Influences of Neighborhood Context, Individual ...

1 downloads 0 Views 225KB Size Report
Mar 4, 2010 - communities (Sampson and Groves 1989, Jacob 2006;. Obertwittler 2004; Osgood and Chambers 2000). A number of studies have concluded ...
J Youth Adolescence (2010) 39:1067–1079 DOI 10.1007/s10964-010-9518-5

EMPIRICAL RESEARCH

Influences of Neighborhood Context, Individual History and Parenting Behavior on Recidivism Among Juvenile Offenders Heidi E. Grunwald • Brian Lockwood Philip W. Harris • Jeremy Mennis



Received: 13 November 2009 / Accepted: 15 February 2010 / Published online: 4 March 2010  Springer Science+Business Media, LLC 2010

Abstract This study examined the effects of neighborhood context on juvenile recidivism to determine if neighborhoods influence the likelihood of reoffending. Although a large body of literature exists regarding the impact of environmental factors on delinquency, very little is known about the effects of these factors on juvenile recidivism. The sample analyzed includes 7,061 delinquent male juveniles committed to community-based programs in Philadelphia, of which 74% are Black, 13% Hispanic, and 11% White. Since sample youths were nested in neighborhoods, a hierarchical generalized linear model was employed to predict recidivism across three general categories of recidivism offenses: drug, violent, and property. Results indicate that predictors vary across the types of offenses and that drug offending differs from property and violent offending. Neighborhood-level factors were found to influence drug offense recidivism, but were not significant predictors of violent offenses, property offenses, or an aggregated recidivism measure, despite contrary expectations. Implications

H. E. Grunwald Beasley School of Law, Temple University, Philadelphia, PA 19122, USA e-mail: [email protected] B. Lockwood (&)  P. W. Harris Department of Criminal Justice, Temple University, Philadelphia, PA 19122, USA e-mail: [email protected] P. W. Harris e-mail: [email protected] J. Mennis Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA e-mail: [email protected]

stemming from the finding that neighborhood context influences only juvenile drug recidivism are discussed. Keywords Juvenile recidivism  Community context  Neighborhood effects  Drug offending

While few empirical studies have examined neighborhoodlevel predictors of juvenile recidivism, the effects of environmental forces have played a leading role in the development of criminological theory and juvenile justice policy. The proliferation of juvenile courts during the early twentieth century has been attributed to concern for neighborhoods unable to prevent delinquency (Harris et al. 2000; Tanenhaus 2004). Shaw and McKay’s (1942) seminal research on delinquency rates in Chicago concluded that the spatial distribution of neighborhood characteristics influences delinquency rates. Even today, consideration of juveniles’ environments as they relate to delinquency influences juvenile court decisions (Fader et al. 2001). Reducing the likelihood that juvenile offenders will commit future offenses is a primary goal of the juvenile justice system. State-level juvenile recidivism rates as high as 55% have been reported (Snyder and Sickmund 2006). In 2003, the rate of juveniles in custody was 307 for every 100,000 juveniles, with more than 92,000 juveniles held in public and private juvenile facilities, according to a 1-day count (Snyder and Sickmund 2006: 201). This figure represents a 28% increase in juvenile confinement since 1991. Moreover, Snyder and Sickmund (2006: 234) estimate the juvenile reincarceration rate at 24%. These recidivism and reincarceration rates are largely attributed to individual and family factors, or to program impact. Relatively little attention, however, has been given to the environmental factors that increase or decrease the

123

1068

likelihood of recidivism. Kubrin and Stewart (2006: 167), in their study of the effects of neighborhood context on adult recidivism, describe this important, yet overlooked area of investigation when noting that: Neighborhood context is fundamental to our understanding of why individuals offend, and potentially even more important for understanding why former offenders offend again, yet we know very little about how the ecological characteristics of communities influence the recidivism rates of this population. The investigation of the effects of neighborhood effects on reoffending is very new in the field of Criminology, with several studies finding that space does influence the likelihood of adult recidivism (Kubrin and Stewart 2006; Kubrin et al. 2007; Mears et al. 2008). This conclusion is supported by the even more sparse research on the effects of spatial factors on juvenile recidivism (LeBaron 2002; Simmons 2001), and can be contrasted with a number of studies examining ecological explanations of delinquency (Bursik 1988; Sampson and Groves 1989; Sampson et al. 1999). This study explores the effects of environmental factors on juvenile recidivism and their differential effects on types of repeat offending. Preliminary hotspot analyses of our data indicated that the spatial distribution of juvenile recidivism differed by offense type: local spatial clusters of different recidivism offense types (drug, property, and violent) were located in clearly different areas of the city. This spatial clustering by recidivism offense type indicates that an investigation of individual- and neighborhood-level effects on recidivism by offense type may add pertinent information to the analysis. Earlier studies of delinquency have specified the type of delinquency that certain neighborhood features are apt to influence. Jacob (2006) found that residential mobility is the best predictor of juvenile property crime while the rate of lone-parent families is the best predictor of violent crime. Sampson and Grove’s (1989) test of social disorganization in Great Britain found that organizational participation and local friendship groups were the strongest predictors of burglary, while ethnic heterogeneity significantly predicted only property crime. Family disruption was found to predict violent crime and unsupervised peer groups predicted both property and violent crime (Sampson and Groves 1989). These findings support an investigation of the impact of environmental factors on juvenile recidivism and, further, of an investigation that distinguishes juvenile recidivism by offense type.

Correlates of Juvenile Recidivism and Offending Prior studies have uncovered a number of individual-level factors that influence the likelihood that a juvenile will

123

J Youth Adolescence (2010) 39:1067–1079

re-offend. Juveniles at highest risk to offend are those who have done so in the past (Cottle et al. 2001; Dembo et al. 1998). Other individual-level predictors of recidivism include gender, race (Dembo et al. 1998), substance abuse (Duncan et al. 1995; Elliott et al. 1985), early childhood misbehavior (White et al. 1990), current age (Snyder and Sickmund 2006), criminal history (Cottle et al. 2001), prior out-of home placement (Myner et al. 1998), peer relations (Akers 1985; Myner et al. 1998), mental health problems (Huizinga et al. 2000; Pullmann et al. 2006), and family problems (Wiebush et al. 1995). Findings from these and other studies have been used to construct risk assessment tools tasked with assigning levels of risk of reoffending to juvenile offenders. Evaluations of these tools have been mixed, with some studies concluding that they provide little or no predictive ability, at least when applied in different settings (Miller and Lin 2007; Schwalbe et al. 2006). It is likely that the inability of risk assessment tools to accurately predict juvenile re-offending is due to the absence of spatial predictors (Webster et al. 2006: 12). Some instruments consider environmental attributes, but risk prediction instruments rarely take geographic or social space into consideration, in spite of evidence suggesting the importance of space when studying delinquency. Our primary theoretical perspective is social disorganization theory. This theory provides an ideal framework for explaining relationships between neighborhood processes and juvenile recidivism rates. Socially disorganized neighborhoods lack informal social controls which in turn increases crime and delinquency in those neighborhoods (Bursik 1988). The proliferation of such ecological considerations within the field of criminology stems from the work of Shaw and McKay (1942), who identified the effects of environmental attributes such as poverty, ethnic heterogeneity, and residential mobility within neighborhoods on rates of crime and delinquency. A number of subsequent studies have operationalized these community-level constructs and confirmed these predictors of offending within communities (Sampson and Groves 1989, Jacob 2006; Obertwittler 2004; Osgood and Chambers 2000). A number of studies have concluded that juvenile crime is dependent on neighborhood processes, particularly where economic disadvantage decreases collective efficacy, which, in turn, increases delinquency rates (Bursik 1988; Sampson and Groves 1989; Sampson et al. 1999). Drug and alcohol availability (Freisthler et al. 2005; Herrenkohl et al. 2000), the spatial concentration of juveniles with delinquent attitudes (Oberwittler 2004), and number of ‘‘unconventional’’ friends (Rankin and Quane 2002) have also been identified as neighborhood-level predictors of juvenile offending. Other studies have integrated micro- and macro-level explanations of delinquency and determined that certain individual attributes can increase an individual’s

J Youth Adolescence (2010) 39:1067–1079

susceptibility to neighborhood forces. Cattarello (2000), for example, found that delinquent peers mediate the effect of social disorganization on delinquency: controlling for peer associations rendered the effects of social disorganization on delinquency insignificant. Similarly, a study by Wikstrom and Loeber (2000) observed that the effects of neighborhood socio-economic factors on serious offending among juveniles is mediated by the risk-level and age of juveniles; the effects of neighborhood context were limited to juveniles with low and middle risk scores and to those with a late adolescent onset of delinquency. Chung and Steinberg (2006) found that the effect of economic disadvantage on persistent delinquency was mediated by parenting behavior and peer deviance. Most research on specific types of juvenile delinquency has done so for offenses related to drug sales and violence. This is not surprising when considering the works of Wilson (1996, 1987) and Anderson (1999, 1990) that describe the structure of urban areas that contribute to increasing rates of both types of offenses. They cite changes in economic conditions, disruption within families, and pervading attitudes that promote criminal activities as causal mechanisms for increases in both drug selling and violent crime within disadvantaged urban communities. Many subsequent studies have provided support for their finding that neighborhood processes, most often relating to economic deprivation and family organization, contribute to juvenile violence (Liberman 2007; Osgood and Chambers 2000; Sampson and Groves 1989). These works indicate that socially disorganized neighborhoods are more likely to experience youth violence than are communities that do not exhibit qualities of social disorganization. More recent research has asked whether drug dealing within neighborhoods can be attributed to neighborhood processes. Little and Steinberg (2006) used an opportunity framework to explain their findings that poor neighborhood conditions and low neighborhood job opportunity influence urban adolescent drug dealing. Their work draws in part on literature that indicated a rising number of opportunities for juveniles to sell drugs in urban areas. They conclude that ‘‘adolescents who sold the most drugs were more likely to live in contexts characterized by high physical and social disorder…’’ (Little and Steinberg 2006: 378). Martinez et al. (2008) support Little and Steinberg’s (2006) finding that drug crime is influenced by neighborhood-level indicators of disadvantage. Additionally, they found that drug activity increases violence within neighborhoods, net of their measures of social disorganization. Their conclusion that ‘‘traditional dimensions of social disorganization predict drug activity which, in turn, leads to higher levels of criminal violence,’’ serves to tie drug and violent offending together in disadvantaged neighborhoods (Martinez et al. 2008: 866). Further, research by Baumer and Gustafson

1069

(2007) has linked neighborhood structure to rates of instrumental crime such as drug trafficking. Few studies have investigated differences in the correlates of recidivism by offense type; rather, most research is concerned with the presence or absence of re-offending in general. Much of the small body of literature on specific recidivism offense types concentrates on sex offenders (Parks and Bard 2006; Rasmussen 1999) or violent offenders (Howell 1995; Loeber et al. 1998). While many studies of the effects of space highlight the importance of community context on violent and drug offending, little research has parceled out the predictors of recidivism based on specific offense types. Research, however, suggests that the disaggregation of recidivism offense type can serve to unmask potentially varying effects of individual and environmental predictors on recidivism. Accordingly, we disaggregate our outcome measure to capture distinct types of juvenile recidivism.

Hypotheses Our sample of youths is likely to comprise the more serious delinquency cases, since cases in which the disposition was probation supervision were excluded. Thus the neighborhoods in which most of our sample members reside are likely to be disadvantaged. We expect, however, that neighborhood-level variables will exert a significant influence on juvenile recidivism. Chung and Steinberg (2006), for example, studied a sample of serious delinquent youths from the same population and found that concentrated poverty was associated with self-reported offending through its effects on neighborhood disorganization and deviant peers. We hypothesize that neighborhood indicators of social disorganization found to predict delinquency will continue to predict recidivism after controlling for individual and family contexts. We also hypothesize that individual and neighborhood predictors of juvenile recidivism will vary depending on recidivism offense type. The literature reviewed above suggests that neighborhood processes should have a significant influence on both drug and violent crime. Relative to drug and violent re-offending, we expect that property offense recidivism will be much less affected by neighborhood attributes.

Methods Data Individual level data were taken from the Program Development and Evaluation System (ProDES) database, a population database of all juveniles committed by the

123

1070

Philadelphia Family Court to community and residential programs between 1994 and 2004. Within ProDES, the data were organized by program experience. That is, a ‘‘case’’ was created for a juvenile each time a decision was made to commit a youth to a program or move the youth to a different program. These data were collected to provide program providers, court personnel, and funding agencies with information on program outcomes. The data include measures of family demographics, juvenile characteristics, criminal history, current offense characteristics and petitions for new offenses. All ProDES data used for this study were collected by CJRC staff. To test the impact of neighborhood-level attributes on juvenile recidivism, any periods of residential program participation were excluded. We included only periods when neighborhood forces could directly affect recidivism. Based on that criterion, 13,000 cases were selected who entered community programs during the period 1996 through 2002–the years when the data were most complete. The data set was further reduced by the removal of females, since females made up a small proportion of subjects (11%), their recidivism rates were less than half that of males, and prior research has demonstrated marked gender differences in the predictors of juvenile delinquency and recidivism (Daigle et al. 2007; Funk 1999; Mazerolle 1998). These considerations reduced our sample for analysis to 10,971 male delinquents committed to communitybased programs by the Family Court. These 10,971 cases included multiple records for the same youth, which represented each program for each youth. Approximately onethird of the juveniles in the sample appear in the dataset twice; approximately 300 juvenile offenders appear more than twice. Since there were too few observations for a longitudinal analysis, we selected the first community program experience for each youth resulting in a sample of 7,061 male juveniles. Of these, 2,565, or 36%, were on aftercare (or parole) status, meaning that their first community program experience followed a period of incarceration. Since youths on aftercare are likely to differ from other delinquents in terms of offense seriousness and/or more troubled family environments (Fader et al. 2001), a measure of aftercare status was included in the analysis. The ProDES system includes home addresses for each juvenile at the time of program participation. ArcView GIS 9.2 was used to geocode the home addresses of the juveniles and assign these addresses to neighborhoods within Philadelphia. The spatial level of aggregation utilized in this study includes neighborhood boundaries delineated by the Philadelphia Health Management Corporation (PHMC), which exhaustively partitions the city into 45 neighborhood polygons. These neighborhood boundaries were constructed by researchers and officials familiar with the spatial

123

J Youth Adolescence (2010) 39:1067–1079

distribution of communities in Philadelphia. While larger than the frequently-used Census geographies of block groups and tracts, this level of aggregation has the advantage of specifically representing the neighborhoods of Philadelphia. Data at the neighborhood level were taken from the results of the PHMC’s Household Health Survey (HHS), a biannual survey of residents from Philadelphia and surrounding counties, and includes items related to health and neighborhood perceptions. The 2000, 2002, and 2004 surveys were used to match the time period of the ProDES data. The PHMC surveyed 4,088 adults in Philadelphia in 2000, 4,133 in 2002, and 4,415 in 2004, with a mean sample size of 4,212 over the three survey periods, and a mean of 93.6 survey respondents per neighborhood. Since the PHMC HHS also includes respondents from the surrounding counties of Philadelphia, a weighting procedure was undertaken so that the data represented the population of the city (for a detailed description of this process, see Garcia et al. 2007). Measures Recidivism This study examines four juvenile recidivism outcomes: an aggregate measure of any new offense and three measures of specific re-offense types (property, drug, or violent). The outcome measure representing drug recidivism captures both drug selling and drug possession. Cases were followed into the adult criminal justice system. The recidivism measure was dichotomous indicating whether or not a juvenile had a new petition filed at any time during participation in the community-based program through 6-months following program discharge (0 = no new petition filed, 1 = new petition filed). Snyder and Sickmund (2006) found that using referral to court to measure recidivism produced an aggregate recidivism rate across several states of 45%. Of our subjects, slightly more than 40% had a new petition filed against them during the study period. The follow-up period starts with program commitment and ends 6 months following program discharge, during which time the youths were exposed to home and neighborhood influences. The average length of time per juvenile spent in the community-based program was 203 days, or approximately 7 months. With the 6-month post-discharge period, the study includes an average follow-up period of approximately 13 months. Preliminary models distinguishing between juveniles who recidivated during their time in treatment and those who reoffended after treatment indicate that the predictors for both outcomes behave similarly.

J Youth Adolescence (2010) 39:1067–1079

Individual-Level Predictors A review of the literature and several preliminary analyses, including binary logistic regression, CHAID (Chi-squared Automatic Interaction Detector), and neural network analyses, were conducted to reduce the number of predictors from several hundred to the fourteen that were included in this analysis. The final set of predictors includes demographic characteristics, family traits, current offense characteristics, and criminal history. Demographic predictors include age at first arrest and race, in the form of dummy variables for white and Hispanic juveniles (black juveniles are the reference category). Parental deviance was measured with items indicating drug abuse and criminal behavior. Receiving public assistance is used to indicate the family’s socio-economic status. A variable indicating aftercare status is also included to identify youths returning directly from residential facilities. Current offense predictors classify the instant offense as violent, property, or sexrelated, based on the initial most serious charge. Lastly, criminal history predictors indicate a prior drug crime, violent crime, or out-of-home placement on the juveniles’ court records. All of the individual-level variables selected were dichotomous (0 = no, 1 = yes), with the exception of age at first arrest, which is continuous. The few studies that have examined neighborhood effects on recidivism have generally included measures of economic disadvantage. This study also includes such a measure in the form of concentrated disadvantage; additionally, we estimate the effects of a potentially protective factor in the form of social capital. Data from the PHMC HHS conducted in 2000, 2002, and 2004 were used to construct indices representing concentrated disadvantage and social capital. Neighborhood Disadvantage An index of concentrated disadvantage was created from a linear combination of four HHS items: adults living below the poverty line, unemployed, on welfare, and yearly income. The first three items were converted into proportions representing the percentage of adults per neighborhood that live below the poverty line, are unemployed, and are on welfare. The fourth item represented yearly income on a scale of zero through seventeen, with seventeen representing adults making more than $250,000 per year. The values of this item were multiplied by -1 so that high values would represent disadvantaged individuals, and thus be consistent with the other items in the concentrated disadvantage index. This index demonstrates a high level of internal reliability with a Cronbach’s alpha of 0.947 in 2000, 0.871 in 2002, and 0.845 in 2004. To further match the data collection period of the ProDES data, and to

1071

account for neighborhood change, the values of this index for each of the 3 years was averaged so that a mean value from 2000 to 2004 was created. This operationalization of neighborhood disadvantage is consistent with studies of the effects of neighborhood context on crime (Baumer et al. 2003; Baumer 2002; Morenoff et al. 2001; Sampson et al. 1997), with two exceptions. The proportion of black residents was excluded for two reasons: it decreased the reliability of the index and, theoretically, race has been used to indicate financial disadvantage, which is already well-represented in the index. Social Capital An index representing social capital was also created from the 2002 and 2004 HHS data. Prior to 2002, the survey did not include these items. This index was constructed from four items relating to perceptions of and levels of participation within their neighborhood: number of local groups and organizations they participate in; a five-category rating (ranging from ‘‘never’’ to ‘‘always’’) of how often neighborhood residents are willing to help their neighbors with basic tasks, such as picking up trash cans and shoveling snow; and responses to two statements on a scale of ‘‘1’’ to ‘‘5’: ‘‘I feel that I belong and are part of my community’’ and ‘‘Most people in my neighborhood can be trusted.’’ This index also demonstrates a high level of internal reliability, with a Cronbach’s alpha of 0.785 in 2002 and 0.868 in 2004. As with the concentrated disadvantage index, a mean value of this item from 2002 and 2004 was used in the analysis. The concept of social capital has been described by some researchers as ‘‘varied, murky, and even circular’’ (Messner et al. 2004: 882). Several researchers, however, have identified two key components of social capital: civic engagement and trust (Kennedy et al. 1998; Messner et al. 2004; Rosenfeld et al. 2001). The social capital indicator in the current study possesses both components. Additionally, the components of this index closely resemble the items used to conceptualize collective efficacy, an index intended to represent favorable neighborhood processes that stand in contrast to socially disorganized communities (Sampson et al. 1997, 1999). It is important here to note the importance of controlling for neighborhood crime rates when estimating the effects of neighborhood context on criminal activity. We found, however, that neighborhood crime rates, including total crime and crime by type, are highly correlated with our social disorganization and social capital constructs. We opted not to include neighborhood crime in our neighborhood scale items, as crime and our items representing social disorganization and social capital are distinct constructs. The inclusion of crime into these latent constructs,

123

1072

while statistically supported, would not be consistent with the theoretical definitions of those measures. To be sure of the similarities between crime and our level-2 constructs, earlier models that included crime rate at level-2 support the findings derived from the models included in this study. While neighborhood crime is not included in the current models, we believe that crime is accounted for by inclusion of level-2 constructs that are highly correlated with crime. Analytic Strategy Univariate descriptive statistics were estimated to describe the predictors and outcomes to be included in the analysis. Bivariate correlations of the individual-level variables were then analyzed to detect potential associations and multicollinearity. The main portion of this analysis includes the estimation of hierarchical linear models (HLM) so that potential neighborhood effects of juvenile recidivism can be identified, while controlling for individual-level variables. Hierarchical models permit the simultaneous inclusion of multiple levels of data and estimations of the amount of variance in the outcome measure that each level of data is responsible for. HLM was employed due to the natural clustering of youth in neighborhoods. In this analysis, juvenile offenders (n = 7,061) comprise the level-1 units of analysis and neighborhoods (n = 45) the level-2 units. For models estimating the effects of dichotomous outcomes, it is suggested that an interval of plausible values is calculated to determine if the outcome varies across level-2 units of analysis (Raudenbush and Bryk 2002). A significant interval of plausible values indicates variation in the outcome between level-2 units of analysis and supports the utility of HLM to analyze the data. These unconditional models were followed by two-level, hierarchical, generalized linear models to estimate individual and neighborhood effects on the odds of recidivism. We use a random intercept model to predict all four outcomes.

Results Univariate statistics for the outcomes and predictors are shown in Table 1. An examination of the mean values of the outcome measures indicates that 40% of the juvenile offenders recidivated, 14% with a drug crime, 10 with a violent crime, and 11% with a property offense. Although drug offense recidivism was the most prevalent recidivism offense type, the proportions of drug, violent, and property reoffenders in the dataset are relatively similar. For race, 11% of the youths were white and 13% Hispanic. The reference category of black juveniles comprised the largest proportion of the population (74%). The average age of the juveniles at the time of their first arrest was 14.2 years. As

123

J Youth Adolescence (2010) 39:1067–1079

with the recidivism offense types, there was little variation between the proportion of juveniles who committed a violent crime as their initial offense (36%) and those who committed a property crime (32%). Few offenders committed a sex offense (6%). Descriptive statistics for the court history predictors indicate that more of the juvenile offenders have a prior drug offense on their criminal record (33%) relative to prior violent offenses (20%). Few juveniles had been placed out of their home due to prior to their instant offense (7.6%). A comparison of these variables by recidivism type indicates that juveniles in this population who recidivate via drug crimes differ from juveniles who recidivated with a violent or property offense (the supporting table can be obtained from the author). Drug crime recidivists are more likely to be Hispanic, have a prior drug arrest and have had a prior out-of-home placement than are person or property reoffenders. An examination of correlations and further follow up of the tolerance statistics indicated no issues of multicollinearity. Therefore, all the juvenile and family variables were eligible for inclusion in the analysis. The neighborhood-level items representing concentrated disadvantage and social capital were, not unexpectedly, found to be highly correlated. As a result, these predictors were entered into separate models. The reliability of the neighborhood estimates for the empty models without predictors is high. Once level-1 predictors are entered into the models, however, individual reliability estimates for each subsequent model dropped to approximately .15. This low reliability results in shrinkage of the log odds of recidivism in the neighborhood estimates back towards the grand mean log odds. There are two structural neighborhood characteristics that can cause shrinkage: (1) large intra-neighborhood variances (noisy level-1 data) and/or (2) small sample sizes. A visual inspection of the unadjusted recidivism rates across neighborhoods shows proportions ranging from 0.3 to 0.7. However, the adjusted estimates are shrunk to a plausible interval of .38–.40 for all recidivism types combined, indicating a significant amount of weighting back towards the grand mean log odds across the neighborhoods. This is indicative of the strong effects that the level-1 variables have on the outcomes, relative to the effects of neighborhood context. Based on the vast literature of individual predictors of recidivism, this is not unexpected. Regardless, the previously-mentioned studies of the effects of neighborhood context on recidivism support the current investigation of space on juvenile recidivism (Kubrin et al. 2007; Kubrin and Stewart 2006; LeBaron 2002; Mears et al. 2008; Simmons 2001). Results of the eight hierarchical generalized linear models are shown in Table 2. Odds ratios, with their subsequent significance, are displayed. Most of the individual-level

J Youth Adolescence (2010) 39:1067–1079 Table 1 Descriptive statistics of individual- and neighborhood-level variables

Variable

1073

Metric

N

Mean

SD

Outcome Recidivism: All

0 = no, 1 = yes

7061

0.40

0.49

Recidivism: Drug crime

0 = no, 1 = yes

7061

0.14

0.35

Recidivism: Violent crime

0 = no, 1 = yes

7061

0.10

0.30

Recidivism: Property crime

0 = no, 1 = yes

7061

0.11

0.31

Level 1: Individual Family receives public assistance

0 = no, 1 = yes

7061

0.31

0.46

Parental drug abuse

0 = no, 1 = yes

7061

0.20

0.40

Parental criminal history

0 = no, 1 = yes

7061

0.16

0.36

Age at 1st arrest (years)

Continuous

7061

14.2

1.7

White

0 = no, 1 = yes

7061

0.11

0.32

Hispanic

0 = no, 1 = yes

7061

0.13

0.34

Aftercare case

0 = no, 1 = yes

7061

0.35

0.48

Prior drug offense

0 = no, 1 = yes

7061

0.33

0.47

Prior violent offense On probation at time of arrest

0 = no, 1 = yes 0 = no, 1 = yes

7061 7061

0.20 0.10

0.40 0.30

Prior placement ever

0 = no, 1 = yes

7061

0.07

0.25

Sex offense

0 = no, 1 = yes

7061

0.06

0.24

Violent offense

0 = no, 1 = yes

7061

0.36

0.48

Property offense

0 = no, 1 = yes

7061

0.32

0.46

Level 2: Neighborhood Concentrated disadvantage

Scale item

45

Below poverty line

Proportion

45

0.17

0.09

Unemployment

Proportion

45

0.07

0.03

On welfare

Proportion

45

0.04

0.03

Income

-17–0

45

-9.21

1.71

Social capital

Scale item

45

Belong

1–5

45

3.07

0.11

Trust

1–5

45

2.75

0.22

Help neighbors

1–5

45

3.45

0.20

Participate

Continuous

45

0.75

0.17

predictors in the models are dichotomous (indicating group membership). A significant odds ratio of 1.5 for a predictor, for example, indicates that, on average, the odds are 50% higher of recidivating for youths with a ‘‘1’’ for that variable. In contrast a significant odds ratio of 0.5, indicates that on average, the odds of recidivating for youths with a ‘‘1’’ for that variable are 50% lower. Age at first arrest and all five of the neighborhood-level variables were standardized before being entered into the models. As a result, their odds ratios are interpreted differently. An odds ratio of 1.5 for one of these variables indicates that, on average, a one standard deviation increase will result in a 50% increased odds that a juvenile will recidivate. Each set of two models for the four outcome measures will be discussed separately. There is very little variance in the models measuring the same outcomes; the same predictors are found to be significant with only minor variation in the effect sizes of predictors. Odds ratios for predictors

are listed first for the model with concentrated disadvantage, followed by the model with social capital. A comparison of the models for each outcome follows. Models 1 and 2: All Recidivism Of the fourteen individual-level variables entered into the models, eight are significant predictors of recidivism. Neither the race variable nor ‘‘age at first arrest’’ are significant predictors of recidivism when all recidivism offense types are combined. Juveniles designated as aftercare cases have a relatively high odds ratio (OR = 1.49; 1.49, p \ .01), indicating that juveniles on an aftercare status are more likely (odds are nearly 50% higher) to reoffend than juveniles who were not. Juveniles who committed property offenses (OR = 1.23; 1.23, p \ .05) are more likely (odds are 23% higher) to recidivate than juveniles who do not commit property offenses. All of the variables representing

123

1074

J Youth Adolescence (2010) 39:1067–1079

Table 2 Odds ratios of individual- and neighborhood-level predictors of juvenile recidivism Model Recidivism type

1 All

2 All

3 Drug

4 Drug

5 Violent

6 Violent

7 Property

8 Property

Individual-level Demographics White

0.89

0.86

0.65**

0.62**

0.82

0.83

1.19

1.20

Hispanic

1.08

1.09

1.33*

1.37**

0.68**

0.66**

1.07

1.06

Age at 1st arrest

0.95

0.95

1.14**

1.14**

0.87**

0.90**

0.90**

0.90**

Parental drug abuse Parental criminality

1.09 1.13*

1.09 1.13*

0.95 1.08

0.95 1.09

1.18 1.27*

1.18 1.27*

1.03 1.04

1.03 1.04

On public assistance

1.10

1.11

1.10

1.12

0.97

0.96

1.12

1.12

Aftercare case

1.49**

1.49**

1.68**

1.68**

1.03

1.03

1.15

1.15

Sex offense

0.53**

0.53**

0.57**

0.57**

0.62**

0.62*

0.53*

0.53*

Violent offense

1.01

1.01

0.88

0.87

1.25*

1.26*

1.05

1.05

Property offense

1.23*

1.23*

1.00

0.99

1.07

1.07

1.59**

1.59**

On probation

1.24*

1.24*

0.92

0.92

1.26

1.26

1.23

1.23

Prior drug offense

1.50**

1.51**

2.81**

2.82**

0.76*

0.76**

0.76*

0.76*

Prior violent offense

1.27**

1.27**

1.16

1.16

1.36**

1.36**

0.98

0.98

Prior placement

1.48**

1.48**

1.23

1.24

1.13

1.13

1.47**

1.47**

Disadvantage

1.07



1.19**



1.00



0.98



Social capital



0.96



0.89*



0.96



1.01

Family

Current offense

Criminal history

Neighborhood-level

** p \ .01, * p \ .05

criminal history have a significant and positive relationship with the likelihood of recidivating. Having committed a prior drug offense (OR = 1.50; 1.51, p \ .01) and having a prior out-of-home placement (any placement, dependent or delinquent at any point in the youth’s life; OR = 1.48; 1.48, p \ .01) are particularly strong predictors of juvenile recidivism. The neighborhood concentrated disadvantage index, however, is not significantly related to the aggregated recidivism outcome. Similarly, the social capital predictor is unrelated to recidivism in Model 2. Models 3 and 4: Drug Recidivism In models 3 and 4, six individual-level predictors are significantly correlated with juveniles who commit drug crimes as their recidivating offense. The attributes Hispanic (OR = 1.33, p \ .05; 1.37, p \ .01) and being older at the time of first arrest (OR = 1.14; 1.14, p \ .01) are both positively related to drug recidivism. In contrast, white juveniles, on average, are less likely than black youths to recidivate through the commission of a drug crime (OR = 0.65; 0.62, p \ .01), holding other variables in the model constant. Juveniles on aftercare status are much more likely to reoffend (OR = 1.68; 1.68, p \ .01) than their

123

non-placed counterparts. Of the current offense variables representing the offenses that brought the juveniles into court, only sex offense is significantly related to drug recidivism (OR = 0.57; 0.57, p \ .01), and this relationship is negative. Only one of the criminal history predictors is significant for drug recidivism: prior drug offense (OR = 2.81; 2.82, p \ .01) exerts the greatest influence of all variables in any of the models. The effect of having a prior drug offense in a juvenile’s criminal history is to nearly triple the odds that the juvenile will reoffend with a drug crime. At the neighborhood-level, both concentrated disadvantage (OR = 1.19, p \ .01) and social capital (OR = 0.89, p \ .05) are significant predictors of juvenile drug recidivism. The odds ratios for both neighborhood predictors are in the opposite but expected directions, indicating that the likelihood of a juvenile offender recidivating with a drug offense increases as the level of disadvantage in a neighborhood increases, and decreases as the level of social capital increases. Models 5 and 6: Violent Recidivism Models 5 and 6 possess two significant demographic variables. The first, Hispanic (OR = 0.68; 0.66, p \ .01),

J Youth Adolescence (2010) 39:1067–1079

indicates that Hispanic juveniles are less likely than black youths to recidivate with a violent offense. Age at first arrest is positively correlated with the target variable (OR = 0.87; 0.90, p \ .01), signifying that juveniles who were older at the time of their first offense are less likely to recidivate with a violent offense. Only parental criminality is significant among the family context variables (OR = 1.27; 1.27, p \ .05): parental criminality is positively associated with violent re-offending. Sex offense is a significant predictor as well (OR = 0.62; 0.62, p \ .05), reducing the likelihood of recidivating via a violent offense for juveniles previously convicted of sexual offenses. Two criminal history variables exert significant influence on violent recidivism, but in different ways. Having committed a prior drug crime significantly reduces the likelihood that a juvenile will recidivate with a violent offense (OR = 0.76; 0.76, p \ .05). In contrast, having committed a prior violent offense increases the likelihood of reoffending with a similar violent offense (OR = 1.36; 1.36, p \ .01). Neither concentrated disadvantage nor social capital is a significant predictor of violent recidivism. Models 7 and 8: Property Recidivism As with Models 1 and 2, neither race variable is a significant predictor of property offense recidivism. Age at first arrest (OR = 0.90; 0.90, p \ .01) is negatively correlated, indicating that younger adolescents are more likely to recidivate with a property crime. No family context variables are significant predictors of property recidivism. Sex offense (OR = 0.53; 0.53, p \ .05), as with all other models, is negatively correlated with property recidivism. The effect of having committed a property offense as the current offense is to increase the odds of reoffending with a property offense by nearly 60% (OR = 1.59; 1.59, p \ .01) over non-property offenders. Prior drug offenders are less likely to reoffend with a property offense (OR = 0.76; 0.76, p \ .01), while juveniles who have been placed out of their home in the past are nearly 50% more likely to recidivate with a property offense (OR = 1.47; 1.47, p \ .01) than are juveniles who have not been placed out of their homes. As with violent offense recidivism in Models 5 and 6, the neighborhood context attributes are not significantly correlated with property offense recidivism.

Discussion This research is among the first to estimate neighborhood effects on juvenile recidivism and is the first to do so while disaggregating juvenile recidivism to measure specific recidivism offense types. The analysis has uncovered several findings that can be used to inform future research and

1075

juvenile justice policy. Social context, in the form of concentrated disadvantage and social capital, is a significant predictor of a particular type of juvenile recidivism: drug reoffending. The significant effects of environmental variables representing community processes are supportive of similar processes described by social disorganization theory. The concentrated disadvantage predictor closely approximates the poverty dimension of social disorganization theory, while the items used to create the social capital index are similar to those used in the construction of the collective efficacy, a concept representing sociallyshared processes that reduce social disorganization in communities. As a result, this study can be said to have found support for the effects of social disorganization on juvenile drug recidivism. Consistent with social disorganization theory, increased levels of concentrated disadvantage increase the likelihood of drug recidivism, while increased levels of social capital decrease the likelihood of drug recidivism. Prior studies that have examined neighborhood correlates of recidivism have not made this offense type distinction (Kubrin et al. 2007; Kubrin and Stewart 2006; LeBaron 2002; Simmons 2001), but our findings regarding drug offense recidivism are consistent with recent research on drug selling among juvenile offenders (Little and Steinberg 2006; Martinez et al. 2008). The specification of these relationships to drug offending and the instrumental nature of drug offending suggest that drug offending, and possibly specialization in this type of crime, is supported by some form of illicit organization. Both Hagedorn (1994) and Decker et al. (2008) argue that drug organizations are typically not highly organized. However, Hagedorn notes that Hispanic kinship ties sustain well developed drug organizations, whereas African American and White drug sellers operate more independently. This may help to explain why being Hispanic is a predictor of this type of offending and reoffending. Using a contingency theory framework, Hagedorn argues that neighborhood conditions both block and create opportunities. Our findings are consistent with this view, both in terms of ethnicity (Hispanic youths) and in terms of social and economic disadvantage. In Philadelphia, the Hispanic community is concentrated in one large area of North Philadelphia with limited socioeconomic opportunities. The combination of poverty, ethnic ties, opportunity, and the economic benefits of drug selling identified in Hispanic communities by this analysis is consistent with Little and Steinberg’s (2006) interpretation of their findings, namely that opportunity to engage in drug selling explains their findings that poor neighborhood conditions and low neighborhood job opportunity influence urban adolescent drug dealing. We should also note that youths involved in drug selling were older than the other youths in the other

123

1076

offense type categories when they first entered the juvenile justice system, indicating that their participation in drug selling was likely to have been less impulsive relative to other recidivists. Our findings also suggest that social disorganization alone is insufficient to explain the involvement of delinquent youths in persistent drug offending. Youths engaged in this behavior are likely to be residing in environments characterized by ethnic ties, economic disadvantage, and opportunities to become involved in an illicit, organized business. Thus a social learning perspective, such as that described by Akers (1985), should be useful for future explorations of this pattern of behavior. It appears likely that youths living in environments characterized by ethnic homogeneity, poverty, and a well-organized drug-trafficking business will be easily drawn into illicit activities that are both encouraged by others and lucrative. The current study did not include a measure of juvenile association with deviant peers during the follow-up period to examine the impact of such peers on the likelihood of recidivism, but future work in this area would be well-served by testing the effects of peer influence on juvenile recidivism. Contrary to the expected findings, neighborhood disadvantage does not influence rates of violent recidivism offenses by juveniles. Of all recidivism offense types, violent recidivism was expected to have the strongest relationship with neighborhood disadvantage, as many studies have identified a link between areas of concentrated poverty and violent crime (Anderson 1999, 1990; Liberman 2007; Osgood and Chambers 2000; Sampson and Groves 1989; Wilson 1996, 1987). The lack of significance of neighborhood context for violent recidivism might again reflect the greater impact of neighborhood processes on repeated involvement in instrumental crimes that include drug selling and which are largely dependent on opportunity. It may also be the case that social disorganization plays a critical role in explaining delinquency in general but that once the focus of analysis is narrowed to comparisons among delinquent youths, social disorganization has little left to explain. When we consider the relatively young age of these youths at their first arrest, the significant impact of parental criminality and their previous arrests for violent offenses, we see a pattern that is more consistent with disorganization at a more micro level, thus supporting the findings of Chung and Steinberg (2006) regarding family dynamics and delinquency among seriously delinquent youths. These findings are consistent with those of Lattimore et al. (1995) who found that parental criminality and family violence were associated with violent offense recidivism among a group of young parolees in California. Their findings, as well as those of Widom (1989), suggest that both parental criminality and parental abuse and neglect increased the likelihood of an arrest for a violent offense.

123

J Youth Adolescence (2010) 39:1067–1079

There appears to be some consistency in offending behavior among violent and property re-offenders, and their relatively low age of first arrest, combined with family problems ranging from parental criminality to abuse and neglect, that suggests both impulsivity and a lack of appropriate parental monitoring and supervision. It may be that our distinctions between person and property offenders are not warranted and that combining them would be more useful. For example, Sampson and Groves (1989) found that family disruption predicted violent crime. Family disruption can be the result of system responses to abuse and neglect, and it can result from a parent being incarcerated as a result of criminal prosecution. The strongest individual-level predictors in our analysis were derived from the juveniles’ criminal history, and suggest specialization of juvenile drug offending. The analysis found that juveniles with prior drug offenses are significantly more likely to re-offend with a drug crime, and are significantly less likely to re-offend with a violent or property crime. The literature on offense specialization has concluded that specialization among juveniles is uncommon. Few offenders specialize at all, and those individuals considered specialists generally age toward specialization (Farrington et al. 1988; Piquero et al. 1999). Note, however, that our drug offenders were likely to be older at the time of their first arrest, suggesting a limited impact of age on specialization. A recent study by Armstrong (2008) suggests that juvenile drug offenders are more likely to specialize: age affects the specialization of violent and property offending, but not drug offending. Thus, it may be that the age effects on specialization found in prior research do not have the same impact on drug offense specialization (Armstrong 2008; Piquero et al. 1999). Limitations There are limitations of this study that should be mentioned. Regarding our neighborhood-level predictors, the components of our concentrated disadvantage and social capital indicators have been used extensively in prior research estimating the effects of space on crime, but there are countless other indicators that could be called upon to determine whether the neighborhoods in this study influence juvenile recidivism rates. Ecological researchers have called for an inclusion of community-level variables beyond those that measure disadvantage; it would likely be wise to heed this advice and consider the effects of variables that capture social interactions, protective factors, and other neighborhood-level indicators that are not commonly found in studies of space and crime. The measurement of social context should also be considered in light of the arguments raised by Rankin and

J Youth Adolescence (2010) 39:1067–1079

Quane (2002) and Chung and Steinberg (2006) regarding the value of social attributes that mediate the relationships between economic disadvantage and delinquency. We did include several measures of parental behavior at the individual level, and found that parental criminality was associated with violent re-offending. Our knowledge about parenting behavior, however, was limited to what appeared in juvenile court records. Our data suggest that some youths, particularly drug offenders, specialize in the types of offenses they commit. We recognize, however, that our data include only one offense transition, and that our offense information is derived from court data. Not only are many offenses not known to the justice system, the criminal justice process acts to siphon off both non-offenders (false positives) and actual offenders (false negatives). Appropriate testing of a hypothesis of specialization would require more than two offenses and would ideally make use of self report offense data. Although such a dataset may be difficult to obtain, it would permit questions beyond the scope of this analysis to be asked. Considering the observed differences between juvenile drug reoffenders and juveniles reoffending with violent and property crimes, future research should consider the possibility of different explanations of recidivism, depending on the kinds of offending involved. For person and property offending, it may be that individual, family, and peer attributes are most critical to understanding subsequent offending, while for drug selling, neighborhood context should also be taken into account. Further disaggregating recidivism to more detailed offense categories may also reveal other patterns of predictors and, hence, more ways to explain recidivism. For example, the theories called upon to explain the neighborhood-level effects identified in our analysis of juvenile drug recidivism suggest that drug selling is much more sensitive to community processes than is drug using. Therefore, future research would be welladvised to investigate what specific spatial factors influence specific types of drug offending. Beyond drug offending, disaggregating violent and property crime might also benefit our understanding of the processes described in this analysis. Auto theft, a property crime, may be classified as an instrumental crime, but we have aggregated it with all other property crimes, thus masking this possibility.

Conclusion The spatial dependency of drug offending, combined with the effects of different family predictors of violent and property offense recidivism, imply the need for multiple explanations of recidivism. Our findings indicate that no single causal model of juvenile recidivism can effectively

1077

explain all types of reoffending. Drug re-offenders, in particular, appear likely to persist in drug offending. This pattern of offense specialization is associated with high levels of economic disadvantage and, in the case of Philadelphia, social isolation of Latino communities (see also Bourgois 2003, who describes a similar pattern in New York’s South Bronx). This finding suggests that the juvenile justice system is unlikely to make headway through punitive measures or by temporary removal of these youths from their home environments. Neighborhood and family contexts should be part of any strategy to reduce juvenile recidivism.

References Akers, R. L. (1985). Deviant behavior: A social learning approach (3rd ed.). Belmont, CA: Wadsworth. Anderson, E. (1990). Streetwise: Race, class, and change in an urban community. Chicago, IL: University of Chicago Press. Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. New York: Norton. Armstrong, T. A. (2008). Are trends in specialization across arrests explained by changes in specialization occurring with age? Justice Quarterly, 25, 201–222. Baumer, E. P. (2002). Neighborhood disadvantage and police notification by victims of violence. Criminology, 40, 579–616. Baumer, E. P., & Gustafson, R. M. (2007). Social organization and instrumental crime: Assessing the empirical validity of classic and contemporary anomie theories. Criminology, 45, 617–664. Baumer, E. P., Horney, J., Felson, R. B., & Lauritsen, J. L. (2003). Neighborhood disadvantage and the nature of violence. Criminology, 41, 39–72. Bourgois, P. (2003). In search of respect: Selling crack in el barrio. New York: Cambridge University Press. Bursik, R. J. (1988). Social disorganization and theories of crime and delinquency: Problems and prospects. Criminology, 26, 519–552. Cattarello, A. M. (2000). Community-level influences on individuals’ social bonds, peer associations, and delinquency: A multilevel analysis. Justice Quarterly, 17, 33–60. Chung, H. L., & Steinberg, L. (2006). Relations between neighborhood factors, parenting behaviors, peer deviance, and delinquency among serious juvenile offenders. Developmental Psychology, 42, 319–331. Cottle, C. C., Lee, R. J., & Heilbrun, K. (2001). The prediction of criminal recidivism in juveniles. Criminal Justice and Behavior, 28, 367–394. Daigle, L. E., Cullen, F. T., & Wright, J. P. (2007). Gender differences in the predictors of juvenile delinquency. Youth Violence and Juvenile Justice, 5, 254–286. Decker, S. H., Katz, C. M., & Webb, V. J. (2008). Understanding the black box of gang organization: Implications for involvement in violent crime, drug sales, and violent victimization. Crime and Delinquency, 54, 153–172. Dembo, R., Schmeidler, J., Nini-Gough, B., Sue, C. C., Borden, P., & Manning, D. (1998). Predictors of recidivism to a juvenile assessment center: A three year study. Journal of Child and Adolescent Substance Abuse, 7, 57–77. Duncan, R. D., Kennedy, W. A., & Patrick, C. J. (1995). Four-factor model of recidivism in male juvenile offenders. Journal of Clinical Child Psychology, 24, 250–257.

123

1078 Elliott, D. S., Huizinga, D. H., & Ageton, S. S. (1985). Explaining delinquency and drug use. Beverly Hills, California: Sage. Fader, J. J., Harris, P. W., Jones, P. R., & Poulin, M. E. (2001). Factors involved in decisions on commitment to delinquency programs for first-time juvenile offenders. Justice Quarterly, 18, 323–341. Farrington, D. P., Snyder, H. N., & Finnegan, T. A. (1988). Specialization in juvenile court careers. Criminology, 26, 461–488. Freisthler, B., Needell, B., & Gruenewald, P. J. (2005). Is the physical availability of alcohol and illicit drugs related to neighborhood rates of child maltreatment? Child Abuse and Neglect, 29, 1049– 1060. Garcia, R. M., Taylor, R. B., & Lawton, B. A. (2007). Impacts of violent crime and neighborhood structure on trusting your neighbors. Justice Quarterly, 24, 679–704. Hagedorn, J. M. (1994). Neighborhoods, markets and gang drug organization. Journal of Research in Crime and Delinquency, 31, 264–294. Harris, P. W., Welsh, W. N., & Butler, F. (2000). A century of juvenile justice. In G. LaFree (Ed.), Criminal justice 2000: The nature of crime: Continuity and change. Washington, DC: National Institute of Justice. Herrenkohl, T. I., Maquin, E., Hill, K. G., Hawkins, J. D., Abbott, R. D., & Catalano, R. F. (2000). Developmental risk factors for youth violence. Journal of Adolescent Health, 26, 176–186. Howell, J. C. (1995). Guide for implementing the comprehensive strategy for serious, violent, and chronic juvenile offenders. Rockville, MD: Office of Juvenile Justice and Delinquency Prevention. Huizinga, D., Loeber, R., Thornberry, T. P., & Cothern, L. (2000). Co-occurrence of delinquency and other problem behaviors. In Juvenile Justice Bulletin. Washington, DC: Office of Juvenile Justice and Delinquency Prevention. Jacob, J. C. (2006). Male and female youth crime in Canadian communities: Assessing the applicability of social disorganization theory. Canadian Journal of Criminology and Criminal Justice, 48, 31–60. Kennedy, B. P., Kawachi, I., Prothrow-Stith, D., Lochner, K., & Gupta, V. (1998). Social capital, income inequality, and firearm violent crime. Social Science and Medicine, 47, 7–17. Kubrin, C. E., Squires, G. D., & Stewart, E. A. (2007). Neighborhoods, race, and recidivism: The community-reoffending nexus and its implications for African-Americans. Race Relations Abstracts, 32, 7–37. Kubrin, C. E., & Stewart, E. A. (2006). Predicting who reoffends: The neglected role of neighborhood context in recidivism studies. Criminology, 44, 165–197. Lattimore, P. K., Visher, C. A., & Linster, R. L. (1995). Predicting arrest for violence among serious youthful offenders. Journal of Research in Crime and Delinquency, 32, 54–83. LeBaron, J. (2002). Examining the relative influence of community context on juvenile offender post-confinement recidivism. Doctoral Dissertation, Department of Criminal Justice, Rutgers University, Newark, NJ. Liberman, A. (2007). Adolescents, neighborhoods, and violence: Recent findings from the project on human development in Chicago neighborhoods. Washington, DC: National Institute of Justice. Little, M., & Steinberg, L. (2006). Psychosocial correlates of adolescent drug dealing in the inner city: Potential roles of opportunity, conventional commitments, and maturity. Journal of Research in Crime and Delinquency, 43, 357–386. Loeber, R., Farrington, D. P., & Waschbusch, D. A. (1998). Serious and violent juvenile offenders. In R. Loeber & D. P. Farrington (Eds.), Serious and violent juvenile offenders: Risk factors and successful interventions. London: Sage Publications.

123

J Youth Adolescence (2010) 39:1067–1079 Martinez, R., Rosenfeld, R., & Mares, D. (2008). Social disorganization, drug market activity, and neighborhood violent crime. Urban Affairs Review, 43, 846–874. Mazerolle, P. (1998). Gender, general strain, and delinquency: An empirical examination. Justice Quarterly, 15, 65–91. Mears, D. P., Wang, X., Hay, C., & Bales, W. D. (2008). Social ecology and recidivism: Implications for prisoner reentry. Criminology, 46, 301–340. Messner, S. F., Baumer, E. P., & Rosenfeld, R. (2004). Dimensions of social capital and rates of criminal homicide. American Sociological Review, 69, 882–903. Miller, J., & Lin, J. (2007). Applying a generic juvenile risk assessment instrument to a local context. Crime and Delinquency, 53, 552–580. Morenoff, J. D., Sampson, R. J., & Raudenbush, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of homicide. Criminology, 39, 517–560. Myner, J., Santman, J., Cappelletty, G. G., & Perlmutter, B. F. (1998). Variables related to recidivism among juvenile offenders. International Journal of Offender Therapy and Comparative Criminology, 42, 65–80. Oberwittler, D. (2004). A multilevel analysis of neighbourhood contextual effects on serious juvenile offending. European Journal of Criminology, 1, 201–235. Osgood, D. W., & Chambers, J. M. (2000). Social disorganization outside the metropolis: An analysis of rural youth violence. Criminology, 38, 81–115. Parks, G. A., & Bard, D. E. (2006). Risk factors for adolescent sex offender recidivism: Evaluation of predictive factors and comparison of three groups based upon victim type. Sexual Abuse: A Journal of Research and Treatment, 18, 319–342. Piquero, A. R., Paternoster, R., Mazerolle, P., Brame, R., & Dean, C. W. (1999). Onset age and offense specialization. Journal of Research in Crime and Delinquency, 36, 275–299. Pullmann, M. D., Kerbs, J., Koroloff, N., Veach-White, E., Gaylor, R., & Sieler, D. (2006). Juvenile offenders with mental health needs: Reducing recidivism using wraparound. Crime and Delinquency, 52, 375–397. Rankin, B. H., & Quane, J. M. (2002). Social contexts and urban adolescent outcomes: The interrelated effects of neighborhoods, families, and peers on African-American youth. Social Problems, 49, 79–100. Rasmussen, L. A. (1999). Factors related to recidivism among juvenile sexual offenders. Sexual Abuse: A Journal of Research and Treatment, 11, 69–85. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage. Rosenfeld, R., Messner, S. F., & Baumer, E. P. (2001). Social capital and homicide. Social Forces, 80, 283–309. Sampson, R. J., & Groves, W. B. (1989). Community structure and crime: Testing social-disorganization theory. The American Journal of Sociology, 94, 774–802. Sampson, R. J., Morenoff, J. D., & Earls, F. (1999). Beyond social capital: Spatial dynamics of collective efficacy for children. American Sociological Review, 64, 633–660. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924. Schwalbe, C. S., Fraser, M. W., Day, S. H., & Cooley, V. (2006). Classifying juvenile offenders according to risk of recidivism. Criminal Justice and Behavior, 33, 305–324. Shaw, C. R., & McKay, H. D. (1942). Juvenile delinquency and urban areas: A study of rates of delinquency in relation to differential characteristics of local communities in American cities. Chicago: University of Chicago Press.

J Youth Adolescence (2010) 39:1067–1079 Simmons, R. D. (2001). Ecological factors that predict recidivism of male juvenile offenders after release from institutional commitment. Doctoral Dissertation, University of South Carolina, Columbia, South Carolina. Snyder, H. N., & Sickmund, M. (2006). Juvenile offenders and victims: 2006 national report. Washington, DC: Office of Juvenile Justice and Delinquency Prevention. Tanenhaus, D. S. (2004). Juvenile justice in the making, studies in crime and public policy. New York: Oxford University Press. Webster, C., MacDonald, R., & Simpson, M. (2006). Predicting criminality? Risk factors, neighbourhood influence and desistance. Youth Justice, 6, 7–22. White, J. L., Moffitt, T. E., Earls, F., Robins, L., & Silva, P. A. (1990). How early can we tell? Predictors of childhood conduct disorder and adolescent delinquency. Criminology, 28, 507–527. Widom, C. S. (1989). Child abuse, neglect and violent criminal behavior. Criminology, 27, 251–271. Wiebush, R. G., Baird, C., Krisberg, B., & Onek, D. (1995). Risk assessment and classification for serious, violent, and chronic juvenile offenders. In J. C. Howell, B. Krisberg, J. D. Hawkins, & J. J. Wilson (Eds.), A sourcebook: Serious, violent, and chronic juvenile offenders. Beverly Hills, California: Sage. Wikstrom, P. O. H., & Loeber, R. (2000). Do disadvantaged neighborhoods cause well-adjusted children to become adolescent delinquents? A study of male juvenile serious offending, individual risk and protective factors, and neighborhood context. Criminology, 38, 1109–1142. Wilson, W. J. (1987). The Truly disadvantaged: The inner city, the underclass, and public policy. Chicago, IL: University of Chicago Press. Wilson, W. J. (1996). When work disappears: The world of the new urban poor. New York: Random House.

1079

Author Biographies Heidi E. Grunwald is Deputy Program Director, Public Health Law Research, Beasley School of Law, Temple University. Her current research areas include research design and methodology, hierarchical linear modeling, and causal analyses from observational data. Recent publications have appeared in Research in Higher Education, Journal of Higher Education, and Journal of Behavioral Health Services and Research. Brian Lockwood is a doctoral student in the Department of Criminal Justice at Temple University. His recent research interests include near—repeat offending the and social networks of juvenile offenders. He has co-authored a book chapter that describes difficulties that can arise when conducting spatial analyses. Philip W. Harris is an Associate Professor in the Department of Criminal Justice at Temple University. His research has focused primarily on the areas of juvenile justice, juvenile correctional strategies, and organizational and system development. Recent publications have appeared in Criminology, Justice Quarterly, Criminal Justice and Behavior, and Evaluation Review. Jeremy Mennis is an Associate Professor in the Department of Geography and Urban Studies at Temple University where he specializes in Geographic Information Science. His recent research has focused on modeling human-environment interaction for urban public health and crime applications. Recent publications have appeared in Annals of the Association of American Geographers, Cartography and Geographic Information Science, and Drug and Alcohol Dependence.

123