Parole,Neighborhood and Reentry Outcomes: A Contextualized Analysis

Abstract

Compared to a large body of literature on the location-sensitivity of policing, relatively less effort has been made to examine whether parole practice is intertwined with the context of neighborhood. Based on longitudinal data of released prisoners, the current study examines the location contingency of parole efficacy in the context of reentry, focusing on the outcomes of recidivism and illicit drug use. Findings suggest that net of the effects of risk factors such as financial difficulty and insufficient family support, respondents who returned to less cohesive communities reported receiving a significantly lower level of support from parole officers. Moreover, parole officers’ support exhibited a significant protective effect against recidivism, and this protective effect was not universal but contextual: Parole officers’ support demonstrated a diminished protective effect for released prisoners who returned to disordered communities. Implications for correction practice and policymaking are presented.

Keywords

reentry recidivism substance use parole neighborhood

Introduction

As one of the many consequences of the U.S. “imprisonment binge”—a prolonged period of mass incarceration from the end of the 20th century to the beginning of the 21st century, the U.S. prison population increased from 330,000 in 1980 to over 2.2 million in 2008 (Lutze et al., 2014; Stahler et al., 2013). It should be noted that incarceration is only one of numerous forms of punishment. If we included the population under community supervision (e.g., probation or parole), a staggering seven million adults are under the supervision of the correctional system (Begun et al., 2016). About 80% of released prisoners will be under parole supervision or other forms of community supervision, which lasts an average of about two years in the U.S. (Hughes et al., 2001; Paparozzi, & Guy, 2009; Petersilia, 2003). Despite various forms of post-release supervision, a troubling 60% of released prisoners under community supervision will return to prison in five years (Markman et al., 2016). How to best integrate former prisoners back into society is one of the most pressing issues for researchers, practitioners, and policymakers.

While the majority of reentry studies test desistance theories (e.g., Farrington, 2001; Laub et al., 1998; Visher et al., 2005; Western et al., 2001) or evaluate correction programs (e.g., Grattett et al., 2008; Hamilton & Belenko, 2019; Hanrahan et al., 2005; Steen et al., 2013; Van Stelle & Goodrich, 2009), a relatively thinner line of research attention has been paid to go beyond individual attributes and examine the interplay of community context and correction supervision. We do not know whether parole supervision is a universal experience or contextual based on the community environment of the parolee. Extant studies have demonstrated that criminal justice practice is sensitive to community location; nonetheless, they are primarily from policing studies (Bass, 2001; Brunson, 2007; Brunson & Miller, 2006; Klinger et al., 2016; Smith, 1986). The current study speaks to these literature gaps by integrating the concepts of community context and parole practice to explain parolees’ reentry outcomes. Before reviewing the literature, it is important to note other ways this research will advance understanding of prisoner reentry and reintegration.

First, extant studies on neighborhood conditions and reentry primarily use neighborhood structural/economic characteristics (e.g., income and poverty rate) to capture the community context (Hipp et al., 2010; Hipp & Yates, 2009; Kubrin & Stewart, 2006; Stahler et al., 2013; Tillyer & Vose, 2011). This leaves neighborhood social cohesion and networks—the core elements that inhibit crime as posited by elaborations of the social disorganization perspective (Bursik & Grasmick, 1993; Maxwell et al., 2018; Sampson et al., 1997)—an under-investigated research question. In this study, parolees’ self-reported measurements of neighborhood cohesion and disorder are used to capture their neighborhood context rather than structural measurements such as neighborhood unemployment and poverty rate. This methodological approach is quite different from that used in other social disorganization studies, yet is uniquely suited to answer the research question of neighborhood contextual effect on parolees’ reentry. The act of an individual should be understood based on his or her subjective cognition of the social circumstances (Agnew, 1992; Merton, 1938). Given that the current study focuses on individual respondents’ reentry outcomes, their perceived community context can legitimately shed light on how this contextual influence affects their reentry outcomes.

Second, past integrative studies on neighborhood structural and individual risk factors for reentry controlled individual respondents’ demographic factors in the model; nevertheless, it is largely unknown whether neighborhood-level influence still exhibits a significant effect on reentry outcomes when respondents’ financial difficulty and family support are controlled. To sever a criminal past, released prisoners have to overcome a wide scope of challenges such as family’s rejection (Austin & Irwin, 2001; Covington & Bloom, 2007; Liu & Visher, 2019; Travis et al., 2001), financial difficulty (Liu et al., 2019; Shinkfield & Graffam, 2009; Visher et al., 2004) and limited access to housing (Bushway, 2004; Lutze et al., 2014). In this analysis, when assessing the roles of parole and neighborhood context in reentry outcomes, I control a wider scope of individual-level risk factors that include financial difficulty and family bonds.

Using longitudinal data that capture adult parolees’ experiences during reentry, I test whether neighborhood context moderates parole efficacy on reentry outcomes, represented by recidivism and drug use. Three research questions lead the study:

Do parolees from different types of neighborhoods receive disparate levels of parole officers’ support?

Do community context and parole officers’ support affect former prisoners’ reentry outcomes?

Is the influence of parole officers’ support on reentry outcomes conditioned by community context?

Literature Review

The Relevance of Neighborhood Cohesion and Disorder in Understanding Reentry Outcomes

People’s behaviors are affected by the environment of their communities (Bursik & Grasmick, 1993; Leverentz, 2010, 2011; Sampson, 2012). Neighborhood context has been an important theme in research on prisoner reentry, with much of the research focusing on the importance of neighborhood economic/structural measures such as poverty rate and unemployment rate. This prism illustrates how the structural disadvantage and resource depletion of a neighborhood creates a difficult context for released prisoners to obtain access to employment opportunities and social services (Hipp et al., 2010, p.953; Kubrin & Stewart, 2006, p. 166). For example, with limited access to community-based programs such as job training and substance abuse treatment, released prisoners fall victim to the “resource desert” environment and are at high risk to experience varied aspects of reintegration failure.

Besides the economic/structural aspect of a neighborhood, researchers also underscored the consequential influence of the social processual aspect of a neighborhood on reentry—how neighborhood cohesion and networks affect individuals’ reentry outcomes (Clear, 2002; King & Maruna, 2009; Leverentz, 2010). Community cohesion and residents’ networks—a non-structural community context that reflects people’s informal interactions and community sentiment—buffer crime and delinquency (Clear, 2008; Clear et al., 2001; Leverentz, 2011). When residents have mutually supportive bonds, they can exert collective efficacy to change the threatening environment by taking good care of their houses and apartments and intervening when seeing incivilities (Sampson et al., 1997; Sampson et al., 1999). It is probable that when released prisoners can draw strength from the cohesiveness of their communities to minimize the risk of reentry failure (Clear, 2008; Morenoff & Harding, 2014). Those who return to a cohesive community may have an easier time building relationship, engaging in civic activity, and developing a feeling of connectedness to the community. In addition, with the networks with neighbors, released prisoners can receive neighbors’ help and support in various aspects of life such as being introduced to a job or help with childcare while they go to work.

However, the sanguine effect of community cohesion on reentry outcomes rest on an assumption that the community is ready to incorporate released prisoners back into the community. In a famous qualitative study on residents’ attitudes toward prisoner reentry in Massachusetts, Leverentz (2010) illustrated a nuanced picture with regard to residents’ willingness to welcome released prisoners back into the community. Several factors affect a community’s readiness to accept released prisoners. When crime issues are considered as a general, abstract problem in local communities, residents have higher levels of punitiveness. This is because crime is seen as abstract and depersonalized, or something that “strangers” do to neighbors. In contrast, when there is public attention to crime issues as well as the social, structural challenges that are associated with crime, residents tend to have a more nuanced understanding of crime and offenders. They tend to hold lower levels of punitiveness and a greater belief in redeemability (Leverentz, 2010).

Among quantitative empirical tests, there has been a handful of tests on the relationship between perceived neighborhood cohesion and youth delinquent behaviors. Generally, perceived neighborhood cohesion was found to be negatively associated with youth respondents’ drug use and vandalism (Lanctot & Smith, 2001; Lin et al., 2012; Simons et al., 2004; South & Baumer, 2001). However, when it comes to predicting adult prisoner reentry outcomes, few quantitative studies have assessed the role of community cohesion and disorder.

The Contextual Nature of Parole Practice

The quality of the parolee–parole officer relationship may play a pivotal role in reentry success. When offenders perceive positive relationships with their parole officers, they report feeling personal loyalty and accountability toward them, which bodes well for future client outcomes (Chamberlain et al., 2018). Within the context of a perceived positive relationship, a parolee might be more willing to confide in an officer and communicate treatment and service needs (Robinson, 2005). Empirical studies on parolee-parole officer relationship and reentry outcomes demonstrated that support from parole officers were positively associated with higher rates of compliance (Blasko et al., 2015; Ireland & Berg, 2008; Vidal et al., 2015), lower rates of drug use (Blasko et al., 2015) and reoffending (Ireland & Berg, 2008). For example, one study found that juvenile offenders who perceived positive relationships with their parole officers had lower rates of reoffending, and this protective effect was strongest for youths lacking parental support. Until now, few reentry studies have explored whether parole practice is sensitive to neighborhood context (Vidal et al., 2015).

Community context is a core concept in our understanding of disparate treatments individuals receive when they have contact with the criminal justice system. Research illustrates that criminal justice actors’ focal concerns explain the variations in policing, prosecution, and sentencing (Albonetti, 1991; Spohn & Cederblom, 1991; Steffensmeier, 1980; Steffensmeier & Demuth, 2000, 2001). The focal concerns perspective begins with the assumption that criminal justice actors constantly make sanctioning decisions; meanwhile, they always need to secure efficiency in the large institutional environment even when they have limited knowledge of the offenders from the cases they are processing (Dias & Vaughn, 2006; Ulmer & Johnson, 2004). For example, judges, facing uncertainty from the limited information of offenders, use important cognitive signposts to make decisions in order to maximally decrease the likelihood of recidivism. Their sentencing decisions reflect their perceptions of (1) the offender’s perceived threat to public safety, (2) the offender’s blameworthiness, and (3) practical constraints of the court bureaucracy (Johnson et al., 2008; Steffensmeier et al., 1998). These perceptions that guide judges to make sentencing decisions can reflect their stereotypes of certain individuals and communities. This is captured by their patterned responses to some types of criminal cases (Cho & Tasca, 2018; Spohn & Cederblom, 1991; Steffensmeier & Demuth, 2001). For example, focusing on court decisions in communities that are more politically conservative, Helms and Jacobs (2002) find offenders who are unemployed, male, and black are perceived to pose a higher threat to the local communities; hence, offenders with these characteristics received longer sentences in court.

Compared to a large body of literature on the location-sensitivity of policing (Bass, 2001; Brunson, 2007; Brunson & Miller, 2006; Klinger et al., 2016; Smith, 1986), less attention has been paid to examine whether parole practice (e.g., parole revocation) is sensitive to neighborhood environment (for an exception, see Lin et al., 2010). Although focal concern theory has been primarily applied to the practice of judges and prosecutors, it is also applicable to parole practices.

Parole officers (hereafter POs), as other criminal justice actors, are sensitive to local politics and culture. As correction officers, they are well-informed about the condition of each community they serve; thus, they can differentiate local communities ridden by disorder from those that are not. Similar to police, prosecutors, and judges, parole officers aim to decrease the risk that a released prisoner poses to local communities. There are uncertainties in a parolee’s likelihood of success; driven by their focal concerns, parole officers may rely on their pre-conceptualization of individuals and community with certain characteristics to make supervision decisions (Huebner & Bynum, 2006; Steffensmeier & Demuth, 2006; Ulmer & Bradley, 2006; Ulmer & Johnson, 2004). If parole officers perceive parolees from disordered neighborhoods as more likely to reengage in crime, they may be more punitive toward this parolee group and impose tighter supervision. In contrast, in a community characterized by cohesion and networks, the social distance between POs and parolees may be shortened by the ties. In this type of community, POs may be more likely to demonstrate a work orientation that is characterized by support.

It should be noted that POs decision-making is qualitatively different from that of courtroom actors (McCleary, 1978). Parole decision making is made through one-to-one interactions and administrative hearings. Parolees have fewer legal rights in the decision-making process than they do in the courtroom. In addition, unlike the decision-making process of judges, POs’ decision-making is largely invisible to the public. Thus, POs are subject to less scrutiny of the general public, legislators, and researchers. However, decisions made by parole officers are not trivial. Whether the liberty of an individual under supervision is revoked can often depend upon which parole officer is supervising the case. There have been many instances in which “Offender A” commits an infraction and has formal action taken against him or her, while “Offender B” commits the same infraction and receives no formal action. Instead, “Offender B” receives a warning when the parole officer, exercising discretion, believes a warning to be a better intervention (Jones & Kerbs, 2007, p. 10).

Another question relevant to the interplay of neighborhood and parole practice is parolees’ receptivity to POs’ advice and instructions. From an interactionist approach (Albonetti, 1991; Becker, 1963; Sudnow, 1965), residents from divergent communities may have disparate levels of confidence in criminal justice practitioners. Offenders who reside in disordered neighborhoods, experiencing or witnessing the harsh treatment community members receive from criminal justice practitioners, may hold less confidence in parole officers. This, in turn, undermines the efficacy of parole officers’ support and supervision during their reentry. In contrast, a cohesive community has more capacity to foster better relationships between criminal justice authorities and residents. Compared to residents from disordered neighborhoods, residents from cohesive communities are less likely to be exposed to police brutality or other forms of discriminative treatments from law enforcement (Kirk & Matsuda, 2011; Kirk & Papachristos, 2011; Nix et al., 2015). Parolees from this kind of community are likely to hold less resistance to POs; as a result, they may be more receptive to the advice and instructions from POs. This, in turn, strengthens the positive effect of PO support and supervision on their reentry outcomes.

While the literature above has added a great deal to what we know, most research examined neighborhood context and parole practice in reentry in isolation; few studies have tested these two factors simultaneously and allow them to interact. This study fills the literature gaps by analyzing data from Returning Home: Understanding the Challenges of Prisoner Reentry, a multistate, a longitudinal study that was conducted by the Urban Institute from 2002 to 2005 (Visher et al., 2003). The data were collected against the background of decarceration when communities saw large numbers of prisoners came back home. One advantage of this reentry data set is that it is one of just a few data sets that captured released prisoners’ interactions with parole officers as well as their perceived neighborhood conditions.

Methods

Data

From 2002 to 2005, researchers from the Returning Home study identified prisoners serving at least one year in state prisons of Ohio, Illinois, and Texas who returned to Cleveland, Chicago and Houston, respectively. Potential respondents were identified either through compulsory prerelease programs where prisoners were already convened (Illinois and Texas) or from lists of individuals who were within 60 days of release (Ohio). A member of the research team provided an overview of the study. Individuals who agreed to participate (more than 80%) were given consent forms and asked to complete a self-administered survey. The samples of prisoners in each state were generally representative of all the prisoners being released to the study areas in the previous year in terms of race, sentence length, and time served. After each prisoner’s release, experienced interviewers conducted two personal interviews within 12 months, which included interviews with those who had returned to prison. Information about respondents’ family relationships, financial stress, interactions with parole officers and perceived neighborhood conditions was collected. The current analysis utilized the data of respondents (N = 454) who were released under parole supervision.

Although the Returning Home study was conducted more than ten years ago, these data are still valuable when it comes to explain the nexus of neighborhood conditions, parole practice and individuals’ reentry outcomes. There has been a vast body of reentry tests; however, few studies examined the influence of community conditions on individuals’ reentry outcomes (see review by Morenoff & Harding, 2014). To some extent, this research work has yet to be conducted because of the difficulty and expense of collecting the relevant data. Returning Home study is one of the few studies that tapped into the conditions of communities to which released prisoners returned. Meanwhile, from a qualitative reentry study using data collected in 2018, Western (2018) found that the mechanism by which troubling neighborhood environment affected crime outcomes was quite consistent with what researchers had found from social disorganization studies that were conducted more a decade ago (Sampson et al., 1997). Therefore, Returning Home data set have its unique value when it comes to disentangle the association between neighborhood conditions and reentry outcomes.

Measures

Dependent variables

Recidivism

Recidivism was captured by the occurrence of a new incarceration after release, measured through official records at one year after release. It was a binary variable; respondents who recidivated had a value of one for this variable while those did not recidivate had a value of zero. Using official records of reincarceration as a proxy for recidivism is not without limitation. Sometimes re-incarceration can occur due to reasons such as a technical violation instead of a new offense. Official records of rearrests are found to be another proxy for recidivism among past studies (e.g., Burraston et al., 2012; Castillo & Fiftal Alarid, 2011). However, due to data limitations, official records of rearrests were not available in the data—there were only respondents’ self-reported rearrests. Respondents may under-report rearrests for various reasons. Meanwhile, rearrests can take place due to reasons other than reoffending—for example, people can be arrested due to loitering. Given the limitations of self-reported rearrests, official records of reincarceration were used as a proxy of recidivism.

Post-release substance use

This variable was created based on six items from an eight-month follow-up interview that asked about the respondents’ frequency of using six main types of drugs—marijuana, heroin, methadone, cocaine, amphetamine, and other illicit drugs—in the past 30 days. Responses were on a scale that ranged from (1) never to (6) daily. About 18.4% of the respondents reported using at least one type of illicit drug. Due to the significant skew in the data, continuous-level analysis change score modeling was inappropriate because the assumptions of heteroskedasticity were violated (Long & Ervin, 2000). A binary variable was created with one indicating a respondent used at least one type of drug and zero indicating that a respondent never used any type of illicit drug.

Independent Variables

Neighborhood level predictors

Neighborhood cohesion¹

The construct of neighborhood cohesion was developed by the Urban Institute (Lynch & Sabol, 2001). This variable was constructed from four items about the respondents’ perceived social cohesion in their neighborhoods. These items asked the magnitude of respondents’ agreement with the statements: (1) You think your neighborhood is a good place for you to live, (2) You care about what your neighbors think of your actions, (3) If there is a problem in your neighborhood, people who live there can get it solved, and (4) You expect to live in this neighborhood for a long time. The responses ranged from (1) strongly agree to (4) strongly disagree. Reliability test results showed that this construct was internally consistent (Cronbach’s alpha = .74).

Neighborhood disorder

This construct was also developed by the Urban Institute and it was constructed based on five items (Lynch & Sabol, 2001). The items assessed the magnitude of respondents’ agreement with the statements: (1) Your neighborhood is not a safe place to live, (2) It is hard to stay out of trouble in your neighborhood, (3) You are nervous about seeing certain people in your neighborhood, (4) Drug selling is a major problem in your neighborhood, and (5) Living in this neighborhood makes it hard for you to stay out of prison. The answers ranged from (1) strongly disagree to (4) strongly agree. Reliability test results showed that this construct was internally consistent (Cronbach’s alpha = .81).

Individual level variables

Financial difficulty

This variable was assessed with four questions asking the respondents how hard it was to make enough money to support themselves, find a place to live, find and keep a job, and pay off debts. The answers ranged from (1) very easy to (4) very hard. Exploratory factor analysis (EFA) was employed to test the unidimensionality of the items. There was only one factor with an eigen value larger than one, and all of the four items loaded relatively strongly onto this factor (Appendix A). Standardized factor scores were used to create the value of the composite financial difficulty. It should be noted that it is problematic to simply add the numeric values of the items to create a latent factor because this practice disregards the items’ loading weights and cannot effectively reduce measurement error (DiStefano et al., 2009).

Family support

The Returning Home study measured respondents’ family support using a well-developed scale from the MOS social support survey (Sherbourne & Stewart, 1991). This variable was constructed based on 10 items: Whether a respondent had someone in the family to get together with to relax, to do something enjoyable with, to spend time with to help him get his mind off things, to love him and make him feel wanted, to listen to him when he needs to talk, to have a good time with, to talk to about himself or his problems, to share his most private worries and fears with, to turn to for suggestions about how to deal with a personal problem, and, finally, to get advice. Answers ranged from (1) strongly disagree to (4) strongly agree. CFA results confirmed the unidimensionality of the items; all of the items loaded relatively strongly onto the composite of family support (Appendix A). Standardized factor scores were used to create the value of this composite.

PO support

The parole officers’ support measure developed by the Urban Institute (Yahner et al., 2008) was used to construct PO support. It was based on seven items asking about respondents’ perception of PO support: (1) parole agent was helpful with his transition, (2) parole agent seemed trustworthy, (3) parole agent gave him correct information, (4) parole agent acted too busy to help him, (5) parole agent treated him with respect, (6) parole agent acted professionally, and (7) parole agent didn’t listen to him. The answers ranged from (1) strongly disagree to (4) strongly agree. The fourth and seventh questions were recoded so that a higher score indicated a higher level of PO support. CFA results confirmed the unidimensionality of the items and all items loaded relatively strongly onto the composite of PO support (Appendix A). Standardized factor scores were used to create the value of this composite.

Legal cynicism

Legal cynicism drives people to fend for themselves and seek gratification in ways “outside” of laws and social norms. Because it is an attitudinal outlook that discourages citizens’ compliance with authority’s decisions, researchers controlled for its effect when they examined the efficacy of law enforcement such as policing and parole supervision (Sampson & Bartusch, 1998). Following past practices, I controlled for its effect when assessing the influence of PO support on reentry outcomes. Legal cynicism was measured by five items developed by Sampson and Bartusch (1998). They tapped into respondents’ agreement on the statements: (1) Laws are made to be broken, (2) It’s okay to do anything you want as long as you don’t hurt anyone, (3) To make money, there are no right and wrong ways, only easy and hard ways, (4) Fighting between friends or within families is nobody else’s business, and (5) Nowadays a person has to live pretty much for today and let tomorrow take care of itself. The answers ranged from (1) strongly disagree to (4) strongly agree.

Control Variables

In this analysis, I included control variables of age, race (1 = white, 2 = black, 3 = other), T1 prior prison terms, and education attainment (1 = 6th grade or less, 2 = 7th to 9th grade, 3 = 10th to 11th grade, 4 = high school grad, 5 = GED, 6 = some college, 7 = college grad, 8 = post-grad study). Table 1 reports the descriptive statistics for all variables used in the regression analysis. The average age of the respondents was 36, and their prior prison terms ranged from 0 to 13. About 78% of them were black while nearly 15% of them were white. On a scale from one to four, the neighborhoods to which respondents returned on average had a disorder level of two. On a scale of one to four, their neighborhoods had an average social cohesion level of 2.78. Generally, respondents reported receiving a decent level of PO support—the mean of this variable was zero on a scale from −3.19 to 1.28 (after standardization, a latent factor has a mean of zero and standard deviation of one).

Table 1.

Descriptive Statistics (N = 454).

Variables	Mean or percentage	Std dev	Min	Max
Dependent variables
Recidivism	14.91%		0	1
Drug use	18.38%		0	1
Independent variables
Financial difficulty	0.00	1.00	−1.96	2.05
Family bond	0.00	1.00	−3.58	1.03
PO support	0.00	1.00	−3.49	1.28
Legal cynicism	0.00	1.00	−2.95	1.83
Neighborhood cohesion	2.78	0.68	1.00	4.00
Neighborhood disorder	2.00	0.61	1.00	4.00
Control variables
Education level	4.04	1.46	1.00	8.00
Age	36.17	10.18	19.00	73.00
White	14.78%
Black	77.96%
Other	7.25%
Prior incarcerations	2.56	1.66	0.00	13.00

Results

Figure 1 provides a visualization of the correlation between PO support and neighborhood conditions. Parole officer support was significantly negatively related to neighborhood disorder while positively related to neighborhood cohesion (Figure 1), at a p-value of .05. Parolees who went back to cohesive neighborhoods reported receiving a significantly higher level of support from their POs than their counterparts who returned to communities where networks and ties were absent. Neighborhood disorder also affected how much support parolees received from parole officers. Those who went back to disordered neighborhoods reported receiving a significantly lower level of PO support compared to those who returned to well-ordered communities.

Figure 1.

Correlation heatmap between neighborhood conditions and PO support.

Next, I examined the effects of neighborhood condition, PO support and parolees’ individual-level characteristics on reentry outcomes. Model 1 from Table 2 demonstrates the results for recidivism. For the categorical variable of race, white was used as the reference group. Several individual-level factors significantly affected this reentry outcome. Financial difficulty during reentry was significantly and positively related to recidivism; when financial difficulty went up by one unit, respondents had a 79% increase in the odds of committing another crime. Meanwhile, PO support exhibited a strong and inhibitive effect on recidivism. When PO support increased by one unit, respondents’ odds of reoffending decreased by 25%. Among control variables, age and prior incarcerations were found to exhibit significant effects on the risk of recidivism. Older respondents had lower risks of recidivism compared to their younger counterparts. Meanwhile, those with more prior incarcerations had higher odds of returning to prison. Switching from the individual to neighborhood predictors, neighborhood disorder aided in the risk of recidivism, with an effect size that was the largest in the model. Every one-unit increase in neighborhood disorder brought a 103% increase in the risk of recidivism. While respondents’ reentry was undermined by their neighborhood disorder issues, they received little benefit from the cohesion of their neighborhoods: Social cohesion exhibited no significant influence on recidivism.

Table 2.

Logistic Regression Results on Recidivism and Drug Use.

	Model 1Recidivism		Model 2Drug use		Model 3Recidivism		Model 4Drug use
Parameters	Estimate^a	OR	Estimate	OR	Estimate	OR	Estimate	OR
Education level	0.16 (0.10)	1.18	−0.07 (0.11)	0.94	0.17 (0.10)	1.19	−0.08 (0.11)	0.92
Age	−0.07*** (0.02)	0.93	−0.02 (0.02)	0.98	−0.07*** (0.02)	0.93	−0.02 (0.02)	0.98
Black	−0.29 (0.28)	0.40	−0.29 (0.26)	0.61	−0.30 (0.29)	0.38	−0.29 (0.26)	0.61
Other	−0.36 (0.45)	0.37	0.09 (0.38)	0.89	−0.35 (0.45)	0.37	0.10 (0.38)	0.90
Prior incarcerations	0.39*** (0.09)	1.47	0.19* (0.09)	1.21	0.38*** (0.09)	1.46	0.19* (0.09)	1.21
Financial difficulty	0.58*** (0.18)	1.79	0.01 (0.17)	1.01	0.56** (0.18)	1.74	0.03 (0.17)	1.03
Family bond	0.18 (0.16)	1.20	−0.27† (0.15)	0.76	0.20 (0.16)	1.23	−0.29* (0.15)	0.75
PO support	−0.30* (0.14)	0.75	0.02 (0.15)	1.02	−0.34* (0.15)	0.72	−0.04 (0.19)	0.96
Legal cynicism	−0.07 (0.15)	0.93	0.02 (0.16)	1.02	−0.12(0.15)	0.89	0.06 (0.16)	1.06
Neighborhood cohesion	0.29 (0.24)	1.33	−0.55* (0.26)	0.58	0.30 (0.25)	1.35	−0.57* (0.27)	0.57
Neighborhood disorder	0.71** (0.28)	2.03	1.00*** (0.30)	2.72	0.82** (0.29)	2.27	0.95** (0.30)	2.59
PO support* Neighborhood cohesion					0.32 (0.22)		−0.42 (0.26)
PO support* Neighborhood disorder					0.38† (0.20)		−0.25 (0.22)

entries are coefficients from logistic regression, with standard errors in parentheses.

†p < .1. *p < .05. **p < .01. ***p < .001.

Model 2 in Table 2 illustrates the effects of predictors on the second reentry outcome—drug use. Among parolees’ individual-level characteristics, I found moderate evidence that strong family bonds protected them against drug use at eight months after release. A one-unit increase in family bonds brought a decrease in respondents’ odds of drug use by 24%. The evidence of this association was moderate because the p-value for this variable was between 0.05 and 0.10. Parole officer support, though found to inhibit recidivism, seems to exhibit no significant effect on respondents’ drug use. Among control variables, only prior incarcerations exhibited a significant effect on post-release drug use. Respondents who had more prior incarcerations were more likely to use illicit drugs after release; every count increase in past prison terms was associated with a 21% increase in the odds of drug use. Switching from individual to neighborhood variables, both neighborhood cohesion and disorder demonstrated significant influences on respondents’ drug use but in opposite directions. Those who returned to cohesive communities benefited from this community condition; a one-unit increase in neighborhood cohesion brought a 42% decrease in respondents’ odds of drug use (odds ratio = .58). In contrast, released prisoners who went back to disordered communities were strongly affected by this context: Every one-unit increase in the severity of community disorder was found to raise respondents’ odds of drug use by 172% (odds ratio = 2.72).

Next, I examined if there was an interaction between PO support and neighborhood conditions in parolees’ reentry outcomes. Model 3 and 4 from Table 2 presents the interactive effect of these two dimensions of factors on recidivism and drug use, respectively. From Model 3, moderate evidence is found for an interactive effect of neighborhood disorder and PO support on recidivism. The evidence is moderate because the p-value for the interaction term is slightly larger than 0.05 but smaller than 0.10. While strong PO support inhibited former prisoners’ recidivism, its protective effect was attenuated when former prisoners went back to disordered neighborhoods (b = −.38). This interactive effect can be illustrated by estimating the effect of PO support in neighborhoods with divergent levels of disorder. For example, in a neighborhood with a disorder level at one (on a scale from 1 to 4), every one unit increase in PO support was found to decrease respondents’ risk to recidivate by 30%. However, this protective effect would be attenuated if respondents went back to more disordered communities. In a neighborhood with a disorder level at three, every one-unit increase in PO support brought a 27% decrease in the risk of recidivism. Moving onto the other dimension of reentry outcome—drug use, there was no interaction effect between PO support and community context; community context did not moderate the effect of PO support on parolees’ drug use (Model 4).

Discussion

Past studies on the location-sensitivity of criminal justice practice have greatly advanced our understanding of disparate treatments that individuals receive due to their neighborhood locations. However, the interplay of neighborhood conditions and parole practice in the context of reentry remains an important and little-investigated research question. Based on data that captured parolees’ neighborhood conditions and the PO support they received, this study assessed the location-sensitivity of parole practice and examines how neighborhood contextual and individual risk factors simultaneously influenced parolees’ recidivism and drug use. Parolees received disparate PO support depending on the conditions of their neighborhoods. Additionally, there is an interplay between neighborhood conditions and PO support in affecting reentry outcomes. Specifically, three key findings emerged from this analysis.

First, PO support is an essential factor for sustaining successful reentry; parolees who reported receiving more support from POs demonstrated a lower rate of recidivism. This effect still held even when other primary predictors of recidivism, such as financial difficulty and family bonds, were controlled. Parole officers’ support, in contrast to surveillance and policing, is characterized by warmth, empathy, respect, and understanding. Although the therapeutic focus of parole was once criticized during the mass incarceration era in the last century, and the U.S. has experienced a shift in parole from a therapeutic to surveillance orientation (Simon, 1993; Travis & Lawrence, 2002), the current analysis indicates that only imposing supervision and surveillance is inadequate to assisting individuals to achieve successful reintegration. PO support is the key to successful parole supervision. This finding is consistent with the findings found from studies on other offender social groups such as juvenile offenders (Vidal et al., 2015) and adult violent offenders (Chamberlain et al., 2018). It has significant policy implications, which will be discussed shortly.

One may argue that parolees’ evaluation of officers’ support as well as the relationship with officers may be influenced by parolees’ trust in authorities and reverence to laws. It is interesting that legal cynicism was not a significant predictor of recidivism or drug use from the model results, given that past studies illustrated its consequential role in people’s delinquent peer involvement, willingness to report crimes to police, and vulnerability to deviance values. It may be that the detrimental effect of legal cynicism is contingent on the outcome on which researchers focus. Future studies should compare the effect of legal cynicism across different reentry outcomes.

Second, net of the effects of individual risk factors, neighborhood conditions exhibited significant influences on the reentry outcomes of parolees. Although attenuated family bonds, financial difficulty, and low PO support were individual-level risk factors that undermine reentry, they represented only part of the story. When individual risk factors were included in the model, neighborhood disorder still exhibited sizable effects on individuals’ reentry outcomes over and beyond individual risk factors. Specifically, neighborhood disorder undermines the reentry process by increasing individuals’ risk of drug use and recidivism. Dwelling in disordered neighborhoods that are ridden with drug activities and other incivilities, respondents may be exposed to violence, bullying, victimization, and the presence of drugs. Dwelling in this kind of neighborhood environment, parolees have a more difficult time keeping remaining drug free and severing a criminal past.

Neighborhood context should be considered as a central factor in developing an explanation for reentry success and failure. It is time to ponder upon whether putting the effort primarily on changing the values, behaviors and employability of individuals is enough to reduce recidivism. It is imperative to provide released prisoners “social rehabilitation”—to help released prisoners build social relationships as a central part of positive reentry processes (Burke et al., 2018). Human relationships influence human behavior and identity. To help released prisoners build humanistic and holistic identities, we should start by encouraging the community to treat them as returning citizens and community members instead of felons. Re-connecting with the community has proven critical to helping released prisoners build a sense of confidence, self-esteem, and establish meaningful social connections with other people (Burke et al., 2018; Cohen, 2019; McNeill, 2019).

Third, there is an interplay between neighborhood context and PO support in the reentry process. Parolees from well-ordered communities received a substantially higher level of PO support. In addition, the inhibitory effect of PO support on recidivism was not uniform across neighborhoods. PO support demonstrated an amplified protective effect against recidivism for parolees who dwelled in well-ordered communities. It is possible that a well-ordered neighborhood poses fewer challenges for individuals to adhere to the terms of their supervision, which increases their chance of having a positive parolee-PO relationship. It is also probable that individuals from better-organized neighborhoods make a more positive impression on criminal justice agencies; thus, they are more receptive to officers’ advice and instructions. In contrast, parolees from disorganized neighborhoods are socially marginalized due to poverty and other forms of disadvantages that come along with the deterioration of their communities. With limited opportunities for education and job training, residents from disorganized neighborhoods can fall prey to their environment and be pushed to engage in illegal activities to financially survive and support their families (Burke et al., 2018; Clear et al., 2003; Hipp et al., 2010). Once snared by the criminal justice system, parolee residents from these neighborhoods will experience another type of marginalization: When they strive to reenter society, the inadequate support they receive from community correction officers, unfortunately, pushes them toward a higher risk of reentry failure.

Residents in disadvantaged neighborhoods, mostly black and brown, face segregation in housing, social service resource, and job opportunities (Anderson, 1999; Wilson, 2012). As a result, their vulnerable social and economic status are sustained and replicated. This study illustrated that the criminalization of racial minorities and lower-income individuals are being replicated as well. As the study reveals, PO support is geographic sensitive; people from disorganized communities receive less PO support and face a higher risk of failing to complete parole and returning to prison. Therefore, racial and income-based disparities have a diffusive effect not only on the police service people receive but also on all aspects of the punishment system.

Although this study has made a genuine contribution to the reentry literature, a few limitations should be noted. First, although this study illustrated the contextual effects of community cohesion and disorder on reentry outcomes, the economic and structural aspect of community context was not examined in this study. Therefore, the confluence of weak community cohesion, poverty rate and depletion of resources on reentry outcomes remains unknown. Future studies should assess how these factors simultaneously affect individuals’ reentry process. Second, the influence of local gentrification on criminal-justice involved individuals’ reintegration was beyond the scope of this study. A group of large cities in the U.S. and European countries experienced extensive gentrification in recent years, which has created new challenges for low-income, urban residents. Few studies have compared reentry lives in gentrifying and non-gentrifying neighborhoods. Future studies should capture the impact of gentrification in reentry outcomes among individuals who reside in metropolitan areas.

Apart from future directions, findings from this study can point toward useful recommendations for improved reentry programming. First, policymakers should properly address the disorder issue in communities. This is not to suggest that authorities should impose the tightest policing in disordered neighborhoods. The draconian policing delivered to minority neighborhood under the stop-and-frisk policy in New York City proved to be a toxic strategy to address neighborhood disorder as it destroyed citizens’ trust in criminal justice agencies (White & Fradella, 2016). Community disorder is a complicated social problem that is rooted in resource depletion, poverty, and lack of cooperation by government agencies; it should be resolved under a collaborative problem-solving orientation. Criminal justice system is not a “catch-all” for societal ills. Dialogue between criminal justice and social service agencies should be initiated and substantial planning to cooperate and facilitate change in disordered communities should be formulated. In addition, policies should be developed based on a thorough understanding of needs and expectations from local residents. Disorder issues are more likely to be resolved when criminal justice and social service agencies have clear understandings of what other departments are doing as well as the specific needs of a community.

Second, this study indicates that respectful and supportive treatment from POs can significantly facilitate parolees’ reentry success. Community correction agencies should develop training programs that strengthen POs’ capacity for communication with parolees. POs should build relationships with parolees based on respect and support, which can foster parolees’ acceptance of POs’ advice and instructions. Past studies demonstrated that parole officers showed significant increases in consistent behaviors (such as reflective listening) upon finishing an MI training workshop (e.g., Miller & Mount 2001). The demonstration of consistency in parole practice will likely enhance officers’ capacity in building rapport with parolees. It is also imperative to include parolees’ reentry outcomes as measures for POs’ appraisal systems to evaluate POs’ work performance.

Third, this analysis captured disparate treatment by POs depending on parolees’ neighborhood conditions. To address this disparity in community correction practices, correction agencies should provide training for POs to enhance their professionalism and help them develop supportive relationships with the communities they serve, especially the vulnerable communities that are depleted of housing, health and other social service resources. Individuals from this type of community have been marginalized by society, and correction agencies should not marginalize them again during community supervision. A supportive relationship between POs and the community they serve can enhance the image of criminal justice practitioners in these communities, which facilitates effective communication between POs and parolee residents. After all, in order to break the malicious cycle of release and reentry failure, a synthesized effort from communities, correction and social service agencies must be fostered.

Footnotes

Appendix

Appendix A.

Item Loadings of Latent Variables.

	Mean	Factor loadings
Financial difficulty	—	—
How hard is it to make enough money to support yourself? (1) very easy to (4) very hard	2.35	0.86
How hard is it to find a place to live?	2.12	0.81
How hard is it to find a job?	2.34	0.77
How hard is it to pay off debts?	2.56	0.76
Family bond
To what extent do you agree with the statement? There was someone in the family with whom I can have a good time. (1) strongly disagree to (4) strongly agree
There was someone in the family with whom I can to relax.	3.35	0.94
There was someone in the family with whom I can do enjoyable things.	3.31	0.96
There was someone in the family with whom I can spend time and get my mind off things.	3.32	0.92
There was someone in the family to love me and make me feel wanted.	3.25	0.95
There was someone in the family to listen to me when I need to talk.	3.54	0.93
There was someone in the family with whom I can have a good time.	3.34	0.90
There was someone in the family to whom I can talk about problems.	3.35	0.91
There was someone in the family with whom I can share my most private worries and fears.	3.24	0.94
There was someone in the family whom I can turn to for suggestions about how to deal with a personal problem.	3.21	0.93
There was someone in the family from whom I can get general advice.	3.39	0.95
Support from parole officers (PO support)	—	—
To what extent do you agree with the statement? Parole agent was helpful with your transition. (1) strongly disagree to (4) strongly agree	2.01	0.72
Parole agent seemed trustworthy.	1.91	0.88
Parole agent gave you correct information.	1.85	0.86
Parole agent acted too busy to help you.	2.08	0.74
Parole agent treated you with respect.	1.79	0.84
Parole agent acted professionally.	1.77	0.89
Parole agent didn’t listen to you.	1.83	0.76
Legal cynicism	—	—
To what extent do you agree with the statement? Laws are made to be broken. (1) strongly disagree to (4) strongly agree	2.96	0.73
It’s okay to do anything you want as long as you don’t hurt anyone.	2.91	0.78
To make money, there are no right and wrong ways, only easy and hard ways.	2.94	0.79
Fighting between friends or within families is nobody else’s business.	2.74	0.68
Nowadays a person has to live pretty much for today and let tomorrow take care of itself.	2.51	0.51

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Lin Liu

Notes

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Parole,Neighborhood and Reentry Outcomes: A Contextualized Analysis

Abstract

Keywords

Introduction

Literature Review

The Relevance of Neighborhood Cohesion and Disorder in Understanding Reentry Outcomes

The Contextual Nature of Parole Practice

Methods

Data

Measures

Dependent variables

Recidivism

Post-release substance use

Independent Variables

Neighborhood level predictors

Neighborhood cohesion 1

Neighborhood disorder

Individual level variables

Financial difficulty

Family support

PO support

Legal cynicism

Control Variables

Results

Discussion

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

ORCID iD

Notes

References

Neighborhood cohesion¹