Modeling the Distribution of Sentence Length Decisions Under a Guidelines System: An Application of Quantile Regression Models

How should sentencing disparity be assessed when decisions are constrained under a sentencing guidelines system? Much of the debate over the measurement of sentence disparity under a guidelines system has focused primarily on using specific values from within the sentencing grid (e.g., minimum recommended sentence) or on using interaction terms in regression models to capture the non-additive effects of offense severity and prior record on length of sentence. In this paper, I propose an alternative method for assessing sentencing disparity that uses quantile regression models. These models offer several advantages over traditional OLS analyses (and related linear models) of sentence length, by allowing for an examination of the effects of case and offender characteristics across the full distribution of sentence lengths for a given sample of offenders. The analysis of the distribution of sentence lengths with quantile regression models allows for an examination of questions such as: Do offender characteristics, such as race or offense severity, have the same effect on sentence length for the 10% of offenders who receive the shortest sentences as they do for the 10% of offenders who receive the longest sentences? I illustrate the application and interpretation of these models using 1998 sentencing data from Pennsylvania. Key findings show that the effects of case and offender characteristics are variable across the distribution of sentence lengths, meaning that traditional linear models assuming a constant effect fail to capture important differences in how case and offender characteristics affect punishment decisions. I discuss the implications of these findings for understanding sentencing disparitites, as well as other possible applications of quantile regression models in the study of crime and the criminal justice system.     

Conditional Race Disparities in Criminal Sentencing: A Test of the Liberation Hypothesis From a Non-Guidelines State

Objectives

To test the liberation hypothesis in a judicial context unconstrained by sentencing guidelines.

Methods

We examined cross-sectional sentencing data (n = 17,671) using a hurdle count model, which combines a binary (logistic regression) model to predict zero counts and a zero-truncated negative binomial model to predict positive counts. We also conducted a series of Monte Carlo simulations to demonstrate that the hurdle count model provides unbiased estimates of our sentencing data and outperforms alternative approaches.

Results

For the liberation hypothesis, results of the interaction terms for race x offense severity and race x criminal history varied by decision type. For the in/out decision, criminal history moderated the effects of race: among offenders with less extensive criminal histories blacks were more likely to be incarcerated; among offenders with higher criminal histories this race effect disappeared. The race x offense severity interaction was not significant for the in/out decision. For the sentence length decision, offense severity moderated the effects of race: among offenders convicted of less serious crimes blacks received longer sentences than whites; among offenders convicted of crimes falling in the most serious offense categories the race effect became non-significant for Felony D offenses and transitioned to a relative reduction for blacks for the most serious Felony A, B, and C categories. The race x criminal history interaction was not significant for the length decision.

Conclusions

There is some support for the liberation hypothesis in this test from a non-guidelines jurisdiction. The findings suggest, however, that the decision to incarcerate and the sentence length decision may employ different processes in which the interactions between race and seriousness measures vary.