Washington State Institute for Public Policy
Intensive supervision (probation)
Juvenile Justice
Benefit-cost estimates updated December 2016.  Literature review updated September 2015.
In this broad grouping of programs, intensive supervision emphasizes a higher degree of surveillance than traditional supervision in the community. This meta-analysis includes only studies of offenders on probation (not parole). The average number of monthly contacts of any kind for studies included in our meta-analysis was 37. Conditions of supervision vary across the studies, but some characteristics include urinalysis testing, increased face-to-face or collateral contacts, or required participation in treatment.

We used multiple regression to test for the possibility of an “interaction,” (a simultaneous effect of two variables) between monthly contacts and treatment. The interaction indicates that more contacts, coupled with treatment, result in a bigger reduction in crime. We only found this effect for parole populations. For probation populations, we found a statistically significant increase in recidivism when there was a combination of more contacts and more treatment.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2015). 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 ($954) Benefits minus costs ($10,240)
Participants ($277) Benefit to cost ratio ($1.35)
Others ($1,995) Chance the program will produce
Indirect ($2,657) benefits greater than the costs 0 %
Total benefits ($5,883)
Net program cost ($4,358)
Benefits minus cost ($10,240)
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 ($804) $0 ($1,898) ($404) ($3,106)
Labor market earnings associated with high school graduation ($138) ($304) ($140) ($65) ($646)
Health care associated with educational attainment ($33) $9 $36 ($16) ($4)
Costs of higher education $21 $18 $7 $10 $56
Adjustment for deadweight cost of program $0 $0 $0 ($2,182) ($2,182)
Totals ($954) ($277) ($1,995) ($2,657) ($5,883)
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $3,985 2008 Present value of net program costs (in 2015 dollars) ($4,358)
Comparison costs $0 2008 Cost range (+ or -) 10 %
We used WSIPP’s annual marginal cost estimate for juvenile supervision (as reported in Washington State Institute for Public Policy (July 2015). Benefit-cost technical documentation. Olympia, WA: Author) to compute a daily cost estimate. The daily cost estimate for probation was multiplied by 9.2, the weighted average months on supervision as reported by the studies included in the meta-analysis.
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 Primary or secondary participant 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 5601 0.035 0.028 18 0.035 0.028 28 0.034 0.230
Technical violations 3 732 0.435 0.319 18 0.435 0.319 28 0.435 0.173
Citations Used in the Meta-Analysis

Barnoski, R. (2003). Evaluation of Washington's 1996 Juvenile Court Program (Early Intervention Program) for high-risk, first-time offenders: Final report. Olympia: Washington State Institute for Public Policy.

Barton, W.H., & Butts, J.A. (1990). Viable options: intensive supervision programs for juvenile delinquents. Crime and Delinquency, 36(2), 238-256.

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

Fagan, J., & Reinarman, C. (1991). The social context of intensive supervision: Organizational and ecological influences on community treatment. In T. L. Armstrong (Ed.), Intensive interventions with high risk youth (pp. 341-394). New York: Willow Tree Press.

Gray, E., Taylor, E., Roberts, C., Merrington, S., Fernandez, R., Moore, ., Great Britain., . . . University of Oxford. (2005). Intensive supervision and surveillance programme: The final report. London: Youth Justice Board for England and Wales.

Hennigan, K., Kolnick, K., Siva Tian, T., Maxson, C., & Poplawski, J. (2010). Five year outcomes in a randomized trial of a community-based multi-agency intensive supervision juvenile probation program. Washington, DC: Office of Juvenile Justice and Delinquency Prevention US Department of Justice.

Land, K.C., McCall, P.L., & Parker, K.F. (1994). Logistic versus hazards regression analysis in evaluation research: An exposition and application to the North Carolina Court counselors’ intensive protective supervision project. Evaluation Review, 18(4), 411–37.

Lane, J. Turner, S., Fain, F., & Sehgal, A. (2005). Evaluating an experimental intensive juvenile probation program: Supervision and official outcomes. Crime and Delinquency, 51(1), 26-52.

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.

Lerman, P. (1975). Community treatment and social control. Chicago: University of Chicago Press.

National Council on Crime and Delinquency. (1987). The impact of juvenile court intervention. San Francisco: Author.

National Council on Crime and Delinquency, & United States of America. (2001). Evaluation of the RYSE Program: Alameda County Probation Department.

Robertson, A.A., Grimes, P.W, & Rogers, K.E. (2001). A short-run cost-benefit analysis of community-based interventions for juvenile offenders. Crime & Delinquency, 47(2), 265-285.

Rodriguez-Labarca, J., & O'Connell, J.P., (2004). Delaware's serious juvenile offender program: an evaluation of the first two years of operation, State of Delaware, Statisical Analysis Center, Doc Num: 100208-040204.

Sealock, M.D., Gottfredson, D.C., & Gallagher, C.A. (1997). Drug Treatment for juvenile offenders: Some good and bad news. Journal of Research in Crime and Delinquency, 34(2), 210-236.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov