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FORECASTING MODELS FOR MANAGING A CHANGING INMATE POPULATION: IMPLICATIONS FOR PUBLIC POLICY

NCJ Number
144236
Journal
Criminal Justice Review Volume: 18 Issue: 1 Dated: (Spring 1993) Pages: 1- 11
Author(s)
C A Heard
Date Published
1993
Length
11 pages
Annotation
Forecasting models may be useful in projecting changes in the inmate population and in assessing changes between age groups in the inmate population over time; such models may become increasingly necessary as the imprisonment rate continues to escalate.
Abstract
Prison overcrowding is a serious concern of Federal, State, and local authorities. The number of offenders under the jurisdiction of Federal and State authorities increased from 196,429 in 1970 to 329,821 in 1980 and to 771,243 in 1990. Although researchers have applied various forecasting models to project changes in the inmate population, little is known about the extent of offender movement by age group over time. Five different prison population projection methodologies are identified: extrapolation time series, ARIMA models, multivariate regression models, disaggregated flow models, and simulation models. A difference equation model is applied to make inmate growth and movement projections for the years 1989 through 2017, based on three scenarios of prison admission growth rates (5, 7.5, and 10 percent). This model indicates that the aging prison population does not represent a major threat to the future management and operation of the correctional system and that increased attention will continue to focus on the younger population and on the implementation of such alternatives as privatization, house arrest, and military-style boot camps as cost-effective strategies to meet the needs of young offenders and also reduce prison overcrowding. 17 references and 3 tables