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Article
Influence of Race in the
Deep End of the Juvenile
Justice System
Ashlin Oglesby-Neal
1
and Bryce Peterson
1
Abstract
Racial inequalities pervade U.S. justice systems and are the focus of a growing body of research.
However, there are fewer studies on racial disparities in juvenile justice settings, particularly on
decisions points at the “deep end” of the system after youth have been adjudicated delinquent. The
current study examines racial disparities in length of stay, institutional misconduct, and community
program placement for youth admitted to the Virginia juvenile justice system from 2012–2017. We
find that black youth have significantly longer lengths of stay and more serious institutional mis-
conduct than white youth. Controlling for legal and extralegal factors eliminates the disparity for
length of stay, but it remains significant for serious institutional misconduct. In recent years, youth of
all races are placed into community programs rather than traditional correctional centers at similar
rates. Disparities for Hispanic youth and other races are difficult to distinguish because few are
admitted to the system.
Keywords
juvenile justice, racial disparity
Racial disparities are nearly ubiquitous in U.S. criminal justice systems and are the focus of a large
and growing body of research. However, studies are still emerging on the extent and scope of racial
disparities in the extant juvenile justice literature. What research does exist suggests that these
disparities are found at most juvenile justice decision points, including referral, adjudication, dis-
position, and waivers to adult court (Puzzanchera & Hockenberry, 2016). This is particularly
problematic given longstanding research findings that people who become involved in justice
systems as children and adolescents are more likely to continue their involvement into adulthood
(Nagin & Paternoster, 1991; Sampson & Laub, 1993). In other words, because youth of color are
more likely to have contact with juvenile justice systems, they are at a greater risk of becoming
involved in criminal justice systems and experiencing diminished life outcomes as adults. It is thus
critical to examine racial disparities in juvenile justice settings.
1
Justice Policy Center, Urban Institute, Washington, DC, USA
Corresponding Author:
Ashlin Oglesby-Neal, Justice Policy Center, Urban Institute, 500 L’Enfant Plaza SW, Washington, DC 20024, USA.
Email: aoglesby@urban.org
Youth Violence and Juvenile Justice
2021, Vol. 19(2) 186-205
ªThe Author(s) 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/1541204020958465
journals.sagepub.com/home/yvj
Racial Disparities in Juvenile Justice Settings
National research has found that racial disparities exist at all levels of the juvenile justice system,
including arrest, diversion, detention, and waivers to adult court (Puzzanchera & Hockenberry,
2016). In this review of prior research, we discuss disparities at particular decision points in the
deep end of the juvenile justice system that are the focus of the current study—placement in secure
confinement, length of stay, and institutional misconduct—and then describe potential sources of
those disparities that can be examined in this study—differences in offending behavior, legal factors,
sociodemographic and risk factors, and treatment by the justice system.
Racial Disparities by Decision Point
The National Disproportionate Minority Contact Databook contains the counts, rates, and relative
rates of case processing outcomes for delinquency offenses by race from 1990 to 2013. According to
their most recent data, which come from over 1,200 counties from 26 states and the District of
Columbia, Hispanic youth and American Indian or Alaskan Native youth have higher rates of
adjudication and residential placement than white youth. Also compared to white youth, black youth
have slightly lower rates of adjudication and higher rates of residential placement, while Asian,
Hawaiian, and Pacific Islander youth have slightly higher adjudication rates and similar placement
rates. In short, racial disparities exist at nearly all decision points of juvenile justice case processing
and these disparities are especially pronounced for black and Hispanic youth.
While some of these decisions (e.g., referrals and adjudication) occur near the beginning of
juvenile justice case processing, many come later, or at the “deep end” of the system. For example,
studies on placement in secure confinement are mixed on whether black youth have disproportio-
nately high rates of placement after controlling for legal and extralegal factors (Davis & Sorensen,
2013; Leiber, 2013; Leiber & Peck, 2015; Peck & Jennings, 2016). Specifically, minority youth
are significantly more likely to receive placement in facilities that emphasize physical regimens,
such as boot camps or wilderness programs, and less likely to receive therapeutic placements than
white youth (Fader et al., 2014). Davis and Sorensen (2013) found that after controlling for arrest
rates, black youth were placed in confinement at rates nearly 70 percent higher than those of white
youth. In a sample of adjudicated youth in Florida, both black and Hispanic youth were signifi-
cantly more likely to receive a residential placement than white youth after controlling for adverse
childhood experiences, demographics, offense type, and many other extralegal factors (Zettler
et al., 2018). In contrast, some studies have found that black youth were less likely to receive out-
of-home placement in a correctional facility (Bishop et al., 2010; Cochran & Mears, 2015), and
Leiber (2013) suggests this may be an attempt to correct the overrepresentation caused by deci-
sions earlier in the juvenile justice system. Still other research suggests that race has no influence
on commitment decisions (Holleran & Stout, 2017).
Once youth are placed out of home, further research on disparities in their length of stay has found
minimal impact of race. Nationally in 2010, committed white and minority youth had spent the same
amount of time in placement (Sickmund & Puzzanchera, 2014). A study of referred youth confined
in Texas found that race and ethnicity had no significant influence on length of stay after controlling
for many legal and extralegal factors (Espinosa & Sorensen, 2016). Winokur et al. (2008) also found
very little difference in the mean length of stay in residential placement by race and security level for
youth in Florida, while Heggeness and Davis (2010) found that black and other minority youth
actually had shorter stays in placements. In 2015, Maggard examined variations in length of stay
before and after the implementation of Juvenile Detention Alternatives Initiative (JDAI) and found
that prior to its implementation, youth of color served significantly longer time in pre-dispositional
secure detention facilities, but the significance was lost post-JDAI implementation. Some current
Oglesby-Neal and Peterson 187
work attributes differences in length of stay to factors other than race, such as age at first arrest,
severity of offense, or facility type (Espinosa & Sorensen, 2016; Walker & Bishop, 2016; Winokur
et al., 2008), though Maggard (2015) theorizes that race effects could potentially exist within other
variables.
There has been limited research on racial disparities in institutional misconduct in juvenile
justice settings, and these results are mixed. A study of youth in California found that race and
ethnicity had no significant influence on sexual, other, and total misconduct (DeLisi et al., 2008).
Rather, age, prior offenses, sex offense history, and self-reported delinquency had a strong and
significant influence. An additional study by DeLisi and colleagues (2010) found that when
accounting for trauma, mental health characteristics, demographics, and offense history, black
youth were significantly more likely to have more sexual misconduct and total misconduct. A
meta-analysis of studies examining the relationship between psychopathy and total, aggressive,
and violent misconduct of youth in institutionalized settings found that the percentage of white
youth in the study had no effect (Edens & Campbell, 2007). Likewise, there is mixed evidence on
the influence of race on misconduct in adult prison settings. Many studies find that people of color
are more likely to engage in violent misconduct than white people (Berg & DeLisi, 2006; Harer &
Steffensmeier, 1996; Huebner, 2003), while others find no significant differences by race (Camp
et al., 2003; Jiang & Fisher-Giorlando, 2002; Steiner et al., 2014). Combined, the research on
youth and adults suggests that racial disparities in institutional misconduct, especially serious
misconduct, are unclear.
There is even less research on the relationship between race and disciplinary practices for
incarcerated youth. Because of the limited research on youth misconduct and discipline in incarcera-
tion settings, we review prior research in school settings. Much can be learned from the literature on
racial disparities in school discipline (McCarthy & Hoge, 1987; Monroe, 2005). Rocque (2010)
found that black students were more likely to receive office referrals during elementary school, even
after controlling for behavior and school effects. Anderson and Ritter (2017) looked at the use of
exclusionary discipline in Arkansas and found that black students were more likely to receive
exclusionary discipline, even after accounting for the type and amount of disciplinary referrals.
However, within schools, eligibility for free and reduced lunch and enrollment in special education
had a larger influence than race. Gregory and Fergus (2017) found that black, American Indian, and
Hispanic youth were significantly more likely than other students to be referred to school admin-
istrators for disciplinary problems, punished by out-of-school suspension or expulsions, and referred
to law enforcement. Skiba and colleagues (2002) found no evidence that racial disparities in school
punishment could be explained by higher rates of misconduct by black youth, while Bradshaw et al.
(2010) reported that black students had significantly greater odds of receiving referrals than white
students. Thus, disciplinary practices seem to fall along racial lines, though more research is needed
in juvenile incarceration settings.
Sources of Racial Disparity
Although it is clear that racial disparities exist across various stages of the juvenile justice system,
much less is known about the sources of these disparities (Kempf-Leonard, 2007; Piquero, 2008).
Prior theoretical examinations of this issue has focused on whether disparities can be explained by
other factors (e.g., differences in underlying offending behavior or other demographic and risk
factors) or whether the justice system treats individuals disparately specifically because of their
different racial and ethnic backgrounds. In this section, we describe prior research on these
theoretical perspectives.
Prior research has theorized that differences in delinquent behavior are the primary driver of
observed racial disparities. Testing this assumption, for example, Beaver and colleagues found
188 Youth Violence and Juvenile Justice 19(2)
that when controlling for self-reported lifetime violence and IQ, black youth were not more likely
than white youth to be arrested, incarcerated, or receive a longer sentence (2013). In contrast, other
studies have found that disparities persist even after accounting for underlying behavior. Davis
and Sorensen (2013) found that after controlling for arrest rates, black youth still were placed in
confinement at rates nearly 70 percent higher than those of white youth and that the level of
disparity decreased over the years studied (1997–2006). In their analysis of 125 studies, Engen
et al. (2002) found that controlling for prior offending reduced the effect of race in juvenile justice
outcomes but did not eliminate it, while controlling for offense seriousness did not reduce the race
effect. Huizinga and colleagues (2007) similarly found that differences in offending behavior, as
measured by self-reported offending, did not explain the disparities between the rates of arrest and
court referral of black and white youth in Pittsburgh, Rochester, and Seattle. Black youth were also
more than two times more likely to be arrested for drug offenses than white youth even though
they reported selling and using them at a lower rate (Kakade et al., 2012). Taken together, these
findings suggest that differences in offending behavior may account for some, but not all, observed
racial disparities.
Similarly, controlling for legal factors, such as prior justice involvement and offense severity,
does not explain the full extent of the racial disparities observed at multiple decision points in the
juvenile justice system. Studies that accounted for prior contact with the juvenile justice system,
severity of prior referral, and whether youth were under court authority at the time of referral, still
found that black youth were more likely to be detained preadjudication and be adjudicated delin-
quent than similarly situated white youth (Leiber & Fox, 2005; Leiber, 2013). However, black youth
were not more likely to receive an out-of-home placement, which may be an attempt to correct for
overrepresentation at earlier stages of case processing (Leiber, 2013). A study evaluating disposi-
tions in Florida found that after controlling for offense severity, prior adjudications, and preadjudi-
cation detention, black youth were less likely to receive severe sanctions such as confinement and
transfer to adult court than white youth (Cochran & Mears, 2015). They also were less likely to
receive diversion or probation and instead receive dismissals. However, there were few differences
between white and Hispanic youth in that study. When examining referrals after a youth’s first
referral to the juvenile justice system, Caudill et al. (2013) found that offense severity and prior
dispositions had a stronger influence than race on receiving a formal disposition. Legal factors are
important to justice outcomes, and their influence varies by the decision point.
Another theoretical explanation of the source of racial disparity is differences in demographic and
risk factors for antisocial behavior, such as socioeconomic status, household structure, problems in
school, or mental health. Huizinga and colleagues (2007) control for many risk factors of arrest, and
found that disproportionate minority contact decreased, but still persisted. The risk factors included
neighborhood, socioeconomic status, household structure (single parent or married), the age of the
mother at her first birth, and educational problems. Leiber (2013) also controlled for and tested the
effect of two extralegal factors, household structure and problems in school, finding that being from
a single parent household increased the likelihood of pretrial detention for black youth, and problems
in school decreased the likelihood of pretrial detention and out of home placement for black youth.
When using these factors as controls, Leiber (2013) found that black youth faced harsher outcomes
in the earlier stages of the juvenile justice system than white youth. Engen and colleague’s (2002)
meta-analysis indicates that after controlling for risk factors associated with race, such as socio-
economic status or family structure, race still influenced dispositions. Controlling for risk factors of
antisocial behavior reduced the effect of race on juvenile justice outcomes but did not eliminate it,
suggesting that risk factor differences are not the only source of racial disparity.
Beyond risk and behavioral factors about the youth, features of the juvenile justice system
process and differential treatment may also be of theoretical relevance to the study of racial
disparities. After controlling for arrest rates, Davis and Sorensen (2013) found higher levels of
Oglesby-Neal and Peterson 189
racial disparity in cases that that involved more discretion, such as those involving drug or public
order offenses. Racial disparities are also greater in the earlier stages of the juvenile justice system
than in the later decision points (Engen et al., 2002). A meta-analysis of waivers from the juvenile
court to the criminal court found the effect of race on waiver decisions was insignificant (Zane
et al., 2016), further supporting that racial disparities lessen in the later stages of the juvenile
justice system.
In sum, findings from these analyses suggest that differential involvement in delinquency,
differences in risk factors, and the processes found in the juvenile justice system all contribute
to racial disparity, but do not fully explain why youth of color, particularly black youth, experi-
ence worse outcomes than their white counterparts (Engen et al., 2002). This could be understood
by the symbolic threat hypothesis, which “attempts to identify the contingencies of juvenile justice
decision making by focusing on the characteristics of youth, especially minorities, and the social
psychological emotions of juvenile court officers” (Leiber & Johnson, 2008). For instance, mem-
bers of the majority group in a society may perceive the minority group as a threat to the status quo
and, as a result, take steps to reduce possible competition (Blalock, 1967). This issue is exacer-
bated for youth of color because their dual status as young and a member of a minority groups
symbolizes negative stereotypes often held by criminal justice decisionmakers (Tittle & Curran,
1988).
Regardless of the underlying reason, disparities in the juvenile justice system is a serious problem
for youth of color. Juvenile justice involvement can lead to a host of undesirable outcomes in the life
of youth (Piquero, 2008), including low educational attainment, unemployment, and adult criminal
justice system involvement. In their evaluation of arrest racial disparities, Kakade et al. (2012) found
that black youth with arrests at baseline were less likely to graduate from high school than white
youth with arrests. Secure confinement is especially associated with negative impacts, such as
school dropout, increased mental illness, less future employment, and increased recidivism (Holman
& Ziedenberg, 2006). Because involvement in the juvenile justice system can affect many life
outcomes, it is important to consider the impact of racial disparities beyond increased contact with
the justice system.
Current Study
The current study contributes to the existing body of research in several important ways. First, we
use data from the Virginia Department of Juvenile Justice (DJJ) to examine disparities at multiple
decision points in the deep end of the justice system where the current research is particularly scarce.
These include placement type, institutional offenses, and length of stay. Second, we build on the
literature by examining the impact of race on these various outcomes after controlling for relevant
legal and extralegal factors. This allows us to more accurately determine the degree to which racial
disparities exist in Virginia’s juvenile justice system, which can then help guide policies around
placement and care and promote more positive outcomes for all youth.
It is important to note that this study focuses on racial disparities, not on disproportionality. For
example, disproportionality occurs when the rate of youth of a particular race or ethnicity involved
in the justice system is greater or lesser than their share in the general population. Disparities occur
when youth of different races or ethnicities enter the justice system under the same circumstances
but receive different treatment or have disparate outcomes as they move from one case processing
decision point to the next. We are not able to compare the rates of juveniles in our study to that of the
general population, which is why we focus on disparity. Still, identifying disparities can help with
understanding how to change current practices and ensure more equitable treatment. To that end, our
primary research questions are:
190 Youth Violence and Juvenile Justice 19(2)
Do racial disparities exist in length of stay, institutional offenses, and placement into com-
munity programs?
What legal and extralegal factors influence any disparities observed at these juvenile justice
decision points?
Racial Disparities in Virginia
Although the primary focus of this study will be on length of stay, institutional offenses, and
placement decisions, prior research on racial differences in the Virginia juvenile justice system
provides helpful context about the system overall and decisionmaking that occurs prior to incar-
ceration. Table 1 below provides the relative rate indices (i.e., the rate for minority youth com-
pared to white youth) for black, Hispanic, and Asian youth at multiple decision points in the
Virginia juvenile justice system, as calculated by the Virginia Department of Criminal Justice
Services (VA DCJS, n.d.). Black youth are nearly three times more likely than white youth to be
referred into the juvenile justice system, and nearly two times more likely to be detained and
placed into secure confinement. They also are less likely to receive diversion and probation,
decisions that would help distance them from the juvenile justice system. Hispanic youth have
similar disparities as black youth, but to a lesser degree. Hispanic youth are 1.5 times more likely
to be detained and 1.4 times more likely to be adjudicated delinquent than white youth. However,
Hispanic youth are not more likely to be placed in secure confinement than white youth. Asian
youth are less likely to be referred, detained, and petitioned than white youth. They also are more
likely to receive diversion and probation than white youth.
The disparities demonstrated by these rates have remained relatively stable for the past several
years in Virginia, and cannot be completely explained away by demographic factors, allegation
severity, or prior offending history. An assessment of DMC in the Virginia Juvenile Justice System
found similar statewide relative rate indices for the years 2007–2010 (Harig et al., 2012). This
assessment evaluated the outcomes of all juvenile justice cases in Fairfax, Richmond, and Norfolk
counties, controlling for age, gender, allegation severity and type, and prior referral history. The
analysis found no significant racial differences in length of stay in preadjudication detention, but
found that black and Hispanic youth were more likely to be adjudicated delinquent and receive a
correctional placement than white youth. These disparities lessened, but most remained significant
after introducing control variables. These prior findings provide some contact for the current study,
specifically that greater disparities may exist between black and white youth than Hispanic and
Table 1. 2012–2013 Virginia Case Processing Summary by Race. Relative Rate Indices (RRI) for Delinquency
Offenses.
Rate Type Minority Black Hispanic/Latino Asian
Referral rate 1.87* 2.74* 1.04* 0.26*
Diversion rate 0.87* 0.78* 0.83* 1.52*
Detention rate 1.63* 1.81* 1.48* 0.75
Petitioned rate 1.04* 1.07* 1.08* 0.85*
Adjudicated rate 1.29* 1.31* 1.37* 1.04
Probation rate 0.79* 0.75* 0.86* 1.30
Placement rate 1.72* 1.98* 0.76 **
Notes: All rates are relative to the rates for white youth.
*Indicates statistical significance. **Insufficient cases for analysis.
Source: Virginia Department of Criminal Justice Services
Oglesby-Neal and Peterson 191
white youth, there may be few disparities in length of stay, and that black and Hispanic youth may be
more likely to receive more restrictive placement types.
Methodology
This study examines decisions that occur after a youth has been adjudicated delinquent and admitted
to direct care in the Virginia juvenile justice system, including facility placement, institutional
offenses, and length of stay. To measure racial disparities, we first calculate the mean of each
measure of interest by race. These averages are helpful for immediately identifying differences, but
they do not control for factors that may account for the differences. Thus, we next employ regression
analyses to control for legal (e.g., offense severity, risk level) and extralegal (e.g., age, school
behavior, mental health, treatment needs) variables. We then compare the differences in rates of
outcomes before and after controlling for related factors to see if any of the disparity can be
attributed to a race effect.
This study’s methods comport with most racial disparity studies, which introduce covariates to
lessen the differences between the groups being compared and to establish that that the observed
disparity is due to a race effect (Bishop & Frazier, 1996; Davis & Sorensen, 2013; Engen et al.,
2002; Guevara et al., 2011; Huizinga et al., 2007; Kakade et al., 2012; Leiber & Fox, 2005; Leiber,
2013). These covariates include potential sources of disparity (differential involvement, risk factor
differences, juvenile justice system structure) as well as other demographic characteristics like age
and gender. Some of the covariates we include are related to race, such as socioeconomic status or
neighborhood, are not be neutral of race (Pope & Feyerherm, 1990), which makes it difficult to
identify the true levels and sources of racial disparity. No single study design can estimate the
“true” race effect, but including legal and extralegal controls in the regression models can lead to
better approximations.
Data
This study uses data on all admissions into the Virginia DJJ from 2012 to 2017. Follow-up data on
the youth extend through April 2018. The analysis of community placement programs is limited to
years in which it was in existence, 2013–2017. The unit of analysis is unique admissions to DJJ, such
that youth who have more than one admission appear in the data set multiple times.
Dependent variables. The three dependent variables in the study are length of stay, serious institu-
tional offenses, and community program placement. Length of stay is the number of days served
under DJJ’s direct care, which includes secure facilities, detention centers, and community place-
ment programs. In October 2015, DJJ implemented a policy change to shorten length of stay. In the
updated 2015 guidelines, the length of stay is determined by the youth’s risk level and the severity of
the committing offense (DJJ, 2015). These new guidelines replaced the 2008 guidelines that used the
severity of the current and prior offenses to determine length of stay. In this analysis, we are unable
to fully assess the impact of this change because we only have length of stay information for youth
admitted through the end of 2017, and not all of these youth had been released at the time of our data
collection. Because of this limitation, this length of stay analysis examines all youth admitted
between 2012 to 2017 who had been released.
Serious institutional offenses are violations for which youth are formally written up, limited to
assault and sexual misconduct. We examine serious institutional offenses rather than all institutional
offenses because conversations with DJJ staff revealed that there is less discretion involved in
writing up youth for these offenses. Institutional offenses are important because they may influence
length of stay and placement or other facets of a youth’s time in custody.
192 Youth Violence and Juvenile Justice 19(2)
Finally, we examine whether youth placement into one of Virginia’s Community Placement
Programs (CPP). In an effort to downsize current detention facilities and allow children to stay in
places closer to their homes, DJJ revitalized its CPPs in 2014, which exist in local detention centers
rather than large correctional centers. The CPPs allow youth to be closer to home and have greater
access to reentry services (DJJ, 2017). Through June 2017, only males between ages 16 and 20 with
remaining lengths of stay under 1 year were admitted to CPPs (DJJ, 2017). Our data coverage
extends through April 2018, allowing us to capture youth admitted from 2012 to 2017 who received
community placements through that time. Placements to CPPs can occur directly after adjudication,
or DJJ can transfer youth to a CPP after they have already been placed in a correctional center.
Independent variables. The primary independent variable in our study is race. Data on racial and
ethnic groups are collected by DJJ as youth enter the system. DJJ records Hispanic ethnicity separate
from race, with the race of most Hispanic youth being “white,” “other,” and “unknown.” DJJ’s
measure of Hispanic ethnicity was missing or unknown for half of youth admitted since 2012. To
address this issue, we employed a novel surname matching procedure that imputes Hispanic ethni-
city. The ethnicity imputation method uses a surname list released by the U.S. Census Bureau in
2007, which contains 151,671 surnames that occurred at least 100 times in the 2000 Census, along
with the frequency with which each name appears among a number of mutually exclusive racial and
ethnic groups (Hispanic, White, Black, American Indian or Alaskan Native, Asian or Pacific Islan-
der, and Two or More Races). People with these surnames represent approximately 90 percent of the
U.S. population (Word et al., 2008).
We minimally standardized the surnames by uppercasing all names and splitting hyphenated
names. We then used exact matching to match these names to the Census surname list. If the first
name in a hyphenated surname did not match, we tried to match the remaining names, resulting in an
overall match rate of 95 percent. The percentage of self-reported Hispanic individuals with a
particular surname in the Census surname list acted as the predicted probability that another indi-
vidual with the same surname would be Hispanic. We identified the optimal cutoff for classifying an
individual as Hispanic (9 percent) using the Liu (2012) method, which maximizes the product of
sensitivity and specificity. Using this cutoff, we then compared the imputed ethnicity variable to the
reported ethnicity variable to assess the methodology’s predictive performance. We found that the
method correctly classified 97 percent of the individuals who had reported ethnicities, with a
sensitivity of 0.91, a specificity of 0.99, and an AUC of 0.95. This method yields a more complete
measure of Hispanic ethnicity, though the results should still be interpreted with caution.
In our study, we combined race and ethnicity into a single, mutually exclusive measure that
includes non-Hispanic black (“black”), non-Hispanic white (“white”), Hispanic of any race
(“Hispanic”), and non-Hispanic youth of any other race (“other”). The other racial group in this
analysis includes Asian/Pacific Islander, American Indian/Alaskan Native, other races, and
unknown. These categories were combined into a single group given their relatively small numbers
in the data set.
We also include many control variables in the regression analyses, such as demographics, legal
factors, and extralegal factors. The demographic factors include age at admission and sex, which is a
binary measure of male or female. The sentencing-related variables are offense tier,projected length
of stay, and commitment type. There are five offense tiers set by DJJ. The offense tiers increase in
severity from one to four, and tier five is a treatment override. Tier one includes misdemeanors and
violations of parole, tier two non-person felonies, tier three felonies against persons, and tier four
class one and two felonies. Tier five is for youth who have an identified need for sex offender
treatment. The projected length of stay comes from a sentencing matrix that combines the offense
tier with risk level for a total of seven projected length of stay ranges, with the shortest being 2–4
months and the longest being 9–15 months. There is an additional option in the matrix for treatment
Oglesby-Neal and Peterson 193
overrides, which does not have a projected length of stay. Commitment type includes three cate-
gories: blended, determinate, and indeterminate.
Our final set of controls account for risk and treatment needs. All youth who enter DJJ receive a
YASI risk assessment, and can receive an overall risk score of low, moderate, or high. This
assessment considers school behavior issues in the last 6 months, which can be none, slight,
moderate, or severe. Youth are also assessed to determine whether they have mental health,aggres-
sion management,substance use, and sex offender treatment needs.
Analytic Approach
The regression method and control variables included in the model vary based on the outcome
variable. Length of stay in days is a continuous variable, but has a finite range of values and
presented some issues with skewness and kurtosis. Serious institutional offenses are measured as
the total number of assaults and sexual misconducts committed by the youth during their time in DJJ
custody. Thus, we determined that a count model would be more appropriate for both the length of
stay and serious institutional misconduct analyses. Two common count models are Poisson and
negative binomial regression. Poisson regression is only appropriate when the data meet the assump-
tion of equidispersion—that is, the conditional means equal the conditional variances (Cameron &
Trivedi, 2007). We tested this assumption using the likelihood ratio test of the overdispersion
parameter and found that negative binomial regression was more appropriate for both analyses.
Finally, we use logistic regression to examine placement into a community program, which is a
binary variable. For all analyses, we use R version 3.5.1.
The independent variables in all of the regression models are largely consistent. All models
include race, age at admission, sex, and treatment needs (aggression management, substance use,
and mental health). For length of stay and community program placement, we also control for
projected length of stay, commitment type, and total institutional offenses. Projected length of stay
is based on the offense tier and risk level of the youth and is used for sentencing, making it important
for length of stay and community program placement. The length of stay models also include
whether the sentence occurred under the new 2015 sentencing guidelines. For the CPP models,
we exclude youth who have a blended sentence, as they are not eligible.
For the models on serious institutional offenses, we include length of stay as an exposure variable.
This is an option in negative binomial regression and other count models to account for units of time
that constrain the frequency of the dependent variable. In this example, youth who were in DJJ
custody for longer periods of time would have had more opportunity to engage in serious institu-
tional offenses. By including length of stay as an exposure variable, the outcome becomes the rate at
which youth engaged in serious institutional offenses relative to the time they remained in custody.
In these models, we additionally control for school behavior and risk level, as these may be related to
behavior while under DJJ’s direct care.
For all three outcomes, we use a stepped approach to regression modeling. The first model only
includes race as the independent variable, while the second models include race along with the legal
and extralegal control variables listed above. This approach allows us to examine the relative
contribution of race when considering additional factors that may influence the outcome.
Population
From 2012 to 2017, there were 2,001 admissions to DJJ’s direct care. These admissions represent
1,858 unique juveniles. Two-thirds of youth were black, while 22 percent were white, 9 percent were
Hispanic (based on our imputation method), and 2 percent were of another race. The vast majority
(92 percent) of youth admitted were boys. On average, youth were 16 when admitted. The age of
194 Youth Violence and Juvenile Justice 19(2)
first arrest was 13, indicating that many youth had become involved with the juvenile justice system
at an earlier age. The most common offense tiers were two (37 percent) and three (35 percent).
Fourteen percent were admitted for tier one offenses, while 12 percent were admitted for tier five
and 2 percent for tier four. Table 2 shows these summary statistics of the study population.
Most youth in the sample had a moderate or high initial risk level, with 28 percent at a
moderate risk and 63 percent at a high risk. During intake risk assessments youth were assessed
on school behavioral issues. Thirty-five percent had severe and 30 percent had moderate school
behavioral issues. Comparatively, 14 percent had slight and 15 percent had no behavioral
issues. In terms of treatment needs, 95 percent of the sample needed aggression management
therapy, 89 percent needed substance abuse treatment, and about half were designated as
needing mental health treatment.
Results
We first examine the means of each outcome by race (Table 3). From these results, Hispanic and
youth of another race had the shortest length of stay, at 11.6 and 11.5 months respectively. Black
youth maintained the longest length of stay at 14.5 months, and white youth had an average length of
Table 2. Summary Statistics. All Admissions to VA DJJ 2012–2017 (n ¼2,001).
Variable Freq Percentage/Mean SD
Race
White 438 22% 0.41
Black 1,337 67% 0.47
Hispanic 179 9% 0.29
Other 47 2% 0.15
Sex
Female 166 8% 0.28
Male 1,835 92% 0.28
Age
At Admission – 16.3 1.1
At first arrest – 13.1 2.0
School behavioral issues
None 297 15% 0.36
Slight 274 14% 0.34
Moderate 600 30% 0.46
Severe 696 35% 0.48
Treatment Needs
Mental Health 1,022 51% 0.50
Aggression Management 1,847 95% 0.22
Substance Abuse 1,680 89% 0.32
Initial risk level
Low 52 3% 0.16
Moderate 560 28% 0.45
High 1,260 63% 0.48
Offense tier
1 279 14% 0.35
2 736 37% 0.48
3 706 35% 0.48
4 34 2% 0.13
5 245 12% 0.33
Oglesby-Neal and Peterson 195
stay of 12.3 months. Black youth were also found to have committed significantly more total serious
institutional offenses (2.03) than white (0.64), Hispanic (0.56), and other (0.72) youth. Youth of all
races had very similar rates of placement into community programs, ranging from 29.7 percent for
other youth to 32.5 percent for white youth.
Table 4 shows the results of the length of stay regression analyses. Model one only includes the
race variable, with white as the reference race group. From this model, black youth have an average
length of stay 1.18 times longer than white youth, significant at the 0.01 level. In model two, we
introduce the additional legal and extralegal controls. From this model, we found that there is no
longer a significant difference between black and white youth in their lengths of stay. However, we
also found that youth identified as Hispanic have a shorter length of stay than white youth, though
this finding only approached statistical significance (IRR ¼0.91, p < 0.1) The Akaike Information
Criterion is lower for the second model, indicating a better overall fit than model one, while the theta
confirms that model two explains a much greater share of the variance.
In addition, we found that youth who were sentenced under the 2015 revised guidelines had
significantly shorter lengths of stay than youth sentenced before that period (IRR ¼0.66, p < 0.01).
This was in line with our expectations as the primary goal of those guidelines was to cut lengths of
stay and shrink the juvenile justice population in Virginia. Model two also indicates that youth who
received a determinate (IRR ¼0.85, p < 0.01) or indeterminate (IRR ¼0.40, p < 0.01) sentence had
significantly shorter lengths of stay than those who received a blended sentence. Again, this was
expected as blended sentences, which involve youth spending the first part of their sentence with DJJ
and the later part in an adult facility, are administered in more serious cases with longer sentences.
Not surprisingly, we further found that youth with longer projected lengths of stay, based on their
offense tier and risk level, had longer actual lengths of stay. For example, youth with a projected
length of stay of 9–12 months remained in custody over two times longer than youth in the reference
category (those with a projected length of stay of 2–4 months) (IRR ¼2.08, p < 0.01). Moreover, for
each additional offense that youth committed in the institution, they were expected to spend 1
percent longer in custody (IRR ¼1.01, p < 0.01). Finally, youth who were assessed to have an
aggression management treatment need spent longer in custody(IRR ¼1.17, p < 0.01).
The second deep end decision point of interest in this study is serious intuitional offenses. We
conducted two negative binomial regression models to demonstrate the impact of race alone and the
additional contribution of other covariates (Table 5). All models include length of stay as an
exposure variable to account for youth who spend a longer amount of time in custody having an
increased opportunity for institutional offenses. In model one, we found that black youth had a
significantly higher expected count of serious institutional offenses compared to white youth. In fact,
black youth had more than three times more serious institutional offenses than their white counter-
parts (IRR ¼3.20, p < 0.01). There were no significant differences between white youth and
Hispanic youth or those of another race. These findings were consistent in model two after we
Table 3. Outcome Means by Race.
White Black Hispanic Other
Actual LOS (days)*** 349 (266) 411 (302) 324 (254) 316 (195)
Total serious inst. offenses*** 0.64 (1.9) 2.03 (5.2) 0.56 (1.4) 0.72 (1.8)
Community program placement 32.5% (46.9%) 31.9% (46.6%) 31.1% (46.4%) 29.7% (46.3%)
Note: Standard deviation in parentheses.
N¼2,001 for LOS and inst. offenses. N ¼1,516 for CPP.
ANOVA test for LOS and inst. offenses, w
2
test for CPP.
*p<0.1; **p<0.05; ***p<0.01.
196 Youth Violence and Juvenile Justice 19(2)
included the control variables, though the magnitude of the effect for black youth decreased slightly
(IRR ¼2.52, p < 0.05).
In model two, we found that many of the control variables significantly influenced how many
serious institutional offenses were committed by youth. Notably, age at admission had an inverse
Table 4. Length of Stay 2012–2017 Regression Results. Negative Binomial Regression. Actual Length of Stay
(days). Incidence Rate Ratio (Standard Error).
(1) (2)
Black 1.18*** 1.02
(1.04) (1.03)
Hispanic 0.93 0.91*
(1.07) (1.05)
Other 0.91 0.91
(1.12) (1.08)
Age at admission 1.01
(1.01)
Male 0.98
(1.05)
2015 Guidelines 0.66***
(1.04)
Projected LOS 3–6 1.47***
(1.16)
Projected LOS 5–8 1.68***
(1.15)
Projected LOS 6–9 1.85***
(1.15)
Projected LOS 7–10 2.03***
(1.16)
Projected LOS 9–12 2.08***
(1.16)
Projected LOS 9–15 2.60**
(1.47)
Projected LOS override 2.98***
(1.16)
Determinate commitment 0.85*
(1.10)
Indeterminate commitment 0.40***
(1.09)
Total inst. offenses 1.01***
(1.00)
Agg. treatment need 1.17***
(1.06)
Sub. treatment need 1.00
(1.04)
MH treatment need 1.02
(1.03)
Constant 348.77*** 252.30***
(1.04) (1.31)
Observations 2,001 1,779
Log Likelihood 13,748.08 11,433.30
Y1.80*** (0.05) 3.96*** (0.13)
Akaike Inf. Crit. 27,504.16 22,906.59
Note: *p<0.1; **p<0.05; ***p<0.01.
Oglesby-Neal and Peterson 197
relationship with the outcome such that each additional year in age was associated with a 30 percent
reduction in serious offenses (IRR ¼0.69, p < 0.01). On the other hand, males engaged in nearly
twice as many serious institutional offenses as females (IRR ¼1.96, p < 0.01). Similarly, youth who
had behavioral issues in school and those diagnosed with an aggression management or substance
abuse treatment need committed significantly more institutional offenses than other youth. We also
found that youth who had a moderate risk level engaged in 50 percent more institutional offenses
(IRR ¼1.53, p < 0.01), while those with high risk scores had twice as many serious offenses (IRR ¼
2.00, p < 0.01) compared to low-risk youth. The Akaike Information Criterion indicates that the
second model is a better overall fit than model one, while the theta confirms that model two explains
more of the variance.
Our final set of analyses examines the relationship between race and whether Virginia DJJ placed
youth into a community placement program (CPP, Table 6). Model one again only included race. In
this model, we found that race had no significant impact on placement. This was maintained in the
second model, which introduced the various legal and extralegal covariates. In model two, being
Table 5. Serious Institutional Offenses 2012–2017 Regression Results. Negative Binomial Regression. Total
Serious Institutional Offenses. Incidence Rate Ratio (Standard Error).
(1) (2)
Black 3.20*** 2.52**
(1.03) (1.03)
Hispanic 0.96 1.08
(1.05) (1.05)
Other 1.14 0.81**
(1.09) (1.09)
Age at admission 0.69***
(1.01)
Male 1.96***
(1.05)
School behav. problem slight 2.08***
(1.05)
School behav. problem moderate 2.28***
(1.04)
School behav. problem severe 2.49***
(1.04)
Agg. treatment need 3.12***
(1.09)
Sub. treatment need 1.11***
(1.04)
MH treatment need 1.63***
(1.02)
Risk level moderate 1.53***
(1.08)
Risk level high 2.00***
(1.08)
Constant 1.14 14.96***
(1.03) (1.22)
Observations 2,001 1,635
Log Likelihood 53,810.89 44,168.80
y0.32*** (0.004) 0.45*** (0.01)
Akaike Inf. Crit. 107,629.80 88,365.61
Note. All models include length of stay as exposure variable. *p<0.1; **p<0.05; ***p<0.01
198 Youth Violence and Juvenile Justice 19(2)
male was associated with significantly higher odds of receiving a placement to the CPP (OR ¼2.71,
p < 0.01). Model two also shows that there was a generally positive relationship between the
projected length of stay and placement in a CPP, though this only approached statistical significance
with some of the covariates. Youth who were diagnosed with a mental health treatment need were
significantly less likely to be placed in a CPP than their counterparts (OR ¼0.24, p < 0.01). Finally,
for each additional institutional offense that youth committed while in DJJ custody, their odds of
being placed into a CPP were decreased by 6 percent (OR ¼0.94, p < 0.01). The AIC and log-
likelihood values suggest that model two fits the data better than model one.
Table 6. Community Program Placement 2013–2017 Regression Results. Logistic Regression. Community
Program Placement Ever. Odds Ratios (Standard Error).
(1) (2)
Black 0.98 1.00
(1.15) (1.18)
Hispanic 0.93 0.97
(1.24) (1.30)
Other 0.89 0.84
(1.46) (1.54)
Age at admission 0.94
(1.06)
Male 2.71***
(1.34)
Projected LOS 3–6 5.47
(3.07)
Projected LOS 5–8 8.32*
(3.00)
Projected LOS 6–9 7.03*
(3.00)
Projected LOS 7–10 7.70*
(3.01)
Projected LOS 9–12 5.51
(3.15)
Projected LOS override 1.53
(3.13)
Agg. treatment need 0.74
(1.32)
Sub. treatment need 1.14
(1.22)
MH treatment need 0.24***
(1.17)
Total inst. offenses 0.94***
(1.01)
Indeterminate commitment 1.52
(1.31)
Constant 0.50*** 0.19
(1.13) (5.08)
Observations 1,516 1,354
Log Likelihood 957.54 673.91
Akaike Inf. Crit. 1,923.07 1,381.82
Note: *p<0.1; **p<0.05; ***p<0.01
Oglesby-Neal and Peterson 199
Discussion
Our first research question was concerning the degree to which racial disparities exist at three
key decision points at the deep end of the juvenile justice system: length of stay, institutional
offenses, and placement into community programs. Overall, the findings from the current study
are mixed. After controlling for relevant legal and extralegal factors, there were no disparities
in the length of stay in custody for youth or in their placement to community programs.
However, our findings indicate that black youth were involved in significantly more serious
institutional offenses than white youth.
Our second research question focused on whether the other covariates in the models influence
any observed racial differences in the measures of interest. In our first set of analyses, we found a
significant relationship between race and length of stay in custody. However, once we added
control variables into the model, the effect of race was no longer significant. Instead, variables
such as being sentenced under the Virginia DJJ’s 2015 guidelines, projected length of stay of
youth based on their offense tier and risk level, their commitment type, and their treatment needs
had the strongest influence on length of stay. Further, these variables substantially improve the
explanatory power of the model, with the theta value increasing from model one to model two.
These results are not surprising. They confirm prior research, which has found no effect of race on
length of stay (Espinosa & Sorensen, 2016; Maggard, 2015). They also highlight the importance of
judicial decisionmaking, individual risk, and clinical needs in understanding how long juveniles
remain in custody.
For the analyses examining serious institutional offenses, defined as assault and sexual miscon-
duct, we observed significant disparities for black compared to white youth, even after controlling
for relevant factors. This finding comports with some of the prior research on youth and adults that
finds black individuals have higher rates of institutional misconduct beyond the influence of other
legal and extralegal variables (DeLisi et al., 2010; Harer & Steffensmeier, 1996; Huebner, 2003).
This also mirrors the school discipline literature, which finds that black youth are more likely to
receive office referrals and experience exclusionary sanctions than white youth (Anderson & Ritter,
2017; Bradshaw et al., 2010; Gregory & Fergus, 2017; Rocque, 2010; Skiba et al., 2002). It is
important to note that, just as the school discipline literature focuses on the sanctions given by the
school rather than differences in the underlying behavior, we are unable to differentiate between
actual underlying behavior and staff responses to that behavior. In other words, our measure of this
decision point may reflect actual differences in the degree to which youth commit serious offenses,
the ways in which DJJ staff differentially respond to youth behaviors, or some combination of these
two actions.
Many of the control variables in the institutional offense modelshad similarly strong impacts on the
outcome. Our findings suggest that male and younger youth committed significantly more serious
institutional offenses than others. This is supported by decades of prior research on the strong link
between age, gender, and criminal and delinquent behavior (Baxendale et al., 2012; Farrington, 1986;
Hirschi & Gottfredson, 1983; Loeber & Farrington, 2014). We also found that youth who had previous
behavioral problems in school were more likely to commit serious institutional offenses in custody,
suggesting that youth who engage in some forms of misbehavior are more likely to engage in more
serious misconduct. Finally, both higher levels of risk and all forms of diagnosed treatment needs
(aggression, substance use, and mental health) were positively correlated with institutional offenses.
These findings could help staff identify youth who are more likely to engage in disruptive and
problematic behavior in custody. For instance, high-risk youth with identified clinical needs should
be targeted for treatment-focused programming and other in-custody interventions.
In contrast to the other measures analyzed, there were no racial disparities in placement decisions
into community programs. It is difficult to interpret these findings through the lens of extant
200 Youth Violence and Juvenile Justice 19(2)
literature. Most prior research on placement decisions focuses on secure confinement, and these
studies have mixed findings on the link between race and placement (Holleran & Stout, 2017; Leiber
& Peck, 2015). The deviation from our study could be explained by the fact that Virginia’s CPPs are
unique placement facilities. These programs are within secure facilities (i.e. juvenile detention
centers), but are smaller, generally allow youth to remain closer to home, and are viewed as more
focused on reentry than other correctional centers. Youth can also go to a CPP immediately, or after
a period in a juvenile correctional center. The clear eligibility criteria for CPP placement may lessen
the possibility of racial disparities at this decision point.
Still, there were other youth characteristics that influenced this decision. Males were much more
likely to be placed into a CPP, though this is because these programs were only available to male
youth during most of the study period. We also found that youth with a mental health treatment need
had significantly lower odds of being placed into a CPP. This may reflect that community programs
have less capacity to serve individuals who need mental health services. Having institutional
offenses was also associated with a significantly lower likelihood, as part of the CPP eligibility
criteria is having no misconduct for a set duration of time.
Limitations
There are several limitations to the current study. First, our findings focused on decision points at
the deep end of the juvenile justice system. While we believe this is an area that has been
under-studied in previous research, we are also limited in understanding how earlier decisions
in Virginia’s system may have created or exacerbated other racial disparities. For example,
previous research has clearly shown that there are disparities in juvenile arrests, diversions,
waivers to adult courts, and other front-end decision points (Puzzanchera & Hockenberry,
2016). Thus, while our findings on disparities in length of stay, institutional offenses, and place-
ment decisions were mixed, it is critical to recognize that these decisions occur later in case
processing and after youth may have already been subjected to disparate treatment.
Further, our findings on youth identified as Hispanic should be interpreted with caution. Although
we used a novel surname matching procedure to identify Hispanic youth and did our best to validate
this method, we cannot be certain that we correctly identified the Hispanic youth in Virginia DJJ
custody. It is likely that we under- or over-estimated the number of Hispanic youth by misclassifying
their ethnicity. Likewise, there were few youth of other racial identities and this group included
several different racial and ethnic groups (e.g., Asian/Pacific Islander, American Indian/Alaskan
Native, other races, and unknown). Thus, findings related to the “other” racial category had limited
power in our models and should also be interpreted with caution.
Another limitation is that there may be sources of racial disparity that we were unable to capture
and include as covariates in the regression models. For example, factors about the specific unit or
facility youth were in might influence levels of institutional misconduct. Moreover, there may be
clustering effects based on the geographic regions in which juveniles lived before they were placed
into custody. Unfortunately, we were unable to include a measure of juveniles’ zip code or county of
residence in our models. Future research could further explore this issue using multi-level modeling
to examine how disparities may exist or vary at these geographic levels.
Finally, because we are relying on official data, we have no way of knowing whether the
reported serious institutional offenses accurately reflect underlying behavior. It is possible that
there were racial disparities in how staff detected or reported on these behaviors that contributed to
the discrepancies we found between black and white youth. This underscores our decision to limit
our analyses to serious institutional offenses, which involve much less staff discretion than lower-
level offenses. For these reasons, we believe future studies should continue exploring whether and
Oglesby-Neal and Peterson 201
why disparities may exist in misconduct, length of stay, and eligibility considerations for com-
munity programs.
Conclusion
At the deep end of the juvenile justice system in Virginia, there are no racial disparities in length of
stay and community program placement. However, there is disparity in serious institutional offenses
for black youth compared to white youth. Legal factors such as the offense severity and sentencing
type have a strong influence on length of stay, while extralegal factors such as school behavior issues
and aggression management treatment need have a strong influence on serious institutional mis-
conduct. Covariates related to the eligibility criteria of placement in community programs had the
strongest influence on whether youth received these placements. Other deep end measures that merit
further research are treatment enrollment and completion, parole decisions upon release, and edu-
cational attainment while incarcerated. Future disparity research should continue to include both
legal and extralegal factors in quantitative analyses, and complement them with qualitative inter-
views of staff and youth.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publica-
tion of this article: This research was supported by the U.S. Department of Justice, Office of Justice Programs,
National Institute of Justice (Grant 2014-IJ-CX-0002).
ORCID iD
Ashlin Oglesby-Neal https://orcid.org/0000-0002-1634-4559
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Author Biographies
Ashlin Oglesby-Neal is a research associate in the Justice Policy Center at the Urban Institute. Her
research includes developing and validating risk assessment tools and evaluating criminal justice
programs and policies. She received a MS in criminology from the University of Pennsylvania.
Bryce Peterson is a principal research associate in the Urban Institute’s Justice Policy Center. His
research focuses on evaluations of criminal justice technologies and correctional policies. He
received his PhD in criminal justice from John Jay College, City University of New York.
Oglesby-Neal and Peterson 205
