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A Multilevel Test of Minority Threat Effects on Sentencing

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Abstract

Prior studies of criminal sanctioning have focused almost exclusively on individual-level predictors of sentencing outcomes. However, in recent years, scholars have begun to include social context in their research. Building off of this work—and heeding calls for testing the racial and ethnic minority threat perspective within a multilevel framework and for separating prison and jail sentences as distinct outcomes—this paper examines different dimensions of minority threat and explores whether they exert differential effects on prison versus jail sentences. The findings provide support for the racial threat perspective, and less support for the ethnic threat perspective. They also underscore the importance of testing for non-linear threat effects and for separating jail and prison sentences as distinct outcomes. We discuss the findings and their implications for theory, research, and policy.

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Notes

  1. We acknowledge that earlier-in-the-process decisions (such as arrest and conviction) may provide a more important context for differential and discretionary justice. However, we believe that it is important and useful to investigate the minority threat perspective in felony sentencing for at least three reasons. First, legal scholars have argued that judges have considerable discretion in assigning the type and severity of criminal sanctions even in the most structured sentencing systems (e.g., Tonry 1988, 1996). Further, empirical evidence has accumulated to suggest that judicial discretion exists and varies across social contexts (e.g., Chiricos and Crawford 1995; Spohn 2000). Second, the minority threat perspective has been applied to explain the association between minority presence and levels of social control. Since sanctioning is considered to be an important crime control undertaking, sentencing decisions provide an important platform from which to test the threat perspective. Third, a number of scholars have examined the effect of the percent of blacks—as an indicator of racial threat—on sentencing decisions. Although the results are mixed, some researchers have found a significant effect of racial threat (e.g., Britt 2000; Myers and Talarico 1987; Weidner et al. 2005).

  2. To be sure, a number of studies have investigated the non-linear relationship between percent black and a range of social control measures (e.g., Kane 2003, 2006; Eitle et al. 2002; Stolzenberg et al. 2004; Stults and Baumer 2007). To our knowledge, however, only Fearn (2005) evaluated the possibly non-linear effects of percent black on sentencing severity.

  3. We thank one of the anonymous reviewers for drawing our attention to this possibility.

  4. Per Steffensmeier and Demuth (2006, 249): “Defendants who are on release pending another case, on probation, on parole, or in custody when arrested have active criminal justice statuses.”

  5. We also ran models with the 15 individual offense dummies: murder, rape, robbery, assault, other violent offense, burglary, larceny, motor vehicle theft, forgery, fraud, other property offense, drug sales, weapons, driving-related offense, and other public order offense, holding other drug offense as the reference category. The findings for the variables of interest (i.e., contextual-level racial and ethnic threat variables) were almost identical.

  6. Four counties in the state of New York did not provide county jail information in the 1999 National Jail Census. For these four counties, we used the jail capacity value for New York City.

  7. The UCR crime index includes seven offenses: homicide, forcible rape, robbery, aggravated assault, burglary, larceny-theft, and motor vehicle theft.

  8. Shaffer (1999, 7) has argued that “unless rates of missing information are unusually high, there tends to be little or no practical benefit to using more than five to ten imputations.” We erred on the side of caution and used ten.

  9. Defendants listed as “other” accounted for 2.4% of the cases and included American Indians and Asians. Because we focus on black and Hispanic threat, and the defendants who were in other race category were few and heterogeneous, we removed these cases from the analysis.

  10. We thus excluded convicted misdemeanants. The reasoning stems from the argument that misdemeanants and felons are sentenced through different sentencing procedures. For example, most state sentencing guidelines regulate only felony crimes (Frase 2005; Tonry 1988).

  11. Given the ordered nature of the dependent variable—the categories are increasingly more punitive, ranging from non-custodial sanctions to prison—an alternative model would be ordinal logistic regression (see Holleran and Spohn 2004). Ordinal models assume the parameters are invariant across the response categories (Long 1997, 141), referred to as the proportional odds assumption. We estimated an ordinal regression model using SAS’s PROC LOGISTIC which provides a test for the proportional odds assumption (HLM 6.0 does not provide this test). The ordinal logistic regression model, however, violated the proportional odds assumption (p < .01). As a result, we analyzed the incarceration decision using multinomial logistic regression models.

  12. Ideally, we could account for potential dependence among counties nested within the same state by running three-level models. However, given that there are only 60 counties nested in 23 states and that 12 of these states only have one county, sufficient degrees of freedom do not exist to compute county-level random effects. For this reason, we proceeded with two-level models.

  13. The variance inflation factors for all the county-level variables were all below 4. In addition, the results of condition indices indicated acceptable levels of collinearity (Hair et al. 1998, 220). The multicollinearity test for all the offender-level variables did not reveal any problems.

  14. Because not all indicted felons were convicted, concerns about potential selection bias arise. Scholars have recommended the inclusion of adjustments for such bias using the Heckman model. However, use of this model is limited to ordinary least square models (Bushway et al. 2007; Griffin and Wooldredge 2006). We acknowledge this important limitation which, unfortunately, characterizes most research on sentencing (see Johnson, 2006, 275).

  15. We did not report odds ratios because odds ratio for the squared term of contextual-level racial and ethnic threat variables is not intuitive; instead, we present figures to facilitate discussion.

  16. We computed the predicted probabilities for each sentence type using the formula provided by Holleran and Spohn (2004, 219–220).

  17. We used Greenberg et al. (1985, 696) method to determine the inflection point, which is -b/2a, where “a” represents the coefficient for the squared term and “b” represents the coefficient for the linear term.

  18. Although we controlled for jail capacity, the measure was not statistically significant in any of the models. Our jail capacity measure, similar to what is used in sentencing studies in general, did not capture the actual capacity of jails to mete out sanctions. For example, it did not take into account the fact that jail bed space may be used for at least two distinct purposes: to sanction individuals to jail terms or to hold them prior to sentencing or transfer to prison.

  19. In this study, we followed the common practice of dichotomizing race into blacks versus whites, and ethnicity as Hispanic versus non-Hispanic. Zatz and Rodriguez (2006, 46) have warned that criminal justice agents may categorize race and ethnicity in highly imperfect ways and “make decisions based on presumed attributes of the racial/ethnic group to which they assume the victim and/or offender belongs.” For that reason, future research may benefit from analyses of within-race and within-ethnicity effects using data that permit such investigations. Rice et al. (2005, 48) have found, for example, that “within ethnicity, racial self-identification plays a galvanizing role in shaping perceptions toward racial profiling,” which suggests that within- and across-race and ethnicity analysis constitute a fruitful line of inquiry.

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Acknowledgments

Data for this study were kindly provided by the Inter-university Consortium for Political and Social Research. The authors thank Shawn Bushway, Stephen Demuth, Carter Hay, Brian Johnson, John Kramer, Xufeng Niu, Mike Reisig, Darrell Steffensmeier, Brian Stults, Gary Sweeten, and Jeffrey Ulmer for their helpful comments and suggestions. We also thank the anonymous reviewers and the Editors for their assistance and insights.

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Authors and Affiliations

  1. School of Criminology and Criminal Justice, Arizona State University, 411 N Central Ave, Suite 600, MC: 4420, Phoenix, AZ, 85004-0685, USA

    Xia Wang

  2. College of Criminology and Criminal Justice, Florida State University, 634 West Call Street, Tallahassee, FL, 32306-1127, USA

    Daniel P. Mears

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Correspondence to Xia Wang.

Appendices

Appendix A

See Table 4.

Table 4 Bivariate correlations for contextual (county-level) measures
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Appendix B

See Table 5.

Table 5 Hierarchical multinomial logistic regression of individual-level variables and contextual controls on the decision to incarcerate
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Wang, X., Mears, D.P. A Multilevel Test of Minority Threat Effects on Sentencing. J Quant Criminol 26, 191–215 (2010). https://doi.org/10.1007/s10940-009-9076-8

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