The relevance of inmate race/ethnicity versus population composition for understanding prison rule violations

Transcription

1 Copyright The Author(s), Reprints and permissions: journalspermissions.nav ; Vol 11(4): DOI: / PUNISHMENT & SOCIETY The relevance of inmate race/ethnicity versus population composition for understanding prison rule violations BENJAMIN STEINER AND JOHN WOOLDREDGE University of South Carolina and University of Cincinnati, USA Abstract The importance of order maintenance in prisons has generated numerous studies of the correlates to inmate misconduct. Very few of these studies, however, have focused specifically on the correlations between inmates race/ethnicity and prison rule breaking. Race and ethnicity could be relevant to an understanding of prison rule breaking if inmates bring their ecologically structured beliefs regarding legal authority, crime and deviance into the institutional environment. Using data from two nationally representative samples of males incarcerated in state facilities, we examined the individual-level effects of an inmate s race and ethnicity on his likelihood of engaging in various forms of misconduct during incarceration, as well as the contextual effects of the racial/ethnic composition of inmate and correctional staff populations on levels of rule breaking. Findings reveal that the effects of an inmate s race and ethnicity differ by offense type, and the racial/ethnic composition of inmates and correctional staff have both main and conditioning effects on levels of misconduct. Implications of these results are discussed within a social control framework. Key Words ethnicity inmate misconduct prisons race Social disorder in a correctional facility has implications for the well-being of both inmates (Irwin, 1980; Bottoms, 1999) and staff (DiIulio, 1987; Lombardo, 1989; Sparks et al., 1996; Bottoms, 1999). The level of disorder in a facility might be 459

2 PUNISHMENT & SOCIETY 11(4) reflected, in part, by the level of inmate misconduct because the latter relates directly to both institutional safety and the degree to which inmates adhere to facility rules (DiIulio, 1987; Reisig, 1998; Bottoms, 1999). The importance of maintaining safe and orderly prisons has generated numerous studies of the correlates to misconduct, and scholars have underscored the relevance of both inmate and environmental characteristics for explaining variations in the level of misconduct (Goodstein and Wright, 1989; Bottoms, 1999; Wooldredge et al., 2001; Camp et al., 2003; Huebner, 2003). Only a few of these studies, however, have focused specifically on the role of an inmate s race and ethnicity for predicting misconduct (e.g. Harer and Steffensmeier, 1996; Camp et al., 2003). Race and ethnicity could be relevant to an understanding of misconduct if inmates bring their ecologically structured beliefs regarding legal authority, crime and deviance into the prison environment (see Sampson and Wilson, 1995). This is not to say that these culturally shaped values are internalized norms, but rather latent beliefs that may influence the likelihood of deviance in environments that are similar to those in which the beliefs were originally shaped (Wacquant, 2001; Sampson and Bean, 2006). Prisons might constitute such environments (see also Bernard, 1990; Harer and Steffensmeier, 1996; Wacquant, 2001). The dearth of attention to the relationships between inmates race/ethnicity and prison rule breaking is unfortunate in light of the changes in the demographic composition of state prison populations which have occurred over the past several decades. Between the late 1970s and the early 1990s the density of African Americans as well as Hispanics in state prisons nearly doubled (Tonry, 1994; Blumstein and Beck, 1999; Mauer, 2006). Even though these trends have stabilized, the representation of both minority groups in state correctional facilities remains disproportionate to their representation in the general population; in fact, African Americans are no longer the minority in a large portion of state operated prisons (Harrison and Beck, 2006; Mauer, 2006). Several ethnographic studies examined the effects of inmate racial and/or ethnic stratification on inmate acculturation shortly after the start of the rapid rise in minority incarceration rates (e.g. Carroll, 1974; Jacobs, 1977; Irwin, 1980), and many quantitative studies of misconduct have included an inmate s race and/or ethnicity as predictors in empirical models. Yet we are aware of only two efforts to examine both the individual and contextual effects of race and ethnicity on inmate rule breaking (see Harer and Steffensmeier s (1996) study of 58 federal prisons for men and Camp et al. s (2003) study of all federal prisons). Using data from two nationally representative samples of men incarcerated in state operated facilities during the 1990s, we follow from these prior investigations by examining the individual-level effects of an inmate s race and ethnicity on his likelihood of engaging in various forms of misconduct during incarceration, as well as the contextual effects of the racial/ethnic composition of inmate and correctional staff populations on levels of rule breaking. This study extends the previous empirical work on race/ethnicity and prison rule breaking by analysis of: (1) data from state facilities as opposed to federal facilities; (2) random effects (instead of fixed effects) of inmate-level predictors across facilities; (3) data from two time periods in order to assess the stability of effects over time; and (4) possible interaction effects on misconduct levels involving the racial composition of both inmates and staff. 460

3 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity MICRO- AND MACRO-LEVEL CONTROLS OVER INMATE BEHAVIORS The primary foci of this study include the potential links between: (1) an inmate s race or ethnicity and their likelihood of misconduct at the individual level; and (2) the racial/ethnic composition of inmate and staff populations and levels of misconduct at the aggregate level. These particular relationships, as well as the other inmate- and facility-level predictors of misconduct examined for purposes of statistical control, can be framed within a social control perspective. The decision to adopt a control perspective stems from the idea that informal control in prison can be linked to a common interest among inmates to maintain order (Sparks et al., 1996; Bottoms, 1999). While control per se reflects formal practices used by correctional staff to maintain order, it also reflects the ability of inmates to realize common goals. A control perspective also recognizes the potential contributions of both micro- and macro-level effects on crime and deviance (Sampson, 1986; Wooldredge et al., 2001). Therefore, concepts which have previously been framed within an importation (individual-level) perspective or a deprivation (aggregate-level) perspective might both be considered reflections of informal social controls. A control model is also inclusive of the hypothesized relationships between inmate race/ethnicity and misconduct at both the micro- and macrolevels of analysis (see, for example, Sampson and Bartusch, 1998; Warner, 2003; Sampson and Bean, 2006). Relationships between race/ethnicity and prison rule violations at the individual level Researchers have often included an inmate s race as a predictor in models of prison deviance. Very few of these scholars, however, have outlined why race may be relevant to the subject, perhaps due to concerns with being misinterpreted (but see Harer and Steffensmeier, 1996). This situation is not unique to studies of inmate deviance (see, for example, Sampson and Wilson, 1995). In an effort to encourage future research on the subject, we advance a theoretical position that may explain why minority inmates might be more likely to engage in violent behaviors in correctional institutions. Due to data constraints we do not test these ideas directly in this study, but instead provide an examination of the potential link between an inmates race and ethnicity and various types of prison rule violations. If race or ethnicity is associated with a higher likelihood of violent infractions, then the ideas discussed below could be relevant to examine in future studies of the subject. The link between race and inmate rule breaking has been grounded in cultural adaptation perspectives on crime and deviance which posit that residents of neighborhoods characterized by concentrated poverty and social isolation may adapt to their circumstances by developing values counter to those of the larger society (see Sampson and Wilson, 1995; Warner, 2003; Sampson and Bean, 2006). Although residents of disadvantaged neighborhoods may share mainstream cultural values, these values become attenuated within certain structural contexts (Warner, 2003; Sampson and Bean, 2006). When the strength of conventional values is weaker, informal controls over individuals behaviors are less effective for preventing deviance (Kornhauser, 1978; Warner, 2003; Sampson and Bean 2006). The conditions in extremely disadvantaged neighborhoods are such that crime (including violence) is tolerated and expected as a part of daily life (Wilson, 1987; 461

4 PUNISHMENT & SOCIETY 11(4) Sampson and Wilson, 1995; Krivo and Peterson, 1996; Anderson, 1999). Feelings of resentment and hostility toward legal authority are also pervasive among residents (Hagan and Albonetti, 1982; Harer and Steffensmeier, 1996; Sampson and Bartusch, 1998; Anderson, 1999). Due to the overrepresentation of nonwhites in these economically and socially disadvantaged neighborhoods, African Americans may be more likely to adopt values related to an underclass culture (Wilson, 1987; Sampson and Wilson, 1995; Sampson and Bean, 2006). Emerging evidence suggests this perspective may also be applicable to other ethnic minority groups (see Sampson and Bartusch, 1998; Sampson and Bean, 2006). If minority inmates are drawn disproportionately from these types of neighborhoods (Rose and Clear, 1998), then these inmates may not hold much respect for the rules of a correctional facility because they question the legitimacy of those rules (Harer and Steffensmeier, 1996; Wooldredge et al., 2001). It follows that rule breaking may be more common among African American and Hispanic inmates because they may bring their ecologically structured beliefs regarding legal authority, crime and deviance into the prison environment (Irwin and Cressey, 1962; Irwin, 1980; Harer and Steffensmeier, 1996). Some might argue that, since incarcerated individuals are removed from the neighborhoods in which they were residing, an observation that minority inmates are more likely to engage in prison rule breaking might reflect support for the idea that culturally shaped beliefs are internalized norms. However, we follow from researchers of communities and crime in theorizing that such beliefs are ecologically shaped (e.g. Sampson and Wilson, 1995; Sampson and Bartusch, 1998). That is, the cultural values held by residents of disadvantaged neighborhoods are not norms, but adaptations to the constraints and opportunity structures which characterize the cognitive landscapes of these neighborhoods (Sampson and Bean, 2006). Although these structurally induced beliefs become latent when individuals are removed from disadvantaged neighborhoods (e.g. incarcerated), they may become relevant if they are pertinent to solving problems posed by an individual s new environment (Becker and Geer, 1960). In other words, individuals who hold cynical beliefs toward legal authority and are more tolerant of deviance will have a higher probability of maintaining some or all of those views when placed in environments that pose similar obstacles as the environment from which they were pulled. For reasons discussed below, we argue that prisons could be such an environment. Our position is not that White inmates who originate from disadvantaged neighborhoods hold conventional beliefs regarding legal authority and deviance (see also Sampson and Bartusch, 1998; Sampson and Bean, 2006). Instead, based on previous research, we speculate that minority inmates are more likely to be drawn from disadvantaged neighborhoods due to: (1) the overrepresentation of minorities in racially/ethnically heterogeneous disadvantaged neighborhoods; and (2) the greater number of the racially/ethnically homogenous disadvantaged neighborhoods that are minority neighborhoods (Sampson and Wilson, 1995; Krivo and Peterson, 1996; Rose and Clear, 1998; Sampson and Bean, 2006). As a result, we speculate that within racial/ethnic groups a greater proportion of African American and Hispanic inmates (as opposed to White inmates) hold ecologically shaped beliefs regarding legal authority and crime that make them more likely to engage in prison rule breaking. We also speculate that within racial/ethnic groups that are drawn from disadvantaged areas the 462

5 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity degree of attenuation of conventional beliefs may be greater among minority inmates because of the differences in the level of disadvantage between minority disadvantaged neighborhoods versus White neighborhoods of disadvantage (Krivo and Peterson, 1996; Sampson and Bean, 2006). For several reasons, the parallels between disadvantaged communities and prison environments might be more relevant for understanding inmate violence as opposed to other types of misconduct. First, ethnographic studies of disadvantaged neighborhoods have documented how these areas are often characterized by cultural values which condone and legitimize violence as a mechanism for attaining status or respect (see, for example, Anderson, 1999). Studies of the prison environment have also underscored the role of violence in status attainment (e.g. Sykes, 1958; Jacobs, 1977; Wacquant, 2001). Second, both disadvantaged neighborhoods and prisons cultivate expectations about aggression at the hands of others (Bernard, 1990; Wacquant, 2001). These expectations generate a fear for self which in turn may evoke protective and potentially violent responses (Bernard, 1990; Harer and Steffensmeier, 1996; Anderson, 1999). Third, drug dealing and other nonviolent crimes might be less prevalent among disadvantaged groups in prison relative to residents of low status neighborhoods because of fewer opportunities for such crimes inside prison (i.e. more limited availability of drugs as well as fewer personal possessions). Thus, if prisons pose similar problems as disadvantaged neighborhoods, then those individuals who hold ecologically shaped beliefs reflecting greater disrespect for legal authority and more tolerance for deviance may be more likely to maintain those views in prison; in particular, their views regarding violence. Since minority inmates are drawn disproportionately from disadvantaged neighborhoods, then they may be more likely to engage in violence in prisons. Reviews of the empirical literature on inmate adaptation and misconduct have revealed mixed findings with regard to race and ethnicity (Goodstein and Wright, 1989; Wooldredge, 1991; Bottoms, 1999), although a meta-analysis did find an overall positive effect of minority status on rule violations (Gendreau et al., 1997). None of these reviews focused on potential offense-specific effects, however. While the evidence is not conclusive, many offense-specific studies have uncovered positive relationships between an inmate s race or ethnicity and violent misconduct (e.g. assaults on inmates), and either a null or negative relationship with other outcomes such as drug offenses (see, for example, Wooldredge, 1994; Harer and Steffensmeier, 1996; Sorensen et al., 1998; Gaes et al., 2002; Huebner, 2003; Gillespie, 2005; Berg and DeLisi, 2006). Other studies, however, have not observed a unique relationship between race or ethnicity and violent misconduct (Wright, 1989; Camp et al., 2003; Griffin and Hepburn, 2006). In one of the more recent studies devoted to this issue, Harer and Steffensmeier (1996) examined data collected in 1988 and 1989 from 58 federal prisons for men. They found a positive relationship between race (African American) and violent misconduct versus a negative relationship between race and drug/alcohol misconduct. When considered along with their other findings, Harer and Steffensmeier s (1996) observations support the relevance of the violence-specific cultural adaptation perspective. It remains to be seen, however, whether their findings generalize to Hispanic inmates, prisoners confined in state institutions or analyses of more recent data. 463

6 PUNISHMENT & SOCIETY 11(4) Contextual effects of the racial and ethnic composition of inmate and staff populations Ethnographic studies of prison environments have documented how demographic changes to the racial composition of inmate populations in the 1960s and 1970s coincided with increased violence and deviance within prisons (e.g. Carroll, 1974; Jacobs, 1977; Irwin, 1980). These scholars observations are consistent with ideas stemming from macro-level theories of social control, such as the idea that increases in the racial heterogeneity of community populations, or in this case prison populations, may weaken informal controls. More specifically, while ethnically diverse groups might share common values (e.g. a desire for order); increasing heterogeneity can obstruct patterns of interaction and communication that bind social organizations together (Kornhauser, 1978; Sampson and Groves, 1989; Bursik and Grasmick, 1993). The dramatic rise in the incarceration rates of minorities coincided with the inmate rights movement which, in turn, introduced legal obstacles to prison administrators abilities to exercise particular mechanisms of formal control (e.g. limits on punitive segregation and the abolition of corporal punishment). The weakening of formal controls allowed racial tension to become an important influence on levels of conflict in state prisons (Carroll, 1974; Jacobs, 1977). Organized groups (often based on race and ethnicity) which existed in urban areas also emerged inside prisons and contributed to conflict between inmates as well as between inmates and staff (Jacobs, 1976, 1977). Regarding the latter, the growing numbers of nonwhite inmates from urban areas were subjected to control and supervision by guards who were predominately White and often from rural areas (Jacobs and Kraft, 1978; Irwin, 1980, 2005; Camp et al., 2003). Thus, when the proportion of nonwhite inmates increased simultaneously with the inmate rights movement, a period of disruption followed in many prisons (Irwin, 1980, 2005). The processes that contributed to elevated levels of violence in the 1970s and early 1980s have since subsided in many states, however, which brings into question the current applicability of a link between the racial composition of inmate populations and levels of inmate misconduct. At the national level, the proportions of African American and Hispanic inmates reached a plateau in the early 1990s (Blumstein and Beck, 1999; Harrison and Beck, 2006), and the related stabilization in many states just happened to coincide with longer sentences of incarceration resulting from legislative changes owing to the get tough movement (Blumstein and Beck, 1999; Austin and Irwin, 2001; Irwin, 2005). 1 Since the racial composition of prisons has stabilized in some states, inmates may have adapted to racial heterogeneity with a common focus on facility order. Drawing from the equal status contact hypothesis, this thesis suggests that conflict may be reduced by equal status contact between majority and minority groups in pursuit of common goals (Allport, 1954). Prisons could be societies where residents generally have equal status and share a common goal (i.e. to do their time in a safe and orderly environment) (Irwin and Cressey, 1962; Sparks et al., 1996; Bottoms, 1999; Trulson and Marquart, 2002). Equal status contact may be relevant to a cultural adaptation explanation of deviance because when the structural inequality experienced by minority residents within the larger population is reduced, the cultural role of social isolation and corresponding adaptation should also be reduced (Sampson and Bean, 2006). Related to the equal status contact perspective, surveys of prison administrators have revealed support for the racial integration of prison inmates (Riveland, 1999; 464

7 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity Henderson et al., 2000). Henderson et al. (2000) also found that prison officials did not report an increase in violence or conflict after integration. Similarly, Trulson and Marquart (2002) found that desegregation of Texas prisons via integrated cell assignment did not result in more violence when compared to violence among segregated inmates. Trulson and Marquart also observed a decrease in the level of racially motivated assaults among integrated cell partners. The equal status contact perspective also posits that the effect of contact is greatly enhanced by institutional supports, one of which might involve local atmosphere (Allport, 1954). Greater heterogeneity among correctional staff might offer a more normalized prison experience for inmates (Camp et al., 2003), and so a potentially relevant measure of a supportive facility culture is the racial composition of correctional staff. More normalized prison environments may facilitate the perception of common interests between members of different race and ethnic groups, thus constituting a mechanism of informal control over inmates. Scholars have also discussed how minority officers may be more efficacious in working with the inmate population (Jacobs and Kraft, 1978; Britton, 1997). On the other hand, disparity between the racial composition of staff and inmates could fuel inmates perceptions of injustice (e.g. Jacobs, 1977; Jacobs and Kraft, 1978). In support of these ideas, Irwin (2005) discussed how the hiring of minority officers is now more common among prison systems in larger states, and these practices have lessened racial tensions between inmates and staff. McCorkle et al. (1995) observed a positive relationship between the ratio of White Black correctional staff and rates of both inmate and staff assaults. Other relevant controls over inmates A reliable examination of race/ethnicity effects on inmate misconduct requires consideration of other variables that might be included in a multivariate model as statistical controls. Such individual- and facility-level predictors of misconduct might also might proxy various aspects of informal controls over inmate behaviors. Specifically, at the individual level, age may be inversely related to deviance because younger inmates often have fewer conventional relationships (with, for example, partners and/or their own children) and are less likely to be involved in activities reflecting more conformist lifestyles (such as full-time jobs). Ample support has been found for a negative relationship between an inmate s age and misconduct (Goetting and Howsen, 1986; MacKenzie, 1987; Wooldredge, 1994; McCorkle, 1995; Sorensen et al., 1998; Harer and Steffesmeier, 1996; Cao et al., 1997; Wooldredge et al., 2001; Gaes et al., 2002; Camp et al., 2003). A commitment to traditional goals and involvement in conventional behaviors also may be suggestive of a greater commitment to conformity (Toby, 1957; Hirschi, 1969). For example, marriage and employment might exhibit some control over an inmate, as he might have more to lose by engaging in deviance (Wooldredge et al., 2001). Inmates who are married or those who were employed prior to their incarceration may also be less likely to commit rule infractions because these inmates have demonstrated some level of conformity (to conventional lifestyles) and, therefore, might be more likely to abide by facility rules. Evidence supporting the relevance of these measures, however, is mixed. (For studies of one or both of these measures, see Goetting and Howsen, 1986; 465

8 PUNISHMENT & SOCIETY 11(4) Toch et al., 1989; Wright, 1989; Wooldredge, 1994; Cao et al., 1997; Huebner, 2003; Jiang and Winfree, 2006). Related to employment, securing a facility work assignment might also reflect to some extent an inmate s stake in conformity or, from a more cynical perspective, a tolerance for conventional activities. Even assuming that many inmates perform facility work merely to appease staff, inmates with facility work assignments could be less likely to engage in deviant behaviors. Also relevant to consider are an inmate s committing offense and prior record (Goetting and Howsen, 1986; Toch et al., 1989; Wooldredge, 1994; Harer and Steffensmeier, 1996; Cao et al., 1997; Harer and Langan, 2001; Wooldredge et al., 2001; Camp et al., 2003; Huebner, 2003; Jiang and Winfree, 2006). The relevance of continuity in individual behavior is well documented (see, for example, Sampson and Laub, 1993). Inmates who have engaged in more serious offenses or have lengthier criminal histories could also be more likely to engage in misconduct because their beliefs regarding the legitimacy of legal authority may be weaker (Alpert and Hicks, 1977; Wooldredge et al., 2001). These measures are particularly important to include when examining the potential effect of race and ethnicity in light of findings regarding differential involvement in criminal activity (e.g. Blumstein, 1982; Langan, 1985). Many inmates suffer from having been physically or sexually abused, which may result in higher odds of misconduct (Toch, 1977; Toch et al., 1989). Drawing from perspectives on personal control, the abuses suffered by inmates may contribute to learned feelings of helplessness (Goodstein et al., 1984). Such feelings interfere with an individual s perceptions of choice, outcome control and predictability of future events. These interferences prevent the formation of coping methods that can alleviate stress associated with uncertainty, thus increasing the odds of acting out in stressful situations. An inmate s mental (ill) health and substance abuse prior to incarceration may similarly affect his odds of misconduct, by contributing to (or reflecting) learned helplessness and increasing the likelihood of maladaptive responses to the prison environment (Toch, 1977; Toch et al., 1989; McClellan et al., 1997). Problems such as a history of abuse or mental illness could also inhibit the forming of attachments to conventional others. Such problems might lessen an inmate s involvement in marriage and employment (Sampson and Laub, 1993). Accordingly, histories of physical/sexual abuse, substance abuse and mental illness may act as inhibitors of informal control, leading to higher odds of deviance. McClellan and her colleagues (1997) found that histories of abuse and mental illness were related to drug use. Toch et al. (1989) observed a relationship between mental illness and rule infractions in general, and Jiang and Winfree (2006) found a relationship between pre-prison drug use and misconduct. Regarding relevant predictors of misconduct at the aggregate level, Irwin (2005) observed that the architectural design of newer facilities and the use of special maximum security facilities for containing disruptive inmates, along with reductions in the level of racial prejudice and informal segregation, have contributed to lower levels of rule breaking across most general confinement prisons. Security and efficiency have guided the planning and design of most facilities constructed after 1980, and newer prisons have enhanced staff members abilities to monitor and control the inmate population, reducing opportunities for deviance (Irwin, 2005). 466

9 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity Differences in facility security level typically coincide with differences in physical environments that may either promote or inhibit rule violations. For example, the environment of a maximum security prison is often more sterile and authoritative compared to less secure facilities. Although maximum security facilities typically contain more guards, they also contain more dangerous and high risk inmates. For all of these reasons, maximum security facilities are generally associated with higher levels of officially reported misconduct (McCorkle et al., 1995; Jiang and Fisher-Giorlando, 2002; Huebner, 2003; Jiang and Winfree, 2006). 2 Another possible influence on levels of misconduct involves the use of work assignments to keep inmates out of trouble. As opposed to being coercive, however, this form of control is remunerative in that inmates may consider work assignments to be valuable, and so they can function as incentives to comply with facility rules (Colvin, 1992; Huebner, 2003). Facilities with higher percentages of inmates with work assignments, therefore, may have lower levels of rule breaking. 3 METHOD The analysis focused on the individual-level effects of an inmate s race and ethnicity on the odds of engaging in various types of misconduct within prison, and the contextual effects of the racial/ethnic composition of both inmate and officer populations on levels of misconduct across prisons. The target population included all male inmates incarcerated in state-operated correctional facilities in the United States with the primary purpose of confinement. Female inmates were excluded from the analysis due to unmeasured structural and cultural differences that exist between many of the state-operated facilities for women versus those for men that may also correlate with misconduct (see Owen and Bloom, 1995; Pollock, 2002; Kruttschnitt and Gartner, 2005; Morash, 2006). In light of the study objectives, the following research questions were addressed: (1) Within facilities, what were the empirical relationships between an inmate s race/ethnicity and the prevalence of assaults, drug/alcohol offenses and other nonviolent rule infractions? (2) Between facilities, what were the relationships between the racial/ethnic compositions of the inmate and the staff populations and the proportion of inmates who engaged in assaults, drug/alcohol offenses and other nonviolent rule infractions? (3) Between facilities, did the interaction between the racial composition of the inmate and staff populations improve prediction of the proportions of inmates who engaged in each category of rule infractions? (4) Did the empirical relationships between an inmate s race/ethnicity and the prevalence measures of misconduct vary in magnitude across facilities and, if so, were these relationships conditioned by facility characteristics? (5) Were the empirical relationships involving race/ethnicity at both the individual and facility levels consistent over time (1991 and 1997)? Samples and data The inmate-level data analyzed for the study were obtained from two waves of the survey of state inmates conducted by the United States Bureau of the Census (1997 Survey of 467

10 PUNISHMENT & SOCIETY 11(4) Inmates in State and Federal Correctional Facilities, and 1991 Survey of Inmates in State Correctional Facilities). The surveys provided nationally representative data on state prison inmates offenses and sentences, criminal histories, personal and family backgrounds, substance abuse and related treatment, as well as their routines while in prison. For reasons mentioned above, we selected only male inmates from each of the larger samples. Also due to structural and organizational differences across facilities based on inmate populations, we excluded inmates housed in coed facilities, facilities serving the District of Columbia and facilities whose primary function was not confinement (e.g. bootcamps). Each time period was examined separately in order to evaluate similarities and differences in findings over time. Despite some differences in the available information across the two data sets, we were able to derive common measures for the analysis described here. The Bureau of the Census used similar sample designs for each time period. For the 1991 inmate survey, the facilities for men were selected from a sampling frame of all 1239 state-operated facilities in the United States (including coed facilities). The 51 largest facilities were selected with certainty, while the remaining facilities were then separated into eight substrata defined by census region (Northeast, Midwest, South and West) and facility type (confinement versus community-based). The facilities were then grouped within each region to ensure adequate representation of population size. After calculating probabilities proportionate to size to determine the sampling interval within each substratum, systematic sampling generated 175 additional facilities. These procedures yielded a final total of 226 facilities for men. In the second stage of sample selection, inmates were selected randomly from a list provided by each facility that included all persons who occupied a bed the previous night. The total number of inmates selected for interviews in each facility was based on facility size. Interviews were completed for 11,163 male inmates between June and August of In addition to excluding inmates from coed facilities, the District of Columbia facilities and nonconfinement facilities, we also eliminated inmates if they were not sentenced (n = 74) or if they had missing data on time served, committing offense or number of prior arrests (n = 321). These decisions reduced the 1991 sample examined here to 8795 males confined in 183 facilities across 39 states. 4 For the 1997 inmate survey, the sampling frame consisted of all 1409 state-operated facilities for men in the United States (also including coed facilities). Information from federal facilities was also collected for the 1997 survey, but those inmates were excluded from our analyses. 5 A similar design as the one employed in 1991 was used to obtain the state facility sample, including the stratification criteria. Important differences involved the automatic inclusion of only the 13 largest facilities for males, and the use of slightly different regional substrata (Northeast except New York, New York, Midwest, South except Texas, Texas, West except California and California). A total of 223 facilities for men were selected. In addition to excluding the inmates from federal, coed and nonconfinement facilities, we also eliminated inmates if they were not sentenced (N = 102) or if they had missing data on time served, committing offense or number of prior arrests (N = 493). These procedures yielded a final sample of 8566 males in 175 facilities across 36 states. 468

11 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity For both data sets, sample weights were derived (by the Census Bureau) based on sample effects and efforts to reflect the population distributions. These weights were normalized for the multivariate analyses. Facility-level data were obtained from three waves (1990, 1995 and 2000) of the Census of State and Federal Adult Correctional Facilities, also collected by the Bureau of the Census. The 1990 and 1995 Censuses provided the sampling frames for the facilities selected for the 1991 and 1997 inmate surveys, respectively, making them conformable to merge with the survey data. Using information contained in both data sets, the survey data were matched to the relevant census data to create the unique data sets used here (some of this information is restricted for 1997 and was obtained with written permission from Interuniversity Consortium for Political and Social Research (ICPSR)). 6 All of the measures included in the final models are described in Table 1. The measures in Table 1 were ultimately selected from the available measures in the data sets by considering their theoretical relevance as demonstrated in the empirical literature, thorough checks for (multi)collinearity, the stability of coefficient estimates (influenced by the number of predictors relative to sample size) and the strength of the zero-order relationships. The outcome measures were created from the inmates self-reports of their individual misconduct histories and included whether an inmate had been written up or charged with a physical assault (on other inmates or staff), drug/alcohol offense or any other nonviolent rule infraction. These dichotomous outcomes described above are subsequently referred to as prevalence measures, defined as whether or not the inmate engaged in the offense. Self-report data potentially contain systematic errors resulting from poor recall and/or underreporting by certain groups of respondents (e.g. Hindelang et al., 1981), however, officially reported inmate misconduct has also been criticized in that it may underestimate actual deviance within an institution (Hewitt et al., 1984). Although both self-report and official measures of misconduct have been considered valid indicators of adjustment (van Voorhis, 1994), the limitations of the outcome measures examined here should be kept in mind when interpreting the findings. Self-reported prevalence measures of prisoner disciplinary records were also examined by Kruttschnitt and Gartner (2005). We focused on the prevalence of misconduct due to the analysis of self-reported officially detected incidents. The magnitude of undetected misconduct among inmates is unknown (Hewitt et al., 1984), and so the incidence of self-reported misconduct which was officially detected may not adequately reflect the inmate s level of involvement in rule violations. Prevalence measures of official misconduct are not without their limitations, but they are inherently less biased than incidence measures because inmates who engage in rule infractions are more likely to be accurately categorized as such, assuming a study period of reasonable length. We distinguished the three types of misconduct (assaults, drug/alcohol offenses and other nonviolent offenses) as opposed to combining them into a single category because, following the discussion of cultural adaptation, the inmate-level effects of race and ethnicity may only be relevant for predicting violent misconduct (see also, Harer and Steffensmeier, 1996). (Similarly, Camp et al. (2003) found that inmate and facility effects on misconduct varied by the type of misconduct examined.) The decision to 469

12 PUNISHMENT & SOCIETY 11(4) TABLE 1 Descriptions of the inmate and facility samples (unweighted) MEASURES X s X s Outcomes Assault(s) on other inmates or staff Drug/alcohol offense(s) Nonviolent offense(s) Level-1 Predictors: Inmates African American Hispanic Age (in years) Married Employed prior to arrest Physically/sexually abused Overnight mental health program Incarcerated for violent offense Incarcerated for drug offense Number of times arrested Used drugs in month before arrest Number of hours at work assignment (past week) Time served (months) N Level-2 Predictors: Facilities Inmate heterogeneity Staff heterogeneity Inmate heterogeneity x Staff heterogeneity Proportion with work assignment Number of years facility in operation Maximum security facility N Level-3: States N Notes: Ratio scales include age, time served, number of hours at work assignment and all level-2 predictors except security level. All other measures dummy coded. focus on these particular outcomes versus examining other outcomes (e.g. assaults on staff) was based on findings from Steiner and Wooldredge (in press). In another analysis Steiner and Wooldredge (in press) compared the magnitude of coefficient estimates derived from models predicting: (1) physical assaults on staff; (2) physical assaults on inmates; (3) inmate and staff assaults combined; (4) possession, sale or use of either drugs or alcohol; (5) possession of stolen property or an unauthorized item (excluding weapons and drugs); (6) escape, possession of a weapon or verbally assaulting a staff 470

13 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity member or another inmate (following Camp et al., 2003); (7) all other forms of misconduct; (8) all nonviolent forms of misconduct; and (9) all misconduct. We found that the three types of misconduct examined here offered the most unique findings across the nine models. Combining assaults on inmates with assaults on staff into a single outcome measure might seem questionable, but findings for all three assault measures listed above were completely redundant across the three outcomes. The combined measure was examined for the analysis presented here because of its inclusion of all assaults. The inmate-level predictors of misconduct included age, African American or Hispanic, married, employed prior to arrest, incarcerated for violent offense, incarcerated for a drug offense, number of prior arrests, physically/sexually abused prior to arrest, reported to have used drugs in the month before arrest, being housed in an overnight mental health program prior to incarceration and the number of hours spent at work assignment in the week prior to the survey. The facility-level predictors related to the racial/ethnic composition of inmate and staff populations included inmate heterogeneity, staff heterogeneity and an interaction term inmate heterogeneity staff heterogeneity. Both heterogeneity measures were derived with Blau s (1977) formula (1 Σp i 2 ), where the sum of the squared proportions of each facility s population of inmates or officers within each racial/ethnic group (p) is subtracted from 1. This measure appropriately considers the number and distribution of groups in the population. From each census we used five groups (Caucasian, African American, Hispanic, Asian/Pacific Islander and American Indian/Alaskan) for the calculation of the heterogeneity measures. The interaction term was created by multiplying the mean-centered values for inmate heterogeneity by staff heterogeneity. Other level-2 predictors included the proportion of inmates with work assignments, whether a facility was maximum security and the number of years facility in operation (as a proxy for facility design). 7 A limitation of the survey data is that the outcome measures and some of the predictors were derived from questions that did not reflect a fixed period of time. These questions were preceded by the phrase, Since your admission, have you..., which would increase the likelihood of misconduct among inmates who have been incarcerated for longer periods of time. To help adjust for this limitation, the measure time served was included as a statistical control variable. Statistical analysis All steps in the analysis involved multi-level modeling techniques due to the examination of both inmate and facility effects on misconduct, and because of the need to recognize the hierarchical structure of the data (inmates nested within facilities, and facilities nested within states). Tri-level data sets were created for each time period with inmates at level-1, facilities at level-2 and states at level-3. Facilities were not identified by name in each inmate data file, but there was enough information available to match inmates in each facility to their corresponding facility in the census data. As noted above, some of the information required for matching was restricted for 1997 and was obtained with written permission from ICPSR. The tri-level data files allowed us to: (a) adjust for correlated error among inmates nested within the same facility as well as for correlated error among facilities within the same state; (b) base the hypothesis tests on the appropriate sample sizes (for inmates 471

14 PUNISHMENT & SOCIETY 11(4) versus facilities); (c) remove (through group mean-centering) between-facility variation in inmate characteristics that might have corresponded with differences in misconduct rates across facilities; and (d) remove between-state variation in facility characteristics that might have corresponded with differences in misconduct rates across states, possibly due to differences in the composition of intake populations, budgets, classification procedures and management practices (through group mean-centering the level-2 measures). 8 More specific to this study, group mean centering the level-2 predictors removed between-state variation in facility environments that could result from statelevel differences in racial segregation policies (see, for example, Trulson et al., 2008). Although we created a three-level data set in order to address all of these issues, it is important to understand that the models displayed here are technically two-level models because they only include measures at the first two levels of analysis (inmates and prisons). This is why findings are displayed for only the first two levels of analysis. The dichotomous outcome measures were examined with Bernoulli regression. The bi-level analyses proceeded in several stages. First, an unconditional model (with no predictors) revealed the variance estimates in each outcome at level-1 (among inmates within facilities) versus level-2 (between facilities within states), versus level-3 (between states). Significant variance in each outcome at level-2, reflected by whether the level-1 model intercepts differed significantly between facilities (p.05), indicated significant differences in the proportions of inmates in a facility who engaged in misconduct. Next, random coefficient models were estimated for each of the level-1 predictors, allowing these effects (in conjunction with the level-1 model intercepts) to vary randomly across facilities. These models revealed whether the inmate-level effects on misconduct varied significantly across facilities (p.05), which would suggest stronger effects in some facilities versus others. Establishing such differences is a necessary prerequisite for estimating cross-level interaction effects (i.e. to examine whether differences in the level-1 effects across facilities might correspond with differences in the characteristics of those facilities). Given the specific focus of the study, cross-level interaction effects were only estimated if the level-1 relationship between race or ethnicity and misconduct varied across facilities. Although all of the level-1 effects were permitted to vary randomly, no other cross-level interactions were examined here. The third stage of the analysis involved estimating both level-1 and level-2 main effects. First, the level-1 predictors were entered as either random or fixed effects, depending on the findings from the random coefficients models. All level-1 model intercepts were designated as random because all varied significantly across the facilities even after the level-1 predictors were added to the model. The level-2 predictors were also entered, allowing for examination of level-2 effects on the level-1 intercepts. In the fourth stage of analysis, the model from stage 3 was expanded to include cross-level interactions (i.e. level-2 effects on the level-1 estimates for race/ethnicity effects on misconduct). A potential concern with using bi-level estimation methods with these data involved the limited number of inmates sampled within some of the facilities. Although both data sets averaged close to 50 inmates per facility (1991, X = 48.08, s = 13.95; 1997, X = 48.95, s = 7.25), the numbers in some facilities dipped below 25, raising concerns about the reliability of the level-1 intercepts and random coefficients. To adjust for this situation, the Empirical Bayes (EB) estimates of level-1 intercepts and slopes were modeled at level-2 (Raudenbush and Bryk, 2002). 472

15 STEINER & WOOLDREDGE The relevance of inmate race/ethnicity The final stage of analysis compared the level-1 and level-2 estimates for 1991 versus 1997 using the equality of coefficients test developed by Clogg et al. (1995). Brame et al. (1998) demonstrated the applicability of this particular test to a comparison of maximum likelihood coefficients between two independent samples. The coefficients were compared between time periods in order to assess the stability of the estimates over time. FINDINGS Table 2 displays the level-1 (inmate) models, Table 3 presents the level-2 (facility) models with main effects only and Table 4 describes the cross-level interactions (facility effects on the significantly varying level-1 relationships). Before delving into specific findings, it is worth mentioning that a significant and sizeable portion of variation in each outcome ( 20%) existed between facilities. Also noteworthy is that even though levels of assaults did not vary across states, levels of both drug offenses and other nonviolent offenses did vary significantly at level-3 (p <.05). Assaults The models predicting assaults (Table 2) revealed that both African American and Hispanic inmates were more likely to commit assaults, although the coefficient for African American was significant only for Other predictors that were relevant for both 1991 and 1997 included an inmate s age, an overnight stay in a mental health program, incarceration for a drug offense, number of prior arrests, pre-arrest drug use, the number of hours an inmate spent at his work assignment and time served. Incarceration for a violent offense was related to the likelihood of an inmate committing an assault in 1991 only, whereas marital status, pre-arrest employment and physical/sexual abuse were related to assaults in 1997 only. Although these last findings were unique to one year, the equality of coefficients tests revealed that only the effect of marital status differed significantly in magnitude over time (and was stronger in 1997). The age effect, although significant in both years, was stronger in Each model accounted for 15 percent of the variation in assaults. Turning to the facility-level main effects (Table 3), neither inmate nor staff heterogeneity affected the level of assaults (or the proportion of inmates who engaged in assaults in a facility). The interaction of the racial composition of the inmates and staff, however, was negatively associated with assault levels in both 1991 and That is, facilities which had higher levels of both inmate and staff heterogeneity corresponded with lower levels of assaults. The proportion of inmates with a facility work assignment was also negatively related to assaults in both time periods, while maximum security facilities corresponded with higher levels of assaults. The number of years a facility was in operation had no effect in either time period. The facility-level predictors explained 34 percent (1991) and 28 percent (1997) of the between-facility variation in assaults. The level-1 race and ethnicity effects on assaults varied significantly across facilities during both time periods (p <.05), permitting examination of potential cross-level interaction effects (or facility effects on the level-1 estimates). The results displayed in Table 4 reveal that both level-1 relationships were conditioned by a facility s security level. In the 1991 models, the effects of race and ethnicity were weaker in maximum security 473