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DISPROPORTIONATE MINORITY CONFINEMENT:
A STATE-LEVEL TEST OF THE RACIAL AND SYMBOLIC THREAT
HYPOTHESES
A Dissertation
Presented to
The Faculty and Department of Justice Studies
College of Juvenile Justice and Psychology
Prairie View A&M University
In Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy in Juvenile Justice
By
Jaya Bolestridge Davis
December 2010
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Certification of Dissertation Approval
TO THE COMMITTEE OF GRADUATE STUDY:
The undersigned on this date examined Jaya Bolestridge Davis for the awarding of the
doctoral degree and hereby certify that the dissertation was inspected by each of us and
was approved.
Approved:
____________________________________________ (Chair)
Jonathan Sorensen, Ph.D.College of Juvenile Justice and Psychology
_____________________________________________
Camille Gibson, Ph.D.College of Juvenile Justice and Psychology
_____________________________________________
Harry Adams, Ph.D.College of Juvenile Justice and Psychology
_____________________________________________ William Kritsonis, Ph.D.Whitlowe R. Green College of Education
Approved:
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Abstract
The issue of disproportionate minority confinement (DMC) in the juvenile justice
system has been a growing concern since the 1960s. The reality by the 1980s was that
minority youth accounted for more than half of juveniles in custody despite research
showing that they did not disproportionately commit crimes. Even with the
acknowledgment of this problem, there was little direction toward correction. At the time,
the only conclusion to offer was that there was a crisis of national magnitude (Krisberg,
2005).
By the mid 1990s, the Office of Juvenile Justice and Delinquency Prevention
(OJJDP) included, as a requirement for a state to receive Federal Formula Grants, a
determination of whether disproportionate minority confinement existed in its juvenile
justice system, identification of its causes, and development and implementation of
corrective strategies (Hsia, 1999). In response, researchers undertook serious
investigation of the issue and states began to document DMC results and progress in the
way of internal investigations and reports filed with the OJJDP (Leiber, 2002).
Subsequently, the Juvenile Justice and Delinquency Prevention Act of 2002 expanded the
requirement to address DMC at all points of the juvenile justice system.
The current study examined the extent to which state juvenile justice systems
have been successful in reducing disproportionate minority contact (DMC), specifically
disproportionate African American placement, since the implementation of the Office of
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population. By employing both broad measures to compare to previous research, and
more refined measures for more accurate testing, this study begins to fill in the gap left
by previous research efforts regarding theoretical grounding and DMC. The findings fail
to support previous research suggesting that a nationwide reduction in DMC is a result of
OJJDP initiatives. Also there is no evidence of racial or symbolic threat with regards to
the overrepresentation of Black juveniles in residential placement.
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Dedication
To my husband, and best friend, thank you for your consistent support and understanding.
Without the sacrifice for our shared vision of different tomorrow, this would not have
happened today.
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Acknowledgements
Thank you to all my family and friends for their unyielding support and understanding
when I had ³to work.´ A special thank you to my grandmother for taking such loving
care of the boys and allowing me the freedom to pursue my dreams.
Thank you to Dr. Everette Penn for igniting the spark of inquisition to examine problems
that I have never personally faced.
Thank you to the Prairie View A&M graduate faculty for their expertise in various fields
that has helped to guide me in my new path.
Thank you to my dissertation committee for making this process as painless as possible
and their efforts at making the final product something that I am proud of.
Thank you to Dr. Jon Sorensen for being my mentor, chair, and friend.
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Table of Contents
Certification of Dissertation Approval ............................................................................. ii
Abstract .......................................................................................................................... iii
Dedication ....................................................................................................................... v
Acknowledgments .......................................................................................................... vi
Table of Contents .......................................................................................................... vii
List of Tables .................................................................................................................. x
List of Figures ................................................................................................................ xi
Appendices.................................................................................................................... xii
Chapter I ......................................................................................................................... 1
Introduction ..................................................................................................................... 1
Purpose of Study ............................................................................................................. 6
Current Study ................................................................................................................ 11
Objectives ................................................................................................................ 11
Limitations .............................................................................................................. 12
Organization ............................................................................................................ 13
Chapter II ...................................................................................................................... 14
Literature Review .......................................................................................................... 14
Theoretical Background ................................................................................................ 14
Social Disorganization .............................................................................................. 15
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Racial Threat ............................................................................................................ 21
Bureaucratic Model .................................................................................................. 28
Benign Neglect ......................................................................................................... 30
Symbolic Threat ....................................................................................................... 34
The Issue of Disproportionate Minority Contact ............................................................ 41
Toward a State-level Assessment................................................................................... 45
Chapter III ..................................................................................................................... 51
Methods ........................................................................................................................ 51
Data Sources ................................................................................................................. 51
Measures ....................................................................................................................... 58
Outcome Measures ................................................................................................... 58
Predictor Variables ................................................................................................... 61
Control Measures ..................................................................................................... 66
Hypotheses .................................................................................................................... 66
Analyses ........................................................................................................................ 67
Limitations .................................................................................................................... 69
Chapter IV .................................................................................................................... 72
Analyses and Results ..................................................................................................... 72
Compliance ................................................................................................................... 72
Racial-Economic Threat and Benign Neglect................................................................. 75
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Compliance ................................................................................................................... 90
Threat Hypotheses ......................................................................................................... 91
Limitations .................................................................................................................... 94
Recommendations ........................................................................................................ 96
Conclusion .................................................................................................................... 97
References ..................................................................................................................... 99
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List of Tables
Table Page
Table 1A: Total juvenile referrals ± 2005 ................................................................ 4
Table 1B: Total juvenile out-of-home placements by offense ± 2005......................... 4
Table 2: Offense comparison by data source ......................................................... 57
Table 3: Descriptive statistics comparing state DMC score and percent change .... 75
Table 4: Descriptive statistics for outcome, predictor and control measures(n=190) .................................................................................................................. 77
Table 5: Principal component analysis of population structure variables............... 78
Table 6: Fixed effects models of adjusted Black-White ratio of juvenile placements
(n=190) .................................................................................................................. 80
Table 7: Descriptive statistics for offense-specific outcome measures (n=190)....... 83
Table 8: Fixed effects models of Black juvenile placements explained by arrest (n=190) .................................................................................................................. 85
Table 9: Full f ixed effects models of Black juvenile placements explained by arrest
by offense (n=190).................................................................................................. 87
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List of Figures
Figure Page
Figure 1: Absolute percent change by state in Black-White disproportionality in juvenile placements after controlling for arrests, 1997 and 2007 ............................. 74
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Appendices
Appendix Page
Appendix A: S ummary of S tate A ssessments............................................................... 119
Appendix B: IRB A pproval Letter ............................................................................... 129
Appendix C: Vitae ...................................................................................................... 130
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Chapter I
Introduction
The issue of disproportionate minority confinement (DMC) in the juvenile justice
system has been a growing concern since the 1960s. When the deinstitutionalization
movement swept the nation in the 1970s, Whites accounted for 75% of the reduction in
incarcerated status offenders; and, when incarceration rates began to rise again during the
early 1980s, minority youth bore 93% of the increase (Krisberg, Schwartz, Fishman,
Gutman, & Joe, 1987). The first legislative reference to DMC, however, came with the
June 1986 testimony of Ira Schwartz of the Center for the Study of Youth Policy before
the House Subcommittee on Human Resources. Schwartz (1986) stated that minority
youth accounted for more than half of juveniles in custody despite research showing that
they did not disproportionately commit crimes.
In the mid-1980s, research from the Children in Custody data series began to be
presented and discussed. Although largely descriptive, the primary indication was that
there was a problem regarding the overrepresentation of minority juveniles in the system;
however, it ³could not offer compelling explanations of the etiological forces behind
those discrepancies´ (Krisberg, 2005, p. vii). Additionally, there were few empirical
studies to add to the conversation. At the time, the only conclusion to offer was that there
was a crisis of national magnitude (Krisberg, 2005).
Two years after the congressional testimony on DMC, in 1988, the Conference of
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amendment to the Juvenile Justice and Delinquency Prevention Act (JJDPA) of 1974 was
authorized requiring states to study and address efforts to reduce overrepresentation of
minority youths if the portion of minority youth detained or confined exceeded the
proportion of such groups in the general population (Feyerherm, 1995). The 1988
Amendment reserved a portion of federal funding to reduce overrepresentation of
minorities in out of home placement, detention, or residential facilities (Kempf-Leonard,
2007). Following the amendment and funding earmark, states began to identify and
address disproportionate minority confinement/contact (DMC) (Hsia, n.d.).
In 1992, DMC became one of the core requirements of the JJDPA (Kempf-
Leonard, 2007). Beginning in fiscal year 1994, the Office of Juvenile Justice and
Delinquency Prevention (OJJDP) included, as a requirement for a state to receive Federal
Formula Grants, a determination of whether disproportionate minority confinement
existed in its juvenile justice system, identification of its causes, and development and
implementation of corrective strategies (Hsia, 1999). In response, researchers undertook
serious investigation of the issue and states began to document DMC results and progress
in the way of internal investigations and reports filed with the OJJDP in the mid to late
1990s (Leiber, 2002). Subsequently, the Juvenile Justice and Delinquency Prevention Act
of 2002 expanded the requirement to address DMC at all points of the juvenile justice
system. Any state that fails to do so may lose 20% of that state¶s formula grant allocation
for the following year (Soler & Garry, 2009).
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juvenile population. Table 1 shows the ratio of referrals and placement by race (A) and
ratio of out-of-home placement by race and offense (B). These data were obtained from
the National Juvenile Court Data Archive which is maintained by the National Center for
Juvenile Justice, and made available by the OJJDP. The National Center for Juvenile
Justice, through funding by the OJJDP, collects individual and aggregate level data
(depending on the availability of information from the specific court) from juvenile
justice courts on delinquency cases processed annually. The unit of count is the number
of cases disposed (definite action taken as a result of referral) on each new referral,
regardless of number of violations per referral. In 2005, 2,135 jurisdictions in 41 states
reported to the National Center for Juvenile Justice, representing approximately 80% of
the juvenile population. National estimates are generated from this nonprobability sample
of juvenile courts (Stahl, Finnegan, & Kang, 2005).
Although there have been targeted measures taken at the state and national level
to address and reduce DMC for more than two decades, a quick glance at Table 1 makes
clear that Black juveniles continue to be referred, detained, placed, and waived to the
criminal court at a higher rate than their representation in the general population which
was approximately 17% in 2005 (Puzzanchera, Sladky, & Kang, 2009). For comparison,
the population share for White juveniles in 2005 was approximately 78% (Puzzanchera et
al., 2009). This overrepresentation holds for each offense category. Additionally, a
disparity exists where White juveniles are referred, detained, placed, and waived to the
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³other´ category does not allow for identification of possible issues within each race.
This other category is used as a racial reference category only and should not be used to
come to a conclusion that DMC does not exist for other races.
Table 1A: Total juvenile referrals ± 2005
Total Total Placed Waived toJuvenile Juvenile out of Criminal
Populationa
Referrals b
Detained Home Court
White 78% 64% 66% 57% 57%
Black 17% 33% 31% 40% 40%
Other 5% 3% 3% 3% 3%
Table 1B: Total juvenile out-of-home placements by offense ± 2005
TotalJuvenile Public
Referrals Person Property Drugs Order
White 64% 51% 61% 54% 59%
Black 33% 46% 35% 44% 38%
Other 3% 3% 4% 2% 3%
Source: Stahl, Finnegan, and Kang (2005)
Note: Hispanic juveniles are not identified separately but placed in categories by race.
aAge for juvenile population is 10 - 17.
bAge for juvenile referral is <12 17
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future inequities (Kempf-Leonard, 2007). By using statistics similar to those offered in
Table 1, it appeared that addressing discretion within the system would make a marked
impact on the overrepresentation of Black juveniles. However, efforts to address DMC
over the last 20 years have shown how the problem is not nearly as easy to fix as it first
appeared (Kempf-Leonard, 2007). Explanations for the overrepresentation of minorities
in the justice system have traditionally focused either on minorities¶ level of differential
involvement in crime or selection bias by the justice system. While either explanation
may have deserved further investigation, due to the rhetoric of advocates such as
Schwartz, the legislative lens was focused on selection bias, specifically the inequitable
use of confinement (Leiber, 2002).
Subsequent to the mandates, an explosion in DMC research was seen in the
1990s, which raised a question of whether the increase was ³a chance convergence of
research and policy interests´ (Feyerherm, 1995, p. 15) that would eventually dissipate or
actually culminate in improvement of the juvenile justice system. City, county, and state
level assessments have continued throughout the ensuing years with varying results
(Leiber, 2002; Pope, Lovell, & Hsia, 2002). These studies have inspected different stages
of processing and locations, employed various methods, and included legal (such as
seriousness of crime and criminal history) and extralegal factors (for example, single
parent headed households and average income of neighborhood) that may have affected
minority overrepresentation (Huizinga et al., 2007).
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The OJJDP states that ³research and practice´ regarding DMC since it became a
core requirement has led to two lessons.
1. In most jurisdictions, disproportionate juvenile minority representation is not
limited to secure detention and confinement but is evident at nearly all contact
points of the juvenile justice system.
2. Contributing factors to DMC are multiple and complex, reducing DMC
requires comprehensive and multipronged strategies that include
programmatic and systems change efforts (In Focus, 2009, p. 1).
In 2008, 66% of juveniles arrested were referred to juvenile court (Puzzanchera,
2009). As is evident in Table 1, once referred, Black juveniles are disproportionately
incarcerated within the juvenile justice system. The negative effects of juvenile
incarceration can ³interfere with successful adult development through the cumulative
continuity of lost opportunity´ (Sampson & Laub, 1992, p. 15). Therefore, the effects of
DMC may have long term negative consequences on the life chances of those involved. It
is important to continue to spend productive energy toward understanding DMC in order
to address possible solutions.
Purpose of Study
The current status of the DMC initiative remains largely unanswered. A
nationwide assessment has found that, on average, there has been a reduction of nearly
one-fifth in the disproportionate Black-White ratio of juvenile placements controlling for
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This unevenness in implementation provides an opportunity to examine variation across
jurisdiction in DMC-related outcomes, while simultaneously testing hypotheses related to
racial disparity and social control.
First, this research will extend findings from Davis and Sorensen (2010) by
comparing state-level juvenile placements, controlling for the groups¶ rate of arrests
during the last decade. If that study was correct in its conclusion that reductions in DMC
were related to the OJJDP initiative, then one would expect states that began addressing
DMC early in the evaluation time frame and made more headway in addressing DMC
will experience larger decreases in Black-White disparities. State reports to the OJJDP
regarding their progress with DMC initiatives will be compared.
H1: The extent to which states have addressed DMC mandates will be inversely
related to the ratio of Black-White disproportionality in juvenile placement rates
controlling for arrest (DMC).
Second, this research will test the racial threat hypothesis regarding minority
incarceration rates. The traditional racial threat hypothesis states that as the Black
population increases in a geographic location, social control will intensify to decrease the
threat of Blacks to the political, economic, and social domination of Whites (Stolzenberg,
D¶Alessio, & Eitle, 2004). Much of the supporting research examines the correlation
between aggregate Black population size and individual-level outcomes, i.e. police
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socially, economically, and politically, and will exercise more social control leading to
greater disparity in Black-White juvenile placement.
H2: The size of the Black population and lower rates of Black unemployment
relative to Whites in a jurisdiction will result in higher levels of DMC.
However, research has shown that once additional variables are introduced, the
relationship between the Black population and social control diminishes. Measures
constructed to capture Black composition, racial inequality, Black immigration, economic
disadvantage, and racial residential segregation have been used as predictor variables,
while Black political power and police presence have been used as control variables
when examining the impact of racial threat on arrests (Parker, Stults, & Rice, 2005).
Introducing these additional variables has led to findings that in areas with large Black
populations, Black arrest rates have decreased (Parker et al., 2005). This finding has often
been explained through the benign-neglect hypothesis (Parker et al., 2005; Stolzenberg et
al., 2004). Because crime, especially crime in large Black populations, tends to be
intraracial, there may be a decrease in manifestation of formal control through arrest due
to minorities being less likely to report crime and due to the allocation of fewer resources
for solving intraracial minority crime (Parker et al., 2005). While an increase in formal
control may be expected when an area experiences an increase in the size of the Black
population, benign neglect predicts that once an area becomes saturated a tipping point
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threat in areas of long standing, large minority populations because, ³Other things being
equal, the degree of relationship between status consciousness and discriminatory
behavior can be expected to decrease as community size and heterogeneity increase´
(Blalock, 1967, p. 70).
Leiber (2002) found lower rates of minority-White disparity at various decision-
making stages in urban areas with higher concentrations of minorities. Because minorities
have become more highly concentrated, this finding suggests a decrease in Black-White
disparity nationwide, as urban areas may drive national results. Due to research findings
that Black-White disparity is lower in jurisdictions with large, stable Black populations, it
is predicted that the relationship between racial composition and DMC may be
curvilinear.
H3: Jurisdictions with large Black underclass populations will have lower levels
of DMC.
A derivative of racial threat is the symbolic threat hypothesis. Instead of the
White elite being fearful of an actual threat of a change in political positioning through
Black population growth as proposed by racial threat (Tittle & Curran, 1988), the
symbolic threat hypothesis posits that the White majority subjectively perceives the poor
and underclass as a threat to the values of ³mainstream America´ (Sampson & Laub,
1993). Poor Black communities have, over time, developed into the underclass (Wilson,
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in decreased disparity of Black-White juvenile placement rates. The established Black
population is no longer considered a racial threat due to its underclass status. However,
drug and public order offenses continue to symbolically threaten the values of middle and
upper class standards and the success of their children.
From a symbolic threat perspective it can be argued that African American
juveniles receive more punitive results in decision-making (i.e. incarceration) due to their
being stereotyped as more dangerous by middle-class standards, regardless of racial
composition (Leiber, Johnson, Fox, & Lacks, 2007). According to the symbolic threat
hypothesis, then, the effect of racial composition on DMC may be moderated by the type
of offenses committed. African American youth continue to symbolically threaten the
status quo regarding the safety and well-being of middle-class youth through drug and
public order offenses, resulting in the continued use of social control mechanisms at a
high rate to control the behavior of Black youth for these offenses.
H4: The percentage of explained disproportionality in juvenile placement rates
will be lower for the offense categories of drugs and public order in comparison to
violent and property offenses irrespective of other variables, except for the size of
the Black youthful population which should exacerbate these differences.
The threat hypotheses predict that African Americans will be treated more harshly
by the justice systems as a means of controlling the threat they pose toward maintaining
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juvenile justice system allows for the opportunity for racial disparities to exist more than
in any other part of a justice system. Because of this opportunity to exhibit greater
discretion in the juvenile system it is possible to uncover stronger support for these
theories when using a juvenile population than relying almost exclusively on adults.
Current Study
Objectives
There are two objectives of the current study. First it will determine the extent to
which state juvenile justice systems have been successful overall in reducing DMC,
specifically disproportionate African American placement, since the implementation of
the OJJDP initiative. Second it will test racial and symbolic threat theories on a juvenile
population.
Blumstein¶s (1982) methodology has been applied to an analysis of racial
disproportionality in incarceration in the juvenile justice system at the national level
(Davis & Sorensen, 2010), but has not been applied to a state-level analysis. By using this
methodology, this study will add to the body of literature addressing the selection bias
versus differential involvement debate in regards to DMC. By partitioning explained
versus unexplained (by arrest) overrepresentation of Black juveniles in the justice system
and correlating the results with state-level efforts to comply with the OJJDP mandate, a
conclusion will be offered regarding the effectiveness of the national initiatives.
Racial threat has been tested using adult populations with mixed results. Often
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previous research, and more refined measures, for more accurate testing, on juvenile
populations, this study will begin to fill in the gap left by previous research efforts.
Limitations
Although the current study will eliminate many potential limitations, a number of
issues remain that must be addressed in advance. Tests of racial and symbolic threat
theories have relied on city/community/neighborhood level data to assess minority threat
criminally, politically, and economically. Due to the fundamental protection of the
juvenile within the juvenile justice system, nationwide data is only available at the state
level. Offering statistics by offense, especially low base rate offenses, for some states
could compromise the anonymity of the juvenile. In order to address this issue, measures
previously used to evaluate city/community/neighborhood data will be adjusted for use at
the state level.
The current research does not include juveniles, often the most serious offenders,
who have been waived or transferred to the adult corrections system. The primary goal of
judicial waiver is the ability to impose more severe sanctions for serious juvenile
offenders than are available in the juvenile system (Fritsch, Caeti, & Hemmens, 1996).
Although only a small percentage of juveniles are waived or transferred to the adult
system each year, race has been found to influence judicial waiver decisions (Fagan,
Forst, & Vivona, 1987). Further, because Blacks have higher levels of involvement in the
most serious crimes and it is typically those who are dealt with in adult courts, they are
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Organization
This research is presented in five chapters. In this chapter, an overview of the
history of DMC and efforts taken to address it was presented. Chapter II discusses the
literature available relating to the theoretical models set out to explain the
overrepresentation of Black juveniles in the juvenile justice system. Also, a detailed
literature review concerning DMC and the prior use of the methods that will be employed
are presented. The data sources, measures, methodology, analyses, and limitations of the
current study are described in Chapter III. Chapter IV presents the findings of the
analyses of each hypothesis proposed. A discussion of the findings and limitations of the
research are offered in Chapter V.
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Chapter II
Literature Review
In introducing his paper on the theoretical bases for inequality, Tittle (1994)
argued that research regarding racial (as well as sexual and class) disparity and formal
social control has largely been a-theoretical. Although much of the research surrounding
DMC has remained descriptive and practical in nature, various theoretical foundations
have been tested in an attempt to explain overrepresentation of minorities under formal
control. Strides have been taken to fill in the ³barren theoretical landscape´ of juvenile
(Sampson & Laub, 1993) and criminal justice decision making. After a brief historical
overview, this chapter will address the previous theoretical research used to explain the
overrepresentation of African Americans in the justice system. It will also offer an
examination of the empirical literature regarding DMC. Finally, this chapter will review
prior research using the methodology employed in the current study.
Theoretical Background
Shortly after the abolition of slavery, researchers began studying and discussing
the overrepresentation of African Americans involved in the criminal, and later juvenile,
justice systems (Du Bois, 1899/2002; Work, 1900/2002a). As African American families
migrated from the impoverished south during Reconstruction to the industrialized north
in hopes of better opportunities, they were often faced with harsh environments.
European immigrants were scrabbling alongside African Americans for a piece of the
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impacted by the industrialization of the United States, African Americans were
continually at a disadvantage (Moses, 1936/2002).
By the turn of the twentieth century it was clear that Black Americans were
involved in the justice systems at a rate disproportionate to their representation in the
general population. Over 100 years later, there has been no conclusive resolution to the
fundamental DMC debate: To what extent are African Americans differentially involved
in crime and delinquency; and alternately, to what extent is their representation in the
justice system a result of selection bias? Addressing this disparity are numerous
theoretical perspectives of crime and delinquency causation that have been adapted to
clarify the overrepresentation.
Like the involvement versus selection debate, theoretical explanations examine
the issue from both sides. On the one hand, theory attempts to explain why African
Americans disproportionately commit crime. On the other hand, the selection bias
theories address why African Americans are disproportionately involved in the justice
systems. A review of theoretical explanations regarding the overrepresentation of African
Americans within the justice system follows.
Social Disorganization
Attempts to theoretically explain the overrepresentation of minorities in the
justice systems is not a recent endeavor. The association between disproportionate
involvements by African Americans with criminal and juvenile justice systems and
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Work, 1900/2002a). Du Bois (1899/2002) wrote of disharmonious social conditions
leading to crime as a result of migration from the newly freed South. He chronicled the
increase of arrest and imprisonment by offense for African Americans throughout the
second half of the nineteenth century to make the case that the increase in crime
commission among young, Black Americans was ³a phenomenon that stands not alone,
but rather as a symptom of countless wrong social conditions´ (p. 44).
Work (1900/2002a) explored the causes of overrepresentation of imprisoned
African Americans in Chicago at the end of the nineteenth century. He suggested that
unemployment and lack of participation in community activities, particularly church, had
caused deterioration in the relationships and informal social control networks of African
American communities. This deterioration was evident in African Americans being
arrested at a rate three to nine times higher than other populations, including foreign-born
immigrants.
The observations of these early scholars contributed to the development of social
disorganization theory, one theory addressing overrepresentation based on differential
involvement. Kornhauser (1978, as cited in Sampson & Groves, 1989) succinctly defined
social disorganization as the ³inability of a community structure to realize the common
values of its residents and maintain effective social controls´ (p.120). Social change;
including immigration, rural-urban migration, residential mobility, and urban growth;
weakens traditional coping behaviors and control institutions (Lanier & Henry, 1998).
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Henry, 1998). Thus a social pathology develops and is evident in crime and deviant
behavior (Williams & McShane, 2004).
The structural factors of social disorganization, as developed by Shaw and
McKay, are economic status, ethnic heterogeneity, rate of participation, and residential
mobility. Rate of participation is evident through local friendship networks and
participation in community activities (Sampson & Groves, 1989). Krohn (1986) uses this
concept of participation rate in his discussion of network density. Bonds developed
through social, work, or school networks increase participation in community activities,
which increases visibility by various community members. When visibility is high,
opportunity of delinquency decreases. Similarly, low economic status decreases the
likelihood of participating in formal and informal groups and activities leading to a
³weaker organizational base´ (Sampson & Groves, p. 780). Residential mobility disrupts
any existing local social bonds and creates a difficulty in developing and maintaining
lasting friendship bonds (Kasarda & Janowitz, 1974). Associated with residential
mobility is family disruption. If a parent leaves the home, available supervision for
children and property decreases. In addition, the community loses a capable guardian to
supervise neighborhood children and property (Sampson, 1987).
Social disorganization is not endemic to minority or racial/ethnic groups. Early
scholars such as Du Bois and Work observed the breakdown of social organization in
relation to African Americans; however, other scholars have used the tenets of the theory
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environment conducive to criminal activity. However, because African Americans
disproportionately reside in areas of social disorganization, it has been used to help
explain their overrepresentation in the justice systems.
Social Control
In addition to social disorganization, social control theory has been offered as an
explanation of Blacks¶ disproportionate involvement in criminal offending. Social
disorganization and social control have similar antecedents. Both schools of thought are
concerned with the wellness of social variables and institutions and the bonds made to
those institutions. The main difference is that social disorganization theorists emphasize
the social ills of the community as pulling the would-be offender into criminal activity.
Social control theorists postulate that deviance is a normal part of society. In
communities where social institutions are weak, bonds have not been successfully forged
to prevent everyday residents from acting in a deviant manner.
Like social disorganization, the concept of social control as it relates to the
disproportionality of African American involvement in the justice systems predates
scholarship most commonly associated with the theory. Frazier (2002), in a 1939 writing,
cited ³absence of communal controls´ as a reason for the high delinquency rate of
African American adolescents (p. 99). In the same year, Work (2002b) claimed that the
rise in African American crime after slavery was the result of the removal of ³restraints´
which had tied them to a social structure, and the new found freedom ³meant the license
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caught. Risk of breaking established bonds to conventional institutions deters crime. If,
however, those bonds either have not developed or have weakened, there is less
deterrence. ³The more weakened the groups to which [the individual] belongs, the less he
depends on them, the more he consequently depends only on himself and recognizes no
other rules of conduct than what are founded on his private interests´ (p. 294). Like social
disorganization, because it relies on a nest of social factors that have weakened bonds
with informal institutions, the theory is more generally applied to crime and delinquency
activity. Again, however, because African Americans are overrepresented among areas of
weak social institutions it has been indicated as a cause of DMC.
Conflict Theory
Following the offender centered crime causation theories, some criminologists
began to view crime as ³relative to legal systems,´ and began to study crime as it is
defined and applied to society (Quinney, 2001, p. 4). Conflict theories see deviance as a
product of social control instead of deviance leading to social control. This set of theories
predicate overrepresentation as a result of selection bias.
Conflict theories are actually a collection of various aspects of conflict. They
share a fundamental assumption that ³societies are more appropriately characterized by
conflict rather than consensus´ (Williams & McShane, 2004, p. 165). If not specified,
most research referring to conflict theory focuses on the power struggle in dual class
societies. Power is used by one class to maintain the consensus of the other class.
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Quinney (2001) defines power from a global perspective as a force used over
others to ³ensure effective coercion,´ to ³affect distribution of values,´ and as
institutional means to enforce values on a population (p. 11). From a practical view,
power is defined as total resources and the degree to which resources are mobilized
(Blalock, 1967). As it pertains to conflict theories, power is unequally distributed and
allows access to the political structure to affect decision-making processes in order to
control others (Quinney).
The importance of discussing power as it relates to crime is significant as crime
control institutions, organizations, programs and policies shape how society is organized
and directly affects individuals¶ lives (Liska, 1992). Crime is a construction of legal
definitions created ³through the exercise of political power´ (Williams & McShane,
2004, p. 170).
³«the conflict perspective asserts that those laws that most protect the interests of
the powerful are most enforced. Assuming that law violations are more
threatening when committed by some people than by others, the perspective
asserts that laws are most enforced against those people who most threaten the
interests of the powerful. Hence the conflict perspective asserts that the greater
the number of acts and people threatening to the interest of the powerful, the
greater the level of deviance and crime control´ (Liska, 1992, p. 18).
Conflict theory is based on a struggle between classes or the domination and
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replace class with race. Many of the characteristics of an upper and lower class system
have been used as a proxy for a struggle between White dominance and minority
subordination. This class/race interchangeability is grounded in early writings of conflict
theory by authors known for their work in the area. As discussed in Liska and Yu (1992),
Turk (1969) included both culture and race in his description of dissimilar subordinate
groups that can be perceived as threatening to the social order. Additionally, the size of
the dissimilar subordinate group affects the perceived threat where larger dissimilar
groups are perceived as more threatening. ³In particular, research suggests that non-
Whites in the contemporary United States are perceived by many people and authorities
as posing a criminal threat´ (Liska & Yu, 1992, p. 55).
Hawkins (1987/2002) criticized conflict theory on its application to race and
crime. He claimed that conflict theory emphasized group subordination and
powerlessness wherein lower-class individuals were said to have fewer resources in
which to resist criminal sanctions. However, race was stated as a function of social status.
Conflict theory¶s failure to address race separately from social status causes problems in
assessing its applicability to subordination on purely racial bases. Hawkins asked the
question, ³to what extent is the treatment of blacks in the United States a function of their
racial as opposed to their purely social status´ (p. 182)?
Theoretical Framework
Racial Threat
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and research projects where conflict theory is the fundamentally tested theory, racial
threat hypothesis is the most accurate description of a conflict theory based on race.
Ousey and Lee (2008) offer a succinct summation of the hypothesis as put forth by
Blalock.
«as a dominant social group, Whites view Blacks, and other non-White minority
groups, as potential competitors who may challenge their ascendant position in
society. Consequently, as Blacks (non-Whites) become more prevalent and less
residentially segregated in a given area, it is hypothesized that Whites will
perceive a greater threat and therefore move to protect the existing status quo via
a variety of discriminatory methods, including unjustly focusing criminal justice
resources at their non-White competitors. Simply put, the logic of racial threat
theory proposed that in the face of an increasing encroachment from Blacks,
Whites will be more motivated to discriminate and therefore will use formal
social control resources, such as arrests, as means of controlling Blacks (p. 324-
325).
Keen and Jacobs (2009) offer an overview of research findings that support the
racial threat hypothesis. As is chronicled by Keen and Jacobs, in communities with larger
populations of Black residents, hostility toward them is higher (Qullian, 1996; Taylor,
1998), fear of crime is higher (Liska, Lawrence, & Sanchirico, 1982; Quillian & Pager,
2001), support for capital punishment is greater (Baumer, Messner, & Rosenfeld, 2003),
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Liska, Lawrence, & Benson, 1981), experience greater use of deadly force by police
officers (Jacobs & O¶Brien, 1998), and have higher arrest rates (Liska, Chamlin, & Reed,
1985). At the state level, states with larger African American populations have higher
incarceration rates (Jacobs & Carmichael, 2001; Western, 2006; Yates & Fording, 2005),
have higher odds of having the death penalty (Jacobs & Carmichael, 2002), and perform
execution more often (Jacobs, Qian, Carmichael, & Kent, 2007).
These studies have encompassed the three areas in which racial threat postulates
Blacks threaten White status quo: criminally, economically, and politically (Eitle,
D¶Alessio, & Stolzenberg, 2002). Studies in the late 1970s and early 1980s began to
show that fear of crime (Liska, Lawrence, & Benson, 1981), size of police force (Jackson
& Carroll, 1981; Jacobs, 1979; and Liska, et al., 1981), and arrest rates (Liska &
Chamlin, 1984) are more highly correlated with the non-White population.
The racial threat hypothesis proposes that as the non-White population increases,
arrests of the non-White population increases. This proposed increase in arrest is a result
of an increase in fear of crime resulting in increased pressure directed toward police to
make more arrests; non-Whites¶ inability to resist arrest; and Whites¶ shared stereotypes
linking non-Whites with crime. Therefore, this hypothesis assumes that as the non-White
population percentage increases, the size of the formal control apparatus increases, the
fear of crime increases pressure to use formal control, and non-Whites¶ ability to resist
formal control decreases, resulting in an overall increase in arrest rates (Liska &
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factors affecting race-specific arrest rates and addressing racial disparities in arrest.
Second, the authors point to racial segregation patterns within cities that may allow for
opportunity structures that affect arrest rates racially. Third, evidence of disparity in
police discretion permitted involving arrests for offense categories of violent and
property index crimes compared to less serious offenses, like drug and public order
violations. Finally, research design flaws led to weak correlations between formal social
control and theoretical frameworks.
A number of recent studies have addressed some of these concerns but have failed
to support the various aspects of racial threat and their effects on arrest. Eitle et al. (2002)
failed to find a significant relationship between the White-to-Black unemployment ratio
and arrest rates. Stolzenberg et al. (2004) used variables testing economic threat including
community disadvantage, geographic location, and unemployment rate, all of which
failed to support the hypothesis. Finally, Parker et al. (2005) found that racial inequality,
defined as disparity between unemployment and educational attainment, had no
significant effect on arrest rates.
Racial threat can also be used to explain the use of formal social control through
increased sentencing of non-White offenders. At the macro level, racial threat is
evidenced by more severe sentencing outcomes in communities with larger Black
populations due to their representation as a threatening population. At the individual
level, Black offenders are sentenced more harshly because of judicial decision makers¶
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economic inequality. They proposed that ³the size of the non-White population and the
degree of non-White poverty will have distinct effects on the non-White imprisonment
rate´ (p. 348). They found that after controlling for community rates of crime or arrests,
percentage of population that was non-White had the greatest influence on imprisonment
in the expected direction.
Andrus (2005) examined what role, if any, racial composition and a state¶s
punitive laws effected African American incarceration rates. He found that at the adult
level, increased African American poverty, unemployment, and population increased the
rate of African American incarceration. For juveniles, Andrus found that a state¶s racial
composition was a predictor in juvenile incarceration rates of African Americans.
Specifically, for every 1% increase in African American population, a state incarcerated
an additional 11 African American juveniles per 100,000.
In addition to the correlated triad involving minorities, fear of crime, and formal
control, racial threat also has economic and political implications. Economic competition
between Whites and Blacks, and competition for jobs and other restricted resources,
increases the level of social control directed toward Blacks. The political portion of racial
threat explains how Blacks are seen as a political threat to Whites, and are subject to
increased formal social control to quell the threat, also known as power threat (Blalock,
1967).
Liska (1992) presented an association between these threatening aspects and the
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likely to use lynching to control political and economic competition. Specifically, the
authors noted the perceived threat was not a fear of Black criminals, but a fear of Black
voters and the economic change realized through Emancipation where four million pieces
of property were transformed to four million competitors (p. 34). Once lynching became
less favorable as a method of social control, other mechanisms such as Jim Crow
legislation, disenfranchisement, judicial discrimination, debt peonage, and violent
intimidation took its place (Tolnay & Beck). Many researchers argue that some of these
mechanisms, as well as formal control through the criminal justice system, remain intact
today.
Each of these relationships (crime, politics, and economics and social control) has
been shown to be curvilinear. The relationship between fear of Black-on-White crime and
the use of the criminal justice system as a formal control mechanism increases until the
Black population approaches parity with the White population, at which point the formal
control mechanisms begin to decrease through what has been termed benign neglect
(Liska & Chamlin, 1984; benign neglect will be addressed in further detail in a
subsequent section). Concerning the relationship between political threat and social
control, once the Black population controls a majority of the political vote, formal social
control toward Blacks would be expected to decline due to their ability to politically
mobilize (Blalock, 1967). The explanation of a ³deceleration in the intensity´ of the use
of social control as it relates to economic threat is explained through the use of ³other
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overlap have been indicated as being associated positively with ³non-state based´ social
control (Eitle et al., 2002, p. 559-560).
Blalock (1967) proposed a ³point of diminishing returns´ when a ³saturation´
effect has occurred in regard to a continuous objective (p. 142), for instance controlling a
minority group. Resources toward social control of minority groups will increase until the
saturation point has occurred. Once the minority group is no longer believed to be a
constant threat, resources spent toward the objective will diminish. Therefore, once the
minority group has been residentially, politically, and economically segregated there will
be no motivation for increased social control measures, and social control efforts will
begin to diminish.
This phenomenon has also been described in detail by Wilson (1987). Wilson
claimed that over time, poor Black communities developed into an underclass due to
residential and economic isolation (Wilson, 1987). This led to a decrease in the perceived
racial threat to White domination where established Black populations were no longer
considered a racial threat due to their underclass status. However, changes in social
composition or an increasing Black population remain threatening (Chamlin, 1989).
As with substituting race for class, Hawkins (1987/2002) took issue when
economic status has been used as a proxy for race. It is theorized that power threat is the
actual or perceived threat a minority group poses as a realistic challenge to White
political and economic control. Therefore, treatment of minority groups by the criminal
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supported by Tittle and Curran¶s (1988) finding of no effect regarding income variables,
but a significant effect regarding race on juvenile justice disposition.
Keen and Jacobs (2009) examined the effect the presence of a Black population
has on adult incarceration rates, specifically addressing the political aspect of social
control and an ³encroaching´ Black population. Their findings support a racial threat
hypothesis. States with substantial increases in Republican presidential candidate voting,
which they correlated with law-and-order campaign platforms, had substantial increases
in racial disparities in prison admissions. They concluded that in the deep South, where
Republican strength and ³covertly racist law-and-order political appeals were most
successful,´ and where the Black population was increasing but had not reached a size
where they had a firm political voice, disparities in prison admissions were most
pronounced (Keen & Jacobs, 2009, p. 231).
However, Eitle et al. (2002) did not find support for the political portion of racial
threat. They noted no statistically significant difference in the use of social control of
Blacks based on voter turnout. However, they did acknowledge that failure to find
significance could have been a product of the difference between voting eligibility and
actual voting. In areas of low voter turnout, Whites may not feel politically threatened
and thus less likely to expend energy on social control.
Bureaucratic Model
Some of the mixed results of racial threat and formal social control may be
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³Overall, it is taken-for-granted that our central object of theorizing in crime and justice
studies is crime. Pursuing a recognized and usable theoretical infrastructure about
criminal justice«has not been an acknowledged priority and certainly does not constitute
a recognized theoretical project´ (p. 169).
However, bureaucratic theorists, also known as rational, structural organizational,
or Weberian theorists, propose that the administration of law will differ depending on the
degree of organizational bureaucracy within an area (Albonetti, 1991; Bridges et al.,
1987). Where bureaucratic adherence is high, legal punishments will be more uniform,
consistent, and executed in accordance with ³universalistic rules of criminal procedure.´
Where bureaucratic organization is low, ³informal criteria´ and ³rules-of-thumb´ will be
evident in punishment (Bridges et al., p. 345).
The practical aspects of the bureaucratic model are most common in terms of
justice decision-making in urban versus rural areas. Due to the high volume of offenders
processed in urban areas, structural organizationalists theorize that the legal process will
be guided by policy, procedure, and formal rules restricting actions of legal actors.
Discretion will be limited by the inability to deviate from stated polices. Therefore, it is
suggested that capricious decision-making will be less likely in urban courts when
compared to rural courts. Where race differences occur, these differences should be the
result of rules of procedure which would discriminate against whole categories of
offenders, for example, in the case of appointed counsel for poor defendants in criminal
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factors in justice decision-making. She proposed that when uncertainties are present,
legal actors rely on habit and social structure to facilitate decision-making in urban areas.
The result may be decision-making based on ³past experience, stereotypes, prejudices,
and highly particularized views of present stimuli´ (Clegg & Dunkerley, 1980, p. 265, as
cited in Albonetti, 1991, p. 249).
Tittle and Curran (1988) tested the rational organization hypothesis wherein they
theorized that racial (as well as socioeconomic and family structure) disparity in juvenile
court processing would be greater in rural areas with fewer cases and lower in urban
areas with higher case volume. This hypothesis was based on the idea that policy would
be more likely to affect processing in urban areas and stereotypes would affect rural area
processing. However, they found no support for this hypothesis and, interestingly, no
support for the alternate hypothesis that urban areas would exhibit greater processing
disparity because high volume would encourage quick decisions based on past
experiences or prejudices. While the disparity was slightly higher in areas of high
volume, the only significant impact race had on processing decisions was in areas of
medium volume.
Benign Neglect
Increasing research has shown that once additional variables are introduced, the
relationship between the Black population and formal social control diminishes.
Measures constructed to capture Black composition, racial inequality, Black immigration,
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Introducing these additional variables has led to findings that in areas of large Black
populations, Black arrest rates have decreased (Parker et al., 2005). This finding has often
been explained through the benign-neglect hypothesis (Chamlin & Liska, 1992; Liska &
Chamlin, 1984; Parker et al., 2005; Stolzenberg et al., 2004).
Benign neglect has been a component of the conflict perspective since the 1970s
(Blau, 1977). Specifically, benign neglect is closely related, although inversely, to the
racial threat hypothesis. Racial threat predicts that as the non-White population increases,
formal control mechanisms, such as arrest, will also increase due to the subordinate
group¶s threat to the dominant group¶s control of economic and political resources. The
benign neglect model suggests that as the non-White population increases, formal control
mechanisms, such as arrest, will decrease due to a higher prevalence of intra- rather than
interracial crime. Benign neglect assumes that police experience less pressure to arrest
when crime victims and offenders are Black due to a perception of it being a family
problem not in need of intervention, lower likelihood of reporting, residential racial
segregation, and devaluation of Black victims.
Diminished pressure to formally resolve intraracial crime has been attributed first
to the perception that ³personal matters should be handled informally´ (Ousey & Lee,
2008, p. 328). ³If police hear about a crime within a family, they are less likely to
recognize it as such, whether by writing an official report or by making an arrest´ (Black,
1976, p. 108).
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offenses become known to police (Warner, 1992). Research has shown that when traffic
violations are excluded, 90% of all citizen/police encounters are initiated by private
individuals (Lundman, 1980, as cited in Warner). Specifically, then, in communities
where there is mistrust toward law enforcement, Black victims are reluctant to report
crime (Ousey & Lee, 2008).
Third, the need for crime control processes are reduced if problem groups are
residentially segregated (Spitzer, 1975). Segregation can be forced through the creation
of urban ghettos (Spitzer, 1975; Wilson, 1987) or by way of the ability of more affluent,
mostly White residents to move out of areas where non-White minorities begin to reside
(Blalock, 1967). Even when minority groups have the financial means to reside in
neighborhoods primarily controlled by the dominant group, the dominant group continues
to possess the means to leave ³invaded´ neighborhoods (p. 141-142). Residential
segregation decreases interracial crime, thereby reducing police pressure to control crime
(Liska & Chamlin, 1984). Residential racial segregation could be ³an instrument of state
control whereby problem populations are managed passively without the need for an
excessive reliance on the police´ (Stolzenberg et al., 2004, p. 693). An inverse
relationship has been found regarding racial segregation and size of the police force
(Liska et al., 1981).
Finally, it has been argued that Black victims receive less investigative attention
due to their representation among the lower social strata. ³The more organized the victim
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more law will be applied to the offender than if the offender has greater cultural standing
than his/her victim. However, if the victim and offender are of comparable cultural
standing, as is the case in most neighborhood crime events, less law will be applied
according to the status of the parties. Furthering Black¶s propositions and regarding the
resources afforded investigation of inter- versus intraracial crime, Hawkins (1983) argued
that, due to the historic positioning of Blacks as inferior to Whites, Black life remains
³cheap.´ Therefore, Black victims of crime will be afforded fewer legal resources.
Chamlin and Liska (1992) compared the effects of the percentage of non-Whites
to arrest rates of both whites and non-Whites in 1972 (Liska & Chamlin, 1984) and in
1982. They found support for the benign neglect hypothesis, whereby in both years the
percentage of non-Whites was negative and strong for both white and non-White arrests.
As the percentage of the non-White population increased, non-White arrests decreased.
The researchers explained this relationship through the benign neglect hypothesis; as the
non-White population increased, so too did the non-White crime victims. ³Lacking
political and economic clout, non-White victims may be unable to legitimate their
complaints as crimes and to pressure police to allocate resources to resolve them´ (p.
112). However as the percentage of non-White population increased, white arrests also
decreased. The authors suggested this may be explained further by way of benign neglect
in that an overreaching climate of neglect produced by the high rate of non-White victims
ultimately decreased the white arrest rate as well.
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Parker et al., 2005). To this end, Ousey and Lee hypothesized that where the race of the
victim is known (mainly index offenses), a higher prevalence of Blacks will result in
smaller Black-White arrest disparities due to intraracial victimization. Additionally,
where there is no clear victim (drug offenses, for example), Black population size will
have little effect on Black-White arrest disparities. However, their research did not
support this hypothesis. In fact, where Black intraracial homicide increased, so did the
Black-White arrest disparity.
Eitle et al. (2002), in testing the racial threat hypothesis, found support for both
racial threat and benign neglect. They found that as the percent of identification of a
Black perpetrator and White victim for a violent felony rose, so too did the likelihood of
Black arrests for violent offenses. However, in support of benign neglect, they also found
that the likelihood of Black arrests did not rise if Black-on-Black crime increased, which
accounted for 60% of reported crime in their study.
SymbolicT
hreat
A derivative of racial threat is the symbolic threat hypothesis. Instead of the
White elite being fearful of an actual threat of a change in political positioning as
proposed by racial threat (Tittle & Curran, 1988), the symbolic threat hypothesis posits
that the White majority subjectively perceives the poor and underclass as a threat to the
values of ³mainstream America´ (Sampson & Laub, 1993). Specifically regarding
juveniles and decision making within the juvenile justice system, Leiber and Johnson
35
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youth, especially minorities, and the social psychological emotions of juvenile court
officers´ (p. 561).
Sampson and Laub (1993) emphasized the perceptions offered by previous
researchers in making a distinction between symbolic threat and other aspects of social or
racial threat. These perceptions include jealousy, envy, fear, and offensiveness (Irwin,
1985; Tittle & Curran, 1988). They suggest that juvenile justice officials may respond
more harshly to the stereotype elicited by symbolic threats such as ³threatening young
black males dealing drugs in poor neighborhoods across the United States´ (p. 290).
This correlation between drug offenses and Black youth is especially salient to the
symbolic threat hypothesis. Arguably, the perception of dangerous young Black drug
dealers is one of the most widely held stereotypes regarding minorities and crime.
Sampson and Laub (1993) outline how race, class, and drugs became interlaced in the
1980s and early 1990s. During the 1980s, arrest rates and referrals to court for drug law
violations decreased significantly for White juveniles, by 28% and 6%, respectively.
However, during the same time period, arrest rates and referrals for drug law violations
increased for Black juveniles, by 25% and 42%, respectively (Snyder, 1990; Snyder,
1992).
Drawing on prior research, Tittle and Curran (1988) theorize that offenses that are
less serious, ³moralistic,´ or ³ambiguous in definition´ will allow greater opportunity for
discretion, and thereby carry a higher risk of increased racial disparity (p. 32). Although
36
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with drug/sex offenses being most influenced by race. When ³moral,´ personal, and
property offenses are separated, the findings show race to have the greatest impact on the
processing of juveniles referred for moral offenses. They conclude that drug and sex
offenses were the source of the largest discriminatory effects in juvenile justice
dispositions, setting up the argument that these ³behavioral manifestations´ represent
qualities that ³frighten white adults or generate resentment and envy´ (p. 52).
At the juvenile level, Tittle and Curran (1988) proposed that disparities based on
status variables vary directly in proportion to the threat posed by minorities to elites.
They proposed that race and age would impact juvenile justice sanctioning. They found
significant differences in sentencing severity based on race whereby nonwhite juveniles
sanctioned in areas of medium threat (10-19% nonwhite) and high threat (20% and higher
nonwhite) received harsher dispositions than those sanctioned in areas of low threat (less
than 10% nonwhite). They also found significant differences in sentencing disparity
based on age. Counties were divided in terms of proportion of the population under that
age of 18; low threat counties (under 25% of the population under 18), medium threat
counties (26-30% of the county was under 18), and high threat counties (over 30% of the
county was under 18). They found that in counties with the highest proportion of youth
under the age of 18, race had a significant impact on sentencing severity. The
combination allowed them to conclude that there is support for conflict theory in regards
to severity of sentencing in areas of large young, minority populations.
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based on status variables would vary directly with the wealth of the population within the
court¶s jurisdiction. However, they found no evidence to support the value dominance
hypothesis.
Sampson and Laub (1993) evaluated county-level structural variations and
juvenile justice processing to test symbolic threat. They examined various decision
making stages to test for the effects of racial inequality, underclass, and social control. At
the petitioning stage, they found racial inequality was most consistently related to all
offenses except drug offenses and had the largest effect on personal and public order
offenses. Their construct for underclass poverty was most significantly related to secure
detention for drug offenses, while racial inequality was significantly related to personal
and public order offenses. When examining out of home placements, underclass poverty
was significant for personal and drug offenses, but racial inequality failed to reach
significance.
Sampson and Laub (1993) further introduced race interactions to uncover any
structural effects. They found that underclass poverty was positively related to secure
detention for personal, property, and public order offenses for Black juveniles but not for
White juveniles. Further, although racial inequality was significantly related to detention
for both races for drug and property offenses, the raw coefficients for Black juveniles
were more than double that for Whites. Wealth of county was significant for the
detention of Black juveniles for personal, property, and drug offenses, but was not
38
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³Consistent with the symbolic threat hypothesis, then, counties characterized by
inequality and/or the presence of a large underclass produce the highest rates of
confinement for blacks, particularly blacks adjudicated for drug offenses´ (p. 305).
At the adult level, research has revealed increased punitiveness for non-White
drug offenders (Myers, 1989) and a growth in the prison population attributed to
admissions of Black drug offenders (Blumstein, 1993). These findings support the idea
that the war on drugs has led to racially discriminatory practices in the criminal justice
system (Jackson, 1992).
Using symbolic threat as a theoretical background, recent studies have explored
the relationship of race in juvenile justice decision making (Leiber, 2010; Leiber &
Johnson, 2008; Leiber, Johnson, Fox, & Lacks, 2007). Leiber (2009) anticipated that
decision makers (juvenile court officers and judges) would be more likely to use secure
detention with Black juveniles due to their perceptions that these youth are more
dangerous or delinquent, engage in drug offenses more, and/or come from dysfunctional
families more. Therefore, decision makers would apply a different ³threshold of
tolerance´ for Black youth due to these perceptions or stereotypes (p. 5). These
perceptions are supported by research conducted on adults where decision makers in the
criminal justice system often stereotype young adult Black males as more ³crime prone
or dangerous´ and not amenable to treatment when compared with older offenders
(Steffensmeier, Ulmer, & Kramer, 1998, p. 764).
39
Whit t t Th i h l i di t d th t t d
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White counterparts. Their research also indicated that race trumped age as a
determination of release. Although White juveniles were more likely to be released the
younger they were, young Black juveniles did not receive the same leniency.
As with his previous study, Leiber (2009) found that being black increased the
odds, almost 2 to 1, of pre-adjudication detention for African American juveniles, but
was not a predictor in post-adjudication decisions when all relevant legal and extralegal
factors were considered. Living in a single-parent household increased the likelihood of
pre-adjudication detention for Black juveniles by almost 2.5 times that of White
juveniles. Race was found to have an interactive effect at other decision making stages.
Black juveniles who were detained pre-adjudication were less likely to receive diversion,
and more likely to be referred for further court proceedings. In fact, none of the Black
juveniles in this study who were detained pre-adjudication received diversion; all were
referred to court. Being Black and from a single parent household increased the
likelihood of being petitioned by 5.5 times that of White juveniles.
Leiber et al. (2007) compared juvenile justice processing of Whites, Blacks,
Native Americans, Asians, and other racial categories to test tenets of symbolic threat.
They found that Blacks were least likely to receive a decision of diversion at intake
compared to all other racial categories; however, they were less likely to be formally
adjudicated than Asian and other minority juveniles. Although age seemed to affect
decision making, where older juveniles were treated more harshly at various decision
40
less likely to result in adjudication for all racial categories with Black juveniles least
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less likely to result in adjudication for all racial categories, with Black juveniles least
likely to be formally adjudicated for drug offenses.
Although Leiber (2009) found evidence to support symbolic threat in earlier
stages of juvenile justice processing, support was not found in all stages. Decision-
making at later stages of processing did not always result in more severe outcomes or
cumulative disadvantage. The author claims that these inconsistencies can be explained
by the many stages and various actors at each stage in the juvenile justice system. All
actors do not share the same perceptions or stereotypes. Further, the leniency shown in
later decision making stages were attributed to a ³correction factor´ to counteract the
harsh earlier stages (p. 18). This correction factor has also been used to explain lower
rates of Black juveniles in detention where there is a high rate of Black juvenile arrests
(Rodriguez, 2007).
Symbolic threat specifically indicates Blacks will be treated more harshly for drug
offenses. Crow and Johnson (2008) found support for this hypothesis. In their findings,
individual- and county-level variables interacted to reveal that the greatest racial disparity
in habitual offender sentencing, when all offenses were taken into consideration, existed
for drug offenses. Larger Black populations were also associated with a higher likelihood
of habitualization for drug offenses. In a summary of DMC research, Piquero (2008)
explained that differential treatment based on race has occured at different stages of the
juvenile justice system in varying degrees, from none to high levels, except for drug
41
symbolic threat the authors point to a spatial opportunity model They theorize that
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symbolic threat, the authors point to a spatial opportunity model. They theorize that
police resources are concentrated in areas of ³spatially distinct Black communities that
are perceived [emphasis added] to be crime µhot spots¶´ (p. 331). Therefore, higher
Black-White arrest disparities will be more likely in areas of high residential segregation
and for crime categories involving greater police discretion. Their research supported this
hypothesis; in cities with greater racial residential segregation there was an increase in
the Black-White disparity for drug arrests, with 0.796% increase in arrest disparity for
each 1% increase in the segregation measure.
The Issue of Disproportionate Minority Confinement
Feyerherm (1995) summarized the complexity of studying and addressing
disproportionate minority contact in the juvenile justice system. He stated that complying
with the OJJDP mandate and producing change could not ³be met by the simple
elimination of a type of treatment or confinement, nor one that for which [sic] success
(compliance) [could] be measured in a simple counting operation´ (p. 6). Subsequently,
the production of a large body of literature has resulted in mixed outcomes. Examinations
of each stage of juvenile justice processing produced widely varying results indicating no
evidence of racial disparity (Barrett, Katslyannis, & Zhang, 2006), disparity at one stage
and no race effect at another (DeJong & Jackson, 1998), or consistent discrimination
throughout the system (Bishop & Frazier, 1996; Conley, 1994; Leiber, 2002).
One of the main findings from this body of research is that racial disparities
42
histories and those charged with more serious crimes those involving weapons and drug
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histories and those charged with more serious crimes, those involving weapons, and drug
offenses received less favorable decisions (DeJong & Jackson, 1998; Wordes, Bynum, &
Corley, 1994). Independent of offense seriousness and other legal matters, race affected
the likelihood of detention (DeJong & Jackson, 1998, Wordes et al., 1994; Wu et al.,
1997) and differential processing of minority youth (Bishop & Frazier, 1996; Leiber
2002). Huizinga et al. (2007) studied self-reported delinquency data in three cities during
1985 through 1988 and official contact/arrest/referral data for the juvenile justice system
to uncover the magnitude of racial effects, if any, on juvenile justice decision-making
after controls were added. They found that DMC at the initial stages of juvenile justice
decision-making could not ³be fully explained by level of involvement in delinquency
nor by delinquency level and risk factors combined´ (p. 42).
According to the differential involvement hypothesis, empirical research
including controls for offense seriousness and prior offending should show a reduction in
or nonexistence of any direct race effects. However, empirical research testing the
selection bias hypothesis would predict that controlling for legal factors will not negate
race effects. In an effort to compare previous research testing the effects of race on
juvenile justice decision making, Engen, Steen, and Bridges (2002) used logistic
regression to eliminate the effects of methodological variation on outcomes. They found
that 29% of studies report direct race effects that disadvantage minorities. Although
controlling for legal factors diminishes some race effects, evidence that these findings
43
increased the probability of a direct race effect. Their research lent support to the
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increased the probability of a direct race effect. Their research lent support to the
selection bias hypotheses in that, independent of legal and other social factors, race still
matters in juvenile justice processing.
Another major finding from this body of research is that small racial differences
in decision making may accrue throughout the process. This cumulative effect wherein
racial disparities ³build up´ as a result of decisions made at various stages during case
processing has been well documented by researchers. Detained juveniles, for instance,
were twice as likely to be adjudicated delinquent in comparison to youths who were not
detained prior to adjudication (Wu et al., 1997). Bishop and Frazier (1996) found that due
to a cumulative effect of many case processing decisions, although minorities made up
29% of cases referred or at delinquency intake, they made up 44% of incarcerated or
transferred youth.
In addition to individual studies, analyses of previous work have been undertaken
to summarize the empirical research on minority representation in the juvenile justice
system. Pope and Feyerherm (1995) reviewed publications regarding minority youth in
the juvenile justice system from 1969 through 1989. They found racial effects generally
present at some stages of processing but not others, although bias could occur at any
stage of case processing. They also noted that when it did exist, racial disparity tended to
accumulate as youths were processed through the system. No relationship was found
between rigor of methodology or data quality and disparity, although controlling for legal
44
However, compared to prior research, the latter studies tended to use complex statistical
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, p p , p
designs and were more likely to result in mixed findings.
Leiber (2002) reviewed state assessment studies to determine the extent of racial
disparities when relevant legal and extra-legal factors were taken into consideration. The
report examined studies from 43 states where data were collected in the mid to late
1990s. Leiber found that at the identification stage minority youth overrepresentation was
evident in every state reviewed, existed at all decision-making points, and was greater in
states with smaller minority populations. African American youths were the most
disproportionately represented minority group. The assessment reports that attempted to
determine the reasons for the overrepresentation were more difficult to compare because
each state¶s assessment procedure and level of methodological sophistication varied
substantially. However, Leiber found ³overwhelming evidence to support the presence of
race effects in juvenile justice decision making´ (p. 13), with 32 states unable to account
for racial disproportionality by minority youths¶ differential involvement in crime.
An effort to update Leiber¶s (2002) state assessment, a review of current state
DMC assessments is offered here. Although the OJJDP lists 21 states as having submitted
studies regarding DMC between the years of 2000 through 2008, only 12 states were
included in the current analysis (indicated in the reference section by an asterisk). The
remaining studies were dropped from consideration for one of the following reasons: the
study used overlapping data discussed in prior studies; the study did not include
45
The 12 states identified represent all regions of the United States and include
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p g
diverse demographic characteristics, particularly with regard to minority populations. The
results from these studies were mixed; the outcomes indicate that for many states and
various decision points, DMC was present. However, no state showed consistent DMC
for any minority group throughout all decision-making points. For minorities, higher
odds of being negatively affected at any particular stage of processing in the juvenile
justice system were evident in particular studies, but this seemed to be the exception
instead of the rule. However, due to the attrition of studies in analyzing stages throughout
the decision-making process, it is difficult to say for certain if it occurred, and to specify
the extent of DMC that arose during various stages of case processing across studies or
cumulatively within studies.
Toward a State-level Assessment
³Disparity is not necessarily tantamount to discrimination´ (Garland, Spohn, &
Wodahl, 2008, p. 5). Disparity reveals that there is a difference in outcome. It is clear
there is a difference in the percent of Black juveniles in the United States population and
the percent of Black juveniles in out-of-home placement. Although this disparity exists,
to what degree, if any, can it be said that the disparity is a result of discriminatory
practices, ³unequal treatment through such things as unfair policies and practices´
(Garland et al., 2008, p. 5)?
States began reporting DMC to the OJJDP shortly after the mandate was in effect.
46
indicates minority overrepresentation (Puzzanchera, Adams, & Snyder, 2008). While this
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measure offers a comparison to the general population, it does not factor in differential
involvement in delinquency. Although arrest rates are not a substitute for involvement,
once factored into placement disparity, a more comprehensive examination can be made
regarding the expected rates of out of home placement. ³Scanning for disparities in
incarceration with no control for arrest rates or criminal involvement can lead to gross
overestimations of racial disproportionality. These inaccuracies can further lead to large
investments of time, money, and manpower in investigation of an illusory problem´
(Garland et al., 2008, p. 31).
Blumstein (1982) pioneered the methodology used to disentangle the explained
portion (due to differential involvement) and the unexplained portion (perhaps due to
system bias) from the total overrepresentation of adult African Americans in United
States¶ prisons vis-à-vis Whites relative to their representation in the population. He
proposed that if no discriminatory practices existed in the criminal justice system after
arrest, then the racial distribution of prisoners incarcerated for a particular crime type
would equal the racial distribution of persons arrested for that crime type. By comparing
crime-specific race ratios at arrest to the distribution of prisoners by crime of conviction
in prison, he was able to estimate the expected racial distribution of incarcerated
prisoners (formula is provided in the methods section). Any differences between the
estimated racial distribution (based on arrest) and actual racial distribution of incarcerated
47
Compared to their representation in the population, Blumstein (1982) noted that
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African Americans were overrepresented in the prison system relative to Whites by a
ratio of nearly 7:1. He found that 80% of the disproportionality witnessed in incarceration
during 1974 was accounted for by differential arrest rates, and thus explained by
differences in the groups¶ propensities to commit serious crimes. While noting that the
remaining 20% of the original disproportionality which could not be accounted for by
differential arrest rates could be partially due to differences in criminal record or
seriousness of the crimes within offense categories, Blumstein acknowledged that some
portion was undoubtedly due to bias in the processing of cases by the justice system. In a
later study, Blumstein (1993) found that a similar portion of the disproportionality in
incarceration rates in 1991 was explained by arrests, but did note that the surge in the
sentencing of drug offenders to prison, a nearly four-fold increase, was having much
more of an impact on disproportionality in the latter, as compared to his earlier, study.
The percentage of disproportionality in incarceration rates explained by arrest increased
from 76% to nearly 94% when those incarcerated for drug offenses were removed from
the equation using the 1991 dataset.
Blumstein¶s work spurred other efforts to gauge the level of racial
disproportionality in adult incarceration rates. Two studies using a similar methodology,
but less sophisticated formula, attempted to account for differences in racial
disproportionality among individual states and regions of the country (Crutchfield,
48
during the 1990s demonstrated the importance of disaggregating arrest data by particular
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offenses when estimating the expected ratios of incarcerated prisoners, finding that only
42% of racial disproportionality in court commitments to prison was explained by
differences in crime-specific arrest rates.
One study examined differences in regional incarceration rates for 1997 utilizing
Blumstein¶s formula to account for crime-specific arrest rates in their measure of
disproportionality (Sorensen, Hope, & Stemen, 2003). Initially observed Black-White
disproportionality in prison admissions varied regionally from 7:1 in the South to 16:1 in
the Midwest; for every newly admitted White prisoner in the South, seven Black
prisoners were admitted, while for every newly admitted White prisoner in the Midwest,
sixteen Black prisoners were admitted. The results showed only small differences in the
portion of disproportionality explained by arrests across regions, 67% on average. The
authors noted, however, that the percent of explained variance was an inadequate figure
for describing differences by race remaining unexplained across regions due to vast
differences in initially observed levels of disproportionality among the regions. By
applying the portion of unexplained variance to the initially observed level of
disproportionality, an adjusted ratio of disproportionality, one which controlled for race-
specific arrest rates, was calculated for each region. While less dramatic, the initial
pattern noted among regions remained similar after controlling for arrests, with the
adjusted ratio ranging from 3.5:1 in the South to 6.5:1 in the Midwest.
49
trends across the United States, roughly partitioning variance in confinement by race into
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that which was explained by arrest, and that which remained unexplained based on arrest
rates and could therefore conceivably result from improper factors working within the
juvenile justice system. Davis and Sorensen found that, on average, there has been a
reduction of nearly one-fifth in the disproportionate Black-White ratio of juvenile
placements controlling for the groups¶ rate of arrests during the years 1997 through 2006,
suggesting that the OJJDP mandate may be having some positive effect. However, after
adjusting for rates of arrest, they reported that Black juveniles were still placed at rates
nearly 70% higher than Whites.
Garland et al. (2008) identified the potential utility of such measures after
reviewing the results of prior studies using Blumstein¶s (1982) method of assessing
prison disproportionality. They stated that due to the variation in disparity across states,
break downs at the state level are a more appropriate use of the method than calculating
an omnibus nationwide measure. Further, Garland et al. pointed out that the research
indicates disparity can change over time and by offense. Drug offenses, for instance, have
consistently had the lowest explained disproportionality of all offense categories. They
suggested examining the level of explained disproprotionality based on arrest for each
offense type. They also noted that the proportion of Blacks in urban areas and other
demographic factors could influence imprisonment disparity. Garland, et al. also argued
that prior prison disproportionality studies have largely neglected theoretical grounding.
50
Following Garland et al.¶s (2008) suggestions, this study will offer state-level
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comparisons of DMC and efforts to reduce it. By using a 10 year study period, 1997-
2006, and separating incarceration disparity by offense based on arrest rates, this study
will allow an examination of change over time and reflect differential disparity, if such
disparity exists, for offenses where opportunities for discretion vary. This study will be
theoretically grounded in racial and symbolic threat hypotheses, addressing previous gaps
in disproportionality research and employing demographic measures that may influence
incarceration disparity. Finally, this study will utilize Blumstein¶s (1982) formula
adjusted to compare levels of disparity across time and jurisdiction as suggested by
Sorensen et al. (2003).
This chapter offered an historical review of theories addressing the
overrepresentation of minorities in the justice systems. It evaluated theoretical and
empirical literature related to DMC. It proposed a need to utilize previously used
methodology which had primarily been employed to examine adult incarceration
disparity to now address DMC in the juvenile justice system. Chapter III describes
specific methods that will be used to test the hypotheses discussed.
Chapter III
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Method
Data Sources
The present research examined state-level variables to assess collective changes
made in DMC in accordance with OJJDP initiatives. Comparisons relied on population
data from the U.S. Census Bureau, arrest data from the Uniform Crime Reporting system
(UCR), and juvenile placement data from the Census of Juveniles in Residential
Placement (CJRP). Information was also culled from state DMC compliance reports
available from the Office of Juvenile Justice and Delinquency Prevention. To test the
racial and symbolic threat hypotheses, state-level variables measuring population
composition, economic disadvantage, and racial segregation were calculated from U.S.
Census Bureau data.
The OJJDP provided the necessary data on incarcerated juveniles. The OJJDP has
administered the Census of Juveniles in Residential Placement (CJRP) on a biennial basis
since 1997 when it replaced the former Children in Custody (CIC) census that had been
conducted since the early 1970s. While the CIC collected only aggregate data on
juveniles held in facilities, the CJRP collects individual-level information regarding the
juvenile¶s gender, date of birth, race, placement authority, most serious offense charged,
court adjudication status, date of admission, and security status. The CJRP requests data
from more than 4,000 public and private residential facilities on each youth assigned to a
52
collects data on placements under the age of 21, adults age 18 and over still serving their
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sentence in juvenile institutions were removed from the current sample. From these data,
race of juveniles was disaggregated to find the actual number of Black and White
juveniles in secure confinement by offense of adjudication (Sickmund, Sladky, Kang, &
Puzzanchera, 2008). Juveniles held in placement outside of the state where their offense
was committed are counted in the CJRP for the state of offense (Sickmund, 2010). For
example, if a juvenile committed an offense in Utah but is held in California, the offense
is counted for Utah. In order to maintain anonymity and preserve the privacy of juvenile
residents, CJRP rounds cell counts in each offense category to the nearest multiple of
three.
Arrest by race and offense for each state were requested directly from the Federal
Bureau of Investigation (FBI). The master file provided by the FBI is constructed from
data collected as part of the Uniform Crime Report (UCR), Crime in the United States
Series (FBI, 2005). These figures were used to calculate the expected racial distribution
of incarcerated juveniles by crime type. UCR data on ethnicity were invalid due to
underreporting. Given that Hispanic is an ethnic designation instead of a race, the vast
majority of Hispanics arrestees were classified as White (Snyder, 2006). To maintain
consistency with racial arrest designations, Hispanics in the CJRP placement dataset were
combined in the White racial category.
Comparing differential involvement based on arrest rates raises the possibility of
53
prevalence of criminal behavior across groups has led to the comparison of such records
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with other forms of source data on criminal behavior. These alternative sources rely on
self-reports of victimization and offending (Morenoff, 2005).
National self-report surveys have called into question official statistics¶ portrayal
of the extent of criminal involvement by Black youth vis-à-vis White youth. Results from
the National Youth Risk Behavior Surveillance System (YRBSS), Monitoring the Future
(MTF), and the National Longitudinal Survey of Youth (NLSY) have indicated small or
nonexistent racial disparities in overall self-reported delinquent behavior (Campaign for
Youth Justice, 2008; Morenoff, 2005). Huizinga et al. (2007) found that while self-
reported delinquency was somewhat higher for minorities in comparison to Whites, the
contact/arrest/referral frequency was disproportionately higher for minorities.
Specifically, African American reported delinquency was 1.1 to 1.5 times higher than
Whites, but the contact/arrest/referral rate was 1.5 to 3.4 times as high.
Research comparing official sources and victim accounts draw conclusions
opposite those relying on self-reports. Hindelang (1978) compared UCR arrest statistics
with National Crime Survey (NCS, a predecessor to the National Crime Victimization
Survey - NCVS) victimization results to uncover differences between race of arrestees
and race of perpetrators, as reported by victims. He found a high degree of
correspondence in the race of perpetrators between the two sources. Although he noted
some discrepancies in race identification between arrestees and the victimization survey
54
arrest rates which were 4.9 times higher (Lynch, 2002). D¶Alessio and Stolzenberg
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(2003) used the National Incident-Based Reporting System (NIBRS) to investigate an
offender¶s race in relation to probability of arrest where the victim was able to identify an
offender¶s race. For offenses of forcible rape, robbery, aggravated assault, and simple
assault, the authors found ³little empirical evidence of systematic racial bias against
blacks´ (p. 1392), and considered their findings to ³refute the argument that racial bias in
policing [was] affecting the arrest rate for blacks´ (p. 1393).
Different explanations have been offered in an attempt to reconcile discrepancies
in findings between research relying on self-report and victimization surveys. Some
studies suggest these differences result from African Americans underreporting their
involvement in criminal activity on self-report surveys (Kirk, 2006; Thornberry & Krohn,
2003). Others suggest that differences lie primarily in the aggregation of offender and
offense types. Piquero and Brame (2008) examined self-reported arrests and official
arrest records among a sample of serious adolescent offenders, finding little evidence of
racial differences between the reporting sources. MTF results showed that while White
youth were more likely to report involvement in more common delinquency such as petty
theft, breaking into buildings, and damaging property, Black youth had higher self-
reported rates of crimes against persons and more serious forms of offending (Morenoff,
2005).
Arrest rates, although not a perfect proxy for involvement, have been used in
55
is found in the total arrests for a period of years. The value of such figures is lessened by
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the varying efficiency and diligence of the police, by discrimination in the administration
of law, and by unwarranted arrests. And yet the figures roughly measure crime´ (2002, p.
44). It remains clear that discrepancies among sources exist. Yet, arrest statistics provide
the only national, longitudinal data source measuring the types of serious crimes
necessary for calculating expected racial differences in incarceration rates across states
over time. Nevertheless, in relying on arrest data, this study is limited in its ability to
detect selection bias resulting from differential law enforcement practices. As examined
herein, Black-to-White disproportionality should be viewed as a measure of system bias
occurring after juveniles have been taken into custody.
Case processing from arrest to final decision can take several months. As with
previous studies, the UCR data from a year prior to the CJRP data were used to allow for
a one-year lag period between arrest and placement (Austin & Allen, 2000; Sorensen et
al., 2003). For example, arrest data from 2002 were used to estimate placement data for
2003. The sample includes 38 states at five observation periods (1997, 1999, 2001, 2003,
and 2006) for a total of 190 observations. Arrest files for Florida, Kansas, Montana,
Vermont, and Wisconsin were incomplete and thus dropped from the analysis. Any state
with a Black population less than one percent of the total juvenile population was also
dropped from the analysis. These states included Delaware, Idaho, North Dakota, South
Dakota, Utah, and Wyoming.
56
order to ensure anonymity of juveniles in states with very low occurrences of certain
ff ( h i id ) h i l b d h i f j il
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offenses (e.g. homicide). These categories are also broader than categories of juvenile
arrests provided in the UCR data. Where possible, UCR categories were directly matched
to incarceration offenses by CJRP definitions. In cases where CJRP did not include an
offense category outlined in the UCR, UCR categories were included under CJRP offense
categories which were the closest definitional match (see Table 2). Juveniles incarcerated
for technical violations had to be excluded from the analysis because their CJRP
categorization did not allow a match with the UCR arrest database.
Population figures for states were obtained by using the OJJDP¶s Easy Access to
Juvenile Populations. This data analysis tool relies on data originally collected by the
U.S. Census Bureau and modified by the National Center for Health Statistics (National
Center for Health Statistics, 2009).
57
Table 2: O ffense comparison by data source
S l l
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State-level
CJRP offense category UCR offense category
Person violent crime index Murder Forcible rape
RobberyAggravated assault
Person other Other assaults
Sex offensesOffenses against family
Property crime index Burglary
Larceny-TheftMotor vehicle theft
Arson
Property other VandalismForgery and counterfeit
FraudEmbezzlement
Stolen property
Drug Drug abuse violations
Public order WeaponsProstitution
DUILiquor laws
DrunkennessDisorderly conduct
Vagrancy
GamblingAll other offenses
Technical violations [None]
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Measures
Outcome measures
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Outcome measures
Blumstein (1982) pioneered the methodology used to disentangle the explained
portion (due to differential involvement based on arrest) and the unexplained portion
(perhaps due to system bias) from the total overrepresentation of adult African Americans
in United States¶ prisons vis-à-vis Whites relative to their representation in the
population. He proposed that if no discriminatory practices existed in the criminal justice
system after arrest, then the racial distribution of prisoners incarcerated for a particular
crime type would equal the racial distribution of persons arrested for that crime type. By
comparing crime-specific race ratios at arrest to the distribution of prisoners by crime of
conviction in prison, he was able to determine the extent of explained disparity in the
racial distribution of incarcerated prisoners.
Sorensen, Hope, and Stemen (2003) noted, however, that the percent of explained
variance in DMC was an inadequate figure for describing differences by race that
remained unexplained across jurisdictions because of differences in initially observed
levels of disproportionality. By applying the portion of unexplained variance to the
initially observed level of disproportionality, an adjusted ratio of disproportionality, one
which controlled for race-specific arrest rates, can be calculated for each state. Garland,
Spohn, and Wodahl (2008) explain how combining the measures of Blumstein (1982)
and Sorensen et al. (2003) yield an effective measure for explaining imprisonment
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summary measure of unexplained disproportionality that remains after controlling for
arrests across the studied period
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arrests across the studied period.
The formula presented by Blumstein (1982) served as the basis for the analytical
procedures performed herein. To calculate the level of racial disproportionality of
juveniles in residential placement explained by arrests ( X ), the following equation was
used:
Ratio of expected Black ± to ± White incarceration rates based on arrests
X = ---------------------------------------------------------------------------------------------
Ratio of Black ± to ± White incarceration rates actually observed
Or, expressed as a percentage,
Expected (Black incarceration rate/White incarceration rate)
X = --------------------------------------------------------------------------------------- 100.
Actual (Black incarceration rate/White incarceration rate)
Blumstein further simplified the formula with the following:
X = 100( R(100 - Q)/(100 - R)Q), (1)
where: Q = the actual percentage of incarcerated juveniles that were Black; and
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To calculate the expected percentage of incarcerated juveniles that were Black
based on arrests the following formula was used:
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based on arrests, the following formula was used:
R = R j, (2)
where: R j = B j F j = the expected percentage of incarcerated juveniles for offense type j
that was Black based on arrests;
B j = the percentage of persons arrested for offense type j that was Black; and,
F j = the percentage of incarcerated juveniles for offense type j.
Offense-specific measures were also calculated using the formula:
X j = 100( R j(100 - Q j)/(100 - R j)Q j), (3)
where Q j = the actual percentage of incarcerated juveniles for offense type j that was
Black; and,
R j = B j.
Using Blumstein¶s formula, the percentage of racial disproportionality left
unexplained by arrest is also simple to calculate, and is simply 1 ± X . As noted earlier,
Blumstein¶s (1982) original analysis found that 80% ( X ) of the racial disproportionality in
incarceration rates was accounted for by differential arrests, while 20% (1 - X ) of the
racial disproportionality in incarceration rates was unexplainable by differential arrests.
61
applying the 20% of the unexplained disproportionality to the actual Black-White ratio of
6.9:1. This is done using the simple formula described by Sorensen et al.:
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6.9:1. This is done using the simple formula described by Sorensen et al.:
(1 ± X )* Br + X, (4)
where Br = Black ratio of incarceration.
By multiplying the proportion of unexplained disproportionality by the actual
observed Black ratio of incarceration, this formula converts the percentage (proportion)
into a ratio, which in the Blumstein example would be (1 ± 0.8)*6.9 = 1.4. As pointed out
by Sorensen et al., however, in order to set the ³White side´ of the ratio to 1, it is
necessary to set the ³Black side´ to its ³expected ratio,´ that portion of the Black
incarceration rate that is to be expected for every point in the White incarceration rate, the
X value. This is done quite simply by adding X back into the Black side of the ratio. In
Blumstein¶s example, the final tally would be calculated: (1 ± 0.8)*6.9 + 0.8 = 2.2. This
means that, after controlling for arrests, Blacks were incarcerated at a rate 2.2 times that
of Whites.
Predictor variables
The OJJDP makes available a catalog of state research reports on DMC wherein
documents submitted to the OJJDP regarding each state¶s efforts at addressing DMC are
listed. Using this resource, a state by state comparison was made with regards to time and
extent to which efforts have been directed toward DMC. Extent was coded on a
dichotomous scale for each of the four part OJJDP objectives; identifying, assessing,
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be met before a state could receive credit for the next objective. For example, if a state
did not meet the requirement for assessment, they could not receive a point for
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q , y p
intervention. It is proposed that those states with the highest cumulative DMC
compliance score should reflect the greatest reduction in Black-White disparity in
juvenile placement rates.
According to the Disproportionate Minority Contact Technical Assistance Manual
(U.S. Department of Justice, 2009), states are required to collect data statewide and from
targeted local DMC reductions sites on a continuing basis, at least every 3 years. The
criterion to meet the identification stage was based on information obtained from the
Summary of States¶ DMC-Reduction Activities Based on FY 2007 Formula Grants
Application (Summary) (Hsia, 2007).
To meet the requirement of assessment, states must generate possible
explanations for DMC, obtain data for comparison over multiple time periods, and
identify most likely reason for DMC in the jurisdiction (U.S. Department of Justice,
2009). Because there was no clear section in the state summary (Hsia, 2007) for meeting
this phase, a review of the Catalog of State Research Reports on Disproportionate
Minority Contact (Catalog) was undertaken (U.S. Department of Justice, n.d.).
States must develop a comprehensive set of interrelated intervention strategies to
reduce DMC in order to satisfy the OJJDP¶s intervention requirement (U.S. Department
of Justice, 2009). This required a review of both the Summary and Catalog. States met
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Finally, the OJJDP¶s monitoring requirement is met when states evaluate
effectiveness of intervention programs (U.S. Department of Justice, 2009). Again, both
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the Summary and Catalog were reviewed. States met this objective if evaluation methods
were mentioned in the ³DMC Reduction Strategies,´ ³Products and Tools Produced,´ or
³State Laws or Guidance´ portions of the Summary, or if a report was listed which
referenced evaluation in the Catalog.
Measures used to test racial threat, benign neglect, and symbolic threat were
based on data obtained from the U.S. Census Bureau unless otherwise indicated. The
Statistical Abstract of the United States (SAUS), including years 1998-2009, provided
population estimates for numerous variables (U.S. Census Bureau, 1999-2010).
Researchers have devised various measures of racial threat (Parker et al., 2005;
Sampson & Laub, 1993; Stolzenberg et al., 2004). A combination of the most relevant
was adapted for the current research. Relative size of the Black population is the most
basic and common indicator for testing racial threat (Ousey & Lee, 2008). Although
relative racial population size has its limitations when testing racial threat, its use makes
it easier to compare results with findings from previous research (Stolzenberg et al.,
2004). Relative size of a state¶s Black population was measured as percent of the state
population that was Black. Population estimations were obtained through the SAUS. No
population estimate was available for 2001, therefore linear interpolation was employed
to obtain estimates from 2000 and 2002 figures.
64
Bureau of Labor Statistics, Local Area Unemployment Statistics (U.S. Department of
Labor, 2010). State level unemployment data were compared to the state population over
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the age of 15 by race. Once a rate was obtained for each race, the Black to White ratio
was calculated as the final form of the variable. The first year these data were available
was 1999. Statistics for 1997 were duplicated from the 1999 figures.
Benign neglect draws on aspects of racial and symbolic threat hypotheses,
wherein areas of high Black population concentrations of an established underclass will
experience less formal control due to an increase in intraracial offending. According to
benign neglect, areas with large Black populations and high levels of concentrated
underclass disadvantage should be related to decreased levels of DMC. To test benign
neglect, a combination of Black composition and underclass was used.
This theory assumes that the level of threat posed by an increasingly Black
population diminishes after some level of ³saturation´ is achieved. As the Black
composition of the population becomes large and stable, or reaches a tipping point, a
significant negative coefficient would be expected. Consistent with the curvilinear
relationship predicted for benign neglect, Black composition was squared in the
regression models to assess this possible relationship.
A number of variables were initially proposed to measure the effects of underclass
on placement disparity as suggested by prior research (Parker et al, 2005; Sampson &
Laub, 1993). However, percent persons below poverty by race and percent persons
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of unmarried mothers under the age of 18 (CDC, 1997, 1999, 2001, 2003) was used as an
underclass indicator.
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Number of births to unmarried women ages 17 and below was compared to the
population of girls between the ages of 12 and 17 by state and race (Puzzanchera, Sladky,
& Kang, 2009). Although some girls under the age of 12 have the possibility of becoming
pregnant, the average age of first menstruation for girls in the United States is 12
(Nelson, 2009). Births under this age should be rare and were unlikely to influence the
outcome. No information was available for unmarried mothers for 2006, so information
for 2003 was duplicated.
To examine the effects of symbolic threat, the percentage of explained variance in
Black-White juvenile placement rates were compared by offense. Symbolic threat draws
on the supposition that Black juveniles involved in drugs and public order offenses
threaten the stability of middle class values, increasing formal control mechanisms. To
support the symbolic threat hypothesis, percentage of explained disproportionality in
incarceration rates for drug and public order offenses should be significantly lower than
the explained disproportionatity in incarceration rates for violent and property offenses.
Differences in explained variance between these two categories of offenses should
remain invariant when controlling for the impact of other theoretical and control
variables. One exception involves a variable which combines race and age of the
population. While the percentage of Black youth in the population was treated as a
66
fluctuate where Black youth populations are high. Black youth was measured as
percentage of Black youth population aged 10-17 years (Puzzanchera, Sladky, & Kang,
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2009).
Control measures
Control measures were introduced into each of the models. Number of state and
local police officers per 100,000 population per state was included to reflect criminal
justice response (Stolzenberg et al., 2004). Police protection for 1997 was unavailable as
was for year 2001; therefore, linear interpolation was used to estimate those data points
from the 1996, 2000, and 2002 figures. To the extent that some states have larger urban
areas than others, which could affect the outcome of the relationships between theoretical
hypotheses and outcome measures, urbanization variables were introduced. These factors
should control for differential processing in a rural versus urban setting as discussed
earlier in regards to a bureaucratic model. As was originally offered by Sampson and
Laub (1993), these variables were percent population in urbanized area, state population
size, and population per square mile. All of these measures were garnered from the SAUS
data series.
Hypotheses
Compliance:
H1: The extent to which states have addressed DMC mandates will be inversely
related to the ratio of Black-White disproportionality in juvenile placement rates
67
H2: The size of the Black population and lower rates of Black unemployment
relative to Whites in a jurisdiction will result in higher levels of DMC.
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Benign Neglect:
H3: Jurisdictions with large Black underclass populations will have lower levels
of DMC.
Symbolic Threat:
H4: The percentage of explained disproportionality in juvenile placement rates
will be lower for the offense categories of drugs and public order in comparison to
violent and property offenses irrespective of other variables, except for the size of
the Black youthful population which should exacerbate these differences.
Analyses
Bivariate analyses were used to assess correlations of state efforts to reduce DMC
and change in Black-White placement disparity (H1). A linear mixed models design was
chosen to analyze the data in relation to H2, H3, and H4. Mixed models can handle
correlated data common with repeated measures of subjects (Linear mixed-effects
modeling in SPSS, 2005). The linear mixed model expands on the general linear model
so that error terms and random effects are allowed to exhibit non-constant variability.
Standard ordinary least squares (OLS) regression assumes that residuals are unrelated to
one another. Mixed models allow for the introduction of variables not explicitly included
from the beginning of the data series that could influence the later outcomes (Phillips &
68
variable (state) is independent of repeated variables, and error terms are independent of
other subject variables.
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In order to examine changes within jurisdictions over time while controlling for
heterogeneity across jurisdictions, a panel design was used. Panel data, or cross-sectional
time series data, utilizes observations of multiple cases over multiple time periods to
examine change over time. The advantages of panel data include exploitation of variation
across time and cases, ability to ameliorate omitted variable bias, and it is ideal for use
with a restricted number of observations (Phillips & Greenberg, 2008). The use of panel
data removes year fixed-effects and can include state-fixed effects to control for
differences across places (Levitt, 2001). Levitt, in assessing the relationship between
state-level crime and unemployment over time, argued that the high number of degrees of
freedom allowed by the analysis of panel data reduced the potential for spurious
coefficients. Phillips and Greenberg (2008) chronicled the use of panel data in a number
of studies including studies of gender, imprisonment and probation ratios across states;
prison admissions; and, determinants of homicide rates across counties.
The parameters were set to fixed effects for the predictor variables herein. Fixed
effects allows the unobserved variables to be associated with observed variables, where
random effects assumes that unobserved variables are not correlated with observed
variables. Unless this association is allowed, as in fixed effects, effects of unobserved
variables cannot be controlled. Also, fixed effects estimates within-individual differences
69
2009). Diagnostics were used in determining the appropriate models, and will be
discussed further in Chapter IV.
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The analyses employed in the current study represent an advance over that
employed in previous DMC studies in the following ways: 1) utilization of panel data and
related appropriate statistics; 2) employment of theoretically-based measures; and, 3)
reliance on Blumstein¶s method of assessing explained versus unexplained
disproportionality in juvenile placements.
Limitations
Although many limitations have been addressed in this and previous chapters,
some issues remain. As mentioned in Chapter I, tests of racial and symbolic threat
theories have relied on city/community/neighborhood-level data to assess minority threat
criminally, politically, and economically. Measures historically used to evaluate
city/community/neighborhood data were adjusted to use at the state level to support
available data. However, measures using state-level data may not provide as good of a fit
conceptually as local data.
Also mentioned in Chapter I, juveniles that have been transferred or waived from
state juvenile justice systems to adult criminal justice systems are not available for
examination in the CJRP. While the UCR maintains arrest data for each juvenile,
juveniles transferred out of the juvenile system to the adult system are not captured in the
CJRP. Because Black juveniles are more likely to be transferred to the criminal justice
70
placements systems for serious crimes due to the fact that Blacks are more often
transferred to the adult system.
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UCR data on ethnicity were invalid. To adjust for the inclusion of Hispanic
juvenile arrestees in the White racial category, Hispanic juveniles in placement were
added to the White CJRP racial category. To maintain consistency, Hispanic and White
juveniles were combined for state population numbers as well. The inclusion of Hispanic
juvenile arrests and placements with White juveniles could also downwardly bias the
level of Black-White racial disproportionality observed because research shows Hispanic
juveniles are arrested and placed at a higher rate than White, non-Hispanic juveniles.
Specifically, the combining of these categories would most likely affect the level of
Black-White disproportionality results in states with large Hispanic populations. To
partially address this concern, population percentage Hispanic was introduced as a
control in the analyses.
As noted earlier, arrest rates are imperfect proxies for involvement in
delinquency. Levels of minority involvement differ according to measurement source
relied on. Nevertheless, arrest statistics are the only national, longitudinal data source
measuring various offenses needed to calculate expected racial differences in
incarceration rates across states over time. Yet, the use of arrest statistics prohibits the
detection of selection bias resulting from differential law enforcement practices. Any
Black-White disparity presented should be viewed as resulting from system bias
71
interior years (1999, 2001, 2003), whereby the mean was taken of the closest year
available before and after the missing year. Data duplication was used for missing data
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points at either end of the time period (1997 and 2006), whereby the figure for the closest
year available was duplicated. These restrictions in variance increase the possibility of
type II error, making the statistical tests of these relationships more conservative.
Chapter IV
Analysis and Results
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This chapter presents the analyses of data and results of testing for each
hypothesis. The test of the first hypothesis and fourth hypotheses are presented
independently, while the second and third are presented in tandem. Testing of the first
hypothesis relies on descriptive statistics and bivariate analysis of the predictor and
outcome measures. Sections testing the remaining hypotheses include descriptive
statistics, a brief discussion of models used, and tables that present the results of
multivariate models.
Compliance
The first hypothesis proposes that the extent to which states have addressed DMC
mandates will be inversely related to Black-White disparity in juvenile placement rates.
The adjusted ratio of Black-White disproportionality in juvenile placement (DMC) was
assessed for each state for each observed time period. The absolute percentage change
across the time period was then calculated for each state from 1997 to 2006. For example,
Texas placed Black juveniles in residential settings at a rate of 1.77 for every White
juvenile controlling for arrests (1.77:1). In 2006, Texas placed Black juveniles in
residential settings at a rate of 1.60 for every White juvenile (1.63:1). To calculate the
absolute percentage change for Texas from 1997 to 2006, the 1997 ratio of
disproportionality was subtracted from the 2006 ratio and multiplied by 100;
73
an increase in DMC. Absolute percent change is a conservative estimate and preferred
over proportional percentage change due to the vastly different state base rates (Sherman
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& Weisburd, 1995).
Absolute percent change for each state across the time period examined is offered
in Figure 1. As is evident in Figure 1, twenty one states experienced reductions in Black-
White disproportionality in juvenile residential placements after controlling for arrest,
while 16 states experienced increases. State reductions in DMC, both in terms of numbers
of states and percentage point differences, outweighed overall increases in DMC.
Although the pattern shown here supports the national reduction noted previously (Davis
& Sorensen, 2010), this state-level examination somewhat changed the expected image.
The aggregated reduction in DMC found at the national level masked rather extreme
state-level fluctuations in levels of DMC reduction.
74
-350.00 -300.00 -250.00 -200.00 -150.00 -100.00 -50.00 0.00 50.00 100.00 150.00 200.00
Nebraska
West Virginia
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Figure 1: Absolute percent change by statea
in Black-White disproportionality in
juvenile placements after controlling for arrests, 1997 and 2006. a
H ii i l d d b h i d Bl k j il l i
IowaColorado
Illinois
Oregon
Washington
Alabama
Georgia
Delaware
North Carolina
ArkansasTennessee
New Jersey
Indiana
Minnesota
Mississippi
Louisiana
California
Texas
United StatesConnecticut
Pennsylvania
Michigan
Virginia
Arizona
Nevada
Missouri
Oklahoma
South Carolina
Ohio
Maryland
Alaska
New York
Kentucky*
New Mexico
Rhode Island
Massachusetts
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Average state DMC compliance scores for states experiencing either increases or
decreases in DMC during 1997 to 2006 are presented in Table 3.
T bl 3 D i i i i i DMC li f
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Table 3: Descriptive statistics comparing DMC compliance scores for states
experiencing reductions or increases in levels of DMC and percent change (n=37)a
Compliance ScoresMean SD
States experiencing reductions in DMC 2.524 1.209
States Experiencing increases in DMC 2.188 1.223
aHawaii was not included because the state experienced no Black juvenile placements in
2006.
The direction of the relationship is as expected, with those states experiencing reductions
in levels of DMC having put more effort into addressing DMC in their jurisdictions, as
indicated by higher compliance scores. The mean difference in DMC compliance scores,
however, between states with reductions or increases in DMC did not reach the .05 level
of statistical significance. Therefore, the findings did not reach the magnitude needed to
support the hypothesis that states which have complied more fully with efforts to address
DMC as mandated by the OJJDP have realized greater reductions in Black-White
placement disparity.
Racial-Economic Threat and Benign Neglect
The outcome variable for H2 and H3 is the adjusted ratio of Black-White
disproportionality in residential placement based on each group¶s rate of arrest. Table 4
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substantially higher than noted in that study during the latter years (68%) of the series,
2003-2006 (Davis & Sorensen, 2010). The discrepancy stems from two differences in
th d l Fi t l 38 t t i d i th t t d d t th
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methodology. First, only 38 states were examined in the current study compared to the
previous study which was nationwide. Second, separate calculations were made by state
prior to averaging in the current study, while figures were aggregated at the national level
in the previous study.
Table 4 also provides descriptive statistics for predictor and control measures. As
would be expected, the standard deviation is higher between states than within states over
time. Once all data were entered and diagnostics run on the variables, necessary
corrections were made to ensure proper form for analysis. Crime index per 100,000 was
logged to correct a positively skewed distribution. Percent living in urban area,
population per square mile, and state population were found to be highly correlated.
Therefore, a principle components analysis was undertaken to create a population
structure factor encompassing the three correlated variables.
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Table 4: Descriptive statistics for outcome, predictor and control measures (n=190)
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Variable Data source Mean Between-state SD Within-state SD
Black/White placement ratio OJJDP CJRP 1.88 0.83 0.10FBI UCR
US Census Bureau
Percentage Black US Census Bureau 12.57 9.53 1.43
Black-White unemployed ratio US Labor Department 2.10 0.62 0.12
Black-White teenage mother ratio Center for Disease Control 3.70 1.47 0.15
Percentage Black youth (10-17) US Census Bureau 16.09 11.57 1.67
Percentage Hispanic US Census Bureau 8.46 9.55 1.62
Police per 100,000 US Census Bureau 286.17 49.09 24.36
Index crime per 100,000 US Census Bureau 140.95 148.61 16.59
Percentage population in urban area US Census Bureau 83.99 11.56 8.66
Population per square mile US Census Bureau 219.69 268.96 12.98
Population size (in 1,000s) US Census Bureau 6,545 6,449 3,392
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Results from the principal components analysis are presented in Table 5. The total
eigenvalue for the population structure factor was 1.595 with 53.179 percent of the
variance explained The factor loadings show that percent population living in an urban
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variance explained. The factor loadings show that percent population living in an urban
area had the strongest relationship to the overall factor, closely followed by population
per square mile, with state population trailing. In light of the factor loadings, the factor
was re-named urbanism.
Table 5: P rincipal components analysis of population structure variables
Variables Factor loadings
Percent population in urban area 0.864
Population per square mile 0.734
State population 0.557
Diagnostics were performed to test for further multicollinearity. First, a
correlation matrix was run to assess if any of the variables appeared to be collinear in
bivariate comparisons. The results indicated that percentage Black, percentage Black 2,
and percentage Black youth showed collinear relationships, as would be expected. The
variance inflation factors were tested for the same purpose and again, percentage Black,
percentage Black 2, and percentage Black youth showed a high degree of collinearity.
Regardless, a decision was made to retain them in the models as conceptual variables to
79
In testing for collinearity other significant relationships were noted, although not
as strong as those mentioned above. Moderate relationships were found to exist between
Confederate South and percentage Black (r= 721) Confederate South and percentage
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Confederate South and percentage Black (r .721), Confederate South and percentage
Black 2 (r=.698), Urbanism and Crime (r=-.546 ), Urbanism and Police (r=.522), and
percentage Black youth and ratio of Black-White unemployment (r=.500). Aside from
the correlated race variables, the variance inflation factors (VIF) showed no extreme
multicollinear relationships between predictor and control variables. The highest VIF
score on this test, Confederate South at 2.8, is within acceptable bounds.
The familiarity of R 2 has led researchers to attempt to extend the test to linear
mixed modeling (Edwards, Muller, Wolfinger, Qaquish, & Shabenberger, 2008). As
noted by Orelien and Edwards (2008), however, the proposed R 2
statistics for linear
mixed models have not performed well. Diagnostics were run among unstructured,
diagonal, and compound symmetry modeling. The information criteria table in SPSS
provides measures for selecting and comparing mixed models with smaller numbers
indicating better fit between the model and data. Accordingly, the compound symmetry
model provided the best fit in this case. Compound symmetry requires constant variation
and constant covariation. Using the compound symmetry model considers variance to be
equal across measurement periods (Linear mixed models, n.d.).
The models testing racial threat and benign neglect are presented in Table 6. The
conceptual variables with no controls are presented in Model 1. Model 2 combines
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that as the Black population grows Whites feel threatened, and their use of formal social
control mechanisms (i.e. juvenile placements) increases in response to the threat. Racial
threat would also be supported by a significant negative coefficient for the ratio of
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threat would also be supported by a significant negative coefficient for the ratio of
Black/White unemployment, indicating that Whites feel economically threatened,
resulting in a corresponding increase in the use of formal social control mechanisms (i.e.
juvenile placements) in response to the threat. The signs for both of the racial threat
variables are in the anticipated direction. However, because they fail to reach the level of
statistical significance, H2 is not supported.
Table 6: F ixed effects models of adjusted Black-White ratio of juvenile placement
Model 1 Model 2Estimate SE Estimate SE
Intercept 1.615 0.377 1.278 1.204
Percentage Black 0.022 0.052 0.083 0.140
Percentage Black 2
-0.001 0.001 -0.001 0.002
Black/White unemployment ratio -0.076 0.084 -0.076 0.087
Black/White teenage mother ratio 0.087 0.055 0.077 0.056
Percentage Black youth (10-17) -0.024 0.106
Percentage Hispanic 0.007 0.015
Police per 100,000 -0.003* 0.002
Index crime (logged) 0.277 0.390
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Benign neglect predicts that once the minority group is no longer believed to be a
constant threat, resources spent toward the objective will diminish. Therefore, once the
minority group has been residentially, politically, and economically segregated there will
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y g p y, p y, y g g
be no motivation for increased social control measures and social control efforts will
begin to diminish. Variables testing the benign neglect aspect of racial threat are
percentage Black 2, Black-White unemployment ratio, and Black-White teenage mother
ratio.
Given the curvilinear nature of the relationship expected between percentage
black and DMC by the benign neglect hypothesis, percentage Black 2 should exhibit a
negative coefficient. Once a certain critical mass is reached in the black population, one
would expect a decrease thereafter in DMC. Consistent with the racial threat hypothesis,
a significant negative coefficient for Black-White unemployment ratio could also be
interpreted as supporting benign neglect, in that as economic disparity increases between
Whites and Blacks, Whites would feel less threatened and, thus, formal social control
mechanisms would not be utilized as often. An additional indicator of underclass, one
more exclusively related to the benign neglect hypothesis, is the ratio of Black-White
teenage mothers. A negative coefficient would indicate that as the Black underclass
increases, as is evident through signs of disadvantage such as teenage motherhood, the
need for formal control would decrease.
The results shown in Table 6 show that the signs for the estimates for percentage
82
direction (positive). Further, none of these variables reach the level of statistical
significance in predicting DMC placements, therefore H3 is not supported.
The only significant estimate in the racial threat model is police per 100,000.
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y g p p ,
Although the coefficient is small, it indicates that as policing increases, disproportionality
in the Black-White ratio of juvenile placements decreases. This finding may appear
counterintuitive as increased policing should, presumably, lead to more arrests of Blacks
and ultimately more juvenile placements. However, the likely explanation for the finding
is that increased policing leads to more arrests of Blacks for less serious crimes that end
in dismissal or community sanctions instead of placements.
Symbolic Threat
The outcome variables for testing H4 are the percentage of disproportionality in
Black juvenile placements that can be explained by arrest for the offense categories of
violent, property, drug, and public order. The symbolic threat hypothesis posits that the
greatest amount of DMC should result from offenses that are symbolic threats to
mainstream Americans, including drug and public order offenses, whereas violent and
property offenses are not expected to be influenced by such forces. Descriptive measures
presented in Table 7 support the symbolic threat thesis in that the percentage of DMC
explained by arrest is much lower for drug and public order offenses. In fact while the
combined level of explained disproportionality for violent and property offenses is
around 100%, indicating parity in treatment by race, the explained disproportionality for
83
Table 7: Descriptive statistics for offense-specific outcome measures (n=190)
Variable Data source Mean Between state SD Within state SD
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Variable Data source Mean Between-state SD Within-state SD
Percent explained ± violent OJJDP CJRP 120.23 52.06 11.22FBI UCR
US Census Bureau
Percent explained ± property OJJDP CJRP 95.35 63.08 8.06FBI UCR
US Census Bureau
Percent explained ± drug OJJDP CJRP 54.59 34.51 6.89FBI UCR
US Census Bureau
Percent explained ± public order OJJDP CJRP 61.31 31.39 4.27FBI UCR
US Census Bureau
84
Symbolic threat draws on the supposition that Black juveniles threaten the
stability of middle class values, increasing formal control mechanisms. Specifically,
Black juveniles that are involved in drug offenses and public order offenses would be
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symbolic of the threat instead of an overt threat as in violent and property offenses. The
bivariate findings in Table 7 support H4, the findings of which suggest that decisions
made once a Black juvenile has been arrested for a drug or public order offense are based
on factors other than the seriousness of the offense.
As a further test of the symbolic threat hypothesis, offense-specific multivariate
models were calculated. In this instance the variable of utmost relevance is percentage
Black youth, for it is the variable which is postulated to incite sanctioning based on
symbolic threat due to a more substantial and visible presence of young ³marauders.´ As
such, one would expect that percentage Black youth influence the level of DMC for drug
and public order offenses, but not for violent and property offenses. The symbolic threat
thesis further predicts that the influence of percentage Black youth would not be
mitigated by the inclusion of other variables, whereas the other conceptual and control
variables could very well be expected to wash out any influence of percentage Black
youth on property or violent offenses.
Table 8 presents the estimates of the conceptual variables for symbolic threat by
offense. There is no evidence that any of these conceptual variables have a significant
association with explained Black juvenile placement based on arrest by offense.
85
Table 8: F ixed effects models of Black juvenile placement explained by arrest by offense (n=190)
Violent Property Drug Public Order
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Violent Property Drug Public Order
Estimate SE Estimate SE Estimate SE Estimate SE
Intercept 84.766*** 25.953 88.465*** 29.777 55.989*** 18.116 52.286*** 15.673
Percentage Black -5.856 9.927 -14.017 8.937 -3.437 6.936 -4.565 5.517
Percentage Black 2 -0.133 0.099 -0.006 0.112 -0.004 0.069 -0.025 0.061
Black/White unemployment ratio 9.374 7.044 2.242 5.641 -2.791 4.924 0.268 3.754
Black/White teenage mother ratio -2.970 4.159 1.410 3.797 0.092 2.905 -1.174 2.343
Percentage Black youth (10-17) 8.293 7.413 10.856 6.253 3.000 5.180 4.748 4.041
p is based on one-tailed significance.
*** p < 0.001
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Table 9 offers the estimates for the complete models for percentage explained for
Black juvenile placements for each offense category. Percentage Black youth is
significant for violent and property offenses. As the population of Black youth increases,
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the percentage of explained Black juvenile placements increases for violent and property
offenses. The relationship remains positive, yet not significant, for drug and public order
offenses as well. This finding contradicts H4; higher Black youth populations do not
appear to symbolically threaten the White elite, and thereby decrease rates of explained
Black juvenile placements based on arrest for drug and public order offenses.
The positive estimate for police per 100,000 indicates that as policing increases,
percentage of explained Black juvenile placement based on arrest for violent and drug
offenses also increases. Consistent with earlier analyses, increased policing may increase
arrests and thereby result in higher rates of explained variation in placement (Table 8 &
9), but overall lower rates of DMC in placement (Table 6). Urbanism is significant for
violent and drug offenses as well. This indicates that states with higher urban populations
have lower percentages of explained variation in placement for those offenses. This
finding does not jibe with Albonetti¶s (1991) version of the bureaucratic model, which
would predict that urban areas, through routinization, are more successful at purging
extra-legal influences such as race during case processing.
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Table 9: F ull fixed effects models of Black juvenile placements explained by arrest by offense (n=190)
Violent Property Drug Public Order
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p y g
Estimate SE Estimate SE Estimate SE Estimate SE
Intercept 85.669 74.664 57.702 98.326 -11.935 54.988 36.230 50.565
Percentage Black -15.976 10.270 -17.526* 9.834 -6.947 7.342 -5.340 6.010
Percentage Black 2 -0.162 0.101 -0.024 0.132 -0.029 0.074 -0.020 0.068
Black/White unemployment ratio 9.185 6.870 2.557 5.682 -2.704 4.851 0.408 3.778
Black/White teenage mother ratio -1.378 4.037 1.700 3.847 0.681 2.898 -0.944 2.393
Percentage Black youth (10-17) 17.376* 8.003 15.751* 7.715 7.296 5.699 5.259 4.576
Percentage Hispanic 0.192 0.970 0.787 1.204 0.898 0.711 0.384 0.641
Police per 100,000 0.238* 0.133 -0.020 0.120 0.196* 0.095 0.105 0.077
Index crime (logged) -49.941 24.551 -19.283 31.536 -19.569 18.043 -10.969 16.451
Urbanism -40.262*** 10.397 -15.207 10.282 -22.761*** 7.500 -7.477 6.289
Non-South 13.552 28.131 48.399 37.279 29.208 20.724 4.546 19.083
p is based on one-tailed significance.
* p < 0.05** p < 0.01
*** p < 0.001
Although the findings from the bivariate analysis in Table 7 are supportive of H4,
findings from the linear fixed effects models are not. While the variables included in
these models failed to explain the low levels of explained disproportionality of Black
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juveniles in residential placement based on arrest for drug and public order offenses, the
pattern in coefficients was quite similar across offense-specific models. In sum, symbolic
threat, H4, does not appear to be empirically supported by the findings herein.
Chapter V
Summary and Conclusion
This research examined the effects of federal government initiatives directed
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toward reducing state level disproportionate minority confinement as well as testing
racial and symbolic threat theories on a juvenile population. In an effort to address the
overrepresentation of minorities involved in the juvenile justice system, beginning in the
mid 1990s the Office of Juvenile Justice and Delinquency Prevention (OJJDP) included,
as a requirement for a state to receive Federal Formula Grants, a determination of
whether disproportionate minority confinement (DMC) existed in its juvenile justice
system, identification of its causes, and development and implementation of corrective
strategies. Varying levels of state compliance have been achieved in accordance with the
requirement, ranging from non participating states to states in full compliance where
current efforts include monitoring ongoing DMC reduction. The hypothesis tested in the
current study sought to determine whether those states that began addressing DMC
earlier, and have thus reached the latter stages of the OJJDP requirements, would show
greater reductions in statewide DMC.
Theoretically, the current research examined three proposed correlations between
minority threat and minority representation in out-of-home placements in the juvenile
justice system. Racial threat hypothesis proposes that as the Black population increases in
a geographic location, social control will intensify to decrease the threat of Blacks to the
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Findings from previous studies that fail to support a linear relationship between
minority population size and justice system outcome have often been explained as benign
neglect. Because crime, especially crime in large Black populations, tends to be
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intraracial, there may be a decrease in manifestation of formal social control due to
minorities being less likely to report crime, and due to the allocation of fewer resources
for solving intraracial minority crime (Parker, et al., 2005). Therefore in areas of high
concentrations of minority populations, the dominant group would have less need to
utilize the formal state justice apparatus and attendant resources; hence the appearance of
leniency toward minorities may be evident.
Finally, symbolic threat maintains that there is a relationship between type of
crime that symbolically threaten the long term welfare of the majority and the use of
social control mechanisms. The symbolic threat hypothesis posits that the White majority
subjectively perceives the poor and underclass as a threat to the values of ³mainstream
America´ (Sampson & Laub, 1993). Specifically, minority youth symbolically threaten
the status quo regarding the safety and well-being of middle-class youth through drug and
public order offenses. This relationship would be evident in the use of social control
mechanisms to control the behavior of Black youth for these offenses, and could result in
high rates of DMC in placements for drug and public order offenses.
Compliance
The first hypothesis was not supported by the findings. There was no satistically
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which attributed a nationwide reduction of approximately 20 percent of the ratio of
disproportionality over the same time period to the OJJDP initiative. This study has failed
to reveal a connection between state-level compliance with the OJJDP mandate and
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reductions in DMC.
While this finding is disappointing given the intention of the OJJDP initiative, it
may be a result of the subjective assessment in measuring the degree of compliance
herein. Upon initial undertaking, this researcher expected to objectively assign a
compliance rating to each state based on information provided through the OJJDP.
During coding it became apparent that clear demarcations of state compliance were
unavailable. Although guidelines were established as to how to code each level of
compliance, guidelines used herein may not match those set by the OJJDP. This
disconnect could have resulted in the null finding.
Threat Hypotheses
The findings failed to support any of the threat hypotheses. Variables testing for
relationships between the conceptually-driven threat hypotheses and disproportionate
juvenile placement controlling for arrest were not significant, including size of the Black
population (racial threat), racial ratio of unemployment (economic threat), racial ratio of
teenage motherhood (underclass disadvantage), large Black population (benign neglect),
and size of the Black youth population (symbolic threat). Most of the relationships were
in the hypothesized direction but all failed to reach the required level of statistical
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examining the relationship between Black population size and incarceration, Wang and
Mears (2010) found that as percent Black population increases in a county, likelihood of
receiving a prison sentence, compared to jail sentence, increased if the offender was
Bl k S h fi di ll d h h l d d h ³ l h ff ´
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Black. Such a finding allowed the authors to concluded that ³a clear threat effect´ was
evident in the 26 states they examined (p. 202).
On the other hand, Ousey and Lee (2008) found little support for the conventional
racial threat framework or the benign neglect model when examining racial arrests at the
city level. A seemingly recurrent theme in this field of research is that the introduction of
³mediating mechanisms´ tends to reduce the likelihood of findings linking threat or
benign neglect with formal control outcomes. The theories have ³intuitive appeal,´
whereby indicators of racial threat can be linked with Whites¶ perception of threat and
use of formal social control. But as Ousey and Lee surmise, ³we also note that the racial
threat argument rests on some very strong assumptions about the ease with which Whites
can and will act as a collective entity to use the criminal justice machinery against
Blacks«perhaps those assumptions are too simplistic´ (p. 347).
Perhaps racial threat has run its course. Research testing these hypotheses from
the 1960s through the mid-1990s found, at times, strong support for an argument that the
observed overrepresentation of minorities in the justice systems could be linked to a
perceived threat by the dominant White majority. The view that the Black minority would
rise up and take Whites¶ jobs, neighborhoods, and political power seemed to be more
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between disproportionate involvement with the justice system was appropriate, and
probably, accurate for the time.
However, as noted by Kempf-Leonard (2007), what was thought to be a quick fix
t d i li i ti DMC i th j il j ti t b i l i
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to reducing or eliminating DMC in the juvenile justice system by simply removing
racially motivated actors turned into a decade¶s long project aimed at addressing the
issue. Since the time Blalock (1967) proposed his thesis concerning racial threat, our
society has changed in some important ways with regards to the Black minority.
Increases in political and economic power have come to fruition for some. But for others,
we have created, or at least allowed, a very different reality. A system of hyper-ghettos
has become a reality in many large cities (Wilson, 1987). Just as it is not possible to make
a difference in DMC by removing a few bad apples from the justice system, neither can
the phenomenon be adequately explained as a collective act of domination. Likely, DMC
is the end result of much deeper social issues.
Research, such as that undertaken in the current study, which examines
disproportionality beginning at the arrest stage has continually shown reductions in
Black-White incarceration disparity over time. Where offense of conviction is
disaggregated, the more serious categories of crime show almost no difference in
incarceration based on arrest. It would appear that the juvenile justice system at least is
aggressively working to minimize its role in the overrepresentation of Blacks in the
justice system. However, this cannot resolve the issue. ³The criminal justice system
94
employment opportunities are truly addressed, one cannot expect to see a one to one ratio
of Black-White representation within the justice system.
Limitations
The current study attempted to control for limitations through design and also
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The current study attempted to control for limitations through design and also
address them as they arose, however, a number of limitations remain. As has been
mentioned, previous studies examining racial threat relationships have focused on smaller
geographic areas ± county, city, neighborhood, etc. In an attempt to expand this research
domain and in keeping with available data, the current project relied on state-level
analyses. Although controls were added to account for state-level differences; such as
urban population, crime, and policing; an ecological fallacy in interpretation could not be
ruled out. It is possible that resultant findings would be different given a similar study
using micro-level information.
The current research does not include juveniles who have been waived or
transferred to the adult corrections system. While only a small percentage of juveniles are
waived or transferred to the adult system each year, race has been found to influence
judicial waiver decisions (Fagan, Forst, & Vivona, 1987). UCR statistics maintain arrest
data for each juvenile, however if a juvenile is transferred to the adult system, the
juvenile would not be tracked on juvenile placement data. Thus, failing to include
juveniles sentenced to adult institutions in the current study may have had a downwardly
biasing impact on the level of Black-White racial disproportionality observed.
95
juvenile arrests and placements with White juveniles could have downwardly biased the
level of Black-White racial disproportionality observed, given that Hispanic juveniles
were likely arrested and placed at a higher rate than non-Hispanic White juveniles.
As was discussed in length in Chapter II arrest rates cannot be used as a proxy for
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As was discussed in length in Chapter II, arrest rates cannot be used as a proxy for
involvement in delinquency. The use of arrest statistics limits the ability to detect
selection bias resulting from differential law enforcement practices. Black-White
disparity can only be viewed as a measure of system bias occurring after juveniles have
been taken into custody.
The method for handling missing data was addressed in Chapter III. Linear
interpolation is an acceptable practice for use in this manner (for example see Phillips &
Greenberg, 2008). Unfortunately, restrictions in variance increase the possibility of type
II errors. Such conservative tests of these relationships lessen the likelihood of finding
significant relationships.
The lack of OJJDP generated records used by the agency to determine state
compliance may have produced null findings for the comparison between state initiatives
and DMC reduction. Factors set by the researcher in making a determination as to what
constituted compliance probably differed from those set by the OJJDP. Although
guidelines here were consistent for each state, their departure from official compliance
may have biased the results.
Generally, the analyses presented here err on the conservative side. Using fixed
96
effects is appropriate for the analyses undertaken herein, it is a more conservative model.
In an effort avoid type I errors, the analyses may have neglected to find significant
relationships that actually exist.
Recommendations
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Recommendations
To address limitations found in the current study, it is recommended that state
agencies or researchers duplicate Blumstein¶s (1982) and Sorensen et al.¶s (2003) method
of calculating disproportionality after controlling for arrest. Access to arrest and
placement data within states would allow the researcher to compare differences by city or
county that could offer a more detailed picture of DMC than is currently obtained through
OJJDP¶s RRI or state-level aggregate models presented here. Availability of more
detailed and localized data would allow the researcher to disaggregate Hispanic juveniles
from White juveniles, offering an additional level of comparison and reducing bias that
occurs when White and Hispanic juveniles are combined. Such information could provide
the state with an honest assessment of what justice system practices are working toward
DMC reduction and what might need to be changed (see e.g. Leiber, 2010).
Analyses of compliance with the OJJDP initiative should be replicated using
official OJJDP records of DMC compliance which are slated to be released. The results
presented here in regards to H1 should be interpreted with caution. If further analysis
results in similar findings, the agency may need to re-examine the effectiveness of the
initiatives. Such measures, however, would not be justified by the current study.
97
qualitative studies that examine which juveniles are arrested, referred, detained, etc. and
why adds a layer to understanding these decisions that is overlooked when using large,
aggregated, secondary data sets. Much of the current research regarding threat-motivated
bias in the justice system is inconclusive or contradictory. This may be a result of the
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j y y y
failure of researchers to commit to a deeper, contextually-based understanding of the
problem.
Finally, and most importantly, DMC cannot be laid solely at the feet of justice
system practitioners. To achieve a true reduction in DMC a multi-disciplinary approach is
needed. First, the political mobilization of key stakeholders will be required to develop
policies aimed at addressing broader social inequalities which spur higher rates of crime
and delinquency among minorities. Second, the efforts and resources of various social
agencies will need to be re-directed or focused on delinquency prevention, which will go
further than simply relying on the justice system to not ³exacerbate´ existing disparities
resulting from broader social forces.
Conclusion
The Bureau of Justice Statistics estimates that 33% of all black males born in
2001 will spend some time in prison during their lifetime compared to 6% of white males
(USDOJ, 2003). The pathway to prison often begins with involvement in the juvenile
justice system. Black juveniles are overrepresented in juvenile institutions at a margin of
more than 3 to 1 considering their representation in the population (Sickmund, 2004).
98
reviewing Davis and Sorensen¶s (2010) findings, J. Chiancone (personal communication,
November, 2009) commented that their findings offered ³a glimmer of hope´ in the fight
to reduce DMC. Juvenile transfer cases have been on the decline through much of the
decade, and the decline for Black juveniles outpaced Whites. In 2007, Black juveniles
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, j p , j
were as likely to be waived to the adult system as Whites (1.0% and 0.9% respectively)
(Adams & Addie, 2010).
However, within the system there remains inequity. Of all racial categories, Black
youth had the highest level of involvement in the most serious offenses categories, person
offenses, at 41 percent. Black juveniles were referred to the juvenile court 140 percent
higher than White juveniles (Knoll & Sickmund, 2010). Involvement and referral rates
cannot be corrected by judicious decision-making. These numbers speak to a larger
problem than how to reduce DMC within the justice system. These statistics point
directly at processes that are occurring before a judge makes a decision to incarcerate.
We, as a society, have created a system where racial inequality is the norm. The natural
lottery of birth should not be allowed to dictate one¶s chances of becoming enmeshed in
delinquency and the juvenile justice system.
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Summary of State Assessments
Appendix A
Citation Study Sites
Time
Period
Racial
Groups
Involved
Decisionmaking
Points
Investigated
No of
Cases
in Pool Research Results
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119
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Anchorage
7/2002 -
6/2003
W, B, NA,
A
intake -
dismissal 312
being African American or Native
American significantly decreases
chances of informal adjustment
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Anchorage
7/2002 -
6/2003
W, B, NA,
A
intake - informal
adjustment 1995
being African American significantly
increases chances of dismissal - only
significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.(2006).
AK -Anchorage
7/2002 -6/2003
W, B, NA,A
intake - petitionfiled 533 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.(2006).
AK -Anchorage
7/2002 -6/2003
W, B, NA,A
formal court
processing -dismissal 115 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Anchorage
7/2002 -
6/2003
W, B, NA,
A
formal court
processing -
adjudicated 587
being Native American or Asiansignificantly increases chances of
adjudication - only significant racial
finding
120
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Fairbanks
7/2002 -
6/2003
W, B, NA,
A
intake -
dismissal 111 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.
(2006)
AK -
F i b k
7/2002 -
6/2003
W, B, NA,
A
intake - informal
dj t t 421 i ifi t i l fi di
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(2006). Fairbanks 6/2003 A adjustment 421 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Fairbanks
7/2002 -
6/2003
W, B, NA,
A
intake - petition
filed 188 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Fairbanks
7/2002 -
6/2003
W, B, NA,
A
formal court
proceeding -
dismissal 66 no significant racial finding
Leiber, M.J., Johnson, J., & Fox, K.
(2006).
AK -
Fairbanks
7/2002 -
6/2003
W, B, NA,
A
formal court proceeding -
adjudicated 104 no significant racial finding
State of Arizona Commission on
Minorities. (2002).
AZ -
Maricopa
Co. 2000
W, H, B,
NA referral 36002
based on RRI - compared to White -
Hispanic 1.16x, African American 2.2x
121
State of Arizona Commission on
Minorities. (2002).
AZ - Pima
Co. 2000
W, H, B,
NA referral 9513
based on RRI - compared to White -
Hispanic 1.14x, African American 2.2x,
Native American x1.6
State of Arizona Commission on
Minorities. (2002).
AZ ±
MaricopaCo. 2000
W, H, B,
NA detention 10158
based on RRI - compared to White -
Hispanic 1.4x, African American 3.4x,
Native American 2.5x
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State of Arizona Commission on
Minorities. (2002).
AZ - Pima
Co. 2000
W, H, B,
NA detention 3113
based on RRI - compared to White -
Hispanic 1.2x, African American 2.8x,
Native American 1.4x
State of Arizona Commission on
Minorities. (2002).
AZ -Maricopa
Co. 2000
W, H, B,
NA probation 5292
based on RRI - compared to White -African American 2.1x, Native
American 1.7x
State of Arizona Commission on
Minorities. (2002).
AZ - Pima
Co. 2000
W, H, B,
NA probation 1944
based on RRI - compared to White -
Hispanic 1.2x, African American 2.5x
State of Arizona Commission on
Minorities. (2002).
AZ -
Maricopa
Co. 2000
W, H, B,
NA commitment 417
based on RRI - compared to White -
Hispanic 1.6x, African American 3.6x,
Native American 2.2x
122
State of Arizona Commission on
Minorities. (2002).
AZ - Pima
Co. 2000
W, H, B,
NA commitment 330
based on RRI - compared to White -
Hispanic 1.6x, African American 3.5x,
Native American 1.5x
State of Arizona Commission onMinorities. (2002).
AZ -
MaricopaCo. 2000
W, H, B, NA adult transfer 401
based on RRI - compared to White -
Hispanic 2x, African American 3.7x, Native American 2.5x
b d d hi
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State of Arizona Commission on
Minorities. (2002).
AZ - Pima
Co. 2000
W, H, B,
NA adult transfer 134
based on RRI - compared to White -
Hispanic 2.2x, African American 5.3x,
Native American 1.7
California Department of Justice.
(2004). CA 2004 W, H, B, A arrests 206201
based on RRI - compared to White -
Hispanic 1.18x, Black 2.5x, Asian .4x
California Department of Justice.
(2004). CA 2004 W, H, B, A
referrals to
probation 169681
based on RRI - compared to White -
Black 2.6x, Asian .4x
California Department of Justice.(2004). CA 2004 W, H, B, A diversion 7877
based on RRI - compared to White -Hispanic .65x, Black .35x, Asian .77x
California Department of Justice.
(2004). CA 2004 W, H, B, A detention 39087
based on RRI - compared to White -
Hispanic 1.3x, Black 1.7x, Asian 1.2x
California Department of Justice.
(2004). CA 2004 W, H, B, A petition filed 86283
based on RRI - compared to White -
Hispanic 1.15x, Black 1.2x
California Department of Justice.
(2004). CA 2004 W, H, B, A
wardship
placement 55129
based on RRI - compared to White -
Hispanic .91x, Black .95x, Asian .89x
California Department of Justice.
(2004). CA 2004 W, H, B, A
transfer to adult
court 1590
based on RRI - compared to White -
Hispanic 3.21x, Black 3.71x, Asian
3.95x
123
Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,Myrent, M., et. al. (2003).
IL - Cook County 1999 W, B, H arrests 21972
African Americans overrepresented for
all crimes; Hispanic for property and
weapons crimes; Whitesunderrepresented for all crimes
African Americans overrepresented for
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Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1999 W, B, H court referral 11228
African Americans overrepresented for all crimes; Hispanic for weapons
crimes; Whites underrepresented for all
crimes
Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1996-1999 W, B, H petition filed 56051
African American malesoverrepresented for all crimes; Hispanic
males for weapons crimes; Whites
underrepresented for all crimes
Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1996-1999 W, B, H
delinquent
adjudication 26118
African American males
overrepresented for all crimes except
weapons; Hispanic males for all crimes;
White males violent, property, and
weapons crimes
Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1996-1999 W, B, H probation 20016
African American males
underrepresented for all crimes;
Hispanic males overrepresented for all
crimes except violent; White males
overrepresented for all crimes
124
Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1996-1999 W, B, H secure detention 2387
African American males
overrepresented for all crimes except
weapons; Hispanic males for property
and weapons crimes; White males drug
crimes
African American males
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Stevenson, P.J., Lavery, T., Burke,
K.S., Alderden, M., Martin, C.,
Myrent, M., et. al. (2003).
IL - Cook
County 1996-1999 W, B, H confinement 3541
African American males
overrepresented for all crimes; Hispanic
males for violent and weapons crimes;
White males underrepresented for all
crimes
The W. Haywood Burns Institute.(2004). KY
7/2001 -6/2002 W, B, H arrests 15081
based on RRI - compared to White -Black 2.49, Hispanic .77
The W. Haywood Burns Institute.
(2004). KY
7/2001 -
6/2002 W, B, H
refer to juv.
Court 8364
based on RRI - compared to White -
Black 2.49, Hispanic .77
The W. Haywood Burns Institute.(2004). KY
7/2001 -6/2002 W, B, H diversion 8188 no difference found
The W. Haywood Burns Institute.
(2004). KY
7/2001 -
6/2002 W, B, H secure detention 10266
based on RRI - compared to White -
Black 1.68, Hispanic 2.29
The W. Haywood Burns Institute.
(2004). KY
7/2001 -
6/2002 W, B, H petition filed 39314 no difference found
The W. Haywood Burns Institute.(2004). KY 7/2001 -6/2002 W, B, H probation 1376 based on RRI - compared to White -Black .9, Hispanic 1.2
Kenny, M., & Mishina, T. (2005). ME 2004
W, B, A,
NA arrest 8446
based on RRI - compared to White - balck 3x, Asian .5x, American Indian
.42x
125
Missouri Office of the State Courts
Administrator. (2004).
MO - 17
judicial
circuits
8/2003-
10/2003 W, B
referrals -
detention v no
detention 4753 no significant difference found
Missouri Office of the State CourtsAdministrator. (2004).
MO - 17
judicialcircuits
8/2005 -10/2005 W, B
case processingdecision 3621
African Americans processed moreformally than Whites
MO - 17
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Missouri Office of the State Courts
Administrator. (2004).
MO - 17
judicial
circuits
8/2007 -
10/2007 W, B commitment 655 no significant difference found
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA arrest 11153
based on RRI - compared to White -
Hispanic 1.21x, Native American 2.43x
Montana Board of Crime Control.(n.d.). MT 2004 W, H, NA referral 11450 no significant difference found
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA diversion 7587
Hispanic and Native American
underrepresented
Montana Board of Crime Control.(n.d.). MT 2004 W, H, NA secure detention 2120
based on RRI - compared to White -Hispanic 1.38x, Native American 1.34x
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA petition filed 1988
based on RRI - compared to White -
Hispanic 1.84x, Native American 1.23x
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA
delinquent
adjudication 472
based on RRI - compared to White -
Hispanic not significant, Native
American underrepresented
126
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA probation 327 not significant
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA
secure
confinement 85
based on RRI - compared to White -
Hispanic not significant, Native
American 2.39x
Montana Board of Crime Control.
(n.d.). MT 2004 W, H, NA adult transfer 45 not significant
8/8/2019 Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee
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(n.d.). MT 2004 W, H, NA adult transfer 45 not significant
South Dakota Department of
Corrections and Council of Juvenile
Services. (2006). SD 2004 W, B, NA arrest 7828
based on RRI - compared to White -
American Indian 2.39x, Black 2.32x
South Dakota Department of
Corrections and Council of JuvenileServices. (2006). SD 2004 W, B, NA detention 2684
based on RRI - compared to White -American Indian 1.39x, Black 1.55x
South Dakota Department of
Corrections and Council of Juvenile
Services. (2006). SD 2004 W, B, NA petition filed 6226
based on RRI - Whites were 1.21x
higher than American Indians and 1.36x
higher than Blacks
South Dakota Department of
Corrections and Council of Juvenile
Services. (2006). SD 2004
W, B, H,
NA
delinquent
adjudication 5337
based on RRI -American Indian not
significant, White 1.18x higher than
Black
South Dakota Department of Corrections and Council of Juvenile
Services. (2006). SD 2004
W, B, H,
NA probation 3291
based on RRI - American Indian 1.21xhigher than White, White 1.32x higher
than Black
South Dakota Department of Corrections and Council of Juvenile
Services. (2006). SD 2004 W, B, NA confinement 111
based on RRI - American Indian 3.61x
higher than White, Black not significant
Rodney, H.E., & Tachia, H.R. (2004).
TX - 3
counties
1/1999 -
12/2000 W, B, H arrest/referral 316 Black 2x more likely than pop #
127
Rodney, H.E., & Tachia, H.R. (2004).
TX - 3
counties
1/1999 -
12/2000 W, B, H preadj detention 232
race not correlated when compared to
arrests
Rodney, H.E., & Tachia, H.R. (2004).
TX - 3
counties
1/1999 -
12/2000 W, B, H adj hearing 93
compared with arrests, Whites more
likely to go to hearing
Rodney, H.E., & Tachia, H.R. (2004).
TX - 3
counties
1/1999 -
12/2000 W, B, H disposition 185 numbers similar to arrest
8/8/2019 Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee
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Rodney, H.E., & Tachia, H.R. (2004). counties 12/2000 W, B, H disposition 185 numbers similar to arrest
VanVleet, R.K., Vakalahi, H.F.,Holley, L., Brown, S., & Carter, C.
(2000).
UT - Odgen,Provo, Salt
Lake 1997
W, B, H,
NA, A
arrest/referral -
person offense 707
compared to Caucasians - Hispanics 9x,African American 41x, Asian 10x,
American Indian 9x
VanVleet, R.K., Vakalahi, H.F.,
Holley, L., Brown, S., & Carter, C.
(2000).
UT - Odgen,
Provo, Salt
Lake 1997
W, B, H,
NA, A
arrest/referral -
property offense 2342
compared to Caucasians - Hispanics 9x,
African American 32x, Asian 10x,
American Indian 12x
VanVleet, R.K., Vakalahi, H.F.,
Holley, L., Brown, S., & Carter, C.
(2000).
UT - Odgen,
Provo, Salt
Lake 1997
W, B, H,
NA, A
detention
hearing nr
compared to Caucasians - Hispanics 3x,
African American 4x, Asian 1x,
American Indian 3x
VanVleet, R.K., Vakalahi, H.F.,Holley, L., Brown, S., & Carter, C.
(2000).
UT - Odgen,Provo, Salt
Lake 1997
W, B, H,
NA, A
probation
decision nr
compared to Caucasians - Hispanics 3x,African American 3x, Asian negative,
American Indian 2x
VanVleet, R.K., Vakalahi, H.F.,
Holley, L., Brown, S., & Carter, C.
(2000).
UT - Odgen,
Provo, Salt
Lake 1997
W, B, H,
NA, A placement in dyc nr
compared to Caucasians - Hispanics 4x,
African American 3x, Asian 1x,
American Indian 3x
Bellas, M.L. (2007).Burlington,
VT10/2004 -
9/2005 W, B, A arrest 139 based on RRI - compared to White -Black .67n.s, Asian .66n.s.
Bellas, M.L. (2007).
Burlington,
VT
10/2004 -
9/2005 W, B, A arrest 152
based on RRI - compared to White -
Black .91n.s
128
Caucasian = W
African American = B
Hispanic = H Native American = NA
Asian - A
8/8/2019 Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee
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