Abstract
Objectives
In light of empirical findings suggesting no substantive main effects of an incarcerated person’s (IP’s) race or ethnicity on the odds of placement in restrictive housing (RH) for rule violations, we investigated whether these effects are dependent on offense severity and context, including characteristics of facilities that could theoretically increase stakeholder reliance on biased stereotypes and also prison staff members’ perceptions of danger and order in a facility.
Methods
Multilevel analyses of race and ethnicity effects on RH decisions, both at the time of the incident (pre-trial) and after the rule infraction hearing, were conducted for all persons admitted to Ohio’s prisons between 2007 and 2016 and found guilty of prison rule violations (N1 = 81,673; N2 = 33).
Results
We found no significant main effects of an IP’s race or ethnicity on the odds of RH placement for rule infractions, either at the time of the incident or as punishment after a hearing, once the types of violations were controlled. Upon further investigation, we found that African American and Latinx IPs were more likely to receive RH for certain insubordination-related violations, which may invoke greater punitive discretion. Race effects were also stronger in prisons with tighter security, where officers generally relied less on IPs’ acknowledgements of their formal authority for rule enforcement, and in facilities for men.
Conclusions
Variance in the magnitude of racial and ethnic disparities in the use of RH for rule violations makes sense across prison settings and, as opposed to general race and ethnicity effects, should guide our understanding of the sources of these disparities with the goal of reducing their impacts.
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Notes
Crouch’s (1985) finding was counterintuitive because more severe punishments were distributed to white inmates in a Texas prison.
Our focus on first rule violation(s) committed by inmates after admission to prison could introduce less familiarity in the process, considering that the average length of time until an inmate’s first rule violation was only eight weeks after admission.
The Latinx in our sample can be further separated into persons with two Latinx parents (1 percent), Latinx – white (about 1 percent), and Latinx – black (0.2 percent). Numbers were too limited within each group to permit useful analyses of their differences in the odds of RH. Therefore, we combined these groups because analysis of the combined group versus an analysis of persons with two Latinx parents produced similar estimates.
One might speculate that time served by first offense could have more of an exponential effect on case outcomes rather than a linear effect whereby persons who have spent more time in prison should “know better” about violating prison rules, and more time may simply compound the attribution of culpability. As such, we explored this possibility in the data and found that time served is modeled best as a constant (linear) effect.
Distinguishing maximum security from all other security levels yielded the most substantive differences in findings. Regarding security classification in Ohio, state law requires processing inmates through a reception center where their risks and needs are assessed before placing them into designated security levels (O.A.C. 5120–9-52). At classification, an inmate is designated as level 1, 2, 3, 4, or 5 and assigned to a facility considered suitable for their supervision. Levels 4 and 5 are maximum security. A few facilities operate at maximum security only but other “mixed” facilities have maximum security units. A Level 1 inmate has the most privileges and is typically housed in facilities with more opportunities for reentry programming, including work camps and community-based work sites. A Level 2 inmate has some freedom of movement and may work or engage in leisure activities, but they are held in facilities with a double perimeter fence with razor wire and armed patrols. A Level 3 inmate has more direct supervision but is still permitted to interact with others in a “general population” setting. A Level 4 inmate is placed in a “control unit” with fewer privileges and more restrictions, but exact conditions depend on their needs and facility availability. A fifth security level designation is reserved for inmates connected to “violent, disruptive, predatory, riotous actions” because they are considered serious threats. They are placed in Extended Restrictive Housing (ERH), including supermax, and are locked in their cells for 22 h or more every day (ODRC, https://drc.ohio.gov/policies/classification).
The individual level officer data used to create these two aggregate facility measures included survey data compiled from random samples of officers and sergeants across all of Ohio’s facilities operating in 2007–08 (Nofficers = 1,390; Nfacilities = 33). The sample was not significantly different from the population of officers and sergeants regarding sex (77% men), race/ethnicity (79% non-Latinx white, 17% non-Latinx black, 1% Latinx, and 3% Native-American, Asian, and other groups), rank (95% line officers versus 5% sergeants), and length of service (μ = 10.2 years). The sample was, however, slightly older than the target population (x = 42.2 vs. μ = 41.4).
It is useful to note that officers who were more likely to perceive themselves as wielding legitimate power included those who were older, non-white, sergeants, and with more experience in the correctional system (p < .01 for each). These same characteristics also corresponded with perceptions of fewer problems with rule enforcement (p < .01 for each except race, p < .05). These empirical relationships suggest that an officer workforce that is more experienced and racially diverse might contribute to a less coercive officer culture.
A different order of the level-1 models might be preferred by some in order to first assess the “legal” effects on RH decisions (with a model including rule violations only), and then examining whether “extra-legal” factors mattered beyond the specific offenses (with a model adding all other covariates, including an IP’s race and ethnicity, to the first model). Based on what has been found in court sentencing studies, there is ample evidence indicating that offense type and severity should matter most for shaping case outcomes (Spohn, 2006, provides an in-depth discussion of this argument). Therefore, we estimated the pre-hearing and post-hearing models with rule violations only and these tables are available from the first author. The second model in this alternative sequence is identical to the third model described here.
The model progression we ultimately chose to display here was a purposeful one. First, we want to see whether racial and/or ethnic disparities in RH decisions exist (Eq. 1). Next, assuming these disparities exist, we want to investigate the extent to which they are attributable to racial and ethnic differences in other background and confinement factors generally (Eq. 2), and then specifically to the types of infractions for which they are found guilty once these other factors are controlled (Eq. 3). Isolating the impacts of rule violation types on the magnitude of race and ethnicity effects when moving from Eq. 2 to Eq. 3 then provides the impetus (potentially) to look more closely at race and ethnicity effects within offenses that might be treated in a more discretionary manner.
Several rule violations corresponded with significantly higher odds of RH (e.g., several violent offenses, theft) while others coincided with significantly lower odds (e.g., drug possession, tattooing). The non-significance of “death” and “escape” resulted only from the very small numbers of deaths and individuals who traveled beyond facility perimeters before apprehension. These effects were the largest in magnitude in the entire pool of offenses.
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Acknowledgements
This study was supported by a grant from the National Institute of Justice (Award #2016-IJ-CX-0013). Data provided by the Ohio Department of Rehabilitation and Correction (ODRC) and from ICPSR 34317. The opinions, findings, and conclusions are those of the authors and do not necessarily reflect those of the Department of Justice and/or the ODRC.
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Wooldredge, J., Cochran, J. How do Racial and Ethnic Disparities Emerge in the Use of Restrictive Housing for Prison Rule Violations?. J Quant Criminol 39, 769–803 (2023). https://doi.org/10.1007/s10940-022-09548-7
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DOI: https://doi.org/10.1007/s10940-022-09548-7