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"Swift, certain, and fair" supervision

Adult Criminal Justice
Benefit-cost methods last updated December 2023.  Literature review updated January 2017.
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Swift, certain, and fair (SCF) is a strategy used by supervising officers to address violation behavior of persons being who are supervised in the community on probation or parole. Probationers or parolees are required to follow rules and conditions (e.g., abstaining from drugs or alcohol) in order to complete their sentence in the community successfully. When officers observe violations of these rules, the premise of SCF is for the officer or judge to 1) quickly address violations (swift), 2) address all violations (certain), and 3) follow specific sanctioning guidelines (fair). Sanctioning guidelines are dependent upon the type of violation and how many violations the probationer or parolee has received in the past. Sanctions for low-level violations are less severe than sanctions for high-level violations, which can result in no more than three days in jail. Swift, certain, and fair aims to structure the use of prison or jail as a sanction for violation behavior, with the goal of decreasing overall costs. The length of supervision can vary depending on the underlying sentence and the population being served.
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 $2,989 Benefits minus costs $10,934
Participants $122 Benefit to cost ratio n/a
Others $5,736 Chance the program will produce
Indirect $2,006 benefits greater than the costs 65%
Total benefits $10,854
Net program cost $80
Benefits minus cost $10,934

^WSIPP’s benefit-cost model does not monetize this outcome.

^^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
31 11 6790 -0.095 0.055 33 -0.095 0.055 43 -0.105 0.069
31 4 5473 -0.194 0.069 33 n/a n/a n/a -0.257 0.001
31 2 316 -0.445 0.156 31 n/a n/a n/a -0.445 0.004
31 3 777 -0.050 0.249 31 0.000 0.187 34 -0.050 0.842
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 $2,833 $0 $5,621 $1,416 $9,870
Illicit drug use disorder Labor market earnings associated with illicit drug abuse or dependence $35 $83 $0 $0 $119
Health care associated with illicit drug abuse or dependence $112 $17 $115 $56 $301
Mortality associated with illicit drugs $9 $22 $0 $494 $525
Program cost Adjustment for deadweight cost of program $0 $0 $0 $40 $40
Totals $2,989 $122 $5,736 $2,006 $10,854
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $4,286 2015 Present value of net program costs (in 2022 dollars) $80
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 (see Technical Documentation) of community supervision delivered by the Washington State Department of Corrections. Second, we include the cost of violation behavior. For this estimate, we rely on cost of SCF violations 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 treatment participants incur less violation costs. 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.
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

Baird, C., Wagner, D., Decomo, B., & Aleman, T. (1994). Evaluation of the effectiveness of supervision and community rehabilitation programs in Oregon. San Francisco: National Council on Crime and Delinquency.

Grommon, E., Cox, S.M., Davidson, W.S., & Bynum, T.S. (2012). Alternative models of instant drug testing: evidence from an experimental trial. Journal of Experimental Criminology, 9(2), 145-168.

Grommon, E., Davidson, I.I. W.S., & Bynum, T.S. (2013). A randomized trial of a multimodal community-based prisoner reentry program emphasizing substance abuse treatment. Journal of Offender Rehabilitation, 52(4), 287-309.

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.

Harrell, A., Mitchell, O., Hirst, A., Marlow, D., & Merrill, J. (2002). Breaking the cycle of drugs and crime: Findings from the Birmingham BTC demonstration. Criminology and Public Policy, 1(2), 189-216.

Harrell, A., Roman, J., Bhati, A., & Parthasarathy, B. (2003). The impact evaluation of the Maryland Break the Cycle initiative. Washington, DC: The Urban Institute.

Hawken, A., & Kleiman, M. (2011). Washington intensive supervision program: Evaluation report. Seattle: Seattle City Council.

Hawken, A., Kulick, J., Smith, K., Mei, J., Zhang, Y., Jarman, S., Yu, T., Carson, C., Vial, T. (2016). HOPE II: A Follow-up to Hawaiʻi’s HOPE Evaluation.

Lattimore, P. K., MacKenzie, D. L., Zajac, G., Dawes, D., Arsenault, E., & Tueller, S. (2016). Outcome findings from the HOPE demonstration field experiment: Is swift, certain, and fair an effective supervision strategy? Criminology & Public Policy, 15(4), 1103-1141.

Mitchell, O., & Harrell, A. (2006). Evaluation of the breaking the cycle demonstration project: Jacksonville, FL and Tacoma, WA. Journal of Drug Issues, 36(1), 97.

O'Connell, D.J., Brent, J.J., & Visher, C.A. (2016). Decide your time: A randomized trial of a drug testing and graduated sanctions program for probationers. Criminology & Public Policy, 15(4), 1073-1102.

Snell, C. (2007). Fort Bend County Community Supervision and Corrections Special Sanctions Court Program. Department of Criminal Justice. Fort Bend County, TX.