Prisoner Reentry And the Life Course: The Role of Race And Drugs (Criminal Justice)

Criminal Justice Recent Scholarship

Edited by Marilyn McShane and Frank P. Williams III

A Series from LFB Scholarly

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Prisoner reentry and the life course The role of race and drugs

Daniel J. O’Connell

LFB Scholarly Publishing LLC New York 2006

Copyright © 2006 by LFB Scholarly Publishing LLC All rights reserved. Library of Congress Cataloging-in-Publication Data O'Connell, Daniel J., 1963Prisoner reentry and the life course : the role of race and drugs / Daniel J. O'Connell. p. cm. -- (Criminal justice: recent scholarship) Includes bibliographical references and index. ISBN 1-59332-152-X (alk. paper) 1. Prisoners--Deinstitutionalization--United States. 2. Ex-convicts-United States--Longitudinal studies. 3. Ex-convicts--Social networks-United States. 4. Social control--United States. 5. Recidivism--United States. 6. Drug abuse and crime--United States. 7. Crime and race-United States. 8. Criminal behavior, Prediction of--United States. I. Title. HV9304.O146 2006 365'.66--dc22 2006022513

ISBN 1-59332-152-X Printed on acid-free 250-year-life paper. Manufactured in the United States of America.

This work is dedicated to my Mother, who was always there, and to my Father, who always will be.

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TABLE OF CONTENTS List of Tables ad Figures ........................................................ix Acknowledgements...............................................................xiii Chapter: 1 LIFE COURSE THEORY AND PRISONER REENTRY ...... 1 Population Heterogeneity and State Dependence ............... .2 Population Heterogeneity and Criminal Propensity............. 3 State Dependence, Social Bonds and the Life Course ................................................................... 5 2 A BRIEF HISTORY OF SOCIAL CONTROL..................... 11 Durkheim Introduces Social Control ................................. 11 From Durkheim to Sampson and Laub .............................. 12 Albert Reis, Personal Control and Social Control ............. 12 F. Ivan Nye: Direct, Indirect and Internal Control............. 13 Walter Reckless: Containment Theory .............................. 15 Gresham Sykes and David Matza: Techniques of Neutralization............................................. 17 Travis Hirschi: Social Bonds Theory................................. 17 3 SAMPSON AND LAUB’S THEORY OF AGE GRADED SOCIAL CONTROL ............................................................. 23 A Life Course Model of Informal Social Control.............. 23 Crime Across the Full Life Course .................................... 40 Criticism and Empirical Tests of Age Graded Social Control .................................................................... 41 4 ADULT SOCIAL BONDS, PRISON AND THE REENTRY PROCESS........................................................... 47 Prison and Adult Social Bonds. ......................................... 48 Getting Busted ................................................................... 50 Getting Out ........................................................................ 53 Human Agency and Images of Self ................................... 56 5 SAMPLE, DATA AND METHODS..................................... 63 Sample .............................................................................. 63 Dependent Variables.......................................................... 65 Independent Social Bonds Variables ................................. 69 Employment Measures ...................................................... 69 Relationship to a Spouse and Children .............................. 71 Criminal Propensity Measures........................................... 72 Risk Seeking, Impulsivity and Aggression ........................ 73 vii

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Table of Contents Drug Treatment and Changing Trajectories....................... 75 Analysis Strategy ............................................................... 77 GETTING OUT: 12 MONTH FOLLOW UP ANALYSES .. 83 Introduction........................................................................ 83 Twelve Month Bivariate Employment Effects .................. 83 Twelve Month Bivariate Marriage Effects ........................ 90 Bivariate Child Rearing Effects ......................................... 91 Summary of 12 Month Bivariate Analyses........................ 92 Multivariate 12 Month Analyses ....................................... 93 Logistic Regression Predicting Reincarceration ................ 93 Ordinary Least Squares Regression Predicting Frequency of Drug Use .................................. 100 Summary.......................................................................... 113 STAYING OUT: 24 MONTH FOLLOW-UP ANALYSES115 Introduction...................................................................... 115 Twenty Four Month Bivariate Employment Effects ....................................................... 116 Twenty-Four Month Bivariate Marriage Effects ............. 122 Twenty-Four Month Bivariate Child Rearing Effects ............................................................... 124 Summary of 24 Month Bivariate Analyses...................... 125 Twenty-Four Month Multivariate Analyses .................... 126 Logistic Regression Predicting Reincarceration and Relapse at 24 Months ................................................ 126 Summary of 24-Month Analyses ..................................... 131 STRUCTURAL EQUATION MODELS ............................ 133 Introduction...................................................................... 133 Structural Equations Models for Employment................. 135 Structural Equations Models for Marriage and Children..................................................................... 141 Final Structural Equations Models of Additive SocialBonds ..................................................................... 146 Summary of Structural Equations Models ....................... 148 CONCLUSION.................................................................... 149 REFERENCES……. ........................................................... 157 INDEX…………………………………….….……….…………………167

LIST OF TABLES AND FIGRES 3.1 Social Bonds Age 15 to 25 by Percent Reporting Drug Deviance ................................................................................ 37 3.2 Social Bonds Age 25 to 32 by Percent Reporting Deviance ................................................................................ 38 5.1 Frequency of Drug Use at Each Interview............................. 66 5.2 Voluntary Versus Compulsory Treatment ............................. 76 6.1 Crosstabulation of Employment at 12 Months by Reincarceration ...................................................................... 84 6.2 Crosstabulation of Employment at 12 Months by Relapse................................................................................... 85 6.3 Crosstabulation of Any Employment at 12 Months by Reincarceration ...................................................................... 86 6.4 Crosstabulation of Any Employment at 12 Months by Relapse................................................................................... 86 6.5 Crosstabulation of > 50% Employment at 12 Months by Reincarceration ...................................................................... 87 6.6 Crosstabulation of > 50% Employment at 12 Months by Relapse................................................................................... 88 6.7 Crosstabulation of Hours Worked at 12 Months by Reincarceration ...................................................................... 89 6.8 Crosstabulation of Hours Worked at 12 Months by Relapse................................................................................... 89 6.9 Crosstabulation of Married at 12 Months by Reincarceration ...................................................................... 90 6.10 Crosstabulation of Married at 12 Months by Relapse.................................................................................. 91 6.11 Crosstabulation of Children at 12 Months by Reincarceration ..................................................................... 92 6.12 Crosstabulation of Children at 12 Months by Relapse.......... 92 6.13 Logistic Regression of Reincarceration at 12 Month Follow-up.............................................................................. 95 6.14 Logistic Regression Predicting Reincarceration at 12 Month Follow-up, by Race .........................................96-97 6.15 Logistic Regression Predicting Relapse at 12 Month Follow-up .................................................................. 98 6.16 Logistic Regression Predicting Relapse at 12 Month Follow-up, by Race .......................................99-100

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6.17 OLS Regression Predicting Frequency of Drug Use at 12-Month Follow-up....................................... 102 6.18 OLS Regression Predicting Frequency of Drug Use at 12 Month Follow-up by Race ..................104-105 6.19 OLS Regression Predicting Ratio of Drug Using Months During 12 Month Follow-up.................................. 106 6.20 OLS Regression Predicting Ratio of Drug Using Months During 12 Month Follow-up, by Race............107-108 6.21 OLS Regression Predicting Reduction in Drug Use From Baseline to 12-Month Follow-up ....................... 111 6.22 OLS Regression Predicting Reduction in Drug Use From Baseline to 12 Month Follow-up, By Race ............... 112 7.1 Crosstabulation of Employment at 24 Months by Reincarceration ............................................................... 116 7.2 Crosstabulation of Employment at 24 Months by Relapse................................................................................. 117 7.3 Crosstabulation of Any Employment at 24 Months by Reincarceration .................................................................... 118 7.4 Crosstabulation of Any Employment at 24 Months by Relapse............................................................................ 119 7.5 Crosstabulation of > 50% Employment at 24 Months by Reincarceration .................................................................... 120 7.6 Crosstabulation of > 50% Employment at 24 Months by Relapse............................................................................ 120 7.7 Crosstabulation of > Hours Worked at 24 Months by Reincarceration .................................................................... 121 7.8 Crosstabulation of > Hours Worked at 24 Months by Relapse............................................................................ 122 7.9 Crosstabulation of Married at 24 Months by Reincarceration .................................................................... 123 7.10 Crosstabulation of Married at 24 Months by Relapse......... 123 7.11 Crosstabulation of Children at 24 Months by Reincarceration ................................................................... 124 7.12 Crosstabulation of Children at 24 Months by Relapse........ 125 7.13 Logistic Regression Predicting Reincarceration at 24 Months ........................................................................... 127 7.14 Logistic Regression Predicting Relapse at 24 Months........ 128 7.15 OLS Regression of Frequency of Drug Use at 24 Months ........................................................................... 129

List of Tables and Figures 7.16 OLS Regression of Ratio of Months Using Drugs at 24 Months ........................................................................... 130 7.17 OLS Regression of Reduction in Drug Use at 24 Months .......................................................................... 131 8.1 SEM Models of Employment Predicting Relapse and Prison ............................................................................ 137 8.2 SEM Models of Employment Predicting Frequency of Drug Use and Prison........................................................ 139 8.3 SEM Models of Employment Predicting Ratio of Drug Using Months and Prison ........................................... 140 8.4 SEM Models of Employment Predicting Reduction in Drug Use and Prison............................................................ 141 8.5 SEM Models of Marriage Predicting Drug Use and Prison ..................................................................... 143 8.6 SEM Models of Married Predicting Level of Drug Use and Prison ..................................................................... 144 8.7 SEM Models of Children Predicting Drug Use and Prison ............................................................................ 145 8.8 SEM Models of Children Predicting Frequency of Drug Use and Prison............................................................ 145 8.9 SEM Models of Social Bonds Predicting Drug Use and Prison ............................................................................ 147 8.10 SEM Models of Social Bonds Predicting Frequency of Drug Use and Prison...................................................... 147

LIST OF FIGURES 5.1 Theoretical Structural Equation Model.................................. 79 8.1 Theoretical Structural Equation Model................................ 133

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ACKNOWLEDGMENTS The list of people who deserve thanks for this would be longer than the work itself, and they know who they are. But I would like to single out my family for being there. Shrive Cox I would like to thank for all his help and laughs. Additionally, this project would not have happened were it not for the research agenda of James Inciardi, and the hands on statistical and research assistance from Steven Martin and Cliff Butzin, as well as the research staff and graduate students at the Center for Drug and Alcohol Studies, who continue to keep me sane. I must also thank the members of my dissertation committee, Lana Harrison, Frank Scarpitti and Raymond Paternoster, all of whom went far above and beyond the call in trying to help me understand (the errors are mine). And, of course, I want to thank my dissertation chair, Ronet Bachman, who talked me into coming to the University of Delaware, then made me glad I did, and then helped me get out (even though I stayed). And lastly and most importantly, my wife, Tami O’Connell, who somehow knew all this would work and without whom, NONE of this would have happened. Thank you all.

This research was supported by NIDA research grant R37 DA06124.

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CHAPTER 1

Life Course Theory and Prisoner Reentry An ongoing theoretical debate in the criminological literature concerns the relative strength of stable internal person level traits and fluid external social factors in explaining a person’s likelihood of criminal involvement (Gottfredson & Hirschi, 1990; Hirschi & Gottfredson, 2000; Moffit, 1993, 2006; Laub & Sampson, 2003; Sampson & Laub, 1993, 2003, 2005). On one side are those who maintain that people develop propensities for crime and other analogous behaviors early in life, and that these traits remain relatively stable throughout the life course. The other side of the argument is exemplified by Sampson and Laub’s (1993) age graded theory of social control, which suggests that changes in social relations over the life course can affect a person’s future involvement in crime in spite of earlier criminal propensities. This debate happens to be taking place at the same time a policy relevant phenomenon has come to the attention of criminal justice researchers, that of prisoner reentry. According to the United State Department of Justice, 635,000 inmates were released from state and federal prisons in the U.S. in 2000 (Bureau of Justice Statistics, 2002). These numbers are estimated to increase in the coming years. This represents a large number of potential criminals returning to their previous communities. What happens to these people as they return from prison is of great importance, not only to criminologists, but also to police, courts and citizens of the communities to which they return. If these individuals successfully reintegrate into society and lead meaningful lives, the probability of further crime is potentially reduced. However, if they remain marginalized and unable to fully incorporate themselves into some form of productive existence, the probability of further crime is increased, at least according to those theorists who posit that change matters. As explained below, the theoretical perspective advanced by Sampson and Laub offers a foundation to test the differential ability of prisoners to successfully reintegrate into a community once they are released from prison. The study outlined here attempts to focus a theoretical eye upon this policy driven issue by testing the effects of Sampson and Laub’s age graded theory of informal social control on a sample of reentering criminal offenders. 1

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The purpose of the study is fourfold. The investigation will first determine whether and to what extent Sampson and Laub’s theory is useful in explaining desistence in a sample of contemporary, highly active, drug involved criminal offenders. Second, the study will expand the body of literature testing Sampson and Laub’s theory by testing the basic tenets of the theory across Black and White racial groups. Do social bonds differentially affect Blacks’ and Whites’ criminal involvement? Are there specific bonds that might serve as a turning point for one group but not the other? Third, the study will investigate whether the theory can be extended to include drug treatment as a potential turning point in the life course. Accordingly the study attempts to bring the agency of the individual back in. Following Giordano’s (2002) suggestion that control theory underemphasizes the decision making process of offenders, and Terry’s (2003) focus on self image, it will be argued that drug treatment can produce the mental decisions required to “go straight,” leading to a turning point. And finally, because one of the main criticisms of Sampson and Laub’s theory is self selection of persons into socially integrating roles, the study will address and control for differing levels of criminal propensity. Critics such and Travis Hirschi and Michael Gottfredson (1995) have maintained that social process theories such as Sampson and Laub’s do not control for self selection of persons with differing levels of criminal propensity entering into both criminal behavior and poor social relations. In order to address this, multiple measures of criminal propensity will be employed in the present study to address Gottfredson and Hirschi’s valid criticisms. Population Heterogeneity and State Dependence The idea that criminals are somehow different from law abiding citizens dates at least to the work Lombroso, which is to say that the concept is as old as criminology itself. The idea that criminals are different is in many ways still alive today, although most modern theories do not suggest that criminals are born different, but rather that processes early in life relating to personality or intelligence have long lasting effects that differentiate people later in life. These theoretical perspectives can broadly be classified as population heterogeneity approaches; theories that assume people sort themselves into groups

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based on some set of characteristics (in this case a propensity towards criminal behavior), and that once this sorting has taken place, membership in these groups remains relatively stable over time. Theories based on the population heterogeneity can be contrasted with those arguing a state dependence approach. That is, a person’s current state (i.e., offending, not offending) is dependent upon some earlier set of events (i.e., having a job, not having a job). For example, many social process theories that suggest attachment to a spouse reduces the likelihood of later crime, or that offending early in life increases the likelihood of later crime (in some cases by decreasing the likelihood of finding a suitable spouse), can broadly be classified as state dependence theories. Population Heterogeneity and Criminal Propensity A number of criminological theories emphasize the stability of antisocial tendencies developed early in life. For example, Moffit (1993, 2006) suggests that neuropsychological differences that manifest themselves at a young age can help explain differences in offending patterns later in life for some segments of the offending population. Wilson and Herrnstein (1985) argue that differences in intelligence, again developed early in life, can partially differentiate offenders from non-offenders later in the life course. Zuckerman (1979) also has long maintained that differences in preference for risk and sensation seeking are latent traits of all individuals and that persons higher on these measures will be more likely to engage in a host of activities, including crime. Perhaps the most popular, and certainly the one that has garnered the most discussion, is Gottfredson and Hirschi's (1990) General Theory of Crime. This theory is based on the concept of low self control, which the authors contend develops early in life, remains relatively stable throughout the life-course, and leads to a higher likelihood of criminal behavior. Although differing in many respects, the theories cited above all share the population heterogeneity perspective. While most sociological theories focus on factors external to the individual as the main cause of crime, population heterogeneity perspectives argue that individuals possess different propensities towards crime that remain stable over time. Once an individual attains a certain age, usually less

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than ten years old, these traits, or propensities, are relatively stable and the intervention of external social factors becomes largely irrelevant to later offending or non-offending. Because Gottfredson and Hirschi (1990) discuss in detail how their theory applies to changes later in life, it is outlined in more detail below. Breaking with the tradition of earlier control theories (Reiss, 1951; Toby, 1958; Reckless, 1967; Matza, 1964; Hirschi, 1969), Gottfredson and Hirschi argue that factors external to the individual have little to do with criminal behavior once a person has developed a propensity towards crime. In his original (1969) social bonds theory, however, Hirschi suggested that people are constrained from committing crime by factors external to the individual. Specifically, he proposed that people who are well attached to society and to conventional institutions are less likely to commit criminal acts (Hirschi, 1969). In Gottfredson and Hirschi’s contemporary (1990) theory, the main element of social control is parental attachment. Parental attachment to a child leads to higher levels of supervision which in turn leads to a more developed sense of self control. Unlike social bonds, which are external to the individual, self control is an internal element of every individual that overrides or preconditions later social bonds to the point that they are not causally relevant to crime. According to Gottfredson and Hirschi, self control exists on a continuum and individuals can fall along the continuum from the low to high end. Those at the low end will “everywhere and always” be more likely to commit criminal acts as well as a host of analogous behaviors than those with higher levels of self control. Gottfredson and Hirschi’s theory of the relationship between crime and self control is straightforward. They begin by describing the nature of crime. Criminal acts, they state "provide immediate, easy and simple gratification (money without work, sex without courtship, revenge without court delays)... are exciting, risky or thrilling... provide few or meager long term benefits, require little skill or planning, and often result in pain or discomfort to the victim" (Gottfredson and Hirschi, 1990 p. 89). Gottfredson and Hirschi then ask what type of person is most likely to engage in this type of behavior. Their answer is a person with low self control. Persons with low self control tend to be "impulsive, insensitive, physical (as opposed to mental), risk taking, short sighted,

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and nonverbal" (Gottfredson and Hirschi, 1990 p. 90). According to Gottfredson and Hirschi, persons with lower levels of self control are more likely to engage in a host of behaviors that are not in their best long term interests, including crime. They suggest that the difference between persons with high self control and those with lower levels of control is that people with high self control are more likely to see and understand the long term consequences of their actions than their lower controlled counterparts. They argue that this is the key to understanding the difference between criminals and non-criminals, and suggest that all criminals exhibit low levels of self control. They do not, however, suggest that all persons with low self control will commit criminal acts, only that they are more likely to do so. The last component of population heterogeneity theories of crime is stability. Gottfredson and Hirschi suggest that high or low self control is instilled in a child by about age eight. Farrington (1991) notes that aggression is measurable at about the same age. Zuckerman also notes the early age at which risk seekers can be separated from non-risk seekers (1979). Persons who develop these traits are more likely to commit crimes than those who do not. As Gottfredson and Hirschi note, once a person has developed self control, they are less likely to become a criminal at any point in their lives than a person who does not develop a necessary amount of self control. Conversely, if a person does not develop a high level of self control by about age eight, they are unlikely to do so later in life, and are thus more likely to engage in criminal activity than their higher controlled counterparts throughout their life. This last component has been controversial, and serves as a good point of departure from population heterogeneity theories to social process and state dependence theories such as that proposed by Sampson and Laub (1993). State Dependence, Social Bonds and the Life Course The idea that what happens to someone at one point in their life affects what happens later in their lives is central to Sampson and Laub’s age graded theory of self control. The underlying proposition is that bonds to significant others and to institutions decreases the likelihood of crime, both early in life and later in the life course. Further, lack of bonds early in life both increases adolescent and early adulthood

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criminality (which directly affects later criminality through the stability of early criminal propensity), and also decreases the likelihood of establishing strong social bonds later in life. Still, Sampson and Laub’s application of a life course perspective allows them to argue for and show how social bonds developed later in life reduce the likelihood of criminal behavior, in spite of early criminal propensities. This aspect of their theory, that prior events affect present statuses, classifies Sampson and Laub’s theory as one of state dependence. The focus on early lack of bonds and the subsequent effect on later offending is a clear example of how early states affect later ones. By clearly articulating how individuals move through different life transitions, and how these transitions are themselves affected by early propensities as well as social processes, Sampson and Laub have contributed one of the most nuanced theories in criminology. The two central concepts of Sampson and Laub's (1993) theory are trajectories and transitions. Trajectories are long term patterns of development, whereas transitions are abrupt occurrences that have the capacity to alter trajectories. When a transition alters a trajectory, it is referred to as a turning point. Using these combined concepts, Sampson and Laub argue that early childhood experiences resulting from social structural bonds of parenting and school set a child on a trajectory that either increases or decreases the likelihood of delinquency. As one travels through a trajectory they pass through transitions, such as justice system involvement, marriage, or entry into the job market. These transitions and any resulting social attachments can take the form of turning points, which alter a person’s trajectory, either increasing or decreasing the likelihood of delinquency (Sampson & Laub, 1993, Laub & Sampson, 2003 ). Sampson and Laub's longitudinal analysis of the Glueck’s (1950) data showed that those individuals who had strong attachments to a spouse and/or an employer were less likely to engage in criminal acts, regardless of adolescent delinquency or low self control factors. That is, those who entered into a meaningful marriage or who were attached to a job were less likely to offend as adults than those who did not have these attachments even after holding constant the variables related to delinquency and criminal propensity. This is not to say that everyone who got married or got a job stopped engaging in criminal behavior. In fact, Sampson and Laub found that those who were identified as delinquent as juveniles were less likely to get married or hold a job, or

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more likely to be in an unstable marriage or in a dissatisfying job than those who did not engage in delinquency. They call this effect "mortgaging the future," in that persons who engage in early criminality are less likely to become involved in the very institutions that are likely to decrease the probability of engaging in crime. While contradictory in some aspects (i.e., population heterogeneity and state dependence), there is much overlap between the self control and social bonds perspectives that differentiates them from other theoretical approaches to crime. Both start from the premise that people are constrained from committing criminal acts by factors that are either internal to the individual (criminal propensity) or external to the individual (social bonds). In this way they differ from other sociological theories in which offenders are purported to be motivated towards crime rather than constrained from crime (Tittle & Paternoster, 2000). The major differences between the perspectives, then, are the internal and external orientation of control and how these mechanisms manifest themselves over time. Sampson and Laub identify three transitions likely to serve as turning points in a person’s trajectory. These are marriage, entry into the workforce, and entry into the military. As persons pass through these life transitions, they become attached to persons and careers in a manner that creates an investment known as social capital (Coleman, 1988). The more social capital one has, the less likely he or she is to engage in criminal activity. Gottfredson and Hirschi claim that marriage or a job does not reduce a person’s likelihood of criminal activity because low self control is relatively stable across the life-course and these events would, therefore, not be expected to have an effect. This exemplifies their notion of person characteristics over situational factors. A person with low self control, they claim, has low self control regardless of their situation. They argue that differences in delinquency in the situational context of marriage or a job will simply be measuring differences in a person’s level of self control. As noted earlier, the main problem with research on state dependence is selection bias, specifically the problem of self-selection. Quasi-experimental research designs do not have the luxury of assigning people to control and treatment groups (e.g., working or not working). People assign themselves to either treatment or control based

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on their own decisions and actions. Without random assignment, the possibility exists that any outcome may be the spurious result of unmeasured population heterogeneity that affected the decision to “self select” treatment or control in the first place. Regarding the effects of adult social bonds on criminal activity, Gottfredson and Hirschi posit that causation is difficult to show because those who choose to get married or work may be the ones with higher levels of self control. A person with low self control, on the other hand, would not be as likely to enter into a relationship that requires self-sacrifice and commitment, such as marriage or stable work. The outcome is clear; those who get married or obtain good jobs are likely to exhibit higher levels of self control, which indicates that they will be less likely to engage in criminal activity. This correlation, of course, introduces problems in establishing causal order for theories that posit marriage and entry into the workforce will reduce criminal activity (Gottfredson & Hirschi, 1990, Hirschi & Gottfredson, 2000). Sampson and Laub acknowledge that not everyone who gets married or gets a job will automatically exhibit lower criminal tendencies, just as Gottfredson and Hirschi recognize that not everyone who exhibits low self control will go on to become delinquent. Rather, they believe, there is an increased or decreased likelihood of criminal behavior where and in what form these tendencies appear (Laub, Nagin & Sampson, 1998). Sampson and Laub, in their age graded theory of informal social control (1993), agree that criminal propensity is an important factor for the development of delinquent tendencies. However, they depart from the premises of population heterogeneity theories in two important ways. First, they highlight the fact that although most persons who engage in criminality in later life can be described as possessing high levels of criminal propensity, most of those who display this propensity early in life do not go on to become criminals. This suggests that other casual influences must be in effect for a person to remain in a criminal lifestyle (i.e., state dependence) (Sampson & Laub, 1993; Laub & Sampson, 2001, 2003). A second and more crucial difference between population heterogeneity theories and state dependence theories is stability. While population heterogeneity theorists maintain that individual differences remain relatively stable over the life-course, Sampson and Laub stress that change does indeed occur. They argue "informal social bonds in

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adulthood to family and employment explain changes in criminality over the life-span, despite early childhood propensities" (Sampson & Laub, 1993, p. 7). Sampson and Laub make a compelling case for their version of the argument. However, there are still many questions left unanswered. For example, the Glueck’s (1950) sample used by Sampson and Laub was comprised of White underclass males. Even though they suggest that racial differences should not matter in their theory (1993, p. 255), they were not able to empirically demonstrate this. Their data also come from an age of economic growth and prosperity. The data for the current project were collected during a period of economic decline. Again, Sampson and Laub argue that theirs is a general theory tha explains crime across time periods and racial groups (1993, p. 252-56). This project will empirically test this proposition by examining whether factors associated with social bonds hold as predictors of desistance in a modern, racially diverse, sample of recently released criminal offenders. In the next chapter, a brief history of social control will be presented (Chapter 2) followed by an exploration of Sampson and Laub’s theory of informal social control (Chapter 3). Chapter 4 will address how Sampson and Laub’s theory can be applied to reentering offenders and the concept of treatment as a turning point. Chapter 5 will outline the sample, methods and specific hypotheses to be tested. Chapters 6, 7, and 8 will discuss the results and Chapter 9 will conclude with a discussion of social bonds, reentry and public policy.

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CHAPTER 2

A Brief History of Social Control Durkheim Introduces Social Control The concept of social bonds dates back to Durkheim (1951,1897) and emphasizes the effect of forces outside the individual that affect the behavior of individuals. According to Durkheim, persons are embedded in social relations in a manner that causes them to conform to the behavior of the group. The extent of this embeddedness has a direct effect on whether an individual will behave in ways that are against the mores of the culture in which that person is (or is not) embedded. Durkheim used the action of suicide to demonstrate his perspective, and showed that levels of embeddedness could predict rates of suicide in a macro-level analysis. He hypothesized that Protestants were less embedded in their religious institutions than were Catholics, and correspondingly found that Protestants committed suicide at a higher rate than Catholics. Likewise, he found the persons who were married were less likely to commit suicide than unmarried persons. The key to Durkheim’s approach was that persons were constrained from doing something by forces outside themselves, by forces that existed in society at large and exuded an influence upon the individual due to the nature of his or her relations. This was different from the classical trait theories discussed earlier that contended that persons were constrained from crime by forces within themselves. As such, Durkheim’s notion was a radical departure from pre-classical theories that argued that criminals were primarily evil or insane. Durkheim’s theory remained at the macro level and did not delineate micro-level processes regarding crime. He was more concerned with behavior in general, which he argued was affected by the two forces of integration and regulation. Integration was described as a level of bondedness or embeddedness that an individual had with a social group, and measured how one subsumed the beliefs and attitudes of the group. Proper integration resulted in the internalization of the collective conscience, or the will of the group. When integration failed, 11

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an individual’s social bonds were weakened, leading to a more individualized manner of thinking and acting that would, in turn, more often open them to the possibility of crime. Regulation on the other hand, is the combination of externalized forces that keeps individuals in line. Regulation becomes more important in advanced societies, according to Durkheim, as levels of integration fail. Regulation is important because it serves as a constraint on behavior. For example, an individual may not agree with the regulation at hand, but they are likely to agree with the social order and recognize the need for regulation. As a result, they comply with the regulation. As social order breaks down or loses its legitimacy, however, the force of regulation is weakened, increasing the likelihood of individualized ways of acting, including crime. While Durkheim’s discussion of crime was as a macro level mechanism used to define the limits of acceptable behavior, his focus on how social processes influence individuals has led to one of the strongest traditions in criminology; that of social control. From Durkheim to Sampson and Laub Durkheim laid the groundwork for what would develop into the various theoretical perspectives that exist under the umbrella of control theory, even though he never laid out a theory of how social forces worked at the individual level. A series of theorists beginning in the 1940s advanced Durkheim’s ideas, developing them further, adding elements and refining others. Sampson and Laub are thus standing on the shoulders of a long tradition. The section that follows discusses the evolution of this theorizing. Albert Reis, Personal Control and Social Control Albert Reis picked up where Durkheim left off with the publication of Delinquency as the Failure of Personal and Social Controls (Reis, 1951). Note that Reis refers to both personal and social control in his title. Personal controls referred to the individual’s capacity to meet goals in ways that did not conflict with the goals and norms of the community. Social control, on the other hand, was the ability of the community to insure that its goals and norms were effective. In some

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ways, then, social control can be seen, according to Reis, as a mechanism for instilling personal controls. Reis drew his conclusions from the examination of the probation records of 1,100 White male juveniles. He found that probation was more likely to be revoked among those whose tests showed weak ego or superego scores on psychiatric examinations, or who did not attend school regularly and were labeled problematic when they did so. He suggested that the two were related, and the weak ego scores indicated a lack of personal control, while the poor school performance was evidence of poor social control. Although Reis has been criticized for drawing tautological conclusions from his data (probation revocations came largely from the recommendation of the psychiatrist, so to say that the score on the exam was an independent predictor of the revocation is tautological) and for ignoring a host of factors related to personal and social control that showed no effect on probation revocation (Vold, Bernard & Snipes, 1998), his contribution was extremely important for the development of social control theory because it outlined a criminological approach that differed from the social disorganization and differential association theories popular at the time. F. Ivan Nye: Direct, Indirect and Internal Control The control approach was further advanced by F. Ivan Nye. Nye was the first to turn the delinquency question on its head, suggesting that conformity is what needed to be explained, not nonconformity (Nye, 1958). Nye also suggested that theorists did not need to seek the process that caused people to become delinquent, but rather those that constrained them from practicing what would be a normal course of action. He assumed that individuals who were not constrained would naturally seek those courses of action that most benefited them. Nye’s assumption was that when those factors that constrained an individual from nonconforming behavior were functioning properly, he or she would act in accord with the norms of society. When, on the other hand, constraining factors were not functioning properly, deviance became a possibility. Nye argued that the family was the most important agent of social control. He suggested four mechanisms of control that influence people: Direct, Indirect, Internalized and that

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which occurred through the availability of alternative means of need satisfaction. Direct control was manifested by outside pressure from parents, but also from teachers and the police if necessary. This involved actual restraint from performing unallowable behaviors, and punishment when those in control discovered behavior against the rules. Indirect control resulted from affection for and integration of the individual into the group to which he or she was supposed to conform. The effect was largely in the family, and Nye argued that those who were close to their families and felt affection for them were less likely to be deviant. The effect was indirect in that no one forced the person to behave or necessarily punished him or her when they did not, Rather, they acted in a fashion that would not antagonize those they were close to. Internalized control was that which the individual used over him or herself. Very similar to the conscience or superego, Nye claimed that persons differed in their ability to override their natural tendencies to act in an impulsive and nonconforming manner. This could occur absent any direct authority or in a situation where the indirect control was ineffective (for example, if the family were certain to not discover the action). Lastly, Nye claimed that a culture that allowed individuals multiple routes to success, and multiple definitions of success exerted a form of control on persons by not forcing them into an overly constrained social situation. A society with a larger number of acceptable means of satisfaction created an environment in which nonconformity was less necessary for more people. Nye tested his ideas on a sample of 780 juveniles aged 9 to 12. He used a survey instrument to ask about their family lives and their involvement in minor forms of delinquency. He scored 25% of the group in the most delinquent category and compared this group to the remaining 75%. He found that those in the delinquent group had more freedom compared to the non-delinquent group. He found a host of measures associated with parental discord to be more prevalent in the delinquent group, as well as those who felt rejected by their parents. He also found that children of single mothers were more delinquent, and that older or eldest children, children from smaller families and rural children were less delinquent than their otherwise situated counterparts. Based on these results, Nye concluded that there was overwhelming evidence that the more delinquent children were less controlled in a large variety of ways.

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Although Nye came under a scathing attack by Jackson Toby (1957), his approach both reflects the influence of Durkheim and holds the seeds of all control theories that followed. Toby, meanwhile, had advanced an approach that is sometimes included under the umbrella of control theories. Toby was concerned with a person’s stakes in conformity. He argued that the more a person had to lose as a result of crime, the less likely they were to commit one. While not stating his ideas as a control theory, the investment one has in conformity can be seen to act as a constraining force keeping a person away from nonconforming behavior. This idea would be taken up later as a key element of Travis Hirschi’s social bonds theory. Walter Reckless: Containment Theory In 1967 Walter Reckless published the fourth edition of his book The Crime Problem, in which he advanced his Containment Theory of Delinquency. Reckless had been investigating the social-psychological aspects of delinquency causation for nearly two decades, publishing with his colleagues a host of papers investigating why so many persons in neighborhoods plagued by supposed crime causing conditions were not criminals (Scarpitti, Murry, Dinitz & Reckless, 1960). Much of this work focused on the self concept, and argued that persons in these neighborhoods who avoided crime were insulated by a conception of themselves as law abiding, conforming citizens. In his Containment Theory, Reckless (1967) argued that there existed pushes and pulls towards criminal behavior. These could take many forms. Pushes could be caused by structural conditions such as poverty or from psychological or even biological factors that pushed one towards the idea of delinquency. Pulls were things such as illegitimate opportunities to commit delinquent acts, and were said to pull a person towards the possibility of delinquent behavior. Reckless and his colleagues contended that everyone experienced these pushes and pulls to a certain degree. What Reckless was interested in was why some people succumbed to these forces and some did not. He argued that persons have both inner and outer containments that must be broken down in order for delinquency to occur.

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Outer containment referred to those things that bond a person to a group, such as meaningful roles, reasonable limits on those roles and behaviors, and reinforcement of the roles and limits by meaningful others in a manner that created a sense of belonging. Interestingly, Reckless pointed out that these factors were important in modern, industrialized societies, but suggested the idea that other factors might replace these in cultures with different organizing principles. Reckless’ key focus, however, was on inner containment. Outer containment could shift or dissolve as a person moved through life, but inner containment, once established, was much more viable. Reckless pointed out four factors of inner containment; the self concept, goal orientation, frustration tolerance, and norm retention. The self concept was seen as a strong inner constraint. Once a person developed a self concept as a non-criminal or non-deviant, they were very unlikely to engage in deviant acts, in spite of exposure to various pushes and pulls towards it. Goal orientation was described as having a sense of direction in life that involved the desire for legitimate and reasonably obtainable goals. Goal orientation was balanced to some degree by frustration tolerance. Reckless recognized that people’s opportunities for successfully reaching their goals were often blocked or at least disrupted, and that this would obviously create frustration. Yet not all frustrated people would turn to deviance. This was partially explained by differing levels of frustration tolerance, the ability of an individual to cope with disappointing life circumstances. Norm retention consisted of a persons “adherence to, commitment to, acceptance of, identification with, legitimation of, and defense of values, norms, laws, codes, institutions and customs” (Reckless, 1967, p. 476). Norm retention was viewed as the acceptance of legitimate means, while goal orientation was viewed as the acceptance of legitimate ends. Reckless saw norm retention as the norm, and was more concerned with norm erosion or the process by which people came to lose their faith in the legitimacy of formally accepted means. It is important to note that Reckless was hypothesizing that people’s level of inner containment, at least regarding norm retention, was fluid, that is, it could change throughout one’s life. When the conflict between static theories and Sampson and Laub’s age graded theory of social control is

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discussed in Chapter 3, this changing effect of different causal mechanisms, or the lack thereof, will become more important. Reckless and his colleagues tested their ideas on samples of mostly White boys in high delinquency areas. The only part of the theory to be thoroughly tested was the self concept (Scarpitti et al., 1960). Results indicated that boys with non-deviant or good self concepts were less likely to be in trouble. They concluded that the self concept could neutralize the pushes and pulls that were inherent in high delinquency areas, which they assumed to be constant across the population of that area (Scarpitti et al., 1960). They were unable to test the other factors in containment theory, but most of the concepts laid out by Reckless and his colleagues became incorporated into other control theory perspectives, where they have generated a fair amount of support (e.g. Hirschi, 1969; Gottfredson & Hirschi, 1990; Sampson & Laub, 1993). Gresham Sykes and David Matza: Techniques of Neutralization While Gresham Sykes and David Matza (1957) did not propose a control theory per se, their “Techniques of Neutralization” are often considered part of the control tradition. Sykes and Matza argued that delinquents were not totally nonconforming in nature, but rather, were persons who obeyed the rules most of the time. Delinquents and nondelinquents alike were largely held in check by the norms of the society in which they lived. In order for these more or less rule obeying individuals to counter the constraining force of social norms, they developed techniques to neutralize the guilt or pressure they felt when they violated strongly held social norms. These techniques of neutralization were the denial of injury, denial of responsibility, denial of the victim, the condemnation of ones condemners, and an appeal to higher loyalties. By invoking any one of these, an offender could neutralize the force that society imposed upon them not to deviate, which then left the possibility of deviance open to them. Travis Hirschi: Social Bonds Theory Perhaps the most well known approach in the social control tradition is Travis Hirschi’s theory of social bonds. This may be due to the fact that Hirschi incorporated much of what those before him had argued into

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one broad theoretical perspective. Where Hirschi differed was on his denial of internal controls, a position he would completely reverse twenty years later. In his book Causes of Delinquency (1969), Hirschi contended that the internal control of Nye and the internal constraint of Reckless were merely manifestations of externalized forces on the individual. It is important to note that social control theory’s evolution since Durkheim has largely been a move from his macro level ideas towards the more micro-level theory of Hirschi and his counterparts. Hirschi suggested, following the long Durkheimian tradition, that crime was likely to occur when a individual’s bonds were loosened. A key difference between Durkheim and Hirschi is that Durkheim was referring to the bond to society, which Hirschi rejected outright. Rather, Hirschi was referring to much more localized bonds, such as those to parents, to school personnel, and to peers. Another point that should be illuminated is what Hirschi did not say. He did not fully agree with philosophers such as Hobbes who contended that men lacked morality. Rather, he said that people differed in their degree of morality, and that some people who were not constrained by social bonds would deviate, not that they all would (Hirschi, 1969, p. 11). He was clear, however, that other criminological traditions, notably strain and cultural theorists, had overstated the foundation that people were inherently moral and needed to be “caused” to become delinquent. Hirschi’s theory of social bonds, then, was designed to explain conformity among those with a tendency to deviate. Conformity can be explained, Hirschi agued, by four interrelated factors; attachment, commitment, involvement and belief. Beginning with attachment, it is important to note that Hirschi was not referring to an internalized attachment in some social-psychological sense. He argued (1969, p. 17-19) that to pursue this line of reasoning is to liken the result of attachment to the superego or conscience. If one has an internalized attachment to society and wishes to act in a manner pleasing to it, then the attachment becomes an internalized moral compass; “I behave in a certain way because by being attached to all these relations, I have adopted the norms of society.” Hirschi claimed this does not stand up to changing circumstances. Noting that suicide and forgery are both known to increase after a divorce (p. 19), Hirschi argued that if we attribute attachment to an internalized affect, we would have to argue that people lose their conscience as a result of

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divorce. Attachment’s effect is thus direct. It is the state of being attached that causes conformity. When this state is lacking, conformity is lacking and deviance is more likely to occur. Children who are more attached to their parents are more likely to feel their presence than those who are less attached. This demonstrates the essence of Hirschi’s social bond. The child is not bonded to society in some Durkheimian sense; he or she is attached to his or her parents. It is what they wish, how the child thinks they will react, the punishment they will instill that guides the child, not some overarching moral reasoning about what should be done. Hirschi also maintained that attachment of parents to children resulted in more surveillance, both because such a family was more likely to enjoy spending time together, and because the level of caring by the parent would lead them to watch over their child. In fact two of the questions Hirschi used to measure parental attachment were the extent to which the mother knows where the child was and who they were with. Commitment for Hirschi was much like Jackson Toby’s (1957) stakes in conformity. In fact, he even calls it that (1969, p. 162). According to this aspect of the theory, the more invested one is in those things that society approves of, such as education, occupation and what Hirschi calls the passage to adult status, the less likely one is to engage in deviance. Involvement received the least amount of attention in Hirschi’s work (e.g., there is one seven page chapter), possibly because it is the most troubling aspect of the theory in terms of his data. Engagement in most activities thought to prevent delinquency, like reading and watching television, were positively correlated with delinquency. Time spent studying was the only thing Hirschi found that had a negative impact on delinquency. He acknowledged that this is in part a measure of attachment and commitment, but the effect remained even after partially controlling for these factors. An interesting discussion in this short section of the book concerns opportunity. Crime, Hirschi points out, takes very little time. Even the most delinquent of children probably spend only a few hours a year actually committing crimes. This makes the “idle hands are the devils workshop” argument problematic because for those who want to commit crime, it is not very time demanding, and for those not predisposed, crime is unlikely to “fill the hours” of a bored child.

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The last element of Hirschi’s bonds theory is belief. This again is a negative statement; it is the absence of beliefs that prevents conformity, not the adherence to some belief system that causes delinquency. This argument was primarily directed toward subcultural theories that suppose children adhere to some different belief structure. Hirschi argued that the more one aspired to conventional action, that is, believed that getting a job and a wife or husband and a house were worth doing were less likely to become delinquent than those whose interest might lie elsewhere. He further argued that belief is a direct result of attachment and commitment (p. 200). Children who are attached to their parents and committed to conventional institutions will be more likely to hold pro-social beliefs than their less attached and committed counterparts. There are some interesting points to note about Hirschi’s approach to bonds. First is Hirschi’s concern with how social bonds affected people differently over the life course. He argued extensively that attachment needs to be conceptualized as an external effect, because to do otherwise was to deny its variability. He noted on page 88, “Attachment may easily be seen to be variable over persons and over time for the same person.” (Hirschi, 1969, p. 88). This is a foreshadowing of Sampson and Laub’s theoretical approach, which is largely focused on how adult social bonds change over the life course. It is also interesting in light of the theory that Hirschi would propose twenty years later with Michael Gottfredson in which they would argue for the stability of criminal propensity and for the unimportance of changing levels of attachment. The other point is that of motivation. Hirschi, like all control theorists is said to turn the causal premises on their head and argue against a “moral man.” As mentioned above, this is not entirely correct, and Hirschi returns to the point when discussing the relationship of delinquent peers (p. 230), which was strong, positive and significant in his data. This relation he said, showed that some level of motivation seemed required; that if no moral persuasions were needed, then delinquent peers should have dropped out of his models when other control variables were entered, which they did not. There is thus room in Hirschi’s original bonds theory for factors such as learning, opportunity or self control. Hirschi later lamented making this allowance (1995), because so many theorists used it to deny the validity of his theory.

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It should be noted that in addition to Sampson and Laub’s age graded theory, other perspectives in the social control school have continued to be advanced. For example, John Hagan’s (1989) PowerControl theory focuses on the differential child raising practices of upper and lower class families and how class differences in terms of patriarchy result in different child raising practices for boys and girls. Charles Tittle (1995), in his Control Balance theory, argues that people are not only the objects of control but also the agents of control. When the amount of control exerted upon a person is in balance with what that person exerts, the chances of delinquency are low. When control gets out of balance, Tittle argues, the chance of delinquency occurring increases. While these perspectives are interesting and of much theoretical value, they are largely beyond the scope of this project and are included here only to point out that the control tradition is moving forward on multiple fronts, including the path laid out by Sampson and Laub, to whom we now turn.

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CHAPTER 3

Sampson and Laub’s Theory of Age Graded Informal Social Control A Life Course Model of Informal Social Control It is interesting that a theory of crime that focuses on the changing nature of variables over time should emerge as novel at this stage of criminological inquiry. Changing behaviors over time is, in essence, the bedrock of every criminological theory. No theory today realistically argues that adult criminals are born criminal in any predicable way, so every theory is a set of propositions about how some persons move from being non-criminal to criminal, or, in the case of pure social control theories, how some persons are refrained from being deviant (including criminals), while others are not. All of the great criminological traditions focus on the changing nature of variables over time. The effect of time on criminal careers is inherent in the concept. Lambda, the rate of offending over a period of time, requires a measurement of time. Onset implies that there was a period prior to offending, and desistance requires that there is a period after offending. Carpenters and professors retire. It is reasonable to assume that criminals do as well. As mentioned in Chapter 2, Hirschi emphasized in his (1969) social bonds theory that the effect of bonds was variable over time. Sutherland argued that changing behavior resulted from the differential association of peers and colleagues, which varied over time. Durkheim focused, in part, on divorcees, and how people changed as a result of losing a loved one; an implied time variation. As such, it is not the focus on time variation that makes Sampson and Laub’s theory. It is how they explain time variation and, more importantly, the scope of their approach. Most theoretical approaches focus on the move from being noncriminal to being criminal, which is usually a focus on adolescence, or the early part of the criminal career. By using the Glueck’s (1950) data, Sampson and Laub were able to show how structural factors affect the 23

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onset of delinquency, how factors both internal and external to the individual work to continue a criminal career, and lastly, how social bonds in adulthood can bring about desistance from a life of crime. This chapter explores Sampson and Laub’s theory in detail so that the reader may fully understand the analysis that follows. Robert Sampson and John Laub were both well recognized and distinguished criminological scholars prior to writing their 1993 book, Crime in the Making: Pathways and Turning Points Through Life. In light of the criminological focus on change noted above, it is worthy to note that one of the things Sampson and Laub were writing in response to was an argument that change didn’t matter (Gottfredson & Hirschi, 1990; Wilson & Herrnstein, 1985). Theories such as Gottfredson and Hirschi’s theory of Low Self Control, and Wilson and Herrnstein’s focus on intelligence and genetic factors maintained that criminal propensities were established early in life and remained relatively stable over the life course. Sampson and Laub and others (Blumstein et al. 1986; Leober, et. al 1989; Farrington , 1991) questioned this stance in an ongoing debate which, among other things, provided some of the rationale and backdrop for Crime in the Making. Sampson and Laub pointed out that the validity of any argument over whether change mattered could only be examined by looking at longitudinal data over a long period of time. If intervening variables had no effect once individual level background predictors were controlled, one would have to conclude that change did not matter. Fortunately, Sampson and Laub were able to utilize a collection of data originally assembled by Sheldon and Eleanor Glueck to investigate their set of questions. The Gluecks conducted research at Harvard University on 1,000 boys born between 1924 and 1935. They collected data from multiple sources, including self report interview data from the subjects themselves, interviews with parents and official police, court and corrections records. The design of the Gluecks’ study was a case by case matched sample in which each delinquent boy was matched with a non-delinquent boy based on age, race, measured intelligence and neighborhood. The subjects were interviewed first at age 10 to 17 years old, then again at ages 25 and 32. Searches of official records filled in some of the major life events for the intervening years, especially criminal justice involvement. The follow up retention rates for the Gluecks’ studies was nothing short of phenomenal. Of the original

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1000 subjects, 880 were located and interviewed for the age 32 followup. When considering that 37 subjects had died, that amounts to a follow up rate of 92% over a 15 year period. Sampson and Laub advance what they call an age graded theory of informal social control and use these data to test it. Their theory, to quote them at length: ... is threefold in nature: 1) structural context mediated by informal family and school social controls explains delinquency in childhood and adolescence; 2) in turn, there is continuity in antisocial behavior from childhood through adulthood in a variety of life domains; and 3) informal social bonds in adulthood to family and employment explain changes in criminality over the lifespan despite early childhood propensities (Sampson and Laub, 1993:7). More generally, Sampson and Laub are thus arguing that micro level variables associated with the family and school are important, but are tied to macro-level structural characteristics. They then argue in point number 2, that continuity in antisocial behavior is important, not dismissing their critics, but rather engaging them. In the third point, they argue that social bonds explain change in adulthood, countering the argument that stability is the rule. They thus address in one theory all three elements of the criminal career paradigm – onset, persistence and desistance- while incorporating the continuity arguments leveled by the criminal careers paradigm’s biggest critics. The remainder of this chapter examines each of the above points in turn: 1) Structural context mediated by informal family and school social controls explains delinquency in childhood and adolescence. Drawing on elements of Patterson’s (1982) coercion theory, Hirschi’s (1969) social bonds and Braithwaite’s (1989) reintegrative shaming, Sampson and Laub propose a family level model of onset that combines discipline, supervision and attachment as predictors of juvenile delinquency. They then link these practices to larger structural

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level processes that allow them to explain how social factors affect delinquency through a mediated school and family process. Regarding discipline and supervision, Sampson and Laub theorize that those children whose parents’ supervision practice was of poor quality or lacking altogether or who practiced harsh and erratic discipline would be more likely to engage in higher levels of delinquency. This follows from Gottfredson and Hirschi’s (1990) premise that behavior must be monitored, anti-social behavior recognized, and anti-social behavior punished in order for a child to learn not to engage in it. It also draws on Patterson’s (1982) notion that punishment must be focused, regular and follow a distinct and known pattern for the child to learn from it. Much has been made of the attachment element of the social bond, and Sampson and Laub include it at all three levels of their theory. At the juvenile delinquency level, attachment works in the traditional way in which Hirschi (1969) first proposed it; children with a strong emotional bond to their parents will be less delinquent that those who are less bonded. Sampson and Laub propose this in the negative as well, arguing that children whose parents show hostility or rejection towards them will be more delinquent than those who do not receive such treatment. This is further shown by measures of the boys’ rejection of the parents, which also indicates a broken bond. The theoretical perspective at the family level is thus a straightforward model of direct and indirect social control with an emphasis on attachment. Sampson and Laub’s model, though, includes macro-level factors that link it to the structure of the culture at large, even though traditional social control theories largely discounted macro-level effects. This was largely because the theories were countering what was considered the faulty underpinnings of macrolevel theories reflected by strain and social disorganization theories (Merton, 1938; Park, Burgess & McKenzie, 1928). Early theorists such as Nye (1958), Reckless (1967), and even Hirschi (1969) disagreed with the premise that people needed to be caused to commit crime. While strain and social disorganization focused on the macro, social control theorists argued that more proximate factors were more important, largely ignoring macro level influences. Sampson and Laub attempted to bring macro level factors back in, arguing that they were important, even though family level effects largely mediated them. That is, Sampson and Laub hypothesized that

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macro level variables would affect the social control of parents over their children. Specifically, they utilized the Gluecks’ matched sampling design to account for differing levels of socio-economic and racial status at the neighborhood level. In addition they measured the following variables in order to account for population heterogeneity in the sample: • • • • • • • • •

Household crowding: comfortable, average and overcrowded. Based on the number of persons per bedroom. Family disruption: intact family, or boy from home where at least one parent was absent. Family size: number of children in the family. Family SES: comfortable, marginal and dependent, based on family income and dependency on outside aid. Foreign born parents: neither, one, or both parents were born abroad. Residential mobility: number of times the family moved during the boys’ childhood. Mother’s employment: whether mother worked. Mother’s deviance: 1 to 3 scale based on official arrest records and whether the mother is addicted to alcohol. Father’s deviance: 1 to 3 scale based on official arrest records and whether the father is addicted to alcohol.

Sampson and Laub modeled social control and delinquency in both separate and combined models. In the models predicting discipline and supervision, the only thing that did not show at least one effect was family disruption. Family size, family SES, foreign born, and father’s deviance were all significant predictors of both the mother’s and father’s harsh and erratic discipline as well as the mother’s supervision. In addition, mother’s deviance had a positive effect on the mother’s level of harsh and erratic discipline and mother’s employment had a negative effect on supervision. In regards to the emotional ties, parental rejection and the boy’s attachment to the parent, family size, crowding and mother’s employment had non-significant effects on either outcome. The remaining measures, with the exception of mother’s deviance on level

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of the boy’s attachment, were all significant and in the anticipated direction. This set of findings is important because they show how macrolevel influences affect those variables hypothesized to predict delinquency. In the next step, Sampson and Laub predicted that these macro level effects would be mediated by the family process variables in a full model predicting official and unofficial delinquency. In these full models, all of the macro level predictors dropped to nonsignificance when the family process variables were controlled, with the exception of family size and crowding. The full model was extremely robust, predicting nearly half of the variation in delinquency (R-square = .49). An interesting note here is that Sampson and Laub found that family size had a negative effect on delinquency. They note that this is counter to their hypothesis. One could, however, make an argument consistent with their overall perspective that would predict this result. They base their notion of social control on Coleman’s idea of social capital. Coleman clearly articulates that the more links one has, the greater his or her social capital, thus the greater the bond. In larger families there are more people. More people increase the number of potential links, thus increasing the potential social capital in the household. If one is bonded to siblings, there is at least the potential that this bond could inhibit the likelihood of delinquency. This may explain the negative effect Sampson and Laub found for family size when predicting delinquency. Sampson and Laub’s first proposition was largely supported by their data. Macro level variables predicted family process variables, which, in turn, predicted juvenile delinquency. They conducted additional tests to address the issue of whether selection effects might have been introduced at this early stage of the offending career. It has been suggested that erratic parenting can actually be the result of misbehaving children (see, e.g. Lytton, 1990). According to this line of theorizing, parent and child are involved in an ongoing reciprocal relationship in which child and parent adapt to each others behavior. A child with an aggressive temperament could presumably affect the way the parent treats the child over time. In order to insure that the implied causal direction of ther model was not misspecified, Sampson and Laub conducted another set of analyses in which the models discussed earlier were reestimated with

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the addition of three variables measuring early childhood temperament. These variables were: • Child difficulty: parents were asked to define the child as difficult or not at a young age. • Tantrums: whether or not temper tantrums were the “predominant mode of response by the child to difficult situations” (Sampson and Laub 1993, p. 89). • Early onset: Self report of age of onset of misbehavior. All of the child effects were significant in the reestimated models, but the significant macro level predictors and all of the family process variables also remained significant. So, while adding to the explanatory power of the full model (R-square=.53), it does not appear that early childhood behavior problems mediated the effect of the other variables in Sampson and Laub’s model. Showing the effect of selection variables at this early stage was important, because much of the debate surrounding Sampson and Laub’s theory is related to selection effects. Indeed, one of the key arguments that Sampson and Laub made is that selection effects are important, but that social processes and change still matter. By modeling both selection and process effects on the onset of juvenile delinquency, Sampson and Laub showed the early stages of lifelong trajectories into and/or out of crime. Socially embedded family factors make up the early stage of the life course and set a child on a path that will be more or less problematic. Changing trajectories are part of their theory, though, and Sampson and Laub next investigated the effect of peers, school and siblings on delinquency. Like their model of family process, Sampson and Laub predicted that the school would mediate the effects of macro level variables. In turn, they argue in the social control tradition that boys who were more attached to school would be less delinquent. Their measures of attachment were likewise straightforward social control measures: • •

Attachment to school: combined measure of boy’s attitude towards school and his academic ambition. School performance: last year’s grades and the number of times the boy was held back a grade.

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As with their family models, Sampson and Laub first used structural characteristics to predict attachment and school performance. Economic status had a significant positive effect on attachment but not on school performance. Residential mobility and larger family size both had significant negative effects on school attachment and performance. Mother’s employment had a significant negative effect on attachment but not performance, while foreign born and mother’s deviance had significant negative effects on school performance but not school attachment. Thus, although the effects were somewhat different for attachment and performance, and the explained variance was low (Rsquare = .16 for attachment and R-square =.08 for performance), there was support for the idea that structural and family factors affected the school bond. The models predicting delinquency (Ordinary Least Squares and Logistic Regression) both showed significant effects of school attachment, but not school performance. The effect of school attachment was the strongest predictor in both models, and the level of variance explained in the models was very high (OLS R-square = .45). Of the structural variables, residential mobility had a significant positive effect, while SES had a negative effect, after other variables were controlled. Both parents’ deviance had significant positive effects on delinquency. Because Sampson and Laub noted earlier that poor family attachment may be a result of problem behavior rather than a cause of it, they also took into account the possibility that school attachment may result from levels of disruptive behavior, rather than causing it. They followed the same strategy for school as they did for family attachment, by re-estimating the models predicting attachment with the inclusion of the early onset, difficult child and tantrums variables. As with the family model, these early onset and temperament variables significantly decreased the level of school bonds and directly increased levels of delinquency. Still, school attachment remained the strongest predictor in the model, when controlling for all of these background variables. Sampson and Laub next turned their attention to deviant peers and siblings in order to investigate whether these common criminological variables mediated the effects already observed. As with the family and school models, Sampson and Laub first regressed the social structural factors on attachment to both delinquent peers and delinquent siblings. Attachment to delinquent peers was

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affected by family size and father’s deviance, with both variables increasing the likelihood of having delinquent peers. For siblings, parent’s deviance as well as foreign born, mother employment and family size increased the likelihood of attachment. In terms of the effect on delinquency, attachment to delinquent peers, but not siblings was positively and strongly related to delinquency. In a final model in which only the significant predictors of juvenile delinquency were included (p.119), the social process variables were the strongest predictors, when controlling for background characteristics. In sum, social control was supported for the family factors of attachment, rejection and supervision, the school level variables of school attachment but not performance and the effect of delinquent peers but not siblings. Thus far, Sampson and Laub have shown how the effects of macro-level variables are mediated by family and school level effects. While factors related to strain and subcultural and social disorganization theories remained important in these models, Sampson and Laub stressed that they played themselves out through processes much more centralized to the individual in regards to crime causation. It is not poverty or social disorganization per-se that causes crime, Sampson and Laub contend, but the effect of poverty on family and school environmental processes that are important. Sampson and Laub, having demonstrated the empirical validity of the first part of their theory, proceed, in Chapter 6 to demonstrate the “Continuity In Behavior Over Time.” 2) There is continuity in antisocial behavior from childhood through adulthood in a variety of life domains While the first part of Sampson and Laub’s theory is intriguing, it is the second – continuity- and third –change- parts that are best known and most debated. It is interesting and perhaps not surprising that Sampson and Laub devote 15 pages of their book to the continuity chapter and 39 pages to the change chapter. Sampson and Laub show, by using numerous models, not nearly as complex as those preceding and following this chapter, that delinquents were much more likely to be arrested at all three adult follow up time points compared to the nondelinquent group (17-25, 25-32, 32-45). Delinquents were also more

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likely to use alcohol or drugs and more likely to score higher on a measure of gambling and risky sex (i.e., with prostitutes), and numerous measures of military misconduct. These differences were found when childhood delinquency was measured officially, through self-parent-teacher ratings or through early childhood tantrums. Virtually all of the relationships were significant. Not only was the relation between childhood and adult delinquency incredibly robust, it completely mediated the effect of the other childhood predictors on adult crime. Sampson and Laub modeled the same variables used to predict childhood delinquency in a model predicting adult delinquency, with the addition of childhood delinquency as an independent variable. Nothing was significant in the model (p.135) except childhood delinquency. In short, that there is continuity in deviant behavior over time, from early childhood through mid adult-hood, is simply unquestionable. In addition to demonstrating continuity of delinquent behavior over time, Sampson and Laub showed that early delinquency decreases the likelihood that one would become attached to the very social bonding institutions that they claimed reduces the likelihood of crime in later life. Delinquents were less likely to graduate high school, had lower occupational commitment, were more likely to be economically dependent on others, had lower job stability, were more likely to be divorced or separated and had weaker attachment to their spouse if they weren’t divorced or separated than non-delinquents. It is thus apparent from Sampson and Laub’s own work that delinquents are more likely to persist in delinquent behavior than are non-delinquents to initiate criminal behavior and that delinquents are self-selecting into poor social relationships. At a basic level then, Sampson and Laub agree with the empirical assessment of their detractors such as Gottfredson and Hirschi. Where the difference emerges is in what the findings mean. And this is the point of departure for the argument between Gottfredson and Hirschi and Sampson and Laub. Gottfredson and Hirschi argue that the continuity in behavior over time results from the underlying propensities of the individuals in the delinquent and non-delinquent groups. This latent trait in Gottfredson and Hirschi’s case is low self control, but as noted in the introduction, other theorists have argued that it could be other things as well. This construct differentiates persons who possess it from those who do not

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in a fashion that explains a host of outcome variables at different points in the life course; that is, early childhood crime and later adulthood crime are both the result of an underlying propensity to commit crime that remains stable across the life course. Nagin and Paternoster (1991, 2000) have called this population heterogeneity; differences in groups within the population. Nagin and Paternoster (1991, 2000) contrast this with the concept of state dependence, according to which a particular outcome is dependent upon some act or process that occurred earlier. From this point of view, early childhood delinquency is seen to directly cause later crime. That is, the current state is dependent of the earlier state. Sampson and Laub, while recognizing the importance of population heterogeneity (p.136), argue in favor of a state dependence approach where, “We emphasize a cumulative developmental model whereby delinquent behavior has a systematic attenuating effect on the social and institutional bonds linking adults to society (for example, labor force attachment, marital cohesion). This raises the further possibility that, in turn, adult social bonds explain variations in adult crime above and beyond those accounted for by early childhood differences. These perspectives are not mutually exclusive, which suggests that both early delinquency and the dimensions of adult social bonding have independent effects on adult crime” (p.138). The above argument and supporting analyses lays the groundwork for the final stage of Sampson and Laub’s theory, that adult social bonds can decrease the likelihood of crime net of early childhood differences. 3) Informal social bonds in adulthood to family and employment explain changes in criminality over the lifespan despite early childhood propensities. It is useful at this point to revisit the theoretical premise that Sampson and Laub bring to the data. Recall that theirs is a life course approach, which according to Elder, views the life course as “pathways through the age differentiated life span, where age differentiation is manifested in expectations and options that impinge on decision processes and the course of events that give shape to life stages,

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Prisoner Reentry and the Life Course

transitions, and turning points” (Sampson and Laub, 1993 p. 8; paraphrasing Elder, 1985, p.17). The first part of Sampson and Laub’s theory explains how social structure is mediated by social control variables that are more proximate to the involved individuals. In the case of delinquents, this sets them out on a deviant trajectory. In life course parlance, they have passed through a set of turning points and transitions that have led them to follow this delinquent path or trajectory. This is another way of framing the issue of continuity; if nothing alters the deviant trajectory, it will continue. The life course perspective, however, works on the assumption that certain things are more or less supposed to happen as one ages. One is supposed to get a job, supposed to settle into a relationship and supposed to desist from crime. The problem with delinquent trajectories is that, according to Sampson and Laub, the process of delinquency itself “knives off the future,” by making these normal age graded transitions less likely. When persons on a delinquent trajectory do manage to develop some form of social bond, be it to a spouse or to a job, this can serve as a turning point leading to a less delinquent trajectory. It is this proposition that Sampson and Laub set out to test in the last part of their book (1993). Data for the adult models are based on interviews done by the Glueck’s at age 25 and 32. These interviews make up the predictor and independent variables for the samples of 17-25 year olds and 25-32 year olds. In some instances Sampson and Laub use data maintained by Vaillant (1977) in order to estimate models out to age 45. Vaillant collected official arrest data on the Glueck sample to age 45. There are, however, no predictor variables available for this time period. Sampson and Laub used multiple measures of deviance in their adult models. The first was whether the person was arrested or not during the particular time period. This forms the dependent variable in their logistic regression models. They also utilized a frequency of arrest variable that was modeled as the number of arrests per year free (essentially a crude Lambda measure). This allowed them to factor in time spent in prison as well as having a more nuanced measure of crime beyond the conservative “did you or didn’t you” variable. Due to the positive skew and large number of zero arrest counts, Sampson and Laub transformed the arrest frequency variable to the natural log of the

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annual arrest per days free. Then, in order to address the OLS violation of using censored data (arrest counts are not continuous variables), they used a Poisson regression model. Lastly, because the Poisson model assumes the probability of an event occurring is independent of the prior process (i.e., that there is no continuity), an error term was added to their models resulting in a negative binomial estimation procedure (Sampson and Laub1993, p.158). These procedures were necessary in order to obtain “true” estimates of the independent variables. Sampson and Laub additionally use general deviance and excessive drinking measures. Measures of adult social bonds focused on job stability, commitment, and attachment to a spouse. Job stability was a composite based on three items; whether the person was employed at the time of the interview, length of time employed on their most recent job, and a three point work habits scale. This variable was measured the same way in the 17-25 and 25-32 waves. Commitment was measured differently at each wave. At the 17 to 25 wave, commitment was constructed as weak or strong based upon how the person expressed his work and educational aspirations. At the 25 to 32 year old wave, commitment was a three point scale based on what actions the person took to improve his occupational status (Sampson and Laub, 1993, p. 143-4). Sampson and Laub used a measure of marriage to predict spousal attachment. They additionally utilized a measure of spousal attachment in models for only the married men. Attachment to a spouse was also measured differently at wave two and wave three. At wave two, attachment was coded weak or strong based on how the person assumed their marital responsibilities. Those who separated or divorced, or were “neglectful of marital responsibilities, financial as well as emotional,” were classified as having weak attachment. Those who “displayed close, warm feelings toward their wives or were compatible in a generally constructive relationship” were classified as having strong attachments to their spouses (p.144). Wave three used these two measures but also added an element of family cohesiveness based on “ the extent to which the family unit was characterized by an integration of interests, cooperativeness, and overall affection for each other” (p.144).

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Prisoner Reentry and the Life Course

These combined concepts formed Sampson and Laub’s measures of adult social bonds, which they theorized would explain criminal desistence in adulthood, net of childhood criminal propensities. Their well-known findings indicated that the increased presence of adult social bonds was significantly related to lower levels of deviance. One straightforward examination of their findings is on page 146 of their book, adapted in Tables 3.1 and 3.2, below. As can be seen, virtually every relationship between a social bond and a measure of deviance was significant and in the hypothesized direction. For example, the variable job stability at age 17 to 25, coded as low, medium and high, was significantly related to alcohol consumption, general deviance and arrest, not only in the concurrent 17-25 time period, but was also predictive of arrest at the 25- 32 year old time period.

Table 3.1: Social Bonds Age 15 to 25 by Percent Reporting Deviance

Drug Use and Deviance 17 to 25 25 to 32

% Alcohol % Deviance % Arrested % Alcohol % Deviance % Arrested

Social Bonds Age 17 to 25 Time Period Employment Stability Career Marital Commitment Attachment Low Medium High Weak Strong Weak Strong 57 34 15* 50 21* 53 17* 31 13 9* 29 15* 31 8* 91 62 60* 82 64* 87 58* 53 19 11* 43 16* 47 11* 47 17 8* 37 14* 54 16* 74 47 32* 70 47* 76 34*

*= Significantly different from Low group p<.05 Note: Adapted from Sampson and Laub, 1993.

Table 3.2 Social Bonds Age 25 to 32 by Percent Reporting Deviance

Drug Use and Deviance 25 to 32 32 to 45

% Alcohol % Deviance % Arrested Arrested

Social Bonds Age 25 to 32 Time Period Employment Stability Career Marital Commitment Attachment Low Medium High Weak Strong Weak Strong 53 19 5* 43 18* 51 6* 46 13 4* 39 14* 52 6 80 44 18* 70 40* 78 29* 64

48

39*

*= Significantly different from Low group p<.05 Note: Adapted from Sampson and Laub, 1993.

59

47*

64

43*

Sampson & Laub’s Age Graded Theory

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Commitment was also significant and in the direction predicted by Sampson and Laub. Persons who had higher aspirations at age 17-25 and who acted on those aspirations at age 25 to 32 were less deviant in both time periods than those who did not possess or act upon these aspirations in both time periods. Marital attachment produced similar results. Those who were well attached at either time period were less likely to be deviant in the concurrent or the consecutive time period. Taken as a whole, these findings are nothing short of unqualified support for the Sampson and Laub theory. Sampson and Laub then went on to perform more stringent analyses, beginning with Logistic and OLS regression models to predict deviance. Commitment lost significance in all but one of these multivariate models. This is partially expected, because one could assume (although Sampson and Laub did not test) that those who have higher career aspirations and act on them are likely to have greater job stability; thus, when controlling for job stability, career commitment drops out of the model. More damaging to their theory, however, was the finding that marriage was not significant in the full sample models. The actual state of being married had no effect on deviance. Sampson and Laub also conducted the same analysis on a sub sample of men who were married. These models showed a significant effect of marital attachment within this group, that is, those who reported a better quality of relationship with their spouse were less deviant than those who reported worse relations. There were also differences on the job stability variables across these groupings. Among the never married men, job stability was significant and negative in both the logistic and the OLS regressions models predicting deviance. In the ever-married men, however, job stability lost significance in the logistic regression while remaining significant in the OLS model measuring frequency of crime. Sampson and Laub suggest that the marriage bond becomes more important than job stability for those involved in marriage, but that job stability still reduced the amount of crime committed by the married men. Sampson and Laub then added childhood delinquency variables to this model. Days incarcerated and arrests per year free were both significant predictors of adult crime. The adult social bonds variables also maintained their significance. Sampson and Laub then tested a series of mediated models to assess how the effect of childhood delinquency affected criminal propensity. The basic conclusion was

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Prisoner Reentry and the Life Course

that childhood delinquency did predict job stability failure; those who were more delinquent were less likely to succeed in their career, and subsequently more likely to remain in a criminal career as adults. Sampson and Laub found that when the effect of delinquency on job stability was modeled, the direct effect of childhood delinquency lost significance. They concluded by suggesting that childhood delinquents mortgage their future prospects by the results of their delinquent acts (i.e., arrest record, severed bonds with others, lack of a job record). Sampson and Laub modeled adult social bonds using multiple measures, and multiple techniques (e.g., Poisson, negative binomial, structural equation models), but their essential findings remained throughout. Whether predicting arrest, frequency of crime, excessive alcohol use or general deviance, ties to social institutions such as work or a spouse led to a reduced likelihood of deviance, even after controlling for background delinquency. Crime Across the Full Life Course In 2003 Laub and Sampson released another book, “Shared Beginnings, Divergent Lives: Delinquent Boys to Age 70.” (Laub & Sampson, 2003). In it, they expanded their earlier (1993) analysis by reporting on follow-up data from the original Glueck sample out to age 70. In the new work, Laub and Sampson give much more weight to decision making and human agency. While much of the work focuses on long term trajectories across the entire lifespan, the attention to the decision making process illuminates how change occurs. They find that, although agency is of prime importance, the effects of informal social control across the life course remain paramount. Those who obtained and maintained meaningful attachments later in life were those who reported the least amount of crime (Laub & Sampson, 2003). One of the most significant findings in the new work is that desistance appears to be the norm. While there are different trajectories for different offender groups (beyond the scope of the current project), in all offender groups, decline in crime and eventual desistance was the found in all cases. While not suggesting that “Crime declines with age,” Laub and Sampson certainly provide the data that shows this to be the case. Rather than focus on age, however, they maintain that age

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is a marker for other things (turning points) that occur through the life course. In unpacking the process of desistance, Laub and Sampson argue that while agency is important, the level of agency does not reach nor require cognitive transformations (e.g. Giordano at al, 2000). Nor do they find in their narratives a sense of “redemption” among those who desisted (e.g. Murana, 2001). These issues are dealt with in the next chapter. Laub and Sampson have thus added an important and novel development to a well-researched proposition. It had long been maintained that social bonds were important in the beginning stages of a delinquent career. Sampson and Laub’s theory proposed that social bonds were equally important in the later stages of a delinquent career, that it is important to consider how bonds evolve over the life course, and more recently, how human agency fits within that process. Their rigorous modeling approach added a great deal of validity to their proposition. Criticism and Empirical Tests of Age Graded Social Control Criticism of Sampson and Laub’s theory has largely focused on the effect of adult social bonds on later criminal behavior. Recent research has focused on offending trajectories and how many different patterns of offending can be discerned across the full life course (see, e.g. Nagin & Tremblay, 2005a, 2005b, Sampson & Laub, 2005; Moffitt, 2006). This research is beyond the scope of this project however as the concern here is the effect of gaining attachments upon release from prison, and is thus focused on the principle concepts of Sampson and Laub’s theory. As discussed in the introduction, the main point of contention between Sampson and Laub and their critics, Gottfredson and Hirschi chief among them, is the meaning of change. On one hand, crime declines with age. This is an empirical reality in criminology. As persons get older, they commit less crime. While this proposition is agreed upon, Sampson and Laub and Gottfredson and Hirschi disagree about its meaning (Hirschi and Gottfredson, 1983, 1995; Gottfredson and Hirschi 1990; Laub and Sampson, 2001; Sampson and Laub 1993, 1995, 1997). Gottfredson and Hirschi argue that, while crime declines,

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the propensity to commit crime remains relatively stable and differentiates persons with either high or low levels of self control in a model of population heterogeneity. Sampson and Laub argue that as persons move through the life course, they sometimes form attachments to meaningful social bonds, and in the cases where this occurs, a desistence from crime is likely to result. As noted earlier Nagin and Paternoster (1991, 2000) termed these two perspectives population heterogeneity and state dependence. Gottfredson and Hirschi represent a population heterogeneity perspective according to which persons sort themselves into groups, or into heterogeneous populations, at a young age. These groups are then differentiated by the sorting variable, in this case criminal propensity, which is caused, Gottfredson and Hirschi argue, by low self control. The group with higher criminal propensity will remain relatively higher in crime throughout their lives. Differences in criminality that are seen between groups merely would be the result of improper sorting early on. That is, a person who desists might not have belonged in the high propensity group to begin with, or is desisting as a natural result of the aging process. Gottfredson and Hirschi maintain that desistance occurs for all persons based solely on age, that is, people in both the high and low propensity groups experience desistance as they age. To put it more empirically, differences found later in life are the result of omitted variables that, if included, would have properly classified these individuals into their appropriate group, thereby nullifying the finding of significant social bond effects. It is important to be clear on this issue, because in spite of the approximately 100 models Sampson and Laub present in their book, it is still possible that omitted variables may, once included, attenuate the effects of their social bonds variables. For example, Sampson and Laub find that job stability decreases the likelihood of adult crime. They control for childhood criminality, as well as using modeling techniques to account for population heterogeneity. Still, if criminal propensity is mis-measured, or under measured, then results indicating a social bonds effect could disappear if propensity were properly measured. Gottfredson and Hirschi (1995) essentially maintain that no model can fully account for criminal propensity, and as such, all results finding social bonds effects in adult criminality are really artifacts of missspecified models, in particular, from omitted variable bias.

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Sampson and Laub simply disagree with this (1993, 1995, 1997, Laub & Sampson, 2001, 2003). They argue that by doing separate analyses for the delinquent and control groups, and by modeling childhood behavior and adolescent delinquency into their adult models, that they have adequately controlled for underlying criminal propensity. Because the same general pattern of results was found in their analysis of the nondelinquent sample (with the obvious exception of the effect of prior childhood delinquency), Sampson and Laub maintain that self-selection is not an issue. In the end, it may be difficult to settle the debate between the two positions. Strong support has consistently been found for self control theory. Pratt and Cullen’s (2000) meta-analysis of twenty-one studies found an effect size of .20, which they noted made self control one of the strongest known correlates of crime. Moreover, the effect remained strong even when controls for other theories were incorporated, and across gender and racially diverse samples. The effect size was stronger in community samples than in offender samples, as well as in cross sectional as opposed to longitudinal studies. Recent studies focusing on social bonds have generally controlled for criminal propensity. There were thus few creditable studies of social bonds alone in the post “General Theory of Crime” era. Indeed, it was partially their dissatisfaction with earlier social bonds theories that lead Gottfredson and Hirschi to formulate a theory of self control in the first place. Studies testing the relative strengths of both theories have tended to find support for both rather than one or the other. This is generally seen as supporting the state dependence life course theories, because they allow for the effect of criminal propensity while population heterogeneity theories are more stringent, suggesting that the effect of bonding measures will disappear when criminal propensity is controlled. Paternoster and Brame (1997), using data from the Nation Youth Survey, found that controlling for static propensity variables did not diminish the significance of social process variables (Paternoster & Brame, 1997). Wright, Caspi, Moffit, and Silva (1999) used data from the Dunedin study in New Zealand to test the effects of social bonds and self control. Using longitudinal data collected from birth to age twentyone, they tested the effect of early childhood self control on adolescent

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social bonds, late adolescent self control, and criminal involvement and found support for both perspectives. In fact, they found that social bonds largely mediated the effects of self control. Persons with lower levels of self control were less likely to form attachments, and persons with attachments were less likely to offend. They additionally found that self control measures diminished the strength of the social control measures, concluding that both elements are important in the study of crime (Wright et al. 1999). The studies above focused on general population samples and on the early stages of offending. Longitudinal studies on offender samples, with the exception of Sampson and Laub’s (1993) are not as common. Horney, Osgood, and Marshall (1995) used a retrospective data collection technique to study the short term effect of changes in local life circumstances (employment, schooling and relationship status) on crime among a sample of recently convicted offenders in Nebraska. They found that improved circumstances were related to decreases in crime while deteriorating circumstances led to increases in criminal behavior. Horney et al. used no direct measure of latent traits in their model, however, relying instead on the distribution of offending to form a propensity function. Using the local life circumstances perspective of social bonds, Piquero, MacDonald and Parker (2002) examined data on 524 parolees in California. Using a longitudinal design Piquero et al. modeled changes in employment and marriage status over seven years. They found a significant negative effect for marriage, but no effect for being employed. They also found that the effects of employment and marriage were largely the same across racial groups. Uggen (2000) used data from the National Supported Work Project to test the effect of a randomly assigned work program among a sample with prior arrest histories. He found that work decreased the likelihood of reoffending among those over twenty-six years of age. Those under twenty-six were no different from the comparison group in terms of arrest, leading him to conclude that the effect of work on crime was age graded. While Uggen used no measure of criminal propensity, the random assignment process should have distributed high and low propensity persons randomly. In a recent study, Banda & Toombs (2002) followed 480 male boot camps graduates in Arkansas for three years. These researchers

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used a proportionate hazard model to test the effects of social causation and social selection on recidivism. They found that social selection variables were important predictors of recidivism but that they did not attenuate the effects of being married or other effects of their social control model. Although controlling for social selection in their analysis, social control measures were collected at baseline and the study was not able to measure the effects of work, marriage and school after release from prison. Critics continue to maintain (Gottfredson & Hirschi 1995) that these findings result from misspecified models. Laub and Sampson (2001) reviewed the literature on desistence to date and concluded that there is ample evidence to suggest that variation in adult social bonds is a fundamental cause of continued persistence in, or desistence from, criminal behavior. Their review found that the factors associated with change in adult crime are much like those found in the literature on the cessation of drug and alcohol abuse which they list as “The decision or motivation to change, cognitive restructuring, coping skills, continued monitoring, social support and general lifestyle change, especially new social networks” (Laub & Sampson 2001, p.39). In concluding their review, Laub and Sampson suggest an agenda for future research. They point out that “… our understanding of desistance has been hampered by the lack of long term studies, especially of those involved with the criminal justice system and other systems of social control” (Laub & Sampson 2001, p.54), and that too much existing data comes from official arrest counts. They suggest that longitudinal studies be conducted on criminal samples (p.55). They also note that most desistance studies gather data retrospectively, and argue that prospective designs are needed. They note that addicts tend to lapse and relapse multiple times prior to reaching long term abstinence, and that investigations should assess whether desistence from crime operates in a similar fashion. Lastly, they note that their original study was conducted on a largely White, male sample that desisted in the 1950s and 1960s. Since life course perspectives maintain that different historical contexts are important for explaining behavior, Sampson and Laub emphasize the need for more current samples that investigate how social bonds operate across different racial and gender groups.

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The research at hand is designed to meet this challenge. The primary goal is to investigate how a life course theory of adult social bonds applies to a modern sample of using criminal offenders recently released from prison. containing subpopulations of different racial groups. The next chapter will delineate the specific research objectives of this project

CHAPTER 4

Adult Social Bonds, Prison, and the Reentry Process While Sampson and Laub make a compelling case for the significance of adult social bonds in decreasing criminal behavior in later life, their theory still lacks sufficient testing to be considered a general theory; that is, one that applies to all types of criminals, all types of crimes, in all time periods (indeed, there is a line of literature best exemplified by Moffit (1993, 2006) that suggests general theories themselves are misspecified). The Glueck’s (1950) data that Sampson and Laub utilized was based on a sample of offenders who came of age during a period of economic prosperity and lived their lives during one of the greatest economic expansions in United States history. The sample was also exclusively White and male and confined to one city in the American Northeast. It is thus unclear whether social bonds have the same effects as those found by Sampson and Laub among different samples. This project attempts to add to the knowledge of adult social bonds by testing Sampson and Laub’s theory across racial groups among a sample of highly active drug addicted offenders released from prison. It is unclear whether different racial groups gain the same type of effects from adult social bonds. Indeed there exist theoretical perspectives that focus almost exclusively on Black, inner city residents, arguing that that the specific life conditions these individuals face are crime producing (Barnard, 1990; Anderson, 1999). One recent attempt to address the issue of race and adult social bonds was Piquero, MacDonald and Parker’s (2002) examination of the effects of local life circumstances on criminal offending across different racial groups. Using data on 525 California parolees followed for five years after release from prison, Piquero et al. modeled how changes in employment and spousal situations affected criminal behavior across Black and White groups. Their findings indicate that, while some types of marriage and employment reduced offending for some types of offenders, racial differences in offending remained, even after controlling for local life circumstances. They suggest that to the extent that racial differences remain once adult social bonds are controlled for, the generality of Sampson and Laub’s theory is called 47

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into question. The analyses presented below will model the effects of social bonds and race, as well as controlling for underlying population heterogeneity; something Piquero et al. were unable to do. Sampson and Laub’s hypotheses have also not been thoroughly tested on samples of modern, active criminal offenders. The Gluecks’ data were gathered at a time when family life was key to American culture and economic expansion was the norm. The current analyses focus on a sample collected at a time when the family is less central to American life and in a period of economic decline (Rubin, 1994). It is possible that, with less emphasis on family life in general, family factors could prove less significant as persons find meaning and attachments in other areas of their lives. It may also be possible that employment is less significant (or more so) as jobs become scarce and harder to find. Also, due to the relationship between drugs and crime (Harrison, 1992), and Sampson and Laub’s (2001) own conclusion that the predictors of abstinence are much the same as the predictors of desistence, their theory will be tested on the addiction careers of the sample at hand. This chapter outlines the specific theoretical issues to be investigated. Prison and Adult Social Bonds. One of the concerns voiced by scholars studying prisoner reentry is that the majority of people returning to communities from prison have few or no social attachments (Petersillia, 2003). Lacking jobs, strong spousal relationships, or social capital in general, as well as possessing a demonstrated propensity for crime, it is feared that these persons are at an extremely high risk of criminal relapse (Travis, 2000; Petersillia, 2003). Sampson and Laub would agree. Without even the consideration of cumulative disadvantage (Sampson & Laub, 1997), one of the things that a period of incarceration does is to destroy any social capital that an offender may have built up. A job is obviously lost and relationships with spouses or significant others strained. This amounts to an utter destruction of accumulated social capital among incarcerated populations. From a state dependence prospective, a period of incarceration can have an indirect effect on future offending. By cutting off bonds from

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socially beneficial institutions and labeling offenders in a way (i.e., a felony conviction) that lessens the likelihood of developing further social capital, incarceration increases the likelihood of reoffending. One thing that must be considered, however, is the nature of this social capital. Sampson and Laub’s research showed that those who persisted in crime into adulthood had lower levels of social capital than those who desisted. One must wonder, therefore, how much social capital a group of incarcerated offenders had when they were arrested. Studies have shown that while many of those in prison were unemployed at the time of arrest and more likely to be single, many were employed and many were married (Petersillia, 2003). If these social bonds did not constrain people from their first criminal offense how can it be assumed that later social bonds will? Life course theory assumes just that, however. Sampson and Laub note that it is the accumulation of bonds and context within a person’s life that cause a bond to take hold at one point while it may not have at some other point in the life course. Elder (1988) notes that in life course scholarship, timing is very important; the structure of events and period of life intermingle with decision making processes in an individual’s life course to either reinforce stability or alter a trajectory and bring about change. While reentry scholars tend to focus on the loss of social capital resulting from a period of incarceration, what they overlook is the severing of links or bonds to old social networks, (e.g. ways of being, and identities.) Laub and Sampson note “a central element in the desistence process is the knifing off of individual offenders from their immediate environment and offering them a new script for the future.” (2001, p. 49) They suggest that military service and marriage enable this knifing off to occur. It could also be argued that incarceration has the same knifing effect. Networks and illegal systems are undoubtedly interrupted by incarceration. Upon release the offender may, or may not, reestablish these ties. Offenders upon release from prison are at something of a zero order. They have either a toehold in some form of positive social capital awaiting them (e.g. a marriage strained (or strengthened) by a period of incarceration, possibly a job, but presumably under the tenuous eye of an employer) or no positive social capital at all. Similarly, however, they likewise are probably not as embedded in the

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criminal lifestyle they left, if only because the role they once played may now be occupied (in a drug dealing example) or they may simply need to learn new hustles (in a property crime example). Either way, incarceration is an interruption of a trajectory. As such, it has the potential to alter that trajectory and serve as a turning point. Obviously, alteration of a trajectory by incarceration is not an overwhelming pattern. Studies by the Bureau of Justice Statistics have shown that 67.5% of offenders released from prison are rearrested within three years and 51.8% are returned to prison (Langan & Levin, 2002). But this means that approximately 50% are not. Something must be happening to these 50% that is different than the other 50%. Sampson and Laub (1998) showed how periods of incarceration decreased the likelihood of later adult social bonds, and thus increased the likelihood of future crime in a concept (outlined above) they refer to as cumulative disadvantage. But there is at least some potential for an interruption effect of incarceration. As argued below, depending in part on what happens to an individual offender during a period of incarceration and reentry, it may result in an interruption of a continuing trajectory or whether it can alter that trajectory towards a process of desistance. Getting Busted In order to provide a clear picture of the process just described, picture a hypothetical offender. Enmeshed in a world of drugs and crime, our offender is probably alcohol and drug abusing if not fully addicted. His (because he is most likely a he) employment record is spotty at best; his relationships, strained. Social control theory (Hirschi, 1969) would focus on his attachments, commitments, involvements and beliefs, most likely find them lacking, and use this to explain his criminal career. But he is not lacking attachments; he is probably attached to a peer structure and set of relationships that supports crime. Indeed, he is probably attached to and skilled at his job, which is street hustling and possibly drug dealing. He is involved in his craft as well, taking time out of his day to earn money through various enterprises, both legal and illegal. While his commitment to this lifestyle would probably be denied (nobody wants to be a junkie), it is what our hypothetical offender knows and, as such, he is probably committed to it. The belief

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structure of offenders is beyond the scope of this project, but one could draw upon the work of scholars like Matza (1969) and Anderson (1999) to argue that their beliefs are well supported. For example, Anderson (1999) argued that among lower class urban Blacks, traditional beliefs have, in some circles, been supplemented by a “code of the streets” that revolves around notion of respect and in many instances, calls for violence. Persons embedded in these belief structures are not lacking in social bonds, indeed, it is their bonds that draw them into the belief structure to begin with. In sum, there are sociological reasons that can explain our offender’s position. Now, let us assume the offender is arrested and incarcerated. His attachments are severed, his job lost. His involvement in any social activity, whether in a positive or negative manner, is essentially nothing. His beliefs remain, but it is reasonable to assume that a rational person performs a beliefs-check when something like a prison sentence interrupts his life (deterrence theory relies upon this is some sense). In short, his trajectory has been dramatically altered, or at least temporarily severed. Sampson and Laub and other social control theorists have focused on the loss of bonds created by incarceration. This assumption is problematic both quantitatively in assuming that our offender has any bonds at all, and qualitatively because the nature of those bonds could potentially be crime inducing. It also is logically inconsistent: lack of bonds increases the probability of crime, but criminals lose bonds in periods of incarceration. Once our hypothetical offender is incarcerated, these bonds have undoubtedly been altered. Most bonds to peers have been at least temporarily severed. The nature of the arrest and conviction process, in addition to simply removing one from the “action,” can have a straining effect on peer networks that are crime producing. Offenders will often testify against one another in criminal proceedings, which makes further business networking (especially in drug cases) highly unlikely. Even without this direct kind of severance, just being out of the loop is bound to decrease an offender’s ties to illegal networks. In addition, his bonds to family have also probably been strained. Going to prison is a traumatic event in a family, taking one person out of the relationship altogether and leaving the remaining partner in a difficult situation. Bills must be paid, children cared for, and social relations retooled. If a relationship to a spouse or significant other was

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strained to begin with, a period of incarceration may end the relationship altogether. If the relationship was strong, incarceration will certainly strain it. But it also gives the non-incarcerated party a certain amount of leverage in motivating behavioral change. Suppose our hypothetical offender was in a strong relationship. Possibly his spouse was trying to get him to change his behavior. Typical addiction patterns indicate that he would attempt change or at least verbally agree to “get his act together.” These attempts would often fail, and when they were successful, would typically not be successful for long. This is the process of cycling in and out of drugs and crime that is well documented by Terry (2003). Presumably, our hypothetical offender was in one of these cycles, probably at a stage of high drug use/criminal activity when he was arrested. This sets up a situation in which his methods of “getting it together” and excuses for not doing so are exposed, allowing the partner room to insist upon certain behavior modifications. In this sense, the act of getting arrested and being incarcerated may interact with having a spouse in a meaningful relationship to make the marriage bond work to reduce crime in the future even though it did not in the past. This change would represent a meaningful transition to a desistance trajectory. The other part of being incarcerated that allows for stronger spousal influences is the fact that the spouse will be in charge of the household while the offender is away. In some instances, the offender may need the permission of the spouse to move back into the household once released, again allowing for the potential to insist on behavioral change. In sum, there are multiple reasons and routes for behavioral change to occur for an incarcerated offender, particularly if his relationship bonds are strong. There is also a large body of literature on deterrence that suggests that those who are more attached to significant others (or jobs for that matter) are more likely to be deterred by an arrest/incarceration experience than those who do not possess these traits (Sherman, 1992).

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Getting Out Once the hypothetical offender is incarcerated, separated from peers and significant others, his criminal trajectory has been at least temporarily interrupted. Depending on what happens to him while he is incarcerated and when he gets out, one of three things may occur: 1) His criminal trajectory will resume as before. He will simply pick up where he left off and continue his line of offending. 2) He will pass through a turning point, changing his criminal trajectory towards a path of desistance. According to Sampson and Laub, this is likely to occur as a result of an accumulation of salient adult social bonds. 3) He will pass through a turning point, changing his criminal trajectory towards a path of increased offending. Cumulative disadvantage will come into play, he will be unable to form bonds as a result of his incarceration, and his offending will increase. For options one and three, Sampson and Laub would argue a cumulative disadvantage approach, in which the incarceration experience and the stigma of a prison record decrease the ability of the offender to accumulate significant social bonds, which in turn leads to a continued or even increased criminal trajectory. Option two is the main focus of Sampson and Laub’s theory, except they do not base it on a post prison experience, but rather through the natural occurrence of turning points in the adult life course. What is different about the perspective outlined here is the focus on the post prison experience. Returning to our hypothetical offender, the possibility of option two may manifest itself in a number of ways. If the person had a strong spousal relationship going into prison, this relationship will have been altered by the experience. Exiting prison with an in tact spousal arrangement puts an offender at an advantage in terms of social bonds over an offender without such an arrangement. As a result of this attachment, and from a simple stakes in conformity

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perspective, the offender has more to lose if he recidivates (Toby, 1957). In addition, he also has an investment in the relationship, and needs to succeed in order to please the partner and keep the relationship going. He is thus pressured towards success. Another route to desistance, and one not explored by Sampson and Laub, is the role of children. Those who have children, and who develop a bond to those children, have an additional link that ties them to something other than a criminal lifestyle. As such, the presence of children may also enable them to change their trajectory toward a path of desistance. Obviously, for some, a spouse or child did not help the offender refrain from crime prior to the current offense. The question then becomes, “why should a spouse or child help him refrain from upon release. The nuance of the life course perspective, however, allows for just that. Our hypothetical offender, through the very act of being arrested and incarcerated, might come to see the value of his spouse and/or child(ren). Thus, failure at one stage of the life course might alter the influence of a particular variable (having a spouse or child) at a later stage of the life course. In sum, one cannot assume that because a particular effect was absent at one point, that it will have no effect at a later stage of the life course. Another route to success growing from Sampson and Laub’s perspective is having a job. An offender who leaves prison with a job is at an advantage over his unemployed counterpart in numerous ways. From Sampson and Laub’s perspective, the main thing an employed person has is the social capital associated with that job. The bond created by the employment gives the offender a meaningful place in society and a meaningful relationship with his employer that increases the likelihood that he will refrain from drug use and criminal behavior. Having a job also increases the likelihood that the offender will not form attachments to less appropriate (from a conformist, law-abiding perspective) peers and networks. This is not necessarily due to being occupied, as suggested by Warr (1998) regarding the marriage effect. Rather, an offender who has a job is likely to form attachments to coworkers that flow outside of the workplace. To the extent that friendships are formed at work and after work activities involve coworkers, these links would provide additional (unmeasured) bonds that further decrease the likelihood of criminal involvement.

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Unfortunately, when the hypothetical offender in the example above is released, he will more often than not be unemployed (Petersillia, 2003). Whether he finds a job or not is an important factor in determining his long term success. It also opens the door for one of the chief criticisms of Sampson and Laub’s theory, that of self selection or selection bias. According to scholars such as Gottfredson and Hirschi, those who are likely to find meaningful employment are those who have higher levels of self control, which also makes them less likely to engage in criminal behavior. This criticism is very problematic for theorists like Sampson and Laub who maintain that factors external to the individual can have significant effects on behavior. In terms of our hypothetical offender, if he happens to have a certain amount of self control, he is more likely to find a job upon release from prison. He is also more likely to remain arrest free. In research of the type conducted by Sampson and Laub and by the current project, he would be a “success” although, according to Gottfredson and Hirschi, the results would be a purely spurious finding based on an unmeasured self control variable. Methodologically, the solution to this dilemma is to adequately control for underlying propensities such as self control, which Sampson and Laub did and the current project will do. Obviously, all of the subjects used in this research have low self control because they are all criminals. Thus, while there may be variation in self control among subjects, it should not be expected to be as great as that found in a non criminal sample. Indeed, research on self control that has focused on only criminal samples has found less robust findings (Piquero & Rosay, 1998; Pratt & Cullen, 2002). Returning to our hypothetical offender, if he is like many persons in prison, he has probably renounced his life of crime and sworn to himself and others that he will not “be back.” He probably has no idea how he will accomplish his new “straight” life, but offenders tend to make an effort upon release from prison to accomplish some form of socially acceptable lifestyle (Terry, 2003). Assuming that all offenders have some form of criminal propensity such as low self control, their likelihood of success at finding a job depends in part on their skills and efforts, in part upon their social networks outside of prison, to a degree upon the efforts of their parole officer, and some proportion of dumb luck. Those who through some set of these factors secure meaningful employment should be better off

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than their less fortunate counterparts in decreasing the likelihood of future drug use and criminal behavior. All of these types of social bonds are hypothesized to decrease the likelihood of future criminal involvement and drug use for released offenders. They are also hypothesized to reinforce one another. An offender who is released from prison who goes home to a spouse and child and has a job, has a lot invested in not returning to prison. He also has a great deal of support to encourage him to stay active in noncriminal behavior. Human Agency and Images of Self Over the past decade, Sampson and Laub have acknowledged the role of human agency in altering trajectories. Their original 1993 work put less emphasis on agency and more on social structural factors. Research and theorizing in the interim period have led them to agree that the decision to go straight is an important part of the desistance process, even though they state, “The men made a decision to go straight without even realizing it.” (Laub & Sampson, 2001 p. 51). Sampson and Laub also focus on the changing identities of the men who desisted. There was a focus in their (1993) life history narratives that suggested the desisters had found a new identity or had defined their past behavior as just that, past. This essentially opens the way for new trajectories to emerge. Some recent scholars have focused on the importance of self image in relation to the desistance process (Giordano, 2002; Terry, 2003). Offenders who desist must first develop a self conception of themselves as non-offenders before they can even hope to be successful upon release from prison. Terry (2003) notes than many offenders develop this new self through involvement in 12 step programs while they are incarcerated. By developing these new images, offenders prepare themselves to enter back into society not as returning criminals but as ex-cons, ex-criminals and recovering addicts. From a social bonds perspective, it could be argued that attachments to pro-social institutions after release would further cement these self images, decreasing the likelihood of future criminal involvement. Those who do not develop such bonds, the argument would follow, would have a

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more difficult time maintaining this self image and be more likely to return to crime. Because the type of treatment regimen the current sample underwent was a therapeutic community model in which the goal is to remake the individual through a peer centered community process (Inciardi et al., 1997), it is suggested that this might pave the way for identity change among those who experienced the treatment regimen. While there are multiple routes to the “straight life,” it is reasonable to assume that among those who underwent treatment, there are likely a substantial number whose “new life” includes such activities as work, relationships, or closer relationships with children (or having children for that matter). This perspective is an important one and will be addressed in the project by testing whether and how enrollment in a drug treatment program affects the likelihood of obtaining social bonds, as well as the likelihood of remaining crime and drug free in the absence of social bonds. Maruna (2001) conducted in depth interviews with a small sample of long time offenders, some of whom were classified as persisters and some who desisted from crime. Maruna suggests that internalized factors dealing with the narratives that offenders create for themselves are more important than externalized conditions such as getting a job. Those offenders who developed what he termed a “redemption narrative” were much more likely to desist than those who did not. What separated the redemption group from the others was their ability to separate themselves from their crimes. Offenders who were able to create an “other self” who was bad and crime committing, and separate that self from what Maruna calls the “core self’” who is good and not crime committing, were able to start a new path or trajectory that was non crime committing (Maruna, 2001). While Maruna focused on the internalized self as a key factor in the desistance process, he also emphasized the importance of outside influences. He notes that most desisters talked about the intervention of some person who believed in or had some kind of faith or investment in the individual as being pivotal to change. He further notes that one of the biggest obstacles desisting offenders face is the acceptance by society of their new selves.

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This research is inextricably related to research on turning points. On the one hand, those in the population heterogeneity camp would argue that those who turned around might have had lower criminal propensity, and that if one were to properly control for this, it would be possible to identify those more likely to desist. On the other hand, while Maruna (2001), Terry (2003) and Giordano (2002) all focus on the agency or internalized decisions and actions of the individual, they all stress the importance of other in the end. There may indeed be a selection effect involved in who decides to “go straight.” But among that group of persons who make the decision, some will form attachments and some, for whatever reason, will not. To the extent that persons looking to change their lives find some form of bond, they would be expected to be more likely to succeed. Laub and Sampson, it should be reiterated, argue that, while agency is important, the type of thought process by the offender need not involve a cognitive transformation or redemptive turn. Rather, they suggest that offenders often find themselves in situations where they have too much to lose to revert to crime (Laub & Sampson, 2003, p 279). Changes are thus not made by conscious decisions, turning points are not “hooks” that are grabbed on to or passed by, because turning points are not even recognized as turning point until long after the fact. Because the type of treatment regimen the current sample underwent was a therapeutic community model in which the goal was to remake the individual through a peer centered community process (Inciardi et al., 1997), it is suggested that this might pave the way for identity change among those who experienced the treatment regimen. While there are multiple routes to the “straight life,” it is reasonable to assume that among those who underwent treatment, there are likely a substantial number whose “new life” includes such activities as work, relationships, or closer relationships with children (or having children for that matter). Connecting drug treatment to social bonds would constitute a significant advance in Sampson and Laub’s theory. Sampson and Laub have argued a detrimental effect of arrest and incarceration. Desistence from crime, however, presupposes, in fact requires, crime. Crime, in turn, often results in apprehension and punishment. It is not likely that criminal sanctions and their stigmatizing effects will be done away with any time in the foreseeable future (and most would argue that they

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shouldn’t be). It is not necessary, however, that criminal sanctions be devoid of treatment and rehabilitative programs. In the 1970s prison was a last resort and treatment was a preeminent focus of criminal justice. While not suggesting that treatment should be the focus of criminal justice, it might serve as a larger part of it. And if in-prison drug treatment can be shown to increase the likelihood of social bonds, and if it were possible to create or increase the likelihood of turning points through treatment programs, then this would have important policy implications. At a more fundamental level, if our theories of crime, and in this case, Sampson and Laub’s theory of age graded social control, are correct, and if our drug treatment programs are functioning properly, then it stands to reason that these programs should be affecting the same variables that are proposed by our theories to diminish further crime. To the extent that this is found, it would be evidence of both the proper theory and the proper treatment method. This question is important and will be addressed in the project by testing whether and how drug treatment affects the likelihood of obtaining social bonds, as well as the likelihood of remaining crime and drug free in the absence of social bonds. Because the data used for this study come from a treatment program evaluation project, approximately one third of those in the sample went through a therapeutic community based transitional living program; one third went through the transitional living program, and the final third went through a regular work release program, without the therapeutic community treatment. Therapeutic communities are peer based live-in drug programs in which offenders are separated from the rest of the inmate population. This isolates them from the effects of the prison culture and allows participants to concentrate on issues related to recovery. It is hoped that this leads to self images as non-offenders and recovering addicts rather than junkies or other negative images they had when they entered prison. Treatment lasts from six to nine months, in addition to a nine to twelve month in-prison treatment regimen. This amount of treatment concentrates on, among other things, instilling in the offenders a strong non-criminal, abstinent identity. It is thus assumed that those who go through treatment will have better outcomes because they have these stronger non-criminal self images.

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In sum, Sampson and Laub have proposed that criminal offenders who accumulate adult social bonds later in life are more likely to desist from crime than those who do not enter these relationships. This project proposes to capitalize on a treatment effectiveness study sample to test this hypothesis. By utilizing arrest and incarceration as a potential interruption of a criminal trajectory, and thus a possible turning point, the study proposes to test whether the accumulation of adult social bonds after release from prison increases the likelihood of criminal desistance and drug abstinence. The sample began the study period incarcerated, so everyone was essentially unemployed and living in prison. Some found jobs right away while others did not. Some returned to ongoing relationships, others did not. The point is, every person in the present sample essentially started with zero social capital- even if only from the time of release until they were picked up at the curb. This condition enables an examination of the accumulation of social capital among a sample of adult prisoners reentering the community and how this social capital affects future offending and drug usage. This leads to the general concept and set of hypotheses for this project: Persons who develop social capital in the form of stable employment, a strong relationship or attachment to a child upon release from prison will be less likely to reoffend, use drugs or to continue in higher levels of drug use than those who do not develop these relations. From this premise, the following conceptual hypotheses emerge: Hypothesis 1: Finding steady employment upon release from prison will decrease the likelihood of arrest and drug use at the 12 and 24 month follow ups. Hypothesis 2: Persons who are employed at the 12 and 24 month follow-up will be less likely to be drug using or be arrested than those who are not employed at each follow up. Hypothesis 3: Persons who spend a greater portion of their at risk and work eligible time employed will be less likely to be arrested and use drugs than those who have less employment over the entire course of the study.

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Hypothesis 4: Persons in a stable relationship upon release from prison will be less likely to be arrested or use drugs at the 12 and 24 month follow up periods. Hypothesis 5: Persons who are in a stable relationship at the 12 and 24 month follow-up will be less likely to be arrested or use drugs than those who are not in a stable relationship at each follow up. Hypothesis 6: Persons who care for a child upon release from prison will be less likely to be arrested or use drugs at the 12 and 24 month follow up periods. Hypothesis 7: Persons who care for a child at the 12 and 24 month follow-up will be less likely to be arrested or use drugs than those who are not attached to a child at each follow up. Because Sampson and Laub base their theory on Coleman’s (1988) notion of social capital, an additive measure in which possessing each element of social capital listed above adds to the level of capital is utilized. An additional set of hypotheses will test the combined effect of social bonds across time periods. Specifically: Hypothesis 8: Persons possessing more additive social capital upon release from prison will be less likely to be arrested or use drugs at the 12 and 24 month follow up periods among individual trajectories. Hypothesis 9: Persons who have more additive social capital at the 12 and 24 month follow-ups will be less drug using and less criminally involved than those who have lower levels of additive social capital at each follow up. This set of analyses will provide a test of Sampson and Laub’s theory on a modern sample of highly active criminal offenders who have just been released from prison. The final set of hypotheses concern whether the effects found in the first phase are constant across racial groups. This will involve performing separate analyses in each of the Black and White groups (there are not enough Hispanics in the sample to allow a reasonable test of Hispanic differences). Although no

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formal theoretical declarations are made, for the purposes of analysis the null hypothesis is that there are no differences across racial and gender groups. A final set of analyses will investigate whether participation in a transitional living therapeutic community drug program affects the level of social bonds among this sample of reentering offenders. The data and analytic technique are discussed in the next chapter

CHAPTER 5

Sample, Data and Methods Sample In 1990 The Center for Drug and Alcohol Studies (CDAS), at the University of Delaware received a grant from the National Institute on Drug Abuse to study the effects of in-prison and transitional living drug treatment programs on relapse and recidivism. The grant called for the creation of a Therapeutic Community based, live-in treatment program (De-Leon, 1985; Inciardi et al., 1997). The program was initiated under the direction of the Principle Investigator (James A. Inciardi) and data collection began in 1992. Over the next five years, Inciardi and his colleagues documented in numerous journal articles the positive effect of this Therapeutic Community based drug treatment on drug relapse and recidivism (Butzin, Martin and Inciardi, 2002; Inciardi, Martin, Butzin, Hooper, and Harrison, 1997; Inciardi, Martin and Butzin, 2003; Martin, Butzin and Inciardi, 1995; Martin, Butzin, Saum and Inciardi, 1999). The most recent set of analyses (Inciardi, Martin and Butzin, 2003), shows that differences in arrest and relapse are still found between the treatment and non-treatment groups five years after release from prison. Based on the success of the original project, a supplemental MERIT award was granted to Inciardi in order to investigate whether a program designed under the direction of drug treatment scholars and implemented under the careful scrutiny of those scholars maintained its effectiveness when absorbed into the day-today functioning of a state run department of corrections. The new project was initiated and data collection began in 1999. The study design called for three groups to be followed; a sample of those who received both in-prison and transitional living treatment (in a halfway house), a sample of those who received only the transitional living treatment, and a sample of those who received no treatment but were released through a standard work release program. The sample at baseline consisted of 750 offenders returning to the community through various reentry mechanisms. Data collection is ongoing, with follow up interviews being conducted at 12 and 24 months after release. Study recruitment was done by posting flyers in 63

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the treatment programs and work release center asking for volunteers. All drug treatment participants were convicted offenders with serious drug problems. Persons in the drug treatment programs, either the transitional group or the in-prison and transitional group, were either ordered into the programs by a judge who determined that they had a drug problem and would benefit from treatment, or were classified into the programs by the Department of Corrections who made a similar determination. Work release participants represented the every-day prison population and were recruited in the same way as the treatment group, but they were not mandated to treatment. The research protocol for all these groups included a baseline and two follow-up interviews. The baseline interview was administered in the treatment program or work release center, and the first follow-up occurred 12 months hence, after the subjects have been released into the community. The subsequent interviews for this analysis were conducted in the community, 24 months after respondents were released. Interviews at baseline and each subsequent follow up were lengthy, representing over a hundred variables per administration, including data on basic demographics, living situations, such as whether respondents lived alone or with spouses and children, criminal history including the age of onset and the number of different types of crime committed (specialization), extensive drug use history, sensation seeking and impulsivity questions, and a battery of HIV/AIDS risk assessments. Follow up surveys elicit detailed event history information on the intervening periods. Participation in the project is voluntary. Participants are paid up to $50 at each of the follow-up interviews, $25 for completing the questionnaire and $25 for giving a urine sample. The analysis that follows used data from this second cohort (as opposed to the original 1992 study). The analyses used baseline and 12 month follow-up data to form the predictor variables in order to investigate whether being married, having children, or having a job 12 months after release from prison predicted arrest and frequency of drug use at 24 months after release from prison, while controlling for differences in criminal propensity. Of the original 750 persons in the sample, 106 were of Latino or Asian descent, or reported multiple racial categories. Because there were not enough cases within these groups to make reliable

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comparisons, they were dropped from the sample, leaving 644 persons in the baseline sample. At the time of this analysis 397 of the 644 had completed the 12 month follow up and 191 of that 397 had completed the 24 month follow up. These samples will form the basis for the analysis that follows. The 397 persons at follow up one will be used to analyze the cross sectional 12 month data, and the 191 persons who have completed the 24 month follow up will form the basis of the 24 month and structural equation analyses. Dependent Variables Because the nature of this project is to examine how social bonds affect drug use and rearrest after release from prison, while controlling for background criminal propensity, a series of dependent variables are utilized at both the 12 and 24 month follow up periods. Social bonds variables are themselves used as dependent variables at 12 months, but become independent variables at the 24-month follow up. Drug use and arrest will also be predicted at both the 12 and 24 month follow up interviews, based on social bonds, while controlling for underlying criminal propensity. Two dependent variables were used to measure whether participants recidivated or relapsed at the 12 and 24 month follow-ups. The first, (IN-PRISON12, IN-PRISON24), are dichotomous measures of whether respondents had been reincarcerated at each of the 12 and 24 month follow up interviews, coded 0= not reincarcerated, 1= reincarcerated. At the 12 month follow-up interview, 43.8% of respondents had been reincarcerated, while 56.2% had not been reincarcerated. Between the 12 and 24 month interviews, 58.3% of respondents had been incarcerated, including those who were incarcerated at the time of the 12 month follow up. To measure the effects of criminal propensity and social bonds on analogous behaviors, a measure of illegal drug use was created for each follow up period (RELAPSE1, RELAPSE2). Respondents who reported no drug use at all during the time period, and who tested negative for drugs at the time of the interview were coded zero on these variables. Those who either reported some drug use during the follow up period or who tested positive for drugs at the time of the interview, were coded one. These will be used as dependent variables in the drug-

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use outcome models. At the 12 month follow up, 57% of respondents reported using some type of illegal drug since release from prison or tested positive for drug, while 43% reported no use and tested negative. At the 24 month interview, 54% reported having used drugs since the last interview or tested positive for drugs, while 46% reported no use and tested negative. Because scholars (e.g. Harrison, 1992) have suggested that pass/fail criteria such as using or not using drugs are overly ambitious and too conservative when measuring progress in recovery for drug users, three additional variables were created to measure drug use in a more nuanced fashion. First, an ordinal level composite variable was created measuring the frequency respondents used drugs, coded 0 = never, through 6 = several times per day. Respondents were allowed to record up to five episodes of drug use, and the episode in which the respondent used most heavily was used to score this variable. Because the question set for the ordinal level drug use variable was the same in all three interviews, it shows clearly the reduction in drug use by the overall sample. Table 5.1 shows the mean and standard deviations for the drug use variable at each interview. Table 5.1: Frequency of Drug Use at Each Interview Baseline (N=644) Mean Freq of Drug Use: Full Sample Freq of Drug Use: Follow up 2 sample

3.78

St. Dev 2.44

4.04

2.34

12-Month Follow up (N= 497) Mean St. Dev 1.79 2.33

2.19

1.58

24 Month Follow up (N= 191) Mean St. Dev 1.58 2.35

1.58

2.35

Because offenders cycled in and out of jail during the follow-up periods, an additional drug use variable was created that measured the proportion of months that offenders were free in which they used drugs. This allowed the analysis to model exposure time at risk. Because these

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variables involved recoding the data into a new series of variables, the interview variables and coding strategies are explained below. The first set of questions on the follow-up one interview asked where the offender lived after leaving the transitional living center and for how long. On the second follow up, respondents were asked where they lived since the date of the first follow-up. Responses were: Out (meaning living in a non-custodial environment), Halfway house, Prison, and Violation of Parole Center. Respondents were asked how long they lived at each place listed, with up to eight different residential situations allowed. By using the time elapsed between interviews and summing the number of months incarcerated or “out,” it was possible to determine the time each respondent spent at risk, that is, on the street and eligible to offend and use drugs. In the drug use section of the interview, respondents were asked the month they first used drugs after being released. Additionally, the interview schedule allowed for drug free periods and periods of relapse (up to five). For each relapse period, respondents were asked how long the drug-using episode lasted. For a drug-using period to end, the respondent must have reported at least one month of non-use. Using this, coupled with the at risk variable described above, a new variable was created in which values represented the proportion of months at risk during which the client used drugs (RATIODRUG). Values of RATIODRUG were between zero and one. At the twelve month interview, the mean was .14 and standard deviation was .25 (minimum = 0, that is, zero at risk months using drugs, the maximum was 1.00, which indicated the respondent used drugs during every eligible, at risk month). At 24 months, the mean had increased to .21 and the standard deviation was .37. Because of the negative skew resulting from the clustering of zero values, these variables were transformed to their natural logarithm for the analyses (Agresti and Finlay, 1986). Because of the chronic, relapsing nature of drug addiction, a final drug use variable was created that measured respondent’s reduction in drug use. While the RELAPSE variables assess the extent to which respondents maintained full sobriety, the frequency of use measured the intensity of their drug use and the ratio of months used accounted for time at risk. This reduction in use variable assesses improvement in drug use. That is, it accounts for how much less frequently offenders used drugs. The variables (REDRUG1 and REDRUG2) were created

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by subtracting the frequency of drug use from each subsequent follow up. REDRUG1 was created by subtracting the frequency of use at the first follow up measure from the frequency of use at the baseline interview. Higher scores on these variables indicate greater declines in drug use. For example, a person who was at the maximum frequency of six at the baseline interview, but reported no use (maxdrg1 = 0) at the 12 month follow up would score a six on the reduction in use variable. Conversely, a person who reported no use at the baseline periods (and there are some in the work release group), but used at the maximum rate at the 12 month follow up would score a negative six on the REDRUG variables. The multiple dependent variable strategy outlined above, and the structure of the data (three time periods) formed the analytic strategy for this project. By using the very conservative measures of incarcerated/ not incarcerated and used drugs/did not use drugs, a very simple and clean set of binary outcomes allowed for relatively straightforward interpretations of the underlying desistence question: did these offenders desist or not. These measures were utilized in cross sectional analysis at each time period. A series of cross tabulation analyses investigated whether any of the social bonds variables affected the outcome variables. These analyses were conducted for the full sample and then for each of the Black and White groups. The frequency of drug use question allowed the analysis to distinguish between the casual user and the full blown addict. While not directly assessing desistence, this set of analyses allowed for a more nuanced test of Sampson and Laub’s theory. The analyses taking time at risk and reduction in drug use into account are the most comprehensive of all. Because offenders cycle in and out of prison, the time at risk approach allowed them to fail more than once- or not - on the drug use measure. For example, a respondent who smokes marijuana in a single month long period, but does so three times per day, will score in the absolute worst category in the RELAPSE and MAXDRUG categories. A daily heroin addict will score the same on the RELAPSE variable, but actually score lower on the frequency of use variable. By additionally using the number of months at risk in which drugs were used, the analysis gains the perspective of examining how these offenders behaved over time. The four analytical approaches (yes/no, frequency, reduction in use, and

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proportion of months at risk used) provides a comprehensive view of the different possibilities regarding drug use, while the conservative measure of criminal justice system failure provides a more policy relevant outcome focusing on whether respondents “made it.” Independent Social Bonds Variables Social bonds variables were drawn from the 12 and 24 month follow up interviews. These variables focus on employment, relationship to a spouse and relationship to children. While they are all purported to measure the same thing (social bonds), they are all measured differently and so are described separately below. Employment Measures Both the 12 and 24 month interviews asked respondents how many jobs they had since the last interview and how many months they held each job. By summing the months working and dividing by the number of months each respondent was at risk, or eligible to work, a variable WORKING was created that is a proportion of months free in which each respondent was actually employed. At the 12 month follow up, 164 persons representing 36.3% of the sample were working. However, 83.5% of the sample had held some form of job since being released from prison. The proportion of months free that respondents worked ranged from zero (no work in any month free) to one (worked during every free at risk month). The mean for WORKING was .53 and the standard deviation was .34. At the 24 month follow up, 37.2% of the sample was working, while 60.2% had held some kind of job in the period between the 12 and 24 month follow up interviews. The mean proportion of months worked at the 24 month interviews was .63, with a standard deviation of .38. Because it can be assumed that working only a few hours per week might not have the same bonding impact as a forty-hour a week job, another variable (WORKHRS) was created which used the product of the proportion of months worked by the hours per month reported. Because respondents reported differing numbers of hours per week depending on the job (ranging from 4 to 60 hours per week), it was decided to use the number of hours from the highest hours per week job

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to form the WORKHRS variable. Using this coding scheme, the minimum hours worked at the 12 month interview was zero, the maximum was 1800, the mean was 259.6 and the standard deviation was 233.98. For the 24 month interview, the minimum was again zero, the maximum was 1440, the mean was 233.16 with a standard deviation of 276.44. These two variables (WORKING and WORKHRS) allowed the analysis to test whether any form of work reduces the likelihood of arrest, and to test whether more involvement in a job produces added benefit to the offender in terms of recidivism and relapse. For ease of presentation, in the cross sectional bivariate analyses, these variables were dichotomized into new variables. For the proportion of months working variable, those who worked half or more of their eligible time were compared to those who worked less than half of the time. For follow up one, 54.2% worked at least half of their eligible months, while 50.5 % of the follow up two interviewees reported working at least half of their eligible time. The hours worked variable was dichotomized by splitting the original variable at the halfway mark, so those under the fiftieth percentile were coded zero, and those above were coded one. This resulted in splitting the follow up one sample at 200 hours and the follow up two sample at 120 hours. Fifty-six percent of the follow up one sample was coded as one, while 53% of the follow up two sample were coded one. The combination of these different working variables allowed the analysis to thoroughly investigate the relationship between employment and relapse and recidivism. It should be noted, however, that working at the time of interview is potentially problematic because it is somewhat tautological. A respondent who is working at the time of the follow up is by definition not in prison. Still, because Sampson and Laub (1993) utilized this measure, it is included here. The variable measuring any employment, while included, is also potentially problematic, because a person could have worked for one day and would be coded as a one. It is unreasonable to expect a strong bond to form for these people. The more nuanced variables measuring the proportion of free months worked and the variables accounting for hours worked provide a more telling picture of the employment relapse/recidivism picture because they account for the quantity of employment people are engaged in.

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The other independent variables, relationship to spouses and children are described below. Relationship to a Spouse and Children The 12-month follow up interview contained little information on spousal and child relations. For the spousal relation question, it asked only whether the respondent was married, remarried, widowed, divorced, separated, never married, or (volunteered) living together. Because this project focuses on the reentry period, the married category was contrasted against the others to form the 12-month marriage variable. At the 12-month interview, 13.5% of the sample were married, while 11.1% of those interviewed at 24 months were married. The question for children at 12 months asked simply: “How many children under 18 live with you most of the time and you get them to school, buy them clothes and feed them?” This variable was recoded to contrast those with children that they live with and care for against those who do not report living with and caring for children. At the twelve month interview 31.2 % reported having a child living with them, while 30.9% of the 24-month respondents reported having one or more children living with them. The additive social bonds measure will simply sum all of the dichotomous responses to whether respondents had a job, were in a relationship, or had children during the interview period to form an ordinal level variable measuring additive social bonds. This set of variables forms the basis of the social bonds part of this analysis. It is hypothesized that respondents who acquire more social bonds upon release from prison will fare better than those who do not, and that those who spend longer periods of time in these arrangements, or whose relationships improve, will fare better than their less bonded counterparts. The regression analysis and structural equation models testing Sampson and Laub’s theory will account for underlying criminal propensity by controlling for background criminality measured at baseline and for the latent trait variables of risk taking and aggression, discussed below.

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Criminal Propensity Measures In order to control for population heterogeneity and because Gottfredson and Hirschi maintain that criminal propensity can best be measured using behavioral indicators, a series of criminal history variables were utilized as proxies for self control. Moffit (1993) suggests that chronic persistent offenders are identifiable by the early age at which they begin offending, and that once this group is identified, population heterogeneity dictates that they will remain more crime prone than later starting offenders. It is thus expected that age of onset will predict failure at both follow ups. Respondents were asked the age at which they first committed any of sixteen offences. An “other” category recorded any offenses that did not fall into the listed offense categories. Using this series of questions, a variable measuring the age of first offense was created (FIRSTCRIM). The minimum age of first reported offense was six, and the maximum was fifty-nine. The mean age of first offense was 16.7 (S.D.= 7.9). Gottfredson and Hirschi suggest that another way to assess self control is by the number of different types of acts reflective of low self control that one engages in. Using the same set of questions utilized in the age of first offense variable above, a variable was created measuring the number of different crimes committed by each respondent. This variable (NUMCRIM) had a minimum of one and a maximum of seventeen different crimes reported. The mean number of different types of crimes reported by respondents was 5.3, with a standard deviation of 3.0 Clearly, then, this is a highly active criminal sample. These two variables provided measures of age of onset and a measure of specialization. The correlation between the two was high (.548), as would be expected by both Moffit (1993) and Gottfredson and Hirschi (1990), because the early starting group, having a higher level of criminal propensity, would be more likely to engage in a greater number of offenses. Following the same logic as the criminal history variables, a set of drug use history variables was created as well. Just as the criminal history variables are intended to control for underlying criminal propensity, these variables are designed to control for propensity

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towards drug use. The first of these (FRSTDRG) is the reported age at which the respondent first used drugs (not including alcohol). The minimum age of first use was six years old, and the maximum, fiftythree. The mean age at which respondents first used drugs was 15.4 (S.D 5.6) years old. Another variable (MAXDRG0) recorded how frequently respondents used drugs in the three months prior to their arrest for the current incarceration offense (mean = 3.78, S.D. = 2.44). Responses ranged from never to several time per day. Risk Seeking, Impulsivity and Aggression While behavioral measures are worthy and well researched predictors of later criminal offending, much prior research has utilized psychological scales as indicators of underlying criminal propensity (e.g., Arneklev et al., 1993; Grasmick et al., 1993; Zuckerman, 1979, 1983). This project sought to add robustness to the behavioral measures above with three sets of psychological variables in order to account for population heterogeneity in the most comprehensive way possible. Self control predictor variables were drawn from the 12 month follow-up interview. Two latent variables were constructed to measure sensation seeking and aggression, and a single item measure of impulsivity was also utilized. A four-item portion of Zuckerman’s (1979) sensation seeking scale was used, with higher scores indicating higher propensity for risk taking. Findings using Zuckerman’s sensation seeking scale have largely found it to be a robust predictor of deviant behavior (Farley, et al., 1979; Newcomb and Mcgee, 1989; Wood et al., 1995; Zuckerman, 1983). The questions used were: I enjoy getting into new situations where I can’t predict how things will turn out. I sometimes do crazy things just for fun. I get a real kick out of doing things that are a little dangerous. I like to test myself now and then by doing something a little risky. Responses were agree and disagree. The scale mean was 1.49, with a standard deviation of 1.44. The alpha for the scale was .74. While a

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higher alpha would have been desirable, this is considered acceptable in the social sciences (see, e.g. Piquero and Rosay, 1998). Five items from the SCL-90R (Derogatis, 1977) were used to construct a volatile temper/hostility factor. Aggressive behavior and volatile temper have been found to be positively correlated with criminal involvement (Brook, Whitman and Finch, 1989; Glueck and Glueck, 1950; Robbins, 1966, 1978; West and Farrington, 1977). Gottfredson and Hirschi (1990) also maintain that having a short temper is a component of low self control. The items for this scale came from a series of questions in which respondents were asked to rate how often during the past week they experienced a list of problems or complaints. Responses were never, rarely, sometimes and very often. The questions utilized were: …Temper outbursts that you could not control. … Having urges to beat, injure or arm someone. …Having urges to break or smash things. …Getting into arguments. …Shouting or throwing things. The mean for this scale was 2.78 with a standard deviation of 3.07. The alpha reliability was .79. Lastly a single item was used as a measure of impulsivity. Respondents were asked to agree or disagree with the statement “I often do things on impulse.” Of respondents at the 12 month interviews, 21.5% agreed with the statement while 78.5% disagreed. The criminal history, drug use, risk seeking, volatile temper and impulsivity variables described above provide one of the more powerful tests of underlying criminal propensity in the literature. Most studies have either behavior or psychological scales at their disposal, but few have both, and even fewer have both in combination with a variety of social bonds variables. Further, to the author’s knowledge no study has measured such a multitude of variables to control for population heterogeneity among a sample of released criminal offenders. As such, then, the analyses below will provide a highly robust and comprehensive test of Sampson and Laub’s theory among a largely ignored sample of contemporary, mostly minority offenders. The hypotheses that flow from these variables are that more severe

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drug and criminal histories as well as higher levels of risk seeking, aggression, and impulsivity will negatively affect the social bonds variables and directly and positively affect the outcome variables. Drug Treatment and Changing Trajectories As discussed in Chapter 4, the impact of agency (Giordano,2002), self image (Terry, 2003) and placing one’s self in a success narrative (Murana, 2001) has been acknowledged by Laub and Sampson (2001, 2003). In addition to differences in social bonds (and thus, according to Sampson and Laub, recidivism and relapse) based on different levels of criminal propensity, there is an acknowledged decision making process that affects how people gain social bonds and how these affect recidivism and relapse. Measuring decision-making processes is, of course, difficult. In order to attempt this, the project used enrollment in a residential, transitional living drug treatment program. The original design proposed comparing three groups: those who received no treatment, those who received only the transitional living treatment and those who received both in prison treatment and transitional treatment. However, the work release group differed significantly from the two treatment groups in terms of drug use in the three months prior to prison and in the reported age of first criminal offense. Independent sample t-tests showed the work release group’s mean score on these variables to be significantly lower than the treatment groups’. Due to the differences in the populations in these groups prior to enrollment in the study, a different mechanism that measured whether offender volunteered for treatment was used. Recruitment for all three treatment groups was voluntary. Respondents who wished to enroll in the study (and be paid) signed up on fliers that were distributed in the programs. Problems arose, however, in two ways. First, because judges sentenced people to treatment based on presentence reports of drug addiction and Department of Corrections classification officials directed others there, the treatment groups were significantly more drug addicted than the non-treatment group. Second, the treatment groups were interviewed at the treatment centers. The work release group had to call the University of Delaware to schedule an interview, had to arrange with the work

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release center to leave on a pass to be interviewed, walk or ride a bus to the research office and keep the appointment. All of this amounted to a self selection effect whereby those more motivated among the control group were more likely to end up in the study by the very act of the inclusion process. In an attempt to overcome this problem, it was decided to use a measure of volunteerism to capture those who have made some form of voluntary attempt to “go straight.” To insure against the possibility of disproportionate follow up based on these groupings, Table 5.2 shows the percent of respondents in each of the four possible groups. As can be seen, there are no significant differences in the representation of each group across follow-up periods. Table 5.2: Voluntary Versus Compulsory Treatment Baseline Treatment Condition No Treatment Compulsory Treatment Voluntary Treatment Both Compulsory and Voluntary Treatment

12 Month

24 Month

N

%

N

%

N

%

183 256 104

28.4 39.8 16.1

110 155 73

26.9 37.9 17.8

56 74 33

29.3 38.7 17.3

101

15.7

71

17.4

28

14.7

The groups represented in Table 5.2 are made up of four mutually exclusive possibilities. There are those who had no treatment. This group represents those in the original work release group who sought no treatment on their own. The compulsory treatment group are those who were in one of the original treatment groups because they were either court ordered or DOC classified, and sought no further treatment on their own. The voluntary treatment group represents those in the original work release group who sought treatment of some form (such as Alcoholics Anonymous, A.A.), or who voluntarily entered the transitional treatment program. The last group is made up of those who were mandated to treatment by a judge or DOC classification but who sought additional treatment on their own (again, via A.A. or other programs).

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Clearly, those who volunteer for some kind of treatment have shown an increased initiative toward making a change in their lives compared to those who have not sought treatment on their own. It may be that this group is better able to form social bonds, and that these bonds may have a stronger effect on relapse and recidivism once formed. In sum, the analysis that follows controls extensively for population heterogeneity, and for the effect of human agency and decision making. Analysis Strategy The analysis that follows proceeds in three steps. First, the 12 month data are examined cross-sectionally. Then, the 24 month data are examined cross-sectionally. Finally, a series of structural equation models will examine the effects of the variables over all three time periods. In order to estimate the effects of social bonds on the various outcome measures within each time period, a series of cross-tabulation analyses was performed. A dichotomized outcome for each social bonds measure was created (reincarcerated, or not, used drugs of not) and cross-tabulated with a dichotomous predictor measure (married, employed, children). These analyses were performed on both the 12 and 24 month data. The sample was then broken down into White and Black groups and cross tabulations conducted for each group. The null hypothesis on these tests is that there are no differences; that is, social bonds, being a general theory, will have the same effect on Blacks as for Whites. Having examined the effect of social bonds on drug use and reincarceration cross sectionally at the bivariate level, the next step was to utilize Ordinary Least Squares (OLS) and Logistic regressions analysis to test whether any significant relationships found in the crosstabulation analysis remained when holding constant background characteristics and criminal propensity variables. The dichotomous reincarceration and drug use variables used in the cross tabulation analysis were used as outcome variables for a series of logistic regression analyses, and the outcome variables that account for time at risk, frequency of drug use and reduction in drug use were used as the dependent variables for the OLS regression analyses.

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Regression analysis was utilized on both the 12 and 24 month samples. The 12 month sample was split on race. Using a Z-test of difference between regression coefficients (Paternoster et. al, 1998), the analysis investigated whether the strength of the social bonds coefficients differed across racial groups, once background characteristics and criminal propensity were held constant. The small sample size of the 24-month date precluded using this split-sample technique however, so regression models were analyzed with race entered as a predictor variable. Since cross-sectional analyses cannot establish causal order because appropriate time order cannot be determined, and to limit possible self-selection bias, a final set of analyses was conducted using structural equation model (SEM) techniques. Figure 5.1 presents the theoretical model that forms the SEM analyses that follows. The criminal propensity and population heterogeneity variables VOLATILE and RISKY and IMPULSIVE are anticipated to have a negative effect on the various social bonding variables at 12 months after release from prison and a positive effect on the likelihood of being reincarcerated and frequency of drug use 24 months after release. The small black arrows leading from SOCIAL BONDS to PRISON and DRUG USE represent the social bonds effects of interest for the present research. Proponents of a population heterogeneity perspective maintain that these effects are not relevant due to self selection by persons who are less crime prone into the bonding relations that proponents of social control claim mediate crime.

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Figure 5.1: Theoretical Structural Equation Model

Baseline Measures

12-Month Follow-up

24-Month Outcome

Variations of the structural equation model above were estimated to test four hypotheses, three on the individual covariates and one on the full model fit. These are specified below. Hypothesis Set One: The full model, which allows the effects of social bonds on reincarceration and drug use to be free, will provide a better fit to the data than the reduced model in which these effects are constrained to be zero. Hypothesis Set Two: Individual propensity variables will have a positive effect on the likelihood of reincarceration and frequency of drug use. Hypothesis Set Three: Individual propensity variables will have a negative effect on social bonds variables. Hypothesis Set Four: Social bonds variables will have a negative effect on the likelihood of reincarceration and frequency of drug use net of propensity variables.

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Separate structural equation models were estimated for each of the social bond variables. Because proponents of life course theories, notably Sampson and Laub (1993), claim that social bonds develop according to Coleman’s (1988) notion of social capital, a final model was estimated utilizing an additive measure of social bonds. This variable (BONDS), was coded zero if an individual was not involved in any of the bonding institutions (work, marriage, or children), one if he or she was involved in one institution, two if they were involved in two, and three if he or she was working, married, and had children. Structural equation models with longitudinal data are both empirically and conceptually superior to many other techniques, such as standard ordinary least squares regression models. By estimating the correlation matrix that exists in the data and then comparing this to the matrix implied by the theoretical model, structural equation models allow researchers to empirically test the extent to which their conceptual design matches the observed relationships in the data. Researchers can also posit different conceptual models and test which ones best fit the data at hand. Structural equation models with panel data allow a researcher to show how variables measured at time one affect the variables at time two, and how both sets of variables influence the outcome measures at time three. The functional form of the models that follow can be expressed as: y1 = βy2 + Γx + ζ where y1 and y2 are endogenous outcome variables, x is a vector of exogenous predictor variables, Γ is a matrix of path coefficients leading from the exogenous variables to the y2 endogenous variables, β is a matrix of path coefficients leading from the y2 to the y1 outcome variables, and ζ is a vector of random error terms. Using maximum likelihood estimation, this equation allows for modeling dynamic processes in panel data and produces both regression estimates of the various paths and an overall matrix estimation that allows for statistical tests of how well the actual data matrix fits the theoretical model matrix. Structural equation models also produce a number of goodness of fit measures to indicate how well the models fit the data. The ones used in this analysis are described below. The goodness of fit index (GFI)

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(Joreskog & Sorbom, 1984) is used to assess relative fit. The GFI statistic is always between zero and one, where a value of one indicates a perfect fit to the data and zero indicates no relation at all. A GFI above .9 is generally considered to indicate an adequately specified model (Hu & Bentler, 1995). Because some researchers note that the GFI is influenced by the degrees of freedom, the Root Mean Squared Error of Approximation (RSMEA) is also reported. The RMSEA serves as a control on the degrees of freedom and rewards more parsimonious models. An RMSEA of .05 or less is optimum, but researchers have noted that a RMSEA of .08 or less indicate an adequate model fit (e.g.. Brown & Cudeck, 1993). The Chi-Square to degrees of freedom is reported as an alternate measure of model fit that accounts for the degrees of freedom in the models. Models with a Chi-square to degrees of freedom ratio of five or less indicate an acceptable fit to the data (Piquero & Rosay, 1998; Smith & Paterson ,1985). To test the difference between the full and reduced models below, the difference in Chi-square statistics between the models is reported. The difference in Chi-square statistics between models has a Chisquare distribution, with the degrees of freedom equal to the difference of degrees of freedom between the more and less parsimonious models (Piquero & Rosay, 1998; Wheaton, Muthen, Alwin, & Summers,1977). This measure will be utilized to test whether allowing the paths from social bonds to the outcome variables to be estimates significantly improves the model fit. Using these combined analyses, this project will attempt to assess the impact of adult social bonds on reincarceration and drug relapse among a sample of offenders as they renter the community. Chapter 6 reports the results of the 12-month analyses, Chapter 7, the 24 month analyses, and Chapter 8, the structural equation models.

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CHAPTER 6

Getting Out: Twelve Month Follow up Analyses Introduction This chapter reports the results of the 12 month post release analyses. The chapter is laid out as follows: First a series of cross tabulation tables are presented showing the effects of social bonds on the various outcome measures. These tables will show the results for the full sample, and for each of the Black and White sub-samples. Following this set of analyses, a series of logistic and ordinary least squares regression analyses are reported testing whether the use of more robust outcome measures or the addition of criminal propensity variables changes what was seen in the crosstabulation results. These regression models were run on the full, Black, and White samples. Regression coefficients for the Black and White samples were tested to see if the differences in coefficients were significant and these results are reported here as well. Twelve Month Bivariate Employment Effects Table 6.1 shows the results of the crosstabulation of being employed at the time of the 12 month interview by having any period of reincarceration during the follow up period. As can be seen, 60.5% of those not employed at the time of the follow up had been incarcerated during the 12 months since they were released from prison. Only 13.9% of those who were working at the time of the first follow-up interview had been incarcerated. All Tables in this analysis report the Chi-Square p-value which in this model was <.001, indicating that the difference between those employed at the follow up and those unemployed was highly significant. As noted in Chapter 5, this comparison is potentially tautological because to be working at the time of the interview, a respondent by definition had to be in the community, which all of those who had not been reincarcerated were. This result should thus be interpreted cautiously, and is included only because past researchers have utilized it (e.g. Sampson and Laub, 1993). 83

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Table 6.1: Crosstabulation of Employment at 12 Months by Reincarceration: % Reincarcerated Total Sample (n=397) Not Employed 60.5%** Employed 13.9% White Sample (n=123) Not Employed Employed

58.5%** 19.5%

Black Sample (n=274) Not Employed 61.4%** Employed 11.9% *= Chi Square p-value < .05, ** = Chi-Square < .01 Table 6.1 also shows crosstabulation of being employed at the time of the 12 month follow up by reincarceration with the sample split by race. Both groups experience a strong work effect, and the Chi-Square significance for both groups is <.001. The effect of working is slightly stronger for Blacks than for Whites, with 19.5% of working Whites being reincarcerated while only 11.7% of Blacks were. Readers are reminded of the tautological effect of working and the small sample size and also should interpret this result cautiously. Table 6.2 shows the full sample cross tabulation of the dichotomous relapse variable (RELAPSE1) by employment at 12 months. In contrast to the reincarceration variable, employment had no effect on relapse. A full 58.5% of those who were unemployed at the 12 month follow up relapsed, while 54.2% of the employed group did so. While the difference is in the anticipated direction, the Chi-Square does not approach significance at .402. Table 6.2 also shows the crosstabulation of employment by relapse for the Black and White groups. While there is virtually no effect of working for the Black group (56.7% unemployed relapsed, while 57.3% of the employed group did so), there is some effect of employment in the White group. Sixty-two percent of the unemployed Whites relapsed, while only 46.3% of the employed group used drugs during the follow-up period. This difference is in the anticipated direction and returned a Chi-Square p-value of .094.

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Table 6.2: Crosstabulation of Employment at 12 Months by Relapse: % Relapsed Total Sample (n=397) Not Employed 58.5% Employed 54.2% White Sample (n=123) Not Employed Employed

62.2% 46.3%

Black Sample (n=274) Not Employed 56.7% Employed 57.3% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Because of the potential tautology in the working variable, the analysis next utilized a measure of whether respondents reported any employment during the follow up period. The variable was potentially problematic as well, because a respondent who worked as little as one day during the period was scored as having been employed. It is difficult to imagine how this might affect the outcome variables, but because it measures whether getting a job has an effect it is reported below. As anticipated, the effect of any employment on reincarceration was insignificant. Table 6.3 shows that 59.5% of the no work group were reincarcerated, while 56.7% of the any work group were. This was once again in the direction suggested by Sampson and Laub, but in no way approaching a significant result. The effect of any employment by race is surprising. The percentage who had any work and were reincarcerated was relatively even across racial groups (45.1% of Whites and 40.2% of Blacks were reincarcerated). Blacks who had no work, however, were much more likely to be reincarcerated than their like situated White counterparts. A full 70.4% of Blacks who had no employment at all since release were reincarcerated during the follow up period, while only 46.7% of nonworking Whites were reincarcerated. The Chi-Square difference for Blacks was significant (p<.001), but was not for Whites (p=.916)

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Unlike the reincarceration variable, there is virtually no difference between racial groups in the effect of any work on any relapse during the 12 months following release from prison. There is no significant difference for either group in the effect of any employment on relapse at 12 months. Table 6.4 shows that the effect of any employment is in the hypothesized direction but the differences do not approach significance for either the total, White or Black samples. Table 6.3: Crosstabulation of Any Employment at 12 Months by Reincarceration % Reincarcerated Total Sample (n=397) Not Employed 59.5% Employed 56.7% White Sample (n=123) Not Employed Employed

46.7% 45.2%

Black Sample (n=274) Not Employed 70.4%* Employed 40.1% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 6.4: Crosstabulation of Any Employment at 12 Months by Relapse % Relapsed Total Sample (n=397) Not Employed 58.5% Employed 54.2% White Sample (n=123) Not Employed Employed Black Sample (n=274) Not Employed Employed

60.0% 57.4% 59.3% 56.3%

* = Chi Square p-value < .05, ** = Chi-Square p-value <.01

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In order to more clearly account for the effect of employment on reincarceration and drug use, the next set of tables controls for how much time respondents were at risk. As explained in Chapter Five, the variable WORKING (representing the proportion of months respondents were on the streets during which they were employed) was recoded for the crosstabulation analyses. Those who worked 50% or more of their eligible time were coded one, while those who worked less than half of their eligible months were coded zero. Table 6.5 shows the result of this crosstabulation on reincarceration 12 months after release from prison. Those who were employed 50% or more of their eligible months at risk were much less likely to have been incarcerated during the follow up period. This effect is pronounced, significant and, as shown by Table 6.5, equivalent across racial groups. Table 6.6 shows the results of working more than half of eligible months by any drug relapse during the 12 month follow up period. Contrary to the findings for reincarceration, neither the full sample nor the Black and White samples showed significant differences by employment status. Differences were all in the hypothesized direction but were not significant. It should be noted, however, that Whites who were employed more than 50% of the eligible months were about 7% less likely to relapse that those whites who were employed less than 50% of the time. Table 6.5: Crosstabulation of > 50% Employment at 12 Months by Reincarceration: % Reincarcerated Total Sample (n=397) Not Employed 55.7%** Employed 34.7% White Sample (n=123) Not Employed Employed

57.7%* 37.7%

Black Sample (n=274) Not Employed 54.9%** Employed 33.1% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01

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Table 6.6: Crosstabulation of > 50% Employment at 12 Months by Relapse: % Relapsed Total Sample (n=397) Not Employed 58.4% Employed 55.3% White Sample (n=123) Not Employed Employed

61.5% 54.5%

Black Sample (n=274) Not Employed 57.4% Employed 57.1% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 The final employment variable accounts for the amount of hours respondents worked during the follow up period. The split point of this variable was at the median hours worked. Table 6.7 shows the results of comparing the more hours worked group to the less hours worked group. The results show a pattern similar to those above, with a smaller percent of those reporting more worked hours being reincarcerated than those who worked fewer hours. Results indicated, however, that the Black portion of the sample was largely responsible for this effect. Although the White group showed a difference in the anticipated direction, it was not significant. In the Black sample on the other hand, the full 21% difference in reincarceration was highly significant (p=.001), indicating that increased employment is important for Blacks, but perhaps not for the White group. Table 6.8 shows the effect of working more hours on drug relapse 12 months after release from prison. None of the comparisons here rise to significant levels. Oddly, those who worked more were more likely to relapse. In the split race comparison, the White group Chi-square pvalue is 1.0, indicating there is absolutely no difference between the group working more hours and the group working less. In the Black group, those working more were more likely to relapse, but again, this difference was not significant.

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Table 6.7: Crosstabulation of Hours Worked at 12 Months by Reincarceration: % Reincarcerated Total Sample (n=397) Not Employed 53.1%** Employed 36.7% White Sample (n=123) Not Employed Employed

50.0% 42.3%

Black Sample (n=274) Not Employed 54.3%** Employed 33.8% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 6.8: Crosstabulation of Hours Worked at 12 Months by Relapse: % Relapsed Total Sample (n=397) Not Employed 54.7% Employed 58.5% White Sample (n=123) Not Employed Employed

57.7% 57.7%

Black Sample (n=274) Not Employed 53.5% Employed 58.9% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 The analyses presented above indicate that there is a clear bivariate relationship between employment and reincarceration. Measured by four different indicators, employment made a significant difference for at least one racial group. It also appears that by two measures, any employment and hours worked, Blacks gained a significant protection reincarceration while Whites did not, indicated that employment could potentially matter more for Blacks than Whites. Relapse, on the other

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hand, appears to be relatively unaffected by employment after release from prison. Twelve Month Bivariate Marriage Effects As can be seen in Table 6.9, the difference in reincarceration between married and unmarried persons was non-significant. Forty-six percent of the non-married group was reincarcerated, while 39.6% of the married group was reincarcerated. For both racial groups, the differences between the married and unmarried groups were nonsignificant. It should be noted that the difference in the White group was 12.5%, but the number of married persons (14) was too small to achieve significance. Table 6.10 reports the effect of marriage on relapse. Again, all relationships are in the anticipated direction, but none of the differences between married and unmarried groups, for either racial group, approach significance. It thus appears that marriage does not have a significant effect on reincarceration or relapse among this sample of offenders. Readers are cautioned, however, that, due to the small number of married persons (n= 53) in the sample, this finding may not be generalizable to a larger sample of offenders. It should once again be noted that, in the Black sample, married persons relapsed at a rate 10% less than their non-married counterparts. Table 6.9: Crosstabulation of Married at 12 Months by Reincarcerated % Reincarcerated Total Sample (n=397) Not Married 44.6% Married 39.6% White Sample (n=123) Not Married Married

48.2% 35.7%

Black Sample (n=274) Not Employed 42.9% Employed 41.0% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01

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Table 6.10: Crosstabulation of Married at 12 Months by Relapse % Relapsed Total Sample (n=397) Not Married 58.1% Married 50.9% White Sample (n=123) Not Married Married

58.2% 57.1%

Black Sample (n=274) Not Employed 58.0% Employed 48.7% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Bivariate Child Rearing Effects The next step in the analysis was to examine the effect of living with and caring for children. In this analysis, those who lived with and cared for a child (or children) were compared to those who either had no children or who did not live with and care for their children. The results for having and caring for children largely mirror those of being married, as indicated in Table 6.11. While again in the anticipated direction, none of the differences between the groups with children and those without rose to the level of significance. Forty-five percent of those without children were reincarcerated in the full simple while 40.7% of those who lived with and cared for their children returned to prison. The effect of having and caring for children was more slightly pronounced for relapse than for reincarceration, but still did not rise to the level of significance. Table 6.12 shows that 59.5% of those without children relapsed, while 52.1% of those with children relapsed. As indicated by the finding in Table 6.12, the difference between the groups with and without children, as with reincarceration, was more pronounced for Blacks than for Whites. The group difference for Whites was 5%, while for Blacks, the difference between the group with children and without was 8%. Again, none of these differences rose to the p < .05 level of significance.

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Table 6.11: Crosstabulation of Children at 12 Months by Reincarceration: % Reincarcerated Total Sample (n=397) No Children 45.4% Children 40.7% White Sample (n=123) No Children Children

46.2% 43.2%

Black Sample (n=274) No Children 44.9% Children 39.8% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 6.12: Crosstabulation of Children at 12 Months by Relapse % Relapse Total Sample (n=397) No Children 59.5% Children 52.1% White Sample (n=123) No Children Children

59.1% 54.1%

Black Sample (n=274) No Children Children

59.7% 51.5%

* = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Summary of 12 month bivariate analyses To summarize the 12 month bivariate analysis, family level social bonds- being married or having children- did not have significant effects on either reincarceration or relapse 12 months after release from prison. Differences were slightly more pronounced for the Black sample than the White, but still, none rose to the level of significance.

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For the more tangible bonds of employment, significant effects were found for reincarceration, no matter how it was measured, but none was found for the conservative measure of any relapse during the 12 month period. The next section reports the results of multivariate analyses on reincarceration and several more robust measures of drug relapse. Multivariate 12 Month Analyses Having demonstrated the effect of multiple working variables in the bivariate analyses, the multivariate analyses will be limited to one employment variable. Because the proportion of free and eligible months respondents actually spent working has the most face validity in terms of establishing social bonds, it was decided to use this variable in the regression models that follow to represent the effect of employment. While the bivariate analyses were presented in order of the independent variables, the multivariate portions of the analyses are presented in order of the dependent variables. First, the results of logistic regression analyses modeling the two dependent variables utilized in the bivariate analyses are presented (any relapse, and reincarceration). Then, because the data allowed a more in-depth examination of drug use, a series of ordinary least squares regression analyses are presented modeling the effect of population heterogeneity and social bonds on the frequency of drug use, the proportion of months free in which drugs were used, and the reduction in drug use from before prison to 12 months after release from prison. Each model is presented for the full sample, followed by a separate model in which the sample was split by race. Logistic Regression Predicting Reincarceration Table 6.13 shows the results of the logistic regression model predicting any reincarceration in the 12 month period following release from prison. This and the other logistic regression tables that follow report the regression coefficient (B), the standard error of the coefficient, the significance of the regression coefficients (Sig), and the odds ratio, or exponentiated beta, represented by Exp(B) in the tables. Odd ratios are

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based on the probability of an event occurring divided by the probability of the event not occurring Turning to Table 6.13, only three variables of the 14 entered into the model showed a significant effect in predicting reincarceration, and race was non-significant when all else was held constant. Gender was strong, significant and negative in predicting relapse, returning log odds of .33. This indicates that the odds of a female returning to prison in the 12 months following release decreased by a factor of .67 compared to a male, holding all other variables constant. Moving down the list in the model, the number of different crimes committed during an offender’s lifetime (#CRIMES) was positive and significant (p <. 05). For each additional type of crime committed, respondents were significantly more likely to be reincarcerated Although this was anticipated by the non-significant findings at the bivariate level, neither being married nor having children significantly affected the probability that a respondent would return to prison. In addition, none of the treatment variables was significant. The only social bond variable to show a significant effect on reincarceration was employment. With a significance level of <.001 and a log odds of .187, the proportion of free and eligible months during which respondents were employed was the strongest predictor in the model. For each increase in the proportion of months worked, the odds of being reincarcerated decreased by a factor of .81, holding constant gender, the other social bonds variables, criminal propensity variables and treatment.

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Table 6.13: Logistic Regression of Reincarceration at 12 Month Follow-up. FEMALE BLACK AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

B -1.097 -.003 -.024 .092 .028 .013 -.050 .068 .359 -.016 -.365 .055 -1.679 .415 -.015 .013 2.077

S.E. .326 .261 .016 .045 .020 .051 .028 .088 .241 .043 .363 .257 .364 .299 .363 .366 .869

Sig. .001 .992 .123 .040 .155 .801 .071 .439 .137 .709 .315 .830 .000 .166 .968 .972 .017

Exp(B) .334 .997 .976 1.097 1.028 1.013 .951 1.070 1.432 .984 .694 1.057 .187 1.514 .986 1.013 7.984

Pseudo R-Square = .21 Readers should note the addition of the column headed BWDIF in Table 6.14, which represents the p-value from the independent samples’ coefficient test. Values of less than .05 in this column indicate significant differences between the Black and White coefficients. This is exemplified by comparing Blacks’ and Whites’ likelihood of reincarceration in the BWDIF column of Table 6.14. There was one significant difference found between the two groups, which was the effect of AGE, as evidenced in the Table (BWDIF = .000). The age effect was much more pronounced for Whites than for Blacks. Each year increase in age significantly decreased the probability of being reincarcerated for the White sample, but not for Blacks. Employment was far and away the strongest predictor of the likelihood of reincarceration for both Blacks and Whites. AGE at first drug use lost significance as a predictor of reincarceration for Whites,

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however. More interestingly from Sampson and Laub’s perspective was the effect of being married among the White sample, which was marginally significant, indicating that married Whites were less likely to be arrested than their unmarried counterparts, holding all else constant. Table 6.14: Logistic Regression Predicting Drug Relapse at 12 Months (Whites) White BWDIF* .818 .000 .389 .905 .408 .414 .821 .674. .542 .124 .516 .165 .788 .723 .891 .953

FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE 1st DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

B -1.234 -1.090 .149 .011 -.038 -.023 .117 .538 -.043 -1.378 .452 -2.051 .257 -.365 -.047 2.103

S.E. .630 .035 .082 .036 .088 .048 .178 .444 .085 .729 .523 .692 .597 .748 .668 1.330

*BWDIF = p-value for Black White Difference Test Pseudo R-Square White = .244, Black .210.

Sig. .050 .002 .070 .752 .667 .628 .511 .226 .610 .059 .388 .003 .667 .626 .944 .114

Exp(B) .291 .897 1.160 1.011 .963 .977 1.124 1.216 .958 .252 1.571 .129 1.293 .694 .954 8.189

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Table 6.14(Continued): Logistic Regression Predicting Drug Relapse at 12 Months (Blacks) BWDIF Black B S.E. Sig. Exp(B) .818 FEMALE -1.063 .395 .007 .345 .000 AGE -.002 .018 .902 .998 .389 # CRIMES .064 .055 .241 1.066 .905 FIRST CRIME .016 .022 .461 1.017 .408 MAXDRG0 .052 .064 .420 1.053 -.072 .036 .045 .931 .414 AGE 1st DRUG .821 RISKY .072 .105 .493 1.075 .674. IMPULSIVE .314 .295 .287 1.131 .542 VOLATILE .017 .050 .739 1.017 .124 MARRIED -.086 .418 .837 .918 .516 KIDS .062 .297 .835 1.064 .165 PROPWORK -1.684 .452 .000 .186 .788 COMP TREAT .444 .357 .213 1.559 .723 VOLT TREAT -.061 .424 .886 .941 .891 BOTH TREAT -.154 .452 .734 .858 .953 Constant 2.004 .972 .039 7.418 *BWDIF = p-value for Black White Difference Test Pseudo R-Square White = .244, Black .210. If one could argue that Sampson and Laub’s theory came out well in the reincarceration model, at least in terms of employment, one would have to admit that their critics seem to fare better in the logistic regression predicting drug relapse, as evidenced by the results in Table 6.15. Employment was non-significant, as were all of the other social bonds variables, even though all were in the anticipated direction. Gender was also non-significant in the relapse model. Criminal propensity variables, however, were relatively strong predictors of relapse. The level of drug use prior to prison was positive and highly significant. Risk seeking was a powerful predictor of relapse and impulsivity was also positive and approached significance (p=.078). This suggests that taken together and holding all else constant, criminal propensity variables had a stronger effect in

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predicting the probability of relapse than all other variables in the model including social bonds. Table 6.15: Logistic Regression Predicting Relapse at 12 Month Follow-up BLACK FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE 1st DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

B .134 .059 .019 .065 .004 .178 -.024 .274 .425 .049 -.354 -.229 -.128 .049 .370 .327 -1.973

S.E. .256 .303 .016 .045 .019 .050 .023 .088 .241 .042 .350 .251 .348 .293 .353 .366 .834

Sig. .600 .844 .223 .148 .827 .000 .293 .002 .078 .239 .312 .361 .713 .867 .295 .372 .018

Exp(B) 1.144 1.061 1.019 1.067 1.004 1.195 .976 1.315 1.529 1.051 .702 .795 .880 1.050 1.447 1.386 .139

Pseudo R-Square = .16 Table 6.16 shows the logistic regression model predicting relapse at 12 months, by race. As with the reincarceration analysis, there were no significant differences between the strength of the predictors by race. Drug use prior to prison and risk seeking were the strongest predictors of relapse for both groups.

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Table 6.16: Logistic Regression Predicting Relapse at 12 Month Follow-up, by Race (Whites) White BWDIF .766 .111 .845 .422 .979 .381 .134 .460 .851 .693 .607 .957 .801 .976 .978 .576

B FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE 1st DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

S.E.

.196 .642 -.031 .034 .044 .084 .039 .040 .182 .096 -.063 .047 .535 .190 .047 .477 .056 .091 -.092 .775 .035 .530 -.127 .674 .203 .624 .374 .745 .416 .722 -1.104 1.608 Pseudo R-Square White = .27, Black = 14.6 *BWDIF = p-value for Black White Difference Test

Sig. Exp(B) .760 .353 .599 .333 .057 .186 .005 .922 .542 .905 .947 .851 .745 .616 .564 .492

1.216 .969 1.045 1.039 1.200 .939 1.708 1.154 1.057 .912 1.036 .881 1.225 1.454 1.516 .331

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Table 6.16(Continued): Logistic Regression Predicting Relapse at 12 Month Follow-up, by Race (Blacks) Black BWDIF .766 .111 .845 .422 .979 .381 .134 .460 .851 .693 .607 .957 .801 .976 .978 .576

FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

B -.024 .030 .064 .002 .179

S.E. .366 .019 .057 .023 .062

Sig. Exp(B) .947 .976 .112 1.030 .264 1.066 .920 1.002 .004 1.196

-.015

.028

.596

.985

.049 .118 .312 .286 .349 .846 .947 .344 .375 .087

1.233 1.030 1.051 .644 .758 .919 1.024 1.490 1.481 .194

.209 .106 .161 .295 .050 .049 -.439 .412 -.277 .296 -.084 .435 .023 .347 .399 .422 .393 .443 -1.642 .959 Pseudo R-Square White = .27, Black = 14.6 *BWDIF = p-value for Black White Difference Test

Ordinary Least Squares Regression Predicting Frequency of Drug Use The next set of tables report the ordinary least squares regression results. All models reported below were significant in terms of the omnibus variable F-test, indicating that all of the models significantly predicted the outcome variables. For ease of interpretation, the models below report the full model adjusted r-square indicating the proportion of linear association in the outcome variable explained by the combined predictor variables.

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For the individual predictors, the unstandardized coefficients (B), their standard errors (Std. Error), and the standardized coefficient for each predictor are reported in order to show the relative strength of each predictor when controlling for all other variables. The last two columns in the tables are the t-statistic (t) for that predictor and the significance (sig) of that t-statistic. Once again, the BWDIF is reported for the race specific models, where a value of less than .05 indicates a significant difference between the Black and White groups for that predictor. Table 6.17 shows the results of the ordinary least squares regression predicting the frequency of drug use during the 12 month follow up period. Using the standardized coefficients to rank order variables in terms of statistical power, the strongest predictor in the model is the number of different crimes committed (Std. Beta = .158). The unstandardized beta for number of crimes committed was .124, indicating that for each additional type of crime a person had committed during his or her life, there was a corresponding increase of .124 on the scale measuring the frequency of drug use during the follow up period, holding all else constant. The second strongest predictor was age, which was negatively related to the frequency of drug use. The negative coefficient of -.119 indicates that for each year older respondents were, they scored .119 less on the frequency of drug use scale, again holding all else constant. Moving down this list in order of predictive power, impulsivity was strong, positive and significant with an unstandardized beta of .118. This means that those who agreed with the statement that they did things on impulse scored .118 points higher on the frequency of drug use scale. These three variables lend strong support for a population heterogeneity approach to the level of drug use after prison. It should also be noted that frequency of drug use prior to incarceration also approached significance with an alpha of .066, lending further weight to a population heterogeneity argument (keeping in mind that all of these variables were measured prior to release from prison). In addition, however, the proportion of eligible months worked was significant (alpha = .026) and in the anticipated direction. The unstandardized beta of -.796 indicates that for each increment one moved up in terms of the proportion of free months worked, there was a corresponding decrease of .796 on the frequency of drug use scale.

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Thus, in spite of the powerful effect of population heterogeneity variables, holding a job in the first year after release from prison was an important predictor of lower levels of drug use. As with the majority of the Logistic Regression models, having children and being married had no significant impact on the frequency of drug use. Table 6.17: OLS Regression Predicting Frequency of Drug Use at 12 Month Follow-up. Un-std Coeff*

Std Coef#

B Std. Error Beta t (Constant) 2.137 .852 2.509 BLACK -.070 .263 -.014 -.265 FEMALE -.359 .312 -.065 -1.150 AGE -.031 .016 -.119 -1.920 # CRIMES .124 .045 .158 2.739 FIRST CRIME .025 .019 .089 1.293 MAXDRG0 .096 .052 .097 1.844 AGE FIRST DRUG -.023 .024 -.057 -.960 RISKY .137 .089 .083 1.549 IMPULSIVE .552 .247 .118 2.232 VOLATILE .017 .042 .023 .415 MARRIED -.093 .361 -.013 -.258 KIDS -.289 .261 -.059 -1.108 PROPWORK -.796 .355 -.115 -2.242 COMP TREAT -.113 .304 -.024 -.372 VOLT TREAT -.032 .363 -.005 -.088 BOTH TREAT .429 .370 .070 1.159 R-Square = .09 * = Unstandardized coefficients , #= Standardized coefficients

Sig. .013 .791 .251 .056 .006 .197 .066 .338 .122 .026 .678 .797 .268 .026 .710 .930 .247

Table 6.18 shows the results of the OLS regression predicting drug use at 12 months by race. Unlike the logistic regression results, the model using the more expansive definition of drug use showed some significant differences by race. The coefficient for the number of crimes committed (the strongest predictor in the full sample model)

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was almost nonexistent for Whites. Number of crimes committed had a strong and significant effect on Blacks. With a standardized beta of .244, the number of different crimes had twice the predictive power of the next strongest indicator. The difference between Blacks and Whites on this variable was significant with a p-value of .05. In contrast, impulsivity was a significantly stronger predictor for Whites than for Blacks. Impulsivity was non-significant in the Black model, but significant and positive in the White model, meaning that, with a Beta of .918, those who reported being impulsive scored almost a full point higher on the frequency of drug use scale than their less impulsive counterparts. Having children and being married were once again nonsignificant. Employment, however, was significant and strong for both the Black and White groups. The beta for each group (Whites = -.902, Blacks = -.878) indicates that for each unit increase in the proportion of eligible months worked, there was a decrease of approximately .9 on the frequency of drug use scale 12 months after release from prison. It should be noted as well that these models, while significant in terms of the full model F-test, explained little of the variation in frequency of drug use. With an R-square of .09, the model only explained 9% of the variation in frequency of drug use.

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Table 6.18: OLS Regression Predicting Frequency of Drug Use at 12 Month Follow-up by Race (White). Std White Un-std Coeff* Coef#t BWDIF Std. B Error Beta t Sig. .151 (Constant) 4.051 1.551 2.612 .010 .843 FEMALE -.386 .589 -.071 -.655 .514 .305 AGE -.066 .033 -.253 -2.030 .045 .050 # CRIMES .005 .078 .006 .059 .953 .827 FIRST .026 .039 .101 .664 .508 CRIME .196 MAXDRG0 .122 .092 .130 1.330 .186 .917 AGE FIRST -.012 .048 -.032 -.251 .802 DRUG .203 RISKY .334 .178 .196 1.878 .063 .024 IMPULSIVE .918 .450 .197 2.038 .044 .465 VOLATILE -.048 .088 -.059 -.541 .590 .873 MARRIED .017 .708 .002 .023 .981 .475 KIDS .053 .509 .010 .104 .918 .975 PROPWORK -.902 .640 -.137 -1.410 .162 .683 COMP REAT -.300 .611 -.063 -.491 .624 .894 VOLT .014 .721 .002 .020 .984 TREAT .326 BOTHTREAT .944 .686 .165 1.377 .172 R-Square White = .09, Black = .07 * = Unstandardized coefficients , #= Standardized coefficients

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Table 6.18(Continued): OLS Regression Predicting Frequency of Drug Use at 12 Month Follow-up by Race (Black). Black BWDIF .151 .843 .305 .050 .827 .196 .917 .203 .024 .465 .873 .475 .975 .683 .894 .326

(Constant) FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP REAT VOLT TREAT BOTHTREAT

Un-std Coeff* Std. B Error 1.856 1.004 -.291 .384 -.027 .019 .196 .059

Std Coef#t Beta -.053 -.107 .244

t 1.848 -.758 -1.425 3.321

Sig. .066 .449 .155 .001

.036

.024

.120

1.499 .135

.093

.065

.093

1.426 .155

-.018

.030

-.043

-.607 .544

.069 .315 .026 -.115 -.374 -.878 -.009

.108 .307 .050 .433 .313 .449 .366

.041 .067 .034 -.017 -.077 -.124 -.002

-.098

.436

-.016

.638 1.025 .509 -.265 -1.197 -1.958 -.024

.524 .306 .611 .791 .233 .051 .981

-.225 .822

.135 .457 .021 .295 .768 R-Square White = .09, Black = .074 * = Unstandardized coefficients , #= Standardized coefficients Table 6.19 shows the results of the OLS regression predicting the ratio of months free in which respondents used drugs. This model shows some support for a population heterogeneity/criminal propensity model. Although risk seeking was the only variable that surpassed a p<.05 level of significance, the number of different crimes, frequency of drug use prior to prison, and impulsivity were all in the appropriate direction and just missed significance, returning alphas of <.10. The only aberration to the pattern was age of first drug use, which was

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significant and negative. Conversely, none of the social bonds variables was significant, when all other variables were held constant. Table 6.19: OLS Regression Predicting Ratio of Drug Using Months During 12 Month Follow-up Un-std Coeff* (Constant) BLACK FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

B Std. Error .124 .095 -.039 .029 .000 .035 .001 .002 .008 .005 -.001 .002 .010 .006 -.005 .003 .022 .010 .045 .027 .000 .005 .050 .040 -.035 .029 -.059 .039 .007 .034 .011 .040 .058 .041

Std Coef#t

t

Sig.

1.313 -1.332 -.004 .444 1.601 -.358 1.814 -2.042 2.244 1.657 -.026 1.241 -1.212 -1.485 .216 .284 1.421

.190 .184 .997 .657 .110 .720 .071 .042 .025 .098 .979 .215 .226 .138 .829 .776 .156

Beta -.070 .000 .028 .094 -.025 .097 -.123 .121 .088 -.001 .065 -.065 -.077 .014 .017 .086

R-Square = .08 * = Unstandardized coefficients , #= Standardized coefficients

Getting Out: Twelve Month Follow-Up Analysis Table 6.20: OLS Regression Predicting Ratio of Drug Using Months During 12 Month Follow-up, by Race (white) Un-std Std Coeff* Coef# White BWDIF Std. B Error Beta t .499 (Constant) .196 .197 .999 .644 FEMALE .036 .072 .054 .503 .007 AGE -.008 .004 -.247 1.990 .441 # CRIMES .000 .010 -.005 -.046 .193 FIRST CRIME .005 .005 .154 1.013 .644 MAXDRG0 .007 .011 .060 .611 .654 AGE FIRST -.008 .006 -.163 DRUG 1.283 .290 RISKY .043 .022 .203 1.953 .937 IMPULSIVE .029 .055 .050 .521 .619 VOLATILE -.004 .011 -.044 -.399 .209 MARRIED .148 .087 .169 1.704 .388 KIDS -.073 .063 -.114 1.159 .367 PROPWORK .003 .079 .004 .038 .143 COMP TREAT .099 .075 .169 1.312 .521 VOLT TREAT .056 .089 .076 .629 .032 BOTH TREAT .206 .084 .291 2.442 R-Square White = .10, Black = .06 * = Unstandardized coefficients , #= Standardized coefficients

107

Sig. .320 .616 .049 .963 .314 .543 .202 .054 .603 .691 .092 .249 .970 .193 .531 .016

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Table 6.20(Continued): OLS Regression Predicting Ratio of Drug Using Months During 12 Month Follow-up, by Race (Black) Black Std Un-std-coeff* Coef# BWDIF Std. B Error Beta t Sig. .499 (Constant) .045 .106 .423 .673 .644 FEMALE -.002 .040 -.004 -.055 .956 .007 AGE .004 .002 .134 1.768 .078 .441 # CRIMES .009 .006 .113 1.519 .130 .193 FIRST CRIME -.002 .002 -.067 -.824 .411 .644 MAXDRG0 .011 .007 .111 1.688 .093 .654 AGE FIRST -.005 .003 -.118 -1.639 .103 DRUG .290 RISKY .017 .011 .100 1.516 .131 .937 IMPULSIVE .034 .032 .069 1.055 .292 .619 VOLATILE .002 .005 .029 .428 .669 .209 MARRIED .025 .045 .036 .557 .578 .388 KIDS -.012 .032 -.024 -.363 .717 .367 PROPWORK -.080 .046 -.110 -1.725 .086 .143 COMP TREAT -.024 .038 -.048 -.620 .536 .521 VOLT TREAT -.008 .045 -.013 -.179 .858 .032 BOTH TREAT -.001 .047 -.001 -.015 .988 R-Square White = .10, Black = .06 * = Unstandardized coefficients , #= Standardized coefficients Splitting the sample dilutes most of the effects found in Table 6.20. The interesting non-significance of AGE in Table 6.20 is also explained when Table 6.21 is examined. There is a significant difference between Blacks and Whites in the effect of age on the ratio of free months in which drugs were used. Although the unstandardized beta of -.008 may seem small, the standardized beta of -.247 makes age the second strongest predictor in the White sample model. Interestingly, in the Black sample, the age affect is the opposite of that found for Whites. For Blacks, the unstandardized beta of .004 means

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that for each one-year increase in age, respondents were more likely to use drugs an additional .004 units of their eligible free months. In terms of social bonds, nothing rose to the level of a .05 alpha significance. However, if we relax our assumptions to .10, there is variation by race. Although not significantly different from each other, in the White model, being married was actually associated with an increase in free months during which drugs were used. Alternatively, for Blacks, employment had a negative effect approaching significance (p= .086), indicating that the more free months in which respondents in the Black sample were employed, the fewer months they used drugs. The final set of models for the 12 month follow up data utilized a reduction in drug use variable. This analysis takes account where an individual started and ended in terms of the frequency of drug use and measures how the predictor variables affected this reduction (or in some cases increase) in drug use. Because the MAXDRG0 (frequency of drug use prior to prison) is used in the creation of the dependent variable, a correlation analysis was performed to insure that these two variables were not disrupting the model. The Pearson’s correlation between MAXDRG0 and the dependent variable was .647, which is considered a strong predictor, but not high enough to warrant further diagnostics. Table 6.21 shows the results of the OLS regression analysis on respondents’ reduction in drug use. Readers are reminded that the dependent variable is conceptually flipped from the preceding analyses, because a higher reduction is considered a “good” thing, and thus positive relationships would be considered in accordance with social bonds variables, whereas the criminal propensity measures would be hypothesized to exert a negative influence. The first variable of interest is the MAXDRG0 measure. This is strong, positive and significant, as expected by the bivariate correlation discussed above. Part of the reason for the positive direction of the relation, even though drug use prior to prison is considered a population heterogeneity measure, is that those who used drugs more frequently prior to prison had further to go to hit the floor as it were. As discussed in Chapter Four, the process of arrest and incarceration serves to interrupt the cycle of addiction, so if one considers incarceration as a stop-gap or break in the addiction cycle, it is reasonable to assume that 12 months after release from prison, a group

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of serious offenders might not have returned to their original rate of usage. The significant criminal propensity measures that behaved according to theory were again the number of crimes committed and the impulsivity measure, which this time were negative. The coefficient of -.124 for number of crimes indicates that for each additional type of crime a person reported committing during his or her pre-prison lifetime, there was a corresponding .124decrease in the reduction of drug use for that individual, net of all other factors. Likewise, those who reported being impulsive experienced a reduction in drug use that was .552 less than their less impulsive counterparts, net of all other factors. Once again, being married or having children were non-significant in predicting reductions in drug use, as were the treatment variables when all else was held constant. Also, following earlier results, employment status was significant (p= .026) and in the anticipated direction. That is, for each additional unit increase in the proportion of free months subjects were employed, there was a corresponding reduction of .796 increase in the reduction of drug use for that individual. Finally, it should be noted that the r-Square for this model was .46, meaning that 46% of the linear variation in the reduction in drug use was explained by the combined predictor variables. While this indicates an exceptionally strong model and suggests that the process of incarceration has an effect on lowering drug use among persons released from prison, it should be noted that the inclusion of drug use prior to prison accounted for most of this explanatory power. Knowing where someone started in terms of drug use increases the ability to predict where they finish greatly. It should also be pointed out that this relatively high R-square is in contrast to the modest, even extremely modest, r-square statistics in the majority of the 12 month models. While all models were significant by full model f-tests, the explanatory power of most was modest. Second, the combined use of criminal propensity and social control variables served as powerful explanatory variables in predicting reduction in drug use among this sample of recently released offenders. And, lastly, it illuminates the importance of measurement when operationalizing drug use. This topic will be returned to in the discussion section.

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Table 6.21: OLS Regression Predicting Reduction in Drug Use From Baseline to 12 Month Follow-up

(Constant) BLACK FEMALE AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

Un-std Coeff* Std. B Error -2.137 .852 .070 .263 .359 .312 .031 .016 -.124 .045 -.025 .019 .904 .052

Std Coef#

t

Sig.

.011 .050 .092 -.123 -.069 .712

-2.509 .265 1.150 1.920 -2.739 -1.293 17.435

.013 .791 .251 .056 .006 .197 .000

Beta

.023

.024

.044

.960

.338

-.137 -.552

.089 .247

-.064 -.091

-1.549 -2.232

.122 .026

-.017

.042

-.018

-.415

.678

.093 .289 .796

.361 .261 .355

.010 .045 .089

.258 1.108 2.242

.797 .268 .026

.113

.304

.018

.372

.710

.032 -.429

.363 .370

.004 -.054

.088 -1.159

.930 .247

R-Square = .46. * = Unstandardized coefficients , #= Standardized coefficients Table 6.22 examines the results of the OLS regression predicting reduction in drug use by race. The only significant difference between Whites and Blacks was, again, the number of different crimes committed. For Blacks, number of crimes was significant and positive, while for Whites it was non-significant. In the Black sample, for each additional offense type reported respondents’ reduction in drug use was .196 lower.

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While not statistically different from each other, there were also important differences in what was significant for each race within the separate models. For Whites, those variables associated with population heterogeneity proved to be the best predictors. Impulsivity (p=.04) and Risk seeking (p=.06) were both significant and negative for Whites, but non-significant for Blacks. Employment status, on the other hand, was positive and significant for Blacks and non-significant for Whites. These differences highlight potential problems for the generality of state dependence theories such as Sampson and Laub’s. Table 6.22: OLS Regression Predicting Reduction in Drug Use From Baseline to 12 Month Follow-up, By Race (White). White

Un-std Coeff* Std. B Error -3.133 1.599

Std Coef#

t

Sig.

-1.959

.053

Beta

BWDIF .401

(Constant)

.893

FEMALE

.386

.589

.054

.655

.514

.947

AGE

.066

.033

.193

2.030

.045

.050

# CRIMES

-.005

.078

-.005

-.059

.953

.821

FIRST RIME

-.026

.039

-.077

-.664

.508

.796

MAXDRG0

.878

.092

.714

9.551

.000

.915

.012

.048

.024

.251

.802

.203

AGE 1ST DRUG RISKY

-.334

.178

-.150

-1.878

.063

.268

IMPULSIVE

.464 .873

VOLATILE MARRIED

-.918 .048 -.017

.450 .088 .708

-.150 .045 -.002

-2.038 .541 -.023

.044 .590 .981

.407

KIDS

-.053

.509

-.008

-.104

.918

.975

PROPWORK

.902

.640

.105

1.410

.162

.746

COMP TREAT

.300

.611

.048

.491

.624

.894

VOLT TREAT

-.014

.721

-.002

-.020

.984

.326

BOTH TREAT

-.944

.686

-.126

-1.377

.172

R-Square, White = .47, Blacks = .43 * = Unstandardized coefficients , #= Standardized coefficients

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Table 6.22 (Continued): OLS Regression Predicting Reduction in Drug Use From Baseline to 12 Month Follow-up, By Race (Black). Black Un-std Std Coeff* Coef# Std. BWDIF B Error Beta t Sig. .401 (Constant) 1.022 -1.508 .133 1.541 .893 FEMALE .291 .384 .041 .758 .449 .947 AGE .027 .019 .083 1.425 .155 .050 # CRIMES -.196 .059 -.190 -3.321 .001 .821 FIRST CRIME -.036 .024 -.094 -1.499 .135 .796 MAXDRG0 .907 .065 .706 13.852 .000 .915 AGE FIRST .018 .030 .034 .607 .544 DRUG .203 RISKY -.069 .108 -.032 -.638 .524 .268 .464

IMPULSIVE VOLATILE

.873 .407 .975 .746 .894 .326

MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

-.315

.307

-.052

-1.025

.306

-.026

.050

-.027

-.509

.611

.115 .374 .878 .009 .098 -.135 R-Square, White = .47, Blacks = .43

.433 .313 .449 .366 .436 .457

.013 .060 .096 .001 .013 -.016

.265 1.197 1.958 .024 .225 -.295

.791 .233 .051 .981 .822 .768

Summary The 12 month results indicated that, on the whole, social bonds related to the family were not significant predictors of relapse for this sample. While most relationships were in the direction anticipated by Sampson and Laub, few rose to significant levels. Employment status, on the other hand, was associated with significant reductions in reincarceration. Even after controlling for 13 other variables associated with population heterogeneity and drug

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treatment, the proportion of free and eligible months respondents were actually employed was a significant predictor of success in terms of remaining free in the community. This was true for both racial groups. In terms of drug relapse in the 12 month period following prison, some interesting racial differences emerged. In general, it can be said that criminal propensity variables were the dominant predictors of relapse for Whites, notably the risk seeking and impulsivity domains. Employment status was not a significant predictor of relapse for Whites, nor were the children and marriage variables. But again, the more consistent pattern for Whites was the effect of risk seeking and impulsivity. Blacks, on the other hand seemed to benefit more directly from being employed, at least as far as drug use was concerned. There was a consistent and negative relationship between being employed a greater proportion of free months and drug use, regardless of the drug use measure used. The effect of the number of different crimes respondents reported committing was stronger for Blacks than Whites, and significantly more so in some models. It is unclear why this would be so, except to indicate that for Blacks, predictors of both criminality and social bonds work very well. The 12 month data are somewhat ambiguous in many respects. While it is certain that having children had no effect whatsoever, most of the other social bond variables affected outcomes for one group or the other. While the 12 month data are vital for showing what happens in the first year after release, Chapter Seven reports the same set of analyses for the time period between the 12 and 24 month interviews in order to see whether social bonds are important as one moves further away from prison.

CHAPTER 7

Staying Out: Twenty-Four Month Follow-Up Analyses Introduction This chapter reports the results of the 24 month outcome analyses. Readers are cautioned to note a number of points. First, 49 persons were incarcerated during the entire follow up period. These were individuals arrested during their first 12 months after release and who remained incarcerated during the 12 to 24 month period. Because these individuals were not eligible to work or become incarcerated during the period covered by this chapter, they were dropped from the analyses. This presented a problem in terms of sample size. With the 49 arrestees gone, the sample for this phase of analyses is 142. While this is satisfactory for the crosstabulation analyses, it presents problems for the regression analyses, especially the analyses split by race. For this reason race specific models are not reported in this chapter. Thus, the multivariate analyses are conducted on only the full sample with a dichotomized race variable entered into the models in order to control for the effect of race. A final cautionary note involves the marriage variable. Only three members of the White sample were married at the 24 month follow up. This number is far too small to be utilized in anything but an anecdotal note (i.e., none were reincarcerated during the 12 to 24 month period). Still, with these caveats in mind, the analyses that follow shed additional light on the relationship between stability, change, and prisoner reentry in the second year after release. The lay out of what follows mirrors the preceding chapter. First crosstabulation analyses using the dichotomized variables are examined to investigate any bivariate relationships between social bonds and reincarceration and relapse. Then a series of logistic and ordinary least squares regression models are reported examining whether any bonding effects held when criminal propensity variables were introduced into the analyses. 115

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Twenty Four Month Bivariate Employment Effects Table 7.1 shows the results of being employed at the 24 month interview. As with the 12 month analyses, those employed at the time of the interview were much less likely to have been reincarcerated during the follow up period. Readers are reminded again of the potentially tautological effect of this comparison, since those working at the time of the interview were by definition not in prison. This analysis is still useful in showing an employment effect, however, and is not purely tautological, as evidenced by the fact that 27% of those employed at the time of the follow up had been incarcerated during the 12 to 24 month post release period. Table 7.1 shows that 72% of those who were unemployed at 24 months had been reincarcerated, while only 27% of the employed group had returned to prison. The Table also shows the effect of being employed at 24 months by race. The pattern that began emerging in the 12 month results continues here, with Blacks gaining a stronger effect of being employed than Whites. In the White sample, 38.5% of working respondents had been reincarcerated, while 75% of the unemployed sample had returned to prison. For Blacks, only 18.9% of the working sample returned to prison while 70.9% of the unemployed respondents were reincarcerated. The differences between groups were significant for both the Black and White samples. Table 7.1: Crosstabulation of Employment at 24 Months by Reincarceration: % Reincarcerated Total Sample (n=142) Not Employed 72.2** Employed 27.0% White Sample (n=50) Not Employed Employed

75.0%** 38.5%

Black Sample (n=92) Not Employed 70.9%** Employed 18.9% *= Chi Square p-value < .05, ** = Chi-Square < .01

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Table 7.2 shows the results of being employed at 24 months by drug relapse in the 12 to 24 month period. Here a phenomenon hinted at in some of the non-significant findings of the 12 month analyses begins to emerge in a more robust fashion. That is, those who report being employed were more likely to relapse than those who were not employed at the time of the interview. A full 73% of the employed group relapsed, while 57% of the unemployed group did so (p=.05). This pattern holds for both the Black and White groups, although the difference was non-significant for the Black sample. In the White group, employed respondents were 28% more likely to relapse than in the unemployed group. Table 7.2: Crosstabulation of Employment at 24 Months by Relapse: % Reincarcerated Total Sample (n=142) Not Employed 57.0%* Employed 73.0% White Sample (n=50) Not Employed Employed

41.7%* 69.2%

Black Sample (n=92) Not Employed 63.6% Employed 75.7% *= Chi Square p-value < .05, ** = Chi-Square < .01 Table 7.3 shows the results of reporting any employment in the 12 to 24 month time period. It is useful to examine these results in two ways. First, those reporting any employment were much less likely to have had a period of incarceration than those who reported no employment. Eighty-eight percent of those reporting no employment were reincarcerated during the follow-up period, while only 47.6% of those who reported some period of working went back to prison. The second way to look at this relationship is that 47.6% of those who reported some employment between 12 and 24 month after their initial release returned to prison during the follow-up. This compares to 52.4% of the any-work group who were not reincarcerated (result not

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shown). While the difference in reincarceration for the any-work group is meaningful (a 5% reduction in recidivism is important), what is driving this model is that those who find no work, utterly fail. With 87.5% of those who report no work returning to prison, lack of any employment in a post release period is an extremely powerful predictor of impending recidivism. The pattern described above holds for both racial groups, although the relation loses significance due largely to the small sample size. In the Black sample while only 11 people reported having had no work at all during the follow-up period, 10 of these, or 90.9% were reincarcerated. It should also be noted that differences in reincarceration in the employed group for Blacks was 11%, that is, 44.4% of those who had any employment during the follow up period were reincarcerated while 55.6% were not. This is a substantial reduction in reincarceration and, when coupled with the failure of the non-employed group, is a meaningful, although tentative, finding. Table 7.3: Crosstabulation of Any Employment at 24 Months by Reincarceration: % Reincarcerated Total Sample (n=142) Not Employed 87.5% Employed 47.6% White Sample (n=50) Not Employed Employed

80.0% 53.3%

Black Sample (n=92) Not Employed 90.9%* Employed 44.4% *= Chi Square p-value < .05, ** = Chi-Square p-value <.01

Table 7.4 shows the results of any employment on relapse. In contrast to the positive result of being employed at the time of the interview, reporting any work in the 12 to 24 month period was nonsignificant. Examining the model by race sheds some light on the nonsignificant findings. While just missing statistical significance (Chi-

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square alphas in the .08 rage for both groups), the counts in the nonworking groups are opposite. These samples are too small to base any realistic conclusion upon, but they do suggest the reason for the nonsignificant findings. Table 7.4: Crosstabulation of Any Employment at 24 Months by Relapse: % Reincarcerated Total Sample (n=142) Not Employed 68.8% Employed 63.5% White Sample (n=50) Not Employed Employed

20.0% 60.0%

Black Sample (n=92) Not Employed 90.9%* Employed 65.4% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 7.5 reports the crosstabulation of those who worked 50% or more of their months free during the 12 to 24 month follow-up period with those who worked fewer than 50% of their free months. While there was an effect of working, with 49.5% of those working more than half of their free months returning to prison while 57.4% of those working less than half of the time were reincarcerated, the effect was not statistically significant. Further, both the direction and the nonsignificance of the relationship was the same for both racial groups. It may be that once offenders have been in the community for a year, working a greater portion of the time has less impact, as suggested by the positive yet non-significant effect in table 7.5. Table 7.6 shows that there is no difference between the group who worked more and those who worked less than 50% of the time in terms of relapse. The table also indicates that this finding was the same for both racial groups. It should be noted that all relationships showed those who worked more were at a greater risk of relapse, but these findings, while consistent with the earlier findings in this chapter, were comparatively small.

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Table 7.5 : Crosstabulation of > 50% Employment at 24 Months by Reincarceration: % Reincarcerated Total Sample (n=142) Worked > 50% of Time 57.4% Worked < 50% of Time 49.5% White Sample (n=50) Worked > 50% of Time Worked < 50% of Time

61.5% 54.1%

Black Sample (n=92) Worked > 50% of Time 55.9% Worked < 50% of Time 46.6% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 7.6: Crosstabulation of > 50% Employment at 24 Months by Relapse: % Relapse Total Sample (n=142) Worked > 50% of Time 63.8% Worked < 50% of Time 64.2% White Sample (n=50) Worked > 50% of Time Worked < 50% of Time

53.8% 56.8%

Black Sample (n=92) Worked > 50% of Time 67.6% Worked < 50% of Time 69.0% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 7.7 shows the result of working more hours as opposed to less. Readers are reminded that this analysis, as opposed to the preceding one, is designed to show the impact of the amount of hours worked when persons worked, as opposed to more months worked while free. The continuing trend in terms of reincarceration is that those who were employed more hours were less likely to return to prison during the 12 to 24 month period than those who worked fewer hours. While the split is somewhat arbitrary when the 50% reporting more

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hours are being compared to the 50% who worked less, the 10% difference between these groups provides some evidence that more time working exerts a positive impact on the likelihood of remaining free 12 to 24 months after leaving prison, although this relationship did not rise to the level of significance (p= .265). Table 7.7 highlights another difference by race in the 24 month analyses. The model was significant and the relation negative for whites, while it was positive but non-significant for blacks. Among the White respondents, working more hours produced a negative impact on the likelihood of reincarceration, with 80.0% of the less hours-worked group being reincarcerated compared to 45.7% of the more hours group. Among the Black respondents, there was almost no impact of working more hours. Table 7.7: Crosstabulation of > Hours Worked at 24 Months by Reincarceration: % Reincarcerated Total Sample (n=142) Worked Hours 49.0% White Sample (n=50) Worked < Hours Worked > Hours

80.0%* 45.7%

lack Sample (n=92) Worked Hours 50.8% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 7.8 continues to confirm the unanticipated positive effect of working on relapse. Although not significant in either the full sample or the split-race analysis, those who worked more hours were more likely to relapse than their less employed counterparts. In sum, and keeping in mind the small sample and thus exploratory nature of these analyses, it appears that employment continues to exert an impact on the likelihood of returning to prison in the 12 to 24 month period after offenders returned to the community. The difference between the 12 and the 24 month analyses in terms of employment was that it did not seem to matter at 24 months how much or how long a

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person was employed. Those reporting any employment or employment at the time of the interview were less likely to be reincarcerated than those who were either currently unemployed or who had not worked at all since the 12 month interview. Those who reported working either more than half of their eligible months or who were in the top half in terms of hours worked were not less likely to be incarcerated than their lesser-employed counterparts, however. Table 7.8: Crosstabulation of > Hours Worked at 24 Months by Relapse: % Relapse Total Sample (n=142) Worked Hours 67.3% White Sample (n=50) Worked < Hours Worked > Hours

46.7% 60.0%

Black Sample (n=92) Worked < Hours 62.1% Worked > Hours 71.4% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 The bivariate results concerning the relationship between employment and the likelihood of drug relapse were unanticipated. While not often rising to significance, the group who reported more employment, regardless of how it was measured was more likely to report a drug relapse in the 12 to 24 months following prison. The next set of analyses report the effect of marriage and having children on the reincarceration and relapse in the 12 to 24 month period after leaving prison. Twenty Four Month Bivariate Marriage Effects The lack of variability in the marriage variable at 24 months makes interpretation difficult and exploratory at best. Only three of the White respondents and 12 of the Black respondents were married at the 24 month interview. At an anecdotal level, though, the direction of the

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relationship regarding reincarceration supports the hypothesis of adult social bonds (of course one could argue that the very fact that so few of these offenders are married supports the hypothesis of a population heterogeneity approach). The unanticipated relationship between social bonds and drug relapse continued in the analysis reported in Table 7.10, even in the anecdotal relationship between being married and relapse. Sixty-three percent of the non-married sample relapsed, while 73.3% of the married sample did so. Table 7.9: Crosstabulation of Married at 24 Months by Reincarceration: % Reincarcerated Total Sample (n=142) Not Married 55.1% Married 26.7% White Sample (n=50) Not Married Married

59.6% 0.0%

Black Sample (n=92) Not Married 52.5% Married 33.3% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Table 7.10: Crosstabulation of Married at 24 Months by Relapse % Relapse Total Sample (n=142) Not Married 63.0% Married 73.3% White Sample (n=50) Not Married Married

55.3% 66.7%

Black Sample (n=92) Not Married 67.5% Married 75.0% *= Chi Square p-value < .05, ** = Chi-Square p-value <.01

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Twenty Four Month Bivariate Child Rearing Effects While the number of respondents who were married was too small to make inferences, the number with children was more substantial (n=48, or 33% or the sample). As shown in Table 7.11, 58.5% of those who did not live with or care for a child returned to prison at some point in the 12 to 24 month period, while only 39.6% of those who cared for a child did so. The effect was more pronounced for Blacks. Only 38.8% of Blacks who were caring for a child returned to prison, compared to 45.5% of whites who were caring for a child. As shown in Table 7.12, the positive relationship between social bonds and relapse continues with regard to caring for children. Perplexingly, those who lived with and cared for children were more likely to report some level of drug use in the 12 to 24 month follow up period compared to those who didn’t live with their children. Table 7.11: Crosstabulation of Children at 24 Months by Reincarceration: % Reincarceration Total Sample (n=142) No Children 58.5%* Children 39.6% White Sample (n=50) No Children Children

59.0%* 45.5%

Black Sample (n=92) No Children 58.2%* Children 37.8% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01

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Table 7.12: Crosstabulation of Children at 24 Months by Relapse % Relapse Total Sample (n=142) No Children 58.5% Children 75.0% White Sample (n=50) No Children Children

51.3% 72.7%

Black Sample (n=92) No Children 63.6% Children 75.7% * = Chi Square p-value < .05, ** = Chi-Square p-value <.01 Summary of 24 month bivariate analyses Employment remained an important predictor of success in terms of reincarceration during the 12 to 24 month period according to the bivariate analyses. Those persons who scored better in term of job status, by either being employed at the time of the interview or reporting any employment since the previous interview, were less likely to return to prison during the study period. This was true for both racial groups. As previously discussed, the amount of employment did not seem to have an impact in the 12 to 24 month time period. The effect hinted at in the 12 month analyses gained robustness in the 24 month data; that is, the positive effect of adult social bonds on relapse. While holding any in-depth discussion until later, this is partially anticipated by a negative correlation between relapse and reincarceration (Pearson R = -.189). Thus, those persons who were not incarcerated during the follow-up period were more likely to relapse. While this appears counterintuitive, when one considers the sample and the period of life these offenders are in, it may not be as surprising as it first seems. Remember the sample is from a drug-addicted population. As such, relapse is anticipated. Also, those who are not reincarcerated have more opportunity to relapse, by virtue of being on the streets for longer periods of time. Finally, those who remain free from prison longer obtain more freedom from parole officers, thus increasing the

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opportunity for relapse. This relationship will be investigated in more depth in the multivariate analyses that follow. While success in the first 12 months following prison appeared to be related to tangible factors, namely employment, the 24 month analysis, while exploratory in nature, saw the effect of family level variables become more important. Both being married and having children were negatively associated with returning to prison during the 12 to 24 month period. The multivariate analyses that follow investigated whether these relationships hold when population heterogeneity is controlled for. Twenty-Four Month Multivariate Analyses The caveats mentioned at the beginning of this chapter are worth repeating here. The small sample size at this stage of the analyses means that results must be considered exploratory and should be interpreted as such. With that in mind, and also considering the effect of sample size on regression standard errors and significance levels, it can be said that very few variables attained significance in the models that follow. While some of the relationships in the logistic regression analyses are relatively strong, none of the ordinary least squares analyses passed the omnibus f-test for full model significance, indicating that neither social bonds nor criminal propensity variables were predictive of intensity, frequency or reduction in drug use. Still, some variables were significant. These relationships are reported below. Logistic Regression Predicting Reincarceration and Relapse at 24 Months Table 7.13 reports the results of a logistic regression analysis of the predictor variables on reincarceration at 24 months. When controlling for all other variables, the only thing that was significant was drug use prior to prison, which positively predicted reincarceration during the 12 to 24 month period. Although not rising to significant levels in this small sample, it is worth noting that all of the social bond variables were negative, and the odds of returning to prison were decreased by

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127

more than half for each unit increase in the number of free months respondents were employed. Table 7.13: Logistic Regression Predicting Reincarceration at 24 Months FEMALE BLACK AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant

B -.558 -.424 -.022 -.056 -.031 .251 .017 -.106 .235 .090 -.688 -.144 -.721 .826 -.009 -.759 1.636

S.E. .576 .445 .030 .080 .033 .090 .045 .169 .443 .080 .741 .495 .584 .493 .628 .635 1.407

Sig. .333 .341 .456 .481 .338 .005 .698 .533 .596 .262 .354 .771 .217 .094 .989 .232 .245

Exp(B) .573 .654 .978 .945 .969 1.286 1.018 .900 1.265 1.094 .503 .866 .486 2.285 .991 .468 5.135

Pseudo R-Square = .22 Table 7.14 shows the results of the logistic regression predicting any relapse in the 12 to 24 month period. As with the 12 month analyses, population heterogeneity variables appear to drive the model. AGE of onset, drug use prior to prison and risk seeking were all significant and positive predictors of relapse. Again, while not significant, the social bonds variables were all positive, indicating that possessing bonds increased the likelihood of relapse

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Table 7.14: Logistic Regression Predicting Relapse at 24 Months .276 .704 -.031 .118 .063 .273

S.E .631 .455 .031 .085 .035 .098

Sig .662 .122 .328 .165 .072 .005

Exp(B) 1.318 2.022 .970 1.126 1.065 1.314

-.014

.051

.786

.986

.490 -.384 -.114 1.130 .789 .141 .608 .984 .298 -2.910

.192 .448 .081 .822 .527 .596 .507 .687 .693 1.531

.011 .391 .157 .169 .134 .813 .231 .152 .668 .057

1.632 .681 .892 3.095 2.201 1.151 1.836 2.675 1.347 .054

B FEMALE BLACK AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT Constant Pseudo R-Square = .22

Although failing the omnibus f-test, the model reported in Table 7.15 once again shows the influence of risk seeking and drug use prior to prison as the only significant predictors of the frequency of drug use during the 12 to 24 month period. Also notable, Blacks used drugs more frequently, when all else was held constant, although this relation was marginally significant with p = .056.Employment and marriage, while non-significant, were once again positively related to drug use.

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Table 7.15: OLS Regression of Frequency of Drug Use at 24 Months

(Constant) FEMALE BLACK AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

Un-std Coeff*

Std Coef#

B Std. Error .259 1.488 .362 .616 .883 .457

Beta

t

Sig.

.174 .588 1.931

.862 .557 .056

-.154 -1.445

.151

.061 .170

-.046

.032

.047 .033 .245

.085 .035 .094

.057 .114 .234

.560 .962 2.602

.577 .338 .010

-.042

.048

-.087

-.875

.383

.350 .145 .007 .165 -.030 .473 .216 .278 -.181

.177 .450 .079 .772 .512 .593 .517 .668 .672

.192 .029 .008 .020 -.006 .071 .042 .042 -.027

1.973 .321 .082 .213 -.059 .798 .418 .417 -.270

.051 .749 .935 .831 .953 .426 .677 .678 .788

Pseudo R-Square = .17 Other than being married, virtually nothing in Table 7.16 approached significance. That is, when holding everything constant, only being married in this analysis significantly predicted the number of free months in which respondents used drugs. Further, marriage was significant but in the opposite direction of that anticipated. The pattern continues in Table 7.17, except in this table, impulsivity is the only significant variable. This lack of significance was anticipated by the failure of the full model f-tests (not shown) to reach significant levels.

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Table 7.16: OLS Regression of Ratio Months Using Drugs at 24 Months

(Constant) FEMALE BLACK AGE

Un-std Coeff*

Std Coef#

B Std. Error -.045 .185 .003 .080 .084 .054

Beta

t

Sig.

.005 .158

-.245 .036 1.549

.807 .971 .125

.004

.004

.116

.972

.334

# CRIMES .005 FIRST CRIME -.003 MAXDRG0 .010 AGE FIRST DRUG -.005

.010 .004 .012

.063 -.085 .091

.513 -.597 .831

.610 .552 .408

.007

-.080

-.672

.503

RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

.021 .054 .010 .096 .066 .070 .067 .079 .077

.185 1.607 -.119 -1.147 -.081 -.705 .229 2.020 -.088 -.724 .115 1.113 .068 .555 .185 1.531 -.035 -.287

.112 .254 .482 .046 .471 .269 .580 .129 .775

Pseudo R-Square = .07

.034 -.062 -.007 .194 -.048 .078 .037 .122 -.022

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Table 7.17: OLS Regression of Reduction in Drug Use at 24 Months Un-std Coeff*

(Constant) FEMALE BLACK AGE # CRIMES FIRST CRIME MAXDRG0 AGE FIRST DRUG

B Std. Error 2.332 1.797 -.616 .743 -.266 .552 .028 .039 -.068 .102 -.042 .042 .103 .114 .021

.058

-.284 -1.092 .052 .090 -.183 -.203 -.533 .961 -.721 Pseudo R-Square = .09

.214 .544 .096 .932 .618 .716 .624 .806 .811

RISKY IMPULSIVE VOLATILE MARRIED KIDS PROPWORK COMP TREAT VOLT TREAT BOTH TREAT

t

Sig.

-.090 -.044 .081 -.071 -.126 .085

1.298 -.829 -.481 .733 -.667 -1.017 .904

.197 .409 .631 .465 .506 .311 .368

.038

.360

.720

-.135 -1.329 -.188 -2.008 .053 .541 .010 .097 -.030 -.295 -.026 -.283 -.090 -.854 .126 1.193 -.093 -.889

.186 .047 .589 .923 .768 .778 .395 .235 .376

Std Coef#

Beta

Summary of 24 Month Analyses The lack of significant findings in these models was not unanticipated, considering the small sample sizes utilized. The lack of full model significance can almost cause these models to be disregarded, and they are largely included for completeness. But the null findings, when coupled with the significant findings in the 12 month analysis may indicate that it is the first year after release that is most important in terms of forming and gaining the effects of bonds. Recall that almost 25% of the sample was not included in the 12 to 24 month analyses because they had been reincarcerated in the first 12 months and had not

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yet returned to the community. By the end of the 12 month period, then, many of those who would fail had done so; creating variability in the reincarceration outcome robust enough that both social bonds and criminal propensity were able to significantly predict the variation. It may also be that, once offenders have been out of prison for over a year, different sets of predictors come into play that are beyond the scope of this analysis. In order to more fully understand the role of adult social bonds and criminal propensity on the reentry process, Chapter Eight reports the results of a series of structural equation models in which population heterogeneity is accounted for at baseline, and social bonds variables in the first 12 months after release are used to predict 12 to 24 month outcomes.

CHAPTER 8

The Reentry Process: Structural Equation Models Introduction This chapter reports the results of a series of structural equation models utilizing data from the baseline, 12 and 24-month interviews. The idea is to test how population heterogeneity affects the likelihood of persons forming social bonds in the first year after release from prison and, in turn, how these bonds affect the likelihood of relapse and reincarceration 24 months after release. Figure 8.1: Theoretical Structural Equation Model

Baseline Measures

12-Month Follow-up

24-Month Outcome

Figure 8.1 shows the conceptual model that forms that analyses that follow. On the left hand side of the model are the baseline measures. Notice that only those variables that showed some measure of significance in the 12 and 24 month cross sectional analyses are included. The volatile temper variable has been dropped, because it did not reach significance in any of the 12 or 24-month models. The treatment variables were dropped as well, as the hypotheses regarding treatment were not born out at either 12 or 24 months, and thus there is 133

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no reason to assume they would have an effect here. Age of onset was also dropped, as was age of first drug use, as neither rose to a level of significance. This approach sets up the most parsimonious set of models possible. Because the nature of the research question is that adult social bonds will have an impact on the outcome measure of relapse and reincarceration, the employment, marriage, and childrearing variables were all tested, regardless of earlier results. Also, in order to account for the correlation between relapse and reincarceration, the error terms for these two variables were allowed to correlate. In order to refresh the reader, a brief review of the theoretical and methodological nexus is provided before reporting the results. Social bonds theorists such as Sampson and Laub have argued that those individuals who form attachments to social institutions later in life will be less likely to be involved in crime, regardless of earlier criminal involvement. Thus, those offenders (who have all by definition been involved in earlier criminal activity) who are married, care for children or have better employment outcomes are less likely to return to crime. This effect is represented in the diagram by the two small black arrows pointing to RELAPSE and PRISON (representing reincarceration). Sampson and Laub would argue that whichever of the three social bonds variables were put in the middle box, there would be a negative impact on relapse and recidivism. Population heterogeneity theorists reject this. Gottfredson and Hirschi have argued repeatedly (e.g., 1990, 1995) that any effect of adult social bonds is a spurious artifact resulting from self selection based on underlying criminal propensity. The models presented here accounted for this in two ways. First by estimating the effect of criminal propensity variables on social bonds as represented by the thick black line in Figure 8.1. AGE, number of crimes, drug use prior to prison, risk seeking, and impulsivity are all hypothesized to have a negative effect on social bonds (race and gender are included as controls). Thus, those who obtained some form of adult bond after release from prison would be hypothesized to be the ones with lower criminal propensity and so would also obtain better outcomes in terms of relapse and reincarceration. This would be a pure population heterogeneity/self selection model.

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The second method of examining whether adult bonds are significant after controlling for population heterogeneity is to test models that allow social bonds to be estimated against models in which these effects are fixed to zero. Because the simplest explanation is always scientifically desirable, a more complex model must be empirically justified (Piquero & Rosay, 1998). To test this approach, reduced structural equation models were estimated in which the social bonds effects were fixed to zero, that is, the paths represented by the small black arrows were not estimated. Full models were then estimated that allowed the paths to be estimated. Because these full models were more complex, they must be statistically justified. This was accomplished by conducting a difference in Chi-Square test. The difference in Chi-Square statistics between a full and reduced model has a chi-square distribution itself, with the degrees of freedom equaling the difference in degrees of freedom between the two models (Piquero and Rosay, 1998). If there is a significant difference between models, the one which allows social bonds to be estimated would be considered superior to the one in which these paths were fixed to zero, because the additional complexity would be justified by the improved model fit. The rest of the chapter is laid out as follows: each social bond variable will be estimated on the outcome variables in turn. A full and reduced model will be presented for each of the employment, marriage and child rearing variables. Finally, an additive measure of social bonds is presented. Structural Equations Models for Employment In keeping with the approach in the multivariate analyses at 12 and 24 months, the employment variable used for the structural equations was the proportion of months respondents were free in the first 12 months after release from prison during which they were employed. Unreported models of the other employment variables were estimated to insure that no major differences were found by employment type. To assist the reader in presentation, only the significant standardized coefficients are reported in the tables. Table 8.1 begins by showing the results of the full and reduced models predicting the dichotomous relapse and reincarceration outcomes. Table 8.1 and all of

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the following tables are laid out as follows. On the left hand side are the lists of endogenous variables to be regressed on the column to their immediate right. To the immediate right (following the dash) are the exogenous variables that predict the variables in the left column. The next column contains the full model standardized coefficients, followed by the reduced model standardized coefficients in the far right column. The bottom of the table contains the Goodness of Fit (GIF) statistic discussed in Chapter Five, followed by the Adjusted Goodness of Fit Index (AGFI). As a reminder, values approaching one on both of these statistics indicate a good fit. A common rule of thumb is that GFI and AGFI statistics above .9 indicate exceptional model fit. Beneath these fit indexes is the Root Mean Square Error of Approximation (RMSEA), which assesses approximate fit and is considered a more realistic measure than the exact fit indexes noted above. Generally, an RMSEA of below .05 is considered and exceptional fit, .08 indicates an adequate fit and an RMSEA above .10 indicates a poorly fitting model. Lastly, the table reports the Chi-Square and Chi-Square difference between the more and less parsimonious models. P-values of less than .05 indicate that the less parsimonious model (which allows the social bonds effects to be estimated) performs significantly better than the model in which these effects are constrained to zero. Turning to the results in Table 8.1, the full model provided an excellent fit to the data by all of the fit measures. The reduced model performed adequately by the GFI (.94), it failed the AGFI (.74) and RMSEA (.15) tests. More importantly, the Chi-Square difference between the full and reduced models is significant (Chi-Square difference = 44.67, DF = 2, p<. 001), which indicates that adding the paths from employment to relapse and prison substantially increased the ability to predict these occurrences, even when the criminal propensity measures were accounted for. Regarding the individual predictors, the strongest effect in the model was the number of free months respondents were employed on reincarceration. Interestingly, the opposite effect of employment on relapse is here seen in a strong significant relationship that holds in spite of controlling for multiple indicators of criminal propensity. Population heterogeneity variables were significant, even though they did not overcome the strength of the employment variables. Frequency of drug use prior to

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prison significantly decreased the number of free months employed. Prior drug use also directly affected both of the outcome variables, increasing the likelihood of reincarceration and relapse. In terms of the psychological indicators, impulsivity positively predicted relapse, while risk seeking predicted reincarceration. Of the control variable effects, age was negatively associated with reincarceration, indicating that older respondents were less likely to return to prison when the other relationships were accounted for. Gender also negatively predicted reincarceration, meaning that females were less likely to return to prison than their male counterparts. Table 8.1: SEM, Employment Predicting Relapse and Prison Full Model Reduced Model Std. Estimate Std. Estimate -.127* WORKING1 --- MAXDRG0 -.127* RELAPSE2 --- MAXDRG0 .190** .157** RELAPSE2 --- IMPULS2 .116* .108 PRISON --- YOURAGE -.087* -.079 PRISON --- MAXDRG0 .219*** .271*** PRISON --- RISKY1 .144** .164** PRISON --- FEMALE -.126** -.160** RELAPSE2 --- WORKING1 .255*** NA PRISON --- WORKING1 -.407*** NA .98 .94 GFI AGFI .90 .74 RMSEA .068 .150 Chi-Square 18.695 63.162 Chi-Square Diff 44.647*** * = p<.10, ** = p<.05, *** = p<.001 Table 8.2 reports the results of the SEM model predicting the frequency of drug use in the 12 to 24 month time period. Both models again provided good fits to the data, according to the GFI (Full = .98, Reduced = .94). The reduced model once again failed to provide an adequate fit according to the AGFI (.75) or RMSEA (.14), however, while the full model provided a good fit by both measures (AGFI = .90, RMSEA = .07). Finally, the Chi square difference between models was

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once again significant (40.19, p<.001) suggesting the model allowing the paths from working to arrest and frequency of drug use to be estimated performed significantly better than the one in which these effects are fixed to zero. One interesting finding in Table 8.2 is that the improvement in fit is almost entirely accounted for by the effect of employment on reincarceration. The effect of employment on drug use is nonsignificant. Recall that in the cross-sectional models, especially at 24 months, the employment to drug use effects were positive and significant. Here the effect is positive but not significant, indicating that those working more at 12 months used drug more frequently than those who worked a fewer number of eligible months. This positive effect is again unanticipated by the either the population heterogeneity or the social bonds approach, but is not inconsistent with the life course progression of these offenders. Those who worked more in the first 12 months after release from prison were less likely to be incarcerated. As a result of that, they were more likely to be free and have money in their pockets. Thus, they are more likely to use more drugs than their incarcerated or unemployed counterparts. The other point worth noting is that in the models of any use, employment was positive and significant, while in the models measuring the frequency of use (and the unreported reduction in use models as well), the effect of working was not significant. Thus, respondents who were working more in the first 12 months after release were significantly more likely to use some drugs; when the intensity or the reduction in use is considered, working was not a positive predictor. Keeping in mind the negative correlation between being incarcerated and drug use, this would indicate that in the model allowing working to be regressed on reincarceration and frequency of use simultaneously, the bonding effect of employment on incarceration overrides the effect on frequency of drug use. From a harm reduction perspective this is an important finding because if we consider any relapse a failure, these respondents would have failed early on, but when employed and in the community, the employment/drug effect seems to level off. It should also be noted that risk seeking and drug use prior to prison directly and significantly affected the results. So, although social bonds are significant and, as evidenced by the full model, needed in order to measure the outcome adequately, criminal propensity still

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plays a strong role. Of the control variables, gender predicted reincarceration, while age was negatively associated with the frequency of drug use. The only thing that significantly predicted employment was drug use prior to prison. The more frequently one used drugs prior to prison, the fewer eligible months one was employed. Table 8.2: SEM, Employment Predicting Frequency of Drug Use and Prison

WORKING1 --MAXDRG2 --MAXDRG2 --PRISON --PRISON --PRISON --MAXDRG2 --PRISON ---

Full Model Reduced Model Std. Estimate Std. Estimate MAXDRG0 -.127* -.127* YOURAGE -.154** -.156** MAXDRG0 .187* .178** MAXDRG0 .219* .271*** RISKY1 .144*** .164** FAMALE -.126** -.160** WORKING1 .065 Na WORKING1 -.407*** Na

GFI AGFI RMSEA Chi-Square Chi-Square Diff

.94 .98 .75 .90 .068 .143 18.695 58.589 40.19 p<.001 *s= p<.10, ** = p<.05, *** = p<.001

The emergent trend continued in the analyses reported in Table 8.3, predicting the ration of free months in which respondent used drugs. Once again the full model provided a significantly better fit to the data than the less complex reduced model, regardless of the fit statistic utilized. Employment was significant and negative predicting prison and non-significant predicting the proportion of free months in which drugs were used. AGE was non-significant in these models, as opposed to the frequency of use models in Table 8.2. It thus appears that age may effect how frequently one uses drugs when they use drugs (i.e., daily, weekly, or hourly), but not necessarily the length of time a relapse episode lasts (i.e., one month, six months).

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Table 8.3 SEM, Employment Predicting Ration of Drug Using Months and Prison

WORKING1 --PRISON --PRISON --PRISON --RADRG2 --PRISON --GFI AGFI RMSEA Chi-Square Chi-Square Diff

Full Model Reduced Model Std. Estimate Std. Estimate MAXDRG0 -.127* -.127* MAXDRG0 .219*** .271*** RISKY1 .144** .168* FEMALE -.126** -.160** WORKING1 .092 Na WORKING1 -.407*** Na

.98 .90 .068 18.695 40.17 *** p<.10, ** = p<.05, *** = p<.001

.95 .75 .143 58.869

The final model assessing the impact of employment and criminal propensity on the reduction of drug use is similar to the first three models. When one considers the strong effect of employment on reduction in drug use in the 12-month model, this might appear surprising. The model reported in Table 8.4, however, accounts for the criminal propensity measures as well as drug use in predicting reduction in drug use and prison at 24 months. Also recall that the reduction in use models were less robust in the 24 month analysis, indicating that employment has a strong effect on drug use in the important months immediately after release from prison, but that once offenders have sorted themselves for over a year, the effect is less significant. It is worth noting at this point that race did not rise to a significant level in any of the SEM models above. While it may be that the effects of certain variables in the models could have differing effects by race, the non-significance of race indicates that once criminal propensity and social bonds are accounted for, race does not make a difference in predicting reincarceration or relapse among this sample of offenders.

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Table 8.4: SEM, Employment Predicting Reduction in Drug Use and Prison

WORKING1 --REDRUG2 --ANYP2D --ANYP2D --ANYP2D --REDRUG2 --GFI AGFI RMSEA Chi-Square Chi-Square Diff

Full Model Reduced Model Std. Estimate Std. Estimate MAXDRG0 -.127* -.127* IMPULS2 -.154** -.157** MAXDRG0 .219*** .271*** RISKY1 .144** .164** FAMALE -.126** -.160 WORKING1 -.083 Na .98 .90 .068 18.695

.95 .75 .143 58.904 40.21 ***

* = p<.10, ** = p<.05, *** = p<.001 Structural Equations Models for Marriage and Children Examining the effects of employment on multiple measures of drug use and reincarceration allowed the analyses above to show that the differences between the full and reduced models were robust and significant. It also became apparent that, although differences appeared in whether drug use was measured dichotomously or in a linear fashion, once the drug use outcome was allowed to vary across a wider range of values, it made little difference in terms of predictors. This is to be expected because the correlation between two variables remains relatively stable even when the metric changes. For this reason, only the dichotomized drug use models and the frequency of drug use model are presented below (unreported models of the ratio of months used and the reduction of use showed almost identical results to the frequency of use models). Table 8.5 reports the results of the SEM model of being married predicting reincarceration and any reported drug use in the 12 to 24 month period, while accounting for population heterogeneity. As hinted at by the non-significance of marriage in the 12 and 24-month cross-

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sectional analyses, marriage is not significant. Additionally, the full model allowing the paths from marriage to relapse and reincarceration to be estimated does not significantly improve the model fit in relation to the more parsimonious reduced model. In fact, the full and reduced models fit the data to almost the same degree. Both provide adequate fits to the data by all fit statistics, but the difference in Chi-Square statistics is non-significant, indicating that the addition of the paths from marriage to reincarceration and relapse do not improve the model. The more parsimonious model using only the population heterogeneity, race and gender measures would thus be preferable to one that includes the marriage variable. In terms of the individual predictors, impulsivity was the only one that predicted marriage, returning a standardized beta of -.126. This indicates that impulsive persons were significantly less likely to be married than their less impulsive counterparts. Risk taking was positive and significant in predicting reincarceration but not relapse. Drug use prior to prison was a positive and significant predictor of both relapse and reincarceration. Gender was significant and negative predicting reincarceration, meaning that females were less likely to be reincarcerated than males. Females were more likely to relapse on drugs, however, but the effect was only significant at p = .07 in the full model. Gender was not significant in the reduced model.

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Table 8.5: SEM, Marriage Predicting Drug Use and Prison Full Model Reduced Model Std. Estimate Std. Estimate MARRIED --- IMPLUSE -.126* -.126* RELAPSE --- MAXDRG0 .164** .157** PRISON MAXDRG0 .265*** .271*** PRISON --- RISKY1 .164** .164** PRISON --- FEMALE .128* .118 RELAPSE FEMALE -.169** -.160** RELAPSE --- MARRIED .097 NA PRISON --- MARRIED -.086 NA .98 .98 GFI .90 .90 AGFI .068 .064 RMSEA 21.347 Chi-Square 18.69 2.65 Chi-Square Dif * = p<.10, ** = p<.05, *** = p<.001 Table 8.6 reports the model predicting frequency of drug use shows much the same thing as the table model predicting any drug use. Once again, the more complex model that estimates the effect of marriage is not justified by the change in Chi-Square model fit. The individual predictors were much the same as well, with risk seeking and drug use prior to prison significantly predicting failure. Gender lost significance predicting frequency of drug use, indicating that while gender might predict any use of drugs at 24 month it did not predict the frequency of use. Race was once again not significant in either model. In sum, the analyses assessing the effect of marriage clearly and consistently favor a population heterogeneity approach. Being married in the first year after release from prison did not affect drug relapse, frequency of drug use or reincarceration in any significant way. Table 8.7 reports the effect of having children on the likelihood of reincarceration and any relapse 24 months after release from prison. The models testing the effect of children look almost identical to the marriage models, except instead of impulsivity negatively predicting the bond, risk seeking was negative and significant in predicting

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whether respondents lived with and cared for their children 12 months after release. Drug use prior to prison, risk seeking and gender had similar effects as in the marriage model. Having children was not significant, as was reflected in the Chi-Square difference between the full and reduced models, which was not significant. Table 8.8 shows the results of the model predicting frequency of drug use at 24 months. Having children was once again not significant. In fact, the only real difference between the measure of any relapse and that of frequency of use was that age was a significant predictor of the frequency of use. Older persons used drugs significantly less frequently than their younger counterparts (actually, the lack of a significant age effect in the other models is what is surprising). Once again the more parsimonious model including only the population heterogeneity variables was statistically more desirable than the more complex model allowing the children effects to be estimated. Table 8.6: SEM, Married Predicting Level of Drug Use and Prison

KIDS --RELAPSE --PRISON PRISON --PRISON --RELAPSE --PRISON --GFI AGFI RMSEA Chi-Square Chi-Square Diff

Full Model Std. Estimate RISKY -.219*** MAXDRG0 .165** MAXDRG0 .263*** RISKY1 .145** FEMALE -.155** KIDS .087 KIDS -.085 .98 .90 .068 18.695 2.301

*= p<.10, ** = p<.05, *** = p<.001

Reduced Model Std. Estimate -.219*** .157** .271*** .164** -.160** NA NA .98 .90 .034 20.999

The Reentry Process: Structural Equation Models Table 8.7: SEM Predicting Drug Use and Prison

MARRIED --MAXDRG2 --PRISON PRISON --PRISON --MAXDRG2 MAXDRG2 --PRISON --GFI AGFI RMSEA Chi-Square Chi-Square Diff

Full Model Std. Estimate IMPLUSE -.126* MAXDRG0 .180** MAXDRG0 .265*** RISKY1 .164** FEMALE -.169** FEMALE .055 MARRIED .020 MARRIED -.086 .98 .90 .068 18.695

Reduced Model Std. Estimate -.126* .157** .271*** .164** -.160 .118 NA NA .98 .91 .060 20.319 1.624

*= p<.10, ** = p<.05, *** = p<.001 Table 8.8: SEM Predicting Frequency of Drug Use and Prison Full Model Std. Estimate KIDS --RISKY -.219** MAXDRG2 AGE -.160** MAXDRG2 --MAXDRG0 .174** PRISON MAXDRG0 .263*** PRISON --RISKY1 .040** PRISON --FEMALE -.155 MAXDRG2 --KIDS -.053 PRISON --KIDS -.085 .98 GFI .90 AGFI .068 RMSEA 18.695 Chi-Square Chi-Square Diff 2.301 *= p<.10, ** = p<.05, *** = p<.001

Reduced Model Std. Estimate -.219** -.156** .178** .271*** .164** -.160** NA NA .98 .90 .034 20.999

145

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Final Structural Equations Models of Additive Social Bonds As a final measure of socials bonds, an additive variable was created in which those who had no attachments were coded zero (n=79), those who possessed one bond (either working, married, or caring for a child at the 12 month interview) were coded one (n=68), those who possessed two bonds were coded two (n=40) and those who were married, caring for children and working (n=4) were coded three. Because Sampson and Laub suggest the bonds work according to Coleman’s (1988) notion of social capital, where bonds tend to link and reinforce one another, the additive measure should perform better than the individual predictors by themselves. Table 8.9 reports the first model predicting any relapse or reincarceration in the 12 to 24 month period. The outcome is clear. Criminal propensity, represented through drug use prior to prison and risk seeking, significantly reduces the attainment of social bonds and directly and significantly increases the likelihood of both relapse and reincarceration. Still, social bonds were a significant negative predictor of reincarceration, and a significant positive predictor of relapse. This is once again evidenced in two ways, first, by the significance of the individual predictors, and, secondly, by the improved chi-square model fit. The difference in Chi-Square statistics was 28.391, with 2 degrees of freedom, which amounts to a significant difference at p<.001. Thus, the model in which social bonds is estimated provides a significantly better fit to the data than the model in which the effect of bonds is constrained to zero. Table 8.10 reports the model predicting the frequency of drug use during the 12 to 24 month period. The effects of risk taking and drug use prior to prison are the same as in the relapse models (as expected). In fact, both the full model and individual level predictors are nearly identical to the relapse model shown in Table 8.9, with two important exceptions. First, AGE was significant and negative in these models indicating that the older one was, the less frequently they used drugs. Secondly, the positive effect of bonds on drug use is reduced dramatically and loses significance, compared to the more conservative any use model reported above. Once again, race was not significant in either model.

The Reentry Process: Structural Equation Models Table 8.9: SEM Predicting Drug Use and Prison

BONDS --BONDS --MAXDRG2 MAXDRG2 --PRISON --PRISON --PRISON --MAXDRG2 --PRISON --GFI AGFI RMSEA Chi-Square Chi-Square Diff

MAXDRG0 RISKY AGE MAXDRG0 MAXDRG0 RISKY1 FEMALE BONDS BONDS

Full Model Std. Estimate -.155** -.152** -.155** .181*** .220*** .114* -.148** .018 -.328*** .98 .90 .068 18.695 24.285***

Reduced Model Std. Estimate -.155** -.152** -.156** .178** .271** .164** -.160** NA NA .96 .81 .117 42.980

Table 8.10: SEM Predicting Frequency of Drug Use and Prison Full Model Std. Estimate BONDS --- MAXDRG0 -.155** BONDS --- RISKY -.152** RELAPSE --- MAXDRG0 .195*** RELAPSE RISKY .124* PRISON --- MAXDRG0 .220*** PRISON --- RISKY1 .114* PRISON --- FEMALE -.148** RELAPSE --- BONDS .243*** PRISON --- BONDS -.328*** .98 GFI .90 AGFI .068 RMSEA 18.695 Chi-Square Chi-Square Diff 29.391*** *= p<.10, ** = p<.05, *** = p<.001

Reduced Model Std. Estimate -.155** -.152** .157** .087 .271** .164** -.160** NA NA .95 .79 .126 48.086

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Summary of Structural Equations Models While holding final discussion for the last chapter, the outcome here is obviously mixed. Employment had a strong and significant effect on reducing the likelihood of reincarceration. In terms of drugs use, those who worked more of their free and eligible months on the street were significantly more likely to report a drug relapse, when all else was accounted for. In terms of frequency of use, months used, or reduction in use, however, employment was non-significant. The other social bonds- marriage and childrearing- had no effect on the outcome measures. In terms of population heterogeneity, the frequency of drug use prior to prison, risk seeking and, to a lesser degree, impulsivity, were all significant predictors of both the social bond measures and the outcome variables. The measures were stable and consistent across models and obviously cannot be discounted. The last chapter discusses the implication of these findings both theoretically and in terms of policy relevance.

CHAPTER 9

Conclusion Sampson and Laub (1993; Laub & Sampson, 2003) proposed a theory in which social bonds that developed in adulthood were hypothesized to decrease the likelihood of criminal behavior, net of underlying criminal propensity. Using data collected in the post World War Two era (Glueck and Glueck 1950), they demonstrated strong empirical support for their approach. Critics have continued to maintain, however, that Sampson and Laub’s approach and models are misspecified. For example, Gottfredson and Hirschi have continuously maintained that Sampson and Laub and others who have tested theories of adult social bonds are really measuring differences in people’s criminal propensity, which, these critics argue, is strongly related to the likelihood of forming bonds in adulthood. This project attempted to analyze these competing approaches among a sample of recently released offenders. The project advanced the literature in a number of ways. The first contribution was to test Sampson and Laub’s theory on a group of modern, drug addicted offenders. Sampson and Laub tested their theory on data collected during a time of rapid economic expansion. Jobs were presumably plentiful and people who wanted to work could easily do so. It was unclear how the social bond of employment would affect a sample of under skilled, undereducated persons with criminal records attempting to reintegrate into this economy. This sample, on the other hand, was reentering the community at a time of economic contraction in which people with even the highest skill level are forced to move from job to job. Because Sampson and Laub proposed their theory as general, that is, a theory that applies to all times all places, all persons and all crimes, it should not matter whether it is tested in the 1940s or in 2003. If it is truly a general theory, the results should be the same. Sampson and Laub also tested their theory on a sample of exclusively White males. The sample used here was largely Black and contained a substantial proportion of women. Again, for a general theory, differences in the racial or gender make-up of the sample should not matter, but tests of this proposition are few (Piquero et al. 2002).

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Sampson and Laub tested their theory on a sample of persons identified as offenders in their youth. Many of these people desisted in the 17 to 25 year-old range. The current sample, with a mean age of 32 years and incarcerated represents a more hardened group of offenders than that examined by Sampson and Laub. Thus, the goal was to test whether the propositions brought forth by Sampson and Laub applied among older, chronic, persistent offenders. Lastly, the analysis presented here brought to bear a battery of observable measures of criminal propensity. Most studies utilize offending rates or a combination of offending rates and age to account for population heterogeneity (Horney et. al, 1995; Piquero et al., 2002). The analyses presented above utilized psychometric scales of risk seeking and aggression, as well as a measure of impulsivity, an indicator of addiction severity prior to prison, age of onset, a measure of criminal diversity (number of different crime types engaged in), as well as age, race, and gender in order to account for underlying differences in the propensity to commit crime. The results of the present study are mixed but clear. Criminal propensity was an important predictor of outcomes, both at 12 and 24 months. In the most robust models utilized, the structural equation models, risk seeking and level of drug use prior to prison (and in some cases impulsivity) were strong predictors of obtaining social bonds and also directly influenced the likelihood of both reincarceration and relapse, regardless of how the latter was measured. The fact that a simple four-question risk seeking scale can predict all of the above outcome variables says volumes for the stability of individual level differences in behavior over time. In addition, however, the results demonstrate that, overall, Sampson and Laub’s theory also was supported in the current analyses; employment after release from prison significantly decreased the likelihood of reincarceration even though marriage and children did not. This is not altogether surprising when one considers again the circumstance and sample of the study. Recall that the sample was a group of people who have relatively lengthy criminal records and addiction problems. Most have never been married and most do not care for their children if they have them. All began this study incarcerated. In terms of criminal propensity, all are criminal and therefore all have low self control, or are impulsive risk seekers or

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whatever change inhibiting theoretical mechanism the reader wishes to apply. In that sense, if any time varying status or state were to have an effect, it would have to be considered substantial to emerge significant among this group. Employment did just that. When reentering the community after a period of incarceration, those respondents who found and maintained employment faired substantially better in terms of reincarceration than those who did not find or did not attempt to find employment. This was true at the 12 and 24-month interviews regardless of how employment was measured (except for the measure of any employment at 12 months), and was true for both Blacks and Whites. The structural equation models provided even stronger support for the proposition that employment reduces the likelihood of reincarceration. Using two approaches, one testing the significance of the individual coefficients, and the other conducting full model goodness of fit tests, the analyses showed that even while controlling for the effect of individual differences in criminal propensity, the ratio of free and eligible months respondents were employed significantly reduced the likelihood of reincarceration. It thus appears that employment has empirical support in Sampson and Laub’s (1993) theoretical perspective when tested on a sample of highly active, modern, mostly minority offenders. This lends support for the proposition that their theory of age graded social bonds is a general theory. It was stated in the introduction that state dependence theories like Sampson and Laub’s were in conflict with population heterogeneity theories such as Gottfredson and Hirschi’s theory of Low self control. Part of this project was to designed to assess which approach faired best in predicting relapse and recidivism among this offender sample. While the results are mixed, this mixed result itself must be taken as evidence in favor of a state dependence model. Gottfredson and Hirschi have continuously maintained that once group differences in criminal propensity are accounted for, the effect of state dependence variables such as working not be significant. This was not the case in these analyses. After controlling for a host of observable factors associated with criminal propensity, employment still had a positive and significant effect on the likelihood of reincarceration. Proponents of a population heterogeneity approach would disagree with the above conclusion. They would note that there almost certainly

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exists a large amount of unmeasured criminal propensity that, if accounted for, would nullify the effect of employment on reincarceration. Additionally, while I have made the argument that the relationship between drug use and employment points to a need for a harm reduction approach, it is not the finding anticipated by state dependence theorists. Sampson and Laub (1993) used alcohol abuse in their models and anticipated and found a negative association between bonds and alcohol use. Here, the association is positive. This, criminal propensity theorists would maintain, is contrary to the proposed hypothesis and thus supports a population heterogeneity approach. The real surprise in these analyses was the positive relationship between employment and relapse. Those who scored better on the employment measures tended to be more likely to relapse. This at first seems counterintuitive, but one must again consider the sample, and also the types of measures used in assessing this process. First, recall the addicted nature of the sample. It has been suggested that using conservative measures of relapse among this type of population, such as “used any drugs” is not realistic (Inciardi and Harrison, 2000). Second, recall how the relationship between unemployment and relapse evolved over the analysis. In the 12-month cross sectional data, employment was not significantly related to any relapse, was negatively related to the frequency of use, was not significantly related to the ratio of free months used, and was strongly and significantly related to the reduction in drug use from pre-prison to 12 months after release. In the 24-month cross sectional outcome data, all of the drug use measures were non-significant when criminal propensity was controlled. In the structural equation models, the ratio of free and eligible months worked was positively and significantly related to any Relapse. In fact, it was the strongest predictor of any relapse. In the frequency of use models, however (as well as in the unreported ratio of free months used and reduction in use models), employment was not significant. Thus, in the first 12 months after release and when controlling for a host of criminal propensity and prior addiction variables, employment was not associated with whether or not people relapsed. But it was related to how severely they relapsed and was strongly related to the reduction in drug use post release relative to pre-incarceration. When these much more realistic and hopeful measures are considered, the

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employment/drug use relationship is pronounced and important during this vital reentry stage. In the second year after release employment did not seem to matter, although readers are cautioned to recall the small sample size and exploratory nature of the findings. Given this, the non-significant findings at 24 months may indicate that people have sorted themselves during the first 12 months and, by 24 months, differences in employment lose significance when predicting relapse (keeping in mind that employment was strongly related to staying out of prison at 12 and 24 months). The full time period structural equation models highlighted how this association manifested itself over the two-year period. Knowing that more free months worked was associated with lower rates of incarceration at 12 months, and using the first 12 months working variable in the structural equations, we know that those who were employed more in the first 12 months were more likely to be in the community in the 12 to 24 month period and thus had greater opportunity to relapse than those who worked less. Being out of prison and being employed is obviously a good thing, but it means that these addicts are free and have money. Thus, some of them may be more likely to relapse. But they do not apparently relapse in the same way as their lesser-employed counterparts, as evidenced by the non-significant finding of employment on frequency of drug use at 12 months. There are two important conclusions from this set of findings. The first is that how we measure drug use, and consequently success and failure, with drug addicted populations matters. If only the conservative “any use” variable were utilized in these analyses, it might be concluded that employment was something of a wash; that it might decrease the likelihood of reincarceration but that at a theoretical level it is contradicted by its positive relation to drug use. The use of the other drug variables allowed the analyses to account for more realistic goals when researching this population. Second, when dealing with addicts, most experts would agree that anything that can keep addicts out of jail and using drugs at a lower rate has to be considered important (Riley and O’Hare, 2000). These analyses showed that, although employment did not stop drug use, it was associated with lower rates of drug use, and was strongly related to lower rates of reincarceration.

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The current project also tested the effect of being married and caring for children. The changing role of marriage and childrearing over the last five decades could potentially change the way bonds work for parents, husbands and wives. It is unclear how this phenomenon would affect the concept of a general theory. If it is proposed that the bonds of “being married,” or “having children,” is a general principle that reduces the likelihood of crime, but over time the actual meaning and manifestation of these bonds changes. Why didn’t these bonds affect success in this sample of offenders? It could be argued that the institutions of marriage and parenthood no longer have the capacity to create the bonds in the way they were originally intended? In reality, the non-significance of marriage and childrearing is not totally unanticipated. These bonds did not preclude crime prior to respondents’ arrests, so it is reasonable to assume they would not now. Also, Sampson and Laub maintain that it is changes in bonds that matter. Although it was hypothesized in Chapter 4 that there were reasons why a period of incarceration could cause a turning point within a relationship, this was not born out by the data. Child rearing and marriage did not change for these offenders (or at least not in a measurable way), and there was thus no corresponding change in behavior. Finally, the analyses tested whether the effects of social bonds differed by race. As mentioned above, there were few differences. There were no differences in child rearing or marriage by race, and the only substantial difference for employment was in regards to finding any employment in the first 12 months after release. Those Blacks who found no work in the first 12 months after release relapsed at a rate of 70% (compared to 46% of whites who had no work and relapsed). Using the population heterogeneity and state dependence approaches, there are two ways this can be interpreted. A state dependent approach would suggest that those Blacks who do not form at least some employment bonds are at an extremely high risk of relapse. A population heterogeneity argument would suggest that there is a group of “hard core” addicts who probably make no attempt to find work after release from prison and relapse relatively quickly, and that membership in this group is correlated with race. In terms of criminal propensity variables, the number of different crime types reported had a differential effect by race in some models.

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Blacks who committed a greater number of crimes were more likely to be reincarcerated, but there was no concurrent finding for Whites. Also, the effect of Age was stronger for whites than for Blacks regarding relapse. Older Whites were less likely to use drugs, while aging did not affect Black offenders’ drug use. These combined effects might indicate that it is harder for Blacks to escape their past than for Whites to do so. The results of this research have obvious policy implications. One theorist known to the author once said that the problem with Sampson and Laub’s theory is that it is no help to the policy maker. For example, if we can’t sentence people to marriage or to be parents, how can the theory affect policy? These analyses suggest that Sampson and Laub do have something to say to policy makers; that employment in the first 12 months after release is vital. It is unclear how one might encourage this. Job programs have been notoriously unsuccessful (Uggen, 2000). However, it may be that the type of employment offenders obtain makes a difference. For example, a job for the social service office may not integrate one into the community, and, in fact, it could further alienate one from the community by fostering resentment. The offenders in the current study were not sent to a program where they worked for some government entity. They went out and found jobs in the private sector. For the policymaker, then, this project would suggest that programs that assist and encourage reentering offenders to find employment in the private sector could mitigate the likelihood of future reincarceration. Policy makers would need to keep in mind, however, the finding that employed, non-incarcerated drug addicts appeared to have a tendency to relapse. The analyses presented here, however, indicates that for employed persons, those relapses may not be as severe or as long lasting as those found in the lesser employed groups. In fact, when one considers the reduction in drug use, it can be said that there is a great improvement over earlier conditions, particularly if one ascribes to a reduction in harm perspective. This research was limited by the small sample size and the inability to clearly delineate the drug treatment program effects. The analysis was also unable to measure the exte4nt to which persions liked their jobs of felt attached to their spouse or children. Future analyses will be conducted when the data collection period ends in late 2004.

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Once the data are complete attempts will be made to model unobserved population heterogeneity and potential change in marriage and child caring status. Replication is the cornerstone of science. As such, the findings here need to be replicated and confirmed by future research. The focus on the reentry period gives researchers an opportunity to investigate how and if offenders are able to reintegrate into the community. To the extent that our policy and theoretical approaches fosters this reintegration, the effect of informal social control may keep them from committing future crime. To the extent that they fail to reintegrate into our communities, we will continue to rely on formal social control once they have committed future crimes.

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INDEX Durkheim, E., v, 11, 12, 15, 18, 23 Education, 19 Elder, G.H., 33, 49 Employment, 8, 25-27, 30-33, 44, 47-50, 54-55, 60, 69-70, 84-89, 93-94, 97, 109-110, 116-118, 121-126, 134-141, 148, 157-163 Families and crime, 8, 13-14, 19, 25-35, 48, 51, 92, 113, 126 Farley, F.H., 73 Farrington, D.P., 5, 24, 74 Giordano, P.C., 2, 41, 56, 58, 75 Glueck, S & Glueck, E., 6, 9, 24, 34, 40, 47, 74, 157 Gottfredson, M., 1-8, 17, 20, 24, 26, 32, 41-45, 55, 72, 74, 134, 157, 159 Grasmick, H.G., 73 Hagan, J., 20 Harrison, L.D., 48, 63, 66, 160 Hirschi, T., v, 1- 8, 15-20, 2326, 32, 4-43, 45, 50, 55, 72, 74, 134, 157, 159 Horney, J., 44, 158 Hu, L., 81 Human agency, 2, 40-41, 5658, 75, 77 Inciardi, J.A., 57, 58, 63, 160 Informal Social Control, 2, 8, 9, 25, 40, 164 Joreskog, K.G., 81 Langan, P.A., 50 Leober, R, 24

Age and crime, 1-9, 16, 20, 2425, 29, 33-36, 39-44, 47, 59, 64, 72-75, 95, 101, 105, 108, 134, 137, 139, 144, 158-159 Agresti, A., 67 Alcohol abuse, 27, 32, 36, 40, 45, 50, 73, 160 Anderson, E., 47, 51 Arneklev, B.J., 73 Bernard, T.J., 13 Blumstein, A., 24 Braithwaite, J., 25 Brook, J.S., 74 Bureau of Justice Statistics, 1, 50 Butzin, C.A., 63 Cognitive transformation, 41, 58 Coleman, J., 7, 28, 61, 80, 146 Criminal careers, 23, 25 Criminal propensity, 2-8, 20, 33, 39, 42-44, 48, 55, 58, 64-65, 71-79, 83, 94, 97, 105, 109-110, 114-115, 126, 132, 134, 136, 139140, 146, 157-162 Cumulative disadvantage, 48, 50, 53 Derogatis, L.R., 74 Desistance, 9, 23-25, 40-42, 45, 50-57, 60 Drugs abuse 32, 48, 50-52, 6061, 65-68, 73, 77, 84, 93, 105, 108-109, 128-129, 138-139, 142-148, 160-163 167

168 Life course, 1-5, 20, 24, 29, 33, 34, 40-45, 49, 53-54, 80, 138 Marriage, 6-8, 35, 39, 44-49, 52, 54, 71, 80, 90, 114-115, 122, 128-129, 134-135, 142-143, 148, 158, 162164 Martin, S. S, 63 Maruna, S., 57, 58 Matza, D., v, 4, 17, 51 Merton, R. K., 26 Moffitt, T.E., 41 Nagin, D.S., 8, 33, 41, 42 Narratives, 41, 56, 57, 75 Newcomb, M.D., 73 Nye. F. I., v, 13, 14, 15, 18, 26 Park, R. E., 26 Paternoster, R., 7, 33, 42, 43, 78 Patterson G.R., 25, 26 Piquero, A. R., 44, 47, 48, 55, 74, 81, 135, 157, 158 Population heterogeneity, 3, 5, 7-8, 27, 33, 42-43, 48, 58, 72-78, 93, 101-105, 109, 112, 114, 123, 126, 127, 132-135, 138, 142-144 148, 158-159, 162, 164 Pratt, T. C., 43, 55 Prison, 1, 34, 41, 45-56, 59-71, 75, 78, 83, 86, 87-88, 9094, 97-98, 101-105, 109110, 114-117, 119-128, 132-148, 158, 160-162 Prisoner Reentry, 1, 9, 48-50, 63, 71, 115, 132, 161, 164

Index Race and crime, 24, 47-48, 78, 84-85, 88, 93, 94, 98, 101102, 109, 111, 112, 115, 116, 118, 121, 134, 140, 142, 146, 158, 162 Reckless, W. C., v, 4, 15, 16, 17, 18, 26 Reiss, A., 4 Rubin, L. B., 48 Sampson, R. J., v, 1-9, 12, 1617, 20, 23-35, 39-61, 68, 70-71, 74-75, 80, 83, 85, 96, 97, 112-134, 146, 157163 Sherman, L. W., 52 Smith, D.A., 81 Sykes, G., v, 17 Terry, C. M., 2, 52, 55-58, 75 Tittle C. R., 7, 21 Toby, J., 4, 15, 19, 53 Trajectories, 6-7, 29, 34, 4041, 49-57, 60-61 Transitions, 6-7, 34, 52 Travis, J., v, 2, 15, 17, 48 Turning points, 2, 6-9, 34, 41, 50, 53, 58-60, 162 Uggen, C., 44, 163 Vold. G. B., 13 Warr, M., 54 West, D.J., 74 Wheaton, B., 81 Wilson, J.Q., 3, 24 Wood, P.B., 73 Wright, B.E., 43 Zuckerman, M., 3, 5, 73