How Universal Is Disproportionate Minority Contact? An Examination of Racial and Ethnic Disparities in Juvenile Justice Processing Across Four States

Abstract

The extent to which disproportionate minority contact (DMC) in the juvenile justice system varies across states remains largely unknown. Using a multijurisdictional sample of 146 counties across four states, the present study utilizes multilevel modeling with cross-level interactions to explore whether there is variation in the influence of race and ethnicity among states across four major juvenile justice processing decisions—preadjudication detention, petition of delinquency, adjudication of delinquency, and judicial disposition. The results highlight the existence of some variation in DMC across the four states, with the variation most pronounced at detention and least pronounced at disposition. The possibility of state variation in DMC underscores the need for state-specific analysis of DMC, what contributes to it, and what can be done to reduce it.

Keywords:

• disproportionate minority contact
• racial and ethnic disparities
• juvenile justice
• state differences
• multiple stages of processing

Acknowledgments

The authors gratefully acknowledge the anonymous reviewers and the Editor for thoughtful guidance on how to improve the study.

Notes

1 A few studies have also used regression analysis to examine regional variation in disparities. Pasko (2002) assessed racial disparities in sentence length for drug offenders across federal courts in the East, Midwest, West, and South, and found that race was positively associated with sentence length in the West only, while Hispanic ethnicity was positively associated with sentence length in the East only. Elsewhere, using correctional data for 38 states, Durante (2020) regressed Black-White and Hispanic-White imprisonment ratios on contextual predictors, including region. The author found that the South was negatively associated with Black-White disproportionality but not associated with Hispanic-White disproportionality. In the juvenile justice context, Davis and Sorenson (2013) examined Black-White placement ratios across 38 states over five time periods; they found no significant association between racial disproportionality and Southern region.

2 As Mears and colleagues (2016, pp. 85, 87) point out, “disproportionality” refers to “racial or ethnic differences that are greater than what would be expected given the group population sizes among those for whom a given outcome is possible,” while “disparity” is often taken to indicate “any disproportionality attributable to overt or covert, or intended or unintended, discrimination against minorities.” While aggregate analyses can illustrate disproportionalities, they are limited in identifying disparities, which requires controlling for possibly confounding case-level and contextual factors.

3 Scholars have suggested several possibilities for why DMC varies across stages of processing (see Engen et al., 2002; Peck & Jennings, 2016). For example, Bishop and colleagues (2010) suggest that each stage of processing will have distinct focal concerns, with some stages focused more on punishment and others more focused on needs assessment and treatment, and this may account for variation in racial and ethnic disparities.

4 Feld (1991, 2017) argues that in the wake of In re Gault (1967), the Supreme Court decision that granted juvenile defendants many due process rights, urban courts responded by “criminalizing” juvenile justice. According to this argument, while rural jurisdictions largely retained the traditional rehabilitative approach—the “pre-Gault” orientation—urban courts shifted to a greater emphasis on due process—the “post-Gault” orientation. Feld (2017) suggests that this more legalistic orientation is less constrained by the original court’s rehabilitative mission and is, by extension, more punitive. This legalism, however, may also contribute to less individualized discretion and hence fewer disparities. As a result, in urban jurisdictions there might be a more equitable but also more punitive court, while in rural jurisdictions there may be a less punitive but also less equitable one (see Zimring, 2014).

5 Others have examined variation in the severity of juvenile court outcomes across states for all defendants rather than variation in racial disparities (see, e.g., Mears, 2006).

6 Using 2000–2012 data from the National Corrections Reporting Program, Stringer and Holland (2016) employed hierarchical linear modeling to examine regional variation in sentence length for drug offenders. The authors computed cross-level interactions between race and region, finding that Black-White sentence length disparities were significantly lower in the South. To our knowledge, this is the only prior study to examine regional variation in racial disparities using multilevel modeling with cross-level interactions.

7 These data were originally collected by Administrative Office of the Courts, Alabama; Judicial Branch, Connecticut; South Carolina Department of Juvenile Justice, South Carolina; and Administrative Office of the Courts, Utah. These agencies and the National Center for Juvenile Justice bear no responsibility for the analyses or interpretations presented herein.

8 Our unit of analysis is referral, not individual youth. Some youths had multiple referrals disposed in 2010. There were approximately 83,651 unique youth among the final sample of referrals (N = 100,358). Since not all states reported case numbers, we identified unique youth based on county, date of birth, race, and sex.

9 The data do not indicate whether an adjudication of delinquency was the result of plea bargaining or a full hearing.

10 It is noteworthy that South Carolina exhibited the lowest rate of petition (41 percent) and the highest rates of adjudication (93 percent) and commitment (23 percent). This likely reflects a difference in institutionalized case processing where only the most serious cases are petitioned, most of which are then adjudicated delinquent (and many of which are committed).

11 In 2010, Black youth made up approximately 31 percent of the youth population (0–17) in Alabama, 12 percent in Connecticut, 34 percent in South Carolina, and 2 percent in Utah, while Hispanic youth made up approximately 6 percent of the youth population in Alabama, 20 percent in Connecticut, 8 percent in South Carolina, and 17 percent in Utah (Puzzanchera, Sladky, & Kang, 2019). This indicates that at referral, Black youth were over-represented by 45 percent in Alabama, 92 percent in Connecticut, 68 percent in South Carolina, and 50 percent in Utah. Hispanic youth were over-represented at referral by 15 percent in Connecticut, while they were actually under-represented in Alabama, South Carolina, and Utah.

12 The Deinstitutionalization of Status Offenders (DSO) requirement of the Juvenile Justice and Delinquency Prevention Act (JJDPA) provides that states that accept federal funding under the JJDPA must not place status offenders in secure detention or locked confinement. However, many states—including Alabama, South Carolina, and Utah—exercise the valid court order (VSO) exception to the DSO requirements. As a result, status offenders were still eligible for detention and placement in these states. Moreover, even though Connecticut moved to completely deinstitutionalize status offenders in 2007, exceptions could still be made for repeat offenders (see Coalition for Juvenile Justice, 2014). This was confirmed in our data, with status offenders detained and placed in all four states (ranging from 1 percent detained in Connecticut to 15 percent detained in South Carolina).

13 A count measure or description of the type of prior referrals would have been preferable; however, not all states provided such data. Additionally, data on prior petitions or adjudications was not available.

14 Correlations between variables were generally weak to moderate (< .5) and tests for multicollinearity (variance inflation factor [VIF]) indicated no serious concerns across all models (VIF < 5). (The exceptions were several high correlations between contextual control variables: percent non-White was strongly correlated with income inequality (.63), and crime rates (.59), and conservatism (-.75), while conservatism was strongly correlated with income inequality (-.51) and crime rates (-.57); the largest VIF values were 4.28 for percent non-Whiten and 3.27 for conservatism, while other values were < 2.5).

15 We also ran a three-level unconditional model with cases nested within counties (level-2) nested within states (level-3). Here, intraclass correlation is calculated for each level of the model (Johnson, 2010). The formula for level-3 intraclass correlation is ρ = (ψ³) / (ψ² + ψ³ + π²/3); the formula for level-2 intraclass correlation is ρ = (ψ² ) / (ψ² + ψ³ + π²/3). The results indicated significant variation in detention across states as well as counties, with a level-3 variance component of .12 (ψ³) and a level-2 variance component (ψ²) of .62. This indicates that before any predictors are added to the model, approximately 3 percent of the variation in detention outcomes is attributable to between-state differences, while 15 percent is due to county-level differences.

16 We also ran a three-level unconditional model. The results indicated significant variation in petition across states as well as counties, with ψ³ =.43 and ψ² = 1.22. This indicates that before any predictors are added to the model, approximately 9 percent of the variation in petition outcomes is attributable to between-state differences, while 25 percent is attributable to county-level differences.

17 We also ran a three-level unconditional model. The results indicated significant variation in adjudication across states as well as counties, with ψ³ = 1.72 and ψ² = 1.13. This indicates that before any predictors are added to the model, approximately 28 percent of the variation in adjudication outcomes is attributable to between-state differences, while 18 percent is due to county-level differences.

18 We also ran a three-level unconditional model. The results indicated significant variation in placement across states as well as counties, with ψ³ =.35 and ψ² = .70. This indicates that before any predictors are added to the model, approximately 8 percent of the variation in placement outcomes is attributable to between-state differences, while 16 percent is due to county-level differences.

19 Among our states, there are several notable differences in how juvenile justice systems are organized. For example, only South Carolina involved the prosecutor as the primary decision-maker at intake, only Alabama involved a largely decentralized juvenile justice system, and a state parole board determines release in South Carolina and Utah (compared to a court agency in Alabama and Connecticut).

20 Given the standard rule of 10 level-2 observations per level-2 variable (Johnson, 2010), this would require 10 states to test one state-level hypothesis—and an analysis of the full population of 51 juvenile justice systems would still be constrained to approximately five hypotheses.