Do You Know Who Your Probationers Are? Using Simulation Modeling To Estimate the Composition of California's Felony Probation Population, 1980-2000

• NCJ Number: 217980
• Journal: Justice Quarterly Volume: 24 Issue: 1 Dated: March 2007 Pages: 28-47
• Author(s): Kathleen Auerhahn
• Date Published: March 2007
• Length: 20 pages
• Publication Format: Article
• Publication Type: Report (Study/Research)
• Language: English
• Country: United States of America

Annotation

This study examined the changing dynamics of felony probation populations in California over two decades (1980-2000), using dynamic systems simulation modeling.

Abstract

The findings show that the probation population in California has aged over the last two decades, mirroring trends in other criminal justice populations. There was substantial growth in offenders ages 25-44 between 1980 and 2000. In contrast, probationers 18-25 years old declined from 53 percent to 38 percent. The racial and ethnic composition of the probation population remained stable between 1980 and 2000. Approximately 30 percent of probationers in 2000 had two or more prior convictions; only about half were first-time offenders. Even more striking was the gradual increase in probationers with convictions for violent offenses, with just over 20 percent of probationers in 2000 having a prior conviction for a violent offense. Although drug offenders composed the greatest proportion of the probation population over the two decades, the relative positions of property and violent offenders were reversed. The fact that California probation populations are composed of an increasing proportion of repeat and violent offenders indicates the importance of the empirical evaluation of the effectiveness of probation in preventing reoffending. Otherwise, public support for probation as an alternative to incarceration for repeat and violent offenders will be undermined. This article provides a detailed description of the study's application of "dynamic systems simulation modeling" in producing these findings. Structurally, such models are similar to those used for weather forecasting and climatology. In contrast to conventional statistical techniques, which generally require the assumption that observations are independent, dynamic systems simulation modeling is based on the integration of system flows over time. 3 figures, 4 tables, and 55 references