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Washington State Institute for Public Policy
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Correctional industries in prison

Adult Criminal Justice
Benefit-cost methods last updated December 2023.  Literature review updated July 2016.
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Correctional industries programs provide jobs within the prison environment for incarcerated individuals. Individuals may work for a private sector employer, a non-profit, or in institutional industries within the prison. Industries commonly include metal fabrication, laundry, furniture assembly, and textile manufacturing. Typically, these jobs are obtained through an application process. While the focus of these programs is not vocational education or training, they are intended to help provide individuals with work experience and marketable job skills.

Length of attendance in the program varies and typically depends on the length of an individual’s sentence. Individuals in these studies typically participated in correctional industries programs for 6 to 12 months.
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2022). The chance the benefits exceed the costs are derived from a Monte Carlo risk analysis. The details on this, as well as the economic discount rates and other relevant parameters are described in our Technical Documentation.
Benefit-Cost Summary Statistics Per Participant
Benefits to:
Taxpayers $2,005 Benefits minus costs $6,684
Participants $0 Benefit to cost ratio $12.68
Others $4,535 Chance the program will produce
Indirect $716 benefits greater than the costs 100%
Total benefits $7,256
Net program cost ($572)
Benefits minus cost $6,684

^^WSIPP does not include this outcome when conducting benefit-cost analysis for this program.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic in order to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the types of program impacts that were measured in the research literature (for example, crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information.

Adjusted effect sizes are used to calculate the benefits from our benefit cost model. WSIPP may adjust effect sizes based on methodological characteristics of the study. For example, we may adjust effect sizes when a study has a weak research design or when the program developer is involved in the research. The magnitude of these adjustments varies depending on the topic area.

WSIPP may also adjust the second ES measurement. Research shows the magnitude of some effect sizes decrease over time. For those effect sizes, we estimate outcome-based adjustments which we apply between the first time ES is estimated and the second time ES is estimated. We also report the unadjusted effect size to show the effect sizes before any adjustments have been made. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured Treatment age No. of effect sizes Treatment N Adjusted effect sizes(ES) and standard errors(SE) used in the benefit - cost analysis Unadjusted effect size (random effects model)
First time ES is estimated Second time ES is estimated
ES SE Age ES SE Age ES p-value
34 12 11827 -0.057 0.018 36 -0.057 0.018 46 -0.084 0.001
34 1 424 0.079 0.086 34 n/a n/a n/a 0.199 0.022
1In addition to the outcomes measured in the meta-analysis table, WSIPP measures benefits and costs estimated from other outcomes associated with those reported in the evaluation literature. For example, empirical research demonstrates that high school graduation leads to reduced crime. These associated measures provide a more complete picture of the detailed costs and benefits of the program.

2“Others” includes benefits to people other than taxpayers and participants. Depending on the program, it could include reductions in crime victimization, the economic benefits from a more educated workforce, and the benefits from employer-paid health insurance.

3“Indirect benefits” includes estimates of the net changes in the value of a statistical life and net changes in the deadweight costs of taxation.
Detailed Monetary Benefit Estimates Per Participant
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Crime Criminal justice system $2,005 $0 $4,535 $1,002 $7,542
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($286) ($286)
Totals $2,005 $0 $4,535 $716 $7,256
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $485 2016 Present value of net program costs (in 2022 dollars) ($572)
Comparison costs $0 2016 Cost range (+ or -) 10%
Per-participant cost estimate provided by the Washington State Department of Corrections.
The figures shown are estimates of the costs to implement programs in Washington. The comparison group costs reflect either no treatment or treatment as usual, depending on how effect sizes were calculated in the meta-analysis. The cost range reported above reflects potential variation or uncertainty in the cost estimate; more detail can be found in our Technical Documentation.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative Discounted Dollars)
The graph above illustrates the estimated cumulative net benefits per-participant for the first fifty years beyond the initial investment in the program. We present these cash flows in discounted dollars. If the dollars are negative (bars below $0 line), the cumulative benefits do not outweigh the cost of the program up to that point in time. The program breaks even when the dollars reach $0. At this point, the total benefits to participants, taxpayers, and others, are equal to the cost of the program. If the dollars are above $0, the benefits of the program exceed the initial investment.

Citations Used in the Meta-Analysis

Berk, J.A. (2009). Essays on work and education: Behind bars and in the free world. Dissertation Abstracts International, 69(11), A.

Bohmert, M.N., & Duwe, G. (2012). Minnesota's Affordable Homes Program: Evaluating the effects of a prison work program on recidivism, employment and cost avoidance. Criminal Justice Policy Review, 23(3), 327-351.

Cox, R.J.A. (2009). An economic analysis of prison labor. Atlanta, Ga: Georgia State University.

Drake, E.K. (2003). Class I impacts: Work during incarceration and its effects on post-prison employment patterns and recidivism. Olympia, WA: Washington State Department of Corrections, Planning and Research Section.

Hopper, J.D. (2009). The effects of private prison labor program participation on inmate recidivism. Dissertation Abstracts International, 69(07), A.

Lutze, F.E., Drapela, L.A., & Schaefer, R.L. (2015). Washington State Correctional Industries: An outcome evaluation of its effect on institutional behavior, employment, and recidivism. Pullman: Washington State University.

Maguire, K.E., Flanagan, T.J., & Thornberry, T.P. (1988). Prison labor and recidivism. Journal of Quantitative Criminology, 4(1), 3-18.

Saylor, W.G., & Gaes, G.G. (1996). PREP: Training inmates through industrial work participation, and vocational and apprenticeship instruction. Washington, DC: United States Federal Bureau of Prisons.

Smith, C.J., Bechtel, J., Patrick, A., Smith, R.R., & Wilson-Gentry, L. (2006). Correctional Industries preparing inmates for re-entry: Recidivism & post-release employment. (Retrieved from United States Department of Justice database; Document No. 214608)

Soderstrom, I.R., Minor, K.I., Castellano, T.C., & Adams, J.L. (2001). An evaluation of a state's correctional industries program. Paper presented at the annual meeting of the Academy of Criminal Justice Sciences, Washington, DC.

Evans, M, & Koenig, S. (2011). Does participation in Washington’s Correctional Industries increase employment and reduce recidivism? Washington State Department of Corrections.

Richmond, K.M. (2009). Factories with fences: The effect of prison industries on female inmates. College Park, Md: University of Maryland.