Racial (in)variance in prison rule breaking

Journal of Criminal Justice 43 (2015) 175–185

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Journal of Criminal Justice

Racial (in)variance in prison rule breaking☆ Benjamin Steiner a,⁎, John Wooldredge b a b

School of Criminology and Criminal Justice, University of Nebraska, Omaha, 6001 Dodge Street, 218 CPACS, Omaha, NE 68182-0149 School of Criminal Justice, University of Cincinnati, 600 Dyer Hall, PO Box 0389, Cincinnati, OH 45221-0389

a r t i c l e

i n f o

Available online xxxx Keywords: Inmate Prison Race

a b s t r a c t Purpose: Sampson and Wilson (1995) argued that the sources of crime are invariant across race, and are instead rooted in the structural differences between communities. This study involved an examination of the applicability of this thesis to incarcerated individuals. Methods: Random samples totaling 2,388 blacks and 3,118 whites were drawn from 46 prisons in Ohio and Kentucky. Race-specific and pooled bi-level models of violent and nonviolent rule violations were estimated. Differences between race-specific models in the magnitude of regression coefficients for the same predictors and outcomes were compared. Results: Findings revealed that individual and environmental effects were very similar between black and white inmates, although rates of violent and nonviolent rule breaking were higher for blacks. Within prisons, black inmates were also more likely than white inmates to engage in rule breaking. The individual-level relationship between race and violence was stronger in prisons with a lower ratio of black to white inmates and in prisons where inmates were more cynical towards legal authority. Conclusions: Findings seemingly refute the applicability of the racial invariance hypothesis to an incarcerated population. © 2015 Elsevier Ltd. All rights reserved.

Introduction Between 1970 and 2003, the United States experienced an unprecedented seven-fold increase in the size of its confined correctional population (Glaze & Parks, 2012; Western, 2006). The growth in the U.S. penal population has affected black persons more so than white individuals; such that, by 2004, black men were eight times more likely to be incarcerated than white men (Glaze & Parks, 2012; Western, 2006). Although much of the racial disparity in imprisonment rates has been attributed to the disproportionate involvement of blacks in crimes that typically result in imprisonment (e.g., Petit & Western, 2004), few researchers have inquired whether incarceration alters the offending patterns of blacks relative to whites during incarceration.

☆ This study was supported, in part, by grants from the National Institute of Justice (Award #2007-IJ-CX-0010) and the National Science Foundation (Award #SES-07155515). The opinions, findings, and conclusions expressed in this presentation are those of the author and do not necessarily reflect those of the Department of Justice or the National Science Foundation. The authors also wish to thank Guy Harris, along with Brian Martin and Gayle Bickle with the Ohio Department of Rehabilitation and Correction, and Ruth Edwards and Tammy Morgan with the Kentucky Department of Corrections for their assistance with the collection of the data for this study. ⁎ Corresponding author. Tel.: +1 402 554 4057. E-mail addresses: [email protected] (B. Steiner), [email protected] (J. Wooldredge).

http://dx.doi.org/10.1016/j.jcrimjus.2015.03.003 0047-2352/© 2015 Elsevier Ltd. All rights reserved.

Offending within a prison involves deviations from the formal rules of conduct that govern and regulate inmate behavior (DiIulio, 1987; Irwin, 2005). If the sources of crime are invariant across race, and instead rooted in the structural differences between communities (Peterson & Krivo, 2005; Sampson & Wilson, 1995; Wilson, 2010), then examination of the relationship between race and deviance among an incarcerated population could be informative because incarceration arranges inmates in structurally similar environments, which could result in similar odds of rule breaking among blacks and whites. On the other hand, ethnographic studies have revealed that black inmates and white inmates experience prison differently (e.g., Carroll, 1974; Goodman, 2008), and so there may be environmental characteristics of prisons that coincide with race-based differences in rule breaking. If these environmental influences perpetuate race-based differences in offending patterns, then the consequences of imprisonment may be more severe for blacks versus whites. Using a rich dataset with information collected from over 5,000 black and white individuals confined in 46 prisons, we examine race-based differences in prison rule breaking.

Racial invariance in prisons Sampson and Wilson (1995) theorized that the structural barriers and social isolation common to disadvantaged neighborhoods encourage cultural adaptations that may legitimate or at least provide a basis for

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cynicism towards legal authority and tolerance of deviance. Since black persons are overrepresented in disadvantaged neighborhoods, they are more likely to be exposed to these cultural orientations that make crime and deviance more likely. However, white residents of these types of neighborhoods should be similarly affected, and so the sources of crime should be invariant across race in similar environments. From Sampson and Wilson’s (1995) statement, it can be inferred that the odds of offending among black and white individuals residing in structurally similar environments should be the same. Researchers have found support for this thesis (e.g., Peterson & Krivo, 2005, 2010; Sampson, 2012). Incarceration relocates individuals of different races into either the same or structurally similar environments (e.g., sterile buildings designed for security, controlled opportunity structures, power differentials between the officials and the inmates, inmates basic needs are provided for). And prisons, much like neighborhoods, are spatially defined macro-social units characterized by structural circumstances (e.g., physical design, rules, supervision levels) and established patterns of interaction that generate environmental stability, despite changes in the composition of inmate and staff populations (Clemmer, 1940; Harer & Steffensmeier, 1996; Steiner & Wooldredge, 2009; Sykes, 1958). Prisons, therefore, offer a unique social laboratory to examine the racial invariance thesis. Researchers have often included race in statistical models of prison rule breaking (e.g., Berg & DeLisi, 2006; Camp, Gaes, Langan, & Saylor, 2003; DeLisi, Spruill, Peters, Caudill, & Trulson, 2013; Delisi, Trulson, Marquart, Drury, & Kosloski, 2011; Delisi et al., 2010; Drury & DeLisi, 2011; Griffin & Hepburn, 2006; Morris, Carriaga, Diamond, Piquero, & Piquero, 2012; Steiner & Wooldredge, 2008, 2013, 2014a,b; Trulson & Marquart, 2009; Wooldredge & Steiner, 2013; Worrall & Morris, 2012), though the evidence concerning race effects across these studies is mixed. For instance, a recent review of the evidence concerning the correlates of prison inmate misconduct revealed that race effects were nonsignificant across the majority of models from related studies, but a sizeable minority of studies did find a significant relationship between race and prison misconduct (Steiner, Butler, & Ellison, 2014). As far as we are aware, only two studies have focused specifically on the link between race and rule breaking among the confined and provided a framework for these findings. Both studies uncovered a positive relationship between race (black) and violence, but either a negative or null relationship between race and the odds of perpetrating nonviolent offenses (Harer & Steffensmeier, 1996; Steiner & Wooldredge, 2009). Thus, the findings pertaining to violence from these studies are not compatible with the racial invariance thesis, while the findings pertaining to nonviolent rule breaking are generally consistent with this perspective. We draw from the racial invariance perspective and seek further clarification regarding the relationship between race and rule breaking among the incarcerated. Based on studies of the link between race and crime in the general population, we examine the potential race-prison rule breaking relationship in two different ways. First, we compare race-specific samples across the distribution of predictors of prison rule breaking (e.g., Krivo & Peterson, 2000). Based on the racial invariance thesis, we expect that once compositional differences in the respective black and white incarcerated populations are controlled, there will be no race-specific differences in rates of prison rule breaking across prisons. Any observed differences in these rates would then be attributed to differences in the prison environments in which black and white persons are placed. We elaborate on these environmental sources of race-specific rule breaking below. Second, we examine the black-white gap in rule breaking within the same prison environment (e.g., Sampson et al., 2005). Also following the racial invariance thesis, we expect that black inmates and white inmates would have similar odds of rule breaking in the same prison. If blackwhite differences in the odds of rule breaking are observed, however, this relationship may differ in magnitude across prisons owing to differences in prison environments.

Environmental sources of race-based differences in prison rule breaking The position that the sources of crime are invariant across race and instead rooted in the structural differences between communities (Sampson & Wilson, 1995) implies that if individuals are placed in the same or similar environments, then their odds of offending should be the same. Such a scenario should occur once individuals are incarcerated, but ethnographic studies have uncovered that black inmates and white inmates experience the same structural prison environments differently (e.g., Carroll, 1974; Goodman, 2008; Jacobs, 1977). Thus, there could be characteristics of prisons that moderate the relationship between race and rule breaking and amplify the consequences of imprisonment among black persons more so than whites. Researchers have uncovered that characteristics of prison environments effect rates of rule breaking and moderate inmate-level relationships across prisons, but the evidence concerning the relevance of any particular prison-level predictor variable is mixed (e.g., Griffin & Hepburn, 2013; Huebner, 2003; Steiner & Wooldredge, 2008, 2009). To our knowledge, no studies have focused on the effects of prison environments on rates of rule breaking among black inmates versus the effects of rates of rule breaking among white inmates. Prison-level structural effects on rule breaking The social structure of a prison refers to the arrangement of social positions, roles and networks of social ties, whereas the environmental structure of a prison refers to the physical design, resources, and distribution of inmates and staff (see Wilson, 2010 for a similar description of social structure in the general population). We expect that certain structural features of prisons may impact rates of rule breaking. For instance, drawing from the racial invariance thesis, the degree of “isolation” experienced by black persons (or white persons) could impact race-specific rates of prison rule breaking because experiencing isolation could generate a fear for self that could in turn generate protective and even violent responses (Anderson, 1999, Harer & Steffensmeier, 1996). Thus, rates of offending among black inmates may be higher when black persons are less represented within a prison, while rates of offending among white inmates may be higher when white persons are less represented. Similarly, the ratio of black to white inmates might also moderate an individual-level race effect if this effect varies across facilities. If, for example, black inmates have higher odds of engaging in rule breaking, then this effect might become weaker in prisons with higher ratios of black to white inmates if these environments are less “isolating” for blacks. The greater relevance of the racial/ethnic composition of the prison population for predicting race-specific rates of misconduct or moderating individual-level race effects might explain why evidence regarding the main effects of the racial composition of inmates across prisons on rates of prison rule breaking is mixed (e.g., Camp et al., 2003; Steiner, 2009; Steiner & Wooldredge, 2009). Administrative control theory predicts that inmate deviance results from ineffectual or weak prison management (DiIulio, 1987; Useem & Kimball, 1989). It follows that prisons that have higher levels of supervision or use disciplinary segregation more frequently might have less deviance owing to the higher certainty of apprehension and punishment of rule violators in these prisons (Steiner, 2009). From an administrative control perspective, then, prisons with higher levels of supervision and/ or a greater use of segregation might have lower levels of rule violations because these factors promote order and safety within a prison (e.g., DiIulio, 1987; Griffin & Hepburn, 2013; Irwin, 2005; Steiner, 2009; Wooldredge & Steiner, 2009). Prison-level cultural effects on rule breaking Researchers of race and crime in the general population have underscored the relevance of neighborhood structure for understanding

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this relationship, but these scholars have also suggested that culture could mediate structural effects. Prison culture reflects a shared view or mode of behavior among the confined (or the officials) that is shaped by being confronted with similar circumstances (e.g., imprisonment) (Kirk & Papachristos, 2011; Wilson, 2010 have offered similar definitions of culture among the general population). Here, we consider the potential effect of a culture of “legal cynicism” on rule breaking. Legal cynicism refers to a cultural orientation in which the rules of conduct and their agents of enforcement are viewed as illegitimate and ill-equipped to ensure an orderly environment (Kirk & Papachristos, 2011; Sampson & Bartusch, 1998). We conceive of legal cynicism as individuals’ collective perceptions regarding the effectiveness and fairness of legal authorities (see Tankebe, 2013 for a similar conceptualization of police legitimacy among individuals). Ethnographic studies of prison environments have uncovered that incarcerated populations develop collective views that are oppositional towards legal authority in response to the structural deprivations or “pains” associated with imprisonment (e.g., Carroll, 1974; Santos, 2004; Sykes, 1958). Researchers have also found that proportionately more prison inmates report more favorable views of facility regimes in prisons with higher densities of inmates who report better interpersonal relationships with staff and/or who perceive that staff treated them fairly and less coercively (e.g., Liebling, 2004; Sparks, Bottoms, & Hay, 1996). “Inmate” legal cynicism, then, is a cultural orientation that develops when individuals’ beliefs regarding the legitimacy of correctional officers become attenuated and are culturally transmitted through social interactions; such that, a common perception regarding the illegitimacy of the officers becomes pervasive across the incarcerated population. If legal cynicism develops as a cultural orientation within a prison, then legal cynicism may also function as a cognitive framework through which the confined view situations and their environment; an assimilative technique that guides their behavior. In prisons with high levels of legal cynicism, for instance, individuals develop a shared view that correctional officers are ineffective and unfair; that they cannot depend on officers to resolve problems. This collective perception regarding officers functions as a constraint on the individuals’ options for problem solving. Under such conditions, the confined may become more selfreliant when resolving problems because they believe that officers will be unresponsive to their needs (see Kirk & Papachristos, 2011 for a related discussion of neighborhoods). In these prison environments, rule breaking may become a means of problem solving. Correctional officers may also demonstrate cynicism toward legal authority via their attitudes toward fellow officers and toward rule enforcement in general. Cynicism towards fellow officers reflects officers’ collective beliefs regarding the conduct of their colleagues, their treatment of the confined, and so forth; in short, officers’ perceptions regarding each other and the legitimacy of their authority (Liebling, 2004). Cynicism toward rule enforcement is derived from officers’ collective beliefs regarding how rules are enforced in their facility (Liebling, Price, & Shefer, 2011; Sparks et al., 1996). Officers’ collective beliefs regarding rule enforcement could also be reflected in their perceptions of the support they receive from administrators (Sparks et al., 1996). Legal officials who feel supported are empowered to believe that they are acting legitimately (Tyler, 2003). However, when officers do not share the same beliefs as the administration regarding how the rules should be enforced, the strength of officers’ beliefs regarding the importance of enforcing the rules may weaken (Sparks et al., 1996). If officers’ are cynical regarding their fellow officers or how rules are enforced, then they may transmit this belief to the incarcerated population (even if unknowingly). Similar to inmate legal cynicism, then, officer cynicism could contribute to greater self-reliance among the inmate population owing to belief that the officers are ineffectual in responding to their needs. Rule breaking may become a means of problem solving under such conditions. The racial invariance thesis predicts that legal cynicism would have similar effects on rates of rule breaking among black and

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white inmates. However, Sampson et al., (2005) uncovered that legal cynicism among residents accounted for a significant portion of the racial gap in violence in the general population. If a racial gap in prison rule breaking exists overall (on average), then inmate and/or officer legal cynicism may explain variability in that gap across prisons. Other individual-level influences on rule breaking The backgrounds and prison routines of black and white inmates may also differ, and these individual-level differences could also coincide with race-based differences in prison rule breaking. Based on the extant research, other individual-level factors that have been linked to rule breaking include: 1) demographic characteristics (age, sex); 2) involvement in conventional behaviors prior to incarceration (marriage, education, employment); 3) a history of involvement in unconventional pursuits (gangs) or behaviors conceptually similar to prison rule breaking (drug use, prior criminality); and, 4) routines during incarceration (work assignment, recreation, visitation) (see, e.g., Bales & Miller, 2012; Berg & DeLisi, 2006; DeLisi et al., 2013; Drury & DeLisi, 2011; Camp et al., 2003; Griffin & Hepburn, 2006; Kruttschnitt & Gartner, 2005; Morris et al., 2012; Steiner & Wooldredge, 2009, 2014a; Wooldredge, Griffin, & Pratt, 2001; Worrall & Morris, 2012). Measures reflecting these individual-level factors have often been examined by researchers, although the significance of the relationships between some of these variables and prison rule breaking have varied across studies based on how the concepts were measured, the type of misconduct examined (e.g., violent versus drug), and the populations and/or related sample sizes under study (see Steiner et al., 2014 for a review). Here, we assess whether the effects of these predictor variables differ for black versus white inmates. As far as we are aware, no studies have assessed race-specific models of prison rule breaking. We also include these measures as statistical controls in the models examining the individual-level relationship between race and rule breaking within prisons. Method This study involves an examination of 1) the individual and environmental sources of race-specific rule breaking across prisons; 2) the individual-level race effect on rule breaking in prisons; and, 3) the moderating effects of environmental characteristics on the relationship between race and rule breaking at the individual-level. Data for the study were collected during 2007 and 2008 via surveys administered to inmates who had served at least six months in the same prison and line-level correctional officers from the 43 state operated confinement facilities in Ohio and Kentucky, and the three privately operated facilities in Ohio. Individuals who had served less than six months were excluded because the survey inquired about individuals’ experiences in prison during the previous six months of incarceration at the same facility. Official data on these individuals and prisons were also collected. Inmate samples and data collection Stratified random samples were drawn from lists of the inmates who had served at least six months in each prison. The sampling frames were stratified by whether individuals had previously been imprisoned in order to capture the experiences of both first-time inmates and those who had previously served time. Samples sizes differed across prisons due to practical constraints dictated by the Ohio Department of Rehabilitation and Correction (ODRC) and the Kentucky Department of Corrections (KDOC). We targeted either 130 or 260 persons from each prison in Ohio and between 100 and 200 persons from each prison in Kentucky, which resulted in a total sample size of 7,294 persons confined within the 46 prisons.1 Some individuals were not available on the day of the survey, reducing the sample to 6,997.2 In order to adjust

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for the differences in the odds of selecting individuals based on these strata and between-prison differences in incarcerated population size, sample weights were created that reflected the inverse of each individual’s odds of selection. These weights were normalized and applied to the analyses reported here. Administration of the surveys in most of the prisons involved surveying general population inmates in designated areas (e.g., gymnasium) and then surveying the inmates in restrictive housing in their cells. We attempted to enhance confidentiality of the responses by having participants complete the surveys outside the direct view of security staff and away from surveillance cameras. Participants were given a survey and voluntary consent form by a member of the three person research team, and each survey was subsequently collected by one of the researchers. Individuals were not compensated for their participation in the study. These procedures resulted in 5,800 completed surveys, however, missing data on some of the surveys reduced the sample to 5,630 (an 80 percent participation rate). For this study, individuals were also removed if they were not black or white, reducing the sample to 5,066 incarcerated individuals.3 Comparisons between the weighted samples and the respective populations of black and white inmates who had served at least six months revealed no significant differences with respect to age, sex, committing offense type, prior incarceration, sentence length, or time served.

Officer samples and data collection Random samples proportionate to size were drawn from lists of all the line officers and sergeants in each prison, yielding within-prison samples ranging in size from 19 to 178 officers, and a total sample of 3,857 officers. An envelope containing a survey, a letter explaining the study (and voluntary consent to participate), and a postage paid return envelope were placed in the officers’ employee mail. Two waves of

follow-up surveys were distributed to non-respondents in Ohio (at 3 and 7 weeks), and one follow-up survey was distributed in Kentucky (at 6 weeks). The difference in follow-up procedures was based on the wishes of KDOC, however, several weeks prior to the administration of the follow-up survey; a reminder announcement was made during roll call encouraging officers who were so inclined to complete the surveys. These procedures resulted in comparable response rates within each state (50 percent overall), and 1,841 usable surveys, typical of multi-site studies of correctional officers (Hepburn, 1985). The sample was weighted to reflect the target population and, in Ohio, was representative in terms of sex, race/ethnicity, rank, and length of service. The Ohio sample was slightly older, however, than the target population (x = 42.42 versus μ = 41.34). For Kentucky, the sample was representative on sex, race/ethnicity, and rank. The parameters for length of service and age were unavailable from KDOC.

Measures Table 1 describes all measures for the analyses. The outcome measures included the prevalence and incidence of violent and nonviolent offenses each person was found guilty of during the six months prior to the survey date. Violent offenses include infractions such as threatening, causing physical harm, or attempting to cause physical harm to an inmate or staff member. Nonviolent offenses include all other offenses except drug offenses. Drug offenses were excluded from the nonviolent offense category because there is a preference in the literature to treat drug offenses as separate offenses (e.g., Harer & Steffensmeier, 1996). We considered examining drug offenses separately, but too few persons were written up for these offenses during the study period (b3 percent) to generate reliable estimates. The outcome measures used here were derived from official data stored in computer databases. Official measures of rule breaking have been found to underestimate the total volume of

Table 1 Descriptions of the samples (weighted) White-non Latino

Black-non Latino

Pooled

Mean

SD

Mean

SD

Mean

Outcome variables Prevalence of violent offense Prevalence of nonviolent offense Incidence of nonviolent offenses

.05 .37 .82

(.22) (.48) (1.67)

.07⁎ .53⁎ 1.52⁎

(.26) (.50) (2.65)

.06 .44 1.12

(.24) (.50) (2.18)

Level 1 variables: individuals Black Age Male Conventional behaviors Gang member Used drugs in month before arrest Incarcerated for a property offense Incarcerated for a drug offense Incarcerated for a public order offense Prior incarceration Security risk level Natural log time served (in months) Natural log # hours at work assignment per week Natural log # hours in recreation per week Number of visits per month N1

-38.90 .93 1.37 .12 .57 .16 .13 .10 .40 1.92 3.75 2.12 1.54 .86 3,118

-(12.04) (.26) (.79) (.32) (.50) (.37) (.34) (.30) (.49) (.72) (.99) (1.43) (1.19) (1.27)

-35.42⁎ .95⁎ 1.11⁎ .20⁎

-(10.87) (.22) (.86) (.40) (.49) (.31) (.42) (.26) (.50) (.79) (1.03) (1.38) (1.10) (1.25)

.43 37.40 .94 1.26 .15 .57 .14 .17 .09 .45 1.99 3.71 2.04 1.55 .83 5,066

(.50) (11.67) (.24) (.83) (.36) (.50) (.35) (.38) (.28) (.50) (.76) (1.01) (1.41) (1.15) (1.26)

.90 .00 .00 .00 .00 46

(.53) (1.00) (1.00) (.95) (.96)

Level 2 variables: prisons Ratio of black to white inmates Administrative control1 Inmate legal cynicism1 Officer legal cynicism – rule enforcement1 Officer legal cynicism – correctional staff1 N2 Notes: 1 Scale created via factor analysis, individual items listed in Appendix A. ⁎ = significant difference between White-non Latino sample and Black-non Latino sample (p b .01).

.58 .11⁎ .23⁎ .07⁎ .52⁎

2.09⁎ 3.66⁎ 1.93⁎ 1.57 .80 2,388

SD

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deviance within prisons, and may also be subject to systematic bias resulting from discretionary reporting or recording on the part of staff (Hewitt, Poole, & Regoli, 1984). Particularly relevant for this study could be differences in how officers treat black inmates compared to white inmates. In another study involving these data, however, we compared the predictors of self-reported assaults, drug, and property offenses to the predictors of comparable measures of officially detected offenses. We found that, regardless of the type of data examined, black inmates and white inmates had similar odds of committing all three offense types (Steiner & Wooldredge, 2008), suggesting that official measures of prison rule breaking are valid indicators of offending in prison among blacks and whites (see also Van Voorhis, 1994). We used the official measures of misconduct here because we were able to consider a broader range of items for the offense groups, which increased the base rates of these offenses and provided for more stable coefficient estimates. The individual-level measures age, sex (male), race (black), gang member, and the measures of committing offense type (e.g., incarcerated for property offense), criminal history, and time served were created using official data. The measures of conventional behaviors, drug use, the number of hours at a work assignment or recreation per week, and the number of visits per month were based on responses to survey questions. Conventional behaviors is an additive scale of three dichotomous variables that assess whether an individual was married, had at least a high school diploma, and was employed prior to incarceration (see Wooldredge et al., 2001). Preliminary analyses revealed that the bivariate relationships between the conventional behaviors scale and the outcomes were stronger than the separate effects of any of the items included in the scale. The measure of gang membership was retrieved from official records of participation in a security threat group (see also Gaes, Wallace, Gilman, Klein-Saffran, & Suppa, 2002). Security risk is an official custody score (1-4) based on the interval in which a person fell on an additive scale comprised of various criminal and institutional history items (e.g., prior convictions, security level during last imprisonment). The measures of the number of hours at a work assignment or recreation per week were capped at 40 hours and the natural log was taken in order to reduce the skew in these distributions. The prison-level measures included the ratio of black to white inmates, administrative control, inmate legal cynicism, officer legal cynicism – rule enforcement, and officer legal cynicism – correctional staff. Other prison-level measures were considered for the analysis (e.g., security level), but they were ultimately excluded based on tests for multicollinearity and the strength of zero order relationships. The three measures of legal cynicism were created by aggregating the responses to the survey items described in Appendix A to the prisonlevel and then factor analyzing the mean responses. Our measure of inmate legal cynicism was created via a principal components analysis of the prison-level means of four survey items (α = .92). For the measures of officer legal cynicism (rule enforcement α = .83, correctional staff α = .75), a principal components analysis of the prison-level means of nine survey items revealed a two factor solution, and so a separate analysis that involved maximum likelihood extraction and varimax rotation was used to create these scales. Administrative control is a scale based on a principal components analysis of three items: the average custody score of the incarcerated population, the ratio of correctional officers to inmates, and the proportion of individuals held in disciplinary housing (α = .65). Component loadings for all items included in these factor analyses are displayed in Appendix A. Prior to estimating the final models, we examined the variables for multicollinearity, which was not a problem here. Statistical analysis As discussed above, we examined the potential race-prison rule breaking relationship in two different ways. First, we compared the magnitude of the effects of the predictors of prison rule breaking across

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race-specific samples. Next, we examined the black-white gap in rule breaking within the same prison. For each analysis, multi-level models were estimated for each outcome due to the analysis of both individual and environmental effects on rule breaking.4 The prevalence measures of rule breaking were examined with hierarchical Bernoulli regression, while the incidence measures were examined with hierarchical Poisson regression with the correction for overdispersion available in HLM 7.0 (see Table 1 for means and standard deviations). Only the incidence measure for nonviolent offenses was examined because too few persons were written up for more than one violent offense during the study period. Unconditional models were estimated in order to derive estimates of variance in each outcome existing at each level of analysis, and to determine whether the between-prison estimates were significant. Next, random effects models including the individual-level variables were estimated. These models revealed whether the individual-level effects on rule breaking varied across prisons. Individual-level effects that did not vary significantly across prisons were treated as fixed, or as having a common “slope” across prisons. For the analyses of race-specific rates of rule breaking, our primary interest was in whether the black-white gap in rule breaking across prisons could be accounted for by the individual and prison-level environmental effects, and so the individual-level measures were grand mean centered in order to control for compositional differences in the respective black and white samples across prisons. The magnitudes of the coefficient estimates for each predictor were compared across the race-specific models using the equality of coefficients test developed by Clogg, Petkova, and Haritou (1995). For the analysis of the pooled sample, our primary interest was in determining: 1) the effect of race within prisons; and, 2) the moderating effects of environmental characteristics on the potential race-rule breaking relationship at the individual-level. For these analyses, the individual-level measures were group mean-centered in order to remove between-prison variation in individual characteristics that might have corresponded with differences in levels of rule breaking across prisons. The advantage of this strategy is that it offers more conservative tests of level-1 effects by reducing the odds of finding spurious level-1 effects due to unmeasured prison-level effects that might also be related to compositional differences in incarcerated populations across prisons. The next step in each analysis involved estimation of: 1) the intercepts-as-outcome models, which provided the main effects of the prison-level environmental characteristics on the outcomes at level-2; and, 2) the “intercepts- and slopes-as-outcomes” models (for the pooled sample only), where environmental effects on the varying level-1 coefficient for a person’s race were estimated [see Raudenbush and Bryk (2002) for a more detailed discussion of the procedures involved in estimate hierarchical models]. Results and discussion The analyses involved an examination of the racial invariance thesis among incarcerated persons. Following from studies of the race-crime relationships in the general population, we assessed a possible link between race and prison rule breaking in two different ways. Our race-specific analyses are presented in Tables 2, 3 and 4. These analyses examined whether individual and environmental characteristics correspond with rule breaking similarly for black versus white inmates. These analyses also examined whether the differences in the rates of rule breaking among black versus white inmates across prisons (see Table 1) remained once relevant compositional and contextual effects were controlled (e.g., Krivo & Peterson, 2000). Next, we assessed the black-white gap in prison rule breaking within the same environment (e.g., Sampson et al., 2005). These within-prison analyses, which are presented in Tables 5 and 6, examined whether black inmates had higher odds of rule breaking than white inmates housed

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Table 2 Race-specific effects on the prevalence of violent offenses

Table 3 Race-specific effects on the prevalence of nonviolent offenses

WhiteBlackz-test non Latino non Latino Individual-level Age

Security risk level

-.03⁎⁎ (.01) .53 (.59) -.11 (.16) .33 (.30) .42 (.26) .32 (.34) -.08 (.38) .59 (.34) .34 (.19) .65⁎⁎

(.16) .04 (.17) .80⁎⁎ (.21) -.18 (.33) .02 (.35) .30 (.22) .50⁎

Natural log time served (in months)

(.24) -.32⁎

(.25) -.29⁎⁎

(.16) Natural log # hours at work assignment per week -.16⁎ (.07) Natural log # hours in recreation per week .11 (.09) Number of visits per month -.07 (.07) Proportion variation within prisons .93 Proportion variation within prisons explained .23 3,118 N1

(.09) -.11 (.06) .12 (.08) -.10 (.07) .89 .19 2,388

Male Conventional behaviors Gang member Used drugs in month before arrest Incarcerated for a property offense Incarcerated for a drug offense Incarcerated for a public order offense Prior incarceration

Prison-level Intercept Ratio of black to white inmates Administrative control Inmate legal cynicism Officer legal cynicism – rule enforcement Officer legal cynicism – correctional staff Proportion variation between prisons Proportion variation between prisons explained N2

-3.51 (.14) -.60 (.37) .07 (.09) .06 (.12) .31 (.20) -.15 (.12) .07 .00 46

Individual-level Age

-.02⁎ (.01) -.71 (.46) -.13 (.12) .36⁎

-3.01 (.15) -.23 (.34) .08 (.16) .35⁎⁎ (.13) -.11 (.13) -.23a (.13) .11 .26 46

Male Conventional behaviors Gang member Used drugs in month before arrest Incarcerated for a property offense Incarcerated for a drug offense Incarcerated for a public order offense Prior incarceration Security risk level Natural log time served (in months)

1.76a

Notes: Maximum likelihood coefficients generated from hierarchical Bernoulli regression reported with standard errors in parentheses. Italicized coefficient indicates relationship varies across prisons (p b .05); ⁎⁎ = p b .01; ⁎ = p b .05; a = p b .10 (level-2 only).

in the same prison (recall that the race effect was group meancentered for these analyses only), and, if so, whether this relationship differed in magnitude across prisons based on differences in prison environments.

Race-specific effects across prisons The race-specific analysis of violent offenses revealed that persons of either race who were younger, higher security risk, or had served less time on their current sentence had higher odds of committing a violent offense. White inmates who spent fewer hours at a prison work assignment were more likely to perpetrate violence, while black inmates who were gang members or incarcerated for a property offense had higher odds of committing a violent offense. None of the other individual-

-.04⁎⁎ (.01) -.09 (.26) -.17⁎⁎

-.04⁎⁎ (.01) .13 (.28) -.10 (.07) -.14 (.12) .14 (.10) .22 (.19) -.10 (.19) .35⁎⁎ (.14) .22⁎ (.10) .38⁎⁎

(.06) .36⁎⁎ (.13) .22⁎ (.11) .43⁎⁎ (.13) .05 (.12) .23 (.15) .26⁎⁎ (.10) .44⁎⁎ (.09) -.13⁎⁎ (.05) -.08⁎ (.04) -.002 (.06) -.07⁎

3,118

2,388

-.68 (.09) Ratio of black to white inmates -.16 (.15) Administrative control -.09 (.07) Inmate legal cynicism .19 (.12) Officer legal cynicism – rule enforcement .08 (.10) Officer legal cynicism – correctional staff .04 (.07) Proportion variation between prisons .06 Proportion variation between prisons .00 explained 46 N2

.11 (.08) -.04 (.18) -.20⁎

Prison-level Intercept

z-test

2.83⁎⁎

(.09) -.24⁎⁎

(.04) .94 .23

Proportion variation within prisons Proportion variation within prisons explained N1

-1.64a

Blacknon Latino

(.07) -.09⁎ (.04) -.03 (.05) -.02 (.04) .92 .18

Natural log # hours at work assignment per week Natural log # hours in recreation per week Number of visits per month

-2.44⁎

Whitenon Latino

-6.56⁎⁎

(.10) .10 (.10) -.05 (.09) .08 (.08) .08 .10 46

Notes: Maximum likelihood coefficients generated from hierarchical Bernoulli regression reported with standard errors in parentheses. Italicized coefficient indicates relationship varies across prisons (p b .05); ⁎⁎ = p b .01; ⁎ = p b .05; a = p b .10 (level-2 only).

level measures impacted the odds of violence for white or black inmates. Despite race group differences in the statistical significance of certain predictor variables, there were no significant differences between black and white inmates in the magnitude of these individuallevel effects (as indicated by the nonsignificant equality of coefficients tests). In other words, none of these effects were conditioned by an individual’s race. Table 2 also shows that rates of violent offenses among black inmates were higher in prisons where inmate legal cynicism was more pervasive, but lower in prisons in which officers were more cynical regarding their fellow officers. None of the other environmental characteristics affected rates of violence among blacks, and none of the environmental effects impacted rates of violence among whites. The effect of inmate legal cynicism on rates of violence among black inmates was significantly greater in

B. Steiner, J. Wooldredge / Journal of Criminal Justice 43 (2015) 175–185 Table 4 Race-specific effects on the incidence of nonviolent offenses

Individual-level Age Male Conventional behaviors Gang member

Whitenon Latino

Blacknon Latino

-.05⁎⁎ (.01) .17 (.23) -.08 (.05) .24⁎⁎

-.06⁎⁎ (.005) -.47⁎ (.23) -.06 (.05) .004 (.13) .14⁎ (.06) .14 (.11) -.22 (.12) .12 (.11) .16⁎ (.08) .26⁎⁎

(.07) Used drugs in month before arrest .28⁎⁎ (.07) Incarcerated for a property offense .29⁎⁎ (.08) Incarcerated for a drug offense -.03 (.14) Incarcerated for a public order .27 offense (.15) Prior incarceration .15⁎ (.07) Security risk level .40⁎⁎ (.07) Natural log time served -.24⁎⁎ (in months) Natural log # hours at work assignment per week Natural log # hours in recreation per week Number of visits per month Proportion variation within prisons Proportion variation within prisons explained N1 Prison-level Intercept Ratio of black to white inmates Administrative control Inmate legal cynicism Officer legal cynicism – rule enforcement Officer legal cynicism – correctional staff Proportion variation between prisons Proportion variation between prisons explained N2

(.05) -.05 (.03) -.01 (.03) -.07⁎ (.03) .90

(.10) -.20⁎⁎ (.03) -.01 (.03) -.12⁎⁎ (.04) -.01 (.03) .92

Table 5 Individual-level effects on rule breaking (all inmates) z-test

Prevalence Prevalence Incidence Violent Nonviolent Nonviolent Intercept

1.97⁎

Used drugs in month before arrest

-2.90 (.06) .34⁎ (.14) -.02⁎⁎ (.003) .01 (.24) -.08 (.06) .33⁎⁎ (.11) .19⁎

-.35 (.07) .47⁎⁎ (.06) -.05⁎⁎ (.01) .10 (.34) -.13⁎⁎ (.05) .11 (.11) .20⁎⁎

Incarcerated for a property offense

(.09) .62⁎⁎

(.06) .39⁎⁎

(.15) -.17 (.18) .23 (.16) .33⁎⁎

(.10) -.01 (.11) .28⁎⁎ (.09) .27⁎⁎

(.07) .39⁎⁎ (.10) -.22⁎⁎ (.05) -.13⁎⁎

(.06) .48⁎⁎ (.07) -.16⁎⁎ (.04) -.09⁎⁎

(.03) .10⁎⁎ (.04) -.04 (.03) .91 .23

(.02) -.01 (.04) -.06⁎⁎ (.02) .93 .22

5,066

5,066

African American Age Male Conventional behaviors Gang member

Incarcerated for a drug offense Incarcerated for a public order offense Prior incarceration Security risk level

2.20⁎

Natural log time served (in months) Natural log # hours at work assignment per week Natural log # hours in recreation per week Number of visits per month

.26

.33

3,118

2,388

-.63 (.08) -.20 (.16) -.16⁎⁎ (.06) .20⁎

.04 (.09) -.24a (.14) -.07 (.07) .23⁎⁎

(.09) .11 (.09) -.04 (.08) .10

(.08) .02 (.08) .02 (.07) .08

.14

.06

46

46

181

-5.56⁎⁎

Notes: Maximum likelihood coefficients generated from hierarchical Poisson regression reported with standard errors in parentheses. Italicized coefficient indicates relationship varies across prisons (p b .05); ⁎⁎ = p b .01; ⁎ = p b .05; a = p b .10 (level-2 only).

Proportion variation within prisons Proportion variation within prisons explained N1

(.16) -.07⁎ (.03) .12 (.08) .19⁎⁎ (.05) .23⁎⁎ (.06) -.14 (.12) .20⁎⁎ (.07) .17⁎⁎ (.05) .35⁎⁎ (.08) -.19⁎⁎ (.03) -.02 (.02) -.07⁎ (.03) -.04⁎ (.02) .09 .35

Notes: Maximum likelihood coefficients reported with standard errors in parentheses. Italicized coefficient indicates relationship varies across prisons (p b .05). ⁎⁎ = p b .01; ⁎ = p b .05.

Table 6 Prison-level main and moderating effects on rule breaking (all inmates) Prevalence Prevalence Incidence Violent Nonviolent Nonviolent Level-1 intercept as outcome Ratio of black to white inmates Administrative control Inmate legal cynicism Officer legal cynicism – rule enforcement Officer legal cynicism – correctional staff

magnitude than the comparable effect among white inmates, while the magnitude of the effect of officer cynicism regarding correctional officers was not significantly different for black versus white inmates. We also observed a difference in the magnitude of the effect of officer cynicism regarding rule enforcement between black and white inmates, but neither of the coefficients depicting the main effects of officer cynicism regarding rule enforcement reached statistical significance. Finally, even after the individual and environmental effects were taken into account, the rate of violence among black inmates remained higher than the rate of violence among white inmates (reflected by the significant difference in intercepts across the race-specific models). The race-specific analyses of the prevalence of nonviolent offenses (Table 3) revealed that regardless of their race, individuals who were

-.32 (.08) .42⁎⁎ (.05) -.06⁎⁎ (.003) -.34⁎

Proportion variation between prisons Proportion variation between prisons explained Level-1 race effect as outcome Ratio of black to white inmates Administrative control Inmate legal cynicism Officer legal cynicism – rule enforcement Officer legal cynicism – correctional staff N2

-2.90 -.10 (.11) .34⁎⁎ (.07) .34⁎⁎

-.35 .30a (.17) .08 (.06) .38⁎⁎

-.32 .25⁎ (.11) .06 (.06) .50⁎⁎

(.06) .24⁎

(.10) .12⁎ (.08) .04 (.06) .07 .62

(.08) .18⁎⁎ (.06) -.0004 (.06) .09 .60

n/a

n/a

46

46

(.08) -.15⁎ (.06) .09 .73 .27 -.50⁎ (.22) .01 (.06) .21⁎ (.10) .11 (.12) .11 (.09) 46

Notes: Maximum likelihood coefficients reported with standard errors in parentheses. ⁎⁎ = p b .01; ⁎ = p b .05; a = p b .10 (level-2 only).

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younger, previously incarcerated, higher risk, had served less time, or spent less hours at a prison work assignment had higher odds of committing a nonviolent offense. Black persons incarcerated for a public order offense were more likely to commit this type of offense, as were white inmates who had less involvement in conventional behaviors, were gang members, used drugs in the month before their arrest, were incarcerated for a property offense, or received fewer visits. For the most part, these between group differences in the individual-level effects on nonviolent rule breaking were not significant. Gang membership did maintain a stronger effect for white inmates compared to black inmates. Administrative control was the only environmental effect that impacted prevalence rates of nonviolent offenses (for black inmates only), but the magnitude of this effect did not differ between race groups. Similar to violent offenses, prevalence rates of nonviolent offenses were higher among black inmates even after accounting for the individual and environmental effects. Table 4 shows that, regardless of their race, persons who were younger, used drugs in the month before their arrest, had previously been incarcerated, were classified higher risk, or had served less time on the current sentence, committed more nonviolent rule infractions. Black inmates who were also male or spent more hours in recreation committed fewer nonviolent infractions, whereas white inmates who were also gang involved, incarcerated for a property offense, or received fewer visits perpetrated more of these offenses. Consistent with the analyses of the prevalence of violent and nonviolent offenses, however, the majority of individual-level effects did not differ significantly in magnitude between race groups. Only the effects of sex and hours spent in recreation differed significantly between race groups; these effects were stronger among black inmates. Table 4 also shows that, regardless of race, incidence rates of nonviolent offenses were higher in prisons where inmate legal cynicism was more pervasive. Officer legal cynicism had no impact on this outcome for either race group. Incidence rates of nonviolent offenses were lower among whites confined in prisons with greater levels of administrative control, while black incidence rates of nonviolent offenses were lower in facilities with higher ratios of black to white inmates. None of these effects differed in magnitude across race groups, however. Consistent with the analyses of the prevalence of violent and nonviolent offenses, black inmates had higher incidence rates of nonviolent offenses compared to white inmates, even when controlling for all individual and environmental effects. Taken together, the results of the race-specific analyses offer little support for the applicability of the racial invariance thesis to incarcerated populations. We did find very few differences in the effects of the individual and environmental characteristics across race groups, suggesting that, while there may be differences in the backgrounds of black persons and white individuals who are incarcerated (Table 1), both individual and environmental effects on rule breaking tend to operate similarly between black and white inmates. Regardless of the type of offense examined, however, black inmates had higher rates of rule breaking compared to white inmates, even after controlling for the individual and environmental effects. Individual-level effects within prisons The within prison analysis (Table 5) revealed that even when controlling for other potentially relevant individual and environmental factors, black inmates were more likely than white inmates to commit a violent offense. Based on the odds ratio generated from the analysis, the odds for black inmates were 39 percent higher than the odds for white inmates. This finding is consistent with the extant research (Harer & Steffensmeier, 1996; Steiner & Wooldredge, 2009). Inmates who were younger, gang members, used drugs before their arrest, were incarcerated for a property offense, previously served time in prison, were higher risk, had served less time on their current

sentence, spent fewer hours at a prison work assignment, and spent more hours in recreation were also more likely to engage in violence. All other effects were nonsignificant. The significant individual-level effects accounted for 23 percent of the within-prison variation in the prevalence of violent offenses.5 Black inmates were also more likely to commit a nonviolent offense (Table 5), even when controlling for the other individual-level predictors. Black inmates had 60 percent higher odds of committing a nonviolent offense than the odds for white inmates. These findings are inconsistent with the null effects of race on nonviolent offenses observed by Steiner and Wooldredge (2009). Consistent with the analysis of violent offenses, individuals who were younger, used drugs before their arrest, were incarcerated for a property offense, previously served time in prison, were classified as higher risk, had served less time, and who spent fewer hours at a prison work assignment were more likely to commit a nonviolent offense. Unique to this outcome, persons involved in more conventional behaviors, those not incarcerated for public order crimes, and individuals who received more visits were less likely to commit a nonviolent offense. The remaining predictors were nonsignificant. The significant individuallevel relationships explained 22 percent of the variation in the prevalence of nonviolent offenses. Results for the incidence of nonviolent offenses (Table 5) were similar to those for the prevalence of nonviolent offenses. Differences included the significant inverse effects of an individual’s sex and hours spent in recreation on the incidence of nonviolent offenses, and the null effects of number of hours spent at a prison work assignment and number of visits. The significant individual-level effects explained 35 percent of the variation in the incidence of nonviolent offenses. Consistent with the analyses of the prevalence of violent and nonviolent rule breaking, black inmates committed more nonviolent offenses than whites. Incidence rates of nonviolent rule breaking for black inmates were 53 percent higher than rates for white inmates. Similar to the race-specific analyses, the findings from these analyses also refute the applicability of the racial invariance thesis to inmate populations. Within the same prison, black inmates were more likely than white inmates to engage in rule breaking. These findings held, even after controlling for a number of other potentially relevant individual characteristics. Main and moderating effects of prison environments Turning to the prison-level main effects (Table 6), the ratio of black to white inmates was not associated with violent offense rates, but prisons with a greater number of black relative to white inmates did have higher rates of nonviolent offending. These findings might reflect the fact that blacks are the minority group in most of the prisons under study here. Across these prisons then, black inmates may be more likely to feel isolated, which could generate a fear for self or resentment and disrespect for the rules. Violent offense rates were also higher in prisons with greater administrative control, but administrative control did not affect nonviolent offense rates. Inmate legal cynicism and officer cynicism toward rule enforcement were positively associated with rates of each type of rule breaking. Rates of violent and nonviolent rule breaking were higher in facilities where the confined were more cynical regarding officials who enforce the rules, and in facilities where correctional officers were more cynical regarding rule enforcement. Inconsistent with our expectation, officer cynicism toward other custodial staff was inversely related to violent offense rates. There was no relationship between officer cynicism toward correctional staff and nonviolent offense rates. The prisonlevel effects explained 75 percent of the between-prison variation in violent offense rates and 85 percent (prevalence) and 90 percent (incidence) of the between-prison variation in nonviolent offense rates. The relationship between an individual’s race and the prevalence of violent offending varied across prisons (see Table 5), permitting an

B. Steiner, J. Wooldredge / Journal of Criminal Justice 43 (2015) 175–185

analysis of possible moderating effects of the environmental characteristics on this relationship (Table 6). The effect of an individual’s race was weaker in prisons with a higher ratio of black to white inmates. However, the positive relationship between race and violence was stronger in prisons with higher levels of inmate legal cynicism. None of the other environmental effects impacted the race-violence relationship. The individual-level relationships between race and nonviolent offenses did not vary across prisons, and so the moderating effects of environmental characteristics were not examined. The relationship between an individual’s race and violence may be weaker in prisons with higher ratios of black to white inmates because these prisons might offer a less isolating environment for black inmates, which could reduce their odds of violence. By contrast, the relationship between race and violence was stronger in prisons with higher levels of legal cynicism. These findings are consistent with Sampson et al.'s (2005) finding from their study of Chicago neighborhoods; that is, differences in the odds of violence perpetrated by black versus white individuals across ecological areas are accounted for, in part, by a culture of cynicism regarding legal authorities. Conclusions Sampson and Wilson (1995) theorized that the sources of crime are invariant across race, and are instead rooted in the structural differences between communities. We examined the applicability of this thesis to incarcerated persons. Incarceration arranges individuals in structurally similar environments, which, based on the racial invariance thesis, could lead to similar odds of prison rule breaking among black and white individuals. Results from several different analyses did not support racial invariance in rule breaking among the confined. We first examined whether the sources of prison rule breaking were invariant across race, and whether race-specific rates of rule breaking across prisons were similar once individual and environmental characteristics were taken into account. We found very few differences in the magnitude of individual and environmental effects across black versus white inmates. Only three significant differences in the magnitude of effects were observed at the individual-level (out of 42 tests), and only two differences were observed at the prison-level (out of 15 tests). The individual and environmental effects considered here could not completely account for the black-white gap in rule breaking, although this gap was reduced when controlling these effects. We also examined the relationship between an individual’s race and rule breaking within prisons. We found evidence that, net of other individual and environmental characteristics, black inmates were more likely than white inmates to perpetrate violent and nonviolent rule breaking. We also found that the relationships between an individual’s race and both the prevalence and incidence of nonviolent rule breaking did not vary across prisons. That is, regardless of the prison in which these individuals were confined, black inmates were more likely than white inmates to engage in nonviolent rule breaking. We did observe, however, that the race-violence relationship did vary across prisons, suggesting that the individual-level race effect was more pronounced in some prisons versus others. We examined the sources of this variation and uncovered that the relationship between an individual’s race and violence was weaker in prisons with higher ratios of black to white inmates. We also found that the race-violent relationship was stronger in prisons where cynicism towards the officials was more pervasive. Thus, the findings from this study seemingly refute the racial invariance hypothesis as applied to incarcerated populations, with the caveat that we may not have controlled for possible effects that might have rendered spurious race effects. That is, net of relevant controls, our findings suggest that black inmates are more likely than whites to engage in violent and nonviolent rule breaking in prison. A conclusion that race is a cause of rule breaking based solely on these findings, however, necessarily ignores any race difference in the enduring effects of exposure to

183

the cultural patterns and social norms that induce crime and deviance, as well as any destination effects that were not included in our models that make crime and deviance more likely. We discuss these ideas below because, in light of our findings, they appear to be important avenues for future study. Conceptualizing incarceration as a point-in-time treatment disregards the possibility that social and cultural environments surrounding families for generations have cumulative effects on family members (Sharkey, 2008). Compared to white persons, black individuals are more likely to reside in disadvantaged areas, and this has been the case for a number of generations (Sharkey, 2008; Wilson, 2010). Further, compared to residents of disadvantaged white neighborhoods, residents of disadvantaged black neighborhoods experience greater levels of social isolation and disadvantage (Krivo & Peterson, 1996; Sampson, 2012). Since the incarcerated population is drawn disproportionately from disadvantaged neighborhoods (Rose & Clear, 1998; Sampson & Loeffler, 2010), black inmates may be more likely to have experienced persistent contextual disadvantage (Sharkey, 2008; Wilson, 2010). As a consequence, black inmates may be more likely than white inmates to have been exposed to cultural orientations characterized by resentment towards legal authority; cultural orientations that induce crime and deviance (Sampson & Bartusch, 1998; Sampson et al., 2005). If these ecologically structured beliefs linger during confinement in prison, then black inmates would be more likely to engage in rule breaking. The findings from this study support an investigation of this possibility. Regarding destination effects, relocation effects are contextually conditioned (Sampson, 2008; Sharkey & Sampson, 2010). That is, the benefits of relocation may only be realized if the destination environment is significantly better in terms of structural advantage. Prison environments are an improvement over disadvantaged neighborhoods in some respects (e.g., available resources for meeting inmates’ basic needs), but prisons also exhibit many of the same ecological qualities of disadvantaged neighborhoods such as social isolation, expectations about aggression at the hands of others, and inmate cultural orientations that are oppositional towards legal authority (Byrne & Stowell, 2007; Sykes, 1958; Wacquant, 2001). Given these environmental similarities, individuals who originate from disadvantaged neighborhoods may draw upon the cultural repertoire they have already developed in order to navigate the problems posed by prison environments (Byrne & Stowell, 2007; Steiner & Wooldredge, 2009). For reasons discussed above, we suspect that black inmates are more likely to be drawn from neighborhoods characterized by social isolation and cultural orientations that induce crime and deviance. Black inmates, then, may be more likely to resort to rule breaking as a means of problem solving, if only because they were culturally conditioned to do so in their prior environment. Our findings related to the ratio of black to white inmates and inmates’ cynicism toward legal authority lend some support to this idea. We found that effect of an individual’s race on violence was stronger in prisons with lower ratios of black to white inmates. We speculated that these prisons might offer a more isolating environment for blacks, which may have increased their odds of violence. We also uncovered that the cultural orientation of inmate legal cynicism was associated with higher rates of violence among black inmates compared to white inmates. Further, we found that, across prisons, black inmates were more likely to perpetrate violence in prisons where cynicism was more pervasive among the confined population. The relevance of legal cynicism for shaping rates of violence among black inmates across prisons and moderating the within prison relationship between race and violence may be due to the cultural congruence between prisons where legal cynicism is more pervasive and the neighborhoods from which black individuals are pulled. As discussed above, black inmates may be more likely than white inmates to have experienced persistent contextual disadvantage and to have been exposed to cultural orientations such as legal cynicism that make crime and deviance more likely

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(Sharkey, 2008; Wilson, 2010). Thus, prisons with higher levels of legal cynicism might feel more similar to black inmates’ neighborhoods of origin, which, in turn, may have encouraged black inmates to draw upon their culturally conditioned modes of behavior in order to solve the problems posed by these prison environments (Sharkey & Sampson, 2010). If these processes are at work, then it can be inferred that exposure to some prison environments perpetuate differences in the offending patterns of black and white inmates, which suggests that consequences of imprisonment in these environments is greater for blacks compared to whites. On the other hand, our findings also indicate that the effect of an individual’s race on violence was weaker in prisons with higher ratios of black to white inmates and black inmates were less likely to perpetrate violence in prisons where cynicism was less prevalent among the inmate population. In their application of the equal status contact hypothesis to desegregated Texas prisons, Trulson and Marquart (2009) observed that positive interracial contact is enhanced by favorable conditions. Though our analysis did not examine racially motivated rule breaking per se, it is possible that Trulson and Marquart’s argument would also apply to racial invariance in all rule breaking. In prisons with higher concentrations of black inmates in conjunction with lower levels of legal cynicism, black inmates might have felt less isolated and more trusting of the officials, both of which could have contributed to similar odds of violence among whites and blacks.6 It could also be that there are other environmental conditions of prisons that could account for variation in the race effect on violence. Trulson and Marquart (2009), for instance, discussed the applicability of Texas’s prisoner classification system, legally mandated change, available prison space, and so forth. Future research should examine whether there are conditions that are more (or less) favorable to reducing racial differences in prison rule breaking. Altogether, the findings from this study offer new insights regarding the race-crime relationship. Across prisons, we found that black inmates have higher rates of rule breaking than white inmates. Within prisons, we found that black inmates were more likely to engage in rule breaking. We also observed a complex interplay between race, violence, and the cultural orientation of inmate legal cynicism; inmate legal cynicism was associated with higher rates of violence among black inmates but not white inmates, and the relationship between race and violence within prisons was stronger in prisons where inmate legal cynicism was more pervasive among the incarcerated population. As discussed above, these findings have important implications for research on the link between race and crime, not to mention prison officials. An understanding of the link between race and crime is an important theoretical question, but also a question that has very real implications (Sampson & Wilson, 1995). It is also clear that imprisonment has disproportionately affected blacks more so than whites (Western, 2006), and so continued effort to understand the link between race, crime, and imprisonment is relevant. Our findings suggest that blacks are more likely to offend in prison than whites. Moreover, we found that some of the racial differences in rates of violent offending were accounted for by destination effects—the ratio of black to white inmates and the level of inmate legal cynicism. From a practical standpoint, prison officials might lower rates of violence in their own institutions by implementing practices that reduce isolation among minority groups (e.g., controlled integration) and promote more positive attitudes towards staff (e.g., providing fair and consistent treatment of inmates). However, these strategies are simply a starting point. Equally important will be further inquiry to understand why blacks are more likely to violate prison rules, and whether there are environmental conditions that either reduce or enhance racial variance in prison rule breaking. The theoretical and practical importance of understanding the link between race and offending underscores the need for this research.

Appendix A. Items and factor loadings for scales

Scale Inmate legal cynicism1 Overall, the correctional officers here do a good job (reverse coded). The correctional officers are generally fair to inmates (reverse coded). Correctional officers treat me the same as any other inmate here (reverse coded). Correctional officers treat some inmates better than others. Officer legal cynicism – rule enforcement1 The rules for inmates are under-enforced in this facility. The warden encourages us to use our best judgment about whether to issue a disciplinary ticket for some rule violations (reverse coded). My immediate supervisor encourages me to use my best judgment about issuing disciplinary tickets (reverse coded). The warden usually supports my decisions regarding when to issue disciplinary tickets (reverse coded). My immediate supervisor usually supports my decisions regarding when to issue disciplinary tickets (reverse coded).

Component Loading .941 .956 .926 .813

.626 .850

.694 .888 .582

Officer legal cynicism – correctional staff1 I do not trust my co-workers. My co-workers treat the inmates fairly (reverse coded). I am proud to work with the staff in this unit (reverse coded). I would transfer to another facility if given the opportunity to do so.

.936 .562 .687 .532

Administrative control Average custody score. Ratio of correctional officers to inmates.2 Proportion inmates in disciplinary housing.2

.916 .571 .849

Notes: 1 facility-level means of item responses; 2 logit transformation of original scale. N2 = 46 facilities.

Notes 1 The data for this study were collected as part of larger project that included a longitudinal element (Ohio only) and so larger sample sizes were sought in 11 Ohio prisons, although the ODRC only granted our request for larger samples in seven of these prisons. The sampling frames in the 11 prisons selected for the longitudinal data collection were restricted to only those individuals who had at least six months of their sentence remaining at the time of the first survey (≈ 85 percent of the inmates in these prisons had > six months remaining). The decisions to pursue larger number of individuals and restrict the samples to only individuals with > six months of their sentence remaining to serve were made to reduce the effects of attrition in the longitudinal analysis. Our goal was to obtain usable information on at least 100 individuals per prison (at least 200 individuals per prison in the prisons selected for the longitudinal data collection). The 30 percent over-sample was included to compensate for refusals and incomplete surveys, based on the recommendations of research staff at the ODRC. In Kentucky, we targeted sample sizes of 200 individuals per prison, but these numbers were adjusted based on the incarcerated population and resource demands placed on individual prisons. Non-English speaking individuals were excluded from the study due to resource constraints. 2 Individuals were unavailable because they had been released or transferred (N = 125), posed a safety risk or were in the infirmary (N = 42), were receiving a visit (N = 44), or were not in the prisons on the date of data collection (e.g., out to court) (N = 86). 3 Only black and white inmates were examined because there were not enough inmates (b 100) from any other racial or ethnic category to facilitate reliable analyses. 4 An argument could be made for estimating a tri-level model; however, preliminary analyses revealed that the majority of these outcome distributions did not vary across states or operating organizations (i.e., Ohio, Kentucky, or Private). For this reason, bi-level models were estimated for all outcomes. 5 In hierarchical analyses of dichotomous outcomes, the meaning of the variance estimates are based on the validity of the assumption regarding the underlying probability distribution of the outcome variable. For the models presented here, the estimates of variance were derived under the assumption that the level-1 random effects conformed to a logistic distribution (Raudenbush & Bryk, 2002). Estimates of variance explained were computed using the formula offered by Hox (2010). 6 Based on the random coefficient and standard error in Table 5, it can be inferred that weaker race effects would be nonsignificant, suggesting that blacks and whites confined in some prisons had similar odds of violence.

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