The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Examining Race and Gender Disparities in 

Restrictive Housing Placements 

Author(s): Melinda Tasca, Ph.D., Jillian Turanovic, 

Ph.D. 

Document Number: 252062 

Date Received: September 2018 

Award Number: 2016-R2-CX-0043 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
Examining Race and Gender Disparities in Restrictive Housing Placements 

National Institute of Justice 

W.E.B. Du Bois Program of Research on Race and Crime 

Project Summary 

August, 2018 

By: 

Dr. Melinda Tasca 

Principal Investigator 

Department of Criminal Justice and Criminology 

Sam Houston State University 

Email: mtasca@shsu.edu 

Phone: (936) 294-1643 

Dr. Jillian Turanovic 

Co-Principal Investigator 

College of Criminology and Criminal Justice 

Florida State University 

Email: jturanovic@fsu.edu 

Phone: (850) 645-0375 

This project was supported by Award No. 2016-R2-CX-0043, awarded by the National Institute of 

Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and 

conclusions or recommendations expressed in this publication are those of the author(s) and do not 

necessarily reflect those of the Department of Justice. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE OF CONTENTS 

Executive Summary............................................................................................................ 1 

Background......................................................................................................................... 2 

Current Research................................................................................................................. 3 

Methodology....................................................................................................................... 3 

Setting............................................................................................................................. 3 

Data................................................................................................................................. 4 

Dependent Variables....................................................................................................... 4 

Independent Variables .................................................................................................... 5 

Table 1 ............................................................................................................................ 6 

Statistical Procedures...................................................................................................... 7 

Findings .............................................................................................................................. 8 

Research Objective #1 .................................................................................................... 8 

Figure 1 ........................................................................................................................... 9 

Table 2 .......................................................................................................................... 10 

Research Objective #2 .................................................................................................. 11 

Research Objective #3 .................................................................................................. 12 

Figure 2 ......................................................................................................................... 13 

Table 3 .......................................................................................................................... 14 

Research Objective #4 .................................................................................................. 15 

Table 4 .......................................................................................................................... 16 

Conclusions....................................................................................................................... 17 

References......................................................................................................................... 19 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

EXECUTIVE SUMMARY 

Placement into restrictive housing is a controversial practice experienced by some inmates during 

incarceration. Nevertheless, little is known about who is placed in restrictive housing and under what 

conditions. Although this correctional management tool is used to isolate inmates who pose a risk to the 

operation and security of an institution, assessments underlying placement decisions are often racialized 

and gendered. Coupled with the seclusion of prisons from public scrutiny and the wide discretion 

afforded to prison officials, there are ample opportunities for extralegal factors to influence treatment. 

In an effort to generate a broader understanding of racial and gender disparities in conditions of 

confinement, this study—supported through NIJ’s W.E.B. DuBois Research Fellowship Program— 

examined restrictive housing placement decisions. 

Using administrative records on all inmates released from prison in one large state between 2011 

and 2014 (N = 33,143), this study assessed racial and ethnic disparities in men and women’s: 1) 

placements into any segregation; 2) placements into particular types of segregation (i.e. administrative 

segregation, disciplinary and mental health segregation); 3) the length of time spent there; and 4) the 

reasons provided for these placements. Descriptive, bivariate, and multivariate analyses were estimated 

to assess these relationships. Given that multiple housing placements were recorded for each inmate (N 

= 124,942), multilevel modeling procedures were used (i.e. hierarchical logistic regression, hierarchical 

negative binomial regression). 

Overall, results indicated significant racial and ethnic disparities in restrictive housing 

placements among men and women, net of legally- and administratively-relevant factors and other 

inmate characteristics. To be sure, Native American men were more likely than Whites to experience 

placements into any segregation, disciplinary segregation, and administrative segregation (ad-seg). 

Latinos and Black men had lower odds of placement into any segregation and also disciplinary 

segregation relative to Whites. At the same time, Native American men and Latinos spent more days in 

any segregation and ad-seg when placed there compared to Whites. Latinos in disciplinary segregation 

also experienced longer placements, while Blacks’ disciplinary segregation placements were shorter than 

Whites. For women, racial disparities were observed in placements into ad-seg and mental health 

segregation. Native American, Latina, and Black women had increased odds of placement into 

administrative segregation relative to their White counterparts. Latinas were less likely than Whites to 

experience placement into mental health segregation. Routine operations (e.g., custody reclassification, 

lateral transfers, inmate population adjustments) was the most commonly cited reason for restrictive 

housing placements across race/ethnicity and sex. 

This project informs research and policy alike. First, this study extends empirical knowledge on 

disparities in criminal justice decision-making to the correctional setting. Second, this project responded 

directly to calls for research regarding the use of segregation and whether it is applied fairly. Our work 

offers insight into the experiences of diverse and understudied groups, particularly Native Americans. 

And finally, this work can be useful for correctional departments when navigating and implementing 

decisions and practices pertaining to restrictive housing. 

[1] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
BACKGROUND 

A significant body of literature is devoted to understanding disparities in crime and punishment. 

A major takeaway from this work is the differential treatment of people of color and women by the 

criminal justice system (Spohn, 2015; Tonry, 2012). These patterns are particularly pronounced in 

incarceration. Despite evidence of slight declines in inmate populations in some jurisdictions, rates of 

female imprisonment continue to climb and the disproportionate number of racial and ethnic minorities 

cycling through our prison systems remains alarmingly high (Blumstein, 2015; Carson, 2018). Inmates’ 

experiences—particularly how they are managed and their conditions of confinement—is an 

underexplored area of inquiry, however. This is a significant gap in knowledge given incarceration 

experiences can profoundly affect people’s lives (National Research Council, 2014). 

A controversial correctional management tool involves placement into restrictive housing. This 

practice alters conditions of confinement by limiting inmates’ access to programming, education, 

recreation, and opportunities for social contact (Butler & Steiner, 2017; O’Keefe et al., 2013). Restrictive 

housing has direct implications for inmate wellbeing, behavior, and reentry (Frost & Montiero, 2016; 

Labrecque, 2015; Kaba et al., 2014; Reiter, 2015; Pizarro et al., 2014). Yet, little is known about who is 

placed in restrictive housing, for what reasons, and under what conditions (Frost & Monteiro, 2016; 

Mears & Bales, 2010). 

Restrictive housing is intended to separate inmates who pose a risk or threat to the operation and 

security of a correctional institution from the general population (Beck, 2015; Browne et al., 2011; Kane 

et al., 2014). How this is defined and applied in practice, however, is far from straightforward 

(Baumgartel et al., 2015; Lanes, 2010; Mears & Watson, 2006). Assessments of “risk” and 

“dangerousness” underlying placement decisions are inextricably linked to race and gender (Arrigo & 

Bullock, 2008; Cochran et al., In Press; Reiter, 2012). Common stereotypes of Blacks, Latinos, and 

Native Americans as prone to crime, violence, and gang involvement, and women as overly emotional 

and needing to be controlled, can contribute to disparate patterns in correctional punishment (Haney, 

2003; Hannah-Moffat, 2005; Toch, 2001; Wright et al., 2012). In addition, prison staff and 

administrators have wide discretion in sanctioning and segregating inmates and prisons are largely 

secluded from public scrutiny (Arrigo & Bullock, 2008; Lanes, 2010; Waquant, 2002). While discretion 

is necessary in unpredictable and dynamic environments such as prisons, it can also open the door to 

discriminatory practices (Lipsky, 1980; Olson, 2016). 

There are growing calls to better understand how segregation is used on a policy front as well. 

In 2016, the U.S. Department of Justice developed a set of guiding principles emphasizing that “this 

practice should be used rarely, applied fairly, subjected to reasonable constraints, and never used as a 

default solution” (p. 1). With this in mind, through NIJ’s W.E.B. DuBois Research Fellowship Program, 

we examined restrictive housing placement decisions to further knowledge on race differences in 

conditions of confinement among men and women. 

[2] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

CURRENT RESEARCH 

Guided by recent calls to better understand restrictive housing practices, and to advance research on 

racial, ethnic, and gender disparities in conditions of confinement, this research addressed the following 

four broad questions: 

1. Does the likelihood of being placed in restrictive housing vary by men and women’s race and 

ethnicity? 

2. Does the likelihood of being placed in a particular type of restrictive housing (i.e. administrative 

segregation, disciplinary segregation, mental health segregation) differ according to men and 

women’s race/ethnicity? 

3. Does the length of time spent in restrictive housing vary according to men and women’s 

race/ethnicity? 

4. Are the reasons documented by correctional staff for placing men and women in various forms 

of restrictive housing linked to race/ethnicity? 

METHODOLOGY 

Setting 

The setting for this study is one state prison system that is characterized by a diverse and rising 

prison population (Carson, 2018). At the time of data collection, this DOC housed more than 40,000 

inmates across 15 institutions (14 men’s facilities; 1 women’s facility). A unit-based custody system 

comprised of four security classifications (minimum, medium, close, and maximum) is used in this state. 

The data contain information on inmates’ placements into 204 different housing units across all 15 state 

prison complexes. Of the 204 housing units, 45 are designated maximum security, restrictive housing 

units. All maximum custody inmates are held in what is known as “restrictive housing.” Maximum 

security inmates are in lockdown the majority of the day, have limited work opportunities, and are closely 

monitored. These inmates are also escorted in full restraints within the institution. They experience little 

recreational time, visitation and phone privileges, and few interactions with other inmates. 

As with all units in this prison system, each maximum custody unit (which will also be referred 

to as a restrictive housing or segregation unit) serves a particular purpose. These include: 1) disciplinary 

segregation, 2) administrative segregation, and 3) mental health segregation. This DOC also has 

designated units for protective custody and medical segregation; however, these forms of restrictive 

housing placements were excluded from the data due to limited cases available across gender and 

racial/ethnic groups. 

There are numerous criteria that govern inmates’ placements into restrictive housing in this state. 

Inmates can be placed into restrictive housing through the standard classification process that determines 

custody risk using a one-to-five scale. Inmates undergo the classification process every six months. Risk 

criteria that determine custody level (i.e. placement into a minimum, medium, close, or maximumsecurity unit) include factors such as type of current offense, institutional misconduct history, gang 

affiliation, age, and other needs (e.g., mental health, substance abuse). In addition, inmates can be 

transferred to segregation units from lower custody levels for reasons such as committing or leading 

others to commit disruptive behaviors, engaging in serious behavior, and when deemed a threat to the 

[3] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

security of the institution. Correctional officers make initial recommendations for placement and these 

recommendations are elevated through an administrative chain of command for final approval. 

It is important to note, however, that inmates are placed into segregation immediately following 

the initial recommendation of officers and held in segregation throughout the administrative review 

process. If a reversal of the placement decision does occur, inmates are then transferred out of restrictive 

housing. When a decision to place an inmate into restrictive housing is upheld, an inmate’s length of 

stay is determined by a variety of factors, depending upon the type of segregation. 

For instance, inmates who have engaged in institutional misconduct can be placed into 

disciplinary segregation for up to 30 days. However, stays can last longer if there are pending 

investigations or if additional violations are committed while in disciplinary segregation. With respect 

to administrative segregation, there are a multitude of reasons for placement into these particular units. 

Validated gang members are held in this form of restrictive housing indefinitely unless they choose to 

engage in a debriefing process and formally renounce their gang membership. Inmates may also be held 

in ad-seg pending investigations or while awaiting decisions or transfers to other types of restrictive 

housing units (e.g., protection-related concerns). Lastly, mental health segregation is used for mentally 

ill inmates who have demonstrated signs or behaviors that suggest they pose an imminent risk of selfharm or harm to staff or fellow inmates. Length of placement into mental health segregation is 

determined by the recommendations of mental health professionals who conduct ongoing assessments 

of these segregated inmates. Taken together, there are many pathways into restrictive housing and there 

is considerable discretion afforded to correctional officials when making these placements. 

Data 

The data for this project were obtained from the Inmate Management System in the state, which 

contains information on all movements in and out of prison, institutional misconduct, inmate risk factors, 

inmates’ demographic characteristics, and criminal history and offense information. Unique among most 

correctional data sources, these data provide detailed information on every housing placement for each 

inmate, including the date and time of each housing placement, the specific unit/type of housing 

placement, and reasons documented by correctional staff for placing inmates in each housing unit. 

The data used contain information on all housing placements since 2010 for all White, Black, 

Latino/a, and Native American inmates in the state with release dates between July 1st, 2010 and June 

30th, 2014 (N = 33,143). Roughly 86.5% of inmates were male and 13.5% were female. Since there are 

multiple housing placements recorded for each inmate across multiple prison complexes, the data have 

a cross-classified hierarchical structure, and are measured at the person-placement level (N = 124,942 

placements). Descriptive statistics for all measures are presented in Table 1. 

Dependent Variables 

Consistent with the four objectives of the study, we examined four dependent variables: 1) 

placement into any segregation, 2) the type of segregation, 3) the length of time spent in segregation, 

and 4) the reason for placement into segregation. Inmates can be placed into restrictive housing multiple 

times, for various lengths of time, and for various reasons. To ensure that important variation was not 

masked within inmates, all dependent variables were measured at the person-placement level. 

First, any segregation is a dichotomous variable that reflects placement into a restrictive housing 

unit for at least 22 hours (1 = yes, 0 = no). This measure is consistent with guiding principles put forth 

by the U.S. Department of Justice (2016). Each housing unit in the data has its own unique code from 

which we can determine whether inmates were placed into a restrictive housing unit. Since housing units 

are not mixed-use, only inmates placed within particular units are subject to restrictive housing 

conditions. Approximately 16% of all inmate placements were into some form of segregation. 

[4] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

Second, three distinct types of restrictive housing were captured: administrative segregation, 

disciplinary segregation, and mental health segregation. Among total inmate placements, 10% were into 

disciplinary segregation, which was the most common form of restrictive housing. Slightly less than 3% 

of all placements were into administrative segregation (2.6%) as well as mental health segregation 

(2.9%). 

Third, we measured the days spent in restrictive housing. We measured days spent in any 

segregation (Mean = 53.8 days), as well as days spent in each type of restrictive housing, including 

disciplinary segregation (Mean = 27.4 days), administrative segregation (Mean = 194.1 days), and 

mental health segregation (Mean = 23.5 days). 

Fourth, and lastly, we examined the reasons for restrictive housing placement that were recorded 

by correctional staff. We grouped specific reasons into meaningful categories. Categories include: 

routine operations (55.8%), protection-related (27.6%), security management (8%), mental health (8%), 

and medical (0.6%) reasons. Specific reasons for placement by correctional personnel were captured in 

the administrative dataset in shorthand text. Common reasons that were coded under routine operations 

included “reclassification,” “lateral transfer” (i.e. moving from one unit to another of the same level of 

custody) and “population adjustment.” Common reasons grouped under the protection-related category 

included terms like “protection” and/or policy numbers linked to protective custody guidelines. Reasons 

that were coded under security-management included terms such as “disciplinary problem” and “pending 

investigation.” The mental health category was almost entirely comprised of language that specifically 

cited mental health in the text, with a small number citing “suicide attempt” or “72-hour evaluation.” 

Finally, all text grouped under the medical needs category explicitly noted the term “medical.” The 

inclusion of correctional staff’s reasoning for placements is important in that it can reveal disparities in 

how inmates are housed, beyond just examining the type of restrictive housing that they are placed into. 

Independent Variables 

Our key independent variable is race/ethnicity. Race/ethnicity was captured using mutually 

exclusive indicators of White (non-Hispanic) (45.1%), Black (13.9%), Latino/a (33.7%), and Native 

American (7.3%). 

Several important covariates were incorporated into analyses that include additional 

demographics and legally- and administratively-relevant factors. Covariates were measured at the inmate 

level (level 2) and at the placement level (level 1) of the data. 

At the placement level (level 1) we included an indicator for time since admission in months 

(Mean = 17.5 months). At the inmate level (level 2) we included measures for age, offense type, inmate 

risk, and institutional misconduct. Forty-seven percent of inmates were younger than 30 years of age. 

Inmates were mostly incarcerated for non-violent offenses (67.8% versus 32.2%) and 61.2% of all 

inmates had incurred at least one disciplinary infraction during their current incarceration term. Inmate 

risk was measured as a composite score that captured inmates’ substance abuse treatment needs, mental 

health treatment needs, prior incarceration history, gang affiliation, and low educational attainment (i.e. 

no high school diploma or equivalent) that were assessed at intake. The composite score is a five-point 

scale in which the mean inmate risk score was 1.63. These covariates are reflective of factors used in the 

inmate classification process that determine inmates’ housing placements, as explained in the previous 

section. 

[5] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Descriptive Statistics. 

Mean (SD) or % Range N Placements N Inmates 

Dependent Variables (Placement-Level) 

Restrictive Housing Placements 

Any Segregation 15.9% 0-1 124,942 33,143 

Disciplinary Segregation 10.4% 0-1 124,942 33,143 

Administrative Segregation 2.6% 0-1 124,942 33,143 

Mental Health Segregation 2.9% 0-1 124,942 33,143 

Days Spent in Restrictive Housing 

Days in Any Segregation 53.8 (125.2) 0-3,540 19,834 7,415 

Days in Disciplinary Segregation 27.4 (38.4) 0-650 12,962 5,768 

Days in Administrative Segregation 194.1 (249.9) 0-3,540 3,215 1,892 

Days in Mental Health Segregation 23.5 (65.2) 0-1,687 3,657 2,087 

Reasons for Restrictive Housing Placement 

Routine Operations 55.8% 0-1 19,834 7,415 

Protection-Related 27.6% 0-1 19,834 7,415 

Security Management 8.0% 0-1 19,834 7,415 

Mental Health Needs 8.0% 0-1 19,834 7,415 

Medical Needs 0.6% 0-1 19,834 7,415 

Independent Variables (Placement-Level) 

Time Since Admission (months) 17.5 (27.0) 0-359 124,942 33,143 

Independent Variables (Inmate-Level) 

Inmate Sex 

Male 86.5% 0-1 124,942 33,143 

Female 13.5% 0-1 124,942 33,143 

Inmate Race 

White 45.1% 0-1 124,942 33,143 

Black 13.9% 0-1 124,942 33,143 

Latino/a 33.7% 0-1 124,942 33,143 

Native American 7.3% 0-1 124,942 33,143 

Age in Years 

18-24 26.7% 0-1 124,942 33,143 

25-29 20.5% 0-1 124,942 33,143 

30-39 27.8% 0-1 124,942 33,143 

40-49 17.8% 0-1 124,942 33,143 

50+ 7.2% 0-1 124,942 33,143 

Offense Type 

Violent 32.2% 0-1 124,942 33,143 

Non-Violent 67.8% 0-1 124,942 33,143 

Inmate Riska 1.63 (1.08) 0-5 124,942 33,143 

Institutional Misconduct 61.2% 0-1 124,942 33,143 

Notes: The data have a cross-classified hierarchical structure, such that placements are nested within 

inmates and prison complexes (N Placements = 124,942; N Inmates = 33,143; N Prisons = 15). a Inmate risk is a composite score based on inmates’ substance abuse treatment needs, mental health 

treatment needs, prior incarceration history, gang affiliation, and low educational attainment. These 

measures were assessed at intake. 

[6] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

 

Statistical Procedures 

We conducted descriptive, bivariate, and multivariate analyses that reflected the research 

objectives and the characteristics of the data. The study began with a variety of descriptive analyses to 

assess the distributional characteristics of the dependent variables and each of the covariates (Table 1 

and Table 4). Second, bivariate statistics (i.e., Pearson chi-square tests and one-way ANOVAs) were 

used to assess the relationships between race/ethnicity and the dependent variables for men and women 

(Figures 1 and 2). Third, multivariate analyses established whether any racial/ethnic disparities in 

restrictive housing placements that existed at the bivariate level could be explained by legally- and 

administratively-relevant factors and/or other demographic characteristics. 

We used multilevel modeling (MLM) procedures designed to account for the nested nature of 

our data. This modeling strategy is appropriate given that our data contain a hierarchical structure. MLM 

allows for the simultaneous investigation of statistical relationships within hierarchical units (i.e., within 

each inmate) and between hierarchical units (i.e., between inmates). When using nested data, the 

assumptions in normal ordinary least-squares regression—that observations are independent, and that 

error terms are uncorrelated—are violated (Hox, 2010; Raudenbush & Bryk, 2002). These issues can 

result in artificially narrow confidence intervals and shrunken standard errors—problems that bias the 

results in favor of statistical significance. MLM techniques resolve these issues by incorporating into the 

statistical model a unique random effect for each organizational unit (i.e., for each inmate). In doing so, 

these models can account for shared variance in level 1 observations (i.e., characteristics of housing 

placements) both within and between inmates. MLM techniques are well-documented in the literature, 

and MLM is frequently used in fields of education, economics, health, and social sciences (see, e.g., 

Raudenbush & Bryk, 2002). 

The multivariate analyses proceeded in the following stages. Beginning with our first research 

objective, hierarchical logistic regression models were estimated to determine whether inmate 

race/ethnicity was significantly associated with placements into restrictive housing for men and women, 

net of covariates (Table 2).1 

For our second research objective, we examined whether race/ethnicity was related to specific 

types of restrictive housing placements for men and women (i.e. disciplinary segregation, administrative 

segregation, and mental health segregation) using hierarchical logistic regression models and controlling 

for all covariates (Table 2). 

Our third research objective was to assess whether there were racial and ethnic differences in the 

length of time male and female inmates spent in restrictive housing. We examined length of time spent 

in any segregation, as well as length of time spent in each type of segregation. Only inmates who had 

been placed in restrictive housing during their incarceration were included in these analyses. These 

models were estimated using hierarchical negative binomial regression and included all covariates 

(Table 3). 

Finally, as stated in our final research objective, we explored whether there were racial and ethnic 

disparities in the various reasons provided for male and female inmates’ placements into restrictive 

housing. Frequency distributions are provided to show patterns of reasons for restrictive housing 

placements (i.e. routine operations, protection-related, security management, mental health needs, 

medical needs) across race/ethnicity and sex (Table 4). 

1 Cross-classified hierarchical logistic regression models were estimated for male inmates since there were multiple 

placements recorded for each inmate, and men can serve time in several different prison complexes throughout the state. 

Cross-classified hierarchical models were not needed for female inmates since there was only one women’s prison complex 

in the state. 

[7] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

FINDINGS 

Research Objective 1: Determine whether there are racial and ethnic disparities in men and women’s 

placements into restrictive housing. 

Figure 1 depicts placements into restrictive housing by race and sex. Each bar represents the 

percentage of restrictive housing placements within each racial/ethnic group. With respect to any form 

of segregation, there were statistically significant race differences for both men and women at the 

bivariate-level. 

Beginning with the male sample, Figure 1 shows that placements into any form of restrictive 

housing were highest among Latinos (18.1%), followed by Native Americans (17.3%), Whites (16%), 

and then Blacks (15.3%). 

Turning to the female sample, placements into any form of restrictive housing were highest 

among Blacks (15.9%), followed by Native Americans (12.1%), Latinas (11.9%), and lowest among 

Whites (8.3%). 

Table 2 provides results from multivariate hierarchical logistic regression models that assess 

racial disparities in placements into any segregation for male and female inmates. These models 

determine whether race differences persist after taking into account legally- and administrativelyrelevant covariates and additional demographic factors (i.e., time since admission, inmate age, offense 

type, inmate risk, and institutional misconduct). 

The results showed that significant racial and ethnic disparities remained among men. To be 

sure, after controlling for covariates, Native Americans (b = .102; odds ratio [OR] = 1.107)) were more 

likely than Whites to have placements into any form of segregation. On the other hand, Blacks (b = -

.223; OR = .800) and Latinos (b = -.115; OR = .891) were less likely to have placements into any 

segregation compared to Whites. There were no significant racial and ethnic differences in any 

restrictive housing placements for women in the multivariate analysis. 

[8] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Figure 1. Placements into Restrictive Housing, By Race, Sex, and Type of Placement. 

Note: Chi-square tests indicate statistically significant race differences across all types of restrictive housing placements (p < .01), with the 

exception of mental health segregation (men). 

** p <.01. 

[9] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

Table 2. Select Parameter Estimates from Hierarchical Logistic Regression Models Assessing Racial Disparities in Placements into 

Restrictive Housing. 

Any Disciplinary Administrative Mental Health 

Segregation Segregation Segregation Segregation 

Male Inmates 

Black 

Latino/a 

Native American 

Inmate-level variance component 

Prison-level variance component 

Between-inmate variance explained 

Between-prison variance explained 

Female Inmates 

Black 

Latino/a 

Native American 

Inmate-level variance component 

Between-inmate variance explained 

b (SE) 

-.223** (.037) 

-.115** (.028) 

.102* (.048) 

1.585** 

2.349** 

.525 

.102 

.172 (.127) 

.018 (.094) 

.040 (.136) 

1.276** 

.373 

b (SE) 

-.258** (.042) 

-.129** (.031) 

.118* (.054) 

1.205** 

3.684** 

.531 

.137 

.124 (.113) 

.164 (.086) 

.103 (.124) 

1.343** 

.340 

b (SE) 

-.134 (.096) 

.079 (.071) 

.457** (.121) 

1.858* 

4.851*** 

.585 

.159 

.683* (.297) 

.558* (.240) 

.689* (.313) 

2.131** 

.483 

b (SE) 

.065 (.183) 

-.386** (.147) 

-.370 (.215) 

1.712** 

.227 

Note: Unstandardized coefficients (b) and robust standard errors (SE) are presented. Analyses for male inmates are estimated using cross-classified 

hierarchical logistic regression models, where placements (N = 108,131) are nested within inmates (N = 28,078) and prison complexes (N = 14). 

Analyses for female inmates are estimated using hierarchical logistic regression models, where placements (N = 16,811) are nested within inmates 

(N = 5,065). Analyses are not presented for male placements into mental health segregation since no race differences existed at the bivariate level. 

All models adjust for time since admission, and include controls for inmate age, offense type, inmate risk, and institutional misconduct. 

**p < .01; *p < .05 (two-tailed test). 

[10] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

Research Objective 2: Determine whether there are racial and ethnic disparities in men and women’s 

placements into particular types of restrictive housing. 

Figure 1 presents placements into disciplinary, administrative, and mental health segregation by 

race and sex. Bivariate analyses revealed that there were significant racial and ethnic differences in 

placements across all types of restrictive housing, with the exception of mental health segregation 

placements among males. 

Beginning with men, Latinos had the highest proportion of placements into disciplinary 

segregation (11.9%), followed by Native Americans (11%) and Whites (11.3%), and the lowest 

proportion was among Blacks (9.6%). Latinos also represented the highest proportion of administrative 

segregation placements (3.5%), followed closely by Native Americans (3.4%) and Blacks (2.6%), with 

the lowest proportion among Whites (2%). 

With respect to women, disciplinary segregation placements were highest among Blacks 

(6.1%) and Latinas (6%), followed by Native Americans (5.3%), and then Whites (4%). Women’s 

administrative segregation placements were also highest among Blacks (3.5%), Native Americans 

(2.7%), and Latinas (2.6%), and lowest among Whites (0.9%). The proportion of placements into 

mental health segregation were highest among Black women (6.2%), and then Native Americans 

(4.1%), followed by Whites (3.4%), and lowest among Latinas (3.3%). 

Table 2 shows estimates from multivariate hierarchical logistic regression models examining 

the effect of race on placements into different types of restrictive housing by men and women, 

controlling for key covariates (i.e., time since admission, inmate age, offense type, inmate risk, and 

institutional misconduct). 

Results revealed that there were no longer race differences in disciplinary segregation 

placements among women in the multivariate analyses, but racial disparities persisted in disciplinary 

segregation among men. Native American men were significantly more likely than their White 

counterparts to be placed into disciplinary segregation (b = .118; OR = 1.125). Blacks (b = -.258; OR 

= .773) and Latinos (b = -.129; OR = .889) were less likely than White men to experience disciplinary 

segregation placement. 

There also continued to be significant racial and ethnic disparities in administrative segregation 

placements for both men and women once covariates were included in the models. Native American 

men (b = .457; OR = 1.579) had greater odds of placement into administrative segregation (ad-seg) 

compared to Whites. For women, Native Americans (b = .689; OR = 1.992), Blacks (b = .683; OR = 

1.980), and Latinas (b = .558; OR = 1.747) were more likely to experience ad-seg placements than 

Whites. 

Finally, race effects remained in mental health segregation placements of women. In particular, 

Latinas (b = -.386) had significantly lower odds of such placements relative to Whites. 

[11] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

Research Objective 3: Determine whether there are racial and ethnic disparities in the length of time 

men and women spend in restrictive housing. 

Figure 2 portrays the average number of days spent in any restrictive housing, as well as 

different types of restrictive housing (i.e. disciplinary, administrative, and mental health segregation) 

by race and sex. Bivariate analyses indicated significant race differences among men’s placements into 

any restrictive housing, disciplinary segregation, and administrative segregation, but not mental health 

segregation. Among women’s placements, significant racial disparities emerged among placements 

into any segregation and administrative segregation only. 

Turning first to the male sample, Native Americans spent the most days in any form of 

segregation (Mean = 70.9 days), followed by an average of 67.5 days among Latinos, 48.5 days among 

Blacks, and 46.5 days among Whites. The longest placements in disciplinary segregation were 

experienced by Latino and Native American men (who averaged 30.4 and 30.5 days in disciplinary 

segregation), followed by Whites (Mean = 27.9 days), and then Blacks (Mean = 22.4 days). A similar 

pattern emerged for length of stay in administrative segregation. Native Americans spent the most time 

in ad-seg, with average placements of 243.1 days. This was followed by Latinos (Mean = 226.5 days), 

Whites (Mean = 177.1 days), and then Blacks (Mean = 172.4 days). 

Focusing next on the female sample, Native Americans served an average of 36 days in any 

form of segregation, while Latinas served average stays of 35 days. Blacks served an average of 23 

days, and Whites served an average of 20.4 days. There were significant race differences in the length 

of time spent in administrative segregation as well, with Latinas spending the longest time in these 

placements (Mean = 121.6 days), followed by Native Americans (Mean = 118.7 days), Whites (Mean 

= 89.8 days), and then Blacks (Mean = 70.3 days). 

Table 3 provides results from hierarchical negative binomial regression models assessing racial 

disparities in days spent in any segregation, disciplinary segregation, and administrative segregation 

for men and women, net of controls. 

Significant racial differences observed in the bivariate analyses remained in the multivariate 

models among males, while racial disparities disappeared in the female models. Native American men 

(b = .255) and Latinos (b = .212) spent more time in any form of restrictive housing placement relative 

to White men. Similarly, Native Americans (b = .194) and Latinos (b = .167) were housed in ad-seg 

longer than Whites as well. Latinos (b = .068) also spent more time in disciplinary segregation 

placements than Whites, while Black men spent less time in disciplinary segregation. 

[12] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Figure 2. Average Number of Days Spent in Restrictive Housing, By Race, Sex, and Type of Placement. 

Note: One-way ANOVAs indicate statistically significant race differences in the number of days spent in restrictive housing for all placements (p 

< .05), with the exception of mental health segregation (men), disciplinary segregation (women), and mental health segregation (women). 

**p <.01; *p < .05. 

[13] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Select Parameter Estimates from Hierarchical Negative Binomial Regression Models 

Assessing Racial Disparities in Days Spent in Restrictive Housing. 

Days in Any Days in Disciplinary Days in Admin. 

Segregation Segregation Segregation 

b (SE) b (SE) b (SE) 

Male Inmates 

Black -.073 (.051) -.224** (.042) -.030 (.075) 

Latino/a .212** (.036) .068* (.029) .167** (.053) 

Native American .255** (.060) .047 (.049) .194* (.081) 

Inmate-level variance component .695** .320** .350** 

Prison-level variance component .154** .093** .086** 

Between-inmate variance explained .607 .603 .620 

Between-prison variance explained .109 .093 .111 

N Placements 18,051 12,128 2,896 

N Inmates 6,597 5,130 1,789 

N Prisons 14 13 11 

Female Inmates 

Black -.110 (.173) .182 (.238) 

Latino/a .195 (.134) -.261 (.271) 

Native American .301 (.189) .102 (.304) 

Inmate-level variance component 1.020** .221* 

Between-inmate variance explained .357 .276 

N Placements 1,763 319 

N Inmates 818 153 

Note: Unstandardized coefficients (b) and robust standard errors (SE) are presented. Analyses for male 

inmates are estimated using cross-classified hierarchical negative binomial regression models, where 

placements are nested within inmates and prison complexes. Analyses for female inmates are estimated 

using hierarchical negative binomial regression models, where placements are nested within inmates. 

Analyses are not presented for women’s time spent in disciplinary segregation, or men’s and women’s time 

spent in mental health segregation, since no race differences existed at the bivariate level. All models adjust 

for time since admission, and include controls for inmate age, offense type, inmate risk, and institutional 

misconduct. 

**p < .01; *p < .05 (two-tailed test). 

[14] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

Research Objective 4: Determine whether there are racial and ethnic disparities in the various 

reasons provided for male and female inmates’ placements into restrictive housing. 

Table 4 presents frequency distributions of reasons for placement into any form of restrictive 

housing across race/ethnicity and sex. Reasons for placement provided by correctional personnel were 

broken down into five categories, as detailed in the methodology section: 1) routine operations (which 

were primarily comprised of specific terms noted by correctional staff as “reclassification,” “lateral 

transfer,” and “population adjustment”); 2) protection-related; 3) security management (specific terms 

most commonly noted by personnel in the data grouped under this category included “pending 

investigation” and “disciplinary concerns”); 4) mental health needs; and 5) medical needs. 

The most commonly cited reason for placement into restrictive housing by correctional staff for 

men and women, across race, was routine operations. More than 60% of Native American men’s 

segregation placements were explained by reasons tied to routine operations. This was also the case for 

58.5% of Black men’s placements, 56.7% of Latinos’ placements, and about half of White men’s 

placements (48%). In addition, 82.6% of Native American women’s restrictive housing placements 

were noted as due to routine operations. The majority of other women’s placements were explained by 

routine operations as well (i.e. White women: 80%; Latinas: 79.8%; Black women: 75.2%). 

For men, the second most common reason noted for segregation was for protection-related 

purposes. Protection was cited the most often for White men (35.3%), followed by Latinos (27.1%), 

Black men (25%), and the least often for Native American men (23.2%). Security management 

concerns were most often cited for the restrictive housing placements of Latinos (8.9%) and least often 

for Black men (6.8%). In contrast, mental health was more commonly reported as a reason for the 

segregation of Black men (9.2%) and least common among Latinos (6.9%). The least reported reason 

for segregation among all men was medical needs. Medical reasons were documented the most often 

for White men (0.8%) and the least often for Latinos (0.4%). 

For women, following routine operations, the second most frequently cited reason for 

segregation was mental health needs. Mental health was cited more often for Black women (18.3%) 

than for other racial groups (i.e. Latinas: 14.8%; White women: 13.5%; Native American women: 

12.3%). Similarly, Black women also comprised the largest proportion of restrictive housing 

placements explained by security management (5.5%), while White women represented the smallest 

portion of the security management category (2.1%). Among placements noted as protection-related, 

like men, protection was most commonly documented for Whites (4.4%). Medical needs, also like 

men, were the least often cited reason for women’s segregation placements. The highest proportion of 

placements due to medical needs were among Native American women (0.5%) and none of the 

placements of White women were explained by medical needs. 

[15] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

Table 4. Administrative Reasons for Inmate Placements into Restrictive Housing, By Race and Sex. 

Routine 

Operations 

ProtectionRelated Security 

Management 

Mental Health 

Needs 

Medical 

Needs N Placements 

Male Inmates 

White Men 48.0% 35.3% 8.7% 7.3% 0.8% 7,697 

Black Men 58.5% 25.0% 6.8% 9.2% 0.6% 2,350 

Latino Men 56.7% 27.1% 8.9% 6.9% 0.4% 6,727 

Native American Men 60.6% 23.2% 8.6% 7.1% 0.5% 1,277 

χ

2 = 239.39** 

Female Inmates 

White Women 80.0% 4.4% 2.1% 13.5% 0.0% 680 

Black Women 75.2% 0.6% 5.5% 18.3% 0.3% 311 

Latina Women 79.8% 2.2% 2.9% 14.8% 0.2% 580 

Native American Women 82.6% 1.9% 2.8% 12.3% 0.5% 212 

χ

2 = 29.26** 

N Placements = 124,942. 

N Inmates = 33,143. 

**p < .01. 

[16] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

CONCLUSIONS 

With support through NIJ’s W.E.B. DuBois Research Fellowship Program, we aimed to increase 

empirical knowledge on racial, ethnic, and gender differences in restrictive housing—a controversial, 

yet poorly understood practice commonly used in correctional settings. Compared to other areas of 

criminal justice, little empirical research exists on disparities in correctional decision-making, 

particularly regarding the use of segregation. As such, four objectives were addressed in this study, 

including whether there were race differences in men’s and women’s placements into 1) any form of 

segregation; 2) specific types of segregation (i.e. disciplinary, administrative, mental health); 3) the 

length of time spent there; and 4) documented reasons for placement. 

On the whole, findings showed significant race effects in restrictive housing placements among 

men and women, net of key covariates (e.g., legal and administrative factors, inmate characteristics) 

that are used in the risk classification process of inmates in this state. Among men, Native Americans 

were more likely to experience placement into any restrictive housing and disciplinary segregation 

compared to Whites, whereas Latinos and Blacks were less likely to experience these placements. 

While Latinos had lower odds of placement into disciplinary segregation relative to Whites, they did 

spend longer periods of time in these units when placed there, however. Blacks spent less time in 

disciplinary segregation than Whites. Native Americans had greater odds of ad-seg placements as well, 

and both Native Americans and Latinos who experienced any form of restrictive housing or 

administrative segregation, spent more time in these placements than Whites. Among women, different 

(and fewer) race effects emerged. Specifically, Native American, Latina, and Black women were 

significantly more likely to experience placement into administrative segregation than White women. 

And, Latinas had lower odds of placement into mental health segregation than their White 

counterparts. The most frequently documented reason for placement into restrictive housing for both 

men and women, across racial and ethnic groups, was routine operations (most commonly recorded 

reasons grouped under this category included “reclassification,” “lateral transfer,” and “population 

adjustment”). 

Together, these findings show that inmates of color experience different treatment from their 

White counterparts when it comes to segregation, even after accounting for important factors like 

institutional behavior and other combined risks (e.g., gang affiliation, prior incarceration history, 

mental health, substance abuse, and education attainment). These decisions are rooted in assessments 

of risk, which may or may not be based on actual behavior (Bench & Allen, 2003; Haney, 2003; 2008). 

Risk perceptions can be influenced by racial and gender stereotypes to predict dangerousness and 

threat (Grekul & LaBoucane-Benson, 2008; Hannah-Moffat, 2005; Poole & Regoli, 1980; Tonry, 

2012; Zatz & Portillos, 2000). Prison officials exercise broad discretion in how inmates are managed 

and placed. This level of discretion is afforded to these professionals because their jobs are challenging 

and often unpredictable. Since correctional staff are responsible for managing large numbers of 

inmates in hostile and dangerous settings, it isn’t necessarily feasible or practical to craft a uniform 

response for every situation that might arise (Olson, 2016). Combined, however, these conditions can 

open the door to differential treatment and outcomes, like some of the findings observed in our work 

here. 

A pattern noteworthy of further discussion from our overall findings pertains to the restrictive 

housing placements of Native Americans, particularly men. It may be that distinctive cultural 

perceptions of this group drive their disparate placements into segregation and their lengths of stay 

while there. Our results differ from typical expectations regarding racially disparate treatment in that 

African American men tend to be most often overrepresented in prisons and in the criminal justice 

system more broadly. To our knowledge, no other study to date has examined restrictive housing 

[17] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

placements of Native Americans. Additional research is needed that explores the correctional 

experiences of diverse groups as there are significant gaps in the existing knowledge base, especially 

with respect to the Native American population. Future research should also examine racial and gender 

differences in segregation in other jurisdictions as we cannot speak to whether our findings are 

generalizable to other systems. Qualitative interviews with correctional officers and administrators 

may also be needed to delve more deeply into reasons underlying disparities in placements. While 

reasons for placements are noted in the data analyzed in our study, they are limited to short-hand text 

and are quite general in nature. Given that there is significant variation in placements into restrictive 

housing across institutions, future work may also benefit from assessing the contextual predictors of 

placements at the facility level. 

Importantly, incarceration experiences have consequences. When inmates are placed into 

restrictive housing, this affects access to programming (e.g., education, treatment), the nature and 

extent of contact they can have with family and friends, and can even impact terms of community 

supervision (Haney, 2008; Lanes, 2010; Rhodes, 2005). Given its potential for producing ill effects, it 

is critical that this correctional practice is reserved for the “worst of the worst” (Butler et al., 2013). 

After all, under certain circumstances, restrictive housing is necessary. At the same time, our findings 

suggest that segregation is not reserved for only the most dangerous or high-risk of inmates in practice. 

Correctional policies allow for inmates to be segregated for a broad range of reasons and there is 

significant discretion in how these decisions are made. This is evidenced by our finding that even after 

controlling for risk factors used by this system, race/ethnicity still predicts placements into segregation. 

Identifying disparities in the use of this correctional management tool is an important first step 

toward improved decision-making processes that can balance both institutional safety and security, 

with the fair administration of justice. Recently, states have begun to make reforms to their restrictive 

housing policies and practices. One particular example involves increased reliance on close custody 

units by some systems for managing disruptive or high-need inmates instead of automatically locking 

down these individuals in traditional segregation. Another example of reforms includes the use of stepdown programs for gang members and other inmates with a history of violence. While step-down 

programs are promising for giving inmates the opportunity for getting out of segregation, they do not 

necessarily help reduce the overuse of this practice upfront. Still, these reforms illustrate some of the 

promising ways correctional systems are attempting to reduce overreliance on segregation that can 

disproportionately impact certain groups. Refinements to policies in all correctional systems on how 

risk should be defined and handled in practice coupled with officer training is also needed to ensure 

that segregation is applied fairly and used only when less restrictive options have been exhausted. 

In the end, our goals were to respond to calls for research regarding the use of segregation and 

to inform both research and policy on disparities in the conditions of confinement. We encourage 

additional scholarship on this timely topic to further build the evidence base that can be fruitful for 

correctional departments in carrying out their challenging work. 

[18] 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

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[21] 

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