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

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Jaya Davis, Dissertation, College of Juvenile Justice & Psychology, Dr. William Allan Kritsonis, Dissertation Committee. Dr. William Allan Kritsonis is a tenured full-professor at PVAMU, The Texas A&M University System.

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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

Prairie View A&M University

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:

____________________________________________ Jonathan Sorensen, Ph.D. College of Juvenile Justice and Psychology

(Chair)

_____________________________________________ 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:

_____________________________________________ Willie Trotty, Ph.D. Dean, Graduate School

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 Juvenile Justice and Delinquency Prevention (OJJDP) initiative. It utilized methodology to analyze racial disproportionality in incarceration in the juvenile justice system at the state level. Additionally, this study tested racial and symbolic threat theories on a juvenile

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population. By employing both broad measures to compare to previous research, and more refined measures for more accurate testing, this study begins to fill in the gap left by previous research efforts regarding theoretical grounding and DMC. The findings fail to support previous research suggesting that a nationwide reduction in DMC is a result of OJJDP initiatives. Also there is no evidence of racial or symbolic threat with regards to the overrepresentation of Black juveniles in residential placement.

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Dedication To my husband, and best friend, thank you for your consistent support and understanding. Without the sacrifice for our shared vision of different tomorrow, this would not have happened today.

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Acknowledgements Thank you to all my family and friends for their unyielding support and understanding when I had ³to work.´ A special thank you to my grandmother for taking such loving care of the boys and allowing me the freedom to pursue my dreams.

Thank you to Dr. Everette Penn for igniting the spark of inquisition to examine problems that I have never personally faced.

Thank you to the Prairie View A&M graduate faculty for their expertise in various fields that has helped to guide me in my new path.

Thank you to my dissertation committee for making this process as painless as possible and their efforts at making the final product something that I am proud of.

Thank you to Dr. Jon Sorensen for being my mentor, chair, and friend.

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Table of Contents Certification of Dissertation Approval ............................................................................. ii Abstract .......................................................................................................................... iii Dedication ....................................................................................................................... v Acknowledgments .......................................................................................................... vi Table of Contents .......................................................................................................... vii List of Tables .................................................................................................................. x List of Figures ................................................................................................................ xi Appendices.................................................................................................................... xii Chapter I ......................................................................................................................... 1 Introduction ..................................................................................................................... 1 Purpose of Study ............................................................................................................. 6 Current Study ................................................................................................................ 11 Objectives ................................................................................................................ 11 Limitations .............................................................................................................. 12 Organization ............................................................................................................ 13 Chapter II ...................................................................................................................... 14 Literature Review .......................................................................................................... 14 Theoretical Background ................................................................................................ 14 Social Disorganization .............................................................................................. 15 Social Control........................................................................................................... 18 Conflict Theory ........................................................................................................ 19 Theoretical Framework ................................................................................................. 21

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Racial Threat ............................................................................................................ 21 Bureaucratic Model .................................................................................................. 28 Benign Neglect ......................................................................................................... 30 Symbolic Threat ....................................................................................................... 34 The Issue of Disproportionate Minority Contact ............................................................ 41 Toward a State-level Assessment................................................................................... 45 Chapter III..................................................................................................................... 51 Methods ........................................................................................................................ 51 Data Sources ................................................................................................................. 51 Measures ....................................................................................................................... 58 Outcome Measures ................................................................................................... 58 Predictor Variables ................................................................................................... 61 Control Measures ..................................................................................................... 66 Hypotheses .................................................................................................................... 66 Analyses ........................................................................................................................ 67 Limitations .................................................................................................................... 69 Chapter IV .................................................................................................................... 72 Analyses and Results ..................................................................................................... 72 Compliance ................................................................................................................... 72 Racial-Economic Threat and Benign Neglect................................................................. 75 Symbolic Threat ............................................................................................................ 82 Chapter V ...................................................................................................................... 89 Summary and Conclusion .............................................................................................. 89

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Compliance ................................................................................................................... 90 Threat Hypotheses ......................................................................................................... 91 Limitations .................................................................................................................... 94 Recommendations ........................................................................................................ 96 Conclusion .................................................................................................................... 97 References ..................................................................................................................... 99

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List of Tables Table Page

Table 1A: Total juvenile referrals ± 2005 ................................................................ 4 Table 1B: Total juvenile out-of-home placements by offense ± 2005......................... 4 Table 2: Offense comparison by data source ......................................................... 57 Table 3: Descriptive statistics comparing state DMC score and percent change .... 75 Table 4: Descriptive statistics for outcome, predictor and control measures (n=190) .................................................................................................................. 77 Table 5: Principal component analysis of population structure variables ............... 78 Table 6: Fixed effects models of adjusted Black-White ratio of juvenile placements (n=190) .................................................................................................................. 80 Table 7: Descriptive statistics for offense-specific outcome measures (n=190)....... 83 Table 8: Fixed effects models of Black juvenile placements explained by arrest (n=190) .................................................................................................................. 85 Table 9: Full fixed effects models of Black juvenile placements explained by arrest by offense (n=190).................................................................................................. 87

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List of Figures Figure Page

Figure 1: Absolute percent change by state in Black-White disproportionality in juvenile placements after controlling for arrests, 1997 and 2007 ............................. 74

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Appendices

Appendix

Page

Appendix A: Summary of State Assessments ............................................................... 119 Appendix B: IRB Approval Letter............................................................................... 129 Appendix C: Vitae ...................................................................................................... 130

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Chapter I Introduction The issue of disproportionate minority confinement (DMC) in the juvenile justice system has been a growing concern since the 1960s. When the deinstitutionalization movement swept the nation in the 1970s, Whites accounted for 75% of the reduction in incarcerated status offenders; and, when incarceration rates began to rise again during the early 1980s, minority youth bore 93% of the increase (Krisberg, Schwartz, Fishman, Gutman, & Joe, 1987). The first legislative reference to DMC, however, came with the June 1986 testimony of Ira Schwartz of the Center for the Study of Youth Policy before the House Subcommittee on Human Resources. Schwartz (1986) stated that minority youth accounted for more than half of juveniles in custody despite research showing that they did not disproportionately commit crimes. In the mid-1980s, research from the Children in Custody data series began to be presented and discussed. Although largely descriptive, the primary indication was that there was a problem regarding the overrepresentation of minority juveniles in the system; however, it ³could not offer compelling explanations of the etiological forces behind those discrepancies´ (Krisberg, 2005, p. vii). Additionally, there were few empirical studies to add to the conversation. At the time, the only conclusion to offer was that there was a crisis of national magnitude (Krisberg, 2005). Two years after the congressional testimony on DMC, in 1988, the Conference of the National Coalition of State Juvenile Justice Advisory Groups was held, wherein the national policy and advocacy body concentrated primarily on DMC. That same year an

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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 (KempfLeonard, 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). Table 1 summarizes the current state of racial representation in the juvenile justice system. The data presented is unadjusted for any measure proposing involvement in delinquency and strictly compares referral and placement percentages to the national

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 criminal court at a lower rate than their representation in the general population for all offenses. Finally, all other racial categories are represented within the system at a rate similar to their population representation. Of course combining all other races into an

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 Juvenile Populationa White Black Other 78% 17% 5% Total Juvenile Referralsb 64% 33% 3% Placed out of Home 57% 40% 3% Waived to Criminal Court 57% 40% 3%

Detained 66% 31% 3%

Table 1B: Total juvenile out-of-home placements by offense ± 2005 Total Juvenile Referrals White Black Other 64% 33% 3% Public Order 59% 38% 3%

Person 51% 46% 3%

Property 61% 35% 4%

Drugs 54% 44% 2%

Source: Stahl, Finnegan, and Kang (2005) Note: Hispanic juveniles are not identified separately but placed in categories by race.
a

Age for juvenile population is 10 - 17. Age for juvenile referral is <12 ± 17.

b

In the early days of DMC reform, the solution to the racial disparity issue appeared uncomplicated. States were to investigate the problem, ³which would probably involve the discretion of officials´ (p. 72), correct it, and put policies into place to prevent

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 the groups¶ rate of arrests during the last decade (Davis & Sorensen, 2010). Other studies have found that jurisdictions vary in the degree to which they have identified, assessed, intervened, and monitored DMC in accordance with the OJJDP mandate (Leiber, 2002).

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 response (Stolzenberg et al., 2004). The traditional racial threat hypothesis suggests that in areas with recent increases in Black population, White majorities will feel threatened

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 may be reached, at which time formal control could be expected to decrease. Racial threat may play a smaller role because of American society¶s increasing heterogeniety and size. Majority group members may see less of an issue with racial

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 decisionmaking 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, 1987) and, consistent with the benign neglect hypothesis, community members largely offend in an intraracial manner when committing violent and property crimes. This leads to a decrease in the perceived racial threat to White domination, which can be evidenced

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 the majority status quo. Although there is a substantial body of research testing threat hypotheses, particularly racial threat, the majority of the research concentrates on adult populations. Tittle and Curran (1988) argued that discretion afforded actors in the

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 broad measures employed to test the theory have failed to support it. Although research addressing symbolic threat has used juvenile populations, the few studies in this area have failed to garner much attention. By employing both broad measures, to compare to

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 disproportionately handled in adult courts. Thus, failing to include juveniles sentenced to adult institutions in the current study will likely have a downwardly biasing impact on the level of Black-White racial disproportionality observed.

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 New World wealth. Urban poverty and crime became a concern for sociologists of the late nineteenth and early twentieth century. Research regarding social contributors to crime was in its heyday (Williams & McShane, 2004). Although many populations were

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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 community factors had been identified prior to the famous work of Shaw and McKay and social disorganization. In the late 1800s, African American scholars began to identify and study disproportionate minority (African American) confinement (Du Bois, 1899/2002;

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). Relationships are superficial and transitory. Impersonality and weak social bonds allow the breakdown of basic institutions (Williams & McShane, 2004). Instead of community cooperation, individuals compete for available, and often scarce, resources (Lanier &

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 in relation to other groups, particularly immigrant populations during Industrialization. Therefore, social disorganization can be used to explain rates of crime in urban areas where economic status, ethnic heterogeneity, and residential mobility contribute to an

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 to do what they pleased´ (p. 91). In the most popular version of social control, Hirschi (2004) argued that more individuals would commit criminal and delinquent acts if not for the fear of getting

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. Conflict theorists are concerned with the power wielded in making laws and their effect on the powerless more than on the individual offender.

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 subordination of classes. But often major concepts such as dominate and subordinate groups, ³elites,´ or ³authorities´ are not clearly defined and thus change depending on the situation (Liska, 1992). In fact, the class-based conflict theory has often been modified to

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 nonWhites 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 In his popular writing on intergroup relations and discrimination, Blalock (1967) made the official leap from class-based conflict theory to race-based conflict theory. Although race and class continue to be interchangeable in many writings, discussions,

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. 324325). 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), and racist candidates receive more political support (Giles & Buckner, 1993). More severe criminal justice outcomes naturally follow more severe justice ideology: cities with higher minority populations employ more police officers (Kent & Jacobs, 2005;

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 & Chamlin, 1984). Ousey and Lee (2008) point out four limitations in research using the racial threat hypothesis and formal social control through arrests. First is the neglect of aggregate

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¶ stereotypes of African Americans as more likely to recidivate (Crow & Johnson, 2008). Bridges, Crutchfield, and Simpson (1987) examined the racial differences in juvenile punishment. The researchers predicted that sanctioning would differ based on

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 historic use of lynching as a form of social control. He contended that lynching increased when the percent of non-Whites increased. Tolnay and Beck (1992) contended that fear of crime was offered as a pretense for the use of lynching, in that Whites were more

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 economic exclusion strategies.´ Discrimination in the workplace and the emergence of a dual labor market have been offered as economic exclusion strategies that reduce statebased social control, but other variables such as split labor markets and worker niche

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 justice system is a response to the threat they pose to authority structures. Hawkins argued that although percent non-White and percent poor have been related, percent poor has shown no effect on certainty of arrest, while race-related variables have. This is

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 explained by observing the differences in processing styles according to population density. The bureaucratic perspective examines how formal social control is applied unevenly to non-White minority groups in different areas. As Kraska (2006) noted,

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 courts (Bridges et al., 1987). Albonetti (1991) suggested that the bureaucratic or rational model can also be used to explain a form of patterned response that could exacerbate the influence of social

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, 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 et al., 2005).

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). Second, intraracial crime affecting a Black population garners less formal attention because there is less likelihood that the crime will be reported to police. Citizen¶s choice to report, or fail to report, crime is a filtering process where not all

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 of a crime, for instance, the more serious is the offense´ (Black, 1976, p. 95). Black argues that ³law varies inversely with cultural distance´ (p. 66). That is, if an offender has lower cultural standing (i.e. less education, fewer resources, etc.) than his/her 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. Ousey and Lee (2008) argued that benign neglect has not been directly tested, other than by Liska and Chamlin (1984). In fact, benign neglect is most often discussed as a possible explanation when racial threat testing does not support that theory (e.g.

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. Symbolic Threat 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 (2008) define symbolic threat as ³a theoretical perspective that attempts to identify the contingencies of juvenile justice decision making by focusing on the characteristics of

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 the authors refer to this as the ³type-of-offense´ hypothesis instead of symbolic threat, the underlying concepts are similar. Their findings reveal that significant race effects impact juvenile justice processing for drug/sex and other misdemeanor/status offense categories

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. Tittle and Curran¶s (1988) value dominance hypothesis proposed that if a defendant¶s community is wealthy, youth who fit into middle-class models would be treated more leniently than those who did not. The researchers suggested that disparities

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 significant for White juveniles. Underclass poverty was significant for placement of Blacks adjudicated for drugs and public order, with drug placements for Blacks being seven times that of White counterparts. These findings led the authors to conclude,

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). Leiber and Johnson (2008) found race and age to be predictors of decision making. Taking relevant legal and extralegal factors into consideration, older Black juveniles were less likely to receive diversion and more likely to be detained than their

39 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 making points and for various races, there was no clear pattern of an interaction effect between being Black and having committed a drug offense. In fact, drug offenses were

40 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. Decisionmaking 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 offense processing, wherein a consistent pattern of racial disparity existed. Ousey and Lee (2008) offer an alternative explanation to high rates of BlackWhite arrest disparity for offenses where police discretion is most likely. Instead of

41 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 present later in the system often resulted from decisions made during the early stages of case processing. As expected, researchers found that legal factors significantly influenced secure detention and referral decisions; juveniles with more extensive prior offense

42 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 exceed chance led them to conclude that ³racial disparities are a reality in juvenile justice processing´ (p. 208). Further, controlling for status characteristics such as sex, age, and income did not eliminate the race effects, and controlling for family characteristics

43 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 and extralegal factors tended to reduce observed level of disparity. Pope, Lovell, and Hsia (2002) conducted a review of research literature from 1989 through 2001. They concluded that race effects continued to be evident in the juvenile processing system.

44 However, compared to prior research, the latter studies tended to use complex statistical 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 appropriate quantitative data; or, the study was inaccessible. A description of the studies, the decision points analyzed, and their results are available in Appendix A.

45 The 12 states identified represent all regions of the United States and include 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. A relative rate index was created to standardize this reporting. The relative rate index is calculated by dividing the percentage of minority juveniles at each decision making point by the percent of that minority group in the general population. A value greater than one

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 prisoners had to be accounted for by factors other than differential involvement in crime, one of which could be bias in the justice system.

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, Bridges, & Pitchford, 1994; Hawkins & Hardy, 1989). While these studies controlled for the level of index crime arrests overall by race, they did not specify the particular mixture of offenses. Austin and Allen¶s (2000) study of racial disproportionality in Pennsylvania

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 racespecific 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. Blumstein¶s methodology has also been applied to an analysis of racial disproportionality in incarceration in the juvenile justice system (Davis & Sorensen, 2010). This study sought to provide a national systemic measure to discern incarceration

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. Finally, they suggested the need to adjust Blumstein¶s (1982) formula when comparing levels of disparity across time or jurisdiction (Sorensen et al., 2003).

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, 19972006, 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 bed in each facility on the last Wednesday of October. An exception to the biennial census occurred in 2005 in that the CJRP surveys for that year were mailed out late in February, 2006. The CJRP mean response rate since 1997 has been 99%. While the CJRP

51

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 discriminatory processes of selection bias at arrest, the measurement of which is necessarily precluded when relying on arrests in calculating the base ratios of Black-toWhite differentials in offending. The accuracy of official records as a representation 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 selfreported 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 for rapists and assaulters, most of the disproportionality in arrests was found to be accounted for by differential involvement. A recent analysis of NCVS data found Black involvement in serious violent crime to be 4.6 times higher than Whites, close to their

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 selfreported 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 attempts to understand the extent of involvement versus systemic selection effects for some time. In Du Bois¶s, The Negro Criminal, originally published in 1899, arrests were used as a measure of involvement in crime. ³The simplest, but crudest, measure of crime

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. CJRP categories for placed juveniles by state are limited to person-violent crime index, person other, property crime index, property other, drug, public order, technical violations, and status offenses. The specific offenses are not divulged at the state level in

56 order to ensure anonymity of juveniles in states with very low occurrences of certain 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: Offense comparison by data source State-level CJRP offense category Person violent crime index

UCR offense category Murder Forcible rape Robbery Aggravated assault Other assaults Sex offenses Offenses against family Burglary Larceny-Theft Motor vehicle theft Arson Vandalism Forgery and counterfeit Fraud Embezzlement Stolen property Drug abuse violations Weapons Prostitution DUI Liquor laws Drunkenness Disorderly conduct Vagrancy Gambling All other offenses

Person other

Property crime index

Property other

Drug Public order

Technical violations Status offenses

[None] Curfew violations Loitering law violations Runaways

58 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 disparities across geographical units (i.e. states or regions). Because the analysis of data spans several years, with varying levels of initially observed disproportionality across states, the adjusted ratios of disproportionality will also be employed to provide a

59 summary measure of unexplained disproportionality that remains after controlling for 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 R = the expected percentage of incarcerated juveniles that were Black based on arrests.

60 To calculate the expected percentage of incarcerated juveniles that were Black based on arrests, the following formula was used: R = ™ Rj, (2)

where: Rj = BjFj = the expected percentage of incarcerated juveniles for offense type j that was Black based on arrests; Bj = the percentage of persons arrested for offense type j that was Black; and, Fj = the percentage of incarcerated juveniles for offense type j.

Offense-specific measures were also calculated using the formula: Xj = 100(Rj(100 - Qj)/(100 - Rj)Qj), (3)

where Qj = the actual percentage of incarcerated juveniles for offense type j that was Black; and, Rj = Bj.

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. While this interpretation is interesting in itself, as noted by Sorensen et al. (2003), when Blumstein¶s formula is used for comparative purposes across time or geographical areas the interpretation would be incomplete without applying these figures to the actual observed Black-White incarceration ratios. In Blumstein¶s case, this would mean

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.: (1 ± X)*Br + X, 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, intervening, and monitoring DMC; with the highest score being 4. If no report had been made regarding these objectives, the state received a 0. For each succeeding objective a state received an additional point for meeting the objective. Preceding objectives had to (4)

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 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 this objective if they had items listed under ³DMC-Reduction Strategies´ in the Summary, or if a report was listed which referenced intervention strategies in the Catalog.

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. Racial unemployment inequality was used as an (inverse) economic indicator of racial threat. This variable is the ratio of Black to White unemployment per state. Information for this measure was obtained from the United States Labor Department,

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 receiving Temporary Assistance for Needy Families by race was not available. Therefore, in addition to (positive) Black to White ratio of state unemployment, Black to White ratio

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 control variable in the general models, in the offense-specific models, symbolic threat suggests that the percentage of explained disproportionality in incarceration rates for drug and public order offenses (but not other offenses) should be expected to negatively

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 controlling for arrest (DMC). Racial and Economic Threat:

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 & Greenberg, 2008). The introduction of repeated effects relaxes assumptions related to the independence of error terms (Linear mixed models, n.d.). The repeated variable (time) is considered to be a marker of multiple observations on a single subject. The subject

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 instead of between-individual differences. In the current study, what has happened over time within each state is examined instead of differences between states. Between-state variation would likely be confounded by unobserved differences between states (Allison,

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 system (Fagan, Forst, & Vivona, 1987), there is likely to be a higher Black arrest rate captured in the matched database relative to the Black placement rate. This could downwardly bias the level of Black-White racial disproportionality observed in juvenile

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 occurring after juveniles had been taken into custody. Where data were missing for predictor variables, linear interpolation or data duplication was relied on. Linear interpolation was used if data points were missing for

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; 1.60 ± 1.77 = -.17*100 = -17% States with a negative percent change showed a decrease in Black-White disproportionality in placement rates, while states with a positive percent change showed

72

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 BlackWhite 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 Nebraska West Virginia Iowa Colorado Illinois Oregon Washington Alabama Georgia Delaware North Carolina Arkansas Tennessee New Jersey Indiana Minnesota Mississippi Louisiana California Texas United States Connecticut Pennsylvania Michigan Virginia Arizona Nevada Missouri Oklahoma South Carolina Ohio Maryland Alaska New York Kentucky* New Mexico Rhode Island Massachusetts

0.00

50.00 100.00 150.00 200.00

Figure 1: Absolute percent change by statea in Black-White disproportionality in juvenile placements after controlling for arrests, 1997 and 2006. a Hawaii was not included because the state experienced no Black juvenile placements in 2006. * Although Kentucky shows an increase of 86 percentage points, Black juveniles have been underrepresented based on arrest rates for the group from 1997-2006.

75 Average state DMC compliance scores for states experiencing either increases or decreases in DMC during 1997 to 2006 are presented in Table 3. Table 3: Descriptive statistics comparing DMC compliance scores for states experiencing reductions or increases in levels of DMCand percent change (n=37) a Compliance Scores Mean SD 2.524 1.209 2.188 1.223

States experiencing reductions in DMC States Experiencing increases in DMC
a

Hawaii 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 provides descriptive statistics for the outcome measure in the first row. On average for states over the entire time frame examined, Black juveniles were placed in residential placement 88% more often than White juveniles. These findings are roughly similar to those noted in a national study during the early years (85%) of this series, 1997-2001, but

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 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 Black/White placement ratio Data source OJJDP CJRP FBI UCR US Census Bureau US Census Bureau US Labor Department Center for Disease Control US Census Bureau US Census Bureau US Census Bureau US Census Bureau Mean 1.88 Between-state SD 0.83 Within-state SD 0.10

Percentage Black Black-White unemployed ratio Black-White teenage mother ratio Percentage Black youth (10-17) Percentage Hispanic Police per 100,000 Index crime per 100,000

12.57 2.10 3.70 16.09 8.46 286.17 140.95 83.99 219.69 6,545

9.53 0.62 1.47 11.57 9.55 49.09 148.61 11.56 268.96 6,449

1.43 0.12 0.15 1.67 1.62 24.36 16.59 8.66 12.98 3,392

Percentage population in urban area US Census Bureau Population per square mile Population size (in 1,000s) US Census Bureau US Census Bureau

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 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: Principal components analysis of population structure variables Variables Percent population in urban area Population per square mile State population Factor loadings 0.864 0.734 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 Black2, 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 Black2, 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 test racial threat, benign neglect, and symbolic threat, respectively. Models run separately, post hoc, indicated that the decision to include all three did not influence results.

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 Black2 (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 R2 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 R2 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 estimates for conceptual and control variables. The conceptual variables testing racial threat is percentage Black and ratio of Black-White unemployment. If racial threat were evident, there should be a significant positive coefficient for percentage Black, indicating

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 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: Fixed effects models of adjusted Black-White ratio of juvenile placement Model 1 Estimate 1.615 0.022 -0.001 -0.076 0.087 SE 0.377 0.052 0.001 0.084 0.055 Model 2 Estimate 1.278 0.083 -0.001 -0.076 0.077 -0.024 0.007 -0.003* 0.277 0.194 0.500 SE 1.204 0.140 0.002 0.087 0.056 0.106 0.015 0.002 0.390 0.145 0.454

Intercept Percentage Black Percentage Black2 Black/White unemployment ratio Black/White teenage mother ratio Percentage Black youth (10-17) Percentage Hispanic Police per 100,000 Index crime (logged) Urbanism Non-South p is based on one-tailed significance. * p < 0.05

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 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 Black2, 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 Black2 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 Black2 and Black-White unemployment ratio are in the anticipated direction (negative) to support benign neglect, the estimate for Black-White teenage mother is in the opposing

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. 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 drug and public order offenses hovers just above 50%, indicating that only about half of the racial disproportionality in arrests for those offenses is explained by arrest. .

83

Table 7: Descriptive statistics for offense-specific outcome measures (n=190) Variable Percent explained ± violent Data source OJJDP CJRP FBI UCR US Census Bureau OJJDP CJRP FBI UCR US Census Bureau OJJDP CJRP FBI UCR US Census Bureau OJJDP CJRP FBI UCR US Census Bureau Mean 120.23 Between-state SD 52.06 Within-state SD 11.22

Percent explained ± property

95.35

63.08

8.06

Percent explained ± drug

54.59

34.51

6.89

Percent explained ± public order

61.31

31.39

4.27

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: Fixed effects models of Black juvenile placement explained by arrest by offense (n=190) Violent Estimate SE 84.766*** 25.953 -5.856 -0.133 9.374 -2.970 8.293 9.927 0.099 7.044 4.159 7.413 Property Estimate SE 88.465*** 29.777 -14.017 -0.006 2.242 1.410 10.856 8.937 0.112 5.641 3.797 6.253 Drug Estimate SE 55.989*** 18.116 -3.437 -0.004 -2.791 0.092 3.000 6.936 0.069 4.924 2.905 5.180 Public Order Estimate SE 52.286*** 15.673 -4.565 -0.025 0.268 -1.174 4.748 5.517 0.061 3.754 2.343 4.041

Intercept Percentage Black Percentage Black2 Black/White unemployment ratio Black/White teenage mother ratio Percentage Black youth (10-17) 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: Full fixed effects models of Black juvenile placements explained by arrest by offense (n=190) Violent Estimate 85.669 -15.976 -0.162 9.185 -1.378 17.376* 0.192 0.238* -49.941 Property Estimate 57.702 -17.526* -0.024 2.557 1.700 15.751* 0.787 -0.020 Drug Estimate -11.935 -6.947 -0.029 -2.704 0.681 7.296 0.898 0.196* -19.569 Public Order Estimate SE 36.230 50.565 -5.340 -0.020 0.408 -0.944 5.259 0.384 0.105 -10.969 -7.477 4.546 6.010 0.068 3.778 2.393 4.576 0.641 0.077 16.451 6.289 19.083

Intercept Percentage Black Percentage Black2 Black/White unemployment ratio Black/White teenage mother ratio Percentage Black youth (10-17) Percentage Hispanic Police per 100,000 Index crime (logged) Urbanism

SE 74.664 10.270 0.101 6.870 4.037 8.003 0.970 0.133

SE 98.326 9.834 0.132 5.682 3.847 7.715 1.204 0.120 31.536 10.282 37.279

SE 54.988 7.342 0.074 4.851 2.898 5.699 0.711 0.095 18.043

24.551 -19.283

-40.262*** 10.397 -15.207 28.131 48.399

-22.761*** 7.500 29.208 20.724

Non-South 13.552 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 political, economic, and social domination of Whites (Stolzenberg, D¶Alessio, & Eitle, 2004). Thus, DMC could result from the dominant group using state resources (justice system) to neutralize the minority threat.

89

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 significant relationship between the level of OJJDP compliance achieved (identify, assess, intervene, and monitor) and change in the ratio of Black-White disproportionate placement controlling for arrests. This finding is in opposition to conclusions of a study

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 significance. These findings were not surprising given that recent literature has offered mixed support for the threat hypotheses in relation to the use of formal social control. When

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 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 than just a passing whim. Our nation¶s history of subjugation and oppression of the Black race bears that out. A theory, or set of theories, which critically examines the relationship

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 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 cannot rectify racial inequalities and social injustices; it will do well if it does not exacerbate them´ (Morris, 1994, p. 258). Until preventative factors such as availability of capable socialization agents, early intervention strategies, educational success, and

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 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. Arrest data does not identify ethnicity as a separate demographic category. Percent Hispanic population was added as a control to reduce the potential impact on the analyses from states with large Hispanic populations. Nevertheless, combining Hispanic

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 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 effects of the predictor variables allowed the unobserved variables to have association with the observed variable where random effects assumes the unobserved variables are not correlated with the observed variables (Allison, 2009). Although the use of 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 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. In light of findings presented here, and in combination with other current research, testing of threat based theories should focus on micro-level decision-making at many decision points occurring throughout the system, including arrest. Quantitative and

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 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). The statistics at the beginning of this decade indicated a bleak future for young, Black males. More than ever a call to address DMC within the juvenile justice system was vital. As a nation, we may have taken notice and started to reverse the trend. When

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 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

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 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 - informal adjustment

1995

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Anchorage

7/2002 6/2003

W, B, NA, A

intake - petition filed formal court processing dismissal

533

no significant racial finding

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Anchorage

7/2002 6/2003

W, B, NA, A

115

no significant racial finding

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Anchorage

7/2002 6/2003

W, B, NA, A

formal court processing adjudicated

587

being Native American or Asian significantly increases chances of adjudication - only significant racial finding

119

120

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Fairbanks

7/2002 6/2003

W, B, NA, A

intake dismissal

111

no significant racial finding

Leiber, M.J., Johnson, J., & Fox, K. (2006). Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Fairbanks AK Fairbanks

7/2002 6/2003 7/2002 6/2003

W, B, NA, A W, B, NA, A

intake - informal adjustment intake - petition filed

421

no significant racial finding

188

no significant racial finding

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Fairbanks

7/2002 6/2003

W, B, NA, A

formal court proceeding dismissal formal court proceeding adjudicated

66

no significant racial finding

Leiber, M.J., Johnson, J., & Fox, K. (2006).

AK Fairbanks AZ Maricopa Co.

7/2002 6/2003

W, B, NA, A

104

no significant racial finding

State of Arizona Commission on Minorities. (2002).

2000

W, H, B, NA

referral

36002

based on RRI - compared to White Hispanic 1.16x, African American 2.2x

121

State of Arizona Commission on Minorities. (2002).

AZ - Pima Co.

2000

W, H, B, NA

referral

9513

based on RRI - compared to White Hispanic 1.14x, African American 2.2x, Native American x1.6 based on RRI - compared to White Hispanic 1.4x, African American 3.4x, Native American 2.5x based on RRI - compared to White Hispanic 1.2x, African American 2.8x, Native American 1.4x

State of Arizona Commission on Minorities. (2002).

AZ ± MaricopaCo.

2000

W, H, B, NA

detention

10158

State of Arizona Commission on Minorities. (2002).

AZ - Pima Co.

2000

W, H, B, NA

detention

3113

State of Arizona Commission on Minorities. (2002).

AZ Maricopa Co.

2000

W, H, B, NA

probation

5292

based on RRI - compared to White African American 2.1x, Native American 1.7x

State of Arizona Commission on Minorities. (2002).

AZ - Pima Co.

2000

W, H, B, NA

probation

1944

based on RRI - compared to White Hispanic 1.2x, African American 2.5x

State of Arizona Commission on Minorities. (2002).

AZ Maricopa Co.

2000

W, H, B, NA

commitment

417

based on RRI - compared to White Hispanic 1.6x, African American 3.6x, Native American 2.2x

122

State of Arizona Commission on Minorities. (2002).

AZ - Pima Co. AZ Maricopa Co.

2000

W, H, B, NA

commitment

330

based on RRI - compared to White Hispanic 1.6x, African American 3.5x, Native American 1.5x based on RRI - compared to White Hispanic 2x, African American 3.7x, Native American 2.5x based on RRI - compared to White Hispanic 2.2x, African American 5.3x, Native American 1.7 based on RRI - compared to White Hispanic 1.18x, Black 2.5x, Asian .4x based on RRI - compared to White Black 2.6x, Asian .4x based on RRI - compared to White Hispanic .65x, Black .35x, Asian .77x based on RRI - compared to White Hispanic 1.3x, Black 1.7x, Asian 1.2x based on RRI - compared to White Hispanic 1.15x, Black 1.2x based on RRI - compared to White Hispanic .91x, Black .95x, Asian .89x based on RRI - compared to White Hispanic 3.21x, Black 3.71x, Asian 3.95x

State of Arizona Commission on Minorities. (2002).

2000

W, H, B, NA

adult transfer

401

State of Arizona Commission on Minorities. (2002). California Department of Justice. (2004). California Department of Justice. (2004). California Department of Justice. (2004). California Department of Justice. (2004). California Department of Justice. (2004). California Department of Justice. (2004).

AZ - Pima Co.

2000

W, H, B, NA

adult transfer

134

CA

2004

W, H, B, A

arrests referrals to probation

206201

CA

2004

W, H, B, A

169681

CA

2004

W, H, B, A

diversion

7877

CA

2004

W, H, B, A

detention

39087

CA

2004

W, H, B, A

petition filed wardship placement

86283

CA

2004

W, H, B, A

55129

California Department of Justice. (2004).

CA

2004

W, H, B, A

transfer to adult court

1590

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; Whites underrepresented for all crimes African Americans overrepresented for all crimes; Hispanic for weapons crimes; Whites underrepresented for all crimes African American males overrepresented for all crimes; Hispanic males for weapons crimes; Whites underrepresented for all crimes African American males overrepresented for all crimes except weapons; Hispanic males for all crimes; White males violent, property, and weapons crimes African American males underrepresented for all crimes; Hispanic males overrepresented for all crimes except violent; White males overrepresented for all crimes

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003).

IL - Cook County

1999

W, B, H

court referral

11228

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003).

IL - Cook County

1996-1999

W, B, H

petition filed

56051

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003).

IL - Cook County

1996-1999

W, B, H

delinquent adjudication

26118

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003).

IL - Cook County

1996-1999

W, B, H

probation

20016

124

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003).

IL - Cook County

1996-1999

W, B, H

secure detention

2387

African American males overrepresented for all crimes except weapons; Hispanic males for property and weapons crimes; White males drug crimes

Stevenson, P.J., Lavery, T., Burke, K.S., Alderden, M., Martin, C., Myrent, M., et. al. (2003). The W. Haywood Burns Institute. (2004). The W. Haywood Burns Institute. (2004). The W. Haywood Burns Institute. (2004). The W. Haywood Burns Institute. (2004). The W. Haywood Burns Institute. (2004). The W. Haywood Burns Institute. (2004).

IL - Cook County

1996-1999 7/2001 6/2002 7/2001 6/2002 7/2001 6/2002 7/2001 6/2002 7/2001 6/2002 7/2001 6/2002

W, B, H

confinement

3541

African American males overrepresented for all crimes; Hispanic males for violent and weapons crimes; White males underrepresented for all crimes based on RRI - compared to White Black 2.49, Hispanic .77 based on RRI - compared to White Black 2.49, Hispanic .77

KY

W, B, H

arrests refer to juv. Court

15081

KY

W, B, H

8364

KY

W, B, H

diversion

8188

no difference found based on RRI - compared to White Black 1.68, Hispanic 2.29

KY

W, B, H

secure detention

10266

KY

W, B, H

petition filed

39314

no difference found based on RRI - compared to White Black .9, Hispanic 1.2 based on RRI - compared to White balck 3x, Asian .5x, American Indian .42x

KY

W, B, H

probation

1376

Kenny, M., & Mishina, T. (2005).

ME

2004

W, B, A, NA

arrest

8446

125

Missouri Office of the State Courts Administrator. (2004).

MO - 17 judicial circuits MO - 17 judicial circuits MO - 17 judicial circuits

8/200310/2003

W, B

referrals detention v no detention

4753

no significant difference found

Missouri Office of the State Courts Administrator. (2004).

8/2005 10/2005

W, B

case processing decision

3621

African Americans processed more formally than Whites

Missouri Office of the State Courts Administrator. (2004). Montana Board of Crime Control. (n.d.). Montana Board of Crime Control. (n.d.). Montana Board of Crime Control. (n.d.). Montana Board of Crime Control. (n.d.). Montana Board of Crime Control. (n.d.).

8/2007 10/2007

W, B

commitment

655

no significant difference found based on RRI - compared to White Hispanic 1.21x, Native American 2.43x

MT

2004

W, H, NA

arrest

11153

MT

2004

W, H, NA

referral

11450

no significant difference found Hispanic and Native American underrepresented based on RRI - compared to White Hispanic 1.38x, Native American 1.34x based on RRI - compared to White Hispanic 1.84x, Native American 1.23x based on RRI - compared to White Hispanic not significant, Native American underrepresented

MT

2004

W, H, NA

diversion

7587

MT

2004

W, H, NA

secure detention

2120

MT

2004

W, H, NA

petition filed

1988

Montana Board of Crime Control. (n.d.).

MT

2004

W, H, NA

delinquent adjudication

472

126

Montana Board of Crime Control. (n.d.).

MT

2004

W, H, NA

probation

327

not significant based on RRI - compared to White Hispanic not significant, Native American 2.39x

Montana Board of Crime Control. (n.d.). Montana Board of Crime Control. (n.d.). South Dakota Department of Corrections and Council of Juvenile Services. (2006). South Dakota Department of Corrections and Council of Juvenile Services. (2006). South Dakota Department of Corrections and Council of Juvenile Services. (2006). South Dakota Department of Corrections and Council of Juvenile Services. (2006). South Dakota Department of Corrections and Council of Juvenile Services. (2006). South Dakota Department of Corrections and Council of Juvenile Services. (2006).

MT

2004

W, H, NA

secure confinement

85

MT

2004

W, H, NA

adult transfer

45

not significant

SD

2004

W, B, NA

arrest

7828

based on RRI - compared to White American Indian 2.39x, Black 2.32x

SD

2004

W, B, NA

detention

2684

based on RRI - compared to White American Indian 1.39x, Black 1.55x based on RRI - Whites were 1.21x higher than American Indians and 1.36x higher than Blacks based on RRI -American Indian not significant, White 1.18x higher than Black based on RRI - American Indian 1.21x higher than White, White 1.32x higher than Black

SD

2004

W, B, NA

petition filed

6226

SD

2004

W, B, H, NA

delinquent adjudication

5337

SD

2004

W, B, H, NA

probation

3291

SD TX - 3 counties

2004 1/1999 12/2000

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).

W, B, H

arrest/referral

316

Black 2x more likely than pop #

127

Rodney, H.E., & Tachia, H.R. (2004).

TX - 3 counties TX - 3 counties TX - 3 counties UT - Odgen, Provo, Salt Lake UT - Odgen, Provo, Salt Lake UT - Odgen, Provo, Salt Lake UT - Odgen, Provo, Salt Lake UT - Odgen, Provo, Salt Lake Burlington, VT Burlington, VT

1/1999 12/2000 1/1999 12/2000 1/1999 12/2000

W, B, H

preadj detention

232

race not correlated when compared to arrests compared with arrests, Whites more likely to go to hearing

Rodney, H.E., & Tachia, H.R. (2004).

W, B, H

adj hearing

93

Rodney, H.E., & Tachia, H.R. (2004). VanVleet, R.K., Vakalahi, H.F., Holley, L., Brown, S., & Carter, C. (2000). VanVleet, R.K., Vakalahi, H.F., Holley, L., Brown, S., & Carter, C. (2000). VanVleet, R.K., Vakalahi, H.F., Holley, L., Brown, S., & Carter, C. (2000). VanVleet, R.K., Vakalahi, H.F., Holley, L., Brown, S., & Carter, C. (2000). VanVleet, R.K., Vakalahi, H.F., Holley, L., Brown, S., & Carter, C. (2000).

W, B, H

disposition

185

numbers similar to arrest compared to Caucasians - Hispanics 9x, African American 41x, Asian 10x, American Indian 9x compared to Caucasians - Hispanics 9x, African American 32x, Asian 10x, American Indian 12x compared to Caucasians - Hispanics 3x, African American 4x, Asian 1x, American Indian 3x compared to Caucasians - Hispanics 3x, African American 3x, Asian negative, American Indian 2x compared to Caucasians - Hispanics 4x, African American 3x, Asian 1x, American Indian 3x based on RRI - compared to White Black .67n.s, Asian .66n.s. based on RRI - compared to White Black .91n.s

1997

W, B, H, NA, A

arrest/referral person offense

707

1997

W, B, H, NA, A

arrest/referral property offense

2342

1997

W, B, H, NA, A

detention hearing

nr

1997

W, B, H, NA, A

probation decision

nr

1997 10/2004 9/2005 10/2004 9/2005

W, B, H, NA, A

placement in dyc

nr

Bellas, M.L. (2007).

W, B, A

arrest

139

Bellas, M.L. (2007).

W, B, A

arrest

152

128

Caucasian = W African American = B Hispanic = H Native American = NA Asian - A

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