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Brief information interventions (for high school students)

Higher Education
Benefit-cost methods last updated December 2018.  Literature review updated November 2017.
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Brief information interventions are communications, such as physical mail and brief conversations, intended to help high school students and their families make accurate judgments about the costs and benefits of attending college. The interventions do not encourage students to attend specific colleges, are non-intrusive, and are designed to reduce confusion about college and financial aid options. The target population is low-income high school seniors, especially those who would be more likely to apply to college if they knew more about the availability of financial aid. Intervention intensity varies among programs—while some programs mail two to three generic letters, others mail packets of information customized to students’ locations and characteristics (e.g. family income).
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 ($31) Benefits minus costs ($256)
Participants ($81) Benefit to cost ratio ($2.49)
Others ($41) Chance the program will produce
Indirect ($30) benefits greater than the costs 42 %
Total benefits ($183)
Net program cost ($73)
Benefits minus cost ($256)
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 ($43) ($96) ($46) $0 ($185)
Costs of higher education $13 $15 $5 $6 $39
Adjustment for deadweight cost of program $0 $0 $0 ($36) ($36)
Totals ($31) ($81) ($41) ($30) ($183)
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $65 2009 Present value of net program costs (in 2017 dollars) ($73)
Comparison costs $0 2009 Cost range (+ or -) 50 %
Program implementation requires management of a computer database and a process scaled to print and mail documents to hundreds of thousands of students. The per-participant cost reflects a weighted average cost of the programs analyzed in the meta-analysis: Hoxby & Turner (2013), Bird et al. (2015), and Bettinger 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
Apply to 4-year college^ 17 2 114478 0.110 0.113 18 n/a n/a n/a 0.110 0.331
Enroll in 2-year college 17 2 63872 0.002 0.023 18 0.002 0.023 18 0.002 0.923
Enroll in 4-year college 17 2 63872 -0.003 0.009 18 -0.003 0.009 18 -0.003 0.738
File a FAFSA^ 17 2 966 -0.070 0.045 18 n/a n/a n/a -0.070 0.126

Citations Used in the Meta-Analysis

Bergman, P., Denning, J.T., & Manoli, D. (2016). Is information enough? evidence from a tax credit information experiment with 1,000,000 students. Working Paper.

Bettinger, E.P., Long, B.T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results from the H&R Block FAFSA Experiment. The Quarterly Journal of Economics, 127(3), 1205–1242.

Bird, K.A., Castleman, B.L., Goodman, J., & Lamberton, C. (2017). Nudging at a national scale: experimental evidence from a FAFSA completion campaign. EdPolicy Works Working Paper Series No 54.

Blagg, K., Chingos, M.M., Graves, C., Nicotera, A., & Shaw, L. (2017). Rethinking consumer information in higher education (Education Policy Program). Washington, DC: Urban Institute.

Hoxby, C., & Turner, S. (2013). Expanding college opportunities for high-achieving, low income students, (SIEPR Discussion Paper No. 12-014). Stanford, CA: Stanford Institute for Economic Policy Research.