Washington State Institute for Public Policy
Risk Need and Responsivity supervision (for individuals classified as high- and moderate-risk)
Adult Criminal Justice: Corrections
Benefit-cost estimates updated May 2017.  Literature review updated December 2016.
This meta-analysis represents a broad grouping of supervision programs which utilize “Risk Need Responsivity” (RNR) principles. Following these principles, corrections officers provide amounts of services in accordance with the individual’s risk for re-offense (risk principle), provide types of services based on the individual's unique assessed needs (need principle), and determine a treatment method that is appropriate for the individual based on individual abilities and motivation levels (responsivity principle). Supervision using RNR principles focuses on high to moderate risk individuals. Interventions are either cognitive behavioral or social learning techniques. This means that the supervising parole or probation officer uses motivational interviewing techniques (e.g., open ended questions, affirmations, elicitation of person reflection, etc.) as well as a behavioral or contingency management style of supervision (i.e., systems of incentives for reinforcement, disapproval, and an emphasis on self-management). Supervising officers may issue violations when individuals violate the conditions of supervision.
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 (2016). 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 $2,947 Benefits minus costs $8,220
Participants $0 Benefit to cost ratio $6.99
Others $5,855 Chance the program will produce
Indirect $790 benefits greater than the costs 98 %
Total benefits $9,592
Net program cost ($1,372)
Benefits minus cost $8,220
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 $2,947 $0 $5,855 $1,479 $10,280
Adjustment for deadweight cost of program $0 $0 $0 ($689) ($689)
Totals $2,947 $0 $5,855 $790 $9,592
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $5,710 2015 Present value of net program costs (in 2016 dollars) ($1,372)
Comparison costs $4,353 2015 Cost range (+ or -) 10 %
There are three components of this per participant cost estimate. First, the cost of supervision is based on WSIPP's analysis of community supervision delivered by the Washington State Department of Corrections (see Technical Documentation). It is reasonable to assume that the cost for RNR supervision is greater than the cost of traditional supervision since RNR supervision relies on specialized officer training with quality assurance, focuses on increased case management with the use of a validated risk assessment, and aims to serve higher risk, higher needs offenders; thus, we assume an additional 20 percent in supervision costs. Second, we include the cost of violation behavior. For this estimate, we rely on the costs reported in: Hamilton, Z., van Wormer, J., Kigerl, A., Campbell, C., & Posey. B. (2015). Evaluation of Washington State Department of Corrections Swift and Certain Policy Process, Outcome and Cost-Benefit Evaluation. Washington State University. Finally, we include the cost to participate in cognitive behavioral therapy with the assumption that most persons on supervision are required to engage in treatment. We assume both the treatment and comparison groups receive community supervision, but that supervision is 20% more costly for the treatment group. We assume that costs for violations are similar for each group. We assume 50% of the treatment group receives cognitive behavioral therapy.
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.

^^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.

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 14 8575 -0.109 0.042 34 -0.109 0.042 44 -0.180 0.001
Technical violations^^ 4 4760 -0.167 0.068 34 -0.167 0.068 44 -0.257 0.241
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).

Braithwaite, H., Turner, S., & Hess, J. (2013). California Parole Supervision and Reintegration Model (CPSRM) outcome evaluation. Center for Evidence-Based Corrections, UC Irvine. Irvine, CA.

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.

Lowenkamp, C.T., Holsinger, A., Robinson, C.R., & Alexander, M. (2014). Diminishing or durable treatment effects of STARR? A research note on 24-month re-arrest rates. Journal of Crime and Justice, 37(2), 275-283.

Pearson, D.A.S., McDougall, C., Kanaan, M., Bowles, R.A., & Torgerson, D.J. (2011). Reducing criminal recidivism: evaluation of Citizenship, an evidence-based probation supervision process. Journal of Experimental Criminology, 7(1), 73-102.

Pearson, D.A.S., McDougall, C., Kanaan, M., Torgerson, D.J., & Bowles, R.A. (2016). Evaluation of the citizenship evidence-based probation supervision program using a stepped wedge cluster randomized controlled trial. Crime & Delinquency.

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.
360.664.9800
institute@wsipp.wa.gov