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Text message reminders (for high school students and graduates)

Higher Education
Benefit-cost methods last updated December 2018.  Literature review updated January 2018.
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Text message reminders target college-intending high school students and graduates. Students receive automated text message reminders on financial aid and college enrollment tasks as well as prompts to reach out for help from designated organizations and counselors if needed. The duration of programs varied between six weeks and six months prior to a student’s expected fall term in college.
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 $696 Benefits minus costs $3,233
Participants $1,749 Benefit to cost ratio $336.10
Others $928 Chance the program will produce
Indirect ($131) benefits greater than the costs 60 %
Total benefits $3,242
Net program cost ($10)
Benefits minus cost $3,233
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 $947 $2,084 $1,026 $0 $4,057
Costs of higher education ($251) ($336) ($97) ($126) ($810)
Adjustment for deadweight cost of program $0 $0 $0 ($5) ($5)
Totals $696 $1,749 $928 ($131) $3,242
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $9 2009 Present value of net program costs (in 2017 dollars) ($10)
Comparison costs $0 2009 Cost range (+ or -) 25 %
Costs include the cost of text message delivery and staff costs for the counselor hired to respond to text messages. Sources: Castleman & Page (2015) and Page & Gehlbach (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.

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 18 2 6269 0.025 0.080 18 0.025 0.080 18 0.025 0.756
Enroll in 4-year college 18 2 6269 0.012 0.057 18 0.012 0.057 18 0.012 0.832

Citations Used in the Meta-Analysis

Castleman, B.L., & Page, L.C. (2015). Summer nudging: Can personalized text messages and peer mentor outreach increase college going among low-income high school graduates? Journal of Economic Behavior & Organization, 115(3), 144-160.

Page, L.C., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open, 3(4), 1-12.