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Washington State Institute for Public Policy
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College advising provided by a peer mentor (for high school students)

Higher Education
Benefit-cost methods last updated December 2018.  Literature review updated December 2016.
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Students in the 11th and 12th grade receive postsecondary education planning support from peer mentors. The peer mentors in the evaluations included in this meta-analysis are undergraduate or graduate students. The peer mentor assists the student with the college application process and gives advice and encouragement on the student’s plans to go attend college. The student meets with their peer mentor in person at the high school, but interactions also take place via text message, email, or over the phone. The length and frequency of interaction with a peer mentor ranges from meeting during the month when students are applying to college (three hours per week for one month) to one hour per month for the entire school year.
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2017). 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 ($635) Benefits minus costs ($325)
Participants $757 Benefit to cost ratio $0.59
Others $1,325 Chance the program will produce
Indirect ($975) benefits greater than the costs 48 %
Total benefits $472
Net program cost ($797)
Benefits minus cost ($325)
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 ($26) $0 ($63) ($13) ($102)
Labor market earnings associated with high school graduation ($2,663) ($5,863) ($2,704) $0 ($11,229)
Health care associated with educational attainment ($889) $243 $970 ($443) ($118)
Labor market earnings associated with higher education $3,184 $7,011 $3,331 $0 $13,526
Costs of higher education ($241) ($634) ($210) ($121) ($1,206)
Adjustment for deadweight cost of program $0 $0 $0 ($398) ($398)
Totals ($635) $757 $1,325 ($975) $472
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $708 2009 Present value of net program costs (in 2017 dollars) ($797)
Comparison costs $0 2009 Cost range (+ or -) 10 %
The per-participant cost of treatment is the weighted average estimate for studies included in the analysis. We calculate the total cost per study using peer mentoring time (estimated using the federal minimum wage) and stipends from Bos et al. (2012).
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
Enroll in 2-year college 17 2 1552 -0.031 0.044 18 -0.031 0.044 18 -0.031 0.474
Enroll in 4-year college 17 2 1552 0.105 0.043 18 0.105 0.043 18 0.105 0.015
Grade point average^ 17 1 1038 -0.022 0.041 18 n/a n/a n/a -0.022 0.593
High school graduation 17 1 1038 -0.088 0.054 18 -0.088 0.054 18 -0.088 0.106

Citations Used in the Meta-Analysis

Bos, J.M., Berman, J., Kane, T.J., & Tseng, F.M. (2012). The impacts of SOURCE: A program to support college enrollment through near-peer, low-cost student advising. Working paper.

Carrell, S.E., & Sacerdote, B. (2012). Late interventions matter too: The case of college coaching New Hampshire. Cambridge, MA: National Bureau of Economic Research.