Portland, ME MSA Portland-Vancouver, OR-WA PMSA Portsmouth-Rochester, NH-ME PMSA Providence-Fall River-Warwick, RI-MA MSA Provo-Orem, UT MSA Pueblo, CO MSA Punta Gorda, FL MSA Racine, WI PMSA Raleigh-Durham-Chapel Hill, NC MSA Rapid City, SD MSA Reading, PA MSA Redding, CA MSA

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110 Reno, NV MSA Richland-Kennewick-Pasco, WA MSA Richmond-Petersburg, VA MSA Riverside-San Bernardino, CA PMSA Roanoke, VA MSA Rochester, MN MSA Rochester, NY MSA Rockford, IL MSA Rocky Mount, NC MSA Sacramento, CA PMSA Saginaw-Bay City-Midland, MI MSA St. Cloud, MN MSA St. Joseph, MO MSA St. Louis, MO-IL MSA Salem, OR PMSA Salinas, CA MSA Salt Lake City-Ogden, UT MSA San Angelo, TX MSA San Antonio, TX MSA San Diego, CA MSA San Francisco, CA PMSA San Jose, CA PMSA San Luis Obispo-Atascadero-Paso Robles, CA MSA Santa Barbara-Santa Maria-Lompoc, CA MSA Santa Cruz-Watsonville, CA PMSA Santa Fe, NM MSA Santa Rosa, CA PMSA Sarasota-Bradenton, FL MSA Savannah, GA MSA Scranton--Wilkes-Barre--Hazleton, PA MSA Seattle-Bellevue-Everett, WA PMSA Sharon, PA MSA Sheboygan, WI MSA Sherman-Denison, TX MSA Shreveport-Bossier City, LA MSA Sioux City, IA-NE MSA Sioux Falls, SD MSA South Bend, IN MSA Spokane, WA MSA Springfield, IL MSA Springfield, MO MSA Springfield, MA MSA Stamford-Norwalk, CT PMSA State College, PA MSA Steubenville-Weirton, OH-WV MSA Stockton-Lodi, CA MSA Sumter, SC MSA Syracuse, NY MSA Tacoma, WA PMSA Tallahassee, FL MSA

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111 Tampa-St. Petersburg-Clearwater, FL MSA Terre Haute, IN MSA Texarkana, TX-Texarkana, AR MSA Toledo, OH MSA Topeka, KS MSA Trenton, NJ PMSA Tucson, AZ MSA Tulsa, OK MSA Tuscaloosa, AL MSA Tyler, TX MSA Utica-Rome, NY MSA Vallejo-Fairfield-Napa, CA PMSA Ventura, CA PMSA Victoria, TX MSA Vineland-Millville-Bridgeton, NJ PMSA Visalia-Tulare-Porterville, CA MSA Waco, TX MSA Washington, DC-MD-VA-WV PMSA Waterbury, CT PMSA Waterloo-Cedar Falls, IA MSA Wausau, WI MSA West Palm Beach-Boca Raton, FL MSA Wheeling, WV-OH MSA Wichita, KS MSA Wichita Falls, TX MSA Williamsport, PA MSA Wilmington-Newark, DE-MD PMSA Wilmington, NC MSA Worcester, MA-CT PMSA Yakima, WA MSA Yolo, CA PMSA York, PA MSA Youngstown-Warren, OH MSA Yuba City, CA MSA Yuma, AZ MSA Note: Bolded MSAs were excluded.

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112 APPENDIX B MEAN PERCENT MISSING RACE DATA Year % missing 1988 4.62 1989 .05 1990 4.84 1991 4.64 1992 4.79 1993 5.06 1994 5.30 1995 5.29 1996 5.94 1997 6.22 1998 5.68 1999 6.02 2000 4.02

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113 APPENDIX C CORRELATION BETWEEN PERCENT RAC E MISSING AND BLACK INCARCERATION RATE Year Correlation 1988 -0.04 1989 -0.06 1990 -0.03 1991 -0.06 1992 -0.08 1993 -0.06 1994 -0.01 1995 0.16* 1996 0.04 1997 0.11 1998 0.14* 1999 0.15* 2000 -0.08 Notes: p < .05, ** p < .01

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122 BIOGRAPHICAL INFORMATION Justin Hayes-Sm ith earned his bachelors degree (Psychology and Sociology) in 2003 and masters degree in 2006 (Sociology) from Western Michigan University. He entered the Criminology, Law and Society program at the University of Florida in 2006. Currently, he is temporary faculty in th e Department of Sociology, Anthropology, and Social Work at Central Michigan University.