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JUSTICE QUARTERLY VOLUME 24 NUMBER 3 (SEPTEMBER 2007)

ISSN 0741-8825 print/1745-9109 online/07/030408-27© 2007 Academy of Criminal Justice SciencesDOI: 10.1080/07418820701485387

The Effect of Racial Inequality on Black Male Recidivism

Michael D. Reisig, William D. Bales, Carter Hay and Xia Wang

Taylor and FrancisRJQY_A_248415.sgm10.1080/07418820701485387Justice Quarterly0741-8825 (print)/1745-9109 (online)Original Article2007Taylor & Francis243000000September 2007MichaelReisigmreisig@fsu.edu

Macrostructural opportunity theorists posit that the unequal distribution ofeconomic resources across racial groups promotes animosities among disadvan-taged minorities, disrupts community integration, and fosters criminal activity.Guided by this framework, we hypothesize that Black ex-prisoners who reentercommunities with high levels of racial inequality are more likely to commit newcrimes. Support for this argument is found for a large group of males (N = 34,868)released from state prisons to 62 counties in Florida over a 2-year period. Wealso find evidence that racial inequality amplifies the adverse effects of person-level risk factors on recidivism for Black ex-inmates. In comparison, the effect ofinequality on White male recidivism is far less meaningful. These findings under-score the need for researchers to consider social context when studying recidi-vism among Black males, and also support the efforts of correctional reformerswho advocate for state resources to assist prisoner reentry.

Keywords crime; inequality; recidivism; reentry

Michael D. Reisig is an associate professor in the College of Criminology & Criminal Justice at FloridaState University. His interests include social ecology, procedural justice, and measurement. Hisresearch has appeared in a variety of journals, including Justice Quarterly, Criminology, Journal ofResearch in Crime & Delinquency, and Criminology & Public Policy. William Bales is an associateprofessor in the College of Criminology & Criminal Justice at Florida State University. His interestsinclude sentencing research, assessing the effectiveness and consequences of punishment andcorrectional strategies, and community re-entry issues among incarcerated adult and juvenile popu-lations. Carter Hay is an associate professor in the College of Criminology & Criminal Justice atFlorida State University. His research deals principally with the causes of individual involvement incrime and delinquency, especially those causes pertaining to the family environment. Recent publi-cations have appeared in Criminology, Journal of Research in Crime & Delinquency, and SociologicalPerspectives. Xia Wang is a doctoral candidate in the College of Criminology & Criminal Justice. Herprimary research involves the use of multi-level modeling techniques to investigate the socialecology of recidivism. This article was a collaborative effort. All four authors significantly contrib-uted to this work, and share equal responsibility for any mistakes. A previous version of this articlewas presented at the 43rd Annual Meeting of the Academy of Criminal Justice Sciences, Baltimore,MD. We thank Kristy Holtfreter and Dan Mears for commenting on previous versions of this manu-script. Correspondence to: Michael Reisig, College of Criminology & Criminal Justice, Florida StateUniversity, Tallahassee, FL, 32306-1127, USA. E-mail: mreisig@fsu.edu

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 409

Introduction

Beginning in the 1970s, new laws to punish convicted felons more harshly wereenacted throughout the United States. These changes contributed to a morethan 300 percent increase in the number of offenders entering American prisonsbetween 1980 and 2000 (Beck & Gilliard, 1995; Harrison & Beck, 2004). Perhapslittle appreciated at the time was the impact these statutes would eventuallyhave on the number of inmates leaving prison systems. Roughly 95 percent of allprisoners one day return to the community (Petersilia, 2003). Not surprisingly,the number of prison releases also grew substantially during this period, from170,000 in 1980 to more than 600,000 in 2000 (Visher & Travis, 2003).

The sheer volume of ex-inmates reentering our communities from prisondirects attention to how they fare upon release. As a general rule, recidivism isquite high. Within three years of release, more than 60 percent of former inmatesare rearrested, roughly 50 percent are convicted of a new crime, and 25 percentare returned to prison on a new sentence (Beck & Shipley, 1989; Langan & Levin,2002; Petersilia, 2003). These patterns have significant implications for thenation’s crime rate. Langan and Levin (2002, p. 5) estimate that released prison-ers account for as much as 11 percent of homicides, 10 percent of robberies, and12 percent of burglaries. On a more positive note, for the roughly 30 percent whoavoid legal trouble in the first 3 years after release, the chances of returning toprison at a later point are relatively low (Greenfeld, 1985).

A growing body of research seeks to explain these variations in recidivism.This work addresses a straightforward question: Why do some individualscontinue with crime upon their release, whereas others appear to pursue a morelaw-abiding lifestyle? A number of significant factors have been identified.Some demographic characteristics are important. For example, recidivism ishighest among males, African Americans, and those under the age of 18 whenreleased (Beck & Shipley, 1989; Langan & Levin, 2002). Also important is thereleased offender’s prior criminal history. Recidivism is greatest among thosewith more prior arrests and with convictions for property and drug offenses(Langan & Levin, 2002). The offender’s immediate postrelease circumstancesalso are consequential. Recidivism is more likely among those who did notcomplete high school (Visher, LaVigne, & Travis, 2004), are unable to find stableemployment (Uggen, 2000), and who lack strong family commitments (Curtis &Schulman, 1984; Fishman, 1986).

This research has advanced knowledge on the factors that help explain recid-ivism. With few exceptions, however, extant research focuses only on the indi-vidual characteristics of former prisoners, with almost no attention devoted tothe social or economic characteristics of the geographic areas to which releasedinmates return. This practice runs counter to the widespread recognition thatinvolvement in crime is affected not just by person-level attributes, but also bythe characteristics of the social environments in which people live. Indeed, agrowing body of criminological research is explicitly devoted to studying theeffects of contextual factors on individual behavior that are maintained even

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after controlling for sociodemographic characteristics that sort people intodifferent social contexts (Bellair & Roscigno, 2000; Cattarello, 2000; Elliottet al., 1996; Peeples & Loeber, 1994; Sampson, Morenoff, & Gannon-Rowley,2002; Stewart, Simons, & Conger, 2002).

Importantly, contextual research on criminal behavior generally has beenlimited to the study of crime and delinquency. To date, only a small number ofstudies (Gottfredson & Taylor, 1988; Kubrin & Stewart, 2006) have examinedthe role social context plays in recidivism. This is a significant oversight. AsVisher and Travis (2003) recently argued, social context may be especiallyimportant for former inmates, because their stay in prison attenuates their tiesto the economic and social institutions important to their successful reentry(also see Lynch & Sabol, 2001). This may leave former prisoners dependent uponthe prevailing structural conditions in the area to which they are returned.Social constraints, such as high levels of poverty or inequality, may greatlyencourage crime (Kubrin & Stewart, 2006; Rose & Clear, 2003). But other condi-tions, such as a strong job market or the availability of needed services, mayoffer a more genuine opportunity to desist from crime.

The present study addresses this void in the literature by examining theeffects of contextual variables on recidivism for a large cohort of releasedinmates. We focus principally on outcomes for the highest-risk group: Blackmales. Prior research indicates that recidivism rates are highest among AfricanAmericans (Langan & Levin, 2002). As discussed below, we find this to be thecase in our own data from the state of Florida. These patterns give specialpriority to research that considers why some Black males refrain from crimeupon release from prison, whereas others continue with crime and reenter thecriminal justice system. In addressing this issue, we also conduct analyses forWhite ex-prisoners to consider what insights emerge from comparing thoseresults with the findings obtained for African Americans.

For theoretical guidance, we draw from Blau and Blau’s (1982) macrostructuralopportunity theory, which has been used extensively in the study of Black involve-ment in crime (Balkwell, 1990; Messner & Golden, 1992; Wadsworth & Kubrin,2004). The theory focuses on economic inequities linked to race, and posits thatracial inequality will be perceived by disadvantaged African Americans as illegit-imate and will give rise to feelings of animosity that promote “diffuse aggression”(Blau & Blau, 1982, p. 119).

In drawing from this theory, we test two hypotheses regarding racial inequal-ity and Black male recidivism. First, we examine whether recidivism rates arehighest among Black ex-inmates who are released to areas with higher levels ofracial inequality. Second, we consider the potential conditioning role of socialcontext. We hypothesize that the presence of racial inequality will amplify theeffects of individual-level risk factors, such as having an extensive criminalrecord. Data from the Florida Department of Corrections on every Black andWhite male released from Florida state prisons from January 1999 to June 2001are used to conduct our analyses. Other data sources, such as the 2000 decennialcensus, are used to capture variations in the contextual variables of interest. We

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now turn to the objective of placing recidivism among Black males in an empiri-cal and theoretical context.

Race, Recidivism, and the Role of Social Context

The issue of race is salient to the discussion of prison reentry. While comprisingjust 13 percent of the US population, African Americans make up nearly half ofboth the prison population and the offenders reentering society from prison(Harrison & Beck 2004). Upon release, Black ex-prisoners also have significantlyhigher levels of recidivism relative to Whites. Langan and Levin (2002, p. 7) reportthat the percentage of released prisoners rearrested within three years is16 percent higher for African Americans. The differences are greater when consid-ering reconviction (18 percent higher for African Americans) and readmission toprison with a new prison sentence (26 percent higher for African Americans).These data support Petersilia’s (2003, p. 30) observation that when discussingprisoner reentry, “race is the elephant sitting in the living room.”

In considering what explains Black recidivism, one possibility is that AfricanAmericans released from prison possess individual characteristics that increasethe odds of recidivism, such as more extensive criminal histories and lowerlevels of education. While these differences may exist, they do not explainhigher recidivism rates among African Americans. Multivariate analyses oftenfind enduring effects of race even when controlling for key individual-levelpredictors of recidivism (Bales et al., 2005; Piquero, McDonald, & Parker, 2002;Smith & Paternoster, 1990; Spohn & Holleran, 2002; Wilson, 2005).

Another possibility is that recidivism among African Americans is closelylinked to the social contexts of the communities to which they are released.These areas may have significantly higher levels of poverty, inequality, andcrime, all of which may promote recidivism. While not assessing recidivismspecifically, a number of studies support this contextual approach to examiningthe race–crime link. These studies find that while higher Black rates of crimeare explained in part by indicators of individual-level characteristics, contextualvariables (e.g., poverty rate and the extent of inequality) also help explain theassociation between race and crime (Kaufman, 2005; Krivo & Peterson, 2000;McNulty & Bellair, 2003; Peeples & Loeber, 1994).

To apply this logic to the study of recidivism for released Black inmates, Blauand Blau’s (1982) macrostructural opportunity theory provides an importanttheoretical guide. The theorists begin by considering the historical importance ofpoverty in macrolevel criminological research. They argue, however, thatpoverty generally has been approached as an indicator of absolute deprivation(i.e., a measure of the extent of economic deprivation relative to some fixedstandard of physical well-being). Blau and Blau contend that the extent of rela-tive deprivation (or inequality) that characterizes a community may be just asimportant for crime. Relative deprivation is defined not in terms of a fixed stan-dard, but rather in terms of the inequality that exists between members of a

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population. According to this view, deprivation is a social construction that iscomparative in nature. Put differently, an individual is “poor” when they “cannotlive in ways which are ordinary for their own community” (Messner, 1982, p. 104).

Significant inequality should be important for crime because it reflects soci-etal or community conflicts “over the distribution of resources” (Blau & Blau,1982, p. 119). Those on the losing end lack the means necessary to mountsuccessful collective action. Frustration over their lower status builds and even-tually manifests itself in “diffuse aggression, with people being driven more byhostile impulses than governed by rational pursuit of their interests” (Blau &Blau, 1982, p. 119). The result, in short, should be higher crime.

This theory has special importance for the study of crime among disadvan-taged minorities. The authors contend that inequality is of greater consequencewhen it is closely associated with an ascribed characteristic such as race. Whenlarge economic differences exist between racial groups, minorities will perceivetheir group’s poverty as an illegitimate, inherited status that violates “the prin-ciple that all men are created equal” (Blau & Blau, 1982, p. 126). These percep-tions undermine the social integration of a community, and entire groups mayperceive the larger social structure through a lens of “injustice, discontent, anddistrust” (Blau & Blau, 1982, p. 119). While these perceptions could fuel collec-tive social action, the very inequality that evokes frustration also hinders such aresponse. A more likely outcome is that disadvantaged minorities will possess“latent animosities” that increase the likelihood of criminal involvement (Blau& Blau, 1982, p. 119).

Blau and Blau’s (1982) own analysis of 125 metropolitan areas found strongsupport for their arguments—racial inequality was positively associated withrates of violent crime. A similar conclusion was reached by both Balkwell (1990)and Messner and Golden (1992), with the latter study finding that racial inequal-ity increased both the total homicide rate and the rate of Black homicide (alsosee Pratt & Cullen, 2005). Other studies (Wadsworth & Kubrin, 2004) find thatracial inequality is not a significant predictor of crime rates or that the effectsare not especially robust (Golden & Messner, 1987; Parker & McCall, 1999).

Most notable for our purposes, however, is that all of these studies focus oncrime rates (primarily for homicide) for general populations. Systematic obser-vations of racial inequality on recidivism rates for African Americans releasedfrom prison are nonexistent. Contextual factors, such as inequality, should beconsequential for ex-offenders who find themselves in very precarious situations.Many released offenders have considerable anxiety about their release (Nelson,Deess, & Allen, 1999), and most are provided little or no governmental assistanceupon reentry (Visher & Travis, 2003). Moreover, gaining legitimate employmentis difficult because their stay in prison has likely weakened their connections tolabor market opportunities, depleted their work skills, and invited greater scru-tiny and suspicion from potential employers (Visher & Travis, 2003).

These problems are likely to be especially challenging for Black ex-offendersreturning to communities characterized by race-based inequality. As Blau and Blau(1982, pp. 118–119) argue, racial inequality has the likely effect of “consolidating

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and reinforcing ethnic and class differences.” Thus, Black prisoners released tocommunities with high racial inequality may face a compounding of risk for recid-ivism—they will be hindered not simply by the normal challenges associated withprisoner reentry, but also by membership in a racial group that is isolated fromemployers, health care services, and other institutions that can facilitate a law-abiding reentry into society. High levels of Black recidivism may be an importantprice a community pays for economic inequities linked to race.

Although Blau and Blau (1982) provide a compelling account of the relation-ship between racial inequality and Black recidivism, we should note an alterna-tive theoretical view of this relationship. Some extensions of Blalock’s (1967)racial threat theory (Eitle, D’Alessio, & Stolzenberg, 2002; Liska & Chamlin,1984) emphasize that racial inequality could increase Black recidivism notbecause it increases actual crime, but because it triggers the increased use offormal social controls. A pattern that supports this possibility is the relationshipbetween the size of racial and ethnic minority populations and the per capitanumber of police officers in US cities (Jackson, 1989; Jacobs, 1979; Kent &Jacobs, 2005). If a strong presence of African Americans is sufficient to increasepolice presence, it is conceivable that high levels of racial inequality could dolikewise. Put simply, Black poverty in the midst of White affluence might leadWhite members of the population to feel especially threatened by crime and topressure political authorities to crack down on it. Greater Black recidivism inareas with high racial inequality could therefore reflect pressures to increasepublic social controls. Although an exhaustive test of this variant of the racialthreat hypothesis is beyond the scope of the present analysis, we conduct ouranalyses in a way that seeks to account for this possibility.

Present Study

This study examines two hypotheses regarding racial inequality and recidivism forreleased Black prisoners. First, we consider whether racial inequality has a directeffect. Are recidivism rates higher among former Black male inmates released toareas characterized by higher levels of racial inequality? And if so, is this the caseonly for Black ex-inmates, or is it true for Whites as well? Second, we determinewhether racial inequality plays an important conditioning role. Does racialinequality amplify the effects of key individual-level risk factors, such as havingan extensive criminal record, being young, and having low education?

Data and Methods

This study uses data from four sources. First, the Offender-Based InformationSystem (OBIS) from the Florida Department of Corrections provides informationabout released prisoners, such as demographics, criminal history, institutionalmisconduct, and recidivism. In all, we gleaned data on every Black and White

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male released back to 62 of the 67 counties from Florida state prisons fromJanuary 1999 to June 2001 (N = 34,868). Black former inmates constitute 61.6percent of the total sample (N = 21,484). Five counties had fewer than 10 Blackex-inmates released to them during the two-year study period. Including thesecases could adversely affect the reliability of our multilevel model estimates(Raudenbush & Sampson, 1999, p. 9). The average number of inmates releasedto each county was 346.51 for the Black sample (range = 11–2,892), 215.87 forthe White sample (range = 10–1,128), and 562.39 for the combined sample (range= 27–4,020) (see Appendix A for details). In the results section, we investigatewhether the unbalanced distribution of prisoners reentering Florida countiesduring the study period influenced the effects of racial inequality on reconviction.

A second data source, the 2000 decennial census, is used to capture varia-tions in structural characteristics (e.g., racial inequality and deprivation)between the counties to which ex-prisoners returned after release from prison.Finally, information on county-level policing deployments was obtained fromthe Florida Department of Law Enforcement (FDLE), and violent-crime datafrom the Uniform Crime Reports (UCR).

Dependent Variable

Recidivism is operationalized as instances where ex-inmates are convicted of anew felony resulting in correctional supervision (i.e., local jail, state prison,and/or community supervision) for the 2 years following release from prison.The outcome measure, reconviction, is dummy-coded (1 = yes, 0 = no). Duringthe 2-year study period, 39 percent of the sample was convicted of a felonyand placed on correctional supervision (45 percent of African Americans and30 percent of Whites).

Contextual Variables

Data from the 2000 census are used to construct several different county-levelindependent variables. Consistent with Wadsworth and Kubrin (2004, p. 658),racial inequality consists of the following items: ratio of White to Black medianfamily income, ratio of Black to White joblessness rates, and ratio of Black toWhite poverty rates. Using the Kaiser–Guttman (or K1) criterion (eigenvalue>1.0), the principal-components analysis reveals these items load on a singlelatent construct: the eigenvalue (λ) is 1.80, and the factor loadings exceed .65.Different composite measures of absolute economic deprivation are constructedfor each sample (i.e., Black, White, and combined) and will be used separately.Economic deprivation, which is a race-neutral measure, is constructed for theanalyses using the combined sample (i.e., both White and Black ex-inmates) andconsists of the following census items: percent of Black and White population inpoverty (factor loading = .93), percent of Black and White families receiving

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 415

public assistance (factor loading = .92), and median family income (factor load-ing = −.90; λ = 2.52). Black deprivation is computed using percent Black popula-tion in poverty (factor loading = .93), percent Black families receiving publicassistance (factor loading = .85), and Black median family income (factor load-ing = −.95; λ = 2.49). Finally, percent White population in poverty (factor load-ing = .89), percent of White families receiving public assistance (factor loading= .90), and White median family income (factor loading = −.89) are used toconstruct White deprivation (λ = 2.39).1 Measures of relative and absolutedeprivation are operationalized as weighted factor scores and have means ofzero and standard deviations (σ) of one.

In Florida, population density and racial/ethnic composition vary widely fromone county to the next. For example, Miami-Dade’s population in 2000 was over2.2 million, and approximately 79 percent of county residents were racial/ethnic minorities. In comparison, the population in Holmes County, which islocated in the panhandle, was under 20,000 and 89 percent Caucasian. Othercounties have relatively small populations (i.e., below 50,000), but are racially/ethnically heterogeneous (e.g., Madison and Gadsden counties). Because ofthese between-county variations, we include two additional measures. Ethnicheterogeneity is constructed by multiplying the percentage non-White residentsby the percentage of White residents in each county (M = .15, σ = .05) (seeRountree & Land, 1996, p. 1360). Urbanism is a weighted factor score featuringthree census items: percentage of population in urban areas, total population,and population per square mile (see Sampson & Laub, 1993, p. 298). The urban-ism factor has an eigenvalue of 2.20 and factor loadings exceeding .80. The QQ-normal plot for urbanism reveals a skewed distribution. To induce normality, weadjust the distribution using a natural log transformation (M = .20, σ = .66).2

We anticipate that violent-crime rates might also influence reconviction like-lihood. Violent-crime rate reflects UCR reported incidents of homicide, robbery,aggravated assaults, and forcible rape per 100,000 citizens. Rates are averagedover 3 years (i.e., 1999, 2000, and 2001) to minimize the impact of year-to-yearfluctuations (M = 642.46, σ = 246.23). Finally, to account for the possibility thateffects of racial inequality on reconviction are a function of variations in formalsocial controls (Blalock, 1967; Liska & Chamlin, 1984), police presence (theaverage number of police officers per 100,000 citizens) is included as a county-level control variable (M = 221.30, σ = 50.63) (see Parker, 2004, p. 628).

1. The mean intercorrelation between the three absolute deprivation factors is .74. Between Whiteand Black deprivation specifically, the bivariate relationship is .63. Although this estimate indicatesa moderately strong association, it is not sufficiently high to conclude that only trivial differencesexist between the economic deprivations experienced by White and Black Floridians. Put differ-ently, these measures possess discriminant validity and will be used in the present study to detectthe potential effect of race-specific economic deprivation. The race-neutral factor will be usedwhen investigating the influence of county-level factors on reconviction for the combined sample.2. Urbanism scores originally ranged from −1.23 to 3.47. Adding a constant term eliminates negativevalues, which is necessary to perform a natural log transformation. Although various values wereattempted, we use 1.5 because it produces a skewness statistic closest to zero. QQ-normal plots forthe other county-level variables (e.g., violent crime rate) reveal more normal distributions.

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Individual-Level Variables

We control for a variety of offender characteristics to increase confidence thatour estimates of the relationship between county characteristics and reconvic-tion are unbiased. Because prior research has consistently shown that priorcriminal activity and justice system involvement are linked to recidivism events(see, e.g., Gendreau, Little, & Goggin, 1996, pp. 581–586), we enter a numberof items from the OBIS into a principal-components analysis for all threesamples. In each sample, two latent constructs emerge. The first component,criminal record, includes the number of prior felony convictions resulting instate correctional supervision, number of prior felony convictions followingrelease from prison resulting in imprisonment, and an overall prior felonyconviction seriousness score.3 Eigenvalues are acceptable for the Black (λ =2.28), White (λ = 2.18), and combined (λ = 2.23) samples (factor loadings >.70).The second component, incarceration history, exhibits high loadings for thenumber of months served in prison (>.70), the number of prison disciplinaryinfractions (>.80), and the custody level at the time of release4 (>.50). Theeigenvalues exceed 1.0 in each of the three samples. Both criminal record andincarceration history are operationalized as weighted factor scores. Formerprisoners’ age (i.e., age in years at the time of release from prison) and educa-tion level are also included in the analyses. Education level is operationalizedusing scores from the Test of Adult Basic Education (TABE), which measures aperson’s grade level in three subjects (i.e., reading, math, and language),which was administered prior to release. Table 1 displays the descriptive statis-tics for the ex-inmate-level independent variables.

Analytic Strategy

The primary objective is to determine whether racial inequality influencesreconviction rates for Black male ex-inmates. A traditional approach to thisquestion would involve using ordinary least-squares (OLS) to regress county-level reconviction rates onto a set of contextual covariates. But these data havea two-level hierarchical structure, so such a strategy would be less than optimalbecause it fails to take into account differences between released prisonersshown to predict recidivism (e.g., criminal history). Another approach would beto estimate a person-level logistic regression model, but doing so discounts thepossibility that ex-inmates nested in the same counties have shared experiencesthat influence their behavior. To address these and other limitations, we make

3. Under Florida’s Criminal Punishment Code, sentencing points are assigned based on the primary(i.e., most serious) offense before the court. Our seriousness scores reflect the 1999 and 2000sentencing guidelines offense points assigned to 52 different offenses (see Burton et al., 2004 fordetails).4. Custody level is coded so that higher scores reflect higher levels of custody (i.e., 1 = community,2 = minimum, 3 = medium, and 4 = close).

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 417

use of hierarchical modeling techniques, which incorporate a unique randomeffect into the statistical model for each county, that produce more robuststandard errors (Raudenbush & Bryk, 2002, p. 100).

We are also interested in the possibility that racial inequality escalates thedetrimental effects of ex-offender level attributes, such as criminal record, onreconviction. To explore this issue, we employ cross-level (or macro-micro)interaction techniques (see Kreft & De Leeuw, 1998, p. 12). Before we pursueeither of these two objectives, we need to determine whether reconvictionlikelihood varies across counties. This is where we turn our attention after firstinspecting the distribution of racial inequality and Black male reconvictionacross counties and the bivariate relationships between the variables used inthe multivariate models.

Results

Mapping Reconviction and Racial Inequality

We begin our investigation by visually assessing the way our two key measures,racial inequality and Black male reconviction rates, are distributed across Florida.To do so, we group these data into four equal groups (or quartiles) that range from“low” (first quartile) to “high” (fourth quartile). Figure 1 reveals that high levelsof racial inequality are present in the northern, central, and southern parts ofthe state. Counties with high levels of racial inequality are scattered throughoutthe northern section of the state, but appear more concentrated along the gulfand atlantic coast in south Florida. When comparing the two maps, we see that

Table 1 Descriptive statistics for ex-inmate characteristics

Black sample White sample Combined sample

(N = 21,484) (N = 13,384) (N = 34,868)

M M M

Variable (SD) (SD) (SD)

Reconviction .45 .30 .39(.50) (.46) (.49)

Age 32.17 33.73 32.77(9.12) (9.86) (9.44)

Education 6.31 8.99 7.34(2.89) (3.10) (3.25)

Criminal recorda .00 .00 .00(1.00) (1.00) (1.00)

Incarceration historya .00 .00 .00(1.00) (1.00) (1.00)

aWeighted factor score.

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32.3 percent (N = 20) of the county classifications match; that is, the quartilesfor both racial inequality and reconviction are the same. For example, the levelof racial inequality in Palm Beach County is in the upper quartile, as is the county’sBlack reconviction rate. Overall, forty-four counties (71 percent) have reconvic-tion rates and inequality levels that are within one quartile of each other.Figure 1 Distibution of racial equality and reconviction rate for Black males across 62 Florida states.Three of the counties in Figure 1 have drastically different levels of racialinequality relative to their Black reconviction rates. For example, TaylorCounty, which is located approximately 51 miles from the capital along the gulfcoast shoreline, has high racial inequality (upper quartile) but their Black recon-viction rate is in the lower quartile.

Two counties (i.e., Osceola and Monroe) have levels of racial inequality inthe lower quartile and Black reconviction rates in the upper quartile. OsceolaCounty is located to the south of the greater Orlando area. Monroe County is thesouthernmost county in the United States and is home to the Florida Keys.Overall, however, the similar distribution of the two key measures acrossFlorida seems to indicate that a moderate relationship exists between racialinequality and Black male reconviction. To investigate this matter more system-atically, we make use of quantitative techniques.

Bivariate Findings

The zero-order correlation matrices presented in Table 2 feature reconvictiondata aggregated to the county level, as well as the contextual variables.5

Because of the small sample size (N = 62) and our interest in the directional

5. Zero-order correlations for the combined sample are provided in the Appendix B.

Figure 1 Distibution of racial equality and reconviction rate for Black males across 62Florida states.

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 419

hypothesis concerning racial inequality and reconviction, we employ one-tailedsignificance tests (p < .05) for the analyses in Table 2. The results in the corre-lation matrix below the diagonal show that reconviction rates among Blackmales are higher in counties with high levels of racial inequality (r = .26). Noneof the other five contextual variables are significantly correlated with reconvic-tion for Black ex-prisoners. In short, we find empirical support for the hypothe-sis that reconviction rates for Black males are highest in counties where adverseeconomic conditions (e.g., income, joblessness, and poverty) disproportion-ately affect Black families. In the correlation matrix located above the diago-nal, we find that racial inequality has no impact on White male reconvictionrates (r = −.08).

Table 2 also presents bivariate correlations between the county-level inde-pendent variables. Racial inequality is positively associated with Black depriva-tion (r = .28) and is inversely related to White economic deprivation (r = −.44).Levels of absolute deprivation are higher in more rural, less densely populatedcounties for Black (r = −.48) and White families (r = −.56). Violent-crime ratesare significantly higher in counties that are ethnically heterogeneous and moreurban, and where the police-officer-to-citizen ratio is higher. Finally, policepresence and violent crime rate are more modestly correlated with racialinequality.

The correlation coefficients between the county-level independent variablessuggest that collinearity should not be a problem in the multivariate analysis.We do not rely solely on bivariate relationships to assess collinearity, but alsomake use of several diagnostic statistics (Belsley, Kuh, & Welsch, 1980). Thetolerance estimates exceed .60 for the Black sample and .50 for the Whitesample. The lowest tolerance estimate (.38) was for economic deprivation inthe combined sample, but the other tolerances in the combined sampleexceed .50. Using established guidelines, these results appear acceptable (seeMenard, 1995, p. 66). The variance inflation factors are all below 3, whichalso suggest an absence of harmful collinearity (see Kennedy, 1992, p. 183).Finally, we evaluated condition indexes for all three samples, the results ofwhich indicate acceptable levels of collinearity (see Tabachnick & Fidell,2001, p. 85).

Hierarchical Logistic Regression Models

To begin, we estimate unconditional models with random effects to gaugelevels of between-county variance in reconviction. The model can be expressed:Logit (reconviction) = β0j, where β0j = γ00 + u0j, u0j ∼ N (0, τ00). Here, γ00 is theaverage log-odds of reconviction across counties, and τ00 is the between-countyvariance for reconviction. The results reveal the variance of u0j is greater thanzero for all three samples, leading to the conclusion that sufficient levels ofbetween-county variance in the reconviction likelihood exists for the Black,White, and combined samples.

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420 REISIG ET AL.

Tabl

e 2

Coun

ty-l

evel

var

iabl

e co

rrel

atio

n m

atri

ces

for

Blac

k an

d W

hite

ex-

inm

ate

sam

ples

(N

= 6

2)

Reco

nvic

tion

ra

teW

hite

de

priv

atio

nRa

cial

in

equa

lity

Ethn

ic

hete

roge

neit

yPo

lice

pres

ence

Viol

ent-

crim

e ra

teU

rban

ism

Reco

nvic

tion

rat

e–

−.06

−.08

.07

−.04

.02

.14

Blac

k de

priv

atio

n−.

18–

−.44

*.2

3*−.

02−.

11−.

56*

Raci

al in

equa

lity

.26*

.28*

–−.

01.2

.2.0

9Et

hnic

het

erog

enei

ty−.

07.1

9−.

01–

.18

.43*

−.11

Polic

e pr

esen

ce.0

1.0

7.2

.18

–.2

2*.0

1Vi

olen

t-cr

ime

rate

.08

.1.2

.43*

.22*

–.2

6*U

rban

ism

.18

−.48

*.0

9−.

11.0

1.2

6*–

Not

e. B

ivar

iate

ass

ocia

tion

s fo

r th

e Bl

ack

sam

ple

are

loca

ted

belo

w t

he d

iago

nal o

f th

e co

rrel

atio

n m

atri

x, a

nd t

he c

oeff

icie

nts

for

the

Whi

te s

ampl

e ar

e fe

atur

ed a

bove

the

dia

gona

l. E

stim

ates

for

the

com

bine

d sa

mpl

e ar

e pr

ovid

ed in

App

endi

x B.

*p <

.05

(on

e-ta

iled

test

).

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 421

The unconditional model also provides the information necessary to deter-mine how much of the total variance in reconviction lies between counties. Inhierarchical linear modeling, the intraclass correlation (ICC) is useful in assess-ing levels of between-county variance, but this estimate is problematic whenestimating hierarchical logistic models because the level-1 variance isheteroscedastic (Raudenbush & Bryk, 2002, p. 298). But if we conceive thelevel-1 model in terms of an unobserved or latent continuous variable, where Zij= Logit (reconviction) + rij, where rij is the level-1 random effect that isassumed to have a standard logistic distribution with a mean of 0 and varianceπ2 / 3 (Long, 1997, pp. 40–50, 69–71), we can compute ICC coefficients using thefollowing formula: ρ = τ00 / (τ00 + π2 / 3) (Raudenbush & Bryk, 2002, p. 334).This method yields ICC estimates of .01 for reconviction for all three samples,which indicates that approximately 1 percent of the total variation in reconvic-tion is between counties. Although relatively small, there are several reasonsfor concluding that the ICC estimates are sufficient. First, it has been arguedthat aggregate effects that explain only small amounts of total variance aretheoretically significant if the variable under observation operates “over a widerange of research areas and dependent variables” (Liska, 1990, p. 299). Theprimary independent variable of interest in this study—racial inequality—hasbeen used in several fields of study to predict a variety of outcomes. But note,too, that ecological researchers have found that modest levels of variancebetween aggregate units can still admit medium effect sizes (see Duncan &Raudenbush, 1999, p. 33). Finally, the ICC tells us little about the potentialconditioning role of racial inequality with person-level risk factors, such ascriminal history. On balance, the evidence suggests that estimating more satu-rated multilevel reconviction models is an appropriate next step.

We continue by estimating combined hierarchical logistic regression modelsto simultaneously investigate the effects of both county and ex-inmate levelfactors on reconviction likelihood. As mentioned previously, reconviction is abinary variable, so the ex-inmate (or level 1) model is expressed:

where β0j is the intercept, βkj is the effect of variable k on the log-odds ofreconviction for county j, and (Xkij − k) represents the value of variable k foroffender i in county j. The county-level model takes the form:

Here, γ00 is the average log odds of reconviction across counties, and γ0m repre-sents the effect of the contextual variable, Wmj, on the intercept in county j.This model is specified with an error term, u0j, which represents the uniqueincrement to the intercept associated with county j. Ex-prisoner and county-level variables are grand-mean-centered.

Logit (reconviction) = + − + + −β β β0 1 1 1j j ij kj kij kX X X X( ) ( ),K

X

β γ γ γ τ0 00 01 1 0 0 0 000j j m mj j jW W u u N= + + + + K , ~ ( , ).

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Table 3 presents the reconviction models for each sample.6 With both ex-inmate and county-level variables employed, the amount of between-countyvariance explained differs across all three models (i.e., 51 percent for Blacks,16 percent for Whites, and 60 percent for the combined sample). Looking to thereconviction model for the Black sample, we see that racial inequality signifi-cantly influences reconviction likelihood for Black male ex-inmates, indepen-dent of the person-level attributes and other county contextual factors.7

6. The hierarchical logistic equations in Table 3 are unit-specific models with robust standard errors.The unit-specific model is better suited for our interest in the within-county effects of ex-inmatelevel variables on reconviction likelihood than other alternatives, such as the population-averagemodel (see Raudenbush & Bryk, 2002, pp. 301–304).7. Because relying on a single observation period, such as one year, can result in misleading andbiased recidivism estimates (see Maltz, 1984, pp. 73–75), we culled reconviction data for everymember of the sample from OBIS for three observation periods (i.e., 1, 2, and 3 years followingrelease). The sign and significance for all of the county and ex-inmate level variables were veryconsistent across time periods for each sample. Concerning the effect of racial inequality on recon-viction for Black ex-inmates, we used Clogg, Petkova, and Haritou’s (1995) test of explanatoryinvariance. We find that the effect of racial inequality does not vary significantly across the 1-, 2-,and 3-year observation periods.

Table 3 Hierarchical logistic regression models for reconviction

Black sample White sample Combined sample

Variable b SE b SE b SE

Intercept −.30** .03 −.98** .04 −.81** .04Ex-inmate levelAge −.05** 0 −.03** 0 −.04** 0Education −.06** .01 −.05** .01 −.06** 0Criminal record .34** .03 .38** .02 .34** .02Incarceration history −.01 .02 −.06** .02 −.02 .02Race (1 = Black) – – – – .41** .04County levelRacial inequality .09** .03 .01 .04 .01 .02Black deprivation −.10* .04 – – – –White deprivation – – .03 .05 – –Economic deprivation – – – – −.08* .04Ethnic heterogeneity −1.06 .56 .7 .83 .34 .48Urbanism .02 .04 .16* .07 .04 .04Police presencea .12 .07 .05 .07 .09 .05Violent crime rateb .05 .12 −.22 .18 −.06 .1Random effectsτ00 .01 .02 .01χ2 109.63** 98.53** 107.64**

Note. Entries are unstandardized coefficients (b).aCoefficients and standard errors multiplied by 100.bCoefficients and standard errors multiplied by 1,000.*p < .05; **p < .01 (two-tailed test).

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 423

Because racial inequality is centered at zero, the results indicate that thelikelihood of reconviction within 2 years is 10 percent higher for every one stan-dard deviation increase in the racial inequality index. Racial inequality explains16 percent of between-county variance associated with reconviction in theBlack sample.8 Consistent with the bivariate findings presented in Table 2, wesee that racial inequality does not affect White male ex-prisoners in the samefashion. Similarly, in the combined sample featured at the far right side ofTable 3, we observe that the relationship between racial inequality and recon-viction is not significant.9

It was noted previously that the number of prisoners released from state facil-ities to the 62 counties included in this study varied widely. We now addresswhether variation in the number of inmates returning to these counties adverselyaffected the results for the county-level variables, especially the effect of racialinequality on Black male recidivism. To do so, we estimate an aggregateweighted least-squares (WLS) regression model for all three samples where thereconviction rate is regressed onto the county-level cluster of variables used inTable 3. Each county is weighted by the inverse of the square root of the numberof inmates released to it during the observation period. Doing so gives greaterweight to counties where fewer ex-prisoners were observed. Weighting strate-gies of this type have been used previously by ecological researchers to handlesimilar situations (see Sampson & Raudenbush, 1999; Terrill & Reisig, 2003). Forthe Black sample, the results show that racial inequality influences reconvictionrates (b = .02, p < .05), net of five county contextual variables. The standardized

8. For the Black sample, the ex-inmate covariates explain 17 percent of between-county variance inreconviction likelihood. The amount of between-county explained variance increases to 35 percentwhen Black deprivation, ethnic heterogeneity, police presence, violent crime rate, and urbanism areadded to the model.9. Of growing concern among ecological researchers are the potential problems associated withspatial dependence (Kubrin & Weitzer, 2003, pp. 393–395). We acknowledge that recidivism rates inone county may be influenced by recidivism in a neighboring county. This may happen for a varietyof reasons, including the fact that ecological predictors of recidivism (e.g., racial inequality) canoverlap county boarders. We test for spatial autocorrelation. Following Baller et al. (2001, p. 572),we employ a nearest-neighbor criterion calculated from the distance between county centroids.Using different neighbor weight matrices for 5, 6, and 10 nearest neighbors (all weights = 1, andlarger counties have larger weights), we first compute Global Moran’s I statistics on the raw recon-viction rates. We use the S-plus spatial module for 1,000 permutations for each Moran’s I statistic.For the Black sample, none of the Moran’s I coefficients are statistically significant (p > .05). Thus,we can conclude that no spatial autocorrelation exists. We also compute Moran’s I statistics usingEmpirical Bayes (EB) adjusted rates. These results are very similar to the results observed using rawconviction rates. But, as indicated by Moran’s I statistics, significant spatial autocorrelation existsfor the White and combined samples. To investigate this matter, we proceed by estimating an aggre-gate simultaneous spatial autoregressive model (or spatial lag model) with the White sample wherethe reconviction rate is regressed onto the county-level cluster of variables used in Table 3.Compared with OLS estimation, the coefficient for racial inequality in the spatial lag model did notchange significantly in magnitude or in significance level. Moreover, the likelihood-ratio test on thespatial autoregressive parameter (or spatial lag term) indicates that, after controlling for ourcounty-level covariates, the parameter estimate is not significantly different from zero. Similarresults were found for the combined sample. Therefore, after controlling for the county-level cova-riates, there is no longer a significant spatial autocorrelation. We conclude, therefore, that spatialautocorrelation does not bias the estimates reported in Table 3.

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regression coefficient (β) is .31. In the WLS models for the White and combinedsamples, the relationship between racial inequality and reconviction rate is notsignificant. These findings suggest that the uneven pattern of inmate releaseacross counties does not adversely affect the results regarding the relationshipbetween racial inequality and recidivism.

An alternative procedure for assessing the influence of racial inequality onBlack male recidivism with these data is to estimate a cross-level interactionterm using the combined sample of White and Black ex-prisoners. To do so, wefirst re-estimate the combined sample model in Table 3, but this time allow therace variable in the ex-prisoner model to vary across counties. We find evidencethat the race slope varies across counties (variance component = .03508, χ2 =121.34, p < .01), so we proceed by specifying the race slope as a function of racialinequality. Doing so entails setting up a cross-level interaction term to determinewhether the effect of race is conditioned by racial inequality. The results showthat the effect of race on reconviction is significant, and that the effect of racialinequality on the race slope is also statistically significant (b = .09, p ≤ .05, two-tailed test). This finding provides additional evidence that Black former-inmatesresiding in counties with higher levels of racial inequality are significantly morelikely to be convicted of a new crime within 2 years relative to other ex-inmatesliving in counties with less relative economic deprivation.

Returning to Table 3, we see that the effect of Black deprivation on reconvic-tion is negative and statistically significant, which is contrary to expectations.Two factors may help explain this finding. The first concerns financial resources.Specifically, counties with high Black deprivation scores may, in relative terms,lack the tax bases necessary to effectively fund public social control agencies(e.g., policing and judicial systems). So, the observed negative relationship doesnot reflect a connection between absolute deprivation and desistance fromcrime for African American males, but rather a lower likelihood that re-offendingresults in arrests and convictions in counties with insufficient governmentalresources. Even though we control for police presence, we are not able to controlfor other measures reflecting resource deprivation in Florida justice systems. Wemust also consider that police–citizen relationships in racially segregated, disad-vantaged communities are oftentimes tenuous (Reisig & Parks, 2000, 2003, 2004;Sampson & Bartusch, 1998). Citizen mistrust of legal agents and their reluctanceto assist officials makes apprehension and conviction of ex-prisoners who re-offend more difficult. We argue that some combination of these two factorsdrives the Black deprivation finding in Table 3. But until our contention issubjected to future empirical scrutiny, it remains conjectural.

The ex-prisoner level estimates in Table 3 indicate that criminal record isthe most influential predictor of reconviction in all three models, including thesample of Black males released from prison.10 The odds ratio indicates that the

10. The correlations between the offender-level variables are all below .40 (see Appendices B andC), and the tolerance estimates (range from .80 to .99), variance inflation factors (range from 1.01to 1.24), and condition indexes are all within acceptable bounds. Given these findings, we concludethat harmful collinearity is not a concern in the ex-inmate (or level 1) model.

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 425

likelihood for Black males being convicted of a new felony within 2 years ofrelease from state prison increases 40 percent with every one standard devia-tion increase in criminal record. Similar results are observed for the other twosamples. Age and education are inversely associated with reconviction for bothWhite and Black ex-inmates. Incarceration history, in contrast, is not a statisti-cally significant predictor of Black male recidivism. Given the predictiveaccuracy of racial inequality at the county-level and the three significant ex-offender level predictors for Black males exhibited in Table 3, we next considerwhether these factors interact so that the detrimental effects of racial inequal-ity observed to this point are most pronounced for Black males who are younger,have more extensive criminal histories, and have low education.

Does Social Context Condition Individual-Level Risk Factors?

Turning attention to the potential cross-level interaction effect of communitycontext, such as racial inequality, and individual-level risk factors on reconvic-tion within 2 years, we begin by estimating a random-coefficient model usingthe sample of released Black prisoners (see Rountree, Land, & Miethe, 1994).According to our specification, the four ex-offender variables are allowed tovary across counties. The results reveal that the slopes for three variables varysignificantly across counties: age (variance component = .01193, χ2 = 90.53, p <.01), education (variance component = .00005, χ2 = 84.95, p < .05), and criminalrecord (variance component = .00514, χ2 = 89.98, p < .01). The incarcerationhistory slope did not vary (variance component = .00209, χ2 = 67.28, p = .27).Next, we add cross-level interaction terms to the hierarchical model by specify-ing the slopes for criminal record, education, and age as functions of all sixcontextual variables. Because we found no evidence that the slope for incarcer-ation history varied across counties, we specify this variable as fixed. Thecounty-level measures are also modeled on the intercept. The results reveallittle evidence of county-level conditioning effects. In Table 4, we present areduced unit-specific hierarchical model featuring the significant cross-levelinteraction terms. We see that police presence amplifies the negative effect ofeducation on individual reconviction likelihood. Simply put, Black ex-prisonerswith higher TABE scores are less likely to be reconvicted in counties with agreater police presence. We see too that Black deprivation moderates the crim-inogenic effect of criminal record: Black released prisoners with more extensivecriminal records are less likely to be arrested and convicted of new offenses incounties where a greater proportion of Black families suffer economic hard-ships. Finally, the effect of racial inequality is positive and statistically signifi-cant on the criminal record slope, which suggests that relative deprivationintensifies the criminogenic effect of criminal record. In short, evidence fromthe cross-level interactions shows that the effects of age, education, and incar-ceration history on reconviction are not conditioned by racial inequality forBlack males. But we do find evidence that the link between criminal record and

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reconviction among Black males is most pronounced in Florida counties charac-terized by high levels of racial inequality.

Because of our interest in racial inequality, we proceed by plotting the condi-tioning effects of racial inequality on criminal record along a prediction graph(see Luke, 2004, pp. 57–58). More specifically, we explore the influence of crim-inal record on reconviction under three distinct conditions of racial inequality,net of person- and county-level variables (see Figure 2). We plot predictedprobabilities for two counties at opposite ends of the racial inequality contin-uum. Hardee County, which is located in central Florida (population = 26,938),has the lowest level of racial inequality (factor score = −1.94). At the oppositeend of the racial inequality continuum (factor score = 2.64) is Saint John’sCounty (population = 123,135), which is in the northern part of the state alongthe Atlantic coast. We also plot the predicted probability of reconviction formean levels of racial inequality.Figure 2 Predicted reconviction probabilities by level of racial inequality.Figure 2 shows that criminal record has a positive effect on reconviction at allthree levels of racial inequality. But Figure 2 also reveals that while the proba-bility of reconviction among Black males does increase in Hardee County, therate of increase is comparatively modest. In contrast, the rise of the SaintJohn’s County line is less linear, increasing rapidly before leveling off at the

Table 4 Cross-level interactions for the Black sample

Variable Coefficient SE Odds ratio t-ratio

Intercept, γ00 −.29 .03 .75 −8.73**Black deprivation, γ01 −.10 .04 .91 −2.42*Racial inequality, γ02 .09 .03 1.1 3.03**Police presencea, γ03 .13 .07 1 1.7Ethnic heterogeneity, γ04 −.93 .62 .39 −1.51Urbanism, γ05 .02 .05 1.02 .47Violent-crime rateb, γ06 .02 .13 1 .15

Age, γ10 −.05 0 .95 −18.26**Education, γ20 −.06 .01 .95 −9.25**

Police presencea, γ23 −.04 .02 .99 −2.24*Criminal record, γ30 .33 .02 1.39 13.89**

Black deprivation, γ31 −.05 .02 .95 −2.04*Racial inequality, γ32 .06 .02 1.07 3.00**

Incarceration history, γ40 −.01 .01 .99 −.65Random effects Variance component χ2

Intercept, τ00 .01194 94.12**Age, τ11 .00008 86.65*Education, τ22 .0001 85.02*Criminal record, τ33 .00406 75.87

aCoefficients and standard errors multiplied by 100.bCoefficients and standard errors multiplied by 1,000.*p < .05; **p < .01 (two-tailed test).

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 427

upper boundary. This indicates that 100 percent of the Black males with themost serious criminal records in Saint John’s County are predicted to be recon-victed. In Hardee County, in contrast, the model predicts that approximatelytwo-thirds of the most serious offenders will be reconvicted. Overall, criminalrecord has a more detrimental effect on the odds of successful reentry whenBlack males exit prison into conditions of increased racial inequality.

Discussion

In following a large cohort of males released from Florida prisons, we observedthat racial inequality in counties where these ex-inmates were released impactsrecidivism among Black males. Specifically, we observed a significant directeffect of racial inequality on reconviction. We found also that racial inequalityamplified the effects of criminal history on reconviction.

One implication of these findings is that future recidivism research shouldaccount for the social context to which Black ex-inmates are released. Prisonersare not released into a social vacuum, but instead reenter communities withdiffering levels of economic inequities that potentially constrain their ability topursue conventional lifestyles. The ability of released prisoners to desist fromcrime is affected not simply by their own attributes, but by the characteristicsof the broader social context they reenter.

We found this pattern to hold for a sample of African Americans in particu-lar, and our findings therefore can inform broad discussions of the distribution

-1.35 0.68 2.70 4.73 6.750

0.25

0.50

0.75

1.00

Criminal Record

Pre

dic

ted

Rec

on

vict

ion

Pro

bab

ilit

ies

Minimum Racial Inequality

Mean Racial Inequality

Maximum Racial Inequality

Figure 2 Predicted reconviction probabilities by level of racial inequality.

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of societal resources (see Rawls, 1971). Black male recidivism is one cost soci-ety pays for racial inequality. With regard to correctional policy, especiallygovernmental action directed toward prisoner reentry, our findings are consis-tent with efforts to target state resources to African Americans who returnfrom prison to communities characterized by unequal distributions of economicresources between racial groups. Prior policy-oriented research has shown thatgovernmental resources directed at marginalized offenders with low reserves ofsocial and human capital can significantly reduce recidivism rates (Holtfreter,Reisig, & Morash, 2004). The challenge with respect to Black offenders inparticular is immense, however, and governmental efforts should proceed withthis understanding. Because research indicates that mass incarceration of Blackmales actually contributes to racial inequality (Western, 2002), modestincreases in state resources for those released from prison may do little toreduce recidivism.

Our findings have key theoretical implications as well that come from thesupport they offer for Blau and Blau’s (1982) macrostructural opportunitytheory. This theory is unique not simply for its emphasis on structural-level(rather than just individual-level) predictors of behavior, but also for its empha-sis on race-based inequality. To date, the theory has been used exclusively tostudy geographic variations in crime rates, especially homicide rates. No studyhas used it to examine recidivism among released prisoners. Our findingssuggest that this is a significant oversight. Consistent with Blau and Blau’s(1982) predictions, racial inequality significantly increased Black recidivism,even when controlling for overall levels of Black deprivation. While we wereunable to examine the mechanisms that intervene between racial inequality andrecidivism, these findings nevertheless are consistent with the idea that racialinequality encourages feelings of anger and alienation that are conducive tocrime. In short, for Black felons released to areas with high racial inequality,the ideal that “all men are created equal” will stand in sharp contrast to thereality of feeling isolated from the economic opportunities that promote a law-abiding reentry into society.

We found limited support for the racial threat perspective (Blalock, 1967),which suggests that racial inequality could increase Black recidivism mainlybecause it triggers formal social controls. Our bivariate results provided somesupport for this possibility in that racial inequality and county police presencewere positively correlated (r = .20). Importantly, though, in the multivariatemodels estimating the effects of racial inequality on reconviction, police presencewas controlled. Thus, while the bivariate correlation was consistent with theracial threat perspective, our multivariate results indicate that this pattern didnot explain the observed relationship between inequality and Black reconviction.

We should note two possible limitations of our research. The first involvesthe focus on counties rather than smaller aggregations, such as neighborhoods.Recent contextual criminological research emphasizes the importance ofsmaller aggregations that may more closely capture the immediate social andeconomic circumstances an individual faces (McNulty & Bellair, 2003). Indeed,

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EFFECT OF RACIAL INEQUALITY ON BLACK MALE RECIDIVISM 429

Kubrin and Stewart (2006) recently observed significant effects of censustract-level poverty on recidivism for a sample of ex-offenders in Oregon. Itshould be emphasized, however, that smaller aggregations are often charac-terized by significant racial and economic segregation (Massey & Denton,1993). Thus, larger aggregations, such as counties and cities, are more appro-priate for assessing the impact of inequality between racial groups. A secondlimitation concerns possible mobility among released offenders. Our data didnot allow us to assess whether ex-prisoners moved out of the county to wherethey were released, so the portion of the cohort that moved and any resultingbias in our contextual analysis are unknown. Using relatively large ecologicalunits (counties as opposed to cities or neighborhoods), however, mitigates thepotential bias resulting from relocation. Mobility patterns among the generalpopulation over a 5-year period show that more than half of individuals whomove stay in the same county (Schachter, Franklin, & Perry, 2003, p. 2).Research from LaVigne and Parthasarathy (2005) suggests that this modestlevel of county outmigration may be true of ex-prisoners as well.

With limitations noted, this research extends prior theory and research byempirically linking racial inequality to Black male recidivism. Future researchthat also considers the nexus between released offenders and social context isstrongly encouraged. Of special policy importance are evaluation efforts identi-fying social interventions that lower recidivism for economically disadvantagedand socially marginalized populations.

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Appendix A. County-Level Prisoner Release Statistics (N = 62)

Appendix B. Ex-Inmate and County Variable Correlation Matrices for Combined Sample

Black sample

White sample

Combined sample

Mean no. of releases 346.51 215.87 562.39Median no. of releases 83.00 101.50 205.00SD 620.31 275.89 862.65Minimum no. of releases 11.00 10.00 27.00Maximum no. of releases 2892.00 1128.00 4020.00Percentiles 10 19.60 18.00 40.50

20 27.20 26.00 58.0030 34.90 40.00 82.9040 53.20 76.00 138.8050 83.00 101.50 205.0060 130.40 147.60 275.8070 264.60 230.40 509.2080 463.40 415.00 806.6090 1,283.80 660.30 2,070.10

Ex-inmate level Y1 X1 X2 X3 X4 X5

Y1 reconviction 1.00X1 age −.13* 1.00X2 education −.11* −.10* 1.00X3 criminal record .12* .26* −.09* 1.00X4 incarceration history .05* −.06* −.07* .19* 1.00X5 race (1 = black) .15* −.08* −.40* .12* .12* 1.00

County level Y1 X1 X2 X3 X4 X5 X6

Y1 reconviction rate 1.00X1 racial inequality .26* 1.00X2 economic deprivation −.18 −.26* 1.00X3 ethnic heterogeneity .33* −.01 .53* 1.00X4 urbanism .35* .09 −.59* −.11 1.00X5 police presence .13 .20 .08 .18 .01 1.00X6 violence crime rate .35* .20 .07 .43* .26* .22 1.00

*p < .05.

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Appendix C. Ex-Inmate Variable Correlation Matrices for Black and White Samples

Ex-inmate level Y1 X1 X2 X3 X4

Y1 reconviction – −.10* −.06* .15* .03*X1 age −.13* – −.07* .11* −.04*X2 education −.05* −.20* – 0.01 −.05*X3 criminal record .09* .39* −.07* – .26*X4 incarceration history .03* −.06* −.01 .15* –

Note. Bivariate associations for the Black sample are located below the diagonal of the correlation matrix, and the coefficients for the White sample are featured above the diagonal. Estimates for the combined sample are provided in Appendix B.*p < .05 (two-tailed test).

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