Going Off the Grid: Assessing Sentencing Departures for Individuals Convicted of Sexual Offenses

Abstract

This research examines factors associated with departures from sentencing guidelines for a sample of individuals convicted of sexual offenses in Pennsylvania between 2004 and 2015 ( N = 26,093). We contribute to the literature on the sentencing of these cases by considering whether the impact of individual and contextual factors is conditioned by the type of sexual offense being sentenced (e.g., assaultive, child pornography, failure to comply with registration). Bayesian multilevel multinomial logit models suggested a number of legal, extralegal, and contextual correlates of departure likelihood, and indicated that the effects of offense severity, prior record, a previous sex offense conviction, multiple concurrent convictions, and court size vary across different subsets of sexual convictions.