Abstract
Sentencing juvenile offenders to life in prison is the most severe criminal penalty available, yet we know little about the factors that produce jurisdictional differences in the use of such sanctions. Political explanations emphasize conservative values and the strength of more conservative political parties. Threat accounts suggest that this sentence will be more likely in jurisdictions with larger minority populations. After controlling for many explanations using count models, the results show that larger numbers of juvenile life sentences are handed out in more politically conservative states with a stronger Republican Party. Findings also show that racial politics is a factor in juvenile life sentences. Those jurisdictions that have the most blacks and have judicial elections sentence the most juveniles to life terms. By highlighting the explanatory power of public ideologies, these findings support political explanations for the harshest criminal punishment directed towards juveniles.
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Notes
While Graham v Florida (2005), seriously limited the imposition of life without parole sentences for juvenile offenders, it did not rule out sentences that ensured that they would serve terms beyond their natural life. In fact, Nellis and King (2009) report that 75% of all juveniles sentenced to life terms did have the possibility of parole. Thus, Graham only affected 25% of the juveniles serving life terms. This study assesses the variation in these severe sanctions for minors across U.S. states.
The Court’s rationale for declaring both the juvenile death penalty (Roper v Simmons, 2005) and juvenile LWOP (Graham v Florida, 2005) unconstitutional was based on a few similar positions. First, they noted that juveniles cannot be subject to such severe sanctions because, among other reasons, they are not as culpable for their crimes due to their limited decision-making capacities relative to adults. They also stated that these punishments were inconsistent with the law regulating the behavior of youth and particular rights granted to them. Thus, they believed that states themselves established a bright line at the age of eighteen at which point minors were capable of making the kinds of decisions associated with adulthood. The Court argued that subjecting juveniles to this most severe sanction with the assumption that they are equally culpable for their crimes is inconsistent with the other areas of the law that deny certain rights to juveniles based solely on their age (i.e. the right to vote, to be on juries, to get a tattoo, to smoke cigarettes etc.) . The basis of such laws led the Court to conclude that state legislatures believed that young people under eighteen lack the maturity and decision-making skills to participate in certain activities. According to the Court, if the state felt juveniles were not responsible for their actions in other capacities then they should not be held entirely culpable for their criminal actions. Thus, they should not be subject to a penalty reserved for those most culpable of heinous acts. Ultimately, however, the Court based their decision on the idea that the punishment was so grossly disproportionate as to be “cruel and unusual” based on “the evolving standard of decency that mark the progress of a maturing society”.
Judicial discretion may be influenced by public demands at critical decision points. Adult court judges are not involved in the transfer decision as that is either legislated (state legislators determine specific crimes that require direct juvenile transfer to adult courts) or determined by juvenile court official. Adult court judges in some jurisdictions do have the discretion to return minors to the juvenile court if they feel the adult court is inappropriate. The primary point of discretion for adult court judges lies with their ability to determine sentencing outcomes (including LWOP). These sentencing outcomes may be swayed by the whims of the public. We believe the public may be more likely to impact judicial behavior when judges are in a jurisdiction without strict sentencing guidelines that limit their discretion. We test this assumption in some models.
We end the analysis in 2001 due to the unavailability of Census derived independent variables. While data for the dependent variable is available through 2006, detailed state-level Census data from the 2010 Census is not available, making accurate interpolation of a number of the independent variables impossible.
The following 28 states were included in the analysis: Alabama, Arkansas, California, Colorado, Delaware, Florida, Illinois, Louisiana, Maryland, Michigan, Minnesota, Mississippi, Missouri, Nebraska, Nevada, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, Tennessee, Virginia, Washington, West Virginia and Wisconsin.
While only having 28 states in the sample represents a risk for potential bias, particularly when the dependent variable is based on a voluntary survey, we believe generalization to the entire U.S. is acceptable given the results from a comparison of important state-level indicators. This comparison shows that there is little difference between those states in the sample and the all fifty states. For example, the percentage of the population that is black in the sample states is 12.5 and for all 50 states it is 10.5. The Hispanic population in the sample is exactly the same as it is for the 50 states (7.7%). Overall juvenile crime rates are nearly the same in the sample states. Juvenile arrest rates for violent crime are 27 per 100,000 for those states in the sample and 23 in all states. Citizen ideology scores were also similar with the sample having a score of 49 and the U.S. having an average score of 47. Regional distribution was also good with the sample including states in all regions of the country. 37% of the sample states were in the South (compared to 32% of all U.S. states). 21% of the sample states were in the West (versus 26% of all states), 32% were in the Midwest (24% of all states) and 10% were in the Northeast (18% of all states). Given the similarities between the sample states and the entire U.S., it is plausible to assume that the results from the analysis should be generalizable to all U.S.
It is important to note that we are not interested in predicting whether or not a juvenile offender who receives a life term in prison actually serves the entire sentence. Rather, what is at issue here is whether trial judges are willing to impose such severe sanctions and how this varies across the country.
The best specification for the percent Black and percent Hispanic variables was determined through the analysis of bivariate plots between each variable and the dependent variable as well as testing different specifications. These tests showed that there is only a linear relationship between% Hispanic and the number of juveniles sentenced to life terms. Tests also showed that percent Black is non-linear such that it takes the form of an inverted U. To allow (mathematically) for the relationship to take this form in the regression model, both the main effect (percent black) and percent black squared are required in the model. The shape of the relationship is largely understood by the sign of each of these variables. In this case, a positive sign in the main effect (% Black) suggests that the relationship starts positive. The negative sign on the% Black squared term suggests that at some point, the relationship turns negative. Thus, having both the main effect and the squared term in the model gives shape to the best specified relationship between% black and juvenile life sentences as an inverted U shape curve.
The Wald chi-square statistic is reported in the Table 3 as it is the only goodness-of-fit statistic available when using the xtnbreg command with a correction for autocorrelation. The Wald statistic is a modified chi-square that is uniquely suited to panel data using a quasi-likelihood estimation technique (GEE) as we are here. Additionally, we use a measure of model fit known as quasi-likelihood information criterion (QIC). Similar to the more conventional Akaike’s information criterion (AIC) that has been a well-established goodness-of-fit statistic for likelihood based model selection (Hilbe, 2011; Long & Freese, 2006; Hardin & Hilbe, 2003), the QIC assesses model fit such that the model with the smaller QIC is considered the better-fitting model (Hardin & Hilbe, 2003, pg. 140). The QIC was developed by Pan (2001) and represents only a minor extension of the AIC, using the quasi-likelihood from the GEE models instead of the log-likelihood established in other estimation techniques. We see from the QIC statistic that the fit improves substantially across models.
Tests of the specific micro processes of this racial political account must be addressed in future research. Here we rely on macro theoretical accounts which suggest that the strength of conservative ideology in a state will generate more juvenile life sentences. Given the support for threat accounts from this study, though, scholars examining these micro processes should consider other individual level studies of juvenile justice decision making. Scholarship has suggested that justice officials maintain racial stereotypes (Tittle & Curran, 1988; Bridges & Steen, 1998; Sampson & Laub, 1993) and Tittle and Curran (1988) have gone further to suggest that criminal justice officials see youth culture itself as “threatening” to mainstream cultural values. If CJ officials hold such views, they may be more likely to use social control mechanisms to control youth. Given our results here, though, macro variation in youth punishment may be a function of particular judges capacity to excessive discretion (the presence of sentencing legislation) and the extent to which they are accountable to public pressure (judicial elections).
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We would like to thank David Jacobs, J. Craig Jenkins for their helpful comments on early drafts of the manuscript.
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Carmichael, J.T., Burgos, G. Sentencing Juvenile Offenders to Life in Prison: The Political Sociology of Juvenile Punishment. Am J Crim Just 37, 602–629 (2012). https://doi.org/10.1007/s12103-011-9135-1
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DOI: https://doi.org/10.1007/s12103-011-9135-1