Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee

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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



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:



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



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.



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.



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.



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



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



Compliance ................................................................................................................... 90

Threat Hypotheses ......................................................................................................... 91

Limitations .................................................................................................................... 94

Recommendations ........................................................................................................ 96

Conclusion .................................................................................................................... 97

References ..................................................................................................................... 99



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



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



Appendices

Appendix Page

Appendix A: S ummary of S tate A ssessments............................................................... 119

Appendix B: IRB A pproval Letter ............................................................................... 129

Appendix C: Vitae ...................................................................................................... 130



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



2

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).



3

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



4

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



5

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).



6

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



7

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



8

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



9

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,



10

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



11

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



12

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



13

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.



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

1



15

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

16



16

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).

17



17

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

18



18

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

19



19

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.

20



20

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

21



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

22



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),

23



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 &

24



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¶

25



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

26



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

27



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

28



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

29



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

30



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,

31



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).

32



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

33



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.

34



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



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



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.

37



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



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



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



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



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



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



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



, 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



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



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



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



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



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



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



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



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



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



(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



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



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



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]

58

Measures

Outcome measures



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

59

summary measure of unexplained disproportionality that remains after controlling for

arrests across the studied period



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

60

To calculate the expected percentage of incarcerated juveniles that were Black

based on arrests the following formula was used:



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.:



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,

62

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



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

63

Finally, the OJJDP¶s monitoring requirement is met when states evaluate

effectiveness of intervention programs (U.S. Department of Justice, 2009). Again, both



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



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

65

of unmarried mothers under the age of 18 (CDC, 1997, 1999, 2001, 2003) was used as an

underclass indicator.



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,



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.



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.



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.



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.



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



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



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



& 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



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

75

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



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

76

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



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.

77

Table 4: Descriptive statistics for outcome, predictor and control measures (n=190)



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

78

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



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



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

80

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



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

81

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



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.



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



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



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




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

86

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,



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.

87

Table 9: F ull fixed effects models of Black juvenile placements explained by arrest by offense (n=190)




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



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



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

90

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



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

91

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



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

92

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 ´



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

93

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



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



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



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



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



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



, 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



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


(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


(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


(2006).

AK -

Fairbanks

7/2002 -

6/2003

W, B, NA,

A

intake -

dismissal 111 no significant racial finding


(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



(2006). Fairbanks 6/2003 A adjustment 421 no significant racial finding


(2006).

AK -

Fairbanks

7/2002 -

6/2003

W, B, NA,

A

intake - petition

filed 188 no significant racial finding


(2006).

AK -

Fairbanks

7/2002 -

6/2003

W, B, NA,

A

formal court

proceeding -

dismissal 66 no significant racial finding


(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


Minorities. (2002).

AZ - Pima

Co. 2000

W, H, B,

NA referral 9513


Hispanic 1.14x, African American 2.2x,

Native American x1.6


Minorities. (2002).

AZ ±

MaricopaCo. 2000

W, H, B,

NA detention 10158



Native American 2.5x




Minorities. (2002).

AZ - Pima

Co. 2000

W, H, B,

NA detention 3113





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


Minorities. (2002).

AZ - Pima

Co. 2000

W, H, B,

NA probation 1944


Hispanic 1.2x, African American 2.5x


Minorities. (2002).

AZ -

Maricopa

Co. 2000

W, H, B,

NA commitment 417




122


Minorities. (2002).

AZ - Pima

Co. 2000

W, H, B,

NA commitment 330




State of Arizona Commission onMinorities. (2002).

AZ -

MaricopaCo. 2000

W, H, B, NA adult transfer 401


Hispanic 2x, African American 3.7x, Native American 2.5x

b d d hi




Minorities. (2002).

AZ - Pima

Co. 2000

W, H, B,

NA adult transfer 134



Native American 1.7

California Department of Justice.

(2004). CA 2004 W, H, B, A arrests 206201


Hispanic 1.18x, Black 2.5x, Asian .4x


(2004). CA 2004 W, H, B, A

referrals to

probation 169681


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


(2004). CA 2004 W, H, B, A detention 39087


Hispanic 1.3x, Black 1.7x, Asian 1.2x


(2004). CA 2004 W, H, B, A petition filed 86283


Hispanic 1.15x, Black 1.2x


(2004). CA 2004 W, H, B, A

wardship

placement 55129


Hispanic .91x, Black .95x, Asian .89x


(2004). CA 2004 W, H, B, A

transfer to adult

court 1590


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




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




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




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




IL - Cook

County 1996-1999 W, B, H probation 20016


underrepresented for all crimes;

Hispanic males overrepresented for all

crimes except violent; White males

overrepresented for all crimes

124




IL - Cook

County 1996-1999 W, B, H secure detention 2387


overrepresented for all crimes except

weapons; Hispanic males for property

and weapons crimes; White males drug

crimes







IL - Cook

County 1996-1999 W, B, H confinement 3541


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


Black 2.49, Hispanic .77

The W. Haywood Burns Institute.(2004). KY

7/2001 -6/2002 W, B, H diversion 8188 no difference found


(2004). KY

7/2001 -

6/2002 W, B, H secure detention 10266


Black 1.68, Hispanic 2.29


(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



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


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


(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


(n.d.). MT 2004 W, H, NA petition filed 1988


Hispanic 1.84x, Native American 1.23x


(n.d.). MT 2004 W, H, NA

delinquent

adjudication 472


Hispanic not significant, Native

American underrepresented

126


(n.d.). MT 2004 W, H, NA probation 327 not significant


(n.d.). MT 2004 W, H, NA

secure

confinement 85


Hispanic not significant, Native

American 2.39x


(n.d.). MT 2004 W, H, NA adult transfer 45 not significant



(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


American Indian 2.39x, Black 2.32x


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



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



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


TX - 3

counties

1/1999 -

12/2000 W, B, H preadj detention 232

race not correlated when compared to

arrests


TX - 3

counties

1/1999 -

12/2000 W, B, H adj hearing 93

compared with arrests, Whites more

likely to go to hearing


TX - 3

counties

1/1999 -

12/2000 W, B, H disposition 185 numbers similar to arrest



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



(2000).

UT - Odgen,

Provo, Salt

Lake 1997

W, B, H,

NA, A

detention

hearing nr



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



(2000).

UT - Odgen,

Provo, Salt

Lake 1997

W, B, H,

NA, A placement in dyc nr



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


Black .91n.s

128

Caucasian = W

African American = B

Hispanic = H Native American = NA

Asian - A



Documents

Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee