Washington State Institute for Public Policy
Mentoring for students: school-based (with volunteer costs)
Public Health & Prevention: School-based
Benefit-cost estimates updated May 2017.  Literature review updated June 2014.
In school-based mentoring programs, mentors and students meet weekly at school for one-to-one relationship building and guidance. Mentors are adult volunteers, school staff, or high school students. Community-based organizations coordinate with school staff and provide mentors with training and oversight. The programs included in this analysis are (in no particular order) the national Student Mentoring Program, Big Brothers Big Sisters, Project CHANCE, SMILE, and other, locally developed programs.
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 $8,708 Benefits minus costs $26,208
Participants $14,425 Benefit to cost ratio $15.19
Others $5,212 Chance the program will produce
Indirect ($289) benefits greater than the costs 73 %
Total benefits $28,056
Net program cost ($1,847)
Benefits minus cost $26,208
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 $132 $0 $322 $65 $519
Labor market earnings associated with high school graduation $8,650 $19,047 $8,736 $0 $36,434
Labor market earnings associated with test scores ($1,213) ($2,671) ($1,179) $0 ($5,063)
Health care associated with educational attainment $2,061 ($564) ($2,251) $1,031 $276
Costs of higher education ($922) ($1,388) ($415) ($460) ($3,185)
Adjustment for deadweight cost of program $0 $0 $0 ($926) ($926)
Totals $8,708 $14,425 $5,212 ($289) $28,056
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $1,539 2005 Present value of net program costs (in 2016 dollars) ($1,847)
Comparison costs $0 2005 Cost range (+ or -) 10 %
The effects of this program represent one year of mentoring. Per-participant cost estimates are based on the Big Brothers/Big Sisters program as described in Herrera, C., Grossman, J.B., Kauh, T.J., Feldman, A.F., & McMaken, J. (2007). Making a difference in schools: The Big Brothers Big Sisters school-based mentoring impact study. Philadelphia, PA: Public/Private Ventures. The cost of volunteer time is based on the Office of Financial Management State Data Book average adult salary for 2012, multiplied by 1.44 to account for benefits. In the evaluated school-based programs, mentors meet with mentees, on average, once per week during the school year. Approximately half of the mentors in the evaluated programs were high school students and were not included in the volunteer cost estimates. Cost estimates exclude 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.

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 2 1694 -0.013 0.049 14 -0.013 0.049 14 -0.013 0.787
Grade point average^ 5 2009 0.024 0.032 14 0.024 0.032 14 0.026 0.409
High school graduation 1 66 0.262 0.265 18 0.262 0.265 18 0.689 0.029
Illicit drug use before end of middle school 1 531 0.109 0.145 14 0.109 0.145 14 0.109 0.321
Office discipline referrals^ 2 547 -0.256 0.123 14 -0.256 0.123 14 -0.509 0.137
School attendance^ 4 1771 0.074 0.038 14 0.074 0.038 14 0.121 0.063
Test scores 3 3489 -0.034 0.029 14 -0.029 0.032 17 -0.034 0.243
Citations Used in the Meta-Analysis

Bernstein, L., Rappaport, C.D., Olsho, L., Hunt, D., Levin, M. (with Dyous, C., . . . Rhodes, W.) (2009). Impact evaluation of the U.S. Department of Education's Student Mentoring Program: Final report. Washington, DC : National Center for Education Evaluation and Regional Assistance.

Converse, N., & Lignugaris-Kraft, B. (2008). Evaluation of a school-based mentoring program for at-risk middle school youth. Remedial and Special Education, 30(1), 33-46.

DeSocio, J., VanCura, M., Nelson, L.A., Hewitt, G., Kitzman, H., & Cole, R. (2007). Engaging truant adolescents: Results from a multifaceted intervention pilot. Preventing School Failure, 51(3), 3-9.

Flaherty, B.P. (1985). An experiment in mentoring for high school students assigned to basic courses. Dissertation Abstracts International, 46(02), 352A.

Herrera, C., Grossman, J.B., Kauh, T.J., & McMaken, J. (2011). Mentoring in schools: An impact study of Big Brothers Big Sisters school-based mentoring. Child Development, 82(1), 346-361.

Karcher, M.J. (2008). The study of mentoring in the learning environment (SMILE): A randomized evaluation of the effectiveness of school-based mentoring. Prevention Science, 9(2), 99-113.

For more information on the methods
used please see our Technical Documentation.