skip to main content
Washington State Institute for Public Policy
Back Button

For individuals classified as lower risk, decrease prison average daily population by 250, by lowering length of stay by 3 months

Adult Criminal Justice: Policy
Benefit-cost methods last updated December 2023.  Literature review updated October 2013.
Open PDF
This analysis describes a hypothetical scenario in which a statewide decrease of 250 prison beds (roughly the equivalent of a state prison wing) would be achieved by reducing the length of stay by three months for lower-risk offenders.
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 ($994) Benefits minus costs $1,865
Participants $0 Benefit to cost ratio n/a
Others ($7,038) Chance the program will produce
Indirect $2,968 benefits greater than the costs 68%
Total benefits ($5,064)
Net program cost $6,929
Benefits minus cost $1,865

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
28 n/a 0 0.018 0.021 30 0.018 0.021 40 0.000 0.001
28 n/a 0 -0.351 0.095 30 -0.246 0.029 30 0.000 0.001
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 ($22) $0 ($56) ($11) ($89)
Crime elasticity: property Crime ($971) $0 ($6,982) ($486) ($8,439)
Program cost Adjustment for deadweight cost of program $0 $0 $0 $3,465 $3,465
Totals ($994) $0 ($7,038) $2,968 ($5,064)
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs ($5,640) 2012 Present value of net program costs (in 2022 dollars) $6,929
Comparison costs $0 2012 Cost range (+ or -) 10%
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

Barnoski, R.P., & Washington State Institute for Public Policy. (2004). Sentences for adult felons in Washington, options to address prison overcrowding: Pt. 2 (recidivism analyses). Olympia, WA: Washington State Institute for Public Policy.

Blumstein, A., & Wallman, J. (2006). The crime drop in America. New York: Cambridge University Press.

Bushway, S.D., & Owens, E.G. (2013). Framing punishment: Incarceration, recommended sentences, and recidivism. Journal of Law and Economics, 56(2), 301-331.

Drago, F., Galbiati, R., & Vertova, P. (2009). The deterrent effects of prison: Evidence from a natural experiment. The Journal of Political Economy, 117(2), 257-280.

Drake, E.K., Barnoski, R., & Aos, S. (2009). Increased earned release from prison: Impacts of a 2003 law on recidivism and crime costs, revised (Doc. No. 09-04-1201). Olympia: Washington State Institute for Public Policy.

Gottfredson, D.M., & National Institute of Justice (U.S.). (1999). Effects of judges' sentencing decisions on criminal careers. Washington, DC: U.S. Dept. of Justice, Office of Justice Programs, National Institute of Justice.

Johnson, R., & Raphael, S. (2012). How much crime reduction does the marginal prisoner buy?. Journal of Law and Economics, 55(2), 275-310.

Kuziemko, I. (2013). How should inmates be released from prison? An assessment of parole versus fixed-sentence regimes. The Quarterly Journal of Economics, 128(1), 371-424.

Levitt, S.D. (1996). The effect of prison population size on crime rates: Evidence from prison overcrowding litigation. The Quarterly Journal of Economics, 111(2), 319-351.

Meade, B., Steiner, B., Makarios, M., & Travis, L. (2012). Estimating a dose-response relationship between time served in prison and recidivism. Journal of Research in Crime and Delinquency, DOI: 10.1177/0022427812458928.

Oliver, B.E. (2011). Recidivism: A multi-level explanation. St. Louis, Mo: University of Missouri, St. Louis.

Snodgrass, G.M., Blokland, A.A.J., Haviland, A., Nieuwbeerta, P., & Nagin, D.S. (2011). Does the time cause the crime? An examination of the relationship between time served and reoffending in the Netherlands. Criminology, 49(4), 1149-1194.

Spelman, W. (2005). Jobs or jails? The crime drop in Texas. Journal of Policy Analysis and Management, 24(1), 133-165.

Spelman, W. (2013). Prisons and crime, backwards in high heels. Journal of Quantitative Criminology, DOI: 10.1007/s10940-013-9193-2.