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Electronic monitoring (probation)

Adult Criminal Justice
Benefit-cost methods last updated December 2024.  Literature review updated December 2014.
Electronic monitoring involves the use of either radio frequency or Global Positioning System (GPS) units to monitor the location of an individual. Electronic monitoring is used to enforce requirements that an individual remain at home except for approved activities such as work, school, or treatment. It may be used in lieu of, or in addition to, confinement and depends on the individual’s sentence.

This meta-analysis includes studies on individuals who were on probation with electronic monitoring. They were compared to similar individuals who received intensive supervision, parole, continuation of sentence, or home confinement without electronic monitoring.
 
ALL
BENEFIT-COST
META-ANALYSIS
CITATIONS
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 (2023).  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 $2,884 Benefits minus costs $11,433
Participants $0 Benefit to cost ratio n/a
Others $5,019 Chance the program will produce
Indirect $2,138 benefits greater than the costs 95%
Total benefits $10,041
Net program cost $1,392
Benefits minus cost $11,433

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the program impacts measured in the research literature (for example, impacts on 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 on how we estimate effect sizes.

The effect size may be adjusted from the unadjusted effect size estimated in the meta-analysis. Historically, WSIPP adjusted effect sizes to some programs based on the methodological characteristics of the study. For programs reviewed in 2024 or later, we do not make additional adjustments, and we use the unadjusted effect size whenever we run a benefit-cost analysis.

Research shows the magnitude of effects may change 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. 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 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
Involvement in the criminal justice system (e.g., arrests, charges, convictions, incarceration) measured through administrative records (e.g. court records, arrests) or self-report.
 30 10 7036 -0.164 0.125 31 -0.164 0.125 39 -0.351 0.130
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 $2,884 $0 $5,019 $1,442 $9,345
Program cost Adjustment for deadweight cost of program $0 $0 $0 $696 $696
Totals $2,884 $0 $5,019 $2,138 $10,041
Click here to see populations selected
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $377 2009 Present value of net program costs (in 2023 dollars) $1,392
Comparison costs $1,405 2009 Cost range (+ or -) 10%
Electronic monitoring costs per day were provided by the Department of Corrections. The Washington State Institute for Public Policy calculated the total cost per participant assuming 30 days on electronic monitoring in lieu of 30 days in confinement (average daily cost for jail and prison).
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.

Bonta, J., Wallace-Capretta, S., & Rooney, J. (2000). A quasi-experimental evaluation of an intensive rehabilitation supervision program. Criminal Justice and Behavior, 27(3), 312-329.

Bonta, J., Wallace-Capretta, S., & Rooney, J. (2000). Can electronic monitoring make a difference? An evaluation of three Canadian programs. Crime and Delinquency, 46(1), 61-75.

Di Tella, R., & Schargrodsky, E. (2009). Criminal recidivism after prison and electronic monitoring (Working Paper No. 15602). Cambridge: National Bureau of Economic Research.

Jolin, A., & Stipak, B. (1992). Drug treatment and electronically monitored home confinement: An evaluation of a community-based sentencing option. Crime & Delinquency, 38(2), 158-170.

Jones, M., & Ross, D.L. (1997). Electronic house arrest and boot camp in North Carolina: Comparing recidivism. Criminal Justice Policy Review, 8 (4), 383-404.

Padgett, K.G., Bales, W.D., & Blomberg, T.G. (2006). Under surveillance: An empirical test of the effectiveness and consequences of electronic monitoring. Criminology & Public Policy, 5(1), 61-91.

Petersilia, J., & Turner, S. (1990). Intensive supervision for high-risk probationers: Findings from three California experiments. Santa Monica, CA: RAND.

Sugg, D., Moore, L., & Howard, P. (2001). Electronic monitoring and offending behaviour - reconviction results for the second year of trials of curfew orders (Findings 141). London: Home Office; Research, Development and Statistics Directorate.