Washington State Institute for Public Policy
Risk Need & Responsivity supervision (for high and moderate risk offenders)
Adult Criminal Justice
Benefit-cost estimates updated December 2016.  Literature review updated December 2013.
For this broad grouping of programs, supervision of adult offenders utilizing “Risk Need Responsivity” (RNR) principles were included in this analysis. The risk principle pertains to interventions commensurate with an offender’s risk for re-offense. The need principle targets offender’s criminogenic needs such as anti-social attitudes or substance abuse. The responsivity principle refers to interventions geared toward the offender’s individual abilities and motivation level. Supervision using RNR principles focuses on high to moderate risk offenders and interventions are either cognitive behavioral or social learning techniques. The corrections officer uses motivational interviewing to engage the offender and supervision is based on a behavioral or contingency management style to motivate, engage, and change the offender’s behavior.
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 $5,642 Benefits minus costs $12,121
Participants $0 Benefit to cost ratio $3.42
Others $11,163 Chance the program will produce
Indirect $320 benefits greater than the costs 100 %
Total benefits $17,125
Net program cost ($5,005)
Benefits minus cost $12,121
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 $5,642 $0 $11,162 $2,828 $19,632
Adjustment for deadweight cost of program $0 $0 $1 ($2,508) ($2,507)
Totals $5,642 $0 $11,163 $320 $17,125
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $4,853 2012 Present value of net program costs (in 2015 dollars) ($5,005)
Comparison costs $0 2012 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.
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 7 3165 -0.243 0.043 33 -0.243 0.043 43 -0.267 0.001
Citations Used in the Meta-Analysis

Bonta, J., Bourgon, G., Rugge, T., Scott, T., Yessine, A., Gutierrez, L., & Li, J. (2011). An experimental demonstration of training probation officers in evidence-based community supervision. Criminal Justice and Behavior, 38(11).

Jalbert, S.K., Rhodes, W., Kane, M., Clawson, E., Bogue, B., Flygare, C., Kling, R., & Guevara, M. (2011). A multi-site evaluation of reduced probation caseload sizes in an evidence-based practice setting (NCJ No. NCJ 234596). Washington, DC: National Institute of Justice.

Latessa, E., Smith, P., Schweitzer, M., & Labrecque, R. (2013). Evaluation of the effective practices in community supervision model (EPICS) in Ohio. Center for Criminal Justice Research: University of Cincinnati.

Robinson, C., VanBenschoten, S., Alexander, M., & Lowenkamp, C. (2011). A random (almost) study of staff training aimed at reducing re-arrest (STARR):Reducing recidivism through intentional design. Federal Probation, 75(2).

Taxman, F.S. (2008). No illusions: Offender and organizational change in Maryland's proactive community supervision efforts. Criminology and Public Policy, 7(2), 275-302.

Trotter, C. (1996). The impact of different supervision practices in community corrections: Cause for optimism. The Australian & New Zealand Journal of Criminology, 29(1), 1-19.

For more information on the methods
used please see our Technical Documentation.