Analyzing the Influence of Judges’ Caseloads Across Conviction Offenses: An Application of Linear Quantile Mixed Models

• In Event: Judicial Actions at Sentencing

Abstract

Extant research indicates accounting for nonlinearity in sentencing guidelines matrices and the nested structure of sentencing data provides more precise estimates of individual and contextual effects. This paper uses linear quantile mixed models, which allow for examination of individual and contextual covariates across the entire sentence length distribution, to assess whether the prevalence of violent, drug, and property offenses in judges’ caseloads influences the length of the sentence imposed for offenders convicted of these crimes. The goals of this paper are to determine whether variation attributable to the judge-level is constant across the sentence length distribution, and to explore direct and cross-level interaction effects to examine whether the kinds of cases judges handle influence punishment severity. Theoretical, methodological, and practical implications will be discussed.

Authors

• Michael Cassidy, Niagara University

• Jason Rydberg, University of Massachusetts, Lowell