Understanding the Role of Neighborhood Typology and Sociodemographic Characteristics on Time to Recidivism Among Adjudicated Youth

• DOI: 10.1177/0093854820924834
• Author: Christina A. Campbell,Valerie R. Anderson,Natasha Moses,Christopher D’amato,Ashlee Barnes,Jordan Papp
• Published date: 01 September 2020
• Date: 01 September 2020
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2020, Vol. 47, No. 9, September 2020, 1079 –1096.

DOI: https://doi.org/10.1177/0093854820924834

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© 2020 International Association for Correctional and Forensic Psychology

1079

UNDERSTANDING THE ROLE OF

NEIGHBORHOOD TYPOLOGY AND

SOCIODEMOGRAPHIC CHARACTERISTICS

ON TIME TO RECIDIVISM AMONG

ADJUDICATED YOUTH

CHRISTINA A. CAMPBELL

University of Cincinnati

ASHLEE BARNES

Michigan State University

JORDAN PAPP

University of Michigan

CHRISTOPHER D’AMATO

VALERIE R. ANDERSON

NATASHA MOSES

University of Cincinnati

This study examined the effect of neighborhood disadvantage and criminogenic risk on juvenile recidivism. The sample

included 893 youths involved in the delinquency/formal probation division of one Midwestern county juvenile court between

2004 and 2010. Juveniles were classified into one of three neighborhood typologies (i.e., Distressed/Disadvantage, Resilient/

Mixed, Benchmark/Advantaged) based on the socioeconomic conditions in their neighborhoods. Survival models revealed

that when examining the effect of neighborhood type, youth who lived in Resilient/Mixed neighborhoods, characterized by

having the most transient residents, yet high graduation rates, were at greatest risk of recidivism. However, neighborhood

effects disappeared after controlling for sociodemographic characteristics and criminogenic risk. Although there was no

significant interaction between neighborhood and risk group classification, there was a significant interaction between risk

group, age, and gender. These findings suggest the need for advanced statistical models that can disentangle the conflated

effects of socioeconomic conditions and sociodemographic characteristics.

Keywords: adjudicated youth; juvenile recidivism; risk assessment; hazard modeling; Youth Level of Service/Case

Management Inventory (YLS/CMI); neighborhood disadvantage

Each year, more than 2 million youths are arrested, costing law enforcement, courts,

detention facilities, and community programs US$7 to US$21 billion annually

(Kirchner, 2014). Although the current juvenile crime rate has been the lowest since the

AUTHORS’ NOTE: This research was supported by King-Chavez Parks Future Faculty Fellowship Program

at Michigan State University.Correspondence concerning this article should be addressed to Christina A.

Campbell, School of Criminal Justice, University of Cincinnati, Cincinnati, OH 45221; e-mail: campb2ci@

ucmail.uc.edu.

924834CJBXXX10.1177/0093854820924834Criminal Justice and BehaviorCampbell et al. / Neighborhoods and Criminogenic Risk

research-article2020

1080 CRIMINAL JUSTICE AND BEHAVIOR

1970s, juvenile crime remains a major concern for law enforcement, tax payers, communi-

ties burdened by crime, and marginalized families who are disproportionately involved in

the justice system (e.g., families from low-income neighborhoods, racial/ethnic minorities;

Snyder & Sickmund, 2006). To address concerns around public safety and reduce fiscal

costs associated with rehabilitation, modern criminogenic risk assessments have been

adopted and provide courts with standardized information concerning the individual-level

(e.g., personality and behavior) and proximal (e.g., peer relationships and family circum-

stances) risk factors necessary to predict the likelihood of future criminal activity (Hoge &

Andrews, 2006). Risk assessments also evaluate court-involved youth criminogenic areas

of need (e.g., education, substance abuse) that should be targeted during court supervision

to maximize rehabilitation. Ultimately, juvenile risk assessment guides decisions and helps

juvenile court practitioners determine which cases should remain under court supervision

and which cases would be most successfully dismissed or diverted (Andrews et al., 2006).

Racial/ethnic, gender, and socioeconomic disparities have sparked continued debates

about the efficacy and fairness of the juvenile justice system’s response to crime (Lipsey

et al., 2010; Onifade et al., 2011). The creation and application of risk assessment tools

have also been used as an evidence-based strategy to decrease the introduction of subjec-

tive bias in juvenile court processing. Ideally, employing risk assessment standardizes

decision making and prescribes treatment, in turn, mitigating biases that may interfere

with aims to target factors associated with delinquency (Schwalbe et al., 2006). Given the

promising ways that risk assessment has informed juvenile justice practice and policies,

including showing reductions in racial disparities (Onifade et al., 2019; Skeem &

Lowenkamp, 2016), many courts have adopted the use of these instruments to improve

interventions, inform rehabilitation needs, increase public safety, and better capture the

pathways of delinquency.

Risk assessment literature and meta-analytic reviews note the utility and predictive valid-

ity of actuarial assessment in North America and abroad, across different offense types

(violent, nonviolent, and sex offenses), and across justice-involved subpopulations (racial/

ethnic minorities and child welfare involved youth; Edens et al., 2007; Olver et al., 2009;

Schwalbe, 2007, 2008; Thompson & Pope, 2005). However, little is known about the func-

tionality of risk assessment across different neighborhood ecologies and the extent to which

socioeconomic neighborhood conditions interact with different levels of risk.

Macro-level theories, such as social disorganization theory, suggest that many neighbor-

hood-level factors are important predictors of delinquency (Sampson & Groves, 1989;

Shaw & McKay, 1942). Juveniles’ experiences are nested within social and geographic

contexts that affect various social controls, and many risk assessment tools do not capture

these salient neighborhood factors such as poverty, joblessness, neighborhood services,

availability of social services, and housing dynamics. For this reason, it is critical that these

measures are examined to fully understand the nature of recidivism risk across neighbor-

hoods with different socioeconomic conditions and further examine how criminogenic risk

classifications interact with neighborhood characteristics (Onifade et al., 2008). The goal of

this study was to examine differences in recidivism risk of low-, moderate-, and high-risk

juveniles across diverse socioeconomic neighborhood contexts.

To date, one peer-reviewed study has examined the intersection of neighborhoods and

juvenile risk assessment (Onifade et al., 2011). It is important to build knowledge in this

area because risk assessment is used in juvenile court processing; therefore, it is imperative