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Juvenile Justice
Benefit-cost methods last updated December 2018.  Literature review updated June 2014.
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Youth in the juvenile justice system are assigned to a mentor, typically a non-professional volunteer, who meets with the youth approximately once a week. Mentors help youth build social capital by engaging in pro-social relationships. Mentors assist youth in gaining access to community resources necessary for reentry (e.g., Alcoholics Anonymous), attend social functions together (e.g., movies or sporting events), and help youth engage in positive decision-making and problem-solving. Mentors typically maintain a minimum one-year commitment to the youth/program.

Studies examining the effectiveness of mentoring for youth who were not in the juvenile justice system were excluded from this review.
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2017). 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 $3,918 Benefits minus costs $8,858
Participants $649 Benefit to cost ratio $3.64
Others $7,543 Chance the program will produce
Indirect $105 benefits greater than the costs 81 %
Total benefits $12,215
Net program cost ($3,356)
Benefits minus cost $8,858
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
Benefits from changes to:1 Benefits to:
Taxpayers Participants Others2 Indirect3 Total
Crime $3,464 $0 $7,379 $1,753 $12,597
Labor market earnings associated with high school graduation $368 $811 $377 $0 $1,557
Health care associated with educational attainment $163 ($45) ($178) $82 $22
Costs of higher education ($78) ($118) ($35) ($39) ($270)
Adjustment for deadweight cost of program $0 $0 $0 ($1,691) ($1,691)
Totals $3,918 $649 $7,543 $105 $12,215
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $2,748 2005 Present value of net program costs (in 2017 dollars) ($3,356)
Comparison costs $0 2005 Cost range (+ or -) 10 %
Per-participant cost estimates are based on the Big Brothers/Big Sisters program as described in Herrera, C., Grossman, J.B., Kauh, T.J., Feldman, A.F., & McMaken, J. (2007). Making a difference in schools: The Big Brothers Big Sisters school-based mentoring impact study. Philadelphia, PA: Public/Private Ventures. The cost of volunteer time is based on the Office of Financial Management State Data Book average adult salary for 2012 multiplied by 1.44 to account for benefits. Cost estimates exclude donated space. In the evaluated community-based programs, mentors meet with mentees, on average, once per week over the course of one year.
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.
Estimated Cumulative Net Benefits Over Time (Non-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 non-discounted dollars to simplify the “break-even” point from a budgeting perspective. 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.

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.

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
Crime 16 7 539 -0.203 0.146 18 -0.203 0.146 28 -0.327 0.044

Citations Used in the Meta-Analysis

Blakely, C.H., Menon, R., & Jones, D.J. (1995). Project BELONG: Final report. College Station, TX: Texas A&M University, Public Policy Research Institute.

Bouffard, J., & Bergseth, K. (2008). The impact of reentry services on juvenile offenders' recidivism. Youth Violence and Juvenile Justice, 6(3), 295-318.

Drake, E., & Barnoski, R. (2006). Recidivism findings for the Juvenile Rehabilitation Administration's mentoring program: Final report. Olympia, WA. Washington State Institute for Public Policy.

Jarjoura, G.P. (2009). Mentoring as a critical tool for effective juvenile reentry. Written testimony submitted to the Congressional briefing on supporting youth reentry from out-of-home placement to the community.

Lane, J., Turner, S., Fain, T., & Sehgal, A. (2007). The effects of an experimental intensive juvenile probation program on self-reported delinquency and drug use. Journal of Experimental Criminology, 3(3), 201-219.

Moore, R.H. (1987). Effectiveness of citizen volunteers functioning as counselors for high-risk young male offenders. Psychological Reports, 61, 823-830.

O'Donnell, C.R., Lydgate, T. & Fo, W.S.O. (1979). The Buddy System: Review and follow-up. Child Behavior Therapy, 1, 161-169.