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SOCIAL ECOLOGY AND RECIDIVISM:IMPLICATIONS FOR PRISONER REENTRY*

DANIEL P. MEARSXIA WANGCARTER HAYWILLIAM D. BALES

College of Criminology and Criminal JusticeFlorida State University

KEYWORDS: prisoner reentry, recidivism, social ecology

Despite the marked increase in incarceration over the past 30 yearsand the fact that roughly two thirds of released offenders are rearrestedwithin 3 years of release, we know little about how the social ecology ofthe areas to which offenders return may influence their recidivism orwhether it disproportionately affects some groups more than others.Drawing on recent scholarship on prisoner reentry and macrolevelpredictors of crime, this study examines a large sample of prisonersreleased to Florida communities to investigate how two dimensions ofsocial ecology—resource deprivation and racial segregation—mayindependently, and in interaction with specific populations, influencerecidivism. The findings suggest that ecology indeed is consequentialfor recidivism, and it differentially influences some groups more thanothers. We discuss these findings and their implications for theory,research, and policy.

In his 2004 State of the Union address, President George W. Bushemphasized the central importance of improving the life chances ofinmates released from prison, noting, “America is the land of the secondchance, and when the gates of prison open, the path ahead should lead to a

* The authors thank the Florida Department of Corrections for providing data forthis article, Avi Bhati and Mike Reisig for their suggestions, Christina Mancinifor her assistance, and Emily Leventhal, Joan Petersilia, the Editor, and theanonymous reviewers for their guidance and many helpful comments. The viewsexpressed in this article do not necessarily reflect those of the FloridaDepartment of Corrections. Direct correspondence to Daniel P. Mears, PhD,Associate Professor, College of Criminology and Criminal Justice, Florida StateUniversity, 634 West Call Street, Tallahassee, FL 32306-1127 (e-mail:dmears@fsu.edu).

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better life” (Travis, 2005: 275). The emphasis stemmed from concern aboutthe limited housing and employment prospects of these individuals and,not least, the likelihood that they will commit crime. With over 630,000inmates returning to communities annually and over two thirds likely to berearrested within 3 years, the cause for concern remains (Langan andLevin, 2002; Petersilia, 2003; Sabol, Minton, and Harrison, 2007). Yet,despite considerable investment in research and programs aimed atimproving prisoner reentry, much is unknown about the factors that influ-ence successful returns back into society (Travis and Visher, 2005).

Juxtaposed against the increased scholarly and policy-maker attentionto prisoner reentry stands a large body of work on social ecology that mayprovide insight into the reentry process. Although many studies haveinvestigated the salience of ecology for a range of crimes (Sampson, More-noff, and Gannon-Rowley, 2002), most notably homicide (Mears andBhati, 2006), this perspective has not systematically been turned to investi-gations of the recidivism of released prisoners (Kubrin and Stewart, 2006;Visher and Travis, 2003). The research gap is notable given several recentreviews that call for systematic investigation of ecological influences oncrime (Pratt and Cullen, 2005; Sampson, Morenoff, and Gannon-Rowley,2002).

A focus on recidivism is especially opportune given the dearth of analy-ses investigating the significance of ecology on released prisoners (Kubrinand Stewart, 2006). It also builds off of and can contribute to theory andresearch that point to the importance of life events, of which release fromprison represents a critical one for offenders (Piquero, Farrington, andBlumstein, 2003; Sampson and Laub, 2005). Few life-course studies inves-tigate how ecology may contribute to offending trajectories; thus, evidencethat it may play a role in recidivism would underscore the need to developmore nuanced accounts of factors that influence such trajectories.

This article aims to contribute to the growing literature on social ecol-ogy, focusing specifically on how ecology may influence the recidivism ofex-prisoners and how it may amplify the recidivism of young minoritymales, whose life chances, on average, are held to be less than those ofwhite males because of social inequality and accumulated disadvantage(Wilson, 1987, 1996). To this end, we draw on two prominent ecologicalperspectives, resource deprivation and racial segregation, to develophypotheses about the recidivism patterns of a large sample of inmatesreleased from Florida prisons. At the same time, we link these perspec-tives with arguments about how they may hold particular implications forgroups associated with or likely to suffer from accumulated social disad-vantage. In doing so, we build off a line of work by such scholars asHaynie and Payne (2006), who show that contextual influences may mod-erate the effect of individual-level characteristics and that such effects may

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vary among racial groups. Specifically, we hypothesize that individualsreleased to resource-deprived or racially segregated areas will have anincreased likelihood of recidivism, that recidivism will be greater amongyoung nonwhite males, and that the influences of resource deprivation orracial segregation will be greatest for this group of released prisoners.

We begin by discussing prisoner reentry as a general phenomenon thatprovides opportunities to test and extend theory, and then we turn to thetheoretical foundation used to develop our hypotheses. After describingthe data and methods, we present the findings and discuss their implica-tions for theory, research, and policy. In doing so, we draw attention to theneed for studies that provide more nuanced accounts of recidivism andthat carefully attend to the ways in which differential law enforcementacross areas and racial and ethnic groups may influence recidivism mea-sures and, in turn, prediction models (Klinger and Bridges, 1997). We alsoemphasize the notion that ignoring social ecology may unnecessarilyundermine efforts to improve reentry.

BACKGROUND

The study of recidivism is of interest for at least two reasons—it affordsan opportunity to glean insight into the causes of crime and the findings,where robust, potentially can be used to inform efforts aimed at reducingpostrelease offending. On both counts, considerable advances may emergefrom systematic attention to factors associated with prisoner reentry out-comes. In recent years, for example, a substantial body of scholarship hasturned to studying patterns of persistence of and desistance from patternsof offending (Piquero, Farrington, and Blumstein, 2003; Sampson andLaub, 2005). In this regard, persistence notably constitutes a central fea-ture of ex-prisoner behavior. For example, a Bureau of Justice Statistics(BJS) study of prisoners released from 15 states found that the mediannumber of prior arrests was 6; 43 percent of the prisoners had served aprior term of incarceration (Langan and Levin, 2002: 2).

At the same time, prisoner reentry has emerged as a prominent policyconcern nationally, which is reflected in the recent $100 million federalinitiative aimed at promoting effective reentry strategies in each of the 50states and in the priority that it has been given under both Democratic andRepublican presidencies (Travis, 2005). The number of individuals leavingprisons is more than four times greater today than it was 20 years ago, withroughly 630,000 inmates released to communities each year (Harrison andKarberg, 2003; Travis and Visher, 2005). As of June 30, 2006, 1,556,518inmates resided in state and federal prisons and another 766,010 were inlocal jails (Sabol, Minton, and Harrison, 2007: 2, 5). In the meantime,incarceration rates have continued to increase, which in turn means larger

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numbers of individuals reentering society after the experience of jail orprison. According to the most recent data available, at midyear 2006, per100,000 U.S. residents, 497 were prison or jail inmates (Sabol, Minton, andHarrison, 2007: 2), with 1 in every 133 U.S. residents incarcerated in astate or federal prison or a local jail (2007: 8).

The profile of inmates suggests that the prospects for successful reentryare dim. In her analysis of the BJS Survey of Inmates in State Adult Cor-rectional Facilities, Petersilia (2005: 45) concluded: “Many, if not most,[prisoners] . . . did not have much to begin with, and have been born with,or have developed, serious social, psychological, and physical problems,”and “will be released to poor inner-city communities with few services andlittle public sympathy for their plight.” Notably, few inmates will havereceived drug or mental health treatment as well as educational or voca-tional training or services (Lynch and Sabol, 2001). Perhaps not surpris-ingly, recidivism is common. In the widely cited Langan and Levin (2002:3) study, 44 percent of released prisoners were rearrested within 1 yearand 68 percent were rearrested within 3 years.

Alongside these facts stands one that is equally striking—until recently,little was known about prisoner reentry and how to improve the outcomesof released inmates. An emerging body of work has systematicallyexamined a range of dimensions, such as the profile of released inmates,the contributions ex-prisoners may make to crime rates, the effects ofsupervision, ways in which in-prison programming and postrelease exper-iences may influence recidivism, and, not least, the impact of reentry oncommunities (Lynch and Sabol, 2001; Petersilia, 2003; Travis and Visher,2005). Although any assessment of reentry arguably should include atten-tion to a range of postrelease outcomes, such as employment, housing, andfamily reunification (Travis, 2005), recidivism constitutes an obviously cen-tral concern for both criminologists and policy makers. Notably, therefore,and despite an increasingly large set of studies on desistance from crime(Piquero, Farrington, and Blumstein, 2003; Sampson and Laub, 2005),much remains unknown about correlates of recidivism, although a smallset of factors has been identified. As Kubrin and Stewart (2006: 166) haveemphasized, studies to date point to several factors associated withincreased recidivism, including demographic characteristics (men, minori-ties, and younger offenders recidivate more), offense characteristics andhistories (those who have committed serious crimes or who have priorrecords recidivate more), offender characteristics (those who have drugproblems and limited education recidivate more), and supervision (thosewho are supervised recidivate more) (see Cullen and Gendreau, 2000;Piehl and LoBuglio, 2005).

Given the salience of social ecology to studies of social phenomena, ingeneral (Massey, 2001), and to crime, in particular (Pratt and Cullen, 2005;

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Sampson, Morenoff, and Gannon-Rowley, 2002), an especially notablegap in the literature is the extent to which ecological conditions influencerecidivism outcomes. That may be caused by a belief among policy mak-ers, practitioners, and perhaps some scholars as well that, fundamentally,“the risk for reoffending is individually determined” (Kubrin and Stewart,2006: 166). To illustrate, risk-prediction instruments used in correctionalsystems typically focus primarily, if not exclusively, on individual-levelcharacteristics (Cullen and Gendreau, 2000). Similarly, some mainstreamcriminological theories and emphases, such as research on the persistenceof and desistance from offending, tend to focus mostly on individual-levelfactors. Regardless, “almost no studies have measured contextual effects”on recidivism (Kubrin and Stewart, 2006: 171), even though such factorsmay contribute to the offending patterns of ex-prisoners or to the type anddegree of law enforcement to which they are subjected. By extension,there has been limited investigation of the extent to which ecology exerts adifferential effect on recidivism among select sub-groups of released pris-oners. This research gap is important because studies suggest that socialecology may affect some groups differently than others, depending on theoutcome, with race presenting one potentially clear divide along which dif-ferential effects may develop (Earls and Carlson, 2001).

That issue assumes particular salience when thinking about social ecol-ogy and reentry, given that racial minorities are substantially over-represented in the correctional system relative to their representation insociety at large. As a general matter, racial divides in American societycontinue to persist and feature prominently as a focus in social scientificresearch (Chiricos, Welch, and Gertz, 2004; McPherson, Smith-Lovin, andCook, 2001). This focus certainly exists within criminology, especially instudies of violent crime, law enforcement, and sentencing laws and prac-tices (Behrens, Uggen, and Manza, 2003; Huebner and Bynum, 2006;Parker, Stults, and Rice, 2005; Peterson and Krivo, 2005; Sampson andLauritsen, 1997; Spohn, 1994). Again, however, few investigations of pris-oner reentry and recidivism have systematically examined the role of racebeyond identifying whether race is correlated with subsequent arrest, con-viction, or incarceration, and often it simply is treated as a statistical “con-trol.” Left largely unaddressed is whether race may interact with otherfactors and, in turn, with social ecology, in predictable directions. Theimportance of this question lies in the fact that if indeed ecology differen-tially influences certain groups of offenders, reentry programs, policies,and practices might need to be modified to take such conditioning effectsinto account to be effective.

This article contributes to theoretical and empirical research on prisonerreentry by focusing attention on the salience of social ecology for recidi-vism and by examining the differential effects ecology may have for some

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groups. In particular, it builds off of efforts to explore ecology and recidi-vism (e.g., Kubrin and Stewart, 2006) as well as builds off of the scholar-ship that emphasizes the salience of race (e.g., Wilson, 1987, 1996) andcumulative disadvantage (Sampson and Laub, 1997). At the same time, itresponds to calls to examine how certain criminogenic factors may amplifythe effects of others (Agnew, 2005; Haynie and Payne, 2006). Our goal isto advance knowledge about factors that contribute to successful reentryoutcomes and to stimulate investigations into the salience of ecology andrace for recidivism and, more generally, offending patterns and trajecto-ries (Piquero, Farrington, and Blumstein, 2003; Sampson and Laub, 2005).

THEORETICAL PERSPECTIVES

Criminologists have undertaken many studies of ways in which socialecology influences crime, especially violent crime (see, e.g., Akins, 2003;Eitle, D’Alessio, and Stolzenberg, 2006; Lee and Ousey, 2005; Liska,Logan, and Bellair, 1998; Messner, Baumer, and Rosenfeld, 2004). Yet,analysis of the contexts to which released prisoners return or how thesemay influence recidivism generally, much less for specific subgroups ofprisoners, remains rare (Clear, Waring, and Scully, 2005; Gottfredson andTaylor, 1985, 1988; Kubrin and Stewart, 2006; Uggen, Wakefield, andWestern, 2005). A central question that remains largely unaddressed iswhether and to what extent various types of ecological conditions affectprisoner reentry and recidivism in particular and, in turn, whether identi-fied effects vary across particular groups.

A plethora of ecological measures can be found in criminologicalresearch, as Pratt and Cullen’s (2005) recent review has documented.Broadly, the measures fall into different theoretical perspectives, includingsocial disorganization, anomie/strain, resource/economic deprivation, rou-tine activity, deterrence/rational choice, social support/social altruism, andsubcultural theories (2005: 392–4). Here, we focus on two measures mostcommonly used in ecological studies—resource deprivation and racial seg-regation—for several reasons.

First, in Pratt and Cullen’s (2005) review, these measures, or indicatorsof them, not only were frequently used in studies of social ecology but alsowere among those that had the strongest associations with crime (2005:399–403; see also Sampson, Morenoff, and Gannon-Rowley, 2002). Sec-ond, resource deprivation arguably constitutes a fundamental staple ofecological studies of crime, as Land, McCall, and Cohen’s (1990) analysisshowed almost two decades ago and it is reflected in its use, directly or inderivative measures, in many contemporary studies (e.g., Eitle, D’Alessio,and Stolzenberg, 2006; Mears and Bhati, 2006; Parker, 2004). Resource oreconomic deprivation, however measured, is expected to increase crime

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through a range of mechanisms indicated by several ecological theories,and the logic of these mechanisms applies equally well to released prison-ers as it does to the general population. Some theories, such as social dis-organization (Bursik and Grasmick, 1993; Shaw and McKay, 1942) andcollective efficacy (Sampson, Raudenbush, and Earls, 1997), suggest thatresource deprivation disrupts patterns of informal social control and per-haps generates criminal subcultures (Akers and Sellers, 2004; Sampson,Morenoff, and Gannon-Rowley, 2002). In addition, strain theories(Merton, 1938) emphasize resource deprivation as a key indicator ofblocked economic opportunities—living in a resource-deprived area sepa-rates an individual from the legitimate economic opportunities necessaryfor conventional success. In short, the prominent place of resource depri-vation in criminological theory and its status as a strong predictor of crimesuggests that a more complete account of recidivism likely should includethis measure.

Racial segregation stands as another central pillar of ecologicalresearch, one especially salient to studies of crime and reentry (Krivo andPeterson, 2000; Peterson and Krivo, 2005; Travis, 2005). Resource depriva-tion and racial segregation frequently are correlated, but they need not beand may contribute to crime in different ways (Peterson and Krivo, 1993).Much research points to high levels of racial segregation in the UnitedStates that cannot be explained by racial differences in income but insteadare linked to patterns of discrimination in which whites geographically iso-late themselves from disadvantaged racial groups (Massey and Denton,1993). A key result of race-based social isolation is that racially segregatedareas often are marked by high levels of joblessness, mortality, and maritaldisruption, as well as by the presence of dilapidated housing and poorerschools (LaVeist, 1989; Massey and Denton, 1993; Wilson, 1987). Racialsegregation is positively related to crime rates as well (Logan and Mess-ner, 1987; Parker and McCall, 1999; Peterson and Krivo, 1993; Sampson,1985). Here, again, social disorganization and strain theories often areoffered to explain this pattern. From a social disorganization perspective(Shaw and McKay, 1942), racial segregation leads to a breakdown in com-munity organization and informal social control in part because residentsperceive that social investments in the community are either not possibleor would not make a difference (Sampson and Wilson, 1995). From astrain perspective, racial segregation blocks economic opportunities fordisadvantaged groups, including racial minorities as well as economicallydisadvantaged whites (Blau and Blau, 1982).1 The social isolation that isimplied by racial segregation serves as a significant barrier to upward

1. Sampson (1985) found that racial segregation had a slightly greater effect oncrime for whites.

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mobility for disadvantaged groups, which produces frustrations and hostilemotivations that lead to crime (Logan and Messner, 1987).

Juxtaposed against these theoretical perspectives stand several strandsof work, such as Haynie and Payne (2006), which argue for investigation ofhow contextual effects may vary among racial groups. Here, we argue spe-cifically that social ecology may have differential effects among youngminority males. First, as a general matter, scholars have long called forinvestigation of how social ecology may not only directly influence recidi-vism but also exert stronger effects on some populations. However, whatGottfredson and Taylor (1988: 133) noted 20 years ago about recidivismresearch remains true today—little is known about person–environmentinteractions. Indeed, one of the few exceptions, as Kubrin and Stewart(2006: 171) have observed, was research undertaken by Gottfredson andTaylor (1985, 1988), who drew on data from a study of Baltimore neigh-borhoods. Their preliminary exploratory study identified no direct ecologi-cal effects on individual-level recidivism but did reveal“person–environment” interactions between individual risk-level andneighborhood-level physical incivilities (1985: 147). In follow-up analy-ses—which included survey data and involved 57 Baltimore neighbor-hoods and 487 offenders (when observer-recorded information aboutphysical incivilities was used, 67 neighborhoods and 619 offenders wereexamined)—they again found no evidence of direct effects of ecology onrecidivism or, in contrast to the earlier study, of interactions (1988: 73–9).Almost 20 years after this study, Kubrin and Stewart (2006) conducted oneof the few other studies of person–environment interactions in their analy-sis of recidivism in Multnomah County, Oregon, and found no significantinteractions (2006: 185).

Second, calls from the broader criminological community for examininginteraction effects have increased in recent years, stemming from theinsight that the effects of many mainstream criminological variables mayvary across different groups or depend on the level of some other vari-ables. More specifically, and of relevance here given our focus on two eco-logical factors that may influence recidivism through strain-producingmechanisms, strain theorists have advocated the investigation of strain-related interaction effects (Agnew, 2005: 113–4).

Third, today, any focus on prisoner reentry and recidivism almost neces-sarily leads to the observation that young minority males are over-represented in prisons relative to their presence in society (Gabbidon andGreene, 2005; Sabol, Minton, and Harrison, 2007). The reason may lie inpart with greater levels of serious offending among this population, asreflected in self-report and official records data (Sampson and Lauritsen,1997: 330). Many proposed explanations for this difference exist, includingthe fact that social disadvantage is more pronounced among minorities in

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amount and effect. That situation in turn may contribute to greater expo-sure to groups, settings, or beliefs conducive to criminal behavior (Samp-son and Lauritsen, 1997: 330–3; see also Agnew, 2005: 141–7; Kempf-Leonard, Chesney-Lind, and Hawkins, 2001: 258–61; Sampson and Laub,1997) and, more generally, may diminish the life chances of young minor-ity males (Wilson, 1987, 1996). Anderson (1998: 81) has written, for exam-ple, that “the hard reality of the world of the streets can be traced to theprofound sense of alienation from mainstream society and its institutionsfelt by many poor inner-city black people, particularly the young” (empha-sis added).

Common to such explanations is the insight that something may beunique to this age- and race-specific group and its experiences, which inturn may lead contextual factors to have differential effects on them(Spencer and Jones-Walker, 2004; Sullivan, 2004). In a national study ofyouth, for example, Haynie and Payne (2006: 796) found that the “struc-tural and behavioral characteristics of adolescents’ peer networks” largelyaccounted for race-specific differences in violent offending committed byyoung people and that racial heterogeneity in social networks exerted agreater crime-reducing effect among black youth as compared with whites.The argument was that “increasing racial heterogeneity and exposure tomore popular friends increases feelings of integration and provides accessto greater and more positive sources of social capital, which in turn inhibitviolence” (2006: 796). By contrast, for whites, racial heterogeneity “maybe highly correlated with lower socioeconomic status among friends,”which in turn would “account for higher involvement in violence” (2006:797). It is precisely this type of pattern that we anticipate may developwhen other types of contextual influences are examined.

HYPOTHESES

Collectively, these observations point to the importance of investigatingnot only the direct effects of social ecology on individual-level offendingbut also its conditioning effect among particular groups, especially youngminority males. Here, drawing on the discussion above, we examine threerelated hypotheses that focus on the influence of social ecology. Our firsthypothesis is that ex-prisoners returning to areas with higher levels ofresource deprivation or racial segregation will have higher levels of recidi-vism, net of individual-level controls, including postrelease supervision,and of ecological-level criminal justice resources. Although we anticipatesuch an effect for all types of recidivism, prior research suggests that theseecological conditions should be especially salient for violent crime and, byextension, recidivism (Mears and Bhati, 2006).

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Our second hypothesis is that young nonwhite males will have dispro-portionately higher recidivism rates relative to young white, older non-white, and older white males, net of individual- and ecological-levelcontrols. Here, we anticipate that the effect will be more evident for vio-lent and drug offending than for property offending, given that studiespoint to the greater involvement of young minority males in more seriousand chronic offending (Kempf-Leonard, Chesney-Lind, and Hawkins,2001; Petersilia, 2003; Sampson and Lauritsen, 1997).2 Our main substan-tive focus is hypothesis three, which suggests that resource deprivation andracial segregation will have greater effects on young minority males. As aprelude to examining that hypothesis, however, we first test whetheryoung minority males are in fact disproportionately likely to recidivate.3Our goal is not to test whether an interaction can be explained by socialecology. Rather, our focus is on social ecology and its direct and condition-ing effects on recidivism. That said, it can be argued that young minoritiesmay be especially likely to recidivate because of the social conditions inwhich they reside. Thus, one might anticipate that any differences in recid-ivism could be explained by controlling for such conditions. On the otherhand, the possible theoretical avenues that might generate higher levels ofrecidivism among this group go beyond social ecology, including thecumulative individual-level disadvantage members of the group may haveor face relative to other groups. Below, we discuss analyses that bear onthat question, but here we reiterate that our focus is on the potential con-ditioning effects of ecology on young minorities.

This distinction bears additional elaboration. Typically, in recidivismmodels, and more generally in crime-causation studies, age and race areincluded as controls. Our focus here differs in that we view the combina-tion of being both young and minority as a unique marker of the accumu-lation of individual-level disadvantage and life experiences that place thisgroup at a greater risk of offending relative to other groups. Ideally, wewould have data that permitted testing that idea, but we do not. However,one can indirectly test the idea by examining a hypothesis premised on thelogic. That is the approach taken with the third hypothesis—it anticipatesthat a greater effect of social ecology will occur among young minoritiesprecisely because of the unique disadvantage and life experiences amongthis group. Should such an effect develop, it supports the notion that dis-advantage or adverse life experiences, or, at the very least, some shared

2. Prior work that links race and criminal behavior may not be generalizable to astudy of recidivism. Nonetheless, it seems reasonable to anticipate that a similarpattern may exist.

3. Disproportionality here will be assessed using interaction terms, in which a statis-tically significant effect indicates an influence on recidivism greater than woulddevelop in an additive model.

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condition or experience among this group, contribute to their greaterlevels of offending.

Our third hypothesis is that the interaction between age and race will bemoderated by social ecology. Specifically, we expect not only that youngnonwhite males will be disproportionately likely to recidivate relative toother groups of ex-prisoners but also that this difference will be greateramong released prisoners living in areas marked by higher levels ofresource deprivation or racial segregation. Our reasoning is that youngminority males occupy a particularly unstable situation in which cumula-tive disadvantage and diminished life chances, relative to that of whites,and of white males in particular, is acute (Agnew, 2005; Wilson, 1987,1996). For this reason, residing in disadvantaged or racially segregatedcommunities, in which life chances may be diminished or where social iso-lation is greater, can be expected to influence them disproportionately. Wedo not expect the effect to be greater for any one type of recidivism.

In the case of resource deprivation, reduced informal social controlsmay be expected to provide disproportionately greater freedom for youngminorities to commit crime. More generally, they may have greater moti-vation to commit crime because of higher levels of cumulative disadvan-tage and, in turn, strain. Thus, any weakening of informal controls mayprovide a disproportionately wider window of opportunity for youngminorities to offend. Viewed somewhat differently, during a crucial lifestage—early adulthood—young minority ex-prisoners not only have accu-mulated considerable disadvantage, but also they face situations in whichprison experiences are considered normal among residents in their com-munities (Petersilia, 2003: 28). For these reasons, any diminishment insocial controls caused by increased resource deprivation might producesubstantially increased recidivism among young minorities.

In the case of racial segregation, young minorities may be especiallyinfluenced by the lack of exposure to other groups and by the isolationthat separates them from the opportunities, real or perceived, to become acontributing member of society. Here, again, this group typically is charac-terized by high levels of cumulative disadvantage. By dint of their age,they face a situation in which they may place special emphasis on whetherthey can succeed in life, and, as they look forward in time, may see limitedprospects for employment, helping their families, or otherwise succeedingin life. In short, for this group, isolation may have especially perniciouseffects in closing real or perceived opportunities for advancement. Haynieand Payne (2006) make an analogous argument, finding that exposure tomore racially diverse social networks has a stronger crime-reducing effectamong blacks as compared with whites. We submit that a similar logic may

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hold for young minority males and may lead racial segregation to increasetheir recidivism more strongly relative to other groups.4

DATA AND METHODS

Data for this study come from information about males released fromFlorida prisons between January 1998 and June 2001 (N = 49,420). Theaverage number of inmates released to each of the state’s 67 counties was738, with a range of 14 to 5,807. Inmate profiles and histories wereobtained from the Department of Corrections’ Offender-Based Informa-tion System. County data were obtained from several sources. U.S. CensusBureau 2000 decennial census data were used to capture county variationsin social-structural characteristics. Data on racial-residential segregationwere obtained from the Population Study Center at the University ofMichigan. The Florida Department of Law Enforcement provided data oncounty police deployments, and the Bureau of Economic and BusinessResearch at the University of Florida provided data for county expendi-tures on public safety. Below, we describe each variable we used. Table 1provides details on the precise coding used, and the appendix provides thezero-order correlations for the study variables.

DEPENDENT VARIABLES

Recidivism is defined as instances in which inmates were convicted of anew felony that resulted in correctional supervision (i.e., local jail, stateprison, or community supervision) any time within 2 years after release. Arecent comprehensive review conducted for the Campbell Collaborationemphasized that most recidivism studies use reconviction as the measureof recidivism (Villettaz, Killias, and Zoder, 2006: 8). Our use of reconvic-tion to measure recidivism thus accords with the bulk of studies in thisarea. More importantly, we are interested in the commission of seriousoffenses after prison release and therefore believe that focusing on felo-nies that result in a conviction is warranted.5 Our review suggests that no

4. For young minority males returning to racially segregated communities that arepredominantly white—the exception rather than the rule—we expect that racialisolation of nonwhites contributes to greater strain. For example, this increasedstrain can amplify perceptions of blocked upward mobility, which in turn resultsin increased offending (see also Anderson, 1998: 95; cf. Haynie and Payne, 2006).

5. Rearrest data for creating recidivism measures were not available. Arrest data inFlorida come from the Department of Law Enforcement’s Computerized Crimi-nal History File, which has a field for whether the crime at arrest was a felony ormisdemeanor. However, for many cases, information in this field is missing.Rearrest is not, notably, an unproblematic recidivism measure. After reviewing arange of issues related to measuring and analyzing recidivism, Maltz (1984) iden-tified two problems. First, “police may have a policy of harassing ex-offenders to

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basis exists for anticipating substantively or statistically different resultswhen using reconviction rather than rearrest as a measure of recidivism(see, e.g., Spohn and Holleran, 2002).

For each released inmate, we had a full 2 years of follow-up data.Although the bulk of recidivism occurs within the first year after release(Kurlychek, Brame, and Bushway, 2006; Langan and Levin, 2002), a 2-yearfollow-up ensures that we are not restricting our focus to inmates mostlikely to fail in the first year. We disaggregated reconviction into threecategories: violent crimes (homicide, aggravate assault, robbery, and sexoffenses, including forcible rape), drug-related crimes (possession, sale, ordistribution of illegal substances), and property crimes (burglary, motorvehicle theft, and larceny).6 Each type of reconviction was dummy coded(1 = yes, 0 = no)—5.5 percent of the sample was reconvicted for a violentoffense within 2 years of release, 15.2 percent was reconvicted for a drug-related offense, and 12.2 percent was reconvicted for a property offense.

INDIVIDUAL-LEVEL VARIABLES

We examine the likelihood of three offense-specific reconviction out-comes for four age and race groups (young nonwhite, old nonwhite, youngwhite, and old white), controlling for educational background, criminalrecord, incarceration profile, and community supervision (see discussionbelow).7 Consistent with our hypotheses about age and race interactionsand with prior research (e.g., Steffensmeier, Ulmer, and Kramer, 1998:765), respondents who were 29 years old or less were coded as “young.”(We investigated different age cutoffs to assess how the results might, if atall, differ.8) In our main analyses, we investigate how two measures of

encourage them to leave the jurisdiction” (1984: 57). Second, with the pressure toclear arrests, police may make more arrests of ex-offenders regardless of theirculpability in a crime.

6. A reviewer wondered whether motion-to-revoke hearings might result in thereconviction measure missing cases wherein a new offense or technical violationresulted in reincarceration but no new conviction. The state of Florida eliminatedparole in 1983, and only 36 percent of the releases examined had any type ofpostrelease supervision. Thus, the possibility for motion-to-revoke proceedings islow. In addition, many inmates in Florida prisons were incarcerated under thestate’s minimum sentence law—85 percent of a sentence must be served. As aresult, the length of postrelease supervision under this second program typicallyis short in duration, which reduces the opportunities for motion-to-revoke casesto occur in lieu of reconvictions.

7. Among nonwhites, 91 percent were black and 9 percent were Hispanic.8. In analyses of two-way interactions between age and race, and three-way interac-

tions among age, race, and ecology, a younger age cutoff (29/30) was more likelyto reveal statistically significant interactions as compared with older cutoffs (34/35, 39/40). The results, available on request, indicate the need to examine varyingage cutoffs in interactional analyses.

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314 MEARS, WANG, HAY & BALES

Table 1. Variable Descriptions and Descriptive StatisticsVariable Definition and Coding Mean SD

Dependent VariablesViolent reconviction Whether ex-prisoners were convicted of a new .06 .23

violent offense resulting in correctional supervision(i.e., local jail, state prison, or communitysupervision) for the 2 years after release from prison(1 = yes, 0 = no).

Drug reconviction Whether ex-prisoners were convicted of a new drug- .15 .36related offense that resulted in correctionalsupervision for the 2 years after release from prison(1 = yes, 0 = no).

Property reconviction Whether ex-prisoners were convicted of a new .12 .33property offense resulting in correctional supervisionfor the two years following release from prison (1 =yes, 0 = no).

Ex-Prisoner-Level Independent VariablesYoung 1 = age (in years) at the time of release from prison .43 .50

<30, 0 = otherwise.Nonwhite 1 = nonwhite, 0 = white. .64 .48Education Scores from the Test of Adult Basic Education 7.33 3.24

(TABE), which measures a person’s grade level inthree subjects (i.e., reading, math, and language) andwas administered prior to release.

Criminal record Weighted factor score extracted from number of .00 1.00prior recidivism events, total number of priorconvictions, and seriousness scores (l = 2.22, factorloadings > .75; Cronbach’s alpha = .819).

Incarceration profile Weighted factor score extracted from custody level, .00 1.00number of disciplinary infractions, and length inprison (l = 1.63, factor loadings > .60; Cronbach’salpha = .567).

Postrelease supervision Whether the offender is supervised by a parole, .36 .48probation, or a community-control officer for aspecified time period after release (1 = yes, 0 = no).

County-Level Independent VariablesResource deprivation Weighted factor score extracted from median family .00 1.00

income, percent female-headed households, percentunemployed, percent poverty, and percent receivingpublic assistance (l = 3.25, factor loadings > anabsolute value of .60; Cronbach’s alpha = .848).

Index of Dissimilarity White/black within county segregation using census 43.29 15.62(racial segregation) tracts as the subareas. Scores range from 0 to 100, in

which larger values reflect higher levels of racialsegregation.

Criminal justice system Weighted factor score extracted from police .00 1.00resources presence, per capita county revenues, and per capita

spending on public safety (l = 1.95, factor loadings >.76; Cronbach’s alpha = .729).

NOTES: N = 49,420 within county; N = 67 between county.

ecology—resource deprivation and racial segregation—separately interactwith the four age–race groups in predicting reconviction. We also con-ducted analyses in which females and larger numbers of age categorieswere included, but we had to restrict the focus to males. Even with the

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SOCIAL ECOLOGY AND RECIDIVISM 315

large sample size, the inclusion of females, who comprised a small percent-age of the releasee population, spread the data too thin, especially whenexamining age and race interactions.

CONTEXTUAL VARIABLES

We focus on two ecology measures, resource deprivation and racial seg-regation, with the latter measure operationalized using the Index of Dis-similarity. This measure, used in ecological-level studies of violence (e.g.,Parker, 2004; Wadsworth and Kubrin, 2004), reflects the evenness withwhich whites and blacks are distributed across the census tracts that makeup a county. More specifically, it measures the extent to which two groupsare distributed evenly across an area, with values closer to 0 indicatinglower levels of segregation and values closer to 100 indicating higherlevels. A given value indicates the percentage of the overrepresentedgroup that would have to move from tracts in which they are over-represented to other tracts to achieve equal representation (i.e., a value of0). Data from the Census Bureau’s 2000 decennial census were used tocreate the resource deprivation variable, which drew on measures—including median family income, percent female-headed households, per-cent unemployed, percent of population living below the official povertyline, and percent of population receiving public assistance—similar tothose used in prior research (Land, McCall, and Cohen, 1990; Pratt andCullen, 2005).9

Although other ecological-level units of analysis could be used (Peter-son and Krivo, 2005), we focus on counties because social and economicconditions vary considerably between counties, and, more importantly forour purposes, because law-enforcement and courtroom practices often areorganized at a county level (see, e.g., Johnson, 2006). Counties do not con-stitute communities by some definitions (Sampson, Morenoff, and Gan-non-Rowley, 2002), but they do reflect a social ecological context to whichoffenders return and have been used in studies of crime and sentencing(Baller et al., 2001; Johnson, 2006; Osgood and Chambers, 2000).Although we cannot demonstrate that released inmates remain in thecounties to which they return, studies suggest that mobility is low and that

9. Resource deprivation and racial segregation are predicted to influence recidivismin similar ways—increases in both are anticipated to increase recidivism, and thiseffect is expected to be greater for young minority males—but for different rea-sons. With the data at hand, we cannot distinguish one causal pathway fromanother, although ultimately identifying an effect and the reasons for one will becritical for differentiating how resource deprivation and racial segregation maycontribute to recidivism. In our study, the two measures are only modestly corre-lated (see appendix), so it is unlikely that they measure the same underlyingconstruct.

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316 MEARS, WANG, HAY & BALES

any moves typically occur within a county. Schachter, Franklin, and Perry(2003: 2) reported, for example, mobility patterns for the general U.S.population over a 5-year period and found that more than half of residentswho move remain in the same county. Research focusing on releasedinmates suggests that a minimal level of county-to-county migration occurs(La Vigne and Parthasarathy, 2005: 2).

CONTROL VARIABLES

We control for offender characteristics to increase confidence that ourestimates of the effects of age, race, and the ecology measures on the threereconviction outcomes are not spurious. Prior research consistently showsthat prior criminal activity and having a prior record may contribute torecidivism (see, e.g., Gendreau, Little, and Goggin, 1996: 581–6). For thisreason, we examined several measures of these constructs using principalcomponents analysis. As expected, two latent constructs emerged. Thefirst, criminal record, consists of the number of prior felony convictionsthat result in state correctional supervision, number of prior recidivismevents (i.e., the number of times an inmate previously was released fromprison and subsequently both convicted of a new felony offense andreadmitted to a Florida prison), and an overall prior felony conviction seri-ousness score.10 The second, incarceration profile, exhibited high loadingsfor the number of months served in prison, the number of prison discipli-nary infractions, and the custody level at the time of release.11 Both crimi-nal record and incarceration profile were operationalized as weightedfactor scores. Education level was operationalized using scores from theTest of Adult Basic Education, which measures a person’s grade level inthree subjects (i.e., reading, math, and language), and it is administered toinmates prior to release.

A potential problem in studies of recidivism using official records data isthat the outcome may reflect both offending and law-enforcement behav-ior (Blalock, 1970; Kubrin and Stewart, 2006; Liska and Chamlin, 1984).12

We therefore included individual- and ecological-level measures of formal

10. Under Florida’s Criminal Punishment Code, sentencing points are assignedbased on the primary (most serious) offense before the court. Our seriousnessscores reflect the 1999 and 2000 sentencing guidelines offense points assigned to52 different offenses (Burton et al., 2004).

11. Custody level is coded so that higher scores reflect higher levels of custody (i.e., 1= community, 2 = minimum, 3 = medium, and 4 = close).

12. This issue, although often noted, is addressed rarely in recidivism studies, andwhen it is, the potential impacts on model estimates clearly emerge. In Kubrinand Stewart’s (2006) bivariate analyses, for example, one of their ecological mea-sures (ICE) has a positive effect on recidivism. After controlling for individual-level factors, the effect becomes slightly negative (the expected effect). The ques-tion develops, what would the effect have been with, say, different individual-

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SOCIAL ECOLOGY AND RECIDIVISM 317

social control capacity and resources. At the individual level, we includeda variable that indicates whether released inmates were under some formof supervision at the time they were released; this variable addresses thepossibility that some inmates are more likely to be caught purely by virtueof greater exposure to supervision. At the ecological level, and followingSampson and Laub (1993: 298), criminal justice system resources is a com-posite variable, which is produced through principal components analysis,that taps per capita county revenues, resources allocated to public safety,and police presence, as measured by the number of law-enforcementofficers per 100,000 residents. This variable, too, controls for the possibil-ity that the recidivism of some individuals may result from differentialexposure to law enforcement. It does not include an explicit measure ofcourt expenditures (although it does include information on county reve-nues), and thus, it is likely to primarily reflect law-enforcement, not court,activities.

ANALYTIC STRATEGY

With multilevel data and a binary outcome, Raudenbush and Bryk(2002) recommend the use of hierarchical generalized linear modeling(HGLM), which incorporates a unique random effect into the statisticalmodel for each county and produces more robust standard errors thannonhierarchical models allow (2002: 100).13 Furthermore, to assess themoderating effect of social context on age and race, cross-level (or macro-micro) interaction techniques were employed (see Kreft and de Leeuw,1998: 12).14 For the analyses, we used HLM 6.0 and present the model

level characteristics or community police presence measures? Kubrin and Stew-art’s (2006) analyses stand out precisely because they attempt to address thisissue; few extant recidivism studies do.

13. One reviewer wondered whether cohort effects might exist. During the period oftime in which inmates were released, no changes were made in Florida releasepolicies; also, the proportion of releases, by the method of release, did notchange to any appreciable degree (Florida Department of Corrections, AnnualReports, FY1998-99, FY1999-00, FY2000-01, http://www.dc.state.fl.us/pub/index.html). In ancillary analyses (available on request), we re-estimated all anal-yses using dummy variables for 1998, 1999, and 2000, holding year 2001 as thereference year. The statistical and substantive significance of the results remainedthe same.

14. We ran models in which we allowed the slopes of young, nonwhite, and young ×nonwhite to vary across counties for violent, drug, and property reconviction.Only the slope of nonwhite for drug reconviction varied significantly (p < .05).Raudenbush and Bryk (2002) have indicated that in such cases the possibility of anonrandomly varying specification for the corresponding variables (in this case,young, nonwhite, and young × nonwhite) is not precluded. Indeed, homogeneitytests for slopes are, they have noted, only a guide (2002: 129), and “if theoreticalarguments suggest that such effects might be present, the analyst should proceed

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318 MEARS, WANG, HAY & BALES

estimates with robust standard errors.15 Because the theoretical logicsunderlying the hypothesized effects of resource deprivation and racial seg-regation on recidivism differ, we present separate sets of models, one forresource deprivation and one for racial segregation.16 However, we alsoreport the results of analyses in which the measures were included simulta-neously; substantively, the findings were largely the same, regardless ofwhether one or both measures were included in the same models. Whenthese measures were omitted, the results also were the same for the test ofan age and race interaction. We discuss these analyses when presenting theresults of the tests of our three hypotheses.

with posing level-2 models for these slopes” (2002: 258). Because of our theoreti-cal focus, we proceed with investigating cross-level interactions. When cross-levelinteractions were estimated in model 3 in tables 2 and 3, only the slope of non-white, for the drug reconviction outcome, was random.

15. Given the multiple comparisons we make in examining three types of recidivism,some researchers might argue for the use of Bonferroni-adjusted p values. How-ever, others have suggested that such adjustments are not necessary or appropri-ate in some contexts, especially with ecological studies (Moran, 2003) andsituations in which small sample sizes or specific hypotheses are being examined(Book, Quinsey, and Langford, 2007; Perneger, 1998). In addition, such adjust-ments increase the risk of type-II errors (Moran, 2003; Perneger, 1998). Finally,although we have a large size of ex-prisoners (N = 49,420), we have a small sizeof counties (N = 67). The latter constitutes a particular concern when cross-levelinteractions are examined. Given the goal of assessing specific hypotheses andthe relative small sample size at the county level, we do not include Bonferroniadjustments.

16. A note on model fit and the interaction effects is warranted. Our focus is ontesting theoretical hypotheses about the interaction of ecology with specific ageand race groups, not, per se, the ability of an interactive model to improve modelfit. However, we compared the model fit of models without cross-level interac-tions to those with them. When examining resource deprivation (table 2), themodel fit improved significantly for drug reconviction but not for violence orproperty reconviction. When examining racial segregation (table 3), the fitimproved significantly for drug use and property reconviction but not for violentreconviction (results available on request). Because the slope of nonwhite fordrug reconviction varies significantly, race seems to have a differential effectacross counties with respect to drug recidivism. However, neither resource depri-vation nor racial segregation explains the variance of the nonwhite slope for drugreconviction, given that the interaction between nonwhite and each of the socialecology measures was statistically insignificant (see model 3 in tables 2 and 3).We should emphasize that our focus is on resource deprivation and racial segre-gation exerting differential effects on specific variables, and specifically the ageand race groups we specify, not differential effects of race across counties,although the latter certainly bears investigation in future studies.

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SOCIAL ECOLOGY AND RECIDIVISM 319

SPATIAL DEPENDENCE

A concern in studies of social ecology is the potential problem of spatialdependence (Kubrin and Weitzer, 2003: 393–5). Following Baller et al.(2001: 572), a nearest-neighbor criterion was calculated from the distancebetween county centroids. Using different neighbor weight matrices for 5,6, and 10 nearest neighbors (all weights equal 1, with larger counties hav-ing larger weights), global Moran’s I statistics on the raw offense-specificreconviction rates were calculated. Then the S-plus spatial module for1,000 permutations for each Moran’s I statistic was used. In addition,Moran’s I statistics using empirical Bayes adjusted rates were also com-puted. As indicated by Moran’s I statistics, spatial autocorrelation was notstatistically significant for either violent reconviction or property reconvic-tion. However, significant spatial autocorrelation emerged when drugreconviction was examined. As a result, for the drug reconviction models,we included a spatial lag specific to drug reconviction.17

RESULTS

Before proceeding to a discussion of the findings that bear directly onour hypotheses, a brief mention of the individual-level effects (presentedin model 1 in tables 2 and 3) is warranted. Recall that our hypotheses donot directly focus on these variables but instead treat them as controls.Nonetheless, it is notable that the statistical significance and direction ofeffects are consistent with prior research. Specifically, the following char-acteristics were generally associated with increased recidivism—beingyoung or nonwhite, having less education, and having a prior record ormore serious incarceration profile (e.g., having a higher custody level,more infractions, or serving a lengthier sentence). In addition, postreleasesupervision was not statistically significant or, for drug and propertyoffending, was associated with less recidivism.

We turn now to the test of our first hypothesis, which anticipated thatresource deprivation and racial segregation would be positively associated

17. The spatial lag specific to drug reconviction was constructed by averaging the rawdrug reconviction rates for the five nearest neighboring counties of each county;most counties in Florida have no more than five such counties. It bears emphasisthat if our theoretical focus were lag effects, then a different approach, such as asimultaneous autoregressive model, would be indicated (Land and Deane, 1992).Here, however, our focus simply is the use of the spatial lag of drug reconvictionas a control for substantive and/or nuisance autocorrelation. Mears and Bhati(2006: 521) recently employed a similar approach and observed, “To the extentthat the lags are included merely as controls, simple conditional autoregressive(CAR) specifications . . . should be sufficient since they account for all the excessvariation (see Anselin, 2003).”

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320 MEARS, WANG, HAY & BALES

Tab

le 2

.R

egre

ssio

n of

Vio

lent

, D

rug,

and

Pro

pert

y R

econ

vict

ion

on A

ge a

nd R

ace

Inte

ract

ion

and

Res

ourc

e D

epri

vati

on Vio

lent

Rec

onvi

ctio

nD

rug

Rec

onvi

ctio

naP

rope

rty

Rec

onvi

ctio

nM

odel

1M

odel

2M

odel

3M

odel

1M

odel

2M

odel

3b

Mod

el 1

Mod

el 2

Mod

el 3

Inte

rcep

t–3

.19*

*–3

.10*

*–3

.10*

*–2

.97*

*–2

.86*

*–2

.89*

*–1

.93*

*–2

.01*

*–2

.02*

*(.

06)

(.06

)(.

07)

(.09

)(.

08)

(.07

)(.

03)

(.04

)(.

04)

You

ng.5

8**

.41*

*.4

1**

.37*

*.1

0.1

0.1

6**

.34*

*.3

3**

(.05

)(.

08)

(.08

)(.

07)

(.06

)(.

07)

(.03

)(.

06)

(.06

)N

onw

hite

.15*

*.0

1.0

11.

21**

1.07

**1.

12**

–.33

**–.

20**

–.19

**(.

05)

(.06

)(.

06)

(.08

)(.

08)

(.07

)(.

04)

(.06

)(.

07)

Edu

cati

on–.

04**

–.05

**–.

05**

–.04

**–.

04**

–.04

**–.

02**

–.02

**–.

02**

(.01

)(.

01)

(.01

)(.

00)

(.00

)(.

00)

(.00

)(.

00)

(.00

)C

rim

inal

rec

ord

.07*

*.0

7**

.07*

*.2

1**

.22*

*.2

2**

.35*

*.3

4**

.34*

*(.

02)

(.02

)(.

02)

(.02

)(.

02)

(.02

)(.

03)

(.02

)(.

02)

Inca

rcer

atio

n pr

ofile

.23*

*.2

3**

.23*

*–.

07**

–.07

**–.

07**

.05*

*.0

5**

.05*

*(.

02)

(.02

)(.

02)

(.01

)(.

01)

(.01

)(.

01)

(.01

)(.

01)

Post

rele

ase

supe

rvis

ion

–.02

–.02

–.02

–.51

**–.

51**

–.51

**–.

32**

–.33

**–.

33**

(.04

)(.

04)

(.04

)(.

03)

(.03

)(.

03)

(.05

)(.

05)

(.05

)R

esou

rce

depr

ivat

ion

.08*

.08*

.08

–.08

*–.

08*

.06

–.01

–.01

–.03

(.04

)(.

04)

(.06

)(.

04)

(.04

)(.

07)

(.03

)(.

03)

(.04

)Y

oung

× n

onw

hite

.26*

*.2

6**

.32*

*.3

2**

–.32

**–.

30**

(.09

)(.

09)

(.06

)(.

05)

(.10

)(.

10)

You

ng ×

res

ourc

e de

priv

atio

n–.

01–.

06–.

04(.

09)

(.11

)(.

08)

Non

whi

te ×

res

ourc

e de

priv

atio

n–.

00–.

10.0

7(.

07)

(.06

)(.

08)

You

ng ×

non

whi

te ×

res

ourc

e de

priv

atio

n.0

2–.

04–.

00(.

10)

(.06

)(.

13)

Ran

dom

eff

ect

Inte

rcep

t, t 0

0.0

4.0

4.0

4.0

4.0

4.0

7.0

3.0

3.0

3c2

152.

79**

153.

90**

153.

63**

401.

44**

397.

73**

178.

77**

167.

69**

168.

68**

172.

10**

NO

TE

S: N

= 4

9,42

0 w

ithi

n co

unty

; N =

67

betw

een

coun

ty. S

tand

ard

erro

rs in

par

enth

eses

. Eac

h m

odel

incl

udes

cri

min

al ju

stic

e sy

stem

res

ourc

es a

s a

mea

sure

of

coun

ty-

leve

l fo

rmal

soc

ial

cont

rol.

a Spa

tial

lag

spe

cifi

c to

dru

g re

conv

icti

on i

s in

clud

ed b

ecau

se o

f sp

atia

l au

toco

rrel

atio

n, w

hich

was

rev

eale

d w

hen

drug

rec

onvi

ctio

n w

as c

onsi

dere

d.b T

he s

lope

of

nonw

hite

is

allo

wed

to

vary

acr

oss

coun

ties

bec

ause

the

var

ianc

e of

the

non

whi

te s

lope

is

sign

ific

ant

at p

< .

01.

*p <

.05

; **

p <

.01

.

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SOCIAL ECOLOGY AND RECIDIVISM 321

Tab

le 3

.R

egre

ssio

n of

Vio

lent

, D

rug,

and

Pro

pert

y R

econ

vict

ion

on A

ge a

nd R

ace

Inte

ract

ion

and

Inde

x of

Dis

sim

ilari

ty Vio

lent

Rec

onvi

ctio

nD

rug

Rec

onvi

ctio

naP

rope

rty

Rec

onvi

ctio

nM

odel

1M

odel

2M

odel

3M

odel

1M

odel

2M

odel

3b

Mod

el 1

Mod

el 2

Mod

el 3

Inte

rcep

t–3

.18*

*–3

.09*

*–3

.06*

*–2

.97*

*–2

.87*

*–3

.01*

*–1

.93*

*–2

.01*

*–2

.06*

*(.

06)

(.07

)(.

08)

(.09

)(.

08)

(.08

)(.

04)

(.04

)(.

05)

You

ng.5

8**

.41*

*.3

2**

.37*

*.1

1.1

5.1

6**

.34*

*.4

4**

(.05

)(.

08)

(.11

)(.

07)

(.06

)(.

09)

(.03

)(.

06)

(.06

)N

onw

hite

.15*

*.0

2.0

21.

20**

1.06

**1.

15**

–.33

**–.

20**

–.08

(.05

)(.

06)

(.09

)(.

08)

(.08

)(.

09)

(.04

)(.

06)

(.08

)E

duca

tion

–.04

**–.

05**

–.04

**–.

04**

–.04

**–.

04**

–.02

**–.

02**

–.02

**(.

01)

(.01

)(.

01)

(.00

)(.

00)

(.00

)(.

00)

(.00

)(.

00)

Cri

min

al r

ecor

d.0

7**

.07*

*.0

7**

.21*

*.2

2**

.22*

*.3

5**

.34*

*.3

4**

(.02

)(.

02)

(.02

)(.

02)

(.02

)(.

02)

(.03

)(.

02)

(.02

)In

carc

erat

ion

prof

ile.2

3**

.23*

*.2

3**

–.07

**–.

07**

–.06

**.0

5**

.05*

*.0

5**

(.02

)(.

02)

(.02

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322 MEARS, WANG, HAY & BALES

with recidivism. Inspection of model 1 in table 2 shows that resource dep-rivation is, as expected, associated with an increased risk of violence—thatis, released prisoners who return to resource-deprived areas are signifi-cantly more likely to be reconvicted of a violent crime. We expected thatthe effect would be stronger for this type of recidivism than for drug andproperty recidivism. In fact, resource deprivation is negatively associatedwith drug recidivism and statistically insignificant for property recidivism.(In separate analyses, available on request, similar results emerged whenracial segregation was included as a control; the main difference was that,in the drug model, resource deprivation was not statistically significant.)Inspection of model 1 in table 3 shows that the level of racial segregation isnot statistically significant in predicting any of the three types ofrecidivism.

For our second hypothesis, we anticipated an interaction effect betweenrace and age such that recidivism would be disproportionately greateramong young nonwhite males, especially for violent and drug crimes.Model 2 in tables 2 and 3 suggests mixed support for this hypothesis. Thetwo-way interaction terms are statistically significant in all six instances—that is, in the violent, drug, and property reconviction models, respec-tively, as well as for resource deprivation (table 2) and racial segregation(table 3), respectively. To show graphically what the terms indicate, figure1 presents predicted reconviction probabilities for each of the four raceand age groups using the table 2 results. Because the pattern is similar tothe results from table 3, the one figure serves to illustrate the findings,regardless of whether deprivation or segregation is examined.

An inspection of figure 1 shows that, as expected, the probability ofviolent reconviction is markedly higher among young nonwhite males ascompared with all three other groups, especially younger and older whitemales. A more striking contrast is evident with drug reconviction, in whichthe probability of reconviction is almost three times greater for youngnonwhite males as compared with young and older white males. By con-trast, and unexpectedly, property recidivism is greatest among youngwhite males, with all three other groups having relatively similar probabili-ties of such recidivism.

Recall that our focus is simply on establishing whether young minoritymales are disproportionately more likely to recidivate relative to othergroups as a logical prelude to testing the hypothesis that resource depriva-tion and racial segregation may more strongly increase recidivism amongthis group relative to the others. However, a reasonable, but logically dif-ferent, question is whether either measure of social ecology substantiallydiminishes the interaction effect. If so, then it might be concluded that anysuch interaction is caused by, if only in part, a different exposure to

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SOCIAL ECOLOGY AND RECIDIVISM 323

Figure 1. Predicted Reconviction Probabilities for Four Ageand Race Groupsa

0

0.05

0.1

0.15

0.2

0.25

Violent Reconviction Drug Reconviction Property Reconviction

Pred

icte

d R

econ

vict

ion

Prob

abili

ties

Young NonwhitesOld NonwhitesYoung WhitesOld Whites

aBased on table 2, model 2.

resource deprivation and racial segregation, respectively. In separate anal-yses, we found no reduction in the magnitude and statistical significance ofthe age and race interaction. That is, the young minority male effectemerged regardless of whether either resource deprivation or racial segre-gation, or both, were included as controls.

Finally, model 3 in tables 2 and 3 provides a test of our third hypothe-sis—namely, the expectation that the age–race interaction effect will itselfinteract with each of our two measures of social ecology (resource depri-vation and racial segregation). A review of table 2 shows that no statisti-cally significant three-way interaction effect emerged—that is, resourcedeprivation did not moderate the effect of the race and age interaction.However, a significant three-way interaction surfaced in table 3 for bothdrug and property recidivism, which indicates that racial segregationmoderates the race and age interaction effect.18 Here, again, to facilitatediscussion, we present these results graphically in figures 2 and 3.

The figures suggest that the moderating influence of ecology variesdepending on the type of offense. Inspection of figure 2 shows that young

18. The results were the same regardless of whether, in table 2, racial segregationwas included as a control or whether, in table 3, resource deprivation wasincluded as a control.

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324 MEARS, WANG, HAY & BALES

Figure 2. Predicted Drug Reconviction Probabilities forFour Age and Race Groupsa

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Index of Dissimilarity

Pred

icte

d D

rug

Rec

onvi

ctio

n Pr

obab

ilitie

s

Young Nonwhites

Old Nonwhites

Young Whites

Old Whites

aBased on table 3, model 2.

nonwhite males indeed have substantially greater probabilities, relative tothe other ex-prisoner groups, of recidivating for a drug crime. This differ-ence diminishes as racial segregation increases. More important, however,is the fact that, contrary to what we hypothesized, the effect of racial seg-regation is not greater among young nonwhite males; indeed, higher levelsof segregation are associated with less, not more, recidivism for this group.By contrast, increased racial segregation increases the recidivism of theother groups, especially old nonwhites.

When we turn to property offending, inspection of figure 3 provides par-tial support for our hypothesis. Young nonwhites are at increased risk ofrecidivism as racial segregation increases, but a similar effect holds forolder whites as well. By contrast, the risk of recidivism decreases amongyoung whites and old nonwhites as racial segregation increases. Notably,at almost all levels of racial segregation, the probability of property recidi-vism among young nonwhite males is lower than that for the other groups.

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SOCIAL ECOLOGY AND RECIDIVISM 325

Figure 3. Predicted Property Reconviction Probabilities forFour Age and Race Groupsa

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00

Index of Dissimilarity

Pred

icte

d Pr

oper

ty R

econ

vict

ion

Prob

abili

ties

Young Nonwhites

Old Nonwhites

Young Whites

Old Whites

aBased on table 3, model 3.

CONCLUSION

Despite the dramatic increase in the numbers of prisoners returning toU.S. society, we know little about the factors that contribute to recidivism,especially how social ecology may influence released inmates. Building offof recent work by Kubrin and Stewart (2006), and heeding calls for greaterunderstanding of the postrelease experiences of prisoners (Petersilia, 2003;Travis and Visher, 2005), this article contributes to the emerging literatureon reentry by examining the salience of social ecology, and its potentiallydifferential effects on young minority males, for the study of recidivism.Our emphasis on ecology stems from the fact that many criminologicalstudies emphasize the role that ecology can play in offending (Sampson,Morenoff, and Gannon-Rowley, 2002). We gave particular attention toresource deprivation and racial segregation because of their centrality tomany crime theories and because studies find these factors to be amongthe strongest ecological predictors of crime (Pratt and Cullen, 2005). Atthe same time, we focused on the question of whether these ecologicalinfluences exert a greater effect among young minority males given thatthis population typically suffers from considerable cumulative disadvan-tage, has a unique set of life experiences, and engages in more serious andchronic offending. In addition, policy concerns about disproportionate

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326 MEARS, WANG, HAY & BALES

incarceration of minorities, especially those who are young and male,make this group particularly relevant in attempts to understand the causesof recidivism (Travis, 2005). Finally, should ecology not only influencerecidivism but also increase the risk of offending among select groups, itwould point to the possibility that effective recidivism policies should takeecology into consideration and develop risk-prediction efforts that con-template conditional ecological effects.

Briefly, we anticipated that 1) recidivism, especially offending thatinvolves violent crimes, would be higher among individuals released toresource-deprived or racially segregated areas; 2) recidivism, especiallyoffending that involves violent or drug crimes, would be higher amongyoung nonwhites; and 3) the interaction of race and age would itself inter-act with social ecology, and, in particular, with levels of resource depriva-tion or racial segregation.

With respect to the first hypothesis, we found that resource deprivationindeed was associated with higher levels of recidivism for violent crime butnot for property crime. Unexpectedly, however, it was associated withlower rather than higher levels of drug crime, although this effect disap-peared when racial segregation was included. No evidence existed of adirect effect of racial segregation. With respect to the second hypothesis,we found, as expected, that violent and drug recidivism, especially drugrecidivism, was disproportionately greater among young nonwhite males.Although not our focus, we also found that this effect was not caused bydifferential exposure to higher levels of resource deprivation or racial seg-regation. Notably, recidivism that involves property crime was greatestamong young whites; all other race–age groups had relatively comparablelevels of recidivism for such crime. Finally, in testing the third hypothesis,we found that resource deprivation did not condition the race–age interac-tion but that racial segregation did lead to higher drug and property recidi-vism. Specifically, we found that young nonwhite males were more likelythan other groups to be reconvicted of drug crimes, but contrary to ourprediction, this difference decreased in more segregated areas, whereas forother groups, increased segregation was associated with an increased riskof recidivism, especially among older nonwhites. For property reconvic-tion, the recidivism of not only young nonwhite males but also older whitemales increased in areas of greater segregation, whereas young whites andolder nonwhites were at less risk of recidivism in areas marked by moresegregation.

What accounts for these findings? At the individual level, the fact thatviolent and drug recidivism was disproportionately greater among youngnonwhite males likely reflects greater exposure to criminogenic influences,of which incarceration itself may be one, that accumulate and amplify one

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another (Agnew, 2005). That this effect persisted regardless of social ecol-ogy—specifically, resource deprivation or racial segregation—suggeststhat, for ex-prisoner populations, cumulative disadvantage or other suchfactors may account for the greater risk of offending, not differential expo-sure to criminogenic environments, as some have speculated (e.g., Agnew,2005: 169). The fact that increased resource deprivation was associatedwith increased violent offending accords with our theoretical expectations.However, it is unclear why it would be associated with decreased drugoffending, although, again, no such effect existed when we controlled forracial segregation.

A symbolic-threat explanation (Chiricos, Welch, and Gertz, 2004;Mears, 2006; Steffensmeier, Ulmer, and Kramer, 1998) might potentiallyexplain the findings; although, as compared with prior studies, we incorpo-rated relatively strong controls for differential law enforcement. Nonethe-less, the fact that greater levels of resource deprivation were associatedwith a lower likelihood of drug recidivism may reflect the possibility thatlaw-enforcement efforts do not target drug crimes in such areas or thatresidents in such communities are less likely to report such crimes to thepolice (Gottfredson and Taylor, 1985, 1988; Klinger and Bridges, 1997).The explanation is plausible, although it seems unlikely that such a patternwould exist for drug crimes but not for violent crimes.

A differential-reporting or law-enforcement explanation may helpexplain why the drug recidivism of young minority males was lower inmore racially segregated communities, whereas for other groups, greatersegregation was associated, as we anticipated, with increased recidivism. Itstrikes us as unlikely that increased segregation actually reduces the drugoffending of young minorities, and it is difficult to envision a coherenttheoretical explanation for such a differential effect. Assuming that segre-gation has an equal effect across groups, the results might develop in asituation in which law-enforcement efforts are guided by considerations ofplaces and groups. Officers may, for example, be more prone to ignore ordownplay drug offending among young minorities in racially segregatedcommunities, viewing it perhaps as a problem about which little can bedone. Residents in such communities may have similar feelings. In bothcases, the result would be that young minorities would be less likely toemerge in any profile that involves official records statistics. Any such pos-sibility must remain speculation (see, however, Klinger and Bridges, 1997).Even so, it bears emphasizing that many accounts exist of minority com-munities that have felt overwhelmed by drug offending and the behaviorof their younger residents, that have not trusted law enforcement to taketheir concerns seriously, and that have faced the burden of accommodat-ing a disproportionate number of returning prisoners (Anderson, 1998;Blumstein, 1995; Clear, Waring, and Scully, 2005; Saxe et al., 2001). In

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328 MEARS, WANG, HAY & BALES

such circumstances, residents, as well as law enforcement, might turn theirattention to other groups over whom they may exert greater control (cf.Petersilia, 2003: 29).

A differential-reporting or law-enforcement explanation also may helpexplain the property reconviction findings. Perhaps in racially segregatedcommunities, law enforcement places more priority on property offendingamong young minorities, possibly in the belief that, relative to attempts tofight drug offending, a significant effect might result. By contrast, as thefirst half of figure 2 intimates, law enforcement may feel that a greaterimpact can be had in racially heterogeneous areas by focusing on drugcrimes among young minority offenders. Any such explanations must, ofcourse, remain speculative absent independent measures of ex-prisoneroffending and of community resident reporting as well as law-enforcementand court practices.

These observations generate a more general concern—namely, recidi-vism studies frequently draw minimally on criminological theory, and yetmany opportunities exist to test and extend theory by examining theoffending patterns of released prisoners. Such a focus is especially war-ranted given the discipline’s increasing emphasis on patterns of persistenceand desistance (Piquero, Farrington, and Blumstein, 2003) as well as onhow social ecology influences criminal behavior (Sampson, Morenoff, andGannon-Rowley, 2002). In fact, the nexus of these two theoretical orienta-tions provides a particularly unique opportunity to extend criminologicaltheory. To date, research on individual-level trajectories has given almostexclusive attention to individual-level predictors; yet clearly social ecologymay be a contributing factor to such trajectories. Indeed, as the currentstudy found, social context (i.e., resource deprivation and racial segrega-tion) is associated, directly or in interaction with other factors, with recidi-vism. This finding in turn echoes that of other recent studies (e.g., Kubrinand Stewart, 2006), which argue for greater attention to the role of socialecology in contributing to the offending patterns of released prisoners.

Here, a comment on aggregation bias is in order. In any ecologicalstudy, a critical question centers around the appropriate unit of analysis(Zatz, 2000). Studies that involve a focus on communities and neighbor-hoods typically must adopt relatively random boundary markers (e.g.,tracts, blocks, and zip codes) and consequently do not necessarily incorpo-rate meaningful socially delimited spatial areas (see, however, Sampson,Raudenbush, and Earls, 1997). The more fundamental issue is that certainvariables (e.g., percent poverty) can be computed at different units ofanalysis (e.g., tracts, blocks, zip codes, cities, counties, states, and coun-tries); yet they may constitute operationalizations of distinct phenomena(Firebaugh, 1978; Land, Cantor, and Russell, 1995; Lieberson, 1985; Liska,1990). To illustrate, intracountry variation in inequality may be caused by

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SOCIAL ECOLOGY AND RECIDIVISM 329

different factors than intercountry variation (Firebaugh, 1999). Similarly,as scholars of unemployment and crime have noted, “the key effect ofaggregation bias pertains to the meaning of the unemployment rate at dif-ferent analytic levels” (Land, Cantor, and Russell, 1995: 57; emphasis inoriginal).

For our study and others aimed at examining the influence of socialecology on individual-level recidivism, the critical question is how best tomeasure ecology. Without doubt, the approach taken in our study—exam-ining the influence of county-level measures of resource deprivation andracial segregation—precludes examination of whether residing in particu-lar areas within a county characterized by a given level of resource depri-vation or racial segregation influences recidivism. More generally, countiesundoubtedly do not capture key dimensions relevant to discussions ofcommunity or neighborhood effects, but as we discussed, they nonethelessreflect a meaningful difference in how diverse areas are socially organized.Moreover, the concern about aggregation bias can equally be applied to astudy that uses communities, however defined within a county, given thatin many areas, the composition of a community can vary dramatically fromone street to the next. Ultimately, a theoretically meaningful basis shouldexist for any given unit of analysis; at the same time, the power of anytheory lies in its ability to generate predictions across a range of units ofanalysis (Gibbs, 1997). In short, as scholars begin to examine the socialecological factors that influence recidivism, they should consider a widerange of potential factors (see, generally, Pratt and Cullen, 2005) and unitsof analysis. When a body of studies has accumulated that takes this broad-ranging approach, it then will be possible to identify consistencies acrossvarious measures of ecology and levels of aggregation and, in turn, todevelop more powerful theories.

A focus on released prisoners provides a strategic venue for testing andextending criminological theories. Clearly, for example, race remains acentral factor in American society (Warren et al., 2006), and symbolic andracial threat theories, along with work on “focal concerns” (Huebner andBynum, 2006; Steffensmeier, Ulmer, and Kramer, 1998), increasingly pointto the salience of select populations who are especially at risk of severesanctioning. As applied to the impacts of these sanctions, such a focus logi-cally generates the notion that these groups may be more exposed tocriminogenic social conditions or subject to more vigilant law enforce-ment. When coupled with the emphasis on social ecology, this theoreticalfocus develops an area of research in which considerable prospects for the-oretical advances exist, particularly investigations into how individual andecological factors intersect to influence both individuals’ behaviors and theresponses of the criminal justice system to them.

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330 MEARS, WANG, HAY & BALES

From a policy perspective, the findings here illustrate the problems asso-ciated with an individual-level bias in risk-prediction efforts to identifyindividuals most likely to recidivate (Kubrin and Stewart, 2006). This biasignores the possibility that social ecology may influence recidivism ratesthrough an influence on offending or law-enforcement behavior (Beckett,Nyrop, and Pfingst, 2006). In either case, a cause for concern develops. Ifcommunity conditions contribute to offending, then such conditions ide-ally should be taken into account when developing release plans. In addi-tion, if they disproportionately influence some groups more than others,then such influences should also be considered in efforts to reduce thelikelihood of recidivism. Not least, if differences in recidivism result fromdifferential law enforcement, then fairness would indicate that law-enforcement, courts, and other criminal justice system agencies take stepsto ensure that certain groups are not targeted, consciously or uncon-sciously, disproportionate to other groups.

Finally, this study underscores the need for increased attention to abasic methodological issue confronting studies of prisoner reentry—theextent to which officially recorded recidivism rates among individualsreflect true offending versus law-enforcement efforts and priorities. Mostrecidivism studies proceed on the assumption that such measures as rear-rest, reconviction, and reincarceration reflect offending only, not the dif-ferential likelihood of certain groups or communities to report crime orthe differential attention or responsiveness of law-enforcement, court, andcorrectional system agencies. These assumptions are problematic, as manyauthors have noted (e.g., Blumstein et al., 1986: 100–7; Gottfredson andTaylor, 1985: 149–50; see, more recently, Sampson, Laub, and Wimer,2006: 475). Consider, for example, the literature on community and “intel-ligence-led” policing, which indicates that police indeed target certainareas, and such areas almost invariably will have unique social, economic,and racial and ethnic characteristics (Grinc, 1994; Ratcliffe, 2003; Skogan,2003). In the current study, we introduced individual- and ecological-levelcontrols to address these possibilities. Ultimately, however, additionalsources of information—such as self-report survey data from released pris-oners and objective assessments of the beliefs, focus, and actual practicesof law enforcement and the courts—will be required to disentangle pre-cisely how much recidivism differences reflect offending versus formalsocial control efforts.

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Daniel P. Mears, PhD, is an associate professor at Florida State Univer-sity’s College of Criminology and Criminal Justice. He conducts basic andapplied research on a range of crime and justice topics, including studies ofjuvenile justice, supermax prisons, domestic violence, homicide, and pris-oner reentry. His work has appeared in Criminology, the Journal ofResearch in Crime and Delinquency, and Law & Society Review, and othercrime and policy journals.

Xia Wang, PhD, recently graduated from the Florida State UniversityCollege of Criminology and Criminal Justice and, in fall 2008, will be join-ing the faculty at Arizona State University’s School of Criminology andCriminal Justice. Dr. Wang is involved in studies of prisoner reentry, juve-nile justice education programs and their impacts, corporate crime, andthe use of multilevel and spatial analyses to test and extend criminologicaltheories. Her work has recently appeared in Evaluation Review and JusticeQuarterly.

Carter Hay, PhD, is an associate professor at Florida State University’sCollege of Criminology and Criminal Justice. His work, published in Crim-inology, the Journal of Research in Crime and Delinquency, and otherleading crime journals, includes studies of the role of parenting in delin-quency, testing the general theory of crime, and examining the role ofcommunity disadvantage in contributing to crime.

William D. Bales, PhD, is an associate professor at Florida State Uni-versity’s College of Criminology and Criminal Justice. Dr. Bales focuseson a range of crime and policy topics, including factors that contribute torecidivism, the effectiveness of electronic monitoring, and tests of labelingtheory. He has published in Criminology, Criminology & Public Policy,Justice Quarterly, and other crime and policy journals.

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340 MEARS, WANG, HAY & BALES

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