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Mentoring: School-based by volunteers (including volunteer costs)

Public Health & Prevention: School-based
Benefit-cost methods last updated December 2017.  Literature review updated May 2018.
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In school-based mentoring programs, mentors and students meet in schools for one-on-one meetings to build interpersonal relationships, support prosocial behavior, and support academic achievement. Among studies in this analysis, mentors are typically college student volunteers; one study includes older peer mentors. Mentors typically receive some initial training and ongoing support throughout the course of the intervention. Participants were elementary and middle school students identified by teachers or school staff as being at risk. At-risk students include students transitioning from elementary to middle school, struggling to meet academic standards, exhibiting behavioral problems, and/or having a history of absences and/or discipline referrals. Participants in the included studies receive weekly mentoring for 2 to 6 months.
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 ($1,717) Benefits minus costs ($10,473)
Participants ($3,543) Benefit to cost ratio ($3.19)
Others ($1,437) Chance the program will produce
Indirect ($1,278) benefits greater than the costs 11 %
Total benefits ($7,976)
Net program cost ($2,498)
Benefits minus cost ($10,473)
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
Labor market earnings associated with test scores ($1,661) ($3,659) ($1,620) $0 ($6,940)
Health care associated with educational attainment ($145) $40 $159 ($72) ($19)
Costs of higher education $89 $75 $25 $44 $233
Adjustment for deadweight cost of program $0 $0 $0 ($1,250) ($1,250)
Totals ($1,717) ($3,543) ($1,437) ($1,278) ($7,976)
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $2,493 2016 Present value of net program costs (in 2016 dollars) ($2,498)
Comparison costs $0 2016 Cost range (+ or -) 70 %
The annual per-participant cost estimate is based, in part, on the mentoring program as described in Bernstein et al. (2009). Cost estimates also include a weighted average estimate of volunteer time as reported in the included studies. The value of volunteer time is based on the Washington State’s Office of Financial Management’s estimate of the average salary for 2016. We multiply this by 1.44 to account for benefits.
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 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
Grade point average^ 11 4 1335 -0.028 0.039 11 n/a n/a n/a -0.030 0.431
Office discipline referrals^ 11 3 172 -0.128 0.111 11 n/a n/a n/a -0.243 0.253
School attendance^ 11 3 1275 0.054 0.040 11 n/a n/a n/a -0.037 0.763
Test scores 11 1 1163 -0.057 0.050 11 -0.041 0.055 17 -0.057 0.253

Citations Used in the Meta-Analysis

Akos, P. (2000). Mentoring in the middle: The effectiveness of a school based peer mentoring program. (Dissertation). University of Virginia, VA.

Bernstein, L., Rappaport, C.D., Olsho, L., Hunt, D., & Levin, M. (2009). Impact evaluation of the US Department of Education's Student Mentoring Program. Final report. NCEE 2009-4047. National Center for Education Evaluation and Regional Assistance.

McQuillin, S., Smith, B., & Strait, G. (2011). Randomized evaluation of a single semester transitional mentoring program for first year middle school students: a cautionary result for brief, school-based mentoring programs. Journal of Community Psychology, 39(7), 844-859.

McQuillin, S., Strait, G., Smith, B., & Ingram, A. (2015). Brief instrumental school-based mentoring for first-and second-year middle school students: A randomized evaluation. Journal of Community Psychology, 43(7), 885-899.