Returning Home: Residential Mobility, Neighborhood Context and Recidivism

Home , Social control theory

RETURNING HOME: RESIDENTIAL MOBILITY, NEIGHBORHOOD CONTEXT AND RECIDIVISM

Dissertation

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

Christopher Michael Huggins, M.A.

Graduate Program in Sociology

The Ohio State University 2009

Dissertation Committee:

Dr. Christopher R. Browning, Advisor

Dr. Ruth D. Peterson

Dr. Paul E. Bellair

Christopher M. Huggins

2009

ABSTRACT

America’s growing prison population has led to an increasing number of released offenders. The high rates of offenders returning to prison after their release, whether through revocation of parole or new criminal behavior, have renewed interest in explanations of recidivism. While researchers have focused on individual level crime theory and characteristics to explain recidivism, one neglected area of study is neighborhood context’s role in recidivism. This is odd because much recent research has endeavored to establish the link between context and crime. The experiences of recently released offenders offer ample evidence that neighborhood context would logically influence recidivism, or the return to criminality, in much the same way as for other criminality, if not more. The few examples of research that attempts to address this gap also fail to incorporate the fact that recently released offenders are likely a highly mobile population, something that could cause error in contextual analysis.

This dissertation attempts to add to the knowledge of contextual effects on recidivism while improving on previously used methods. Using a unique dataset of released offenders in Ohio that contains serial residence and violation information, this project tests a social disorganization model of recidivism, while also analyzing mobility patterns. A third analysis uses a unique property of the dataset: the fact that it includes prisoners sentenced under indeterminate and determinate sentencing guidelines. This

ii offers the chance to assess the deterrent aspects of the switch in sentencing as well as how deterrence interacts within neighborhood context.

The first analysis of residential mobility patterns answers four questions: do recently released offenders move, where do recently released offenders move, who lives where initially, and who moves? The results indicate that offenders do move and move at a higher rate than the general public, with most offenders in the study experiencing at least one move in the first year after release. These movements are mostly lateral, or to neighborhoods that are similar to the neighborhoods offenders reside in immediately after release. Neighborhood of initial residence is related most notably to race and whether an

offender is released into a halfway house. Finally, movement is related to employment,

race, and living in a halfway house.

The second analysis of contextual effects on recidivism, using discrete time

multilevel logistic regression models to predict likelihood of a parole violation and arrest,

reveals support for social disorganization theory. The results show neighborhood

economic disadvantage and residential stability are related to individual offender’s

likelihood of recidivism. Individual characteristics, like demographic characteristics,

prior offending history, employment and a time variant measure of residential mobility

are also related to recidivism.

The third analysis uses a quasi-experimental method to test deterrence between

parolees and PRC offenders. After propensity score matching a sample of both groups,

the results reveal little evidence of a deterrent effect of length of potential punishment on

recidivism beyond a consistent parolee/PRC effect. Interestingly, contextual measures

only mattered for parolees, indicating that deterrence and context may interact.

iii The results from this set of analyses indicate three major findings. First, while

prisoners move, that movement is mostly lateral, meaning that contextual research that

has not taken movement into account is likely unbiased. Second, neighborhood context does matter for recently released offenders, as offenders in economically disadvantaged neighborhoods and less residentially stable neighborhoods are more likely to recidivate.

Third, recently released offenders showed little evidence of being deterred, although there was some evidence that parolees in disorganized neighborhoods were more likely to recidivate than parolees in better neighborhoods, indicating a potential undermining of deterrence in disorganized neighborhoods.

DEDICATION

For my parents

ACKNOWLEDGEMENTS

While a dissertation is officially the work of a sole author, anyone who has actually written a dissertation knows that they would never be completed without the help of many people. For that reason, I must acknowledge the contributions of the following people: my advisor Chris Browning, committee members Ruth Peterson and Paul Bellair, my ODRC supervisor Brian Martin, the Ohio State sociology faculty, my graduate student colleagues, and my family and friends. This document is a tribute to their patience, understanding, expertise, and advice.

VITA

January 22, 1979 ...... Born – Pittsburgh, PA

2001...... B.A. Sociology, Transylvania University

2003...... M.A. Sociology, The Ohio State University

2004 – present...... Graduate Teaching and Research Associate, The Ohio State University

FIELDS OF STUDY

Major Field: Sociology

vii

TABLE OF CONTENTS

Abstract...... ii

Dedication...... v

Acknowledgments...... vi

Vita...... vii

List of Figures...... ix

List of Tables ...... xi

Chapter One: Introduction ...... 1

Chapter Two: Recidivism at the Individual Level...... 10

Chapter Three: Recidivism at the Contextual Level...... 25

Chapter Four: Data and Measures...... 47

Chapter Five: Residential Mobility of Recently Released Offenders...... 68

Chapter Six: Multivariate Analysis of Contextual Effects on Recidivism ...... 89

Chapter Seven: Parole vs. PRC: A Quasi-experimental Analysis of Deterrence ...... 115

Chapter Eight: Conclusion...... 141

References ...... 152

viii

LIST OF TABLES

Table 4.1 The Operationalization of Dependent, Individual and Neighborhood Variables ...... 60

Table 4.2 Descriptive Statistics for Main Analysis Variables ...... 61

Table 5.1 Comparison of Residences by Means of Contextual Variables...... 84

Table 5.2 Comparison of Movement Patterns of Parolees...... 85

Table 5.3 OLS Regression Models of Neighborhood Disadvantage Index by Demographic, Prior Offending and Residence Characteristics ...... 86

Table 5.4 Poisson Regression Models of Number of Residences by Demographic, Prior Offending, and Residence Characteristics ...... 87

Table 6.1 Discrete Time Multilevel Logistic Models of Violation Behavior...... 107

Table 6.2 Discrete Time Multilevel Logistic Models of Arrest Behavior...... 109

Table 6.3 Hazard Rates for Violation ...... 111

Table 6.4 Hazard Rates for Arrest ...... 112

Table 7.1 Means for Pre-matched and Post-matched Cases ...... 132

Table 7.2 Logistic Regression Models of Violation by Demographic and Prior Offending Characteristics ...... 133

Table 7.3 Logistic Regression Models of Arrest by Demographic and Prior Offending Characteristics ...... 134

Table 7.4 Multilevel Discrete Time Logistic Regressions of Violation Behavior For Combined ...... 135

Table 7.5 Multilevel Discrete Time Logistic Regressions of Violation Behavior for Parolees ...... 136

Table 7.6 Multilevel Discrete Time Logistic Regressions of Violation Behavior for PRC offenders ...... 137

Table 7.7 Multilevel Discrete Time Logistic Regressions of Arrest for Combined...... 138

Table 7.8 Multilevel Discrete Time Logistic Regressions of Arrest for Parolees ...... 139

Table 7.9 Multilevel Discrete Time Logistic Regressions of Arrest for PRC Offenders...... 140

LIST OF FIGURES

Figure 4.1 Ohio Census Tracts and First Residences of ODRC Data...... 62

Figure 4.2 Ohio Census Tracts and Second Residences of ODRC Data ...... 63

Figure 4.3 Ohio Census Tracts and Third Residences of ODRC Data ...... 64

Figure 4.4 Ohio Census Tracts and Fourth Residences of ODRC Data...... 65

Figure 4.5 Ohio Census Tracts and Fifth Residences of ODRC Data ...... 66

Figure 4.6 Ohio Census Tracts and Sixth Residences of ODRC Data...... 67

Figure 6.1 Cumulative Probability of Violation by Month Six by Race and Neighborhood Economic Disadvantage ...... 113

Figure 6.2 Cumulative Probability of Arrest by Month Six by Race and Neighborhood Economic Disadvantage ...... 114

CHAPTER ONE

INTRODUCTION

The increase in the prison population that occurred in the 1980s and 1990s has taxed the United States’ criminal justice system, increasing the need for a better understanding of what causes individuals to engage in criminal behavior. That increase, though, also means that thousands of inmates return to the community each year. Equally problematic is the prospect of a return to criminality from this growing population of released offenders. Estimates indicate that of the roughly 2 million offenders incarcerated in the nations jails and prisons, 600,000 are released each year (Visher and

Travis 2003). In Ohio alone, 25,624 prisoners were released in 2002 (LaVigne and

Thomson 2003). These offenders have already engaged in criminality and face the new challenge of returning to a non-prison environment. Many of these offenders will fail this challenge. Recidivism studies paint a bleak picture, estimating between one half and two thirds of offenders will be arrested for a new felony crime or serious misdemeanor within three years of release (Langan and Levin 2002). This only represents a fraction of the true problem, as it captures new criminal behavior. Other released offenders will be returned to prison for a technical violation of the terms of their supervision. The problem of misbehavior while on parole is huge. Austin (2001) estimates that a full 40 percent of new prison admissions in a given year are parole violators, meaning they have been

1 arrested for a new crime or technically returned. That represents approximately 240,000 offenders. Anyone with the desire to reduce the strain felt by the criminal justice system would be well-advised to investigate and understand the conditions and causes of recidivism during the period of time offenders are on parole.

Studies of recidivism have well established the general characteristics of offenders that correlate with recidivism. But, these studies have limitations. This dissertation project seeks to improve the knowledge of how recidivism functions by addressing two limitations of the current research. First, there are only a couple of research projects that take contextual variables into account in explaining recidivism behavior, even though the link between contextual factors and both neighborhood crime rates and individual crime rates has been well established. Logic dictates that if context matters for crime, it also matters for recidivism, which is simply a return to crime after a formal period of punishment. Second, little attention has been paid to the question of where offenders live after their release and what their mobility patterns are. The next section provides a further discussion of the importance of context and released offender movement.

The importance of neighborhood context

Studies of crime and recidivism often make the mistake of focusing solely on individual differences between people to determine what causes one person to engage in criminality and another person to avoid it. These studies forget that individual behavior is often the result of social forces operating at a contextual level. In essence, they assume that individual factors alone influence individual behavior. This is a problematic

2 assumption, however, based on what researchers know about neighborhood characteristics and how they relate to crime. Shaw and McKay, working more than sixty years ago, showed how neighborhood crime rates were related to structural characteristics of the neighborhood itself, rather than the individual characteristics of those who made up the neighborhood’s population. Certain neighborhoods, then, are more likely to experience crime.

Neighborhoods, like the people who reside within them, are different and exert different influences on individual behavior. High poverty areas may attract criminals, creating greater criminal opportunities for the individuals who live in high poverty neighborhoods. Stable neighborhoods may encourage collective behavior that benefits residents. The growing evidence of an influence of where individuals reside on various outcomes, such as health, crime, and educational attainment, demands attention from recidivism researchers.

Neighborhood context is important for two reasons. First, there is substantial empirical evidence, discussed in chapter three, that context matters for crime, so it should also matter for a return to crime. Of the few studies that have attempted to assess the effects of context on recidivism, some have used problematic measures of context, such as county, rather than more localized measures. Other studies have focused exclusively on neighborhood economic structural components, without looking at other pertinent structural variables, such as residential stability and population composition. Research that corrects these problems and shows the specific relationship between neighborhood environment and recidivism would benefit the general contextual literature, the specific

3 recidivism contextual literature, and help establish another potential hurdle, where an offender is released to, that could impede returning prisoners.

The second reason the investigation of neighborhood context’s effect on recidivism is important stems from the policy implications that such knowledge would generate. If neighborhood context does play a role in whether or not a released offender returns to a criminal pathway, criminal justice agencies would benefit from addressing where their released prisoners plan to live. While it is unrealistic to expect dramatic changes given the unique challenges of placing an offender when he or she leaves prison, the general policy of returning offenders to different yet similar areas to those in which they resided at the time of their original offense could be rethought, with an eye towards placing offenders in more economically secure and residentially stable neighborhoods.

The next section describes the importance of better understanding how much and where offenders move after release from prison.

The importance of residential mobility of released offenders

Finding a stable place to stay is a crucial aspect of a released offender’s post- prison life. In fact, offenders can not be released until a plan is established detailing who they will live with and where. Potential residences are screened by parole officers, who look for obvious red flags like the inability to locate an address, weapons in the home, drug and alcohol use in the home, whether convicted offenders live in the home, and whether the residents are willing to comply with the searches and loss of rights that a parolee accepts. Parolees are almost always released to family members or significant others rather than living alone, although a significant number are also released to halfway

4 homes for drug and alcohol treatment. Offenders are thus often in a situation where they

may be living in a temporary situation, either in a halfway house residential setting or

living with family members while they plan a more permanent housing arrangement.

This observed pattern leads one to believe that recently released offenders are likely to

experience a high rate of residential mobility relative to the general population.

But, what does this have to do with recidivism? The answer to this is unclear.

Although the questions seems obvious, researchers have not asked what causes a parolee

to move, what those patterns of mobility are, and whether or not residential mobility is

related to recidivism. There are two reasons why the investigation of recently released

offenders is an important avenue for research. First, as described above, parolees are

uniquely positioned to move. The ramifications of this position need to be understood to

better understand recidivism. Do parolees who move recidivate more? Do parolees

move to certain neighborhoods that could also influence their criminal behavior? Is the general trajectory an upward climb from neighborhood of release to the next neighborhood of residence, a downward slide, or simply a transition from one neighborhood to a structurally similar neighborhood? The answers to these questions can

teach researchers about the theoretical causes of recidivism and should have important

policy implications for prisoner reentry.

Second, the movement patterns of recently released offenders are an important

area for research because of the methodological implications for recidivism research on

neighborhood context. Typically, recidivism research on neighborhood context measures

neighborhood structural characteristics of the neighborhood offenders are released to and

models the influence of individual and neighborhood characteristics on recidivism. The

5 underlying assumption in models of this ilk is that offenders’ first residence is the residence that offenders are living in, even though some recidivism studies observe offenders for up to three years after their release. It is important to know how many parolees are actually staying put in these initial release neighborhoods, how many move, and what the pattern of that movement is in order to avoid over- or underestimating or underestimating the contextual effects on recidivism. Residential mobility of released offenders is a crucial arena for research for both theoretical and practical reasons. The next section briefly describes the theoretical background of this dissertation.

Important theoretical predictors

The central claim of this dissertation is that recidivism is the result of both individual and contextual factors. For that reason, the analyses contained within are motivated by both individual and contextual theories. Of the potential contextual theories of recidivism, such as social capital and collective efficacy theories of contextual effects, social disorganization theory is the most testable by the data analyzed. The Ohio

Department of Rehabilitation and Correction (ODRC) data that is explored in the analytical chapters is a unique dataset comprised by institutional records and sanction, residence, programming, and employment data collected by a team of coders. This has been paired with census tract data for offender residences, resulting in data that can test structural influences of recidivism, such as those favored by social disorganization theory. Of course, individual characteristics must also be controlled and explained.

As most recidivism research has solely used individual characteristics to explain recidivism behavior, there are many more individual theories of recidivism than

6 contextual theories of recidivism. These are discussed in greater detail in the next chapter, but almost any individual level crime theory has been used to similarly explain recidivism. Strain theory, rational choice theory, life course theory, opportunity theory, labeling theory, conflict theory, and social learning approaches to recidivism have revealed strong individual correlates with a return to criminality. Control theory, however, can also explain those correlates, while also generally conforming to the theoretical assumptions of social disorganization theory. For that reason, the support from the empirical literature, and constraints of the dataset, the multivariate analyses of recidivism use a control model to explain the individual characteristics associated with recidivism in conjunction with a social disorganization model to explain the neighborhood structural characteristics associated with recidivism. The next section describes the research questions to be answered by this dissertation.

Research questions

There are three empirical chapters contained in this dissertation. Each of those chapters attempts to answer research questions raised by the gaps in previous literature, the tenuous methodological assumptions of previous research, and the real world ramifications of criminal justice system policies. The first empirical chapter uses the fact that coders who constructed the ODRC data were instructed to collect information on all released offenders’ addresses during the course of their supervision. Most of the small amount of recidivism research that has addressed where offenders live usually only contains information on the residence of release. This raises some questions that are unanswered by the extant literature. First, are offenders more likely than average citizens

7 to move? Second, what individual characteristics of offenders are correlated with more individual residential mobility? Third, does context play a role in where released offenders move? Fourth, is there a general upward trend in offender movement? A general downward trend? What is the pattern of mobility? These questions are answered through an analysis of total number of residences, the census tract characteristics of offender residences, and regression analysis of the individual and contextual factors that may influence how many residences a released offender has.

The second empirical chapter answers the main research question of this dissertation: does neighborhood context influence recidivism behavior above and beyond the individual characteristics associated with recidivism? Neighborhood measures of economic disadvantage, residential stability, and racial composition from the census tract are tested along with pertinent individual characteristics. The analysis again takes advantage of the unique ODRC data by utilizing a multilevel discrete time modeling strategy, which allows for the analysis of time variant variables, including neighborhood characteristics.

The third and final empirical chapter uses another unique feature of the ODRC data, the fact that some released offenders are on traditional discretionary parole while others are on judicial sentence determined post-release control (PRC). The shift from parole to PRC was a result of the switch from indeterminate sentencing to determinate sentencing which eliminated traditional parole. This switch also resulted in a large disparity in available prison time punishments for parolees and PRC. The final empirical chapter tests a deterrence model to see if the reduction in prison time penalties for PRC

8 offenders resulted in an increase in violation and arrest behavior with and without

neighborhood considerations.

The answers to these research questions represent significant contributions to the

extant literature on recidivism and the understanding of how parolees negotiate the post-

prison environment. The first empirical chapter will establish the movement patterns of

parolees, something missing from the extant literature, and will shed light on a potential

problem of contextual analysis of recidivism. The main empirical chapter, through the

use of multiple neighborhood structural measures and a superior modeling strategy that

takes both context and change over time into account, will also add to the relatively small

number of contextual analyses of recidivism. The final empirical chapter will have

important policy implications about how deterrence operates for recently released

offenders and how this operates within the neighborhood context. The next section

describes the contents of the following chapters.

The organization of chapters

This introductory chapter has established the central focus of this dissertation; the

rest of the chapters are arranged to explore this focus. Chapter two discusses the two realms of individual level empirical research: research that uses general crime theory to explain recidivism and research that examines a unique aspect of returning prisoners’ lives that may explain their inability to escape a criminal lifestyle. Chapter three examines the theoretical links between neighborhood context and recidivism. Special attention is given to the few empirical tests of neighborhood structural characteristics’

effects on recidivism. Chapter four, five, and six contain the empirical analyses

9 described above. Chapter eight concludes the dissertation by highlighting important findings, contributions, and directions for future research.

CHAPTER TWO

RECIDIVISM AT THE INDIVIDUAL LEVEL

Recidivism is the return to criminality after formal punishment. Although estimates of recidivism are varied due to inconsistencies in how recidivism is measured, somewhere between one-third and two-thirds of inmates will be rearrested for a felony or serious misdemeanor within three years of their release (Beck and Shipley 1989; Langan and Levin 2002). With so many offenders returning to crime and so many resources spent on imprisoning them, it is important to understand the factors that lead to recidivism. If these factors were better understood, the criminal justice system could operate a system of selective incapacitation or split population, where only those offenders with high propensities to reoffend are dealt with in resource intensive ways, like imprisonment.

Unfortunately, there has been little success in correctly identifying who will never recidivate, so the models of recidivism must be further refined (Cushing-Daniels 2005).

The central claim of this dissertation is that the factors that lead to recidivism operate at both the individual level as well as the neighborhood level. This chapter will explore the individual level factors, while the next chapter will look at the understudied, yet potentially important neighborhood level factors.

The existing literature that investigates recidivism does so in two major ways.

First, much literature looks at recidivism as criminality like any other and seeks to use

11 general crime theories to explain recidivism. For some researchers, there is nothing inherently unique about recidivism that general theories of criminality can not explain, other than the fact that recidivism requires prior formal punishment for criminal behavior.

These studies use recidivist populations for study, but largely use general crime theory as explanation for recidivism. But these approaches tend to overlook or downplay the important recognition that while recidivist crimes may be explained like original crimes, they are in fact a slightly different animal. Recidivists experience unique social forces that shape how they function in the world in a different manner than other non-formally punished individuals. The second main thrust of recidivism literature then tries to understand the unique causes of recidivism. These studies often look at an aspect of offending that may be unique to reoffending rather than general offending, such as specific type of crime committed to earn formal punishment or history of offending.

General crime theories and recidivism

Most studies recognize that released offenders face certain challenges to reintegration into mainstream non-criminal society. Research has established an understanding that there are structural barriers to successful reentry such as economic hardship and the limited options for sufficient employment (Richards and Jones 1997) as well as legal barriers, licensing restrictions, and lost rights of citizenship (Stanley 1976;

Allen and Simonsen 1995) that make recidivism more likely. These barriers can be in a control context, as potential problems that can be overcome with enough pro-social bonds.

12 When parolees are released from prisons, they have several needs, including an

attempt to establish or reestablish relationships, the need to support themselves

financially, and to follow the guidelines set for them by the supervisory agency, which

may include drug and alcohol counseling, sex offender education, or completion of an

adult education program. Borrowing from Hirschi’s original conception of control theory

(1969), this pressure to fulfill certain needs could be defused with adequate bonds. These

pro-social bonds include attachment to conventional others, commitment to conventional society, involvement with conventional society, and belief in conventional society.

Attachment to conventional others typically means having meaningful relationships with others, such as parents, peers, school, neighbors, or coworkers, that will help control deviant or criminal impulses. For example, Sampson and Laub (1990) find disparity between the criminality of married and non-married individuals, regardless of their previous delinquency. In some respects, marriage was a turning point that took former delinquents off of a criminal pathway. Parolees with more stable relationships with others should recidivate less and delay recidivism. Commitment to conventional society means having investment in conventional society which would be lost if an individual

returned to criminality. Jackson Toby (1957), writing before Hirschi, described this

commitment as a “stake in conformity” that prevents individuals from engaging in crime.

Parolees who are successfully employed should feel a bond to conventional society that

unemployed parolees do not feel and should recidivate less and delay recidivism.

Employment also has the added benefit of potentially alleviating some of the financial

needs of released offenders. Involvement with conventional society and belief in

conventional society are harder to measure. Involvement is usually looked at as the

13 amount of time someone has to get up to no good. This is the classic “idle hands are the devil’s workshop” argument that individuals not acting prosocially are more likely to act antisocially. Involvement is somewhat captured in measuring employment and programming, although this is not perfect. Employment can actually open up opportunities to crime (for example employee pilferage) that non-employed parolees do not have. Still, it is expected that the majority of employed parolees will have less recidivism than unemployed parolees. Belief, as an internal control on behavior, is not captured at all by looking at residence, employment, or supervision programming. This is problematic, but belief is found to have less control than other bonds such as commitment and attachment (Agnew 1991). Also, as this study looks at parolees, there is little evidence, especially the high rates of recidivism, that these individuals would have greatly changed their belief in the rightness of conventional rules, as they have already spent time in prison after breaking those rules.

One study that empirically tests a control model of recidivism looks at the social support an offender has when he reenters mainstream society as a measure of control on behavior. Breese et al. (2000) take a rare qualitative approach to understand the dynamics at play when an offender is returned to society. Relying on an incarcerated interviewer’s discussions with his fellow inmates, they build the case of the simultaneous importance for social support, but also social support’s ineffectiveness to completely combat the institutional barriers to a full reintegration. The authors use a control framework to define social support as the involvement in legitimate work and activities, belief in mainstream values, and commitment by and to others (2000: 3). Instrumental

(material) support, normative support, psychological support, existential support, and

14 social (fun and entertainment) support are all important dimensions of support that ex- offenders need. The studied offenders, all having recidivated as they were asked about prior experiences getting out of prison, report having problems adjusting even when social support is present. The authors recommend halfway houses as a way of smoothing the transition from prison to home because of its formal support system.

Another study that endorses a control theory approach to recidivism looks at the impact of substance abuse on adolescent recidivism. The research examined 500 juvenile offenders and tests internal and external controls, combining Hirschi’s classic control model (1969) with Gottfredson and Hirschi’s general crime theory (1990). Both the offenders and the parents of the offenders were surveyed as to the substance use of the adolescent. Indicative of the importance of internal controls on recidivism, substance use was predictive of recidivism even when controlling for prior delinquency, gender, ethnicity, age, follow-up time, or data source. Both self-reports and parental reports of substance use were predictive of recidivism. Denial of use by offenders, but reports of substance abuse by parents and ignorance or denial of use by parents, but reports of use by offenders both increased the likelihood of recidivism when compared to cases where both offenders and parents reported no substance use. The researchers believe this is the result of a denial effect when offenders deny but parents report substance use (trying to avoid punishment), or of parental supervision, a lack of external control, when parents deny but offenders report substance use (inadequate monitoring of child’s activities by parents) (Stoolmiller and Blechman 2005). Another test of the general crime theory tested the influence of low self-control on parole failure. Low self-control did predict

15 parole failure of those released by the California Youth Authority, but did not influence the timing of recidivism.

Stakes in conformity, the commitment social bond, was also found to be an important predictor for domestic violence recidivism. Woolredge and Thistlethwaite

(2002) analyzed court dispositions for three thousand misdemeanor and felony domestic violence in Cincinnati, Ohio. An offender’s stake in conformity, measured as living in the same residence for five or more years and a measure of education, was found to relate to likelihood of re-arrest for domestic violence. More educated offenders and those who had experienced residential stability were less likely to reoffend, although a measure of economic status (essentially measuring employment) did not reveal significant results.

The authors also found that aggregate levels of stakes in conformity, essentially neighborhood measures of residential stability and socioeconomic status, were also related to re-arrest, with individuals living in more stable and wealthier neighborhoods less likely to reoffend. This will be explored more fully in the next chapter discussing contextual effects on recidivism.

The needs parolees have when released from prison may be thought of in another way. These needs place pressure on parolees. This pressure may result in a return to criminality. Borrowing from Merton’s classic strain theory (1938), this pressure results from an imbalance between the goals ex-offenders need to meet and their ability to legitimately meet those needs, which is hampered by the barriers described above. One study of Swedish prisoners found recidivism increased when parolees experienced deficiencies in key areas, such as educational attainment and financial resources (Nilsson

2003). Higher levels of deficiencies across a number of different areas (education,

16 financial, housing, social relations, and health) also increase the likelihood of recidivism.

Using a representative sample of 346 inmates, Nilsson finds that a prison term significantly reduces the legitimate opportunities available to former inmates.

Recidivism results because of this marginalization and social exclusion that prevents former inmates from successfully meeting their goals.

Life-course theory has also been combined with social control theory to explain recidivism. Drawing on the Sampson and Laub (1990) model for criminal desistence that assumes increased levels of bonding during adulthood are turning points in life-course trajectories of offending behavior, 600 graduates of a boot camp were observed for a five-year period to measure differences in recidivism based on social bonds and gender

(Benda 2005). Hazard models show that peer associations and job satisfaction are more important predictors of recidivism and length of time to recidivism for men than for women. Living with a criminal partner, number of children, and number of relationships are more important predictors for women than for men. Evidence seems to indicate that pro-social commitment and involvement is more important for men than for women, while pro-social attachments are more important for women than for men.

Another general crime theory that has been applied to recidivism is rational choice theory. One empirical study of adolescent felony offenders found some evidence that recidivism operates along rational choice principles. Rational choice theory posits that if punishment is swift, certain, and severe, offenders will be deterred from committing crime or delinquency (Gibbs 1975; Tittle 1980; Zimring and Hawkins 1973) because the risks of criminality outweigh the rewards (Piliavin et al. 1986). Almost 1400 serious felony offenders were interviewed on a number of dimensions, including

17 offending, perceived justice, and perceived costs. Regression analysis revealed that perception of sanction risk, experience of punishment, and perceived and experienced reward of crime influence self-reported offending. Risks and punishments decrease offending, while perception or experience of rewards (social or personal) increase offending likelihood (Fagan and Piquero 2007).

Labeling theory can also explain recidivism. Chiricos and colleagues (2007) investigated felons who were given probation. At the judge’s discretion, some offenders can avoid formal labeling by having their formal adjudication of guilt withheld. These offenders do not suffer the same stigma of criminality, do not lose any rights, and are in fact not considered to have committed a felony. Probationers who did not receive a formal label recidivated less in the following two years than those who did receive a formal label. The effect of the label was greater for women, whites, and older first-time offenders.

Reintegrative shaming theory has also been tested by using recidivating populations. Reintegrative shaming theory holds that a community’s reaction to criminality can be either reintegrating or stigmatizing, with reintegration leading to lower amounts of offending behavior and stigmatization leading to higher amounts of offending behavior (Braithwaite 1989). This is an especially interesting theory for recidivism studies, because it is entirely based on how treatment by formal authorities of an offender can lead to recidivism. Reintegrative shaming does not explain initial criminality, only the likelihood of a return to criminality after punishment. An analysis of Australian tax offenders tested how perception of treatment by the Australian Tax Office was related to future reports of tax evasion. The authors found support for a reintegrative shaming

18 model, with offenders reporting less tax evasion two years later if they felt their treatment by formal authorities to be more reintegrating rather than stigmatizing (Murphy and

Harris 2007).

The common thread of this line of literature is that recidivism can be explained using traditional crime theories, such as strain, control, developmental, or rational choice.

This study borrows from social control theory for its model of individual level recidivism for a couple of reasons. First, it is one of the most testable individual level theories to use with the ODRC data. While certainly not designed to test control theory, the available individual predictors best fit control theory. For example, employment can proxy a commitment to conventionality and can be interpreted as a stake in conformity. There are no similar predictors that could help test labeling theory (no comparison non-labeled group), rational choice theory (no information on perceived benefits of a return to crime or perceptual differences in certainty or severity of punishment), or strain theory (no measure of blocked opportunity) to name just three. Second, social control theory links well with social disorganization theory as both are control theories at different levels of analysis. Third, there is compelling empirical support for control theory as an individual level theory of recidivism. The choice of social control theory to provide a model of individual level recidivism is done with knowledge that there are other factors and theories, as described above, that have been shown to explain recidivism, but this is true of much criminological literature. In the absence of one perfect model, this study chooses social control theory because of its testability and fit with social disorganization theory. Including specific correlates of recidivism, as described in the next section, should reduce the possibility of potentially confounding variables.

19 While a few of the studies recognize the unique position offenders returning to life outside of prison occupy, most of the studies ignore this or instead focus on offending populations that recidivate without a period of incarceration. For the purposes of this study, studies that look at recidivism after a period of incarceration are the most important. They reveal that there are factors that are unique to offenders returned to society from prison that are not factors for offenders who may have received formal punishment, but escaped incarceration. Research which focuses on recidivism as a unique and separate phenomenon from general crime is discussed in the following section.

Specific factors relating to recidivism

The second major thread of recidivism literature looks at specific factors that lead to recidivism. This research tends to focus less on theory to explain a return to criminality, instead centering on key characteristics of individuals that influence recidivism. These characteristics include gender, race, and age. Other research takes a more criminal justice policy approach to recidivism, treating recidivism as an outcome to measure the effectiveness of policies or to determine where valuable and limited resources should be spent. Examples are discussed below.

There is strong empirical evidence that the demographic characteristics of offenders matter in the likelihood of an individual’s return to criminality after formal sanctioning. One characteristic of interest is gender. There are reasons to believe that men and women differ in the causes of recidivism. Jones and Sims (1997) find that men were rearrested more often than women across all types of crime, although the gaps were

20 smaller for property crimes compared to violent crime. Interestingly, drug crimes were

related to unemployment issues for women, but not for men, indicating that there may be

some differences in exactly how men and women recidivate. Another study that focused

exclusively on women found that receipt of welfare was correlated with lower levels of

recidivism, but legal responsibility of children increased recidivism (Hanley and Latessa

1997). This was explained by the added financial stress of children on women, something that is clearly not there for male offenders. While many, if not most, male offenders have children, few make child support a priority. Overall, however, men’s and women’s recidivism may be different because men’s and women’s criminality is different, with men more likely to engage in more serious criminality such as violent crime (Steffensmeier 1995).

Another study found reconviction, a very stringent measure of recidivism as it requires not only criminal activity but also arrest and eventual conviction, was strongly correlated with demographic factors like gender and age. The study analyzed reconviction risks for a very large (almost 35,000 inmates) cohort of released offenders in

England and Wales. The authors use Cox proportional hazard models to determine that younger offenders, men, offenders with more prior convictions, and specific offenses, such as burglary, theft and fraud, are associated with higher risks for reconviction.

Interactions revealed that men’s and women’s risk for reconviction became more similar when the number of prior convictions increased, indicating that career criminals recidivate equally regardless of gender (Bowles and Florackis 2007). More interventions into higher-risk for reconviction offenders’ lives are recommended.

21 Research has also been conducted on how different racial groups recidivate. As the United States of America is racially stratified, it is reasonable to expect that even among ex-prisoners there are benefits to being a member of the dominant racial group and costs to being a member of a racial minority group. African Americans are overrepresented in the criminal justice system, accounting for almost half of prisoners and those returning to society (Harrison and Beck 2005). Research has shown that

African-Americans are more likely than Whites in terms of rearrest, reconviction, and readmission for a new sentence (Langan and Levin 2002:7). The research that attempts to account for this difference has found a racial effect beyond such common factors for recidivism such as prior criminal history (Bales et al. 2005; Spohn and Holleran 2002).

Part of this lingering race effect on recidivism may be explained by a racial bias within the system or by the social contextual differences experienced by African-Americans and

Whites. A more in depth explanation of the possible social contextual differences experienced by African-American and White offenders is offered in the next chapter.

The demographic characteristics of juvenile and adult offenders have been well studied, so it is no surprise that meta-analyses have been conducted to try and collate all of the varying results about just what kinds of information is helpful when trying to build the profile of a potential recidivist. Meta-analyses seek to empirically test the importance of variables by analyzing the results of prior published research to see what the common and disputed findings are and the relative strengths of variables. A 1996 meta-analysis of adults was conducted by Gendreau et al. and a 2001 meta-analysis of juvenile recidivism was conducted by Cottle et al. Their results are discussed below.

22 Gendreau et al. (1996) analyze 131 studies of adult recidivism, defined as arrest,

conviction, incarceration, parole violation, or a combination of these outcomes. Their

results indicate that age, gender, and race, when collapsed into one predictor domain, are

strongly associated with recidivism. In addition, criminogenic needs, or specific

obstacles that released inmates face when reintegrating into society that could lead an

individual back to crime, are highly associated with recidivism, although they are

increasingly ignored by criminal justice policymakers. Prior history of criminal behavior is also highly predictive of a future return to criminality. An investigation into the static, or unchanging characteristics of an offender that cannot be changed (e.g. age at first arrest), and dynamic, or variable characteristics of an offender that can change (e.g. employment), risk factors of recidivism reveal that there are merits to both approaches.

The notion of static or dynamic factors being important in recidivism is intriguing because of the theoretical implications that these factors imply. Static factors imply that criminality is a relatively stable characteristic. This appears to fall in line with the self- control measure of Gottfredson and Hirschi (1990) or the typological approach of

Moffitt. If static factors are highly correlated with recidivism, it indicates that current living conditions of an offender may not work to dissuade them from a return to criminality. Instead, rehabilitation of the ultimate cause of the lack of self-control that led to the initial criminality would be necessary. Alternatively, dynamic factors imply that criminality is the result of specific needs or lack of controls. This appears to fall in line with more of a strain or control model of criminality, where current strains or the

lack of controls would lead an offender back into criminality. If dynamic factors are

highly correlated with recidivism, it indicates that current living conditions, such as

23 residence, employment, education, counseling, and other forms of treatment would be

potentially important reasons to refrain from a return to criminality.

Cottle et al. (2001) analyze 23 studies of juvenile recidivism, defined as re-arrest

for any type of offending after initial criminal justice contact. Much like the previous

meta-analysis, they grouped predictors into eight categories, including demographics,

history of offending, family and social factors, educational factors, measures of

intelligence, substance use, clinical factors and risk assessment. Much like for non-

delinquents, offense history was particularly salient in predicting future recidivism.

Other findings indicate that demographic factors, such as race and socioeconomic status,

and family and social factors, such as family problems and use of leisure time, are

important predictors of juvenile recidivism.

These meta-analyses reveal that along with demographic characteristics, there are

other factors that are specifically related to recidivism. One of the most important factors

is prior criminal history. The more serious the criminal record of an individual, the more likely they are to recidivate (Hepburn and Albonetti 1994; Kruttschnitt et al. 2000). A serious criminal record indicates both a commitment to criminality as well as a higher level of punishment, usually longer incarceration, which can affect an offender’s chances of reintegrating into mainstream society. Some research has found a correlation between length of prison stay and an increase in the likelihood of recidivism (Gainey et al. 2000).

Longer jail and prison terms can stigmatize, making the social transition from incarceration to freedom more difficult, and can institutionalize, making the internal transition from incarceration to freedom more difficult.

24 Another factor related to recidivism is type of punishment. Deterrence theorists believe that an adequately certain, severe, and swift punishment can deter future criminality. Inadequate or lax punishment, while not causing recidivism, would be seen as failing to deter criminality. Studies have shown that intermediate sentences, such as work release and house arrest rather than traditional incarceration or probation, are not associated with an increase in recidivism, negating a deterrence model of recidivism

(Ulmer 2001). Time spent on electronic monitoring, an alternative to jail in most cases, delays and reduces recidivism (Gainey et al. 2000). There is a question as to whether type of punishment and severity of offense are conflated. One study found little difference in recidivism based on punishment finding that whether sentenced to community control or prison in Florida, with both groups recidivating at about an 80% rate (Smith and Akers 1993).

In conclusion, this chapter has endeavored to show two distinct lines of inquiry in the recidivism literature. While some use recidivism as a means of testing general crime theories, others find recidivism a topic of research in itself, seeking to test various factors related to recidivism and evaluating criminal justice policies and practices. The mechanisms that lead to recidivism for the purposes of this study are taken from social control theory and include the commitment to, involvement with, attachment to, and belief in conventionality. Both these lines of research operate at the individual level, however. In the next chapter, contextual factors that could contribute to recidivism are discussed, specifically how structural factors at the contextual level can reduce the social control of neighborhoods on individuals.

CHAPTER THREE

RECIDIVSM AT THE CONTEXTUAL LEVEL

Recent research has established the importance of environmental context in a

variety of outcomes including violence (Morenoff et al. 2001), adolescent development

(Elliott et al. 1996) and health (Ross et al. 2000; Aneshensel and Sucoff 1996).

Logically, the likelihood of an individual returning to crime after a stay in prison should

also be related to where that individual lives. There has been a relative lack of attention

to the contextual effects on recidivism, however. In this chapter, I will review the

existing literature that researches neighborhood context and recidivism and discuss social

disorganization theory as an explanation of how context could influence recidivism.

From classic urban theory to collective efficacy

The work on neighborhood effects on a variety of outcomes is based in earlier

sociological work. The roots of contextual effects can be traced back to two sources: the urbanists’ recognition of the importance of city life and Shaw and McKay’s ecological take on disorganization. These are the predecessors of today’s social disorganization, social capital, and collective efficacy conceptions of contextual effects.

Classical urban theory posited that city life had a strong, largely negative effect on urban residents (e.g. Durkheim [1893] 1984, Maine [1862] 1960, Simmel ([1903] 1997,

26 Tonnies [1887] 1940, Wirth 1938). There was a recognition that environment alone, in

this case the urban environment, could influence values, feelings, and behaviors, usually

in a negative way. There was also a recognition that with the increased freedom from

tradition that city life represented, there was also an increase in subcultures, some of

which could be labeled as delinquent, deviant, or criminal. This classical work has been

challenged by more recent work that has disputed the unique effects of urban

environments, favoring an argument that the people who live in cities are responsible for

any perceived environmental effect. Some write that city environments have limited

effects on their populations (e.g. Gans 1962, Lewis 1952, Suttles 1968, Whyte 1955) and

that any influence might exist due to the demographic composition of a given population

and the culture they create, although this continues to fuel some debate as researchers investigate the original questions and continue to find new answers to them.

This more modern research has focused on a number of outcomes including unconventionality, interpersonal social bonds, and tolerance with differing results (Tittle

1989). Alienation has also been used to test the influence of urban environment, as alienation can be thought of as a feeling of powerlessness(Seeman 1959, 1971, 1983).

Research has found that alienation is related to urban living net of compositional factors

(Geiss and Ross 1998). A more recent approach to urbanism has looked at disorder

(Skogan 1986, 1990; Wilson and Kelling 1982), as a potential correlate with crime.

Disorder, whether physical disorder or social disorder, influences individuals to feel as though they have no control over their surroundings or the outcomes of situations they encounter, can cause fear of crime, and is associated with urbanity. In sum, all of these, mostly negative, outcomes of urban living such as alienation, unconventionality or

27 disorder can potentially help explain why urban environments are associated with high crime rates. At the very least they reveal that social context, the environment that one lives in, must be considered as a factor that influences values, feelings and behaviors.

While prior research establishes that urban environments can affect those living there, it does not take into account arguments that cities have influences beyond their immediate and arbitrary boundaries (Hummon 1990, Tittle and Gramsick 2001, Wirth

1938, Zukin 1995). Clearly, urbanism is not just a matter of environmental context, but of social organization, the ways in which humans coexist together. This social organization, or lack thereof, of the populace that inhabits an immediate environment can certainly explain as much or more of the link to criminality as social context.

The second historically important recognition of context’s impact upon criminal behavior comes from the work of Shaw and McKay. Working from the Chicago School model of human ecology that emphasized the importance of place in explaining human behavior, Shaw and McKay (1942) drew on the extensive data they had collected from their study of delinquency within Chicago. In this study, they created many maps that located “hotspots” of delinquent activity in certain specific Chicago neighborhoods.

Their most interesting finding, and the impetus for their theory of social disorganization, was that while these neighborhoods experienced vast population change over the course of their thirty year study, the offending patterns remained largely the same. This led to the realization that arguments about compositional aspects of these populations and their effect on criminality could not ignore that there might also be neighborhood specific aspects that were also contributing to the juvenile crime rates experienced by those neighborhoods. After further analysis, they posited that the juvenile delinquency rates of

28 neighborhoods were explained by poverty, racial and ethnic heterogeniety, and residential instability.

The development of social disorganization theory truly begins when Shaw and

McKay (1942) recognize that these factors can not explain individual level offending, only neighborhood rates of offending. To bridge this theoretical gap, they developed the idea of social disorganization as the linking mechanism between neighborhood conditions and individual behavior. According to Shaw and McKay (1942), areas that are high in poverty, racial and ethnic heterogeneity, and residential instability are less socially organized than areas that are wealthier, more racially and ethnically homogeneous, and more residentially stable. The reason for this is that socially disorganized areas lack the parts that make up viable, smoothly operating communities. These structural factors are theorized to influence a neighborhood’s ability to achieve goals through the marshalling of resources and collectivities, or like-minded groups of people. Unstable and racially heterogeneous populations can disrupt the networks and collectivities needed to achieve neighborhood goals, because constantly changing neighbors may be less likely to socialize and trust each other. Poverty can limit the resources at hand to achieve goals.

These three contextual measures of social disorganization have been empirically tested, with support for all three measures (Bellair 1997, 2000; Sampson and Groves 1989).

Shaw and McKay have been criticized for assuming a cultural component was necessary for delinquency, believing that communities with high levels of delinquency had delinquent/criminal values incorporated into the institutions of the community.

Kornhauser (1978) refutes a subcultural explanation of delinquency, believing that social structure precedes the development of culture. For Kornhauser, social disorganization

29 “produces weak institutional controls, which loosen the constraints on deviating from

conventional values. Social disorganization also results in defective socialization, when conventional values have not been adequately internalized” (Kornhauser 1978; pg.31).

Kornhauser thus posits that disorganization is the most important neighborhood level predictor of criminality. This is essentially a control model of criminality, with cultural considerations rendered moot because of the inability for oppositional culture to exist.

Neighborhoods which do not experience poverty, racial and ethnic heterogeneity, and residential stability are able to control those who live there and their behavior. Social disorganization, with the three structures identified by Shaw and McKay resulting in a lack of both internal and external control, fails to control crime and delinquency in neighborhoods.

The three structural factors identified by Shaw and McKay have been used by social disorganization theorists as the antecedents for explanations of crime. Control theorists have shown a link between structure and a lack of control over residents, especially in terms of commitment to conventionality. For them, socially disorganized neighborhoods lack the institutions and networks between residents that lead to social control in a neighborhood. Social control leads to less crime and delinquency as neighborhoods are better able to achieve collective goals, such as maintaining adequate supervision over juveniles that might curb criminal tendencies. Other more modern conceptions of social disorganization point to concentrated disadvantage as another potential reason for social disorganization.

Concentrated disadvantage moves beyond the three factors of Shaw and McKay’s classic conception of social disorganization to incorporate how some neighborhoods have

30 become increasingly isolated from and abandoned by mainstream conventional society.

This idea is the result of several economic factors that have left cities in far different

conditions than when Shaw and McKay were studying them. While Shaw and McKay

witnessed cities largely based on the concentric zone model of Park and Burgess (1925),

the move to a post-industrial economy had changed how cities operated. The growth of

suburbs, the loss of industry in central cities, and the aging of city centers left some

neighborhoods experiencing extreme levels of poverty, disorder, and segregation. This

phenomenon has led to concentrated disadvantage in these neighborhoods. Those left in

these concentrated, disadvantaged neighborhoods are the “truly disadvantaged” according to William Julius Wilson (1987) because they experience life without the benefit of mainstream institutions. Mainstream institutions are important because without them inhabitants experience less attachment to mainstream employment, a lack of social control, and high levels of crime. This is a slightly modified version of social disorganization, because the isolation these neighborhoods experience heightens the negative outcomes, such as crime, that residents suffer. Concentrated disadvantage and the exacerbated effects of isolation have been tested empirically with support. Krivo and

Peterson (1996) found that violent crime in extremely disadvantaged neighborhoods was geometrically greater than violent crime in even highly disadvantaged neighborhoods.

This pattern held for property crime as well, although the difference was less severe.

Another expansion upon the social disorganization model comes in the form of the systemic model of social disorganization. The systemic model improves upon the

Shaw and McKay model and the concentrated disadvantage model by making explicit the social control mechanisms that an adequately socially organized neighborhood has.

31 These mechanisms are centered on the networks within neighborhoods. While maintaining the fundamental importance of residential stability, poverty, and racial and ethnic heterogeneity, the systemic model adds networks, both primary relational networks and secondary relational networks (Bursik and Grasmick 1993). These networks are often measured by the density of ties, how often neighbors get together, and participation in local organizations (Bellair 1997). More network ties and more active networks are supposed to influence the crime rate by their ability to instill social control and effectively socialize children.

The systemic model, then, relies on the idea that structural components, such as poverty, influence a neighborhood’s networks, which are directly related to a neighborhood’s ability to maintain social control on its residents. This informal social control is called made up of both private control and parochial control in the systemic model. Bursik and Grasmick (1993) also include public, or formal, social control as well.

The systemic model has been empirically tested. Bellair (1997) tested how social interaction among neighbors influenced crime rates. As expected, neighbors who socialized once or more a year lived in neighborhoods with lower levels of burglary, motor vehicle theft, and robbery. Interestingly, more frequent interaction amongst neighbors did not lead to lower crime rates. Instead, relatively infrequent interactions were the best, with Bellair likening them to the weak ties that can help individuals find jobs (Granovetter 1973).

Bellair (2000) again tests the systemic model. Instead of testing interaction, surveillance is used as a proxy of informal social control. If neighbors are willing to informally supervise the activities of others in their neighborhood, this informal social

32 control can benefit all members of a neighborhood. Results indicate that informal

surveillance does indeed curb robbery and stranger assault, while burglary increases

informal surveillance.

Another perspective on informal social control comes from Silver and Miller

(2004). They formulate a broader model of informal social control in a neighborhood.

For them informal social control is the result of structural factors, social ties, legal

cynicism, neighborhood attachment, satisfaction with the police, and composition. They

find that neighborhood attachment, defined as how much someone likes living in their

neighborhood, and satisfaction with the police are antecedents of informal social capital,

mediating the influence of disadvantage and immigrant concentration.

The systemic model can be criticized for focusing too much on network

interactions, without recognizing other important characteristics that foster informal

social control. In that sense, the systemic model is compatible with, but not necessarily

the same as, a model of social capital. Social capital has been defined in many ways, but

a particularly useful definition comes from Robert Putnam, who defines social capital as

“features of social organization, such as networks, norms, and trust, that facilitate coordination and cooperation for mutual benefit” (1993: 36) An example of social

capital’s impact could be seen in child-rearing. The more resources a resident of a

neighborhood can draw upon to effectively socialize, supervise, and control their children the more social capital they possess. The next major advancement upon the social disorganization framework improves upon the systemic model by incorporating measures of trust and shared values in addition to network relationships. Social capital and collective efficacy is that improvement. Collective efficacy combines measures of social

33 cohesion and trust along with the ability to draw upon institutional resources to create social control. This is an improvement because while social capital has been shown to correlate with social control, collective efficacy is often shown to be an important step between the two. Social capital’s effect is diminished when there is no ability to actuate change. In essence, collective efficacy is social capital that is focused on creating social control, thereby reducing crime and delinquency. Empirical research backs this claim that more than networks and social ties amongst neighbors are important in reducing crime.

Collective efficacy research shows that neighborhoods that possess collective efficacy beyond the usual measures of social capital fare better in terms of crime and delinquency. Sampson et al. (1997) find that social cohesion and trust mediate the structural measures of social organization from Shaw and McKay (1942). Neighborhood clusters in Chicago that are higher in collective efficacy are lower in perceived violence, violent victimization, and homicides. Collective efficacy seems to link structure and crime. Morenoff et al. (2001) focus on primary relational networks, such as kin and friendship ties, and find that these networks create the opportunity for informal social control but neighborhoods with collective efficacy experience lower homicide rates.

In addition to explicit empirical tests of measures of collective efficacy, collective efficacy helps explain anomalous findings about residential stability and close social ties.

Both residential stability and close social ties, key parts of classic social disorganization theory and the systemic model, have been found to actually foster criminality. In Mary

Patillo-McCoy’s (1999) study of Groveland, a middle-class African-American community in Chicago, she found that the very fact that residents knew each other for a

34 long time and had established close social ties contributed to criminality because residents were hesitant to call in formal authorities to deal with the crimes of individuals they knew. In other words, the stable dense networks that are usually associated with informal social control were actually responsible for a reduction in informal social control. These neighborhoods instead had criminal networks that were a part of the mainstream networks. Patillo-McCoy makes the case that this difference is based on the racial composition of the neighborhood; that middle-class African-American neighborhoods are different than other middle-class neighborhoods, in that they are spatially closer to lower-class neighborhoods so are more likely to experience the problems of lower-class neighborhoods and are less likely to experience the benefits that accrue to middle and upper-class neighborhoods.

Another test of collective efficacy came from the recognition that social networks can be used by offenders for their illegitimate benefit in ways that would counter the argument for collective efficacy. Browning et al. (2004) test a negotiated coexistence model, whereby offenders use their social capital for crime, negating the normally prosocial benefits of collective efficacy. Testing this model on data from Chicago neighborhoods finds that the importance of both social networks and collective efficacy in influencing crime rates is reduced in neighborhoods where there is a high level of network interaction and reciprocal exchange. Social capital and even collective efficacy are not panaceas against criminality. Instead, they work prosocially in most neighborhoods where criminal networks and offenders are not part of the mainstream.

Social disorganization studies, whether testing concentrated disadvantage, the systemic model, or collective efficacy, are still basically testing a structural model of how

35 context influences individual behavior. Before there is collective efficacy, before there is informal control, before there is social capital in a neighborhood, there are the structural antecedents outlined by Shaw and McKay sixty years ago. Residential stability, racial and ethnic heterogeneity, and poverty are still important measures of a neighborhood’s ability to achieve collective goals and their influence over individual behavior. With so much extant research on context and crime, it should be no surprise that a recent meta- analysis of macro-level predictors of crime found ecological measures such as concentrated disadvantage among the strongest, and social disorganization theory among those theories receiving the most support (Pratt and Cullen 2005). This study now looks at how these factors, when combined with individual factors, influence recidivism of previous imprisoned offenders.

Context and recidivism

To this point, the research described has looked at crime as the outcome measure of social disorganization. But as mentioned, recidivism should logically work in the same way. Neighborhoods that are high in turnover, racial and ethnic heterogeneity and poverty should provide environments that are more conducive to a return to criminality than neighborhoods that avoid these structural problems. Recidivism is expected to be worse in neighborhoods that experience isolation and concentrated disadvantage. The three types of control, private, parochial and public social control should all help prevent recidivism according to the systemic model. Collective efficacy should help reduce recidivism. For example, for the average offender recently released from prison, the neighborhood conditions that they experience could dramatically influence whether or

36 not they return to criminality. If a neighborhood lacks prosocial networks, or worse, has active anti-social, criminal networks that are either connected to or accepted by mainstream networks, the chances to reoffend are much greater than in neighborhoods where criminality is kept at bay through the trust, social cohesion, and expectations for social control of its residents. There could also be unique effects of context and recidivism that do not follow the normal effects of context on crime. Recidivism requires a return to criminality after formal punishment. For the purposes of this study, formal punishment means an imprisonment of at least one year. Parolees, unlike other criminals, will experience their social context after a period of being formally removed from that social context. They may lack embeddedness in the community or neighborhood. This could result in parolees being more influenced by the social context in which they live.

For a parolee trying to reestablish his or her life, they may rely on the resources within a community to establish a conventional life. This could be harder in socially disorganized neighborhoods. Alternatively, a lack of embeddedness in a socially disorganized neighborhood may reduce the size of the contextual effect on recidivism. Parolees who are not part of a non-conventional neighborhood may be able to stay clear of criminality.

At the very least, disorganized neighborhoods are going to present an individual recently released from prison with less resources to draw upon than more organized neighborhoods. This is especially problematic because disorganized neighborhoods have higher crime rates, meaning more residents of those neighborhoods will face formal punishment for their crimes, leading to prison. When individuals are released from prison, they usually return to the same or similar neighborhoods from which they were formally removed. This means that most criminals, upon release from prison, are

37 probably returning to neighborhoods that contribute to or at least fail to prevent

criminality.

The effect of social disorganization on recidivism has gone largely ignored by

criminological research. There are a few empirical tests, however, that show that the

predicted relationship holds true. Perhaps the best example is a study of probationers and

parolees by Kubrin and Stewart. They recognize the logical reasons why recidivism

would be even more susceptible to neighborhood issues. According to Kubrin and

Stewart (2006), with parolee status comes difficulties that non-offenders do not face. As

their legitimate opportunities may be even more constrained than non-offenders, ex-

offenders may be more affected by the opportunities that their neighborhoods provide.

Kubrin and Stewart (2006; pg.172) write, “where ex-offenders live greatly affects their

ability to reintegrate into society. By providing an environment either rich or deficient in

resources, place of residence tangibly affects the quality of day-to-day living and

influences the range of opportunities available” whether through the availability of

institutional resources (Elliot et al. 1996) or personal networks (Rose and Clear

1998:455-456). Clearly, recidivism can result from living in a resource deprived

neighborhood.

Kubrin and Stewart (2006) studied both probationers and prisoners who were

released in Portland, Oregon. They had two main goals for their research: to understand

what individual-level factors influence recidivism and to understand how neighborhood socioeconomic status affected recidivism. Their analytic strategy was to estimate two- level hierarchical logistic models, with random intercept and fixed slopes, as their interest was primarily to evaluate if their was neighborhood influence on recidivism above and

38 beyond individual level factors. Controlling both individual-level factors, such as race, age, gender, prior offending, status in prison, and type of offense, and neighborhood socioeconomic status, they found that offenders released to neighborhoods that experience concentrated disadvantage are more likely to recidivate than offenders released to other neighborhoods. Using an alternate measure that incorporates concentrated advantage found that offenders released to neighborhoods that experience concentrated advantage are less likely to recidivate than offenders released to other neighborhoods. Individual-level factors that increased the likelihood of recidivating included sex, race, age, supervision level, type of offense, and prior arrest record. Men are more likely to recidivate than women, minority offenders are more likely to recidivate than others, and younger offenders are more likely to recidivate than older offenders, consistent with individual-level research on recidivism (Gainey, Payne, and O’Toole

2000; Gendreau, Little, and Goggin 1996; Hepburn and Albonetti 1994; Listwan et al.

2003; Schwaner 1998; Spohn and Holleran 2002; Ulmer 2001). Other individual level- factors that correlate with recidivism include supervision level while on parole or probation, prior offense, the severity of the offense and drug use, again consistent with prior research (Listwan et al. 2003; MacKenzie et al. 1999; Ulmer 2001).

The study by Kubrin and Stewart provides a blueprint for the concerns of this study. The central analysis will deal with this question of how social context affects recidivism. This study will test a social disorganization model of neighborhood impact on individual processes, namely that the more socially disorganized a neighborhood is, the less social control it will maintain over its residents. This lack of control will lead residents of disadvantaged neighborhoods to be more likely to recidivate, beyond the

39 individual level factors that correlate with recidivism. This promises to shed light on the

neighborhood contextual effects that influence individual behavior and important

advance over previous social disorganization research which focuses mainly on

neighborhood crime rates (Kubrin and Weitzer 2003). Control, then, will be tested at both levels of analysis. Individual controls, such as attachment to conventional others and stakes in conformity, will be tested along with contextual controls on behavior.

While Kubrin and Stewart’s study is very similar to the concerns of this project, there are still several research directions that can be taken. First, Kubrin and Stewart included probationers in their study. While probationers can certainly recidivate, the effect of neighborhood returned to after release is going to be a fundamentally different experience for probationers and parolees. Probationers are not going to experience nearly as long a separation from the neighborhood where they originally committed a crime. It seems strange to include them in a study of neighborhood effects on recidivism as the effect of original neighborhood context and returning neighborhood context could be conflated. Second, their contextual effect is limited to advantage or disadvantage. A stronger and richer understanding of neighborhood-level effects on recidivism would include measures of residential stability and heterogeneity to fully test the Shaw and

McKay model of social disorganization. This project incorporates these measures into its analysis. Third, while there is little doubt that their findings are accurate, this project hopes to extend it by focusing on a sample of an entire state’s released prisoners rather than just one city’s. Fourth, this project includes some potentially controlling individual level factors such as rehabilitation programming and employment. Fifth, Kubrin and

Stewart measured recidivism by arrest by police. This is but one measure of recidivism,

40 although arrest is the most frequently used measure of recidivism. This project will have

the ability to use violation behavior while under supervision in addition to arrest, which

because of the nature of supervision, might capture more recidivism more accurately.

Combining the two could potentially provide a better understanding of recidivism.

Along with Kubrin and Stewart (2006), Mears et al. (2008) provides the best test

of social ecology’s impact on recidivism. They analyzed prisoners released in Florida to

understand if county resource deprivation and racial segregation measures explained

reconviction for violent crimes, drug crimes, and property crimes. While racial

segregation did not have an independent effect, it did interact with age and race variables,

albeit differently based on crime. Young nonwhite offenders’ chances of reincarceration

for property crimes went up as the amount of segregation increased, a pattern also

experienced by older whites. Young nonwhite offenders’ chances of reincarceration for

drug crimes actually decreased in more segregated areas. Overall, this study shows the

importance of an ecological approach to recidivism and the sometimes nuanced nature of

the relationship between measures of different levels.

Other empirical tests of recidivism and neighborhood have focused not only on

neighborhood’s impact upon recidivism, but also on how the removal of offenders to

prison and the return of former prisoners to private residence impacts neighborhoods.

Essentially testing a measure of formal or public social control, Rose and Clear (1998)

find neighborhoods that experience high levels of formal social control or, in other words, rely on police arrests and imprisonment to maintain any semblance of organization experience greater social disorganization. Part of this social disorganization can be explained by the traditional sources of disorganization, but Rose and Clear argue

41 that some of that disorganization comes from the removal of residents from neighborhoods because of lengthy prison sentences. Contrary to expectations, public social control can exacerbate social disorganization by breaking down the networks that occur within a neighborhood. While those networks may be positive or negative, in some areas the number of people removed leads to a breakdown of social control. Socially organized areas, on the other hand, which experience infrequent reliance on public control, benefit from an incapacitation policy.

The concept of coercive mobility set out in Rose and Clear (1998) is explicitly tested by Clear et al. (2003), which measures neighborhood incarceration rates and their influence on neighborhood crime rates. Neighborhood incarceration rates represent a previously unexplored part of residential instability. In addition to the exit of offenders from neighborhoods to prisons, there are also ex-offenders being released back to the community after a stay in prison. Using data from Florida, Clear et al. (2003) find that the rate of released offenders returning to a neighborhood is related to the neighborhood’s crime rate the following year. In addition, there is support for the idea that coercive mobility breaks down social control when a neighborhood experiences high levels of admissions to prison. While low rates of admissions had little impact on social control, moderate rates reduced crime, and high rates increased crime.

Race and economic disadvantage

The theoretical importance of neighborhood context on recidivism brings up a larger question of the relative importance of race and neighborhood economic disadvantage. As described above, researchers, have found that predominantly African-

42 American neighborhoods are distinct from White neighborhoods (Sampson and Wilson

1995; Sampson 1987; Patillo-McCoy 1999). Essentially, this difference manifests itself

in less of a protective shield against crime of economic advantage in African-American

neighborhoods than in White neighborhoods. Patillo-McCoy (1999) describes how a

predominantly African-American middle class neighborhood in Chicago experienced

higher crime rates than similarly economically advantaged White neighborhoods. She

points out that African-American middle class neighborhoods tend to be located closer to

less advantaged neighborhoods than White middle class neighborhoods, limiting the

power of the neighborhood itself to maintain the informal social control that social

disorganization theory predicts would accompany a relatively stable, homogeneous, and

economically advantaged area. This has led to researchers questioning how much

context and race are entangled in explaining the race difference in offending and

recidivating.

One attempt to understand this interaction between race and neighborhood,

Kubrin et al. (2007) use hierarchical modeling techniques to explore how the neighborhood socioeconomic status combines with individual-level characteristics in explaining recidivism. They find that neighborhood SES is an important predictor of recidivism, with a one-unit increase in their neighborhood disadvantage index leading to a twelve percent increase in the likelihood of offending (Kubrin et al. 2007:25).

Alternatively, decreasing the percentage of impoverished residents in a community resulted in a sixty-two percent drop in the likelihood of recidivism (Kubrin et al.

2007:26). Unfortunately, their data lacked the diversity of neighborhoods necessary to test a neighborhood race model, but logic dictates that if neighborhoods are different

43 based on racial composition and African-American neighborhoods tend to garner less benefit from economic advantage than white neighborhoods, even when controlling for

SES, then part of the difference in individual recidivism rates between African-American and White offenders should be explained by context. This “place versus race” argument crops up frequently in ecological literature, such as in Bellair and McNulty’s (2005) study of ecology, verbal ability, and violence. They find that verbal ability alone does not account for the black/white differences in violence. Instead, concentrated disadvantage, working directly and indirectly through verbal ability and school achievement, accounts for much of the race violence distinction.

Place and race could interact so that neighborhood economic disadvantage’s influence on recidivism is different for individuals of different races. For example, predominantly African-American neighborhoods’ ability to maintain social control may be less related to economic disadvantage. African-Americans, more likely to live in predominantly African-American neighborhoods, may experience less contextual influence on recidivism than whites, who are more likely to live in neighborhoods where economic disadvantage has a stronger relationship to social control and, thus, recidivism.

In their empirical test of neighborhood resource deprivation on offending behavior,

Mears et al. (2008) hypothesize that resource deprivation is no more influential on released black offenders than other offenders. They write, “for ex-prisoner populations, cumulative disadvantage[at the individual level] or other such factors may account for the greater risk of offending, not differential exposure to criminogenic environments” (Mears et al. 2008:327). While this study finds no evidence of a greater relationship between resource deprivation and black offenders, research has not untangled the relationship

44 between neighborhood economic status and race. Based on how economic disadvantage

could operate differently on social control, neighborhood disadvantage may exhibit less

influence on black offenders’ recidivism than on white offenders’ recidivism. Chapter 6

includes an analysis of this interaction to better understand how race and place combine to influence recidivism and criminality.

Determinate sentencing, deterrence, and context

The final analytic focus of this project seeks to assess the ability of sanctioning measures to curb behavior after a shift from indeterminate sentencing to determinate sentencing. Indeterminate sentencing used to be the standard for most jurisdictions in the

United States (Seiter and Kadela 2003). Critics of the policy believed that the deterrent effect of prison was being undermined because it was rare for offenders to serve the maximum time allowed by an indeterminate sentence. Determinate sentencing emerged, with so-called “truth-in-sentencing” laws being passed in many states. The “truth-in- sentencing” law that changed Ohio policy took effect in July 1996. It essentially eliminated parole, instead replacing parole with Post Release Control (PRC). Although

PRC may seem like a semantic change, there are some important differences. PRC must be included in an offender’s sentence. So, for example, an offender may receive 3 years of imprisonment and 1 year of PRC. Under parole guidelines, the offender may have been sentenced to 5 to 7 years, getting out after 3 years of imprisonment. The big difference is the potential penalty for a released offender under state supervision. For the

PRC offender, the maximum return to prison time a parole officer can give for supervision violation behavior is their PRC term (in the previous example 1 year). For

45 the parole offender, the maximum return to prison time a parole officer can give is the

entire previous sentence (in the previous example 4 years). This has lead to parole officers encountering situations where PRC offenders become disgruntled with the PRC conditions of supervision and actively seek to finish their supervision in prison and then be released without any supervision by a parole officer. This is obviously not available for parole offenders. Parole officers feel they have less ability to control PRC offenders under their supervision. With data on both PRC and parole offenders, this project seeks to evaluate whether the truth in sentencing initiative is deterring or unwittingly encouraging violation behavior by released offenders or whether parole officers are incorrect in their assumption that their power to control offenders has been diminished by the policy change. This is important in a state that spends well over one billion dollars on

the prison budget to house over forty-five thousand inmates (Austin 2001). If the current

“truth-in-sentencing” policy leads to more recidivism, then budgets and number of

prisoners are going to rise.

The difference between PRC and parole is also important in terms of the

contextual factors related to recidivism. If parolees experience a greater deterrent effect

based on the punishments available to parole officers when compared to PRC offenders,

the contextual effects could be attenuated. For example, residential instability, racial and

ethnic heterogeneity, and poverty may not have the influence for this specific subset of

offenders because the fear of a lengthy return to prison may overcome the lack of social

control present in some disorganized neighborhoods. Alternatively, because PRC offenders may experience fewer consequences to further offending, PRC offenders may be less susceptible to the contextual effects that influence recidivism.

46 Others have written about the difference in deterrence that may be experienced by residents of different neighborhoods. Deterrence works best if there are both formal and informal sources of control. Formal sources are the punishments associated with a crime, while informal sources are the social costs associated with crime. Some neighborhoods have the ability to institute informal social control, some do not. Therefore, some neighborhoods may produce a greater deterrent effect than others. “Legal punishments interact with both an offender’s human capital and with the social context of his or her daily interactions, to produce or compromise deterrent effects” (Fagan and Meares

2008:184). Working from a social disorganization framework, neighborhoods that experience more poverty and residential instability should compromise the deterrent potential of formal punishment. This could mean that for both parolees and PRC offenders, neighborhood context is related to deterrence. The neighborhood effects on deterrence are assessed in Chapter 7.

Lack of parole has the potential to lead to negative outcomes. The use of indeterminate sentences and parole helped keep dangerous, high-risk criminals in prison longer, helped ensure an adequate release plan for inmates, helped provide motivation for good behavior by inmates while in prison, and helped establish the needs for supervision or potential treatment and rehabilitation after release (Seiter and Kadela 2003). The shift away from indeterminate sentences towards the use of determinate sentences thus has the potential to lead to more recidivism in the future. A potential test of indeterminate and determinate sentencing in terms of recidivism is important because of the policy implications the results could yield.

CHAPTER FOUR

DATA AND MEASURES

This dissertation project seeks to understand and explain recently released offender mobility and recidivism through quantitative data analysis. Quantitative data analysis requires an in depth description of data sources, measures and analytic strategies.

This chapter contains that detailed information on the data source used in the analyses in this project, the measures developed from those data to analyze recently released offender mobility and recidivism, and the analytical strategy employed to assess the data.

Data source

The primary data source for the analyses contained in this dissertation is a unique dataset created by the Ohio Department of Rehabilitation and Correction (ODRC). The

ODRC created the data to test a new policy dealing with the sanctioning of offenders.

Essentially, the ODRC wished to test whether or not the use of a standardized sanctioning grid to punish parolees when they violated the conditions of their release would result in changes in how parolees were dealt with by ODRC and overall offender recidivism. To accomplish this policy test, data were gathered on a stratified random sample of offenders released prior to and after the policy’s implementation. These two samples included all of the female offenders who met selection criteria and proportionally selected male offenders based on release type, region of release, and sex offender status, with a twenty percent oversampling to allow for missing or unusable cases. The two samples number

1,040 for the pre-sanction grid sample, 1,012 for the post-sanction grid sample, and 2,052

48 in total. These 2,052 released offenders were observed for the first 12 months after their

release, unless the offenders were recommitted, revoked, or given their final release.

While the sanction grid proved to have little effect on overall recidivism, it did reduce

reliance on revocation hearings, local jail detention, and a better coordination between

offender risk level and violation punishment (Martin and Van Dine 2008). While such a

sampling design could create the possibility of bias, it is important to note that the

researchers found no differences between the samples when they conducted discrete time

logistic analyses of recidivism behavior.

For the 2,052 offenders included in the sample, the ODRC combined existing

institutional data with data obtained by a team of coders who read through and coded

pertinent information from parole officer case files. The coders were trained to extract the

most possible information from parole officer files, using field notes, sanction receipts,

hold orders, parole violator at large declarations, and other documents from physical files, as well as electronic database files, to piece together an incident based account of offender behavior. The institutional data included basic demographic information, sentencing information, offense information, and other agency collected data. The coder data included detailed event data on violations, employment, programming, and housing, including start and end dates, event types, and event outcomes.

These ODRC data are appropriate for answering the questions of this dissertation project for a number of reasons. First, they contain many of the standard variables used in recidivism research. Not only does the dataset contain all the standard demographic variables, it also contains information on risk assessments, release type, and prior offending. Second, they are unique in that there are multiple measures of recidivism

49 behavior available. As the data were collected by coders from parole officer files, recidivism behavior can be measured by violation behavior or arrest, allowing for a comparison between the two. Third, they contain date of event information for violations, programming, and housing which allow for discrete time analysis that has not been performed in recidivism studies. This is a crucial feature most recidivism data lack, because, as described in the chapter on recently released offender residential mobility, allowing for time varying constructs can provide a more accurate understanding of the dynamics of post-prison life. Fourth, the presence of housing information allows for the possibility of a multi-level discrete time analysis. The housing information provides for an analysis of how residence and neighborhood conditions change over time and their effect on recidivism because the addresses can be geocoded and matched up with census information at the tract level.

The second data source for the analyses is the 2000 U.S. Census of Population and Housing Summary File 3. The serial residence data, as many as ten different addresses, were geocoded and matched up with census data. The geocoding process entailed matching the addresses with the TIGER/Line Shape files for Ohio streets.

TIGER stands for topographically integrated geographic encoding and referencing system and are used in conjunction with geographic information system software to combine demographic data with place data. Every attempt was made to make sure that the address from the ODRC data was correctly matched with the addresses in the

TIGER/Line Shape files. Low scored matches and addresses that were unmatched were manually checked by locating the address using the Federal Financial Institutions

Examination Council’s (FFIEC) geocoding website

50 (www.ffiec.gov/Geocode/default.aspx) or Google maps (maps.google.com). Once the addresses were properly matched, a TIGER/Line shape file for Ohio census tracts matched the addresses with census tract numbers. Again at this stage, every effort was undertaken to accurately marry address and tract. Addresses that were close to tract lines were again manually checked at the FFIEC website. Figures 4.1 through 4.6 show the distribution of released offenders in Ohio for their first six residences, after six there are far fewer cases. The census tract numbers now attached to each residence for the 2,052 cases were then merged with various constructs from the 2000 census, such as proportion of residents in poverty and proportion of residents on public assistance. Census tract is a common level of analysis for contextual effects. Although there is some debate as to what measure, such as county, tract, or block group, is the best to capture neighborhood, past research has shown that tract and block group produce similar results (Gephart

1997). County is likely too crude a measure, so tract is preferred here.

After the creation of a single merged dataset that contains the agency data, coder data, and census data, the data had to be transformed into a format suitable for discrete time multilevel regression analysis. An analytic strategy of this type requires a person- period dataset, one that contains a line of data for each person for each period of observation. The original ODRC dataset, like most data, is person-oriented, meaning there is exactly one line of data for each person or case. To transform a person-oriented datafile into a person-period datafile, a researcher must have two pieces of information.

The first piece of information is whether or not an individual experienced an event. For this project, the event under analysis is recidivism, which can be operationalized in multiple ways. The second piece of information is duration to that event. For this

51 project, with parolee data up to the first year out from release, the period of duration is defined as each one month period after release until the period in which an offender recidivated. An individual case where the parolee does not recidivate and lasts the entire observation period would have twelve lines of data. A case where the recidivism behavior occurs during the total observation period has lines of data for each month until the month the behavior occurs. Other cases may have less than the full twelve lines of data if for some reason they stopped being observed. This could occur for several reasons, including early release, transfer to another state, or even death. These cases are considered censored, but are important to include in the analysis because they still include analytically viable periods of observation (Barber et al. 2000). This research used a “Prsnperd” utility within a STATA add-on called “Dthaz” (Dinno 2008) which takes the two pieces of information and the person-oriented datafile and outputs a person- period datafile that contains time indicators, which are a set of dummy variables indicating the period of observation for each line of data, the predictors, including time- varying predictors, the event variable, which is a dummy variable indicating whether or not the event occurred, and the censoring variable, which is a dummy variable indicating if the case is censored. The person-period datafile is the basis for the main analysis.

Measures

The following section describes the independent and dependent measures. These measures are theoretically driven and used in the main analysis to test the relationship between neighborhood context and recidivism. Table 4.1 lists the measures. Table 4.2 provides the means, standard deviations, and minimum and maximum values for the

52 measures for the 1653 valid cases that made up the ODRC data before the person-period data transformation.

Dependent Variables

There are two dependent variables available for the main analysis of neighborhood context and recidivism. Recidivism is measured as a return to any type of violation behavior, such as drug use during supervision, missing office visits, or any other non-compliance with the rules of supervision. The second measure of recidivism is a return to criminality, captured by an arrest of some kind. Although there is some debate about what is the best measure of recidivism, researchers have used arrest frequently and with success (Maltz 1984). Other measures such as revocation of parole or recommission to prison for a new offense are more entangled with the criminal justice process than simpler measures of behavioral recidivism, such as evidence of violation behavior or arrest, and can be less reliable (Kubrin and Stewart 2006). These are also the only two options that will support a discrete time multilevel analysis, as these are the only two behaviors likely to be captured within the coder data. Reconviction, revocation, or recommission is likely to occur after the twelve months due to the time needed for an arrest, trial or parole board meeting, and sentencing.

Violation recidivism is measured as a dummy variable, where 0 is no evidence of violation behavior and 1 is evidence of any violation behavior. Arrest recidivism is measured as a dummy variable, where 0 is no arrest and 1 is arrested. These two measures indicate recidivism in a given month.

Independent Variables

53 The independent variables include both neighborhood level and individual level variables. Both are described in this section.

Neighborhood characteristics. To assess the contextual effects on recidivism, several neighborhood characteristics are used to test social disorganization theory. These neighborhood characteristics include measures of economic disadvantage, residential instability, and racial composition. Data on neighborhood characteristics comes from the

2000 U.S. Census of Population and Housing, the most recent census to the time period of the ODRC data.

Economic disadvantage is measured as an index of four variables which indicate a neighborhood’s economic and occupational viability. These four variables are percent of residents in poverty, percent of residents on public assistance, percent of residents unemployed, and the reverse coded proportion of a neighborhood’s median income relative to the state average. Higher values indicate greater economic disadvantage. As this index is a time varying construct, it was first constructed for each month in the person-oriented data file. Scale analysis indicated this index had a Cronbach’s alpha greater than .70 for all months. Residential instability is measured as an index of two variables which indicate a neighborhood’s turnover of population. The two variables are percent of owner occupied homes and percent of residents over the age of five living in the same home as they were five years ago. Higher values represent a more stable neighborhood. Racial composition is measured as the proportion of African Americans in a neighborhood.

Individual characteristics. Included in the analysis are individual level factors that could influence recidivism. Some of these individual level factors include

54 demographic characteristics, past offending, and residential stability while under supervision. These data come from the ODRC agency data and coder data contained in the sanction grid dataset.

Demographic characteristics are largely included as controls. Parolee sex is included as a dummy variable, with female as the reference category. Parolee race is included as a single dummy variable, where black offenders are coded as 1 and white and other race offenders are the reference category. Other race offenders make up only one percent of the total sample. Parolee age is included as a continuous variable as the age of the offender in years when they were released into supervision. Release type refers to the fact that some released offenders are serving determinate sentences and are on post release control (PRC), while others were sentenced before the change to determinate sentences and are on parole. The experience of supervision is largely the same for both

PRC and parole, but release type is included as a control. Release type is included as a dummy variable, with PRC as the reference category.

Past offending is highly related to future criminality. For that reason, past offending is captured in several different ways. The first measure of past offending is a variable that measures the felony level of their imprisonment offense. Felony level is reverse coded, as Felony 1 is more severe than Felony 5. A second measure of past offending is the risk assessment level assigned to the offender by the ODRC. This risk level is determined by a risk score instrument which assigns a risk score based on offending history. The measures of history include prior convictions (adult or juvenile), prior commitments of more than one year (adult or juvenile), recent commitment free period of three years prior to current commitment, whether an individual was considered

55 a violator at large at the time of the current commitment, whether an offender has a prior

parole or probation revocation, and whether an offender was forty years or older at the

commencement of the current commitment. Risk level is included as dummy variables

for high risk, medium risk, and sex offender risk, with low risk as the reference category.

Sex offender risk is included because sex offenders are automatically assessed as high

risk, regardless of their offending behavior. A third measure of past offending is whether

or not an individual was a member of a gang. Gang affiliation increases the likelihood of

recidivism. Gang affiliation is included as a dummy variable with no gang affiliation as

the reference category.

Other individual characteristics include a measure of residential stability.

Residential stability refers to whether or not an offender experienced a move in a given

month. This time variant variable is included as a dummy variable, with no move as the

reference category. Programming is also measured. Programming refers to counseling,

therapy, education, and other treatment programs that seek to aid a released offender after

their potential needs are assessed upon institutional exit. Programming is included as a

time variant dummy variable with no program successfully completed as the reference

category. Employment is also measured. Although ODRC coders attempted to gather

employment information in the same manner as violation, programming, and housing

data, the employment event data was rife with missing information rendering it useless

for analysis. The only useable employment measure is a global measure of any evidence

that an individual was actively employed, full or part time, or self-employed during their time under supervision. Employment is included as a dummy variable with some evidence of employment as the reference category. In models assessing arrest, number of

56 violations is included as a continuous variable of the number of total violations of the

conditions of supervision a parolee committed while under supervision.

Analytic strategy

The analysis is presented in the following manner. First, the next chapter

analyzes the mobility of recently released offenders. This chapter seeks to answer the

first research question about how and why offender mobility could affect research into

recidivism. Second, the following chapter contains the main analysis of the dissertation.

That chapter presents time only models of recidivism, individual level only multilevel models of recidivism, and multiple models of neighborhood predictors of recidivism.

Third, the final analytical chapter contains an analysis of how offenders released to parole differ from offenders released to PRC and what that means for determinate sentencing, neighborhood effects, and whether the two interact.

The first analytical chapter seeks to address the question of whether or not recently released offenders change residences. This is an important question because any research on contextual effects that does not take movement into account risks misestimating neighborhood effects. Offenders could move to better neighborhoods causing underestimation of neighborhood effects. Offenders could move to worse neighborhoods causing overestimation of neighborhood effects. Or, offenders could move laterally to similar neighborhoods. To address this question, the first analytical chapter describes the patterns of parolee mobility in the ODRC data. Linear regressions detail who moves and where.

57 The central concern of this dissertation is the evaluation of contextual effects on recidivism. Multilevel modeling strategies are the best way to evaluate contextual effects. Multilevel models take into account the nested nature of neighborhoods.

Individuals within the same neighborhood may be more similar than randomly selected individuals or individuals who live in different neighborhoods. This violates assumptions of independence and can lead to standard errors that are biased by being smaller than they should be, increasing the chance of making an erroneous claim about the significance of a relationship. Multilevel models also allow for the investigation of individual level phenomena while appropriately including variables at other levels. Another reason multilevel models are appropriate for this analysis is that they allow for the partitioning of variance into different levels, telling the researcher how much variance in recidivism is caused by individual and contextual effects. Multilevel models are also useful in that they provide for the analysis of possible cross-level interactions (Raudenbush and Bryk

2002).

The multilevel modeling strategy employed by the main analysis is a discrete time multilevel logistic regression. The nature of the data, which provides timed event data on violations (including arrest), programming, and housing, allows for an analysis that takes time into account. Discrete time techniques are used because of the presence of time variant and time invariant factors as well as the presence of censored observations. This is the best way to take advantage of change over time, while also accounting for the hierarchical nature of the data. Logistic regression is used because the dependent variables in the main analysis are binary variables, violating the OLS assumptions of normality (Barber et al. 2000). The equations for this modeling strategy are:

58 12 ⎛ hijt ⎞ ηijt = αj0 + ∑αjt + βXij + βXijt = ln⎜ ⎟ t=1 ⎝1− hijt ⎠

αj0 = γ 01Zjt + uj0

αjt = γt0 where Xij represents a time-invariant person-level covariate for case i in neighborhood j,

Xijt represents a person-level covariate for case i in neighborhood j and time period t

(which allows these covariates to vary across time). Zjt represents a time-variant neighborhood-level covariate for neighborhood j. Time varies from month 1 to month

12. This is the best analytical strategy because it accounts for change over time in a way that most multilevel modeling does not. Most multilevel modeling assesses neighborhood effects on some individual level outcome. The inherent assumption is that these neighborhood effects do not change over time. While this is probably true for some outcomes, for recidivism, such as in Kubrin and Stewart (2006), the chance that an offender moves during the year of observation is certainly significant. Kubrin and

Stewart, then, are really assessing the neighborhood effects of the neighborhood characteristics of first residence on recidivism. The multilevel discrete time logistic approach can improve on this by having multiple points in time, in this analysis 12, where time variant characteristics, such as neighborhood characteristics, are allowed to in fact vary. The next chapter begins the assessment of how much of a problem the assumption that offenders do not move is to contextual effects literature.

One concern with using a multilevel discrete time logistic regression analytic strategy is the problem of censoring. Data can be either left-censored or right-censored

(Barber et al. 2000; Rabe-Hesketh and Skrondal 2005). Left censoring occurs when the event of interest occurs before the observation period. This is not a concern for this

59 project, as offenders can not recidivate before they are released from prison and the

observation period begins immediately upon release. Right censoring occurs when the

event does not occur during the observation period or when the individual no longer is at risk for the event. Including cases that are right censored does not produce biased results

(Reardon et al. 2002). Neither left nor right censoring is a concern of this project because of its retrospective nature.

The final analytical chapter addresses a policy issue related to recidivism. As

Ohio changed from indeterminate sentences to determinate sentences in 1996, the ODRC data contains offenders sentenced under parole guidelines and under post release control

(PRC) guidelines. This provides a unique opportunity to address deterrence theory from a quasi-experimental methodology. First, the chapter provides a comparison between the two groups. Second, linear regression shows the differences in number of violations between the two groups. Finally, the policy implications of the switch from parole to

PRC are discussed.

Dependent Variables Violation Did offender violate rules of supervisions? (0=no, 1=yes) Arrest Was released offender arrested? (0=no, 1=yes) Independent Variables Neighborhood Characteristics Economic Disadvantage Time variant scale consisting of percent in poverty, percent on public assistance, percent unemployed, and median income Residential Stability Time variant scale consisting of percent of owner occupied homes and percent of population over 5 in the same home as 5 years ago Racial Composition Time variant measure of percent African- American Individual Characteristics Sex 0=Female, 1=male Race 0=White or other, 1=Black Age Continuous variable of offender age at release Employment 0=Evidence of some employment, 1=No evidence of employment Felony Level Reverse coded categorical variable, where highest felony level=5, lowest felony level=1 Risk level Three dummy variables for high risk, medium risk, and sex offender risk, with low risk as the reference group Gang affiliation 0=no, 1=yes (only in violation models) Parole 0=PRC, 1=Parole Number of violations Continuous variable of the number of violations while under supervision (only in arrest models) Move Time variant measure of whether offender changed residences in a month, 0=no, 1=yes Program Completion Time variant measure of whether offender successfully completed programming in a month, 0=no, 1=yes Table 4.1 The Operationalization of Dependent, Individual and Neighborhood Variables

Std. Variable Mean Dev. Min Max Dependent Variables

Violation 0.56 0.50 .00 1.00 Arrest 0.39 0.49 .00 1.00

Independent Variables Neighborhood Characteristics

Economic Disadvantage* 0.68 0.13 .01 1.16 Residential Stability* 0.54 0.15 .06 .87 Proportion Black* 0.37 0.34 .00 .99

Individual Characteristics

Male 0.81 0.39 .00 1.00 Black 0.50 0.50 .00 1.00 Age 35.18 10.41 18.80 82.34 No employment 0.27 0.45 .00 1.00 Felony Level 3.24 1.33 1.00 5.00 High Risk 0.14 0.35 .00 1.00 Medium Risk 0.33 0.47 .00 1.00 Sex Offender Risk 0.18 0.38 .00 1.00 Gang affiliation 0.14 0.34 .00 1.00 Number of Violations 1.48 1.91 .00 10.00 Parole Status 0.29 0.45 .00 1.00 Moved* 0.04 0.07 .00 1.00 Program Completion* 0.22 0.37 .00 1.00 Notes: N=1653

* Denotes time variant variable, mean and standard deviation by month

Table 4.2 Descriptive Statistics for Main Analysis Variables

Figure 4.1. Ohio Census Tracts and First Residences of ODRC data

Figure 4.2. Ohio Census Tracts and Second Residences for ODRC data

Figure 4.3. Ohio Census Tracts and Third Residences for ODRC data

Figure 4.4. Ohio Census Tracts and Fourth Residences for ODRC data

Figure 4.5. Ohio Census Tracts and Fifth Residences for ODRC data

Figure 4.6. Ohio Census Tracts and Sixth Residence for ODRC data

CHAPTER FIVE

RESIDENTIAL MOBILITY OF RECENTLY RELEASED OFFENDERS

The first substantive issue of this project is to understand the patterns of mobility

of recently released prisoners. This chapter will show the extent to which recently

released offenders on parole change residences, whether these residence changes result in

prisoners living in neighborhoods with more disorganization, and what characteristics of

parolees make them more or less likely to change residences. The implications for

multilevel research on recidivism are also discussed.

Do recently released offenders move?

The first question that needs to be addressed is whether or not parolees are likely

to change residences after their release from prison. This is an important question for a number of reasons. First, if parolees do move at a high rate, research that does not allow for change in neighborhood may misestimate neighborhood effects on a host of outcomes. Second, if parolees move, it highlights one of the major obstacles that parolees must overcome, securing a stable residence. While it is unreasonable to assume that parolees will remain indefinitely with those they are released to, high turnover could indicate that parolees experience difficulty mending the social bonds they may have once had with relatives and friends. Within the literature on recidivism, researchers recognize that housing is often a concern for recently released prisoners, although there is a relative

69 lack of research on the actual impact of residential mobility on recidivism. Third, if parolees move, there are policy implications for how prison agencies determine viable residences for inmates to be released to. Clearly, if parolees are moving because their immediate post-prison living situations are untenable, prison agencies may need to rethink their practices. Parolee populations moving and changing could demonstrably affect an agencies ability to deliver post-release services.

The parolees included in this dataset move, and move more than the average population. According to the Census Bureau, in a one year period from March 1999-

2000, 43 million Americans or 16 percent of the overall population changed residences

(Census Bureau 2000). The percentage of parolees moving, however, was much greater.

Of the 2052 parolees included in the dataset, 80 had no first residence information of any kind. Of the remaining 1972 parolees, 1141 had more than one residence within 1 year of release from prison, or 57.9%. Only 831 of 1972, or 42.1%, stayed in the same residence for the entire first year after release from prison. Obviously, this is far greater than would be predicted based on the national average for the general population. This makes sense, though, considering the multitude of reasons for moving facing recently released prisoners. As mentioned above, most parolees live with a relative or friend upon release, but this can have financial and emotional costs. Parolees may want to alleviate the burden they may cause others. Parolees may also want the freedom that living on their own may represent, especially after a experiencing a period of incarceration. More prosaic concerns may cause a parolee to seek new residence, such as access to employment or special needs programming, such as substance abuse or sex offending counseling. But if finding the initial residence unacceptable was the only reason for

70 moving, it does not explain why so many recently released offenders are serial movers,

changing their residence many times within that first year of release.

The parolees included in this dataset reported 2.03 residences on average within the first year, while those who moved at all reported 2.78 on average. This indicates that most within the sample did not move or moved once, but those that did move were more likely to move again. The vast majority of parolees (1884 of the 1972) moved three times or less. Less than 5% of parolees moved more than three times, although two cases reported 10 residences, the most within the first year of release. Although not the norm, these 5% represent parolees who had serious problems obtaining and maintaining stable places to live. It is accurate to say that most offenders had multiple residences within their first year of release and that for most offenders the initial address that they were released to was not the address that they were living in at the end of that first year.

In terms of living arrangement, the most likely living arrangement upon initial release was living with a parent, with almost one-third of valid responses indicating such a living arrangement. Those who lived with any relative (parent, sibling, other relative, or domestic partner/spouse) made up about two-thirds of valid responses. By far the second largest single initial living arrangement was some kind of halfway house living situation, with about 22% of valid responses.

By the second residence, living arrangements have changed. The most likely living arrangement reported for second residences is living with a spouse or significant other, followed by living alone, living in a halfway house, living with a parent, and other relative situations. This seems to reveal the pattern that was described above, parolees

71 feeling the need to establish their own residence or to find a voluntary domestic relationship rather than one out of familial obligation.

Although one can argue that there is the chance that this sample of released offenders is unique in its mobility patterns, this is unlikely due to the fact that the sample was built to be as representative of Ohio parolees as possible. It is also unlikely that Ohio parolees are any more or less mobile than other parolees, as parolees everywhere face similar circumstances and housing needs when they are released from prison. Instead, it is likely that parolees do not necessarily stay in the residences that they are initially released to from prison. This is problematic for much research into neighborhood effects on recidivism. While some research, such as Reisig et al. (2007), focuses on county level effects, some research, such as Kubrin and Stewart (2006), focuses on census tract level effects on recidivism. The fact that some parolees move after their release is not as troubling for county level research, as most moves are likely within counties, especially since longer moves would probably require paperwork and processing by the supervisory agency. There is a greater concern for research at the census tract level, as a relatively small move in actual distance could be a significant move in terms of census tract. If, as

Kubrin and Stewart (2006) do, researchers only collect data on initial residence, they are trying to build models of neighborhood effects with data that may not reflect the actual residences of their sample. Kubrin and Stewart (2006) address this concern in a footnote, writing “we believe that although ex-offenders may relocate, they are not likely to move frequently…In addition, it is probably the case that when ex-offenders do move, they are likely to move within census tracts than across them.” (pgs. 174-175). They cite a study that found that three to four months after release 88% of parolees were still at their initial

72 residence (LaVigne and Parthasarathy 2005). While this may be true, they analyze recidivism for a period of 12 months, assuming constant neighborhood effects for all 12 months. The sample for this study found a much higher likelihood of movement, with

57.9% changing residences at least once during the first twelve months of release. But, what of their claim that these moves are most likely within tract rather than across tract?

If this is the case, there is no statistical issue, as movers would experience the same neighborhood conditions as assumed by a static residence measure, assuming neighborhood conditions are measured at the census tract level. In this study, there are

962 valid cases that experienced at least one move, and had an address that could be matched with census tract data for both the initial residence and the second residence

(with valid cases being defined as cases that contain tract data on both initial and subsequent residence so that there is a chance of a match). Of these 962 moves, only 143 were within the same tract. This pattern of relatively few moves occurring within tract is seen across all moves, as only 41 of 398 valid second move cases were within tract, only

19 of 146 valid third move cases, and only 6 of 50 valid fourth move cases. This trend refutes the claims of researchers such as Kubrin and Stewart that most moves are within tract. Another reason this is unlikely is that parolees do not return to census tracts equally. As Clear, Rose and Ryder (2001) show in their research on coercive mobility, some neighborhoods are more likely to experience an influx of parolee residents. These neighborhoods tend to be more urban. Urban areas have census tracts that are geographically small. Thus, parolees may be more likely to live in areas where a small geographic move actually represents a change in census tract. Of course, a move across census tracts does not necessarily represent a dramatic change in the types of contextual

73 influences that could affect recidivism. If the census tracts are similar to each other in terms of advantage or disadvantage, the statistical pattern would largely be the same, if technically inaccurate. In the next section, this issue of whether or not parolees move to similar census tracts is explored.

Where do recently released offenders move?

While there are certainly many reasons why someone who has just been released from prison would want to move, the real concern for researchers of contextual effects on recidivism is whether or not movers are choosing to move to significantly different neighborhoods. Again, if the neighborhoods (and census tracts) that the parolees are moving to are not substantially different than the initial neighborhood of residence, then researchers can simply use that initial residence to draw valid conclusions about contextual effects. If the neighborhoods that parolees are moving to are substantially different than their initial neighborhood of residence, however, this is not the case and could pose two very different scenarios depending on the characteristics of the new neighborhood of residence. Most contextual effects research is based upon disorganization theory, which holds that a disorganized or disadvantaged neighborhood is more likely to result in a whole range of negative health, criminal, and social outcomes.

If the initial residence is significantly “better” in terms of key contextual measures (such as poverty rate, unemployment rate, etc) than the neighborhood that the parolee is actually living in, then the estimations of contextual effects on recidivism would be overestimated. In this scenario, a researcher is more likely to find no support or limited support for contextual effects on recidivism, because it would appear that rates of

74 recidivism are not linked to neighborhood context. This would result in a Type II error.

If the initial residence is significantly “worse” in terms of key contextual measures than the neighborhood that the parolee is actually living in, then the estimations of contextual effects on recidivism would be underestimated. In this scenario, a researcher is unaware that the recidivism is probably not linked to social context (at least from a disorganization perspective) and might erroneously reject the null, resulting in a Type I error. Obviously, in both cases, this is problematic for researchers. It is vital, then, to establish whether parolees are moving upwards, downwards, or laterally when they are moving, to know whether researchers are overestimating, underestimating, or accurately estimating contextual effects on recidivism.

To assess the characteristics of the residences that parolees are released to and move to, Table 5.1 compares seven common measures: percent unemployed, percent on public assistance, percent in poverty, median income, percent of homes owner occupied, percent of population over the age of five in same home for five years, and percent

African-American. These measures come from census tract data obtained from the 2000 census. The table shows the average values for each place characteristic for each of the first six residences that could be geocoded to show the pattern of movement for parolees.

The table also shows the difference in average values for each place characteristic between stayers and movers. Parolees who only have one residence for the entire study period are considered stayers, while parolees who have more than one residence are considered movers. The first six rows of the table show the general path that parolees follow as they move from residence to residence. Using the first residence as a baseline, the place characteristic means indicate that, although the differences are not great, the

75 second neighborhood of residence is often more advantaged, economically. All of the

economic indicators show that movers are moving to better neighborhoods, as percent in

poverty, percent on public assistance, and percent unemployed all go down, while median

income goes up. This trend reverses, however, if a parolee has more than one move. A

move to a third residence and beyond indicates a move to a more disadvantaged

neighborhood, as the place characteristics show neighborhoods with more poverty,

unemployment, and individuals on public assistance, and a smaller median income. This

presents an interesting picture for researchers. If movers’ second residence is in an economically stronger neighborhood, it may indicate that the influence of social context on recidivism is being overestimated. But, the second observation that parolees who move often trend downward, indicates that the estimated may be underestimated for that group. The overall small difference between the means for all neighborhoods indicates that this may not be a serious concern for researchers. Means comparison tests strengthen this claim. The economic indicators are almost all insignificantly different for later residences when compared to the first residence, although this pattern is different for the stability and racial composition measures. Those measures experience many more significant differences for later residences compared to first residence.

More insight into the residential patterns of parolees can be garnered from observing the differences in means of place characteristics between movers’ census tracts and stayers’ census tracts. Mover’s initial census tracts of residence are economically disadvantaged when compared to stayers’ census tracts. Means comparisons reveal that movers’ first residence are significantly different than stayers’ residences for all place characteristics, except racial composition of neighborhood. This indicates that parolees

76 who move start off in worse neighborhoods, so by moving they may be trying to improve their position. Although this data does not provide reliable information on why people moved, the fact that parolees are moving to better neighborhoods indicates a desire to remain successful while on parole. Movers’ last census tracts of residence are similar to stayers’ census tracts of residence, except for public assistance and racial composition.

This similarity again indicates that the researchers’ estimations of contextual effects on recidivism may be accurate.

To further understand these movement patterns, Table 1 also shows the means of the six census tract measures without those parolees who were released to halfway houses. This is important to analyze because parolees who are released to halfway houses are almost all going to move, but will likely move for far different reasons than the average parolee. Parolees released to halfway houses may have special needs, such as mental health issues or substance abuse issues, or may have no viable residence plans as they exit prison. In addition, halfway houses are more likely to reside in poorer neighborhoods, so may skew first residence means on place characteristics down. The means of place characteristics of movers’ first residence, movers’ last residence, and all parolees’ first residence without parolees released to halfway houses further show that not accounting for change in residence may not cause statistical error when estimating contextual effects on recidivism. The means for these three residence types are all remarkably similar. The means also indicate that non-halfway house parolees live in better neighborhoods than halfway house parolees. Interestingly, when parolees released to halfway houses are removed, movers’ initial residences tend to be better than stayers’ residences on most of the place characteristics. Comparing movers’ initial residence and

77 last residence while removing parolees released to halfway houses, indicates that movers tend to move laterally, as the means for the place characteristics are very similar.

Another way to assess whether parolees move to better or worse neighborhoods is to analyze whether they move from areas of high poverty to areas of low poverty or from areas of low poverty to areas of high poverty. Table 5.2 shows the movement patterns of parolees by whether they move up to an area with low poverty rates, move down to an area with high poverty rates, or move laterally. This is shown for the first four moves because the number of parolees with more than five valid residences (and thus four moves) is small. High poverty is considered to be a census tract with more than 30% of residents living below the poverty line. Low poverty is considered to be a census tract with less than 10% of residents living below the poverty line. As with Table 5.1, the first move is slightly different than later moves. The first or only move for movers seems to represent a move up for most parolees, with almost twice as many moving upwards, from areas of high poverty (201), as moving downwards, from areas of low poverty (111).

Again, this indicates that the first move or only move may be caused by parolees trying to better their position by moving to better neighborhoods. The pattern for subsequent moves is less clear. There are still more movers moving upwards than downwards at the second move, but the disparity is much less than for move one. For moves three and four, there are very similar numbers of parolees moving in both directions.

To better understand this pattern, Table 5.2 also displays the numbers of movers for the first four moves that moved from one extreme to the other. In other words, Table

2 enumerates parolees who moved from an area of low poverty (less than 10%) to an area of high poverty (more than 30%) and from an area of high poverty to an area of low

78 poverty. The pattern follows the one described above, with the first move having far more positive moves when compared to subsequent moves. In terms of individual characteristics predicting extreme movement patterns, an analysis of the ten percent of parolees who experienced the biggest moves up and down in neighborhood poverty revealed no significant differences.

Table 5.2 provides more evidence for researchers of contextual effects on recidivism that initial residence may be enough information to create accurate models.

For all moves, most parolees move laterally. A lateral move means that the change in context is not great and thus might cause only a small discrepancy when estimating. The upwards and downwards moves after the first move also are less patterned, meaning there seems to be less of an explainable reason for the moves, and they may balance each other out when estimating. In conclusion, it appears that while parolees do move and do not move within census tracts, there is no observable pattern beyond the first move.

Researchers are likely capturing contextual effects on recidivism accurately by knowing only the initial residence after release from prison. The next section examines if there are individual characteristics that lead some released offenders to reside in certain neighborhoods.

Who resides where?

Before an analysis of what characteristics influence recently released offenders to move, it makes logical sense to see where they start. Part of the reason for why someone moves may stem from the neighborhood they are released into after completing their prison sentence. It is also instructive to analyze where offenders end up. Table 5.3

79 provides two models that seek to explain the economic disadvantage of offenders’ first

and last neighborhoods based on demographic characteristics and prior offending history.

The two biggest factors in determining the starting neighborhood positions of recently

released offenders are race and whether or not they were released to a halfway house.

Both characteristics are associated with a significant increase in first neighborhood disadvantage. Blacks are released to economically worse off neighborhoods than whites, while those who are released to a halfway house are also released to economically worse off neighborhoods. These findings match theoretical expectations.

There is much more action in the modeling of last residence neighborhood disadvantage. In addition to the already mentioned patterns of race and halfway house residence, risk assessment is also found to be an important predictor. More specifically,

those who are assessed as high risk, medium risk, and sex offenders are more likely to

move to economically worse off neighborhoods than low risk offenders. Risk

assessments are designed to assess an offender’s potential for recidivism, so it is

unsurprising that those who are more likely to offend move to poorer, potentially more

crime prone areas. The race effect can be explained as the continuation of the general

pattern of minorities living in worse neighborhoods that was displayed in the model for

first residence. And, although being released to a halfway house logically explains the

first residence disadvantage index because of where halfway houses usually are located,

the continued influence of that may represent a population unable to move into better

neighborhoods because of the substance abuse or mental health issues that led them to the

halfway house in the first place. Having analyzed where people are released to, the next

80 section examines if there are individual characteristics that may lead some parolees to move more than others.

Who moves?

The sections above looked at whether or not parolees moved and where they moved by examining census tract measures. This section examines the question of parolee movement at the individual level, using individual measures to predict movement and direction of movement. Based on the literature, the mobility characteristics of parolees are likely related to demographic characteristics, prior offending, and residence characteristics. Table 5.4 provides three models predicting total number of places lived within the first year of release from prison. Model 1 includes just demographic and prior offending characteristics, Model 2 includes those characteristics and first release residence neighborhood disadvantage index, Model 3 substitutes in first release residence neighborhood stability index and proportion of population Black for disadvantage index,

Model 4 substitutes last release residence neighborhood disadvantage index for first release, and Model 5 substitutes last release neighborhood stability index and proportion

Black for disadvantage index. The number of total residences is the dependent variable in these Poisson regression models. Poisson models are appropriate because number of residences is a count variable, violating normality assumptions of OLS regression. All models contain robust standard errors. Significant predictors in Model 1 include race, age, employment during supervision, initial residence being a halfway house or other transitional facility, and risk level. Again, the importance of accounting for whether or not a parolee is released to a transitional facility is highlighted, as those individuals are

81 almost certain to change residence within the year and thus are likely to change census tract. Also, the importance of work as a way of establishing one’s own life is displayed, as lack of employment is a barrier against movement. Younger parolees are also more likely to move than older parolees. Parolees who are deemed high risk or medium risk on the corrections department risk assessment tool (an instrument that predicts likelihood of recidivism based on offending and demographic characteristics) are more likely to move than offenders who are deemed low risk. Black parolees are less likely to move than white parolees, although this pattern does not hold for later models.

In Model 2, when first residence neighborhood disadvantage is included, these demographic and prior offending predictors are similar. Neighborhood disadvantage, though, does not explain why someone moves. Model 3 also exhibits similar patterns, only the inclusion of a neighborhood race measure causes the individual measure to be insignificant. Residential stability is not significant, but a measure of percent Black in a neighborhood is. This lends credence to the segregation and isolation hypothesis of

Wilson (1987), as the more segregated the neighborhood, the less likely someone is to move.

Models 4 and 5 show different patterns, partially because they only analyze movers, or parolees who have a different last residence than first residence. Thus, these models explain moves beyond one. Evidence of this is seen in the first residence halfway house measure. In the previous models this was very predictive of total moves relative to all released offenders. But, when compared to offenders who have moved, it becomes insignificant. Perhaps unsurprisingly, the last residence characteristics largely do not predict total number of moves. This indicates that first residence characteristics are much

82 more important factors in determining whether someone moves or not. Again, this is unsurprising from a causal ordering standpoint. The neighborhood disadvantage of last residence is weakly significant, indicating the downward trajectory that habitual movers may experience. These general models were also tested, in analyses not reported in the table, by logistic regression on whether a parolee moved from or stayed in their initial residence. The patterns were largely similar, although being male was a predictor of staying. Overall, it is hard to answer the question of who moves. It is difficult to put too much explanatory power into these results without knowing whether the moves are made for positive reasons (in pursuit of a job, desire to live on one’s own) or negative reasons

(experiencing strained relationships with people who took the parolee in after release from prison). Unfortunately, as these analyses are built from secondary data, the data do not have the necessary measures from parolees about why they moved.

In sum, this chapter has tried to create a better understanding of residential mobility patterns of parolees by examining a sample of parolees and their mobility patterns for the first year they were on parole. One can conclude that parolees do move, move more frequently than the general populace, and likely move outside of their initial residence census tract. While this could pose problems for researchers examining contextual effects on recidivism, most of this movement is lateral, meaning parolees do move to similar census tracts, especially for all moves beyond the first move. The first move tends to be upward, suggesting that the misestimation error is likely a slight overestimation of contextual effects on recidivism, if there is any error at all. Finally, although some demographic characteristics and prior offending characteristics are significant predictors of total residences, it is hard to understand which parolees move

83 without knowing why they move. There is some evidence that residence characteristics are related to numbers of moves, perhaps indicating people trying to move to better their lives, or not moving because they are trapped in bad neighborhoods.

Unemployed Public Below Median Assistance Poverty Income Average All Ohio Tracts 6.1 4.0 12.9 48313.50

First Residence 9.7 7.8 22.4 36410.17 Second Residence 9.3* 7.4 21.4 37044.67 Third Residence 9.2 7.1* 21.3 36541.45 Fourth Residence 9.6 7.4 22.8 35821.46 Fifth Residence 10.2 7.8 23.8 34651.80 Sixth Residence 8.4 6.4 19.9 34467.10

Stayers 9.1 7.5 20.4 37906.81 Movers 1st residence 10.2** 8.0** 23.9** 35296.19** Movers last residence 9.1 7.2 21.2 37155.09 Movers last no halfway 8.6 6.8** 19.8 38384.86 Movers 1st residence no halfway 8.4** 6.5** 19.1** 38761.72 All first residence no halfway 8.6 6.8** 19.5 38610.23

Owner Same Proportion Occupied Home Black Average All Ohio Tracts 67.0 57.2 15.4

First Residence 53.5 53.4 38.2 Second Residence 53.1 52.2* 33.0* Third Residence 54.6 53.0 32.7* Fourth Residence 50.3* 51.1* 28.5* Fifth Residence 50.6 51.4 34.7 Sixth Residence 52.8 53.1 23.4*

Stayers 57.6 55.6 38.1 Movers 1st residence 50.5** 51.5** 38.2 Movers last residence 53.9** 52.5** 32.2** Movers last no halfway 56.6 53.2** 30.2** Movers 1st residence no halfway 58.7 54.8** 31.1** All first residence no halfway 58.7 55.7 34.3** Notes: Columns report percentages except for Median Income * t<.05, 1st residence comparison ** t<.05, Stayers comparison

Table 5.1. Comparison of Residences by Means of Contextual Variables

Move 1 Move 2 Moving to areas of more poverty 111 11.5% 54 13.6% Moving to areas of less poverty 201 20.9% 72 18.1% Moving to lateral areas 650 67.6% 272 68.3% Total 962 398

Number extreme low to high 28 2.9% 11 2.7% Number extreme high to low 55 5.7% 16 4.0%

Move 3 Move 4 Moving to areas of more poverty 20 13.7% 6 12.0% Moving to areas of less poverty 23 15.8% 9 18.0% Moving to lateral areas 103 70.5% 35 70.0% Total 146 50

Number extreme low to high 9 6.2% 4 8.0% Number extreme high to low 4 2.7% 1 2.0%

Table 5.2. Comparison of Movement Patterns of Parolees

First Residence Last Residence B S.E. B S.E. Demographic Characteristics Male -.003 .007 -.009 .010 Black .083 .006 *** .077 .008 *** Age .000 .000 .000 .000 First Halfway House .121 .007 *** .032 .009 *** Parole .001 .007 -.001 .010

Prior Offending Felony .002 .002 .002 .003 High risk .016 .009 t .044 .013 *** Medium risk .009 .007 .036 .010 *** Sex offender risk .015 .009 t .029 .012 *

Intercept .374 .612 R2 (Adjusted) 0.224 0.121 Notes: N=1806 N=979 ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed

Last Residence analyzes movers only

Table 5.3 OLS Regression Models of Neighborhood Disadvantage Index by Demographic, Prior Offending and Residence Characteristics

Model 1 Model 2 Model 3 B S.E. B S.E. B S.E. Demographic Characteristics Male 0.019 0.035 0.006 0.035 0.007 0.035 Nonwhite -0.063 0.028 * -0.072 0.030 * -0.030 0.034 Age -0.004 0.001 ** -0.005 0.001 ** -0.005 0.001 ** No employment -0.113 0.032 *** -0.109 0.033 ** -0.107 0.033 ** First Halfway House 0.341 0.031 *** 0.355 0.035 *** 0.359 0.039 *** Parole 0.038 0.035 0.021 0.035 0.024 0.035

Prior Offending Felony 0.005 0.012 0.012 0.012 0.013 0.012 High risk 0.169 0.043 *** 0.171 0.044 *** 0.170 0.044 *** Medium risk 0.112 0.034 ** 0.109 0.035 ** 0.106 0.035 ** Sex offender risk 0.015 0.041 -0.025 0.042 -0.022 0.042

Residence --- Disadvantage index, first --- 0.073 0.108 --- Stability index, first ------0.156 0.096 Proportion Black, first ------0.129 0.046 **

Disadvantage index, last ------Stability index, last ------Proportion Black, last ------

Intercept 0.724 0.714 0.850 Deviance 1133.201 1029.272 1023.635 N=1934 N=1806 N=1806 Table 5.4 Poisson Regression Models of Number of Residences by Demographic, Prior Offending, and Residence Characteristics Continued

Table 5.4 Continued

Model 4 Model 5 B S.E. B S.E. Demographic Characteristics Male 0.051 0.032 0.053 0.032 t Nonwhite -0.039 0.026 -0.022 0.029 Age -0.003 0.001 t -0.002 0.001 t No employment -0.053 0.029 t -0.051 0.029 t First Halfway House 0.036 0.030 0.036 0.030 Parole 0.013 0.029 0.014 0.029

Prior Offending Felony 0.002 0.011 0.001 0.011 High risk 0.042 0.037 0.047 0.037 Medium risk 0.038 0.031 0.043 0.031 Sex offender risk -0.028 0.036 -0.026 0.036

Residence Disadvantage index, first ------Stability index, first ------Proportion Black, first ------

Disadvantage index, last 0.178 0.103 t --- Stability index, last --- -0.138 0.080 t Proportion Black, last --- -0.039 0.041

Intercept 0.922 1.114 Deviance 320.546 320.731 N=979 N=979 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed

Model 4 and 5 only contain movers

CHAPTER SIX

MULTIVARIATE ANALYSIS OF CONTEXTUAL EFFECTS ON RECIDIVISM

The previous chapter explored the residential mobility of recently released

offenders and described how these patterns of mobility could affect the analysis of

contextual effects on recidivism. This chapter further explores contextual effects on

recidivism by estimating discrete time multilevel models of behavioral recidivism. The

chapter focuses on answering the following questions: 1) to what extent do individual level characteristics affect recidivism; 2) to what extent do neighborhood level characteristics affect recidivism; and 3) what cross-level interactions exist that modify the impact of these individual characteristics based on neighborhood of residence?

Violation recidivism

Table 6.1 presents the multivariate results from the discrete time multilevel logistic regression models of violation behavior. Model 1 predicts violation behavior while under supervision with only time controls. Model 2 predicts violation behavior with time, individual characteristics and neighborhood clustering controlled. Models 3,

4, and 5 predict violation behavior with time, individual characteristics, neighborhood controls and the three neighborhood characteristics included. Model 6 adds the significant cross-level interaction to the full models. Estimates for all models come from using the xtmelogit command in STATA 10.0 (Rabe-Hesketh and Skrondal 2005).

90 The estimates presented in Model 1 indicate that there is a real time pattern to violation recidivism. The earlier time periods are significantly different than the twelfth month in terms of increased likelihood of violation behavior. This indicates that there is a slightly higher propensity to commit a violation early after release, relative to the reference month (month 12).

Model 2 adds individual characteristics to the time controls from Model 1.

The effects of these individual characteristics on violation behavior are remarkably consistent across all the models. All of the individual level predictors are significant except for the successful completion of a program. Demographically, males are more prone to violation behavior than females, blacks are more prone to violation behavior than other racial groups, and younger offenders are more prone to violation behavior than older offenders. Unsurprisingly, employment, even broadly defined as any evidence of a job during the course of the supervisory period, reduces the likelihood of violation behavior. Of the three measures of severity of past offending, one has a somewhat unexpected relationship with violation behavior. While the high risk, medium risk, and sex offender risk assessment levels are all more likely to violate the terms of supervision than a low risk assessed offender and offenders who have gang affiliation are more likely to engage in violation behavior than non-affiliated offenders, the felony level measure indicates that felon level is negatively related to violation behavior. Because felony level is reverse coded so that Felony 1, the highest felony level or the worst type of crime, is high and Felony 5, the lowest felony level, is low, a negative relationship seems to indicate that the worst of the worst are less likely to violate the terms of supervision than those who commit less serious felonies. Parolees are less likely to engage in violation

91 behavior than those offenders released to PRC. And lastly, the time variant measure of individual residential stability reveals that offenders who move are more likely to commit a violation of the rules of supervision.

Models 3 through 5 add time variant neighborhood characteristics to the individual characteristics. These neighborhood characteristics are modeled separately because of problems of multicollinearity. Model 3 includes neighborhood disadvantage.

Model 4 includes residential stability. Model 5 includes the proportion of black residents. Of these three characteristics, only residential stability has a significant effect on violation behavior. Individuals living in neighborhoods that are more stable are less likely to commit a violation of the rules of supervision.

Model 6 adds a cross-level interaction to Model 3. Specifically, Model 6 is

Model 3 with an interaction term for race and neighborhood disadvantage. The interaction term indicates that the effect of neighborhood disadvantage, significant and positively related to violation behavior in this model, is less for blacks. In other words, neighborhood disadvantage exerts less of an effect on black violation behavior than on other race violation behavior.

Arrest recidivism

Table 6.2 presents the multivariate results from the discrete time multilevel logistic regression models of arrest behavior. Model 1 predicts arrest behavior with only time controls, with no regard to neighborhood. Model 2 predicts arrest behavior with time, individual characteristics and neighborhood controlled. Models 3, 4, and 5 predict arrest behavior with time, individual characteristics, neighborhood controlled and the

92 three neighborhood characteristics included. Models 6 and 7 add the significant cross-

level interactions to the full models. Estimates for all models come from using the

xtmelogit command in STATA 10.0.

Overall, the models of arrest behavior are similar to the models of violation

behavior, although there are some differences. Model 1 shows a possible time pattern to

arrest behavior. The first seven months are significantly different than the twelfth month

and positively so, indicating that if an offender is going to return to offending the

likelihood is greatest immediately after release from prison. Further evidence of the possible time pattern of violation and arrest is presented in Tables 6.3 and 6.4. Table 6.3 shows the hazard rates for all cases for violation and Table 6.4 shows hazard rates for all cases for arrest. The hazard is greatest for violation immediately after release and steadily declines until the rate plateaus for months eight through twelve. For arrests, however, the month with the highest rate is month two. In fact the hazard rate for month two is twice as high as for months one or three. After month two, there is a steady downward trend like for violation, although there are a couple upward bumps (like month eleven). By the end of the observation period, the hazard rate for arrest also drops much lower than the rate for hazard rate for violations (.0148 compared to .0473) indicating that the chance for a violation remains higher than the chance for an arrest. This apparent time pattern may be the result of unobserved heterogeneity between those who offend, and thus are censored in the analysis, and those who do not offend. In this scenario, the actual likelihood of offending could be stable over time, but only appears to be related to time because there are less high rate offenders and more low rate offenders in the later time periods. An analysis of the likelihood of violation and arrest for just the first two

93 months finds similar patterns for individual characteristics when compared to the full

models, indicating no big differences in individual level predictors of recidivism for those who offend early, and would only contribute one or two person-periods to the main analysis, and those who offend late or do not offend at all, and would contribute ten to twelve person-periods to the main analysis.

Model 2 adds the individual characteristics to the time controls contained in

Model 1. As with the analysis of violation behavior, the effects of these individual characteristics on arrest behavior are mostly consistent across all the models. All of the individual level predictors are significant in this model except for the race variable. Once again, males are more prone to arrest behavior than females and younger offenders are more prone to arrest behavior than older offenders. Employment still seems to have a positive effect on behavior, reducing the likelihood of arrest. As with violation behavior, the two measures of severity of past offending behave in the same manner with arrest behavior. While the high risk, medium risk, and sex offender risk assessment levels are all more likely to experience arrest than a low risk assessed offender, the felony level measure still indicates that felon level is negatively related to arrest behavior. The number of violations while under supervision, obviously not included in the violation behavior models, is a strong predictor of arrest behavior. And lastly, the time variant measures of individual residential stability and program completion reveal that offenders who move are more likely to be arrested and those who successfully complete a treatment or other type of program are less likely to be arrested.

Models 3 through 5 add time variant neighborhood characteristics to the individual characteristics. Again, these neighborhood characteristics are modeled

94 separately because of problems of multicollinearity. Model 3 includes neighborhood disadvantage. Model 4 includes residential stability. Model 5 includes the proportion of black residents. Both neighborhood disadvantage and residential stability are significant predictors of arrest behavior. Individuals living in economically disadvantaged neighborhoods are more likely to be arrested, while individuals living in neighborhoods that are more stable are less likely to be arrested.

Models 6 and 7 add cross-level interactions. Specifically, Model 6 is Model 3 with an interaction term for race and neighborhood disadvantage and Model 7 is Model 3 with an interaction term for felony status and neighborhood disadvantage. In Model 6, the interaction term indicates that the effect of neighborhood disadvantage, significant and positively related to arrest behavior in this model, is less for blacks. In other words, neighborhood disadvantage exerts less of an effect on black arrest behavior than on other race arrest behavior. This is similar to what was observed for the violation behavior models. In Model 7, the interaction term indicates that the effect of neighborhood disadvantage is less for those with higher felony status. In other words, neighborhood disadvantage exerts less of an effect on higher felony offender’s arrest behavior than on lower felony offender’s arrest behavior.

Discussion

The first goal of this chapter was to assess the effect of individual characteristics on recidivism behavior. For both violation of supervision rules and arrest, individual characteristics are an important predictor of recidivism. First, demographic characteristics are important. As previous research on recidivism has shown, males and

95 the young tend to recidivate more often (Gendreau et al. 1996). In terms of race, there was a stark contrast in the effect of race on violations and arrest. While race was found to have an effect on violation behavior, race was not found to have an effect on arrest behavior except for the interaction between race and neighborhood economic disadvantage. The exact reason for this difference is unclear. There may be a race bias in terms of how strict surveillance is on parole/PRC that would lead racial minorities to garner more violations relative to whites. This kind of bias would not show up in the arrest measure because arrest, unlike a violation, may come from outside the parole office and parole agency. Race has been shown to be a predictor of behavioral recidivism in the past (Bales et al. 2005; Spohn and Holleran 2002).

Second, employment is clearly a consistent predictor of violation behavior and arrest. The fact that employment has such a strong connection to recidivism is hardly surprising. As discussed in Chapter 2, economic concerns are paramount to the recently released offender. Those who can secure employment may find, from a control theory perspective, a commitment to maintaining a non-criminal life, involvement in a non- criminal life, and may foster relationships with non-criminal others, or attachments that might control criminal tendencies. Alternatively, from a strain perspective, finding work could alleviate some of the “pressures” that offenders often face when returning from prison, whether it be simply economic or status based. Of course, this finding could also be somewhat misleading. While it is tempting to assume that released offenders who find jobs are less likely to recidivate because of the jobs themselves and the benefits of being employed (money, routine, non-criminal relationships), a simpler explanation might be that those who desire to engage in criminality do not even consider legitimate work,

96 while those who have decided to reform do. The fact that someone was or was not

employed would then be symptomatic of a behavioral change rather than an explanatory

cause.

Third, the results show the importance of prior criminal history when assessing

recidivism. As prior literature (Gendreau et al. 1996; Cottle et al. 2001) predicts, an elevated risk level (risk assessments being based on offense history) is associated with a greater likelihood of re-offending. This pattern is also true for gang affiliation in the violation models and the total number of violations while under supervision in the arrest models. Surprisingly, felony level behaves somewhat differently. Tables 6.1 and 6.2 showed a consistent negative relationship between felony level and violation behavior and arrest, meaning higher felons i.e. “the worst of the worst” were less likely to recidivate than lower level felons. This is unexpected because the most severe felons receive the most severe punishments and thus experience prison for the longest time.

Because of the prison environment and the long break in relationships with family and friends, logic would dictate that the highest felons would be more likely to return to crime. We may see the opposite for a couple of reasons. First, the types of crimes that would receive the highest felony charges may be less likely to be repeated. Murder, for example, is a first degree felony but is not necessarily likely to be repeated. Lower degree felonies, such as theft of more than 500 dollars but less than 5000 dollars (a fifth degree felony) and or aggravated trafficking in drugs (a fourth degree felony), are more likely to be repeated. Of course, this assumes specialization in offending behavior, such that a first degree felon only commits first degree felonies. Another possible explanation is that the potential penalty for violating the terms of supervision may be greater. For

97 example, those offenders released to parole on indeterminate sentences may face a far

longer period of incarceration should their parole be revoked relative to those who

committed a less serious felony. This is due to the fact that felony 1 offenders could receive sentences that range from 3 to 10 years of imprisonment while felony 5 offenders could receive sentences of 6 to 12 months. Obviously, for parolees who face the remainder of their original sentence should their parole be revoked, this provides incentive to not recidivate. Regardless of how they function, this project includes these measures as controls on current recidivism.

Fourth, parole status is important for both violation behavior and arrest. Being a

parolee rather than being released to PRC reduces recidivism, although the effect is larger for violation behavior than for arrest. This is best explained by the fact that the potential time to serve in prison should one violate the rules of supervision is so much greater for

parolees than for those on PRC. Parolees are also more likely to have served a longer

prison sentence due to the nature of the data, so are likely to have committed a higher felony. Felony status is also indirectly associated with recidivism as described above.

The full ramifications of parole status on recidivism are discussed in the next chapter.

Fifth, the time variant individual characteristics of experiencing a residential move and the successful completion of non-sanction based programming are both significant predictors of arrest, although only moving is predictive of violation behavior.

Clearly, moving can disrupt recently released offenders. People may move to establish their own residence, lessen the burden on those who agreed to house them on release, or to avoid conflict. Those who move may experience a loss in attachments to non-criminal others or experience too much freedom too soon. This could contribute to a return to

98 criminality. The successful completion of non-sanctioned based programming is more

likely a symptom of a desire to stay on a non-criminal pathway rather than a potential

cause of recidivism. This may be predictive of arrest and not violations because violation

behavior encompasses so many behaviors, some of which are not related to new criminal

behavior.

In terms of individual effects on recidivism, there were few surprises.

Demographic characteristics, prior offending, parole status and the time variant measures

were all significant predictors of recidivism. In terms of theory, there is evidence of support for a control theory of recidivism. The patterns of the individual effects on recidivism show evidence of how attachment, commitment, involvement and belief can control the deviant impulse. Attachment can be seen in the employment and change in residence measures. If a recently released offender is employed, he or she has access to non-criminal networks and recidivism may be controlled. If a recently released offender moves, this could disrupt their networks and recidivism may not be controlled as effectively. Commitment can be seen in employment and parole status. If a recently released offender is employed, he or she has a stake in conformity, something to lose should he or she recidivate. Similarly, parolees may be more controlled than offenders on PRC because of what they would lose should they recidivate. Involvement can be seen in employment and the successful completion of a program. Both of those endeavors display time spent in a non-criminal manner. Finally, belief can be seen in the successful completion of a program and perhaps in employment. Completing programming can be interpreted as displaying a desire to change stemming from a newfound internal belief about conventionality, specifically because the measure does not

99 include programming that is part of a punishment for the offender. This could also apply to employment, as discussed above, if one assumes that actually looking for and finding some kind of non-criminal work displays an internal belief in the conventional ways of earning money. Unfortunately, a true test of a control theory model of recidivism is impossible with the ODRC data. For example, true measures of attachment would include data about peer and family networks and their orientation towards criminality.

But, the assessment of individual effects on recidivism was only the first goal of this chapter. Now, the second goal is discussed.

The second goal of this chapter was to assess the neighborhood effects on recidivism. The first observation of these effects is that the individual level effects changed little after the inclusion of neighborhood level predictors. In other words, the individual level effects seem to act independently on recidivism. This makes sense based upon the literature discussed in Chapter 2. The findings of Gendreau et al. (1996), Cottle et al. (2001), Spohn and Holleran (2002) among others show the importance of individual level predictors on recidivism. What is somewhat surprising, from a contextual effects perspective, is the near complete lack of change. This will be discussed in greater detail below.

The neighborhood effects on recidivism in this study generally confirm prior disorganization theory. For both violation and arrest behavior, neighborhood effects are important predictors of recidivism. Residential stability is inversely related to both violation behavior and arrest while neighborhood economic disadvantage is directly related to arrest. These two measures of social disorganization in a neighborhood generally behave as they should according to social disorganization theory. Classic

100 social disorganization theory holds that poverty, residential instability, and racial and

ethnic heterogeneity are associated with higher rates of criminality in a neighborhood

(Shaw and McKay 1942). While this project is interested in individual recidivism, there

is little reason to believe that social disorganization works differently for first offenses

rather than a return to crime. The fact that released offenders living in neighborhoods

with more economic disadvantage are more likely to be arrested supports social

disorganization theory and the findings of Kubrin and Stewart (2006). The same is true

for the fact that released offenders who reside in neighborhoods with more home owners

and a higher percent of the neighborhood having lived there for at least 5 years are less

likely to be arrested or commit violations while under supervision. This adds to the

Kubin and Stewart (2006) finding because they focused solely on economic disadvantage

and did not look at residential stability in their analysis. One reason that neighborhood disadvantage does not exhibit the same effect for violations as it does for arrest may stem

from the nature of what constitutes violation behavior. Violation behavior is an admittedly broader measure of recidivism, in that some violations are more rules infractions rather than exhibitions of deviant or criminal tendencies. Because neighborhood disadvantage may be more related to real criminality than residential stability, the effects of disadvantage may be seen more fully in arrest models rather than in violation models.

While the results confirm social disorganization theory’s postulates about the effects of a disorganized environment on recidivism, the actual links between neighborhood disadvantage and residential stability and criminality are unfortunately untested in these models. This is because the three ecological causes of disorganization

101 (poverty, racial and ethnic heterogeneity, and residential instability) in the Shaw and

McKay model are not directly related to behavior. Instead, the presence of these disorganizing agents is supposed to reduce the informal social control of a neighborhood i.e. the informal check on behavior that comes not from formal authority but from family, neighbors, and other social institutions. Informal social control has been conceptualized in a couple of ways. The two most well-known formulations of informal social control are social capital and collective efficacy. Social capital is usually defined as the networks and relationships between members of a community and the institutions within that community (Coleman 1990). Collective efficacy is usually defined as the ability of a community to draw upon their social capital to achieve community goals, such as monitoring young people or preventing criminality (Sampson et al. 1999). For proponents of social capital and collective efficacy, neighborhood characteristics contribute to recidivism, but only because those neighborhood characteristics undermine the formation of informal social control. This provides a reason why the individual level factors were so unaffected by the inclusion of the neighborhood level constructs. The individual factors included are not measures of social capital, collective efficacy, and, thus, informal social control and so directly act on recidivism in a way that is not significantly related to neighborhood characteristics. Theory does provide an explanation for why individual factors can directly influence recidivism, as described above. Another reason individual factors are important is because of social selection.

One criticism of the contextual effects literature has been the claim that any neighborhood effects are the byproduct of the reasons people live in certain neighborhoods, rather than the neighborhood characteristics themselves. In essence, this

102 argument claims a selection bias or compositional explanation for why contextual effects

exist. For example, in the Shaw and McKay social disorganization model, poverty is one

of the three reasons why a neighborhood may fail to create informal social control among

its residents. Poverty, then, precedes informal social control which precedes criminality,

or for this project, recidivism. From a selection perspective, however, this link would be

spurious. Instead, the true cause of recidivism would be related to the characteristics of the people that conspired to make them live in that neighborhood. In this model, poverty would precede neighborhood which would largely be independent of recidivism. While how much of the relationship between neighborhood effects and recidivism is due to selection is unknown, the individual level factors do account for potential selection criteria. For example, inclusion of individual mobility helps control for selection bias in terms of residential stability. Unfortunately, the ODRC data, as they come from prison records when socioeconomic status is irrelevant, do not include a measure of income or any other measure of social class. Still, the inclusion of other demographic characteristics do help control for compositional factors that may be related to both the neighborhoods that individual released offenders live in and recidivism. Of course, in the case of released offenders, most return to the areas in which they previously lived, or to nearby areas, although the exact neighborhood of residence may be related to whomever allows them to reside with them after their time in prison and wherever that, often, family member or friend lives.

The significance of residential stability in both violation and arrest models bears discussion. Part of the reason social capital and collective efficacy models are superior to social disorganization models is because they incorporate the links between

103 neighborhood characteristics and crime. They are thus supposed to account for why residential stability has been found to have a complex relationship with crime. While

Shaw and McKay and much subsequent research has found the traditional relationship between stability and recidivism, Patillo-McCoy (1999) found that stability in a neighborhood actually inhibited informal social control and fostered criminal behavior.

While this project finds in accordance with classic social disorganization theory, the relationship between residential stability and recidivism needs further exploration, specifically with more proximate predictors of informal social control.

In addition to residential stability, this project advanced the tests of social disorganization theory on recidivism by including a measure of racial makeup of neighborhood. The measure was not a significant predictor of recidivism. This could be a result of a poor measure of heterogeneity or a lack of effect. Other measures were tried with little effect, including a squared term which would make sense because if the proportion of African-Americans in a neighborhood increased to a point then the neighborhood would be getting more homogeneous again, but this was also insignificant.

If there is simply no discernible relationship between neighborhood racial makeup and recidivism, this exhibits a stark contrast to social disorganization theory. Moreover, one would expect from the work of William Julius Wilson (1987; 1996) that neighborhoods experiencing high segregation and high poverty would be the most likely to contribute to recidivism, as they represent neighborhoods that are impaired in their ability to provide access to mainstream employment, instill social control, and prevent criminality among residents. In models not presented, concentrated disadvantage, measured by including both a measure of neighborhood racial segregation and economic disadvantage, was not

104 related to recidivism. While this project may have broached the topic of race, neighborhood, and recidivism, more analysis is needed.

The effect of time on neighborhood effects on recidivism is also somewhat unclear. While the discrete time multilevel logit models account for time, neighborhood effects over time may not be constant. An analysis of just the first two months of person- months, instead of all twelve, revealed that none of the neighborhood effects were significant predictors of violation and arrest. This could indicate that neighborhood effects are more important the longer someone is out of prison, or that neighborhood effects are more important in determining the recidivism rates of those not predisposed to an early return to criminality.

Finally, the interaction terms presented in Model 6 of Table 6.1 and Model 6 of

Table 6.2 merit discussion. The first conclusion is that neighborhood disadvantage does not influence behavior equally for all races in terms of violation behavior as well as arrest behavior. While there is no consistent main race effect on recidivism, this interaction term indicates that the influence of neighborhood conditions is more important for whites than for blacks. In other words, the likelihood of whites recidivating is increased more in a disadvantaged neighborhood than the likelihood of blacks recidivating. This can be clearly seen in Figures 6.1 and 6.2. Figure 6.1 shows the cumulative predicted likelihood of violation behavior for the first six months out as neighborhood disadvantage increases for whites and blacks. Figure 6.2 shows the cumulative predicted likelihood of arrest behavior for the first six months out as neighborhood disadvantage increases for whites and blacks. Of course, an interaction could exist because there is greater variability in neighborhoods based on race. In this scenario, a greater effect of neighborhood

105 disadvantage on recidivism could be observed for whites and others because there is greater variability in the types of neighborhoods whites reside in relative to blacks. In other words, neighborhood disadvantage could matter equally for blacks and for whites, but the data would conceal this fact. An analysis of variance of neighborhood economic disadvantage for blacks and whites revealed no significant difference. While the mean for white and other races neighborhoods was lower on the economic disadvantage scale than black neighborhoods, there was similar variance. This goes against research that has shown that blacks and whites live in observably different neighborhoods. But, this research focuses on a subset of the population that may not experience such a wide disparity: parolees.

Why do these interactions exist? The difference in recidivism by race may be a consequence of the differential nature of white and black neighborhoods. As described by Patillo-McCoy (1999), black neighborhoods, even middle-class black neighborhoods, may not offer the same protection from crime that white neighborhoods do. So, neighborhood conditions may have less effect on released African-American offenders than on whites, because the difference in social control in poor white neighborhoods compared to middle class white neighborhoods may be much greater than for poor black neighborhoods compared to middle class black neighborhoods. This counters the prediction of Mears et al. (2008) that resource deprivation would be more influential on released black offenders.

This chapter has shown that recidivism is influenced by both individual and neighborhood factors. Important predictors of recidivism are demographic characteristics, prior offending, experiencing a move, neighborhood disadvantage, and

106 residential stability. The results generally confirm prior research, while extending the extant research on social disorganization and recidivism in both methodological, through the use of multilevel discrete time logistic regression, and theoretical, by including measures of residential stability and racial composition, ways. The significant interaction terms also show the complex relationship between neighborhood economic disadvantage and individual race. The results also show the importance of parole status in predicting recidivism, a finding the next chapter explores in greater detail.

107

Model Model Model 1 2 3 Factors b s.e. b s.e. b s.e. Intercept -2.538 *** 0.043 -2.725 *** 0.049 -2.722 *** 0.048 Time Controls Month 1 1.025 ** 0.214 0.599 ** 0.224 0.599 ** 0.224 Month 2 0.744 ** 0.218 0.415 † 0.226 0.414 † 0.226 Month 3 0.598 * 0.221 0.336 0.229 0.334 0.228 Month 4 0.528 0.224 0.316 0.231 0.313 0.231 Month 5 0.236 0.236 0.036 0.243 0.035 0.243 Month 6 0.363 0.235 0.178 0.243 0.177 0.243 Month 7 0.129 0.248 -0.017 0.256 -0.018 0.256 Month 8 -0.031 0.260 -0.111 0.266 -0.113 0.266 Month 9 0.135 0.256 0.036 0.263 0.034 0.263 Month 10 -0.334 0.289 -0.414 0.297 -0.415 0.297 Month 11 -0.036 0.273 -0.036 0.278 -0.038 0.278 Individual Characteristics MaleA --- 0.544 *** 0.115 0.550 *** 0.115 BlackB --- 0.278 ** 0.081 0.248 ** 0.085 Age --- -0.022 *** 0.004 -0.022 *** 0.004 No employmentC --- 0.762 *** 0.085 0.757 *** 0.085 Felony level --- -0.125 *** 0.032 -0.125 *** 0.032 High riskD --- 0.979 *** 0.124 0.970 *** 0.124 Medium riskD --- 0.578 *** 0.098 0.574 *** 0.097 Sex offender riskD --- 0.470 *** 0.124 0.464 *** 0.124 Gang affiliationE --- 0.230 † 0.109 0.222 * 0.109 ParoleF --- -0.305 ** 0.095 -0.303 ** 0.095 MoveG --- 0.994 *** 0.128 0.996 *** 0.128 Program CompletionH --- -0.084 0.095 -0.084 0.095 Neighborhood Measures Economic Disadvantage ------0.368 0.319 Residential Stability ------Proportion Black ------

Disadvantage*Black ------

Random Effects Intercept Variance Component 0.248 .206 0.198 Slope Variance Component ------

Table 6.1. Discrete Time Multilevel Logistic Models of Violation Behavior (11265 cases in 926 tracts)

Continued

108 Table 6.1 Continued Model 4 Model 5 Model 6 Factors b s.e. b s.e. b s.e. Intercept -2.720 *** 0.048 -2.726 *** 0.049 -2.695 *** 0.050 Time Controls Month 1 0.605 ** 0.223 0.598 ** 0.224 0.595 ** 0.224 Month 2 0.417 † 0.226 0.414 † 0.226 0.412 † 0.226 Month 3 0.335 0.228 0.335 0.229 0.332 0.229 Month 4 0.312 0.231 0.316 0.231 0.312 0.231 Month 5 0.038 0.243 0.036 0.243 0.033 0.243 Month 6 0.182 0.243 0.177 0.243 0.179 0.243 Month 7 -0.014 0.256 -0.017 0.256 -0.018 0.256 Month 8 -0.112 0.266 -0.110 0.266 -0.115 0.266 Month 9 0.034 0.263 0.035 0.263 0.031 0.263 Month 10 -0.413 0.297 -0.414 0.297 -0.417 0.297 Month 11 -0.036 0.278 -0.036 0.278 -0.036 0.278 Individual Characteristics MaleA 0.572 *** 0.115 0.543 *** 0.115 0.547 *** 0.115 BlackB 0.216 ** 0.083 0.299 ** 0.098 0.259 ** 0.084 Age -0.022 *** 0.004 -0.022 *** 0.004 -0.022 *** 0.004 No employmentC 0.755 *** 0.085 0.764 *** 0.085 0.751 *** 0.085 Felony level -0.125 *** 0.032 -0.125 *** 0.032 -0.127 *** 0.032 High riskD 0.955 *** 0.124 0.979 *** 0.125 0.968 *** 0.124 Medium riskD 0.566 *** 0.097 0.579 *** 0.098 0.572 *** 0.098 Sex offender riskD 0.457 *** 0.123 0.470 *** 0.124 0.454 *** 0.124 Gang affiliationE 0.212 * 0.108 0.229 * 0.109 0.223 * 0.109 ParoleF -0.294 ** 0.095 -0.305 ** 0.096 -0.310 ** 0.095 MoveG 0.987 *** 0.127 0.992 *** 0.128 0.995 *** 0.128 Program CompletionH -0.086 0.095 -0.083 0.095 -0.084 0.095 Neighborhood Measures Economic Disadvantage ------0.451 0.320 Residential Stability -0.816 ** 0.266 ------Proportion Black --- -0.052 0.142 ---

Disadvantage*Black ------1.277 * 0.597

Random Effects Intercept Variance Component 0.179 0.209 0.196 Slope Variance Component ------0.000 Notes: A. Reference is Female E. Reference is no gang affiliation B. Reference is White and Other F. Reference is PRC C. Reference is Employed G. Reference is no move during month D. Reference is Low risk H. Reference is No program completion *** p<.001, ** p<.01, * p<.05, one-tailed, † p<.05, two-tailed

109

Model Model Model 1 2 3 Factors b s.e. b s.e. b s.e. Time Controls Month 1 1.269 *** 0.313 -0.310 0.349 -0.315 0.349 Month 2 2.254 *** 0.302 1.253 *** 0.322 1.244 *** 0.322 Month 3 1.423 *** 0.313 0.767 * 0.330 0.754 * 0.330 Month 4 1.259 *** 0.318 0.797 * 0.332 0.784 * 0.332 Month 5 0.929 ** 0.330 0.530 0.345 0.519 0.345 Month 6 1.012 ** 0.330 0.714 * 0.344 0.709 * 0.344 Month 7 0.743 * 0.344 0.554 0.357 0.546 0.357 Month 8 0.544 0.357 0.412 0.369 0.401 0.369 Month 9 0.549 0.360 0.459 0.371 0.455 0.371 Month 10 -0.158 0.421 -0.292 0.441 -0.295 0.441 Month 11 0.529 0.365 0.549 0.376 0.542 0.376 Individual Characteristics MaleA --- 0.317 * 0.153 0.331 * 0.153 BlackB --- -0.009 0.107 -0.077 0.111 Age --- -0.023 *** 0.006 -0.024 *** 0.006 No employmentC --- 0.761 *** 0.107 0.750 *** 0.107 Felony level --- -0.127 ** 0.042 -0.131 ** 0.042 High riskD --- 0.953 *** 0.161 0.936 *** 0.160 Medium riskD --- 0.630 *** 0.130 0.614 *** 0.129 Sex offender riskD --- 0.452 ** 0.172 0.448 ** 0.171 ParoleE --- -0.229 † 0.122 -0.229 † 0.122 Number of Violations --- 0.577 *** 0.034 0.573 *** 0.034 MoveF --- 1.010 *** 0.159 1.007 *** 0.159 Program CompletionG --- -0.640 *** 0.134 -0.633 *** 0.134 Neighborhood Measures Economic Disadvantage ------0.956 * 0.430 Residential Stability ------Proportion Black ------

Disadvantage*Black ------Disadvantage*Felony ------Intercept -3.210 *** 0.05 -3.910 *** 0.087 -3.904 *** 0.086 Intercept Variance Component 0.069 0.452 0.433 Slope Variance Component ------

Table 6.2. Discrete Time Multilevel Logistic Models of Arrest Behavior (13164 cases in 926 tracts)

Continued

110

Table 6.2 Continued

Model Model Model 4 5 6 Factors b s.e. b s.e. b s.e. Time Controls Month 1 -0.304 0.349 -0.311 0.349 -0.363 0.352 Month 2 1.253 *** 0.322 1.252 *** 0.322 1.214 *** 0.324 Month 3 0.764 * 0.330 0.766 * 0.330 0.729 * 0.331 Month 4 0.793 * 0.332 0.796 * 0.333 0.767 * 0.333 Month 5 0.529 0.345 0.529 0.345 0.507 0.345 Month 6 0.719 * 0.344 0.714 * 0.344 0.700 * 0.344 Month 7 0.554 0.357 0.553 0.357 0.537 0.357 Month 8 0.411 0.369 0.411 0.369 0.395 0.369 Month 9 0.457 0.371 0.459 0.371 0.446 0.371 Month 10 -0.294 0.441 -0.292 0.441 -0.300 0.441 Month 11 0.545 0.376 0.548 0.376 0.541 0.376 Individual Characteristics MaleA 0.339 * 0.153 0.318 * 0.154 0.321 * 0.154 BlackB -0.065 0.109 -0.021 0.126 -0.071 0.115 Age -0.024 *** 0.006 -0.023 *** 0.006 -0.025 *** 0.006 No employmentC 0.755 *** 0.107 0.760 *** 0.107 0.746 *** 0.109 Felony level -0.129 ** 0.042 -0.127 ** 0.042 -0.129 ** 0.042 High riskD 0.939 *** 0.160 0.953 *** 0.161 0.941 *** 0.162 Medium riskD 0.615 *** 0.129 0.629 *** 0.130 0.610 *** 0.131 Sex offender riskD 0.444 ** 0.171 0.453 ** 0.172 0.451 ** 0.173 ParoleE -0.225 † 0.122 -0.229 † 0.122 -0.231 † 0.123 Number of Violations 0.572 *** 0.034 0.577 *** 0.034 0.582 *** 0.035 MoveF 0.998 *** 0.159 1.011 *** 0.159 1.011 *** 0.160 Program CompletionG -0.638 *** 0.134 0.033 *** 0.187 -0.639 *** 0.135 Neighborhood Measures Economic Disadvantage ------1.044 ** 0.433 Residential Stability -0.818 * 0.352 ------Proportion Black --- 0.065 0.185 ---

Disadvantage*Black ------1.433 † 0.835 Disadvantage*Felony ------Intercept -3.901 *** 0.086 -3.909 *** 0.087 -3.885 *** 0.087 Intercept Variance Component 0.424 0.452 0.361 Slope Variance Component ------0.435

A. Reference is Female B. Reference is White and Other C. Reference is Employed D. Reference is Low Risk E. Reference is PRC F. Reference is No Move G. Reference is No Program Completion *** p<.001, ** p<.01, * p<.05, one-tailed, † p<.05, two-tailed

111

Beginning Cumulative Total Failure Hazard Rate Month 1 1653 0.1404 0.1404 Month 2 1419 0.2324 0.1071 Month 3 1265 0.3028 0.0917 Month 4 1147 0.3618 0.0846 Month 5 1003 0.4025 0.0638 Month 6 904 0.4448 0.0708 Month 7 820 0.4759 0.0561 Month 8 752 0.5010 0.0479 Month 9 706 0.5286 0.0552 Month 10 658 0.5451 0.0350 Month 11 628 0.5661 0.0462 Month 12 592 0.5866 0.0473

Table 6.3 Hazard Rates for Violation

112

Beginning Cumulative Total Failure Hazard Rate Month 1 1653 0.0520 0.0520 Month 2 1565 0.1726 0.1272 Month 3 1364 0.2217 0.0594 Month 4 1281 0.2612 0.0507 Month 5 1165 0.2885 0.0369 Month 6 1077 0.3169 0.0399 Month 7 1009 0.3379 0.0307 Month 8 949 0.3546 0.0253 Month 9 906 0.3710 0.0254 Month 10 869 0.3790 0.0127 Month 11 846 0.3944 0.0248 Month 12 813 0.4033 0.0148

Table 6.4 Hazard Rates for Arrest

113 .6

.55

.45

Probability of Violation .4

.35

.4 .6 .8 1 Neighborhood Economic Disadvantage White and other Black

Figure 6.1 Cumulative Probability of Violation by Month Six by Race and Neighborhood Economic

Disadvantage

114 .35

.25

Probability of Arrest .2

.15

.4 .6 .8 1 Neighborhood Economic Disadvantage White and other Black

Figure 6.2 Cumulative Probability of Arrest by Month Six by Race and Neighborhood Economic

Disadvantage

115

CHAPTER SEVEN

PAROLE vs. PRC: A QUASI-EXPERIMENTAL ANALYSIS OF DETERRENCE

The previous empirical chapters of this dissertation focused on the topics of

released offender movement patterns and the contextual effects of neighborhood of

residence on recidivism. This chapter also focuses on recidivism, but in a different way.

The analyses presented here take advantage of a unique aspect of the ODRC data: the fact

that contained within the sample of prisoners released in 2003 and 2005 there are

individuals sentenced under indeterminate sentencing guidelines and those sentenced

under determinate sentencing guidelines. A quasi-experimental analysis is conducted to determine if that switch in sentencing policy has changed the recidivism behavior of returning prisoners. Neighborhood context is included in this analysis to better understand how deterrence and neighborhood context interact. This chapter explores the

difference in potential consequences for recidivism, explains why this matters from a

deterrence perspective, presents the results of the quasi-experimental analysis, and

discusses the theoretical and policy implications of those results.

The importance of Ohio’s switch

In 1996, Ohio completed a switch from indeterminate sentencing to determinate

sentencing. Indeterminate sentencing, the model followed by the majority of states in the

20th century, allows for a broad range in sentence (3-8 years or 5-15 years) on the belief that an offender will serve the appropriate sentence based on their unique characteristics.

116 These characteristics will be reviewed by a parole board, who will release an offender to

parole when they feel that offender has served enough time and has a chance of

successfully returning to the community. There is discretion, then, at sentencing (by the judge) and at release (by the parole board).

Critics of indeterminate sentencing felt this discretion was detrimental to the criminal justice process. Some felt that the discretion allowed under an indeterminate sentencing policy would never be allowed by another branch of law, such as contract disputes, because of the perceived arbitrariness of the decision making process (Reitz

2001). Others pointed to the fact that under indeterminate sentencing, offenders may only actually serve a fraction of their term of sentence. These arguments have led to changes in Ohio and other states, to rectify the sentencing of criminal offenders.

The rectification of sentencing in Ohio came in 1996 with the introduction of determinate sentencing. Although determinate sentencing is a bit of a catch-all term, in

Ohio it refers to the presumptive sentencing guidelines that govern felony sentencing and the abolishment of parole and its replacement by a judicial release mechanism (Reitz

2001). Under this system, judges still have some discretion in sentencing decisions, but offenders, once sentenced, will serve that term minus any good time credit. Offenders will serve a mandatory minimum term before release to supervision can be considered

(Wicharaya 1995). The biggest change is in the release decision. Gone is the parole board, except for rare cases such as life sentences, with judicial release instead. This means that offenders are not being assessed for their likelihood of success upon return to the community, but are instead being released based solely on the length of commitment.

The length of time under supervision is also regulated by legislative statute under

117 determinate sentencing. In the previous system, the length of time on parole was essentially the length of the remaining sentence, meaning offenders released faced years of prison should they violate the terms of their parole and have their parole revoked.

Good behavior under supervision would be rewarded by giving released offenders their

“final release” from supervision, essentially ending their criminal justice experience.

Under determinate sentencing, the length of time under supervision in the community, must be explicitly set by the judge. This time is usually five years for F-1 felonies and sex offenses, and three years for other F-2 through F-5 felonies, although this can be reduced.

Ohio’s switch from indeterminate sentencing and a parole board to determinate sentencing and judicial release had more than legislative consequences. Seiter and

Kadela (2003) point to four consequences of the abolition of parole boards. First, the truly dangerous criminals, those who are most likely to reoffend and those least likely to convince a parole board to grant parole, may get released sooner because determinate sentences tend to be shorter than indeterminate sentences. Second, parole boards were better able to ensure potential parolees had a viable release plan in place, such as where they would live and where they would look for work. Third, parole boards and the possibility of leaving prison earlier than expected could produce better behaving inmates, although some argued this simply led to offenders “playing the system” by maintaining a rehabilitating facade. Fourth, parole boards could set special conditions that specifically addressed offenders post-release special needs, such as attending certain treatment programs. These changes could all potentially impair released offenders from succeeding while under supervision.

118 Further changes in supervision accompanied this switch in sentencing. As parole was based on a treatment model whereby offenders would be released from prison after their needs had been addressed, post-release control underwent a transition from a philosophy of helping and counseling offenders to a system more focused on limiting the potential risk that released offenders pose to the community through the use of increased surveillance and punitive sanctions for misbehavior while under supervision (Feeley and

Simon 1992). This surveillance and punishment approach has resulted in resources being shifted away from assisting offender reintegration and towards incarceration for offenders who misbehave.

This chapter focuses on yet another potential consequence of switching from indeterminate sentencing to determinate sentencing. Specifically, the switch changed the potential punishments of violating supervision guidelines. As described above, the maximum amount of time that a parolee faces should they violate their parole is the remainder of their original indeterminate sentence. The maximum amount of time a Post-

Release Control (PRC) offender faces is the, usually, 3-5 years set by the judge in their determinate sentence. This is a wide disparity. Table 7.1 shows the means of various characteristics in the ODRC data for all offenders, just parolees, and just PRC offenders.

While largely similar on most characteristics, the maximum time left on the sentence for

parolees is 14.6 years, while for PRC offenders it is 2.84 years. This is an average

disparity of almost 12 years. Clearly, the length of time that would have to be served in

prison after a parole revocation is vastly different for parolees and PRC offenders.

The difference in possible punishments facing parolees and PRC offenders is

intriguing when one considers deterrence theory. In classic deterrence theory, behavior

119 will be avoided if punishment is swift, severe, and certain (Beccaria 1764; Tittle 1980;

Zimring and Hawkins 1973). Deterrence, then, is most likely in situations where punishment is swiftest, severest, and most certain. In the case of parolees and PRC offenders, punishment for violation of supervision is different in severity, but not swiftness and certainty. Still, the eleven year difference in average punishment should produce a deterrent effect for parolees that is not seen in PRC offenders. The switch from parole to PRC then could have had some unintended consequences, such as increased violation behavior by PRC offenders because of the reduction in punishment.

Other concerns of deterrence research are less problematic here. For example, deterrence relies upon individuals knowing the potential punishments for their actions. Not knowing could lead to behavior that appears to result from a lack of deterrence, when in fact it is a matter of ignorance. Both parolees and PRC offenders should know the specifics of their own cases and the punishments they face. Similarly, deterrence research sometimes focuses on perceptual differences of the possibility of sanction between offenders that could attenuate deterrent effects (see Paternoster and Piquero 1995; Piquero and

Paternoster 1998; Schoepfer et al. 2007; Stafford and Warr 1993). Again, for this analysis, there should be little in group differences in perceptual differences.

This is also interesting from a contextual perspective. Social disorganization theory holds that neighborhood structural conditions limit the formation of social control, specifically informal social control. Deterrence theory notes that the formal punishment associated with formal social control may be less important than the social ramifications faced by criminals (Fagan and Meares 2008). Informal social control, then, is crucial in affirming the cost of criminality. As neighborhoods vary in their ability to generate

120 informal social control, so would those neighborhoods vary in their additive deterrent

effect to formal punishment. Additionally, socially disorganized neighborhoods, lacking

in informal social control, may be forced to rely upon formal authorities and formal legal

control in dealing with crime, further diminishing the efficacy of informal social control.

Simultaneously, reliance upon formal legal controls, such as incarceration, reduces the stigma associated with formal punishment, reducing the deterrent effect of formal punishment. This is similar to the work on coercive mobility, whereby high incarceration rates are related to social disorganization (Rose and Clear 1998; Clear et al. 2003). A constant influx of released offenders and outflux of offenders to prisons would likely attenuate any deterrent effect of formal punishment. Recently released offenders living in socially disorganized neighborhoods may thus feel less of a social cost for recidivating than offenders who face the social costs of recidivating from the family, neighbors, friends and institutions that are associated with informal social control. In general, deterrence would be most likely in socially organized neighborhoods and compromised in socially disorganized neighborhoods.

There are thus three possibilities that Ohio’s switch from indeterminate to determinate sentences could have created in terms of offender recidivism. First, released offenders could violate more than they did previous to the law change. This would support a deterrence argument, as potential punishment has been substantially reduced.

A second possibility is that offenders violate less than they did previously. This would refute deterrence theory. A third possibility is that violation behavior has been largely unchanged by the switch from parole to PRC. If deterrence does in fact work for offenders under supervision, the analysis should reveal a consistent effect of parole status

121 and potential punishment on recidivism. In terms of neighborhood context and deterrence, social disorganization theory holds that neighborhood effects on the likelihood of offending would be related to structural characteristics. On the whole, deterrence should be more likely in socially organized neighborhoods. Economic disadvantage and residential instability should limit any deterrent effect of potential punishment. Parolees, because they face greater punishment, may be more influenced than PRC offenders, who face lower punishment, by neighborhood context due. The remainder of this chapter seeks to uncover which of these possibilities most characterizes the switch from parole to PRC.

Analytic strategy

To accurately assess the differences between groups based on punishment, an experiment could be conducted that would randomly assign released offenders to either a control group or an experimental group and then observe their behavior. Unfortunately, this is impossible with the ODRC data. Assignment to either parolee or PRC status is not random, violating the possibility of a true experimental design. The next best approach is to create a quasi-experimental design whereby the individuals analyzed are as similar as possible to each other, reducing competing influences on behavior, and maximizing the observed effect of “treatment”, in this case parole status. This quasi-experimental design should allow for the assessment of the independent role of parole status on violation rate and arrest, while controlling for important demographic characteristics, supervision experiences, and prior offending.

122 The first step in assessing the influence of the policy shift from parole to PRC in

Ohio is to compare the two groups of offenders and their recidivism characteristics.

Table 7.1 presents the means for two recidivism outcomes, violation rate and arrest.

Violation rate is very similar for both parolees and PRC although parolees seem somewhat less likely to be arrested. This is somewhat surprising given the consistent effect of parole status in the analyses of recidivism in Chapter Six. Even though this table shows similar averages, there could still be differences in violation rate and arrest after controlling for other characteristics. For example, as parolees tend to be older, they may violate less often based on age, not on parole status. The differences on key demographic, supervision, and prior offending variables are also included in Table 7.1.

They show significant differences between the two groups, most notably in terms of age, race, prior offending, and, of course, the potential prison term they face if their parole or

PRC is revoked and they are returned to prison. These differences pose confounding influences on recidivism, making it difficult to assess the independent role of parole status. To overcome this, propensity matching is used to match a subset of parolees and

PRC offenders on key variables so that the role of parole status can be accurately measured. This technique corrects for differences and reduces group heterogeneity allowing for more accurate estimation.

Retrospective propensity score matching is an often used approach to the dilemma of trying to compare groups based on treatment when the groups differ in other observable ways, differences which could bias any estimation of treatment effects. This analysis follows the conventional procedure of propensity score matching. First, a predicted probability or propensity score was estimated by running a predictive model of

123 treatment group selection. This propensity score is the basis of the later matching algorithm. The model was estimated in a parsimonious fashion, specifying group selection as a function of age, race, sex, felony status, risk level, gang association, region of Ohio the offender was from, and several indicators of incarceration offense type, such as assault, drug trafficking, or robbery. Later models retain age, race, sex, felony status, and risk level as controls to further control for differences. The second step is the actual matching procedure, whereby cases are paired together based on their propensity score.

In this analysis, treatment cases were matched to control group cases based on how well they compared on their propensity scores in a nearest neighbor without replacement approach. This approach means that once a case is matched, it can not be matched to another case. To further ensure the quality of the matching procedure, a caliper restriction was put in place so that only cases within .01 in propensity units of each other could be matched together. This caliper restriction does two things. First, it increases the reliability of the matching procedure by only allowing matches for very similar cases.

Second, it restricts the number of cases available for the subsequent analyses. Common support also restricts potential matches, matching only treated cases that have characteristics that can be observed within both treated and untreated cases. Essentially, this procedure reduces the 1653 viable parolee and PRC cases to a matched sample of

680 cases, but these cases are the most similar of the two groups.

The success of the propensity score matching procedure can be viewed in Table

7.1 columns 4 and 5. Most of the variability in the individual characteristics observed in columns 2 and 3 has been removed or significantly reduced. To further minimize the differences between the groups, these characteristics are included in the analytical models

124 as statistical controls. The resulting matched sample of 680 cases (340 parole; 340 PRC)

is used in the recidivism analyses in this chapter. The variables used in these analyses are

the same as those used in the previous chapter except for two. The first new measure

included in the analyses in this chapter is a measure of the maximum time remaining on

an offender’s sentence should they return to prison. This was calculated by subtracting

the year of release from the maximum year of expiration of an offender’s post-release

supervision. This variable ranges from one year to 129 years, as some parolees have

sentences that go far beyond a natural life-span. The second new variable is a count of

the number of residences, rather than a binary indicator of a move. This measure is only

used in the logistic regressions as a more informative measure of move. The time-variant

measure is included in the contextual models. Later models that include neighborhood

context use person-period files created from the matched file. In these models, violation

once again is a binary variable of whether or not an offender committed a violation. The

next section describes the results of the analyses of the propensity score matched

samples.

Results

Table 7.2 presents the logistic regression results of violation rate by demographic

and prior offending characteristics. The first column contains the results of the regression

run with all characteristics for the pooled matched sample and includes a dummy

indicator of parole status. The second column contains the regression results with all characteristics run with only the matched parole sample included. The third column

125 contains the regression results with all characteristics with only the matched PRC sample

included.

The results exhibit some differences across models. While the direction of effect

is largely consistent, the significance of variables in the pooled and PRC models is not

observed in the parole models. Age is not a significant predictor for parolees, but is for

PRC offenders. The prior offending measures are also different, with felony status

predictive for parolees but not PRC offenders and high risk and medium risk predictive

just for PRC offenders. More importantly, the results also undermine any claims of

deterrence based upon the difference in punishment available for parolees and PRC

offenders. This can be observed in the first model of Table 7.2. In the first model, there

is no significant difference between parolees and PRC based on that status alone. If the switch from parole to PRC and the concomitant reduction in prison penalty time available stopped deterring offenders from committing violations, one would expect to observe a difference based on parole status. In addition, especially in the parole status specific models, one would expect that the maximum time remaining on a released offender’s sentence would exhibit a negative relationship with violation rate. For both, the relationship is positive but insignificant, although larger for PRC offenders. The lack of significant effect for parolees is especially telling given the wider range of potential punishment than for PRC offenders. The results of the regressions on violation rate do not indicate a deterrent effect of prison punishment for released offenders.

Of course, there could simply be no relationship between parole status and recidivism, although this seems unlikely based on the results of the previous chapter. To assess this, logistic regressions for arrest were run to determine if parole status mattered

126 for a different measure of behavioral recidivism, one that should operate independently of any concerns about the shift from parole to PRC. Table 7.3 presents the binary logistic regression results of arrest by demographic and prior offending characteristics. The first column contains the results of the regression run with all characteristics for the pooled matched sample and includes a dummy indicator of parole status. The second column contains the regression results with all characteristics run with only the matched parole sample included. The third column contains the regression results with all characteristics with only the matched PRC sample included.

The results again tell an interesting story about parole status and recidivism. The demographic characteristics are more consistent than in the violation rate models. Sex is significant for parolees and the pooled sample, but not PRC offenders. Prior offending exhibits some differences, with felony status significant for parolees, but not PRC, while high risk and medium risk are significant for both. More importantly for the focus of this chapter, the parole status indicator is significant in the pooled sample model, indicating a difference between parolees and PRC offenders, with parolees less likely to be arrested.

Maximum sentence remaining, insignificant for all three models, reveals no deterrent effect for any group, nor one group more than another.

The models to this point, however, do not take context into account. It could be the case that neighborhood structure has attenuated the link between punishment and deterrence. The next models use multilevel discrete time logistic regression to see if controlling for neighborhood economic disadvantage and residential stability change the influence of maximum prison time remaining on recidivism for both parolees and PRC.

127 Tables 7.4, 7.5, and 7.6 present the results of the discrete time multilevel logistic

models of violation behavior for the pooled sample, parolees, and PRC offenders,

respectively. Tables 7.7, 7.8, and 7.9 present the results of the discrete time multilevel

logistic models of arrest behavior for the pooled sample, parolees, and PRC offenders, respectively. The first model in these tables includes just individual level predictors. The second model in these tables includes individual level predictors and neighborhood

economic disadvantage. The third model in these tables includes individual level

predictors and neighborhood residential stability. All the models in these tables include

but do not present the time dummy variables.

The results largely replicate the findings of the previous chapter, in terms of the significance and direction of individual and contextual variables. What is important for this chapter, however, is the significance of the parole status variable in the pooled model, the lack of significance for the maximum time remaining variable, and how neighborhood context and parole status interact. Parole status is significant in both the violation and arrest models. This means that even after the propensity matching, there remains a significant difference between parolees and PRC offenders in terms of recidivism. This is evidence of a potential deterrent effect, as the matched parolees were less likely to violate and be arrested than the matched PRC offenders. This support for a deterrence effect is reduced by the continued insignificance of the maximum time remaining variable. None of the models showed a significant effect of punishment, one that should be strongest for parolees. Instead, all of the models show a direct relationship with recidivism and maximum time remaining, meaning that as maximum time remaining increased, so did the chance of a violation or arrest. Finally, the neighborhood context

128 variables played out in interesting ways. For both violation behavior and arrest, economic disadvantage and residential stability were only significant in the pooled sample and parolee models, not PRC offender models. The effect of neighborhood of residence appears to be different for parolees and PRC offenders. Disadvantage and stability operated as classic social disorganization theory would predict, but only for parolees. Perhaps, then, parolees are able to be deterred, but the neighborhoods that they live in are worse than the neighborhoods of PRC offenders, which weaken that deterrent effect. This seems unlikely, as the neighborhoods that parolees and PRC offenders live in do not differ significantly in terms of mean or variance of disadvantage or stability.

Another explanation is that neighborhood characteristics are simply more salient for parolee offending. While PRC offenders may reoffend at similar rates across all neighborhoods, parolees are more likely to reoffend in disorganized neighborhoods because of the attenuation of the deterrent effect of formal punishment in disorganized neighborhoods. A third possible explanation is that parolees experience neighborhood context for longer time than PRC offenders. While propensity score matching did reduce differences in overall likelihood of a violation or arrest, parolees may return to criminality at a slower rate than PRC offenders. Parolees contribute an average of 8.27 person-months to the violation analyses, while PRC offenders only contribute 7.22. For arrest, the disparity is even greater: parolee (9.66) vs. PRC offenders (8.48). In essence, parolees are out for an average of one month more than PRC offenders.

Overall, there is little evidence that the switch from indeterminate sentencing to parole and thus the switch from discretionary release to judicial release resulted in a weakening in the potential deterrent effect of the prison punishment available for parole

129 violators. Instead, there is ample evidence that there is simply no deterrent effect of that

potential prison punishment. The increased potential punishment associated with parolee

status did not explain violation or arrest behavior in individual level models, although

there was still a parole status difference in individual level arrest models and in both

violation and arrest contextual models. The results show support for a reformulated

theory of deterrence. Wright et al. (2004) summarize this perspective as a self-control modulated deterrence theory that expects less effect of punishment for crime prone groups. Parolees and PRC offenders are certainly crime prone groups, so may possess low self-control that causes them to behave in non-rational ways, leaving them less susceptible to deterrence. Alternatively, Zimring and Hawking (1973) discussed

“marginal offenders”, those not socialized into a life of crime, as the only group susceptible to deterrence, unlike committed offenders and non-offenders. Parolees and

PRC offenders may be committed offenders and may be unlikely to be deterred from crime. The next section discusses the theoretical and policy implications of these results.

Theoretical and policy implications

The analyses contained within this chapter have both theoretical and policy implications. First, there appears to be little support for deterrence theory as decreasing the available punishment for violation behavior was unrelated to the violation rate. The switch from parole to PRC seemed to have little effect on overall violation rate although parolees are somewhat less likely to be arrested. These differences can not be blamed on differences between the sample of parolees and PRC offenders analyzed as propensity score matching was used to minimize the confounding effect of other variables and

130 highlight the “treatment” effect of the change in parole status that accompanied the switch from parole to PRC. The second implication of these results is that any policy relying upon punitive measures intended to ensure parolee or PRC offender compliance with the rules of supervision is likely to meet with failure. This is in line with prior research on a punitive response to parole violation behavior. A review of research on surveillance-oriented strategies suggests that heavy use of punitive sanctions will not result in compliance but instead can actively encourage a return to more serious criminal behavior (Aos et al. 2006).

If the increased threat of punishment does not deter released offenders from violating the rules of their supervision or deter them from committing new criminal acts, what policies make sense for prisoner reentry? Drawing on the analyses from this chapter and the previous chapter, it seems clear that certain aspects of reentry must be addressed. First, residential mobility of released offenders is associated with recidivism.

Reentry plans need to ensure stable environments for offenders to return to lest they return to criminality. Second, employment is also a key factor in determining whether or not an offender returns to a life of crime. Reentry plans that fail to address how released offenders will support themselves will result in more recidivism. Third, program completion, while not associated with violation behavior, is associated with a reduction in recidivism, giving support to the old-fashioned parole approach to prisoner reentry that focused on assessing the needs of returning prisoners and providing for the treatment of those needs. Fourth, the risk assessment tools that have been established do a good job of predicting who needs additional supervision while on parole or PRC. Offenders who are assessed as higher risk do in fact recidivate more than offenders who are assessed as

131 lower risk. Finally, reentry plans should pay more attention to the quality of neighborhoods that returning offenders reside in, as neighborhood characteristics can influence recidivism. While most offenders return to the same or similar neighborhoods that they resided in when committing their incarceration offense, sending returning prisoners to these neighborhoods may contribute to their inability to maintain a non- criminal lifestyle.

Overall, this chapter has shown a lack of support for a deterrence model of parole supervision. The theoretical and policy implications of the analyses support a return to the parole system of indeterminate sentencing, not because of the additional prison time available for parole violators, but because of the lack of effect of punitive strategies of supervision engaged in by surveillance and punishment approaches. The next chapter discusses the findings, implications, and limitations of this dissertation.

132

Matched Matched Pooled Parole PRC Parole PRC Variable Mean Mean Mean Mean Mean Dependent Variables Violation 0.56 0.53 0.57 0.55 0.55 Arrest** 0.39 0.34 0.40 0.36 0.36

Independent Variables Male*** 0.81 0.90 0.77 0.89 0.89 Black*** 0.50 0.57 0.47 0.54 0.55 Age*** 35.18 39.68 33.34 37.77 37.34 No employment 0.27 0.25 0.28 0.26 0.30 Number of Residences 1.90 1.93 1.88 1.98 1.92 Felony Level*** 3.24 3.94 2.96 3.60 3.60 High Risk*** 0.14 0.23 0.10 0.22 0.22 Medium Risk*** 0.33 0.25 0.36 0.28 0.31 Sex Offender Risk*** 0.18 0.21 0.16 0.21 0.22 Maximum Time Remaining*** 6.26 14.60 2.84 12.06 3.48 Notes: N=1653 N=481 N=1172 N=340 N=340 Pre-matched means significantly different for parole and PRC ***p<.001 two-tailed **p<.01 two-tailed

Table 7.1 Means for Pre-matched and Post-matched Cases

133

Matched Sample Matched Sample Matched Sample Combined Parole PRC B S.E. B S.E. B S.E. Demographic Characteristics Male 0.640 0.297 * 0.766 0.421 t 0.692 0.443 Black 0.604 0.186 ** 0.609 0.262 * 0.609 0.274 * Age -0.038 0.010 *** -0.015 0.016 -0.051 0.013 *** No employment 0.964 0.210 *** 1.225 0.305 *** 0.697 0.310 * Number of Residences 0.566 0.099 *** 0.504 0.126 *** 0.718 0.166 ***

Prior Offending Felony -0.191 0.085 * -0.303 0.117 * -0.198 0.178 High risk 1.282 0.280 *** 0.645 0.397 1.883 0.424 *** Medium risk 1.050 0.238 *** 0.708 0.344 * 1.221 0.341 *** Sex offender risk 0.925 0.267 ** 0.787 0.385 * 0.797 0.439 t

Maximum Time Left 0.000 0.012 0.000 0.012 0.060 0.146

Parole -0.281 0.203 ------

Intercept -0.337 -0.890 -0.390 R2 (Pseudo) 0.188 0.170 0.230 Notes: N=680 N=340 N=340 ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed

Table 7.2 Logistic Regression Models of Violation by Demographic and Prior Offending Characteristics

134

Matched Sample Matched Sample Matched Sample Combined Parole PRC B S.E. B S.E. B S.E. Demographic Characteristics Male 0.548 0.314 t 1.220 0.507 * 0.114 0.438 Black 0.241 0.187 0.261 0.273 0.211 0.267 Age -0.046 0.010 *** -0.040 0.018 * -0.051 0.013 *** No employment 1.292 0.200 *** 1.487 0.291 *** 1.113 0.291 *** Number of Residences 0.362 0.084 *** 0.322 0.115 ** 0.412 0.131 **

Prior Offending Felony -0.129 0.082 -0.229 0.112 * -0.240 0.175 High risk 1.614 0.283 *** 1.228 0.424 ** 1.971 0.396 *** Medium risk 1.097 0.250 *** 0.797 0.376 * 1.223 0.343 *** Sex offender risk 0.530 0.296 t 0.573 0.452 0.115 0.459

Maximum Time Left 0.003 0.012 0.000 0.013 0.188 0.144

Parole -0.539 0.208 ** ------

Intercept -0.484 -1.310 -0.240 R2 (Pseudo) 0.200 0.192 0.217 Notes: N=680 N=340 N=340 ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed

Table 7.3 Logistic Regression Models of Arrest by Demographic and Prior Offending Characteristics

135

Matched Sample Matched Sample Matched Sample Combined Combined Combined B S.E. B S.E. B S.E. Demographic Characteristics Male 0.545 0.206 ** 0.554 0.202 ** 0.573 0.204 ** Black 0.301 0.121 * 0.247 0.124 * 0.252 0.122 * Age -0.022 0.006 *** -0.023 0.006 *** -0.023 0.006 *** No employment 0.720 0.129 *** 0.701 0.127 *** 0.707 0.127 *** Move 0.753 0.202 *** 0.744 0.201 *** 0.742 0.201 ***

Prior Offending Felony -0.152 0.053 ** -0.150 0.052 ** -0.151 0.052 ** High risk 1.188 0.194 *** 1.147 0.193 *** 1.152 0.194 *** Medium risk 0.881 0.171 *** 0.846 0.170 *** 0.850 0.171 *** Sex offender risk 0.650 0.195 ** 0.633 0.193 ** 0.646 0.193 **

Maximum Time Left 0.002 0.007 0.002 0.007 0.002 0.007 Parole -0.331 0.132 * -0.318 0.130 * -0.317 0.130 *

Neighborhood Context Economic Disadvantage --- 0.709 0.459 --- Residential Stability ------0.700 0.374 t

Intercept -2.736 -2.731 -2.732 VC 0.143 0.086 0.094 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 4949 cases in 479 neighborhoods

Table 7.4 Multilevel Discrete Time Logistic Regressions of Violation Behavior for Combined

136

Matched Sample Matched Sample Matched Sample Parole Parole Parole B S.E. B S.E. B S.E. Demographic Characteristics Male 0.637 0.297 * 0.664 0.291 * 0.707 0.300 * Black 0.202 0.169 0.093 0.170 0.149 0.171 Age -0.014 0.011 -0.015 0.010 -0.015 0.011 No employment 0.823 0.181 *** 0.812 0.170 ** 0.835 0.179 *** Move 0.935 0.286 ** 0.927 0.283 ** 0.941 0.287 **

Prior Offending Felony -0.192 0.070 ** -0.195 0.068 ** -0.197 0.070 ** High risk 0.763 0.272 ** 0.717 0.257 ** 0.713 0.274 ** Medium risk 0.620 0.251 * 0.595 0.232 * 0.588 0.252 * Sex offender risk 0.552 0.284 t 0.483 0.280 t 0.513 0.284 t

Maximum Time Left 0.001 0.007 0.000 0.007 0.001 0.007

Neighborhood Context Economic Disadvantage --- 1.659 0.656 * --- Residential Stability ------1.027 0.520 *

Intercept -2.833 -2.844 -2.838 VC 0.072 0.000 0.058 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 2683 cases in 260 neighborhoods

Table 7.5 Multilevel Discrete Time Logistic Regressions of Violation Behavior for Parolees

137

Matched Sample Matched Sample Matched Sample PRC PRC PRC B S.E. B S.E. B S.E. Demographic Characteristics Male 0.471 0.269 t 0.467 0.269 t 0.480 0.269 t Black 0.376 0.163 * 0.389 0.169 * 0.344 0.168 * Age -0.026 0.008 ** -0.026 0.008 ** -0.026 0.008 *** No employment 0.549 0.164 ** 0.551 0.164 ** 0.545 0.164 ** Move 0.540 0.282 t 0.544 0.283 t 0.529 0.283 t

Prior Offending Felony -0.219 0.106 * -0.221 0.106 * -0.216 0.106 * High risk 1.432 0.238 *** 1.438 0.239 *** 1.430 0.238 *** Medium risk 0.958 0.218 *** 0.966 0.219 *** 0.947 0.218 *** Sex offender risk 0.491 0.294 t 0.489 0.294 t 0.511 0.296 *

Maximum Time Left 0.083 0.087 0.083 0.087 0.083 0.087

Neighborhood Context Economic Disadvantage --- -0.169 0.600 --- Residential Stability ------0.382 0.528

Intercept -2.234 -2.235 -2.235 VC 0.000 0.000 0.000 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 2266 cases in 283 neighborhoods

Table 7.6 Multilevel Discrete Time Logistic Regressions of Violation Behavior for PRC Offenders

138

Matched Sample Matched Sample Matched Sample Combined Combined Combined B S.E. B S.E. B S.E. Demographic Characteristics Male 0.473 0.238 * 0.488 0.232 * 0.509 0.233 * Black 0.132 0.137 0.029 0.141 0.049 0.139 Age -0.030 0.008 *** -0.031 0.007 *** -0.030 0.007 *** No employment 0.985 0.151 *** 0.971 0.132 *** 0.970 0.132 *** Move 0.992 0.216 *** 0.966 0.213 *** 0.956 0.213 ***

Prior Offending Felony -0.137 0.060 * -0.133 0.060 * -0.135 0.060 * High risk 1.388 0.228 *** 1.340 0.210 *** 1.351 0.210 *** Medium risk 0.964 0.199 *** 0.923 0.194 *** 0.926 0.194 *** Sex offender risk 0.436 0.245 t 0.421 0.241 t 0.447 0.242 t

Maximum Time Left 0.004 0.009 0.004 0.009 0.004 0.009 Parole -0.472 0.158 ** -0.444 0.149 ** -0.454 0.149 **

Neighborhood Context Economic Disadvantage --- 1.362 0.527 * --- Residential Stability ------1.167 0.425 **

Intercept -3.523 -3.518 -3.521 VC 0.031 0.000 0.000 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 5789 cases in 479 neighborhoods

Table 7.7 Multilevel Discrete Time Logistic Regressions of Arrest for Combined

139

Matched Sample Matched Sample Matched Sample Parole Parole Parole B S.E. B S.E. B S.E. Demographic Characteristics Male 1.000 0.411 * 1.043 0.413 * 1.106 0.415 ** Black 0.111 0.200 -0.092 0.209 0.024 0.204 Age -0.026 0.013 * -0.032 0.013 * -0.028 0.013 * No employment 1.140 0.201 *** 1.168 0.203 *** 1.147 0.202 *** Move 1.270 0.303 *** 1.289 0.308 *** 1.266 0.306 ***

Prior Offending Felony -0.216 0.083 * -0.215 0.085 * -0.219 0.085 * High risk 1.081 0.330 * 1.088 0.332 ** 1.009 0.334 ** Medium risk 0.745 0.303 ** 0.772 0.304 * 0.695 0.303 * Sex offender risk 0.478 0.374 0.415 0.376 0.406 0.376

Maximum Time Left 0.001 0.009 0.001 0.009 0.001 0.009

Neighborhood Context Economic Disadvantage --- 3.006 0.838 *** --- Residential Stability ------1.726 0.624 **

Intercept -3.771 -3.838 -3.791 VC 0.000 0.000 0.000 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 3151 cases in 260 neighborhoods

Table 7.8 Multilevel Discrete Time Logistic Regressions of Arrest for Parolees

140

Matched Sample Matched Sample Matched Sample PRC PRC PRC B S.E. B S.E. B S.E. Demographic Characteristics Male 0.245 0.355 0.247 0.355 0.251 0.351 Black 0.226 0.226 0.217 0.235 0.176 0.233 Age -0.040 0.012 ** -0.040 0.012 ** -0.040 0.012 ** No employment 1.063 0.248 *** 1.062 0.249 *** 1.039 0.249 *** Move 0.751 0.328 * 0.748 0.329 * 0.727 0.329 *

Prior Offending Felony -0.293 0.147 * -0.293 0.147 * -0.290 0.145 * High risk 1.863 0.368 *** 1.859 0.369 *** 1.838 0.368 *** Medium risk 1.234 0.321 *** 1.228 0.324 *** 1.207 0.321 *** Sex offender risk 0.219 0.429 0.220 0.429 0.231 0.428

Maximum Time Left 0.187 0.119 0.187 0.118 0.187 0.117

Neighborhood Context Economic Disadvantage --- 0.107 0.837 --- Residential Stability ------0.516 0.715

Intercept -2.589 -2.587 -2.576 VC 0.720 0.719 0.657 Notes: ***p<.001 two-tailed **p<.01 two-tailed *p<.05 two-tailed t p<.05 one-tailed 2638 cases in 283 neighborhoods

Table 7.9 Multilevel Discrete Time Logistic Regressions of Arrest for PRC Offenders

141

CHAPTER EIGHT

CONCLUSION

The large numbers of offenders returning to communities and the high rate of

recidivism among those released offenders pose a policy problem for agents of the

criminal justice system. Reentry is a crucial time, but one that has largely been

researched at an individual level. This dissertation has sought to expand the current

knowledge about reentry and recidivism by investigating two salient parts of that reentry

process: where recently released offenders live and how much they move after their

release from prison. Where recently released offenders live is important because of the

potential neighborhood structural causes of recidivism, a woefully under-investigated

phenomenon. Similarly, little is known about the movement of recently released

offenders. By analyzing a unique dataset, this dissertation offers some initial answers

about that movement, suggesting that offender movement, while substantial, is likely a

mostly lateral process. In addition to researching the reentry process, this dissertation

investigated the potential deterrent effect of the state of Ohio’s switch from indeterminate sentencing to determinate sentencing. Although there was little evidence of a deterrent effect on violation behavior, the relationship between context and parole status indicated that a connection between deterrence and neighborhood context could exist. These and other findings are discussed in the next section.

142

Summary of findings

This section describes and discusses the main findings from the empirical

chapters of this dissertation. First, the findings of the analysis of mobility patterns among

recently released offenders are described. Second, the main findings of the multivariate

models of recidivism behavior are discussed. Third, the lack of a deterrent effect found

by individual level and contextual level models of recidivism in the final empirical

chapter are described.

The first major finding of this dissertation is that offenders do move, move more

than the general public, and generally move laterally in terms of economic and stability

indicators. This is an important finding because it fills a gap in the extant literature on

prisoner reentry. While large distance movement across counties or states is probably

rare, due to the bureaucratic problems of reassignment to new parole offices and officers,

short distance movement is fairly frequent, with most of the parolees in this data

reporting more than one residence during their period of supervision. It is safe to assume

that most of these moves occur between census tracts rather than within, which

potentially poses a problem for contextual research focused on neighborhood effects on

recidivism. This problem is minimized by the pattern of recently released offender

movement, which showed that most offenders who move, roughly 70%, move to

neighborhoods of similar economic characteristics, such as poverty rate. In fact, extreme

moves from areas of high or low poverty to areas of low or high poverty are relatively

rare. This means that researchers can make the assumption that the neighborhood that an offender is released to and the associated structural characteristics of the neighborhood

143 represent the actual neighborhood conditions that parolees experience during their reentry period.

The second major finding of this dissertation is that certain offenders are more likely to experience movement while under supervision. Younger offenders move more often than older offenders, higher risk offenders move more often than lower risk offenders, and offenders released to halfway houses are more likely to move than offenders released to live in private residences. Interestingly, lack of employment was associated with a reduction in total moves, suggesting that parolees may be moving to obtain employment or, once employed, use their newly obtained resources to secure a new domicile. Finally, offenders released to more stable neighborhoods and neighborhoods with a greater proportion of black residents were less likely to move. This is reminiscent of the hyper segregated and isolated neighborhoods argument made by

Wilson (1987; 1996).

Turning focus to the second empirical chapter that focused on a contextual analysis of recidivism, the third major finding of this dissertation is the support shown for a control model of individual level influences on recidivism. Key individual factors that were shown to reduce recidivism include employment, not changing residences, and successfully completing programming. Employment can be viewed as a commitment to a non-criminal lifestyle, providing a stake in conformity. Not changing residences means less disruption in networks, perhaps strengthening attachments. Successfully completing programming indicates a belief in the system and the ability to change. Although this dissertation lacks the ability to test a true control model, and there certainly competing

144 explanations for the findings, the results do not rule out interpretation from a control

framework.

A fourth major finding is the continued support for other well-researched

individual level characteristics that are associated with recidivism. Recidivism literature

has shown that sex, prior offending, and age are significant predictors of recidivism. A somewhat anomalous finding is the lack of a consistent race effect. Although race was a predictor of violation behavior, it was not of arrest behavior. Perhaps black parolees are surveilled with greater intensity than offenders of other races leading to a race effect that is present for violation behavior but not general recidivism behavior like arrest.

A fifth major finding is the importance of neighborhood contextual effects on recidivism. The results of the multilevel discrete time logistic regressions show that neighborhood economic disadvantage is associated with an increased likelihood of arrest.

This lends credence to the similar finding of Kubrin and Stewart (2006), while also extending the validity of a social disorganization framework of neighborhood context on recidivism. This extension of validity comes from the methodology, a methodology that allows for time varying covariates, including time varying contextual covariates, especially important in light of the findings from the analysis of the mobility patterns of recently released offenders. Similarly, the results extend the theoretical framework to include not only economic indicators of structural social disorganization, but also stability indicators. The little research on social context and recidivism had only tested this one facet of Shaw and McKay’s classic social disorganization model. The results show that more stable neighborhoods inhibit the likelihood of both violation behavior and

145 arrest. These neighborhood effects remain even after controlling for compositional factors that could determine where offenders live.

The sixth major finding of this dissertation is the interaction between individual race and neighborhood disadvantage in terms of offender recidivism. This interaction was observed for both violation behavior and arrest. Essentially, the results show that black offenders and offenders of other races experience a different effect of neighborhood disadvantage. Black offenders are much less influenced by neighborhood disadvantage than white offenders or offenders of other races. Instead, black offenders experience an increase in likelihood of arrest as disadvantage increases, but far less of than the increase other races experience. For violation, the pattern is even more severe, with black offenders’ likelihood decreasing as disadvantage increases, while white offenders and other race offenders experience a dramatic upswing in likelihood of a violation. While some critics of contextual research have pointed out that such an effect may be the result of the types of neighborhoods that different races live in, the neighborhoods that white offenders and black offenders live in are more similar than for the general public, and are not significantly different from each other in mean or variance of economic disadvantage.

This perhaps lends support to those who believe that middle class black neighborhoods do not offer the same protection from criminality that white neighborhoods do, so there is less distinction of the effect of economic disadvantage on recidivism.

Turning focus to the last empirical chapter that presented a quasi-experimental analysis of the deterrent effect of prison sanctions, the seventh major finding of this dissertation is that there was no discernible increase in violation behavior or arrest resulting from Ohio’s change from indeterminate sentencing to determinate sentencing

146 and the concomitant change from parole board discretionary parole to judicially determined Post Release Control. The likelihood of recidivism appears to be independent of the possible prison punishment available to be administered to recently released offenders. The analysis also investigated whether or not socially disorganized neighborhoods attenuated the deterrent potential of formal sanctions. While inconclusive, the results did show that parolee behavior, the group most likely to be deterred because they faced the greatest potential punishment, was influenced by neighborhood context, while PRC offender behavior was not. This is important in light of the propensity matching that attempted to create a control and treatment group as similar as possible to each other. This indication of a possible relationship between deterrence and neighborhood context is but one of many future directions for research discussed below. But first, the next section describes some theoretical and policy implications of these findings.

Theoretical and policy implications

The findings of this dissertation have several implications for both theory and policy. Ultimately, the analyses largely confirmed theories of recidivism both at the individual level and the contextual level. Social disorganization theory was applied to the contextual findings, while control theory was applied to the individual findings. These advance the extant literature on recidivism, but do not really extend either theory. True advancements would require a data set with more specific measures of social control.

The final empirical chapter, though, did extend theory. While inconclusive at best, the finding that parolees’ recidivism is influenced by neighborhood context in a way that

147 PRC offenders’ recidivism is not suggests that there is a connection between informal social control and deterrence. This relationship could help explain the lack of an identifiable deterrent effect of post-prison sanctions. Parolees and PRC offenders both return to neighborhoods that are far more economically disadvantaged and unstable relative to the means for Ohio. While PRC offenders may be less deterred because of a lack of punishment, there might not exist a huge disparity in recidivism behavior because the neighborhoods that parolees reside in do not pair the formal costs of punishment with the social costs of punishment, costs which may be equally important in determining behavior (Fagan and Meares 2008). Thus, the findings implicate deterrence theory must incorporate a measure of neighborhood attitudes, values, and norms towards criminal punishment. From the coercive mobility work of Rose and Clear, it is apparent that some communities are more accustomed to the influx and outflow of returning prisoners and convicted offenders. With formal costs of punishment normative for a section of a community, individual offenders would likely not bear social costs for criminal behavior and their behavior would be less likely to be deterred.

The findings also contain policy implications for prisoner reentry and recidivism.

More attention must be paid to where offenders live after their period of incarceration.

Ideally, offenders would not return to the same neighborhoods that they lived in when they committed their imprisonment offense. These neighborhoods contain the same criminal attachments and lack of viable alternatives that may have resulted in the original criminal behavior. Moreover, halfway houses present particularly problematic destinations for offenders, as they are typically situated in disadvantaged neighborhoods.

Movement patterns showed that offenders tend to move laterally when they move,

148 although there was some evidence that offenders moved to secure employment or

employment helped offenders secure a move. Either way, the neighborhoods that

offenders are released to are unlikely to be neighborhoods that they can succeed in, with

success defined as maintaining a non-criminal lifestyle. This is important because

movement itself was associated with an increase in the likelihood of recidivism, indicating that a stable residence is necessary for that non-criminal lifestyle to exist.

Finally, the lack of a deterrent effect of formal punishment suggests that the

adoption of a surveillance approach to offender management may lead to more

criminality. Not only does surveillance value detection of rule-breaking, it signals a

move away from a reentry philosophy focused on addressing the needs of prisoners

reentering communities after years behind bars. The findings suggest that as formal

punishment will not work to control behavior, behavior must be controlled through

providing a stake in conformity, attachments to non-criminal others, and a belief in a non-

criminal lifestyle. The next section describes some limitations of this dissertation and

directions for future research.

Limitations and directions for future research

Working with an existing data set means adapting research around some

limitations. The limitations of this dissertation project include an inability to directly test

more advanced social capital and collective efficacy theories of informal social control.

While social disorganization is linked to both social capital and collective efficacy, social

disorganization lacks a measure of the connections between social structural

149 characteristics and individual outcomes. Research in the future should specifically test

these theories for recidivism, something that has not been done as of yet.

A similar limitation is the lack of data that would allow for a better test of an

individual model of recidivism. While social control theory has been used to explain

recidivism, the lack of more, and more specific, measures of attachment, commitment,

and involvement is potentially problematic especially when there are also no variables to

test competing theories. Future research could include a comparison of alternative

individual level theories while conducting a contextual analysis of recidivism.

Another limitation of this research is that while the findings suggest some patterns

of recently released offender mobility, these patterns are surface level. The findings

describe the incidence of offender mobility, who moves, and where they move, but not in

a complete way. First, there are several potentially important factors that are missing

from the available ODRC data. For example, marital status could help explain movement patterns, as offenders who are released to non-spousal relatives may be more likely to try and find a place of their own. Similarly, it would be instructive to know whether or not an offender had children. Another key variable that was lacking in the ODRC data is education. The ODRC coders attempted to code this information, but it was unusable due to a high level of missing data. Second, one significant question that remains to be answered is why offenders move. Again, the ODRC coders attempted to code this, but as they were coding from parole officer files and not obtaining the information from the parolees themselves, the data were unreliable and riddled with missing values. Why offenders move is crucial in determining whether or not offenders are moving to improve their standings or whether they are moving to avoid conflict and grief. Possible future

150 research should also investigate the unique experiences of offenders who are released to halfway houses. Their experiences are most likely different than other offenders, their reasons for moving are also different, and understanding their patterns of mobility would likely shed light on how this population negotiates life after release.

Another limitation of this study is the lack of a measure of ethnicity from within the ODRC data. The available race categories are black, white, asian, Indian, and other.

There is no measure of Hispanic or Latino ethnicity. This poses a potential problem, as it is certain that Ohio’s prisons house some offenders who would self-identify as Hispanic or Latino. This might explain the uncertain race effect finding within the main analyses.

Future research would do well to further explore race with an appropriate measure of ethnicity.

Future research might explore whether contextual effects on recidivism are solely related to urban setting. The figures of offenders’ residences in Chapter 4 showed that while most offenders are released to urban environments, some are released to relatively rural areas. Research could then explore whether rural offenders recidivate as often and whether context is as important in determining a return to criminality. The present results could also be tested against a model that includes information on economic opportunity and employment to determine whether specific neighborhood economic indicators are more important than others. For example, is poverty the problem that leads to recidivism or a lack of available employment?

Another avenue of research would be to further investigate the timing of recidivism. The analyses showed some evidence of the possibility of a typology of parolees: those who are likely to quickly return to crime and those who are more likely to

151 return to crime only after some time out. If some offenders are predisposed to returning to criminality, the contextual influences on their behavior may be less than for those who are less decided about a return to crime.

Future research could also address some potential confounding problems of this dissertation. While there is plenty of evidence that violation and, especially, arrest are interesting measures of recidivism, there is still some debate over whether or not these are the best measures of recidivism. While these are the available measures for this project to produce a discrete time analysis, future researchers could analyze discrete time to revocation, reconviction, or reincarceration. Another potential confounding problem is the spatial nature of social context. While the state wide nature of this data probably minimizes the impact of spatial autocorrelation, future neighborhood effects on recidivism could uncover whether or not recidivism is similar to violence and other outcomes that do experience spatial issues. A spatial analysis could also investigate whether the race/neighborhood economic disadvantage interaction is still significant after accounting for the spatial location of African-American and White neighborhoods.

Finally, while linear time was assessed and found to not be a statistically better measure of time in the discrete time multilevel models, future research may explore the timing issues associated with recidivism. Do certain released parolees return to crime faster than others and why? What characteristics are associated with a quicker return to crime?

Finally, future research should further explore the link between neighborhood context and deterrence. Neighborhood context could lower the social costs of criminality, but could also determine values of perceived likelihood of punishment,

152 another factor associated with deterrence. The existing literature lacks research on this potential confounding effect on deterrence.

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