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Mentoring for students (non-Big Brothers Big Sisters): community-based (taxpayer costs only)

Public Health & Prevention: Community-based
Benefit-cost estimates updated December 2017.  Literature review updated June 2014.
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In community-based mentoring programs, volunteer adults are paired with at-risk middle and high school students to meet weekly at locations of their choosing for relationship building and guidance. Community-based organizations provide the adult mentors with training and oversight. Mentors are expected to build relationships with mentees with the aim of improving a variety of outcomes including crime rates, academic achievement, and substance abuse. This analysis includes evaluation findings for (in no particular order) the Washington State Mentors program, Across Ages, Sponsor-a-Scholar, Career Beginnings, the Buddy System, and other locally developed programs.
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 $4,680 Benefits minus costs $11,954
Participants $7,361 Benefit to cost ratio $5.05
Others $3,865 Chance the program will produce
Indirect ($1,002) benefits greater than the costs 64 %
Total benefits $14,904
Net program cost ($2,950)
Benefits minus cost $11,954
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 $487 $0 $1,224 $241 $1,952
Labor market earnings associated with high school graduation $3,726 $8,206 $3,776 $0 $15,708
Property loss associated with alcohol abuse or dependence $0 $0 $1 $0 $1
Health care associated with educational attainment $870 ($239) ($954) $433 $109
Costs of higher education ($403) ($606) ($182) ($201) ($1,391)
Adjustment for deadweight cost of program $0 $0 $0 ($1,475) ($1,475)
Totals $4,680 $7,361 $3,865 ($1,002) $14,904
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $2,280 2016 Present value of net program costs (in 2016 dollars) ($2,950)
Comparison costs $0 2016 Cost range (+ or -) 123 %
The per-participant cost estimate is based on the average costs for two out of the five programs included in the analysis. Due to the broad variation in program intensity represented in this analysis, we estimate program costs for a low-intensity program and for a high-intensity program and assume that the average represents an approximation of typical program costs. We estimated the cost for Baltimore Youth Bureaus, a group-mentoring program lasting one year, based on Hanlon et al. (2002). We computed the cost for Sponsor-a-Scholar, a one-on-one mentoring program lasting three years, based on program costs presented Johnson (1999). We report the average cost of these two programs as the per-participant cost, and vary the program costs by 123% in our benefit-cost model. Cost estimates exclude the cost of volunteer time and donated space.
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’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.

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 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
Alcohol use before end of middle school 1 76 -0.037 0.224 16 -0.037 0.224 26 -0.119 0.596
Cannabis use before end of middle school 1 76 -0.081 0.224 16 -0.081 0.224 26 -0.260 0.246
Crime 1 235 -0.089 0.488 16 -0.089 0.488 26 -0.286 0.620
Grade point average^ 3 322 0.033 0.087 16 n/a n/a n/a 0.106 0.226
High school graduation 2 758 0.101 0.143 18 0.101 0.143 18 0.293 0.040
Illicit drug use before end of middle school 1 235 -0.064 0.488 16 -0.064 0.488 26 -0.205 0.675
Illicit drug use in high school^^ 1 43 -0.352 0.224 16 -0.352 0.224 26 -0.352 0.117
School attendance^ 1 76 0.058 0.224 16 n/a n/a n/a 0.186 0.406
Smoking in high school^^ 1 43 -0.212 0.223 16 -0.212 0.223 26 -0.212 0.343

Citations Used in the Meta-Analysis

Aseltine, R.H., Dupre, M., & Lamlein, P. (2000). Mentoring as a drug prevention strategy: An evaluation of Across Ages. Adolescent and Family Health 1(1), 11-20.

Buman, B., & Cain, R. (1991). The impact of short term, work oriented mentoring on the employability of low-income youth. Minneapolis, MN: Minneapolis Employment and Training Program.

Cave, G., & Quint, J. (1990). Career Beginnings impact evaluation: Findings from a program for disadvantaged high school students. New York, NY: Manpower Demonstration Research Corporation.

Hanlon, T.E., Bateman, R.W., Simon, B.D., O'Grady, K.E., & Carswell, S.B. (2002). An early community-based intervention for the prevention of substance abuse and other delinquent behavior. Journal of Youth and Adolescence, 31(6), 459-471.

Harmon, M.A. (1996). Reducing drug use among pregnant and parenting teens: A program evaluation and theoretical examination. Dissertation Abstracts International, 56(08), 3319A.

Johnson, A. (1999). Sponsor-a-Scholar: Long-term impacts of a youth mentoring program on student performance. Princeton, NJ: Mathematica Policy Research.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov