Ecologies of juvenile reoffending: A systematic review of risk factors

Journal of Criminal Justice xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Journal of Criminal Justice journal homepage: www.elsevier.com/locate/jcrimjus

Ecologies of juvenile reoffending: A systematic review of risk factors Leah A. Jacobsa, , Laura Ellen Ashcrafta, Craig J.R. Sewalla, Barbara L. Folbb, Christina Mairc ⁎

a

School of Social Work, University of Pittsburgh, 2217D Cathedral of Learning, 4200 Fifth Avenue, Pittsburgh, PA 15260, United States of America Health Sciences Library System, University of Pittsburgh, 200 Scaife Hall 3550 Terrace Street, Pittsburgh, PA 15261, United States of America Graduate School of Public Health, Department of Behavioral and Community Health Sciences, University of Pittsburgh, 6136 Public Health, 130 De Soto Street, Pittsburgh, PA 15261, United States of America

b c

ARTICLE INFO

ABSTRACT

Keywords: Ecological effects Recidivism Juvenile justice Risk factors Systematic review

Purpose: Research on juvenile reoffending has experienced an ecological turn, marked by an impressive expansion of studies that test the relation between elements of residential context and reoffending. Yet, to date, little consensus exists regarding what ecological factors matter, how they affect reoffending, and for whom they matter most. To address this gap, this study takes stock of research that tests the relationship between ecological factors and reoffending among youth. Methods: A systematic review, in accordance with PRISMA-P guidelines, of quantitative studies (k = 27) was conducted. Evidence was synthesized quantitatively (i.e., meta-analytically, tabularly) and qualitatively (i.e., narratively). Results: A variety of ecological factors have been tested, but results are inconsistent and reflect relatively few contexts and samples. The most frequently tested factor, concentrated disadvantage (k = 15), is a predictor of rearrest (pooled OR = 1.09, p = .01). Inconsistent findings regarding other factors seem to reflect sample and study characteristics. Conclusions: Research to date does not indicate summarily rejecting or accepting ecological factors as risk factors for reoffending. To further clarify the ecology-reoffending relationship and inform recidivism reduction interventions, future research should sample from unexamined contexts and test theoretically meaningful relationships via approaches that strengthen causal inference.

1. Introduction Repeat offending among justice-involved youth is a sizable problem with public safety consequences. Nearly half of youth adjudicated for a delinquent or criminal act will reoffend, and those who continue involvement in crime as adults ultimately account for the majority of all crime (Bullis, Yovanoff, Mueller, & Havel, 2002; Moffitt, Caspi, Harrington, & Milne, 2002; Moffitt, Caspi, Rutter, & Silva, 2001). Yet, not all youth reoffend. Identifying differences between those who do and do not reoffend—and related risk and protective factors for reoffending—is one of criminology's central aims. Researchers have identified risk and protective factors for reoffending to inform risk assessment instruments and, hypothetically, to guide recidivism reduction interventions (Kennealy, Skeem, & Hernandez, 2017; Singh et al., 2014). Much of this work has focused on individual (e.g., anti-social cognitions) and relational (e.g., peer associations) risk factors for reoffending. Despite progress in these areas, the ability to predict and prevent reoffending remains limited (Schwalbe,

⁎

2007). If the individual and relational factors examined to date only partially explain reoffending among youth, what other factors may have predictive utility? Social structural, economic, and systemic ecological factors, also referred to as “macro,” “environmental,” or “neighborhood” effects, represent other potential sources of risk for reoffending. In the past decade, numerous studies of reoffending among youth have emerged as part of a broader “cottage industry” (Sampson, Morenoff, & Gannon-Rowley, 2002) in ecologically-oriented research. In the wake of this ecological turn, consensus has yet to be reached regarding what ecological factors matter for reoffending, how they unfold to affect reoffending, or for whom they matter most. This paper takes a step toward filling this gap by systematically reviewing research on the ecology-reoffending relationship among youth, and qualitatively and quantitatively assessing the effects of ecological factors on youth reoffending.

Corresponding author. E-mail address: [email protected] (L.A. Jacobs).

https://doi.org/10.1016/j.jcrimjus.2019.101638 Received 24 September 2019; Received in revised form 26 October 2019 0047-2352/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Leah A. Jacobs, et al., Journal of Criminal Justice, https://doi.org/10.1016/j.jcrimjus.2019.101638

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

1.1. Risk factors for reoffending

factors for juvenile reoffending (see Fig. 1). Contemporary research on the ecology-reoffending relationship is primarily built upon prior research on the ecology-crime relationship and the ecology-delinquency relationship. We describe these two areas of research below, highlighting the ways in which their theoretical bases and empirical findings may and may not support the ecology-reoffending relationship. Where possible, we turn to prior systematic and narrative reviews, which offer important distillations of these foundational bodies of research but have yet to be completed for research focused on the ecology-reoffending relationship. Research on the ecology-crime relationship has long established that crime and delinquency rates are elevated in communities that share certain social and structural features (Pratt & Cullen, 2005; Sampson et al., 2002). Theories of social disorganization and, relatedly social capital, cohesion, and control, are among those most popular for explaining the relationship between community features and crime and delinquency rates. In the most basic sense, theorists in this vain argue that disorganized communities reflect the intersection of poor economic conditions, residential turnover, and elements of demographic and family composition (e.g., ethnoracial diversity and concentration, concentration of youth, female-headed households; e.g., Sampson, 1986a; Shaw & McKay, 1942). When these factors combine, their “concentration effects” produce crime and other negative social outcomes (Wilson, 2012). Social capital, collective efficacy, and control theorists (Bursik Jr & Grasmick, 1993; Sampson, Raudenbush, & Earls, 1997), argue that the relationship between these social and structural features and crime rates are at least in part explained by related reductions in formal and informal social capital and control, which are necessary to collectively control behavior and guard against the occurrence of crime (see also Wilson, 2012). This latter group of theories, with their ability to connect macro- and microforces to crime and delinquency, have increasingly garnered attention (see, e.g., Groff, 2015; Sampson, 2012; Warner, 2014). Empirical research provides support for the ability of social disorganization-related theories to explain variation in crime and delinquency rates between communities, while some research indicates alternative theories may also be relevant. In a meta-analysis of “macro” predictors of crime (k = 216), Pratt and Cullen (2005) found neighborhoods with higher crime rates tended to be distinct in dimensions relevant to social disorganization and social capital. Specifically, high

As we suggest above, research on risk factors for juvenile reoffending has been dominated by a focus on individual and relational characteristics. Several reviews provide useful summaries of this literature (see Cottle, Lee, & Heilbrun, 2001; Dowden & Brown, 2002; Loeber & Dishion, 1983; Piquero, Jennings, Diamond, & Reingle, 2015; Wibbelink, Hoeve, Stams, & Oort, 2017). Generally, age, gender, and race predict reoffending (Piquero et al., 2015), with younger, male, and Black youth being more likely to reoffend– though there is some evidence that the effect of race disappears when conditioned on socioeconomic status (Cottle et al., 2001). Reoffending is also often, though not always, predicted by delinquent history, negative use of leisure time, family and parenting factors, association with anti-social peers, substance use, neurodevelopmental factors, and mental health problems (c.f. internalizing and severe disorders; Basto-Pereira, Começanha, Ribeiro, & Maia, 2015; Cottle et al., 2001; Wibbelink et al., 2017). The relevance of some of these factors to reoffending also varies along demographic dimensions (Basto-Pereira et al., 2015). Prior research on individual and social factors highlights some important indicators for risk prediction and prevention. Yet, a systematic review of standardized risk assessment tools (k = 28; Schwalbe, 2007) indicated many instruments informed by this literature are marginally more accurate in predicting reoffending than flipping a coin (mean AUC = 0.64, where AUC = 0.50 indicates the instrument is as likely to incorrectly predict recidivism as it is to correctly predict reoffending). In the absence of tools with greater predictive utility and interventions that target known risk factors, repeat offending among youth continues. 1.2. The ecological (Re)turn in research on reoffending: Foundations and challenges In 1942, Ernest Burgess wrote, “If we wish to reduce delinquency, we must… think of its causes more in terms of the community and less in terms of the individual” (in Shaw & McKay, 1942, pp. xiii). Nearly 80 years after Burgess wrote about the relationship between residential context and delinquency, scholars of juvenile reoffending are heeding Burgess' words. This ecological turn, or return, is evidenced by a significant increase in the number of publications focused on ecological

Fig. 1. Count of manuscripts that test the relationship between ecological factors and juvenile reoffending (1983 – present). 2

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

crime neighborhoods were high in concentrated disadvantage (poverty, racial composition, unemployment, and family disruption) and residential mobility, and tended to be low in collective efficacy and social ties (measures of social interactions and strength of social ties; e.g., social capital; mean effect sizes = 0.17–0.41). Pratt and Cullen also found unsupervised local peer groups and inequality (e.g., the Gini Index or measures of group differences in socioeconomic status) were relatively strong predictors of crime rates, indicating theories other than social disorganization, such as theories of relative deprivation and/or contagion (see Dishion & Tipsord, 2011; Odgers, 2015), may also help explain variation in crime rates between communities. Beyond research on macro-level causes of community crime and delinquency rates, research on the ecology-reoffending relationship is informed by a corpus of scholarship on the cross-level effects of ecological factors on individual-level outcomes, including delinquency. In a review of 40 observational studies, Sampson et al. (2002) found that, much like crime and delinquency rates, indicators of youth wellbeing were predicted by structural factors of concentrated disadvantage and residential mobility, as well as social ties and norms (including social cohesion, social control, and surveillance), institutional resources (e.g., presence of schools, social, or health services), and routine activities (i.e., everyday spaces that present opportunities for delinquency, like transportation nodes; see Felson, 2013). Three studies included in Sampson et al.'s review focused on delinquency. Two of these studies found neighborhood disadvantage was associated with delinquency among some youth, depending on age and individual risk factors (see Seidman et al., 1998; Wikström & Loeber, 2000). The third study found neighborhood conditions were not associated with delinquency, but that more “proximal” social factors (e.g., exposure to delinquent peers) were highly predictive (see Lanctot & Smith, 2001). In addition to these observational studies, several papers from the Moving to Opportunities experiment (MtO; see Sanbonmatsu et al., 2011) have focused on delinquency. In MtO, low income families were randomized to one of three groups: 1) those who received a housing voucher for use in a low-poverty neighborhood, 2) those who received a housing voucher for use in any neighborhood, and 3) those who stayed in their current public housing (the control). For behavioral measures of delinquency (parent-reported or self-reported delinquent behavior), early comparisons between those who relocated to less disadvantaged communities and control group members were mixed. Some MtO analyses indicated moving to a less disadvantaged context reduced boys' behavior problems, and others found no evidence of effects on behavior problems (Katz, Kling, & Liebman, 2001; Leventhal & Brooks-Gunn, 2003). Later results indicated movement to less disadvantaged contexts increased delinquency among boys who moved as teens, and produced no statistically significant effects for girls or younger boys (Schmidt, Krohn, & Osypuk, 2018). For administrative measures of delinquency (i.e., arrests), girls in MtO who moved to less disadvantaged contexts had fewer arrests for property and violent crimes compared to control group members, while their male counterparts had reductions in arrests for violent crimes but increased arrests for property crimes (Sciandra et al., 2013; see also Kling, Ludwig, & Katz, 2005; Ludwig, Duncan, & Hirschfield, 2001a). These effects attenuated over time, along with changes in neighborhood conditions. Together, macro-level research on crime and cross-level research on delinquency provides some support for the relationship between ecological factors and reoffending, and some insight as to what ecological factors may matter and how. Ecological factors, especially those related to disadvantage, residential instability, social capital and cohesion, the presence of unsupervised peer groups, and inequality, differentiate crime rates across communities. In some cases, these ecological factors also predict individual level delinquency. Studies on the ecology-delinquency relationship also suggest ecological effects unfold through more proximal forces, interact with age and gender (and related differences in social networks and individual experiences), and are contemporaneous (i.e., as residential contexts change, delinquency risk

changes; effects are situational, and not the result of an indelible effect made during a specific developmental period; Sciandra et al., 2013). They also suggest that effects may vary depending on how delinquency is operationalized (i.e., as self-reported or observed behavior verses administratively recorded criminal justice outcome, like arrest). Though macro-level research on crime rates and cross-level research on delinquency have offered these important insights, their application to research on reoffending also faces potential limitations. First, assuming theory and evidence from research on the ecology-crime relationship can be directly applied to the individual behavior of youth amounts to the ecological fallacy (i.e., inferring delinquent behavior among residents from their community's crime and delinquency rates). Second, research on the ecology-delinquency relationship may differentiate delinquent behavior across the general population but not within a sub-population of previously adjudicated youth. Ecological factors have heterogeneous effects. Thus, it is possible, for example, that ecological factors have stronger effects on offending or reoffending among those who possess fewer or weaker individual risk factors, while those who possess more or stronger individual risk factors may offend or reoffend regardless of residential context (see Wikström & Loeber, 2000). By focusing on youth from the general population or from lowincome families, estimates of ecological effects on delinquency from the studies described above may be greater than the effects of ecological factors on reoffending for justice-involved youth, who likely possess more (or more significant) individual and relational risk factors by virtue of their prior system involvement. In the most basic sense, questions of whether or how ecological factors predict crime rates or delinquency in the general population, are distinct from questions of whether or how ecological factors predict reoffending among justice involved youth. In light of these distinctions, understanding and strengthening research on the ecology-reoffending relationship requires an independent synthesis of evidence. 1.3. Methodological considerations for studying the ecology-reoffending relationship Beyond a dedicated focus, distilling research on ecological risk factors for reoffending requires attention to the methodological specificities of both risk factor research and research that tests relationships across units of analysis (i.e., from macro factor to individual behavior). The traditional risk factor framework, proposed by Kraemer et al. (1997) and adapted to offending by Murray, Farrington, and Eisner (2009) (see also Monahan & Skeem, 2013), defines risk factors as correlates that temporally precede outcomes and defines causal risk factors as factors that vary and alter outcomes as a result of their variation. While experiments are well-suited to establishing causality, well-designed observational studies may closely approximate their findings. Observational studies that approximate experimental studies tend to (a) be well-controlled (i.e., measure potential confounders prior to risk factors, and use comparison groups or statistical procedures to account for confounders, e.g., propensity scoring, multivariate regression), and (b) measure within-individual change in risk factors. The ability of such observational studies to identify risk factors and likely causal relationships between risk factors and reoffending is important, given experiments are often difficult or impossible to conduct where risk factors for reoffending are concerned. Identifying ecological risk factors for reoffending brings additional challenges. Scholars of ecology face an array of methodological complications, which have been discussed in depth elsewhere (see Diez Roux & Mair, 2010). These challenges include the modifiable areal unit problem (MAUP) and uncertain geographic context problems (UGCP), which speak to the introduction of error dependent on how geographic units are defined and differential exposure of individuals to those units (Fotheringham & Wong, 1991; Kwan, 2012). Scholars of ecology also face challenges in establishing causal risk factors (Diez Roux & Mair, 2010). By virtue of their very scale, exposure to macro-level factors is not easily manipulated and, as a result, experiments are seldom 3

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

for the search concepts, ecological characteristics and reoffending, from on-topic articles and preliminary database search results (see search strategy in Appendix 1).1 We also contacted experts to request suggestions for relevant studies until saturation was reached (i.e., until no new studies were recommended). This resulted in 59 recommendations, 7 of which were added to the review. In total, we retrieved and reviewed 2719 unique citations (see Fig. 2).

employed. In the absence of such techniques, ecological research is challenged by the potential for social selection, whereby the factors that lead people to live in certain contexts may confound the association between the qualities of those contexts and outcomes (Cheshire, 2012). As previously discussed, one experimental study (MtO) has examined the relationship between moving to less disadvantaged contexts and delinquency. We know of no experimental study that has examined residential movement's effect on reoffending among youth. Still, there is good reason to take stock of what research does exist. First, as previously discussed, well-designed observational research can speak to causal risk factors for reoffending and, in turn, inform interventions to curb reoffending. Second, identification of risk factors for reoffending, even if not causal, has relevance to risk assessment and the identification of youth likely to reoffend. Studies that identify ecological risk factors can also provide insight into the complex relationships between contexts and outcomes; they can identify which elements of environments are associated with outcomes, assess specific patterns of interaction between individuals and their environments, and test group differences across these associations and patterns (Sharkey & Faber, 2014). As noted by Sharkey and Faber (2014), researchers should avoid reducing such complexity to questions of whether or not residential context “matters,” and instead pursue research that can answer “when, where, why, and for whom…residential contexts matter” (p. 562). The current study heeds this advice, while responding to calls for systematic reviews that speak to risk factors and possible causes of reoffending.

2.2. Study selection Study abstracts were reviewed by two reviewers (LEA and CJRS) with conflicts resolved through consensus and discussion with the principal investigator (LAJ). Full texts of included studies were double coded (LEA and CJRS) with conflicts reconciled through discussion and review by the principal investigator (LAJ). Studies that successfully met criteria at both levels of screening were included for data extraction and critical appraisal. Two reviewers (LEA and CJRS) culled study information, using an extraction form in Qualtrics (Qualtrics, Provo, UT). Data extracted included sample characteristics, follow-up, geographic unit, statistical approach, ecological exposures, controls, outcomes, and results. We also noted studies that tested mediation and moderation, in order to examine how and for whom neighborhood environments relate to recidivism. Conflicts in extracted data were reconciled through review by the principal investigator (LAJ). References and review procedures were managed with systematic review software, DistillerSR (Evidence Partners, Ottawa, Canada).

1.4. Current study

2.3. Synthesis and critical appraisal

The aim of this paper is to synthesize and assess research on the ecology-reoffending relationship. Specifically, we systematically review studies to answer two research questions: (1) What, if any, ecological factors predict reoffending among youth? And, (2) where research supports the relationship between ecological factors and reoffending, what is the nature of that relationship (i.e., how and for whom do ecological factors matter for reoffending)? In the remainder of this paper, we synthesize studies to identify likely socio-structural, economic, and justice systemrelated ecological risk factors for reoffending. We then assess what these studies say about where, when, why and for whom contexts matter for reoffending, and whether current research provides correlational or causal support. We conclude by discussing lessons learned, emphasizing implications for future research that seeks to strengthen risk prediction and inform interventions to reduce reoffending and related collateral consequences.

We employed both qualitative and quantitative synthesis and analytic strategies. First, to establish a minimum level of quality for included studies and to speak to the kind of support (e.g., correlational or causal) provided for the ecological factor-reoffending relationship, we used Murray et al.’ (2009) methodological quality checklist. We scored each study based on its ability to establish correlates, risk factors, and causal risk factors (see Appendix 2 for a description of criteria; see Murray et al., 2009 for a detailed discussion). Based on McCoach's (2018) criteria for assessing multilevel research reporting, we added criteria for (1) number of geographic units and participants per geographic unit, and (2) adequacy of approach to analyzing spatial data.2 Prior to assessing support for specific ecological factors, we classified the ecological factors examined. Adapting Pratt and Cullen's (2005) classification of macro crime predictors, we assessed evidence for factors in three broad groups (see Table 2)– social-structural (e.g., concentrated disadvantage), economic (e.g., unemployment), and justice system-related (e.g., policing). Within these groups, we categorized variables into 15 factors, combining like with like based on operational definitions (e.g., combining median household income and poverty rate into the “income” factor).

2. Methods This study followed the guidelines set forth in Preferred Reporting Items for the Systematic Review and Meta-Analysis Protocols (PRISMAP; Moher et al., 2015), with further guidance from Murray et al. (2009). Our inclusion criteria (see Table 1 for further detail) yielded studies that (1) were conducted in the United States from January 1983 to April 2019; (2) sampled people with index offenses before the age of 18; (3) tested the relationship between an ecological factor and an outcome of re-offense (self-reported or administratively recorded); (4) had an observation period of six months or greater; and (5) used a longitudinal design. Together, these criteria resulted in a body of studies that tested the degree to which one or more ecological factors predicted future offending among previously adjudicated youth, most often above and beyond (or in interaction with) other individual risk markers or factors for reoffending (i.e., gender, age, and delinquency history or risk score).

1 After testing, relevant and productive terms were used to search the following databases: Criminal Justice Abstracts (Ebsco), SocINDEX (Ebsco), and PsycINFO (OVID). Limits for English language and publication date from 1983 to present date were used. Grey literature sources from these databases were retained and supplemented by simplified searches run in the National Criminal Justice Reference Service Virtual Library (https://www.ncjrs.gov/library.html). Criminal Justice Abstract search terms are listed in Appendix 1. The remaining searches are available upon request. 2 For a detailed discussion of quality criteria for multilevel studies, see Hancock et al. (2010). We also modified Murray et al.'s criteria by treating longitudinal studies that relied on previously collected administrative datasets as retrospective data. We did so because the reliability of these data may vary depending on the quality of administrative record keeping procedures and typically administrative systems lack the capacity to fully assess data quality. This is a divergence from Murray et al.'s (2009) approach which only considers selfreport data as retrospective.

2.1. Information sources and search strategies We included peer reviewed journal articles and studies from grey literature (e.g., conference papers, government reports, book chapters). A librarian trained in systematic review methods (BLF) harvested terms 4

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

Table 1 Inclusion criteria. Category

Definition

Location and date Sample Predictor variable: Ecological factor

United States, January 1, 1983 – April 22, 2019a Youth; i.e., sample was adjudicated before the age of 18 (Spruit, van Vugt, van der Put, van der Stouwe, & Stams, 2016) A characteristic of youths' residential context measured in individual units (e.g., self-reported neighborhood social capital) or in aggregate (e.g., median income in a census tract)b; see Table 2 for a descriptions of ecological factors A self-reported or administratively recorded violation, delinquent, and/or criminal act, following prior adjudication (see Andersen & Skardhamar, 2017)c The period of observation for individuals in the sample was six months or greaterd Quantitatively tested the relationship between an ecological factor and reoffending, with ecological factors preceding reoffending

Dependent variable: Reoffending Follow-up Longitudinal or Experimental Design

a Given relevant methodological advancement and the uniqueness of the social and juvenile justice context in the United States, we constrained our review to articles published between January 1983 and April 2019 and those conducted within the United States. b We included studies on residential mobility (i.e., measuring the effect of moving on reoffending) only in cases where ecological factors were considered in addition to mobility. c By this definition, the re-offense may have occurred in adulthood. d Observation periods less than six months can render unreliable measures of reoffending, depending on the operationalization of reoffending and administrative processing times (see Ostermann, Salerno, & Hyatt, 2015).

Next, we assessed support for the ecological factor-reoffending relationship and the nature of this relationship. The high degree of variability in ecological factor measurement precluded the use of a quantitative data synthesis (i.e., meta-analysis) in most cases. As such, we tabulated frequencies for significant tests of each factor's association with reoffending and narratively synthesized these tabulations with other abstracted study information (e.g., study design, quality, etc.; Popay et al., 2006). We also narratively synthesized results from moderation and mediation analyses in order to speak to potential differences in effects by sub-groups and the mechanisms through which ecological factors may impact reoffending. Where possible, we assessed support for the ecology-reoffending relationship meta-analytically. To do so, we used a random effects approach to calculate a pooled log odds for the effect of the ecological factor on reoffending, accounting for within (i.e., sampling) and between (i.e., due to differences in study sample and characteristics) study variability (Hedges & Vevea, 1998). To account for the small number of studies included in the meta-analysis and overlap in data sources, we used a robust model and clustered by study data source. We examined study heterogeneity with Q and conducted sensitivity analyses, running the model repeatedly with each study removed. Analyses were conducted with the metafor package in R (Viechtbauer, 2010).

had mixed gender samples; six had male-only samples and one had a female-only sample. Five studies focused on serious offenders, and three focused on first time offenders. To measure ecological factors, researchers commonly drew from secondary data sources. They typically coded ecological factors based on the Census tract (n = 8), zip code (n = 7), or neighborhood (self-defined or other ecologically valid source; n = 9) of residence, and less commonly used Census block or distance from youths' homes. Only one study (Bright, Hurley, & Barth, 2014) used large geographic units, such as counties, regions, or states. As illustrated in Table 4, studies operationalized “reoffending” most often as a binary criminal justice outcome (e.g., arrest, reconviction, arrest for a specific delinquent act; n = 20), and less often as a behavioral outcome (i.e., self-reported delinquent behavior; n = 5). Six studies examined reoffending by type (e.g., violent, drug, property). A handful supplemented their analysis of binary outcomes by also examining time to arrest, arrest counts, or charge severity. Generally, studies were well-designed for the assessment of risk factors, longitudinally testing the degree to which ecological factors predicted reoffending among youth, above potential confounders (see Table 4). Observation periods ranged from six months to twelve years. Except Wolff et al.'s study (2017), all used a single residence (typically measured at baseline) to represent residential context for the entire follow-up period (i.e., treating ecological factors as invariant). Except Barnes (2017), all controlled for individual-level risk markers or factors for reoffending. None included comparison groups or employed matching or propensity score approaches to compare those exposed or unexposed to ecological factors. Researchers most often analyzed the relationship between ecological factors and reoffending with logistic regression. Nearly half utilized a multilevel framework (i.e., modeling within and across ecological unit effects; e.g., hierarchical logistic regression). Assessing evidence of variation in study results by the abovementioned study characteristics, some patterns indicating variation in effects by geographic unit, analytic approach, and outcome definition emerged (see Table 5). We found few differences in tests that did and did not support the relationship between ecological factors and reoffending in terms of the geographic unit used, with the exception of zip code. Looking across all studies, a larger proportion of studies with significant effects used zip code as their unit of analysis. Compared to methods like logistic regression, we found ecological factors were less frequently statistically significant predictors where structural equation modeling (SEM) was used to test associations. As for differences by outcome definition, statistically significant associations were more common when reoffending was measured as a binary criminal justice outcome (i.e., arrest or reconviction; 36/63 tests; k = 18), than as a binary self-reported delinquent behavior (5/16 tests; k = 5). Where offense-specific outcomes were tested, drug-related offenses were most reliably predicted (5/6 tests; k = 6).

3. Results Twenty-seven studies met inclusion criteria (see Table 3). To contextualize our assessment of the ecology-reoffending relationship, we first describe study characteristics, including factors tested, location, design, measures, and statistical analyses. We note patterns that indicate variation in effects based on these characteristics. Next, we assess evidence for the relationship between specific ecological factors and reoffending. Finally, we describe study results as they pertain to tests of moderation and mediation, summarizing existing research on the social processes at play in the ecology-reoffending relationship. 3.1. Study characteristics and methods Study characteristics and methods are summarized in Tables 3 and 6. Although each of the 27 studies presented one or more unique tests of the relationship between an ecological factor and reoffending, we observed some overlap in samples. Eight studies drew data from a juvenile justice database in Florida, four studies drew data from a juvenile justice database in Philadelphia, Pennsylvania, and four studies used data from a longitudinal study of serious youthful offenders in both Maricopa County, Arizona, and Philadelphia, Pennsylvania (see Mulvey, 2011). In other words, nearly 60% of studies reflect just three locations. In terms of sample characteristics, mean age ranged from 13 to 20. Most 5

Journal of Criminal Justice xxx (xxxx) xxxx

Identification

L.A. Jacobs, et al.

Records identified through database searching (n = 3,716)

Additional records identified through other sources (n = 59)

Records after duplicates removed (n = 2,719)

Eligibility

Full-text articles assessed for eligibility (n = 60)

Studies included in quality assessment (n = 32)

Included

Screening

Records screened (n = 2,719)

Studies included in synthesis (n = 27)

Records excluded (n = 2,130)

Full-text articles excluded, with reasons (n = 557) (not in United States, population, qualitative/mixed methods, systematic narrative review, reprint, no report of association between ecological factor and reoffending, other, adult)

Records Excluded with reasons (n = 5) (observation period less than 6 months, insufficient information after at least three attempted contacts)

Fig. 2. Summary of search and review results.

3.2. What, if any, ecological factors predict reoffending among youth?

that shape residential contexts. These factors include concentrated disadvantage, crime and disorder, demographic composition, racial/ ethnic inequality, residential stability, population density, access to resources, and presence of positive adults. More than half of sociostructural factor tests demonstrated statistically significant protective or risk enhancing effects on reoffending (42/80 tests; k = 25). Concentrated disadvantage (typically comprised of poverty, unemployment, female-headed households, and receipt of public benefit measures) was by far the most commonly tested factor, and the only factor tested in enough studies to permit meta-analytic summation. A statistically significant risk factor for reoffending in 19 of 36 tests (k = 18), tabular analysis indicated concentrated disadvantage's effects were mixed. To assess whether these studies indicate an overall statistically significant relationship between concentrated disadvantage and reoffending and to calculate a pooled effect for concentrated

To assess which aspects of justice-involved youths' residential contexts (i.e., ecological factors) are most commonly tested and supported by existing research, we tabulated the frequency of factors tested and examined the statistical significance of tests of association between each factor and reoffending (see Table 6). These associations represented the main effects of ecological factors on reoffending, above and beyond control variables. We discuss 15 factors in three ecological domains: social-structural, economic, and justice system-related factors. Moving from most commonly to least commonly tested, we describe evidence relevant to each. 3.2.1. Social-structural factors A majority of studies focused on the social and structural factors 6

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

Table 2 Ecological factor domains, definitions, and variables. Ecological domain

Nominal definition

Variables

Synergy of social and economic factors representative of “truly disadvantaged” communities (Sampson et al., 1997; Wilson, 2012) Crime and physical and/or social incivilities (Sampson & Raudenbush, 1999) Population characteristics

Population density Access to resources Positive adults

The strength of relationships within communities (Putnam, 2001) The concentration of delinquent peers that may provide networks for and models of delinquent behavior (Warr, 1996) The unequal distribution of poverty by racial or ethnic group The degree of mobility in an area, which may disrupt social ties (Shaw & McKay, 1942) Geographic concentration of residents Access to community resources Presence of positive adults

Concentrated disadvantage, resource deprivation index (multidimensional measure usually including poverty, unemployment, receipt of public benefits, female-headed households, % children, and % Black) Social disorder (self-report), physical and social disorder (self-report), witnessing violent crime (self-report), crime rate Racial/ethnic heterogeneity, % unmarried, % youth, female headed households with youth, immigrant concentration Social capital (self-report) Area juvenile recidivism rate, area juvenile drug crime recidivism rate, area juvenile property crime recidivism rate, area juvenile violent crime recidivism rate Racial inequality, ethnic inequality Residential instability (multidimensional measure of % of homes owner occupied, length of residence) Density (# of persons per geographic unit) Self-reported access to resources in youths' communities Self-reported presence of positive adults in youths' communities

Economic factors Income Income Inequality Unemployment

Annual income or financial status of households The degree to which income is unequally distributed The percent of the population unemployed

Income per capita, poverty rate Concentrated affluence (ICE) Unemployment rate

Justice-related factors Community Services Policing

Rehabilitative services to which delinquent youth may be referred Police interventions^f

Count of community services per geographic unit Deterrence (multidimensional measure of arrests and convictions), Arrests (multidimensional measure of arrests and court referrals)

Social-structural factors Concentrated disadvantage Crime and disorder Demographic Social capital Delinquent local youth Racial/ethnic inequality Residential instability

disadvantage, we limited tests to analyses with the same outcome measure (re-arrest) and measure of association (odds ratio). For studies with multiple tests, we selected the test representative of the overall sample (i.e., excluding tests in sub-samples and models including interactions between concentrated disadvantage and other factors). Results from a random effects meta-analysis of these studies (k = 15), clustering by data source, indicate concentrated disadvantage had a small, statistically significant effect on re-arrest (see Fig. 3). For approximately every standard deviation increase in concentrated disadvantage, the odds of reoffending increased by 9% (OR = 1.09, p = 0.01).3,4 After concentrated disadvantage, crime and disorder was the most commonly investigated domain. Five out of twelve tests of crime and disorder factors yielded significant results (k = 7). Like concentrated disadvantage, results from crime and disorder were mixed. Self-reported experiences of neighborhood crime (i.e., perceived crime, witnessing neighborhood violence, and perceived disorder) were associated with increases in recidivism in three studies (β = 0.12–0.49; Bright, Hurley, & Barth, 2014; Chauhan & Reppucci, 2009; Leverso, Bielby, & Hoelter, 2015), and three studies found no evidence of association (Barnes, 2017; Piquero, Cardwell, Piquero, Jennings, & Reingle Gonzalez, 2016; Veeh, 2015). Effects held only for the outcome of self-reported violent reoffending in one study (β = 0.34; Leverso et al., 2015), and the effect of witnessing violence only held for self-reported general and non-violent reoffending in another study (β = 0.20 and.12; Chauhan & Reppucci, 2009). The one test of crime concentration (i.e., the crime rate) found an

inverse relationship between crime and the count of new charges for youth; as crime concentration increased, youths' number of charges decreased (β = −0.15, p < .05; Yan, 2009). When present, the effects of crime and disorder related variables appear specific to re-offense type and, at times, protective. Demographic indicators of residential contexts (i.e., racial/ethnic heterogeneity, percent married, percent youth, percent female headed housing with youth, and immigrant concentration) were statistically significant predictors in few tests (3/11; k = 8). Immigrant composition was the only statistically significant demographic predictor of reoffending. However, the direction of immigrant concentration's effects were contradictory; two studies reported immigrant concentration (measured as % foreign born and % linguistically isolated) had a protective effect (β = −0.05 - -0.10, p < .01; Wolff, Baglivio, Intravia, & Piquero, 2015; Wolff, Intravia, Baglivio, & Piquero, 2018) and one reported immigrant concentration among Latino immigrants (measured as % foreign born and % Latino) had a risk enhancing effect (β = 0.44, p < .05; Jeong, 2011). Aside from this complicated picture of immigrant concentration and reoffending, after adjusting for other factors, the demographic composition of a youths' residential context seems unrelated to recidivism. Few studies examined other social-structural factors. For residential instability (i.e., home ownership and length of residence) one out of three tests found it predicted reoffending but in a protective direction (β = −0.25; Jeong, 2011).5 One out of five tests (k = 2) of social capital found it predicted drug-related reoffending (β = −0.12; Grunwald, Lockwood, Harris, & Mennis, 2010), and one study found positive adults in the community predictive of reoffending (AUC = 0.59; Barnes, 2017). All seven tests of delinquent local youth (i.e., living among recidivating justice-involved youth; k = 2) predicted individual-level recidivism (β = 0.63–1.19; Harris, Mennis, Obradovic, Izenman, & Grunwald, 2011; Mennis & Harris, 2011). Specifically, general and crime-specific neighborhood recidivism rates (i.e., recidivism for any, drug, violent, or property crimes) predicted general and crime-specific

3 Concentrated disadvantage in these studies represents an aggregation of four to seven social and economic indicators. Each of these indicators was standardized, though not every study reported standardization procedures. Inspection of standard deviations indicated the mean standard deviation across studies was equal to 1.02. Therefore, we interpret each unit increase in concentrated disadvantage as approximating a standard deviation increase in concentrated disadvantage. 4 The parameter estimate from LeBaron (2002) was an outlier, though we were unable to confidently identify a reason for the larger effect size in this study. Conducting the analysis without LeBaron included, we found the effect of concentrated disadvantage decreased from OR = 1.09 to OR = 1.08, but remained statistically significant.

5 We have concerns about the operationalization of residential instability in this study. We attempted to contact the author to clarify the definition of residential stability and were unsuccessful.

7

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

Table 4 Quality assessment. Study authors

Baglivio et al. (2017) Barnes (2017) Bright, Hurley, & Barth, 2014 Bursik (1983) Chauhan and Reppucci (2009) Craig et al. (2017) Craig (2019) de Beus and Rodriguez (2007) Grunwald et al. (2010) Harris et al. (2011) Intravia et al. (2017) Jeong (2011) LeBaron (2002) Leverso et al. (2015) Lockwood and Harris (2015) McReynolds (2004) Mennis and Harris (2011) Piquero et al. (2016) Veeh (2015) Wolff et al. (2015) Wolff et al. (2016) Wolff et al. (2017) Wolff et al. (2018) Wright and Rodriguez (2014) Wright et al. (2014) Wright et al. (2016) Yan (2009)

1. Correlate score 1a

1b

1c

1d

1e

1 1 0 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 0 1 0 0

1 0 1 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0 0 0 1 1 1 0 1 0 1 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0

0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0

2. Design adequacy for spatial data

3. Design adequacy for establishing risk factors

4. Design adequacy for establish causal risk factors

Total

1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1

2 3 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 3 3 2 2 2 2 3 3 2 2

5 2 5 2 2 5 5 5 5 5 5 2 5 5 5 5 5 5 5 5 5 6 5 5 5 5 5

11 8 11 6 9 11 11 11 12 10 10 8 9 13 12 11 9 13 13 12 10 12 12 11 14 12 9

Note: Scores are not intended to assess overall study quality, but to assess quality specifically as it relates to establishing the relationship between ecological risk factors and reoffending. In some cases, this relationship was not a primary aim of the study assessed. For example, some studies control of ecological risk factors in order to strengthen their assessment of individual level relationships. For further detail, see Appendix 2 and Murray et al. (2009).

provider location and recidivism. One found youth who lived in the state where a delinquency reduction program originated and from which it was administered nationally, were less likely to recidivate than those who lived outside the originating and administrating state (β = −0.13; Bright, Hurley, & Barth, 2014). The other study found the relationship between community service providers and recidivism varied depending on the number of providers present in youths' residential contexts; the presence of any provider was negatively associated with recidivism (β = −0.19), while the presence of two or more had either no statistically significant effect or a recidivism-enhancing effect, depending on the outcome measured (Yan, 2009). Measuring policing, Bursik (1983) found deterrence in residential contexts (the rate at which police contacts result in an arrest or petition) discriminated recidivists from nonrecidivists (r = 0.31, p < .05), while the sheer amount of policing (the rate of police contacts in a community) did not. Too few studies of justice-related factors at a macro-level have been studied to draw conclusions regarding their relevance to reoffending.

individual recidivism among youth residents. One study examining the role of racial and ethnic inequality in disadvantage found small risk increasing and protective effects, respectively (β = 0.06, β = −0.06; Wright, Turanovic, & Rodriguez, 2016). The only study of population density found it predicted the odds and hazards of recidivating (β = 0.65, log(HR) = 0.35; LeBaron, 2002). The only study examining perceived presence of neighborhood resources found it predictive of reoffending (AUC = 0.63; Barnes, 2017). Conclusive statements regarding the relevance of these social-structural factors to reoffending is prohibited by their infrequent inclusion in studies. 3.2.2. Economic factors Relatively few studies (k = 4) examined the relationship between economic aspects of residential contexts and reoffending. Of those that did, few tests yielded significant results (2/7 tests). Specifically, income (0/3 tests; k = 2) and unemployment (0/2 tests; k = 2) were not associated with reoffending, while economic inequality (i.e., disparity in economic affluence measured by the Index of Concentration at the Extremes; ICE) was a statistically significant predictor (2/2 tests; k = 2). For economic inequality, as a neighborhood's proportion of affluent families increased, youths' risk of recidivism decreased (β = −0.83, β = −0.58; Baglivio, Wolff, Jackowski, & Greenwald, 2017; Wolff, Baglivio, Piquero, Vaughn, & DeLisi, 2016). Tests of economic factors seem to provide less support for the amount of absolute disadvantage, than for relative disadvantage or advantage—though these results should be interpreted with caution, given the small number of studies.

3.3. How and for whom do ecological factors matter for reoffending? To understand the social processes through which ecological factors may increase or decrease recidivism, we turn to results from the 15 studies that included some assessment of moderation or mediation. Eleven studies examined moderating effects of ecological factors. Five studies conducted mediation analyses. 3.3.1. Group differences in ecological vulnerability To answer for whom ecological factors matter, studies most frequently examined variation by gender and ethnoracial group membership. In the case of gender, risky environments appear more strongly associated with reoffending for female youth than male youth. Compared to their male counterparts, females had a greater risk of reoffending as concentrated disadvantage (McReynolds, 2004; Wolff,

3.2.3. Justice system-related factors A minority of studies examined justice system-related factors (i.e., the presence of and proximity to community service providers, the rate of policing, and the effectiveness of policing), with mixed results (3/9; k = 3). Two studies indicated some relationship between service 8

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

more disadvantage (β = 2.32). Six studies examined variation by race or ethnicity in the associations between reoffending and concentrated disadvantage, ethnic and racial inequality, immigrant concentration, and disorder. For concentrated disadvantage, Wright and Rodriguez (2014) found no evidence of variation by race and ethnicity; Chauhan and Reppucci (2009) found concentrated disadvantage was significantly associated with reoffending for neither White nor Black youth post-stratification; and Craig et al. (2018) found concentrated disadvantage was significantly associated with re-arrest for neither White nor Latino youth post-stratification. For inequality, Wright et al. (2016) found effects of racial inequality were slightly weaker for Latino than White youth (interaction β = −0.08), but did not vary between Black and White youth. They found no evidence of effect variation for ethnic inequality. Wright and Rodriguez (2014) found the effect of immigrant concentration on reoffending trended protective and was slightly lower for Latino youth than White youth (β = −0.08), but found no significant difference between Asian/Pacific Islander youth and White youth. The one study examining effect variation for physical and social disorder, did not find variation in effects by race or ethnicity (Piquero et al., 2016). In sum, effects of neighborhood environments on reoffending seemed to differ by gender, while variation by race or ethnicity was generally less evident. Twelve studies tested interactions between ecological factors and dynamic factors (i.e., intervenable; Bonta & Andrews, 2016). Studies examined the moderation of ecological risks by relationship factors, substance abuse, anxiety, and program participation (e.g. diversion, restorative justice). Craig et al. (2018) found slightly smaller effects for concentrated disadvantage among those with low social bond quality (β = 0.07) than those with high social bond quality (β = 0.10), though the authors did not test whether these differences were statistically significant. Baglivio et al. (2017) found concentrated affluence had a greater effect on reoffending among youth high in relationship- and/or substance abuse risk, than those lower in relationship risk (i.e., negative peer and non-family influences) and/or substance abuse risk—though these interactions were quite small (β = 0.02). Baglivio and colleagues

Table 5 Study results by outcome. –

+

NS

Total tests

Total studies

Re-arrest or reconviction (binary)

7

29

27

63

20

Re-arrest (any) Re-arrest (new charge) Re-arrest (specific) Reconviction/reincarceration

4 1 0 2

13 5 9 2

13 5 7 2

30 11 16 6

12 4 4 3

Self-reported offending (binary)

1

4

11

16

5

New offense (self-report) New offense (specific; self-report)

1 0

2 2

6 5

9 7

4 2

Other

2

6

13

21

3

Count of new offenses Count of new offenses (severity weighted) Time to new offense

2 0 0

2 2 2

6 5 2

6 5 6

2 1 1

Notes: – = statistically significant (p < .05), negative association between an ecological factor and the index outcome; + = statistically significant (p < .05), positive association between an ecological factor and the index outcome; NS = association between an ecological factor and the index outcome was not statically significant. Because some studies include multiple outcomes, the sum of the Total studies column is > 27.

Baglivio, Intravia, Greenwald, & Epps, 2017; Wright & Rodriguez, 2014; c.f. Craig et al., 2017) and immigrant concentration (Wright & Rodriguez, 2014) increased. In the case of males, exposure to less disadvantaged environments seemed to present a paradoxical risk. Wolff et al. (2017) found youth who moved were at increased risk of reoffending regardless of the degree of disadvantage in their new neighborhoods. However, males were at greatest risk of reoffending when they moved to an area with less disadvantage (β = 1.99), while females were at greatest risk of reoffending when they moved to an area with Table 6 Summary of main effects. Domains

Ecological factors

Social -Structural Predictors Concentrated disadvantage Crime and disorder Demographic Social capital Delinquent local youth Racial/ethnic inequality Residential instability Population density Access to resources Positive adults Economic Predictors Income/poverty Income Inequality Unemployment Justice System Related Predictors Community Services Policing All Tests and Studies

–

+

NS

Total tests

Total studies

6

36

38

80

25

0 1 2 0 0 0 1 0 1 1

19 4 1 1 7 2 0 2 0 0

17 7 8 4 0 0 2 0 0 0

36 12 11 5 7 2 3 2 1 1

18 7 8 2 2 1 3 1 1 1

2

0

5

7

4

0 2 0

0 0 0

3 0 2

3 2 2

2 2 1

2

4

3

9

3

2 0

3 1

2 1

7 2

2 1

10

40

46

96

27

Notes: - = statistically significant (p < .05), negative association between the index ecological factor and reoffending; + = statistically significant (p < .05), positive association between the index ecological factor and reoffending; NS = association between the index ecological factor and reoffending was not statically significant. Because some studies include multiple ecological factors, the sum of the total studies columns for social-structural, socioeconomic, and juvenile justice system factors is > 27. 9

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

Fig. 3. Forest plot of the distribution of concentrated disadvantage's effects on re-arrest. Note: Random effects model used. Variation between studies was statistically significant (Q = 104.87, p = .001). Standard errors are robust to clustering by data source.

protective effect of immigrant concentration was partially explained by decreased parental incarceration and increases in living with both parents, but not by community relationships (Wolff et al., 2018). Of studies that disaggregated mediation analyses by race (k = 3), two found variation by race in how ecological effects unfolded to affect reoffending (c.f. Veeh, 2015). Chauhan and Reppucci (2009) found that when race was taken into account, the direct effect of concentrated disadvantage disappeared, parental abuse directly predicted reoffending among White girls, and witnessing community violence directly affected reoffending among Black girls. Wolff et al. (2018) found that immigrant concentration reduced recidivism among White and Black youth by decreasing parental incarceration, but it also decreased recidivism among Black youth by increasing the probability of living with two parents. In sum, the same structural factors affected reoffending among youth in varied ways depending on their race.

did not find concentrated disadvantage interacted with relationship or substance abuse risk, but McReynolds (2004) found those with anxiety disorders were less affected by concentrated disadvantage than those without anxiety disorders (β = −0.10). Together, these results suggest variation in ecological effects exists by individual-level risk factors, although differences in effects were small and depended on the individual characteristic (i.e., risk of reoffending is increased by substance abuse problems and decreased by anxiety). Two studies examined the way in which youths' environments may alter the effect of interventions. In their study of a diversion program, de Beus and Rodriguez (2007) found that a restorative justice program reduced risk of offending and had a greater effect on youth who lived in higher poverty areas than those in low poverty areas (Poverty rate 11–20% vs. Poverty rate < 11%: β = 0.41; Poverty rate 31–40% vs. Poverty rate < 11%: β = 0.44). In contrast, Jeong (2011) found that a restorative justice intervention that failed to reduce recidivism in general did not have different effects for youth depending on features of their residential contexts.

4. Discussion In the past fifteen years, research on juvenile reoffending has experienced an ecological turn, wherein numerous studies on the relationship between ecological factors and juvenile reoffending have emerged. To take stock of these efforts and inform future research, we have systematically reviewed and synthesized studies on this relationship. We find that, despite the production of an impressive body of research on the ecology-reoffending relationship, samples reflect relatively few regions and conclusive evidence on specific ecological factors is lacking. In some cases, we find ecological factors consistently predict reoffending, but that they have been tested too infrequently to confidently assert their role as risk factors. In other cases, ecological factors have been tested in multiple studies, but their effect on reoffending is inconsistent across those studies. Below, we unpack these findings– speaking to differences in study characteristics, sample characteristics, and theoretical bases, in relation to inconsistencies in ecological effects. We conclude by distilling key takeaways to advance research on the ecology-reoffending relationship. Before discussing key findings, we note two study limitations. First, reducing existing research to the number of times ecological risk factors were, or were not, statistically significant predictors is a crude analytic

3.3.2. Potential mechanisms Studies conducted mediation analyses to examine the social processes through which ecological characteristics impact youths' reoffending. These studies examined potential mediators of concentrated disadvantage, social capital, and immigrant concentration. Specifically, they tested the degree to which social factors (i.e., prosocial relationships and activities, concentration of delinquent peers, peer abuse, community relationships), familial factors (i.e., parental abuse, maternal risk, living with both parents, parental incarceration), and individual experiences or characteristics (i.e., goal blockage, witnessing violence, reading level, and pro-social orientation) mediated or partially mediated disadvantage, social capital, or immigrant concentration. Results indicated that concentrated disadvantage indirectly increased reoffending in part by exposing youth to delinquent peers (Wright, Kim, Chassin, Losoya, & Piquero, 2014), decreasing access to prosocial relationships, activities, and opportunities (Intravia, Pelletier, Wolff, & Baglivio, 2017; Wright et al., 2014), blocking goals (Wright et al., 2014), and exposing youth to violence (Chauhan & Reppucci, 2009). Social capital decreased reoffending by increasing youth's pro-social orientation (Veeh, 2015). A 10

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

strategy. On its own, this approach fails to account for variation between studies that may be due to study design, analysis methods, or population characteristics. Further, a lack of statistical significance is not equivalent to “no relationship”. That said, where possible we conducted meta-analytic synthesis, providing an overall estimate of the ecological effect of concentrated disadvantage on re-arrest. Where meta-analysis was impossible (i.e., due to the variability in the ecological factors tested, the measures used, and the small number of comparable studies), we carefully reviewed and extracted key study characteristics, assessing and making transparent both the characteristics that may account for variation and overall study quality as it pertained to measuring ecological effects. Second, our search gleaned very few papers that tested systemic factors (e.g., policing practices) and no papers that focused on policy factors (e.g., sentencing policies). It is possible that this lack of representation reflects our search terms, which included “neighborhood”, but not “county” or “state.” Though we did include generic spatial terms like “ecology”, “context”, “environment”, and “multilevel”, which theoretically should have captured studies focusing on larger units of aggregation, our neglect of explicitly querying these larger units may have skewed our analysis toward ecological factors more proximate to youth and may not reflect a neglect of these factors in scholarship. With these limitations in mind, we summarise five key findings. Findings one and two speak to our first research question- what, if any, ecological factors predict reoffending among youth? The remaining findings speak to the nature of the relationship between ecological factors and reoffending, in terms of how and for whom they affect reoffending and the type of relationship supported (i.e., correlational, risk factor, or causal risk factor).

collective efficacy) to determine their relevance. Our results also point to two additional potentially relevant theoretical orientations. First, the predictive utility of income inequality and racial/ethnic inequality indicate theories of relative deprivation and social position may be applicable to reoffending (for an overview, see Odgers, 2015). It is possible, for example, that reoffending is affected by not only disadvantage where youth live but also the subjective experience of being in a relatively low social position in a highly unequal context. Further, in such unequal contexts, justice involved youth may be particularly susceptible to stereotyping and related sanctions placed on youth who occupy relatively low social positions. Second, our finding that offender concentration predicts reoffending seems to indicate the relevance of theories of “contagion,” which account for the spread of delinquency between youth (Dishion & Tipsord, 2011). 2. Study characteristics—especially outcome operationalization– may explain the inconsistent link between ecological factors and reoffending As noted above, results from tabular analyses of ecological factors broadly and our meta-analysis of concentrated disadvantage specifically (Q = 104.87, p = .001) indicated effects varied across studies. Such variation may be explained by study characteristics and design features. Though disentangling how features differentially explain effect variation is limited by the overlap of features within studies, there is some indication that effects vary by ecological unit, analytic method, and outcome definition. In terms of geographic unit, we found few differences in studies that did and did not find a significant relationship between ecological factors and reoffending. The one exception to this finding was zip code, which was more common among studies with significant effects. Notably, however, the majority of zip code studies are those from Florida. Therefore, the relation between zip code and statistically significant effects may be spurious, and may actually represent sample or data specificities of the Florida studies. In fact, when we limited our analysis to the studies included in our meta-analysis of concentrated disadvantage, we found statistically significant relationships were more common in studies with smaller, ecologically valid units (i.e., neighborhoods). These findings suggest that the geographic unit of analysis is likely to affect the sensitivity of tests of ecological factors. It also suggests that what constitutes an appropriate geographic unit is study specific and dependent on the ecological factor of focus. In addition to geographic unit, we noted many of the studies reviewed present coefficients for ecological factors after adjusting for a variety of covariates, and that effect sizes tended to decrease as the number of covariates increased. In some cases, analyses adjusted for potential mediators of the relationship between ecological factors and reoffending (e.g., parental employment problems, substance use, etc.). Studies that formally test potential mechanisms through which ecological factors may affect youth (e.g., SEM studies) are critical to understanding causal pathways and potential points of intervention. However, studies that reduce factors such as concentrated disadvantage to their direct (i.e., main) effect, above individual and social risk factors without formal tests of mediation, may “overcontrol” and falsely reduce the magnitude of ecological effects (Sampson et al., 2002). Such conservative models have important implications for interpretation– readers may erroneously conclude that ecological factors do not “matter” for reoffending. The relationship between ecological factors and reoffending also differed by the outcome tested. The proportion of statistically significant effects was greater among studies that defined reoffending as a criminal justice outcome (i.e., re-arrest or re-incarceration) than those that defined reoffending as a behavioral outcome (i.e., self-reported reoffending). Several explanations may exist for this difference. Studies that use self-report data may differ from studies that rely on pre-existing administrative data in terms of overall quality. By relying on administrative data, studies may be marked by measurement error due to non-

1. Of ecological factors tested and related theories, some- but not all- appear relevant to reoffending Overall, current research provides mixed support for the role of ecological factors in shaping reoffending; about half of tests yielded statistically significant associations (see Table 6). Of ecological factors, concentrated disadvantage received the most attention, and so we were able to test its effects meta-analytically. Meta-analysis results indicated that, when outcomes are restricted to re-arrest, concentrated disadvantage has a small, statistically significant pooled effect on reoffending (OR = 1.09, p = .01). Yet, the effect of concentrated disadvantage varied across studies— a point to which we return below. Of other factors, results indicated that economic and neighborhood demographic measures (c.f., immigrant concentration) consistently failed to predict reoffending, while economic inequality and offender concentration most consistently predicted reoffending. Youth living in neighborhoods characterized by large differences in wealth among residents and high concentrations of justice-involved peers tended to reoffend more often than those living in neighborhoods with less inequality and fewer justice-involved peers. Though the effects of inequality and offender concentration were consistent predictors, we cautiously highlight them as they have been tested in only a handful of studies and locations. Similarly, research on the effects of residential instability, social capital, and justice system related factors is too nascent to draw conclusions as to their effects on reoffending. Theoretically, these findings provide some support for prior applications of social disorganization theory to the study of reoffending, but are unable to speak authoritatively to other popular theories. Specifically, we find that a key feature of social disorganization theory, concentrated disadvantage, increased youths' risk of re-arrest. Though we found that demographic and absolute economic measures on their own failed to predict reoffending, this supports the idea that it is the synergistic concentration of social and economic factors that produce deleterious effects in disorganized neighborhoods (Wilson, 2012). As for social capital, cohesion, and control-oriented theories, too few studies have tested relevant factors (e.g., social capital, cohesion, or 11

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

research staff's unreliable or inaccurate recording of addresses, ecological variables, or control variables. However, if this were the case, we would expect the additional noise introduced by such error to decrease associations and statistical significance for tests of ecological effects on reoffending, not increase them, as we found. We argue that the potential introduction of a third, unaccounted for, variable is a reasonable alternative explanation for the greater number of statistically significant findings in studies that use criminal justice outcomes than self-report outcomes. When defined as re-arrest or reincarceration, reoffending is to some degree dependent on surveillance, especially for low-level or non-violent offenses. Empirical comparisons of self-reported offending and arrests yield strong, but variable and imperfect, correlations between the two (Piquero, Schubert, & Brame, 2014). Because police differentially surveille risky neighborhoods and respond to delinquent behavior (Fagan & Davies, 2000; Sampson, 1986; Sampson & Loeffler, 2010), police surveillance becomes a potential confounder of the relationship between residential contexts and reoffending. In other words, youth who live in riskier neighborhoods may be more likely to reoffend when reoffending is defined as re-arrest or reincarceration not solely because of the level of ecological risk that exists in their neighborhood, but also because of the degree to which their neighborhood is policed. The fact that tests of ecological effects on offense-specific outcomes were most consistently significant for drugrelated offenses, which are more likely to reflect surveillance practices than behavior, provides further support for the argument that ecological effects may be confounded by policing practices.

Chauhan and Reppucci (2009) found disadvantage's effect disappears in race-specific analyses, a likely function of racial differences in exposure to disadvantage. This study also indicated that disadvantage increases reoffending indirectly for White girls by increasing poor parenting and for Black girls by increasing exposure to violence. Together, these results indicate that even if interactions are insignificant, the effects of disadvantage are racialized– Black youth are more likely to live in disadvantaged contexts and the causal pathway through which disadvantage leads to reoffending, to some degree, differs by race (for further discussion, see Piquero et al., 2005). 4. Ecological risk factors likely affect reoffending through youths' social environment, opportunities, and perspectives Results from the studies reviewed here build on traditional theories of social disorganization and control, testing how socio-structural community features promote reoffending. Specifically, mediation analyses indicate several logical pathways through which these factors, namely concentrated disadvantage, may affect reoffending. These studies find that the effect of disadvantage decreases, often becoming insignificant, when social factors are entered into models. Such findings support notions that disadvantage contributes to reoffending by increasing exposure to other, more proximal, risk factors (Chung & Steinberg, 2006). We find concentrated disadvantage is mediated by delinquent peers (Wright et al., 2014), access to prosocial relationships, activities, and opportunities (Intravia et al., 2017; Wright et al., 2014), goal blockage (Wright et al., 2014), and community violence (Chauhan & Reppucci, 2009). The one study that investigated factors mediating social capital found youths' pro-social orientation mediated the effect of social capital on reoffending (Veeh, 2015), and the study examining factors mediating immigrant concentration found youths' familial characteristics mediated the effect of immigrant concentration on reoffending (Wolff et al., 2018). Thus, existing research adds to and in many ways compliments factors typically identified by social disorganization theorists as mediators of the relationship between concentrated disadvantage and negative social outcomes (e.g., social capital, social cohesion), suggesting social and structural forces (e.g., disadvantage, and potentially social capital and immigrant concentration) affect reoffending largely by increasing exposure to harmful social influences (peer and familial), decreasing prosocial opportunities, and changing the way youth view the world.

3. Ecological effects vary by gender, while variation by race is more complex Social outcomes are largely considered the product of individual and environmental factors in interaction. Individually-oriented risk prediction studies absent consideration of context risks reductionism. On the other hand, research on macro forces absent consideration of within group or within context variation risks over-predicting delinquency, especially for those in disadvantaged contexts (Piquero, Moffit, & Lawton, 2005). In turn, tests of interaction between individual factors and ecological factors are necessary to fully understand the ecology-reoffending relationship. We found too few studies investigated differences in ecological effects by dynamic characteristics (e.g., substance use, psychological characteristics) to draw conclusive findings. Receiving more attention, however, some demographic characteristics do seem to interact with ecological factors to effect reoffending. Generally, ecological factors have gendered effects. Here, females appeared more susceptible to disadvantaged contexts than their male counterparts, while males appeared less or even paradoxically affected (i.e., more likely to reoffend in less rather than more disadvantaged contexts). These results align with prior research in non-justice involved samples, such as the gender differences in neighborhood effects on delinquency from MtO previously noted (Ludwig, Duncan, & Hirschfield, 2001b; see also Kling et al., 2005; Sciandra et al., 2013). Because females comprise a small portion of justice-involved samples, conclusions drawn from studies that do not oversample or focus on females may underestimate the role of ecological factors in predicting recidivism for this sub-group. Where effects of disadvantage and inequality predict reoffending, they seem to do so regardless of race. Interactions between neighborhood characteristics and race were rarely significant; they largely provided little support for differences between Black and White youth, and provided mixed support for differences between Latino and White youth. These results align with previous research, which mostly finds the effects of concentrated disadvantage on crime are “racially invariant” (Sampson, Wilson, & Katz, 2018). Results from stratified mediation analyses, however, suggested that the relationship between some ecological factors and recidivism are difficult to parse out from race, and that the effect of disadvantage on reoffending may be mediated by different factors for Black and White youth. For example,

5. Existing research permits assessment of ecological risk factors for reoffending, but not causal risk factors The majority of studies included in this review were well-designed to establish ecological risk factors for youth reoffending. Studies utilized longitudinal designs, wherein they measured ecological factors in youths' residential contexts at study start (usually the time of their reentry into the community or post-disposition) and measured reoffending within the study's follow-up period. The time ordering between ecological factors and reoffending permits interpretation of statistically significant ecological effects as risk factors. Of the studies reviewed, all but one included individual-level predictors of reoffending (e.g., age, gender, race, and history of offending) to adjust for potential confounders of the ecology-reoffending to relationship. However, only one study used a quasi-experimental design to strengthen causal interpretation of the ecology-reoffending relationship. In this study, Wolff et al. (2017) measured the relationship between upward-, lateral-, and downward mobility (i.e., moving into a less, similar, or more disadvantage). This study has advantages in terms of measurement and design. First, tracking residential moves over time instead of relying on a single address at observation start is likely to improve measurement validity. Twelve percent of U.S. households with children move at least once per year (U.S. Census, 2018), and those who are poor are nearly twice as likely to move (U.S. Census, 2017a). In turn, relying on a single address, as have most studies, may provide 12

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

inaccurate data on residential contexts for somewhere between 12% and 23% of youth sampled. These data become even less likely to reflect residential contexts when observation periods extend beyond one year. Second, by isolating the sample to movers and examining within-individual change in exposure to an ecological risk factor (e.g., moving from high to low disadvantage), the study design increases confidence that the detected effect on reoffending was due to neighborhood disadvantage and not some other related factor. Based on these findings, we offer three recommendations for future research. First, future research should enhance measurement. Appropriate measurement is the starting place for assessing relationships between constructs (Murray et al., 2009). When measuring ecological risk factors, researchers should expand upon single self-report and secondary sources that locate risk within a single perceived or administratively defined geographic unit. Of the studies reviewed here, Wolff et al.'s (2017) use of multiple addresses and Harris et al.'s (2011) use of spatial decay represent strategies to increase measurement sensitivity and accuracy. In addition to these examples, strategies like spatial interpolation (Bader & Ailshire, 2014), and activity space measurement and ecological momentary analysis (e.g., Browning et al., 2017; Browning & Soller, 2014) are also feasible and offer multiple advantages (e.g., inclusion of subjects who reside in low population density areas, assessment of conditions at ecologically valid proximities, and establish times at which youth are particularly vulnerable). To disentangle delinquent behavior from criminal justice surveillance, studies could combine self-report with administrative measures (Farrington, 2007). Where the cost of primary data collection is prohibitive, studies could follow the lead of Grunwald et al. (2010) and others who examined effects by offense type, given surveillance is less likely to confound the relationship between ecological factors and violent offenses than lower-level offenses. Second, future research should fill existing gaps by diversifying samples and through strategic variable selection. Despite growth in the number of studies in this area, existing research represents overlapping samples and relatively few contexts. Sixteen of the 25 studies that reported location drew samples from the same three places—the State of Florida, the City of Philadelphia, and to a lesser extent Maricopa County, Arizona. These locations reflect specific demographic profiles, justice and law enforcement systems, and policy climates. For example, Florida and Philadelphia have Latino populations that differ considerably from other states and cities, with populations mostly of Cuban and Caribbean, but not Mexican, origin (see U.S. Census, 2017b). A full discussion of the potential influence of this unique ethnic profile on our understanding of the relationship between residential context and recidivism is beyond the scope of this discussion. However, in the most basic sense, the unrepresentative nature of these studies indicates our limited understanding of the ecology-reoffending relationship for the majority of Latino youth in the U.S. Enhanced generalizability could be achieved by targeted sampling of under-sampled regions, including states with large juvenile justice systems and Mexican American populations (e.g., Texas and California), and rural areas. As for strategic variable selection, the research reviewed here suggests that purely demographic and economic factors can likely be set aside as risk factors for reoffending, but further research on the role of concentrated disadvantage, crime and disorder, concentration of offenders and economic inequality is warranted. With respect to concentrated disadvantage, with additional studies it will be possible to use meta-regression techniques to test factors that explain variability in findings. In addition, given their potential for policy intervention and the relatively little attention afforded them, future research should investigate the role of criminal justice, social service, and other institutional ecological factors in shaping offending. Finally, absent in all but one study gleaned from our review, future research should go beyond risk factors to explore the role of ecological factors that may protect against reoffending (e.g., the presence of social services). Third, to inform theory and identify potential targets of recidivism

reduction interventions, future research should test theoretically meaningful relationships, and use designs that strengthen causal inference to do so. Several studies to date represent exemplars of theoretically relevant research (e.g., Chuahan & Repucci, 2009; Harris et al., 2011; Wolff et al., 2016, 2018; Wright et al., 2014), testing ecological factors likely to relate to specific individual-level risk factors and/or specific measures of reoffending. Future researchers who seek to follow in the footsteps of these scholars could go beyond tests of social disorganization to also test theories of social contagion and relative deprivation. For example, the macro application of contagion theory embedded within several studies in this review could be tested by examining mediation of offender concentration by characteristics of peer relationships. If the concentration of justice involved youth in an area fails to predict reoffending by altering peer relationships, then this concentration may be linked to reoffending through factors unrelated to contagion and in turn indicate the need for alternative theories. More generally, causal inferences regarding these relationships, and as a result theory refinement, would be strengthened by further use of longitudinal and quasi-experimental designs. Wolff et al.'s (2017) repeated measurement of residential characteristics among youth who moved provides a creative solution for measuring within individual change in exposure to concentrated disadvantage. Other strategies include those that permit between group comparison and account for confounding (e.g., propensity score matching, inverse probability weighting), and assess causality (e.g., G-estimation, intervening variable analysis, simulation modeling, and g computation; Ertefaie, Nguyen, Harding, Morenoff, & Yang, 2018; Snowden, Rose, & Mortimer, 2011). Ultimately, in the absence of theoretically directed research, repeated selection of generic risk factors, with little regard to how they may increase reoffending among some youth, risks reducing this area of investigation to a proverbial horse race wherein variables are pitted against one another. Further, the absence of such research stymies the development of interventions able to reduce risk- or increase protective factors for reoffending. Avoiding this pitfall requires testing factors that are theoretically meaningful given existing research on ecological effects, developmental science, and criminology. 5. Conclusion In recent years, research on the ecology-reoffending relationship among youth has grown exponentially. This paper systematically reviewed this body of research to provide a much-needed synthesis of findings. We find some evidence that concentrated disadvantage predicts reoffending and that measures of inequality and offender concentration seem worthy of further investigation. We also find purely economic and demographic (aside from immigrant concentration) ecological factors are poor predictors of reoffending. These findings lend some support for traditional theories of social disorganization, but also suggest refined theories should address the transmission of delinquency between youth within communities, the role of relative deprivation in affecting reoffending, and the role of access to opportunities and psychological states in mediating the relationship between socio-structural forces and reoffending. Ultimately, however, definitive conclusions as to theoretical foundations and the predictive utility of ecological factors, above established individual and relational factors, would be premature. Informing risk prediction, theory, and intervention will require studies that test the effect of specific ecological factors above established risk factors in geographically and demographically diverse locations, data collection across multiple time points, and incorporation of quasi-experimental techniques to strengthen causal arguments. Though taking stock of research on the ecology-reoffending relationship highlighted the need for further work, it has also provided key lessons for building on existing research to better understand risk factors where youth live—a necessary precursor to identifying how ecological risks may be alleviated, reoffending reduced, and public safety enhanced. 13

n = 12,302; ♂ = 86%; Mean age = 17.2; Zip codes (n = 563; ♂ = 22) n = 278; ♂ = 66.5%; Mean age = 15.4; Community (n/a) n = 5000; ♂ = 63.4%; Mean age = 15.3; Neighborhood (n/a) n = 938; ♂ = 93.2%; Mean age = 15; First-time serious offenders; Community (75; unreported)

n = 122; ♂ = 0%; Mean age = 16.8; Census tract (94; range: 1–3)

n = 25,461; ♂ = 77%; Mean age = 17; Census tract (unreported) n = 25,461; ♂ = 77%; Mean age = 17; Census tract (unreported) n = 9255; ♂ = 64%; Mean age = 15.1; Eligible for diversion; Zip code (unreported)

Baglivio et al. (2017) Florida

Chauhan and Reppucci (2009) [location not provided]

Craig, Baglivio, Wolff, Piquero, and Epps (2017) Florida

14

de Beus and Rodriguez (2007) Maricopa County, AZ

Craig (2019) Florida

Bright, Hurley, & Barth, 2014 Southeast US Bursik (1983) Cook County, IL

Barnes (2017) Midwest County

Sample & Geographic Unit

Study

Table 3 Summary of study characteristics and results.

LR

Re-arrest (any)

Re-arrest (any)

Re-arrest (any)

LR

LR

New offensespecific (selfreport)

New offensespecific (selfreport) Reconviction/ reincarceration

Re-arrest (any)

Re-arrest (any)

Outcome

SEM

LR

LR

AUC

Multilevel LR

Statistical test(s)

Poverty level variables (dummy coded): -Poverty 1 (< 10% of residents below poverty line) -Poverty 2 (11–20% below poverty) -Poverty 3 (21–30% below poverty) -Poverty 4 (31–40% below poverty)

Concentrated disadvantage (CD; α)

Concentrated disadvantage (CD; α)

Witnessing violent crime (WVC; selfreport)

Concentrated disadvantage (CD; α)

Neighborhood crime (self-report) Deterrence (α: community-specific probability of apprehension and sectioning) Rate (α: rate of police contacts, rate of court referrals)

Program location (in vs out of state)

Perceived safety Access to resources Positive adults

Concentrated disadvantage (CD; α) Concentrated affluence (CA; α)

Ecological factor(s) Relationship risk (+)* Substance abuse (+)* Relationship risk Substance abuse

Poverty 2 x Diversion program (+)***

Poverty 3 x Diversion program (+) Poverty 4 X Diversion program (+)*

0.041(0.062) −0.079(0.082) 0.132(0.101)

CD: -Black > Hispanic > White (not tested) -Male > Female (not tested)

CD affected total antisocial, delinquent, and violent behavior through witnessing violence for Black girls, but not White

Rate x HDOC (a filed petition that results in commitment to DoC- type of outcome): standardized canonical discriminant coefficients = 0.340 (p < .05); i.e., the relationship between neighborhood policing and recidivism is stronger among those who are convicted and sentenced to detention than those who receive other kinds of petition outcomes SEM models disaggregated by race (Black vs White):

n/a

CA x CA x CD x CD x n/a

Groups comparison

[reference category]

0.06(0.02)***

0.07(0.02)***

Total antisocial behavior: -CD: [direct effect ns] -WVC: 0.20(0.08)** Violent behavior: -CD: [direct effect ns] -WVC: [direct effect not reported] Delinquent behavior: -CD: [direct effect not reported] -WVC: 0.12(0.06)*

[not significant; coeff. Not shown]

Program location (in state): −0.13(0.04)** Neighborhood crime: 0.12(0.05)** (standard. Canonical discriminant coeff.) = 0.310*

AUC = 0.55 AUC = 0.63** AUC = 0.59*

0.187(0.032)** −0.828(0.172)**

Main Effect β(se)

n/a

n/a

(continued on next page)

-SEM models supported an indirect effect through witnessing violence on total anti-social behavior [combined effect = 0.20(0.08)**] and delinquent behavior [0.12(0.06)*], but not violent behavior -For the outcome of delinquent behavior, CD also had an indirect effect via reading achievement n/a

n/a

n/a

n/a

n/a

Mediation

L.A. Jacobs, et al.

Journal of Criminal Justice xxx (xxxx) xxxx

15

n = 323; ♂ = 100%; Mean age (at first arrest) = 13.3; Probation; Census tract (230; unreported)

Jeong (2011) Marion County, Indiana

LeBaron (2002) New Jersey

n = 7166; ♂ = 100%; Mean age = 15.7; Neighborhood (45; unreported)

Harris et al. (2011) Philadelphia, PA

n = 24,791; ♂ = 77%; Mean age = 16.3; Zip codes (n = 510; x= ̅ 60.8) n = 782; ♂ = 62.1%; Mean age = 12.6; Census block group (unreported)

n = 7061; ♂ = 100%; Mean age = 14.2; Neighborhood (45; unreported)

Grunwald et al. (2010) Philadelphia, PA

Intravia et al. (2017) Florida

Sample & Geographic Unit

Study

Table 3 (continued)

Multilevel survival models & growth curve model LR & Cox PH regression

Multilevel LR

Multilevel LR

Multilevel LR

Statistical test(s)

% unemployed % unmarried % youth Density Concentrated disadvantage (CD; α)

Per capita income

Logistic regression -Per capita income: 0.220(0.230) -% unemployed: 0.009(0.020) -% unmarried: 0.018(0.015) -% youth: 0.027(0.021) Density: 0.648(0.260)* -CD: 0.405(0.131)** Cox PH -Per capita income: 0.106(0.138) -% unemployed: 0.006(0.012) -% unmarried: 0.007(0.009) -% youth: 0.017(0.013) -Density: 0.348(0.113)** -CD: 0.256(0.076)***

0.443(0.146)* −0.250(0.114)*

Immigrant concentration (α) Residential instability (α) Re-arrest (any); Time to new crime

0.484(0.118)***

Concentrated disadvantage (CD; α)

Re-arrest (any)

Any crime -CD: 0.068 -Social capital: −0.041 Drug crime -CD: 0.174** -Social capital: −0.117* Violent crime -CD: 0 -Social capital: −0.041 Property crime -CD: −0.020 -Social capital: 0.010 Any crime -% Female-headed HH w/ child: 0.010(0.055) -Area recidivism rate for any crime: 1.389(0.169)** Drug crime -Area % vacant property: −0.020(0.062) -Area drug recidivism rate: 0.944(0.113)** Violent crime -Area violent recidivism rate: 1.122(0.130)** Property crime -Area property recidivism rate: 1.194(0.139)** 0.145(0.027)**

Main Effect β(se)

Concentrated disadvantage (CD; α)

Area recidivism rate for property crime (ln)

Area recidivism rate for drug crime (ln) Area recidivism rate for violent crime (ln)

Area recidivism rate for any crime (ln)

% Vacant housing (ln)

% Female-headed household w/ children (ln)

Social capital (self-report; α)

Concentrated disadvantage (CD; α)

Ecological factor(s)

Re-arrest (new crime)

Re-arrest (any)

Re-arrest (any)

Outcome

n/a

Family Group Conferencing (FGC) x CD (−) FGC x Immigrant concentration (−) FGC x Residential instability: (−)

n/a

n/a

n/a

Groups comparison

n/a

n/a

(continued on next page)

Prosocial relationships and activities partially mediate CD

n/a

n/a

Mediation

L.A. Jacobs, et al.

Journal of Criminal Justice xxx (xxxx) xxxx

n = 904; ♂ = 87%; Mean age = 16.0; Serious offenders; Neighborhood (n/a)

n = 5517; ♂ = 100%; Mean age = 14.2; Treatment recipients; Neighborhood (n = 45; unreported)

Leverso et al. (2015) Maricopa County, AZ & Philadelphia, PA

Lockwood and Harris (2015) Philadelphia, PA

16

n = 914; ♂ = 79.8%; Mean age = 14.7; Probationers; Zip codes (281; range: 1–47) n = 7166; ♂ = 100%; Mean age = 15.7; (n/a)

n = 1354; ♂ = 86%; Mean age = 16.0; Serious offenders; Neighborhood (unreported)

Mennis and Harris (2011) Philadelphia, PA

Piquero et al. (2016) Maricopa County AZ & Philadelphia County, PA

McReynolds (2004) Texas

Sample & Geographic Unit

Study

Table 3 (continued)

Negative binomial regression

LR

LR

Multilevel LR

LR

Statistical test(s)

Count of new charges

Re-arrest (any)

Re-arrest (any)

Re-arrest (new crime)

New offense (self-report)

Outcome

Social disorder (self-report)

Area property recidivism rate (x10)

Area violent recidivism rate (x10)

Area drug recidivism rate (x10)

Concentrated disadvantage (CD; α)

Concentrated disadvantage (CD; α)

Social disorder (self-report)

Ecological factor(s)

Drug crime -Area drug recidivism rate: 0.626(0.075)** Violent crime -Area violent recidivism rate: 1.019(0.111)** Property crime -Area property recidivism rate: 0.820(0.094)** −0.021(0.049)

Any crime -Concentrated disadvantage: 0.077(0.042) Drug crime -Concentrated disadvantage: 0.140(0.144)* Violent crime -Concentrated disadvantage: 0.020(0.087) Property crime Concentrated disadvantage: −0.020(0.078) −0.01(0.02)

Time period: baseline to Wave 1 All crime -Social disorder: 0.077(0.553) Violent crime -Social disorder: 0.166(0.103) Nonviolent crime -Social disorder: 0.077(0.097) Time period: baseline to Wave 3 to Wave 4 All crime -Social disorder: 0.166(0.105) Violent crime Social disorder: 0.336(0.130)** Nonviolent crime -Social disorder: 0.122(0.110)

Main Effect β(se)

Social disorder x Black (−)*Social disorder x Hispanic Social disorder x White Social disorder: Blacks < Hispanics (ns) Social disorder: Hispanics < Whites (ns) Social disorder: Whites < Blacks (ns)

n/a

CD: Boys > Girls (not tested) CD x Gender (+)* CD x Anxiety disorder (−)*

n/a

n/a

Groups comparison

n/a

n/a

n/a

n/a

n/a

(continued on next page)

Mediation

L.A. Jacobs, et al.

Journal of Criminal Justice xxx (xxxx) xxxx

17

n = 681; ♂ = 100%; Mean age = 19; Serious offenders; Neighborhood (n/a)

n = 10,5573; ♂ = 69%; Mean age = 16.5; Probation; Census tract (3547, x̅ = 51)

n = 26,960; ♂ = 77%; Mean age = 17.0; Zip codes (n = 748; x̅ = 71)

N = 13,096; ♂ = 68%; Mean age = 14.8; First-time offenders; Census tract (unreported)

n = 26,242; ♂ = 77%; Mean age = 17; Zip code (n = 619, x̅ = 42)

n = 945; ♂ = 82%; Mean age = 20.0; Serious offenders; Census block (808; x= ̅ 1.37) n = 12,660; ♂ = 60.8%; Mean age = 14; Census tract (364; unreported)

Veeh (2015) Philadelphia, PA & Phoenix, AZ

Wolff et al. (2015) Florida

Wolff et al. (2016) Florida

Wolff et al. (2017) Florida

Wolff et al. (2018) Florida

Wright et al. (2014) Philadelphia, PA & Phoenix, AZ

Wright and Rodriguez (2014) Maricopa County, AZ

Sample & Geographic Unit

Study

Table 3 (continued)

Criminal behavior (selfreport)

SEM

Re-arrest (any)

Reconviction/ reincarceration

Multilevel SEM

Multilevel LR

Re-arrest (any)

Re-arrest (new crime); Time to new crime

Reconviction/ reincarceration

New offense (self-report)

Outcome

LR

Multilevel LR & Cox PH regression

Multilevel LR

SEM

Statistical test(s)

01(0.02)

Immigration concentration (α)

Immigration concentration variance component = 0.03

CD variance component = 0.03

0.02(0.02).

Concentrated disadvantage (CD; α)

−0.062(0.042) 0.034(0.032) −0.031(0.032)

0.007 −0.057** (total effect)

Concentrated disadvantage (CD; α) Immigrant concentration (IC; α)

Concentrated disadvantage (CD; α) Racial/ethnic heterogeneity (α) Residential instability (α)

0.176(0.032)*** 1.878(0.146)*** 1.872(0.147)*** 1.700(0.072)***

0.049(0.013)** −0.104(0.012)** Multilevel model (variance explained) γ00 = 0.119 γ10 = 0.072 γ01 = 0.066 0.149(0.027)** −0.582(0.057)** 0.154(0.027)**

0.059(0.045) (not reported)

Main Effect β(se)

Concentrated disadvantage (CD; α) Upward mobility move Lateral mobility move Downward mobility move

Concentrated disadvantage (CD; α) Concentrated affluence (CA; α) Resource deprivation index (RDI; α)

Concentrated disadvantage (α) Immigrant concentration (α)

Physical/social disorder (self-report) Social Capital Inventory (self-report)

Ecological factor(s)

Sex x Immigration concentration (+)*** Latino x Immigration concentration (−)* Asian Pacific Islander x Immigration concentration (−) Sex x CD (+)*** Latino x CD (−) Asian Pacific Islander x CD (+)

Youth who faced combination of temperament problems (low control & high emotionality) and lived in disadvantaged area were significantly more likely to be rearrested than youth without these individual and ecological risk factors. CD: Girls > Boys (not tested) Upward move: Boys > Girls (not tested) Lateral move: Girls > Boys (not tested) Downward move: Girls > Boys (not tested) White: IC predicted reoffending (−) directly and indirectly through parental incarceration Black: IC predicted reoffending (−) directly and indirectly through living in two-parent household, and parental incarceration Hispanic: IC had no direct or indirect effect on reoffending n/a

n/a

n/a

Groups comparison

n/a

(continued on next page)

CD increases goal blockage, which increases exposure to deviant peers and, ultimately, reoffending

IC predicted reoffending (−) directly and indirectly through living in a two-parent household, and reduced parental incarceration

n/a

n/a

-Social capital at 36 months increased pro-social orientation at 48 months, which decreased self-reported offending -Prosocial orientation did not predict social capital at 48 months -Model fit was not good for CD -Findings did not vary by SES or race n/a

Mediation

L.A. Jacobs, et al.

Journal of Criminal Justice xxx (xxxx) xxxx

18

n = 13,138; ♂ = 62%; Mean age = 14.0; White/ Black/Latino; Zip code (n = 50; unreported)

n = 1408; ♂ = 64%; Mean age = 14.0; First-time offenders; Census tract (144; unreported)

Wright et al. (2016) Maricopa County, AZ

Yan (2009) St. Louis, MO

Poisson regression & GEE

Multilevel LR

Statistical test(s)

Count of new charges; Count of new charges (weighted by severity)

Re-arrest (any)

Outcome

Community services (categorized into 0/1/2/3+)

Crime rate (categorized into quartiles)

Concentrated disadvantage (CD; α; categorized into low/moderate/high)

Racial/ethnic heterogeneity (α)

Ethnic inequality (α) Racial inequality (α) Concentrated disadvantage (α)

Ecological factor(s) −0.06(0.02)* 0.06(0.02)* 0.02(0.03) [controlling for ethnic inequality] 0.03(0.05) [controlling for racial inequality] 0.31(0.55) [controlling for ethnic inequality] 0.08(0.54) [controlling for racial inequality] Count of charges -CD moderate (vs low): −0.000(0.067) -CD high (vs low): −0.310(0.072)*** -Crime rate0 (< 1st quartile; reference group) -Crime rate1 (1st-2nd quartile): −0.136(0.087) -Crime rate2 (2nd-3rd quartile): 0.011(0.087) -Crime rate3 (> 3rd quartile): −0.154(0.078)* -Community services1 (vs zero): −0.194(0.091)* -Community services2 (vs zero): 0.401(0.074)*** -Community services3 (vs zero): 0.243(0.062)*** Count of charges (weighted by severity -CD moderate (vs low): −0.023(074) -CD high (vs. low): −0.287(0.075)*** -Crime rate0 (< 1st quartile; reference group) -Crime rate1 (1st-2nd quartile): −0.188(0.115) -Crime rate2 (2nd-3rd quartile): 0.013(0.118) -Crime rate3 (> 3rd quartile): −0.139(0.116) -Community services1 (vs zero): −0.162(0.097)Ϯ -Community services2 (vs zero): 0.433(0.081)*** -Community services3 (vs zero): 0.002(0.072)

Main Effect β(se)

n/a

Racial inequality x Black [with White race as reference group]

Ethnic inequality × Black Ethnic inequality x Latino Racial inequality x Latino (−)*

Groups comparison

n/a

n/a

Mediation

Notes: (+) = moderator increased strength of association with ecological variable; (−) = moderator decreased strength of association with ecological variable; > /≤ groups comparison of effect of ecological variable; Ϯ p < .10, ⁎ p < .05, ⁎⁎ p < .01 ⁎⁎⁎ p < .001; ♂ = % male; x = average number of subjects per geographic unit; α = composite measure; AUC = Area Under the Curve; CD = concentrated disadvantage; CA = concentrated affluence; CV = community violence; GEE = general estimating equations; LR = logistic regression; PH = proportional hazards; RDI = resource deprivation index; SES = socioeconomic status; SEM = structural equation modeling; ln = natural log; ns = not significant; n/a = not applicable; unreported = geographic unit N/average/range not provided in article. We have concerns about the operationalization of residential instability in this study. We attempted to contact the author to clarify the definition of residential stability and were unsuccessful.

Sample & Geographic Unit

Study

Table 3 (continued)

L.A. Jacobs, et al.

Journal of Criminal Justice xxx (xxxx) xxxx

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

Funding

Acknowledgments

This work has been financially supported by the University of Pittsburgh's Center for Interventions to Enhance Community Health (CiTECH). The content solely reflects the ideas of the authors and does not necessarily represent the views of the sponsor.

The authors thank Dr. Edward Mulvey of the School of Medicine at the University of Pittsburgh, and Dr. Ray Engel and Dr. Rachel Gartner of the School of Social Work at the University of Pittsburgh for their thoughtful feedback on manuscript drafts. We also would like to acknowledge the contribution of research assistant, Kelly Nissley, B.S.W., who assisted with a scoping review that informed the literature reviewed here and who helped pilot the review protocol used in this study.

Declaration of Competing Interest The authors know of no conflicts of interest. Appendix 1. Search strategy Criminal Justice Abstracts (Ebscohost) Search run April 22, 2019. S19 S18 S17 S16 S15 S14 S13 S12 S11 S10 S9 S8 S7 S6 S5 S4 S3 S2 S1

S10 AND S16, Limit to Publication Date: 19830101–20,190,422 S10 AND S16, Limit to English language only S10 AND S16 S13 OR S14 OR S15 “collective efficacy” OR ecology* OR environment* OR Inequality OR Multilevel OR “social context” OR “social factors” Parolee* N3 (cluster* OR concentrate*) s11 N2 S12 (affluent OR characteristics OR condition* OR context* OR disadvantage* OR dynamics OR econom* OR effect* OR employment OR environment OR factors OR impoverished OR income OR level OR poverty OR racial OR resources OR sociodemographic* OR socioeconomic* OR structur* OR surrounding OR unemployment OR variables) (neighborhood* OR communit*) S1 OR S2 OR S3 OR S4 OR S5 OR S6 OR S7 OR S8 OR S9 return n2 custody parole N3 violat* repeat N2 offen* reoffen* OR re-offen* reincarcer* OR re-incarcer* re-imprison or reimprison OR re-imprisoned or reimprisoned OR re-imprisonment or reimprisonment reconviction OR re-conviction OR reconvicted OR re-convicted rearrest OR re-arrest OR rearrested OR re-arrested OR rearrests OR re-arrests recidivism OR recidivist OR recidivating OR recidivated OR recidivate OR recidivistic OR recidivized

692 709 723 56,687 50,865 3 8093 289,227 69,505 8667 46 235 4043 1674 271 41 413 647 7014

Appendix 2. Quality assessment criteria definitions 1. Correlate Score (out of 5) 1a. Adequate sampling method 1 Total population sampling or probability sampling 0 Convenience sample or case-control sampling 1b. Adequate response rates 1 Response and retention rates ≥70% and differential attrition ≤10% 0 Response rate < 70% or retention rate < 70% or differential attrition > 10% 1c. Adequate sample size 1 Sample size ≥400; if multilevel, > 30 geographic units and > 5 individual per unit 0 Sample size < 400; If multi-level, < 30 geographic units and < 5 individuals per unit 1d. Good Measure of Ecological Variable 1 Reliability coefficient ≥ 0.75 and reasonable face validity or criterion or convergent validity coefficient ≥ 0.3 or more than one instrument or information source used to assess correlate 0 None of the above 1e. Good Measure of Outcome 1 Reliability coefficient ≥ 0.75 and reasonable face validity, or criterion or convergent validity coefficient ≥ 0.3, or more than one instrument or information source used to assess outcome 0 None of the above 2. Spatial Data Score (out of 3) 3 Study addresses autocorrelation (clustering) and more than one of the following: MAUP, UGCP, spatial autocorrelation, and sparseness 2 Study addresses autocorrelation (clustering) and at least one of the following: MAUP, UGCP, spatial autocorrelation, and sparseness 1 Clusters standard errors, uses multilevel modeling, or relies on non-clustered data 0 Does not address non-independence of observations 3. Design Adequacy for Establish Risk Factors (out of 3) 3 Prospective data (or study of fixed risk factor, including secondary analyses of data previously collected as part of a prospective study) 2 Retrospective data (including administrative data previously collected) 1 Cross-sectional data

19

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al. 4. Design Adequacy for Establishing Causal Risk Factors (out of 7) 7 Randomized experiment; Targeting a risk factor 6 Controlled non-experimental study; With analysis of change 5 Controlled non-experimental study; No analysis of change 4 Inadequately controlled study; With analysis of change 3 Study without a comparison group; With analysis of change 2 Inadequately controlled study; No analysis of change 1 Study without a comparison group; No analysis of change

Fagan, J., & Davies, G. (2000). Street stops and broken windows: Terry, race, and disorder in New York City. Fordham Urban Law Journal, 28, 457. Farrington, D. P. (2007). Advancing knowledge about desistance. Journal of Contemporary Criminal Justice, 23(1), 125–134. https://doi.org/10.1177/1043986206298954. Felson, M. (2013). Routine activity approach. In R. Wortley, & L. Mazerolle (Eds.). Environmental criminology and crime analysishttps://doi.org/10.4324/ 9780203118214-12. Fotheringham, A. S., & Wong, D. W. S. (1991). The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning, 23, 20. Groff, E. R. (2015). Informal social control and crime events. Journal of Contemporary Criminal Justice, 31(1), 90–106. *Grunwald, H. E., Lockwood, B., Harris, P. W., & Mennis, J. (2010). Influences of neighborhood context, individual history and parenting behavior on recidivism among juvenile offenders. Journal of Youth and Adolescence, 39(9), 1067–1079. Hancock, G. R., Mueller, R. O., Stapleton, L. M., Mueller, R. O., Mueller, R. O., Stapleton, L. M., & Mueller, R. O. (2010). The reviewer’s guide to quantitative methods in the social sciences. https://doi.org/10.4324/9780203861554. *Harris, P. W., Mennis, J., Obradovic, Z., Izenman, A. J., & Grunwald, H. E. (2011). The coaction of neighborhood and individual effects on juvenile recidivism. Cityscape: A Journal of Policy Development and Research, 13(3), 33–56. Hedges, L. V., & Vevea, J. L. (1998). Fixed-and random-effects models in meta-analysis. Psychological Methods, 3(4), 486. *Intravia, J., Pelletier, E., Wolff, K. T., & Baglivio, M. T. (2017). Community disadvantage, prosocial bonds, and juvenile reoffending: A multilevel mediation analysis. Youth Violence and Juvenile Justice, 15(3), 240–263. *Jeong, S. (2011). Long-term effects of restorative justice conferencing on future criminality: The Indianapolis experiment. PhD dissertationLansing, MI: Michigan State University. Katz, L. F., Kling, J. R., & Liebman, J. B. (2001). Moving to opportunity in Boston: Early results of a randomized mobility experiment. Quarterly Journal of Economics, 116(2), 607–654. https://doi.org/10.1162/00335530151144113. Kennealy, P. J., Skeem, J. L., & Hernandez, I. R. (2017). Does staff see what experts see? Accuracy of front line staff in scoring juveniles’ risk factors. Psychological Assessment, 29(1), 26–34. https://doi.org/10.1037/pas0000316. Kling, J. R., Ludwig, J., & Katz, L. F. (2005). Neighborhood effects on crime for female and male youth: Evidence from a randomized housing voucher experiment. Quarterly Journal of Economics, 120(1), 44. Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54(4), 337–343. https://doi.org/10.1001/archpsyc.1997.01830160065009. Kwan, M.-P. (2012). The uncertain geographic context problem. Annals of the Association of American Geographers, 102(5), 958–968. https://doi.org/10.1080/00045608.2012. 687349. Lanctot, N., & Smith, C. A. (2001). Sexual activity, pregnancy, and deviance in a representative urban sample of African American girls. Journal of Youth and Adolescence, 24. *LeBaron, J. (2002). Examining the relative influence of community context on juvenile offender post-confinement recidivism. New Brunswick, NJ: Rutgers University Press. Leventhal, T., & Brooks-Gunn, J. (2003). Moving to opportunity: An experimental study of neighborhood effects on mental health. American Journal of Public Health, 93(9), 1576–1582. https://doi.org/10.2105/AJPH.93.9.1576. *Leverso, J., Bielby, W., & Hoelter, L. F. (2015). Back on the streets: Maturation and risk factors for recidivism among serious juvenile offenders. Journal of Adolescence, 41, 67–75. *Lockwood, B., & Harris, P. (2015). Kicked out or dropped out? Disaggregating the effects of community-based treatment attrition on juvenile recidivism. Justice Quarterly, 32(4), 705–728. Loeber, R., & Dishion, T. (1983). Early predictors of male delinquency: A review. Psychological Bulletin, 94(1), 68. Ludwig, J., Duncan, G. J., & Hirschfield, P. (2001a). Urban poverty and juvenile crime: Evidence from a randomized housing-mobility experiment. Quarterly Journal of Economics, 116(2), 655–679. https://doi.org/10.1162/00335530151144122. Ludwig, J., Duncan, G. J., & Hirschfield, P. (2001b). Urban poverty and juvenile crime: Evidence from a randomized housing-mobility experiment. Quarterly Journal of Economics, 116(2), 655–679. https://doi.org/10.1162/00335530151144122. McCoach, D. B. (2018). Multilevel modeling. In G. Hancock, L. Stapleton, & R. Mueller (Eds.). The Reviewer’s guide to quantitative methods in the social sciences (pp. 292–312). Routledge. *McReynolds, L. S. (2004). Prediction of juvenile recidivism: Influence of gender, psychiatric disorder, and neighborhood disadvantage (PhD dissertation). New York, NY: Columbia University. *Mennis, J., & Harris, P. (2011). Contagion and repeat offending among urban juvenile delinquents. Journal of Adolescence, 34(5), 951–963. https://doi.org/10.1016/j. adolescence.2010.12.001. Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. J. (2002). Males on the life-course-

References Andersen, S. N., & Skardhamar, T. (2017). Pick a number: Mapping recidivism measures and their consequences. Crime & Delinquency, 63(5), 613–635. https://doi.org/10. 1177/0011128715570629. Bader, M. D. M., & Ailshire, J. A. (2014). Creating measures of theoretically relevant neighborhood attributes at multiple spatial scales. Sociological Methodology, 44(1), 322–368. https://doi.org/10.1177/0081175013516749. *Baglivio, M. T., Wolff, K. T., Jackowski, K., & Greenwald, M. A. (2017). A multilevel examination of risk/need change scores, community context, and successful reentry of committed juvenile offenders. Youth Violence and Juvenile Justice, 15(1), 38–61. https://doi.org/10.1177/1541204015596052. *Barnes, A. R. (2017). An assessment of protective factors in predicting juvenile reoffending. Michigan State University. Ph.D, Retrieved from https://search.proquest.com/ docview/1899858867/abstract/45B0877A0ECC4F7CPQ/1. Basto-Pereira, M., Começanha, R., Ribeiro, S., & Maia, Â. (2015). Long-term predictors of crime desistance in juvenile delinquents: A systematic review of longitudinal studies. Aggression and Violent Behavior, 25, 332–342. https://doi.org/10.1016/j.avb.2015.09. 012. *de Beus, K., & Rodriguez, N. (2007). Restorative justice practice: An examination of program completion and recidivism. Journal of Criminal Justice, 35(3), 337–347. Bonta, J., & Andrews, D. A. (2016). The psychology of criminal conduct (6th ed.). Florence, GB: Routledge. *Bright, C. L., Hurley, S., & Barth, R. P. (2014). Gender differences in outcomes of juvenile court-involved youth following intensive in-home services. Journal of the Society for Social Work and Research, 5(1), 22–45. Browning, C. R., Calder, C. A., Ford, J. L., Boettner, B., Smith, A. L., & Haynie, D. (2017). Understanding racial differences in exposure to violent areas: Integrating survey, smartphone, and administrative data resources. The Annals of the American Academy of Political and Social Science, 669(1), 41–62. https://doi.org/10.1177/ 0002716216678167. Browning, C. R., & Soller, B. (2014). Moving beyond neighborhood: Activity spaces and ecological networks as contexts for youth development. 31. Bullis, M., Yovanoff, P., Mueller, G., & Havel, E. (2002). Life on the “outs”—Examination of the facility-to-community transition of incarcerated youth. Exceptional Children, 69(1), 7–22. https://doi.org/10.1177/001440290206900101. Bursik, R. J., Jr., & Grasmick, H. G. (1993). Neighborhoods and crime: The dimensions of effective community control. . *Bursik, R. (1983). Community context and the deterrent effect of sanctions. Evaluating performance of criminal justice agencies (pp. 165–181). . Retrieved from https://www. nhlbi.nih.gov/health-pro/guidelines/in-develop/cardiovascular-risk-reduction/ tools/cohort. *Chauhan, P., & Reppucci, N. D. (2009). The impact of neighborhood disadvantage and exposure to violence on self-report of antisocial behavior among girls in the juvenile justice system. Journal of Youth and Adolescence, 38(3), 401–416. Cheshire, P. (2012). Are mixed community policies evidence based? A review of the research on neighbourhood effects. Neighbourhood effects research: New perspectives (pp. 267–294). Springer. Chung, H. L., & Steinberg, L. (2006). Relations between neighborhood factors, parenting behaviors, peer deviance, and delinquency among serious juvenile offenders. Developmental Psychology, 42(2), 319–331. https://doi.org/10.1037/0012-1649.42.2. 319. Cottle, C. C., Lee, R. J., & Heilbrun, K. (2001). The prediction of criminal recidivism in juveniles: A meta-analysis. Criminal Justice and Behavior, 28(3), 367–394. https://doi. org/10.1177/0093854801028003005. *Craig, J. (2019). The potential mediating impact of future orientation on the ACE-crime relationship. Youth Violence and Juvenile Justice, 17(2), 111–118. *Craig, J., Baglivio, M., Wolff, K., Piquero, A., & Epps, N. (2017). Do social bonds buffer the impact of adverse childhood experiences on reoffending. You Violence and Juvenile Justice, 15(1), 3–20. Diez Roux, A. V., & Mair, C. (2010). Neighborhood and Health. Annals of the New York Academy of Sciences, 1186(1), 125–145. https://doi.org/10.1111/j.1749-6632.2009. 05333.x. Dishion, T. J., & Tipsord, J. M. (2011). Peer contagion in child and adolescent social and emotional development. Annual Review of Psychology, 62(1), 189–214. https://doi. org/10.1146/annurev.psych.093008.100412. Dowden, C., & Brown, S. (2002). The role of substance abuse factors in predicting recidivism: A meta-analysis. Psychology, Crime and Law, 8(3), 243–264. Ertefaie, A., Nguyen, A., Harding, D. J., Morenoff, J. D., & Yang, W. (2018). Instrumental variable analysis with censored data in the presence of many weak instruments: Application to the effect of being sentenced to prison on time to employment. The Annals of Applied Statistics, 12(4), 2647–2673. https://doi.org/10.1214/18AOAS1174.

20

Journal of Criminal Justice xxx (xxxx) xxxx

L.A. Jacobs, et al.

mobility experiment on crime and delinquency. Journal of Experimental Criminology, 9(4), 451–489. https://doi.org/10.1007/s11292-013-9189-9. Seidman, E., Yoshikawa, H., Roberts, A., Chesir-Teran, D., Allen, L., Friedman, J. L., & Aber, J. L. (1998). Structural and experiential neighborhood contexts, developmental stage, and antisocial behavior among urban adolescents in poverty. Development and Psychopathology, 10(2), 259–281. Sharkey, P., & Faber, J. W. (2014). Where, when, why, and for whom do residential contexts matter? Moving away from the dichotomous understanding of neighborhood effects. Annual Review of Sociology, 40(1), 559–579. https://doi.org/10.1146/ annurev-soc-071913-043350. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas: A study of rates of delinquents in relation to differential characteristics of local communities in American cities. Chicago, IL: University of Chicago Press. Singh, J. P., Desmarais, S. L., Sellers, B. G., Hylton, T., Tirotti, M., & Van Dorn, R. A. (2014). From risk assessment to risk management: Matching interventions to adolescent offenders’ strengths and vulnerabilities. Children and Youth Services Review, 47, 1–9. https://doi.org/10.1016/j.childyouth.2013.09.015. Snowden, J. M., Rose, S., & Mortimer, K. M. (2011). Implementation of G-computation on a simulated data set: Demonstration of a causal inference technique. American Journal of Epidemiology, 173(7), 731–738. https://doi.org/10.1093/aje/kwq472. Spruit, A., van Vugt, E., van der Put, C., van der Stouwe, T., & Stams, G.-J. (2016). Sports participation and juvenile delinquency: A meta-analytic review. Journal of Youth and Adolescence, 45, 655–671. https://doi.org/10.1007/s10964-015-0389-7. U.S. Census Bureau (2017a). Geographic Mobility in the Past Year by Poverty Status: 2013–2017 American Community Survey 5-Year Estimates. Retrieved from https:// factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_ 17_5YR_S0701&prodType=table. U.S. Census Bureau (2017b). Hispanic or Latino by Specific Origin: 2013–2017 American Community Survey 5-Year Estimates. Retrieved from https://factfinder.census.gov/ faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_B03001& prodType=table. U.S. Census Bureau (2018). General Mobility of Family Householders 15 to 54 Years, by Type of Household and Presence and Age of Own Children Under 18 Years: 2017 to 2018 Annual Social and Economic Supplement. Retrieved from https://www.census. gov/data/tables/2018/demo/geographic-mobility/cps-2018.html. *Veeh, C. A. (2015). The interaction effects of subjective and structural factors on crime among formerly incarcerated males (PhD dissertation). Denver, CO.: University of Denver. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. Warner, B. D. (2014). Neighborhood factors related to the likelihood of successful informal social control efforts. Journal of Criminal Justice, 42(5), 421–430. Warr, M. (1996). Organization and instigation in delinquent groups. Criminology, 34(1), 11–37. https://doi.org/10.1111/j.1745-9125.1996.tb01193.x. Wibbelink, C. J. M., Hoeve, M., Stams, G. J. J. M., & Oort, F. J. (2017). A meta-analysis of the association between mental disorders and juvenile recidivism. Aggression and Violent Behavior, 33, 78–90. https://doi.org/10.1016/j.avb.2017.01.005. Wikström, P.-O. H., & Loeber, R. (2000). Do disadvantaged neighborhoods cause welladjusted children to become adolescent delinquents? A study of male juvenile serious offending, individual risk and protective factors, and neighborhood context. Criminology, 38(4), 1109–1142. https://doi.org/10.1111/j.1745-9125.2000. tb01416.x. Wilson, W. J. (2012). The truly disadvantaged: The inner city, the underclass, and public policy (2nd ed.). Chicago, IL: University of Chicago Press. *Wolff, K. T., Baglivio, M. T., Intravia, J., Greenwald, M. A., & Epps, N. (2017). The mobility of youth in the justice system: Implications for recidivism. Journal of Youth and Adolescence, 46(7), 1371–1393. https://doi.org/10.1007/s10964-016-0498-y. *Wolff, K. T., Baglivio, M. T., Intravia, J., & Piquero, A. R. (2015). The protective impact of immigrant concentration on juvenile recidivism: A statewide analysis of youth offenders. Journal of Criminal Justice, 43(6), 522–531. *Wolff, K. T., Baglivio, M. T., Piquero, A. R., Vaughn, M. G., & DeLisi, M. (2016). The Triple Crown of antisocial behavior: Effortful control, negative emotionality, and community disadvantage. Youth Violence and Juvenile Justice, 14(4), 350–366. https://doi.org/10.1177/1541204015599042. *Wolff, K. T., Intravia, J., Baglivio, M. T., & Piquero, A. R. (2018). The protective impact of immigrant concentration on juvenile recidivism: A multilevel examination of potential mechanisms. Crime & Delinquency, 64(10), 1271–1305. https://doi.org/10. 1177/0011128717739608. *Wright, K. A., Kim, B., Chassin, L., Losoya, S. H., & Piquero, A. R. (2014). Ecological context, concentrated disadvantage, and youth reoffending: Identifying the social mechanisms in a sample of serious adolescent offenders. Journal of Youth and Adolescence, 43(10), 1781–1799. Wright, K. A., & Rodriguez, N. (2014). A closer look at the paradox: Examining immigration and youth reoffending in Arizona. Justice Quarterly, 31(5), 882–904. https://doi.org/10.1080/07418825.2012.700057. *Wright, K. A., Turanovic, J. J., & Rodriguez, N. (2016). Racial inequality, ethnic inequality, and the system involvement of at-risk youth: Implications for the racial invariance and Latino paradox theses. Justice Quarterly, 33(5), 863–889. *Yan, J. (2009). A multidisciplinary study on juvenile recidivism and multilevel impacts: Risk factors, neighborhood features, and juvenile justice intervention. PhD dissertationColumbia, MO: University of Missouri.

persistent and adolescence-limited antisocial pathways: Follow-up at age 26 years. Development and Psychopathology, 14(1), 179–207. Moffitt, T. E., Caspi, A., Rutter, M., & Silva, P. A. (2001). Sex differences in antisocial behaviour: Conduct disorder, delinquency, and violence in the Dunedin longitudinal study. Cambridge, UK: Cambridge University Press. Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., ... PRISMA-P Group (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/ 10.1186/2046-4053-4-1. Monahan, J., & Skeem, J. L. (2013). Risk redux: The resurgence of risk assessment in criminal sanctioning. Federal Sentencing Reporter, 26, 158–166. Mulvey, E. P. (2011). Highlights from pathways to desistance: A longitudinal study of serious adolescent offenders (No. NCJ 230971). Washington, D.C.: Office of Juvenile Justice and Delinquency Prevention. Murray, J., Farrington, D. P., & Eisner, M. P. (2009). Drawing conclusions about causes from systematic reviews of risk factors: The Cambridge Quality Checklists. Journal of Experimental Criminology, 5(1), 1–23. https://doi.org/10.1007/s11292-008-9066-0. Odgers, C. L. (2015). Income inequality and the developing child: Is it all relative? American Psychologist, 70(8), 722–731. https://doi.org/10.1037/a0039836. Ostermann, M., Salerno, L. M., & Hyatt, J. M. (2015). How different operationalizations of recidivism impact conclusions of effectiveness of parole supervision. Journal of Research in Crime and Delinquency, 52(6), 771–796. https://doi.org/10.1177/ 0022427815580626. *Piquero, A. R., Cardwell, S. M., Piquero, N. L., Jennings, W. G., & Reingle Gonzalez, J. M. (2016). How well do the adolescent risk factors predict re-arrest frequency across race/ethnicity among serious adolescent offenders? Journal of Crime and Justice, 39(1), 189–209. Piquero, A. R., Jennings, W. G., Diamond, B., & Reingle, J. M. (2015). A systematic review of age, sex, ethnicity, and race as predictors of violent recidivism. International Journal of Offender Therapy and Comparative Criminology, 59(1), 5–26. https://doi. org/10.1177/0306624X13514733. Piquero, A. R., Moffit, T. E., & Lawton, B. (2005). Race and Crime: The Contribution of Individual, Familial, and Neighborhood-Level Risk Factors to Life-Course-Persistent Offending. In D. F. Hawkins, & K. Kempf-Leonard (Eds.). Our children, their children: Confronting racial and ethnic differences in American juvenile justice. (pp. 202–244). The John D. and Catherine T. MacArthur Foundation. Piquero, A. R., Schubert, C. A., & Brame, R. (2014). Comparing official and self-report records of offending across gender and race/ethnicity in a longitudinal study of serious youthful offenders. Journal of Research in Crime and Delinquency, 51(4), 526–556. https://doi.org/10.1177/0022427813520445. Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., ... Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC Methods Programme. https://doi.org/10.13140/2.1.1018.4643. Pratt, T. C., & Cullen, F. T. (2005). Assessing macro-level predictors and theories of crime: A meta-analysis. Crime and Justice, 32, 373–450. Putnam, R. D. (2001). Bowling alone: The collapse and revival of American community. New York, NY: Simon and Schuster. Sampson, R. J. (1986). Effects of socioeconomic context on official reaction to juvenile delinquency. American Sociological Review, 51(6), 876–885. https://doi.org/10.2307/ 2095373. Sampson, R. J. (1986a). Neighborhood family structure and the risk of personal victimization. The social ecology of crime (pp. 25–46). New York, NY: Springer. Sampson, R. J. (2012). Great American city: Chicago and the enduring neighborhood effect. Chicago, IL: University of Chicago Press. Sampson, R. J., & Loeffler, C. (2010). Punishment’s place: The local concentration of mass incarceration. Daedalus, 139(3), 20–31. Sampson, R. J., Morenoff, J. D., & Gannon-Rowley, T. (2002). Assessing “neighborhood effects”: Social processes and new directions in research. Annual Review of Sociology, 28(1), 443–478. Sampson, R. J., & Raudenbush, S. W. (1999). Systematic social observation of public spaces: A new look at disorder in urban neighborhoods. American Journal of Sociology, 105(3), 603–651. https://doi.org/10.1086/210356. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 5382(277), 918–924. Sampson, R. J., Wilson, W. J., & Katz, H. (2018). Reassessing “toward a theory of race, crime, and urban inequality”: Enduring and new challenges in 21st century America. Du Bois Review: Social Science Research on Race, 15(1), 13–34. https://doi.org/10. 1017/S1742058X18000140. Sanbonmatsu, L., Ludwig, J., Gennetian, L., Duncan, G. J., Kessler, R. C., Adam, E., & Lindau, S. (2011). Moving to Opportunity for Fair Housing demonstration program - Final impacts evaluation. 330. Retrieved from National Bureau of Economic Research website https://www.huduser.gov/publications/pdf/mtofhd_fullreport_v2.pdf. Schmidt, N. M., Krohn, M. D., & Osypuk, T. L. (2018). Modification of housing mobility experimental effects on delinquency and educational problems: Middle adolescence as a sensitive period. Journal of Youth and Adolescence, 47(10), 2009–2026. https:// doi.org/10.1007/s10964-018-0859-9. Schwalbe, C. S. (2007). Risk assessment for juvenile justice: A meta-analysis. Law and Human Behavior, 31(5), 449. Sciandra, M., Sanbonmatsu, L., Duncan, G. J., Gennetian, L. A., Katz, L. F., Kessler, R. C., & Ludwig, J. (2013). Long-term effects of the moving to opportunity residential

21