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Washington State Institute for Public Policy
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College advising provided by counselors (for high school students)

Higher Education
Benefit-cost methods last updated December 2018.  Literature review updated January 2018.
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Students in the 10th-12th grade meet in person with advisors at the high school for college-focused advising sessions. A typical session with an advisor addresses the application and enrollment process, career path readiness, and post-secondary education plans. The number of advising sessions varies by program, some sessions are held only during the time when students are applying to college, other programs require visits in multiple grades. The length and frequency of interaction with an advisor ranges from one-hour meetings once or twice a year to one-hour meetings every two to three weeks during the application season.
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 $5,215 Benefits minus costs $21,511
Participants $12,057 Benefit to cost ratio $28.13
Others $6,156 Chance the program will produce
Indirect ($1,124) benefits greater than the costs 98 %
Total benefits $22,304
Net program cost ($793)
Benefits minus cost $21,511
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 higher education $6,660 $14,666 $6,969 $0 $28,295
Costs of higher education ($1,446) ($2,609) ($813) ($726) ($5,593)
Adjustment for deadweight cost of program $0 $0 $0 ($398) ($398)
Totals $5,215 $12,057 $6,156 ($1,124) $22,304
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $705 2009 Present value of net program costs (in 2017 dollars) ($793)
Comparison costs $0 2009 Cost range (+ or -) 40 %
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 Washington State compensation costs (including benefits) of counselor and staff time as reported by the Office of the Superintendent of Public Instruction. Additional program cost estimates provided by Castleman & Goodman (2015) and Barr & Castleman (2017).
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 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 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 3 9207 -0.019 0.060 18 -0.019 0.060 18 -0.019 0.744
Enroll in 4-year college 17 4 11952 0.194 0.063 18 0.194 0.063 18 0.194 0.002
Persistence into 2nd year^^ 17 1 1687 0.349 0.070 19 n/a n/a n/a 0.349 0.001

Citations Used in the Meta-Analysis

Barr, A., & Castleman, B. (2017). The Bottom Line on college counseling. Working paper.

Belasco, A.S. (2013). Creating college opportunity: School counselors and their influence on postsecondary enrollment. Research in Higher Education, 54(7), 781-804.

Castleman, B., & Goodman, J. (2015). Intensive college counseling and the enrollment and persistence of low income students. HKS working paper.