Washington State Institute for Public Policy
Tutoring: Supplemental Educational Services (under Title I)
Pre-K to 12 Education
Benefit-cost estimates updated May 2017.  Literature review updated May 2015.
Current federal education law directs school districts who do not make "Adequate Yearly Progress" toward student proficiency standards to provide "Supplemental Educational Services"—primarily out-of-school-time tutoring—to eligible students at no charge to students and their families. Providers of SES include local and national for-profit and non-profit organizations as well as school districts themselves (unless they are identified as “in need of improvement” under AYP or have a waiver). Delivery methods (e.g., one-on-one, group, or online) vary; the amount of tutoring ranges from approximately 20 to 40 hours. This analysis estimates the impact of offering SES in school districts throughout the United States on reading and math test scores.
BENEFIT-COST
META-ANALYSIS
CITATIONS
The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2016). 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 $838 Benefits minus costs $744
Participants $1,726 Benefit to cost ratio $1.44
Others $702 Chance the program will produce
Indirect ($830) benefits greater than the costs 58 %
Total benefits $2,436
Net program cost ($1,692)
Benefits minus cost $744
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 $809 $1,782 $793 $0 $3,385
Health care associated with educational attainment $72 ($20) ($79) $36 $10
Costs of higher education ($44) ($37) ($12) ($22) ($115)
Adjustment for deadweight cost of program $0 $0 $0 ($844) ($844)
Totals $838 $1,726 $702 ($830) $2,436
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $1,550 2010 Present value of net program costs (in 2016 dollars) ($1,692)
Comparison costs $0 2010 Cost range (+ or -) 30 %
Average costs are estimated in the range ($1,100 to $2,000) reported in Heinrich, C.J., Burch, P., Good, A., Acosta, R., Cheng, H., Dillender, M., Kirshbaum, C., . . . Stewart, M. (2014). Improving the implementation and effectiveness of out-of-school time tutoring. Journal of Policy Analysis and Management, 1-34.
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 Primary or secondary participant 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 22 293256 0.029 0.010 11 0.021 0.011 17 0.029 0.006
Citations Used in the Meta-Analysis

Deke, J., Gill, B., Dragoset, L., & Bogen, K. (2014). Effectiveness of Supplemental Educational Services. Journal of Research on Educational Effectiveness, 7(2), 137-165.

Heinrich, C.J., Burch, P., Good, A., Acosta, R., Cheng, H., Dillender, M., . . . Stewart, M. (2014). Improving the implementation and effectiveness of out of-school time tutoring. Journal of Policy Analysis and Management, 1-34.

Munoz, M.A., Potter, A.P., & Ross, S.M. (2008). Supplemental Educational Services as a consequence of the NCLB legislation: Evaluating its impact on student achievement in a large urban district. Journal of Education for Students Placed at Risk, 13(1), 1-25.

Munoz, M.A., Chang, F., & Ross, S.M. (2012). No Child Left Behind and tutoring in reading and mathematics: Impact of Supplemental Educational Services on large scale assessment. Journal of Education for Students Placed at Risk, 17(3), 186-200.

Springer, M.G., Pepper, M.J., & Ghosh-Dastidar, B. (2014). Supplemental Educational Services and student test score gains: Evidence from a large, urban school district. Working Paper. Journal of Education Finance, 39(4), 370-403.

Zimmer, R., Gill, B., Razquin, P., Booker, K., & Lockwood, J.R. (2007). State and local implementation of the No Child Left Behind Act: Volume I - Title I school choice, supplemental educational services, and student achievement. Washington DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development, Policy and Program Studies Service.

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.

For more information on the methods
used please see our Technical Documentation.
360.664.9800
institute@wsipp.wa.gov