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Washington State Institute for Public Policy
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Tutoring: By adults, one-on-one, non-structured

Pre-K to 12 Education
Benefit-cost methods last updated December 2018.  Literature review updated June 2014.
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The tutoring programs included in this analysis provide one-on-one assistance to struggling students in English language arts and/or mathematics. The evaluated programs typically allow tutors to exercise their own discretion when selecting and implementing tutoring strategies. The programs typically serve early elementary school students and provide, on average, about 30 hours of tutoring time to an individual student each year. The tutors are non-certificated adults (e.g. instructional aides and community volunteers) who receive approximately two hours of training per 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 $1,194 Benefits minus costs $2,480
Participants $2,465 Benefit to cost ratio $2.66
Others $1,027 Chance the program will produce
Indirect ($715) benefits greater than the costs 72 %
Total benefits $3,972
Net program cost ($1,492)
Benefits minus cost $2,480
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 $1,128 $2,483 $1,100 $0 $4,711
Health care associated with educational attainment $67 ($18) ($73) $34 $10
Adjustment for deadweight cost of program $0 $0 $0 ($748) ($748)
Totals $1,194 $2,465 $1,027 ($715) $3,972
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $1,425 2013 Present value of net program costs (in 2017 dollars) ($1,492)
Comparison costs $0 2013 Cost range (+ or -) 10 %
In the evaluations included in the meta-analysis, the average non-structured one-on-one tutoring program provides 30 hours of intervention per student and two hours of training time per tutor. The estimate assumed that certificated teachers provide approximately four hours of planning support and oversight. To calculate a per-student annual cost, we used average Washington State compensation costs (including benefits) for a K–8 teacher and instructional aides as reported by the Office of the Superintendent of Public Instruction.
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
Test scores 7 12 6253 0.061 0.018 7 0.029 0.020 17 0.062 0.001

Citations Used in the Meta-Analysis

Baker, S., Gersten, R., & Keating, T. (2000). When less may be more: A 2-year longitudinal evaluation of a volunteer tutoring program requiring minimal training. Reading Research Quarterly, 35(4), 494-519.

Cobb, J.B. (2000). The effects of an early intervention program with preservice teachers as tutors on the reading achievement of primary grade at risk children. Reading Horizons, 41(3), 155-173.

Cook, J.A. (2001). Every moment counts: Pairing struggling young readers with minimally trained tutors. Dissertation Abstracts International, 62(08), 2714A.

McKinney, A.D. (1995). The effects of an after-school tutorial and enrichment program on the academic achievement and self-concept of below grade level first and second grade students. Dissertation Abstracts International, 56(06), 2176A.

Rimm-Kaufman, S.E., Kagan, J., & Byers, H. (1999). The effectiveness of adult volunteer tutoring on reading among 'at risk' first grade children. Reading Research and Instruction, 38(2), 143-152.

Ritter, G.W. (2000). The academic impact of volunteer tutoring in urban public elementary schools: Results of an experimental design evaluation. Dissertation Abstracts International, 61(03), 890A.

Weiss, J.A., Thurlow, M.L., Christenson, S.L., & Ysseldyke, J.E. (1989). Paired reading with adult volunteer tutors as a reading intervention for students with reading difficulties. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA. Retrieved from ERIC database. (ED305606)

Zimmer, R., Hamilton, L., & Christina, R. (2010). After-school tutoring in the context of No Child Left Behind: Effectiveness of two programs in the Pittsburgh Public Schools. Economics of Education Review, 29(1), 18-28.