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Washington State Institute for Public Policy
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State and district early childhood education programs

Pre-K to 12 Education
Benefit-cost methods last updated December 2018.  Literature review updated December 2013.
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In this analysis, we include pre-kindergarten programs funded by states or school districts that are universal or that target low-income students. Comparison students could have received any other child care options available in the community, including care by family members, another preschool program, subsidized or unsubsidized child care, or Head Start.
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 $12,989 Benefits minus costs $26,802
Participants $14,311 Benefit to cost ratio $4.63
Others $7,730 Chance the program will produce
Indirect ($850) benefits greater than the costs 83 %
Total benefits $34,180
Net program cost ($7,377)
Benefits minus cost $26,802
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
Crime $1,809 $0 $3,983 $908 $6,700
Labor market earnings associated with high school graduation $6,377 $14,043 $6,444 $0 $26,864
K-12 grade repetition $403 $0 $0 $203 $606
K-12 special education $2,676 $0 $0 $1,335 $4,011
Health care associated with educational attainment $2,148 ($585) ($2,330) $1,082 $315
Costs of higher education ($1,317) ($1,118) ($367) ($662) ($3,463)
Subtotals $12,096 $12,340 $7,730 $2,866 $35,032
From secondary participant
Labor market earnings associated with employment $895 $1,970 $0 $0 $2,865
Public assistance ($2) $1 $0 ($3) ($4)
Subtotals $893 $1,971 $0 ($3) $2,861
Adjustment for deadweight cost of program $0 $0 $0 ($3,713) ($3,713)
Totals $12,989 $14,311 $7,730 ($850) $34,180
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $6,934 2012 Present value of net program costs (in 2017 dollars) ($7,377)
Comparison costs $961 2012 Cost range (+ or -) 10 %
Our per-participant estimate reflects the total cost of Washington State’s Early Childhood Education and Assistance Program (ECEAP), including administrative costs per slot plus the amount of state-subsidized child care subsidies distributed to kids in ECEAP ( Comparison group costs reflect the range of other options that low-income children in Washington might receive, including state-subsidized child care and Head Start. Comparison group costs were calculated by dividing the amount of state-subsidized child care subsidies distributed to ECEAP-eligible families who did not participate in ECEAP by the number of children (30,936). The number of eligible students includes all Head Start (HS) students; while HS eligibility is up to 130% of the federal poverty line (FPL), students under 100% FPL are given first priority ( and personal communication with Nicole Rose, Department of Early Learning, Early Learning Management System on December 4, 2013).
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.

*The effect size for this outcome indicates percentage change, not a standardized mean difference effect size.

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 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
Crime 4 Primary 1 902 -0.251 0.174 26 -0.251 0.174 36 -0.251 0.149
High school graduation 4 Primary 2 1184 0.231 0.091 21 0.231 0.091 21 0.231 0.011
K-12 grade repetition 4 Primary 4 2023 -0.351 0.068 12 -0.351 0.068 12 -0.351 0.001
K-12 special education 4 Primary 3 1670 -0.118 0.193 14 -0.118 0.193 14 -0.118 0.544
Test scores 4 Primary 17 10799 0.303 0.029 4 0.064 0.031 17 0.303 0.001
Earnings* 32 Secondary 1 5253 0.024 0.042 33 0.000 0.000 34 0.024 0.566
Employment 32 Secondary 1 5253 -0.003 0.017 33 0.000 0.000 34 -0.003 0.851
Public assistance 32 Secondary 1 5253 0.000 0.040 33 0.000 0.000 34 0.000 1.000

Citations Used in the Meta-Analysis

Barnett, W.S., Frede, E.C., Mobasher, H., & Mohr, P. (1988). The efficacy of public preschool programs and the relationship of program quality to efficacy. Educational Evaluation and Policy Analysis, 10(1), 37–49.

Barnett, W. S., Jung, K., Youn, M., & Frede, E.C. (2013). Abbott preschool program longitudinal effects study: Fifth grade follow- up. New Brunswick, NJ: National Institute for Early Education Research.

Frede, E., Jung, K., Barnett, W. S., Lamy, C.E., & Figueras, A. (2007). The Abbott Preschool Program longitudinal effects study (APPLES): Interim report. New Brunswick, NJ: Rutgers University, National Institute for Early Education Research.

Gormley Jr, W. T., & Gayer, T. (2005). Promoting school readiness in Oklahoma: An evaluation of Tulsa's pre-k program. The Journal of Human Resources. 40(3), 533-558.

Gormley, W. T., Jr., Gayer, T., Phillips, D., & Dawson, B. (2005). The effects of universal pre-k on cognitive development. Developmental Psychology, 41(6), 872-884.

Gormley, W. T., Jr., Phillips, D., & Gayer, T. (2008). Preschool programs can boost school readiness [Supplemental material]. Science, 320, 1723-1724. doi: 10.1126/science.1156019.

Hustedt, J.T., Barnett, W.S., Jung, K. & Thomas, J. (2007). The effects of the Arkansas Better Chance program on young children's school readiness. New Brunswick, NJ: Rutgers University, National Institute for Early Education Research.

Hustedt, J.T., Barnett, W.S., Jung, K., & Figueras-Daniel, A. (2009). Continued impacts of New Mexico pre-k on children's readiness for kindergarten: Results from the third year of implementation. New Brunswick, NJ: Rutgers University, National Institute for Early Education Research.

Lipsey, M.W., Hofer, K.G., Dong, N., Farran, D.C., & Bilbrey, C. (2013). Evaluation of the Tennessee voluntary prekindergarten program: End of pre-K results from the randomized control trial. Nashville, TN: Vanderbilt University, Peabody Research Institute.

Malofeeva, E., Daniel-Echols, M., & Xiang, Z. (2007). Findings from the Michigan School Readiness Program 6 to 8 follow up study. Ypsilanti, MI: High/Scope Educational Research Foundation.

Peisner-Feinberg, E.S., & Schaaf, J.M. (2011). Evaluation of the North Carolina More at Four Pre-Kindergarten Program. Chapel Hill, NC: University of North Carolina, FPG Child Development Institute.

Quay, L.C., McMurrain, M.K., Minore, D.A., Cook, L., & Steele, D.C. (1996). The longitudinal evaluation of Georgia's prekindergarten program: Results from the third year. Paper presented at the Annual Meeting of the American Educational Research Association, Atlanta, GA.

Reynolds, A.J., Temple, J.A., White, B.A., Ou, S.R., & Robertson, D.L. (2011). Age-26 cost-benefit analysis of the child-parent center early education program. Child Development, 82(1), 379-404.

Reynolds, A.J. &Temple, J.A. (1995). Quasi-experimental estimates of the effects of a preschool intervention. Evaluation Review, 19(4): 347-373.

Schweinhart, L., Xiang, Z., Daniel-Echols, M., Browning, K., & Wakabayashi, T. (2012). Michigan Great Start Readiness Program evaluation 2012: High school graduation and retention findings. Ypsilanti, MI: High/Scope Educational Research Foundation.

Vance, B.J. (1967). The effect of preschool group experience on various language and social skills in disadvantaged children: Final Report. Stanford, CA: Stanford University.

Weiland, C., & Yoshikawa, H. (2013). Impacts of a prekindergarten program on children' mathematics, language, literacy, executive function, and emotional skills. Child Development, 84(6), 2112-2130.

Wong, V.C., Cook, T.D., Barnett, W.S., & Jung, K. (2008). An effectiveness-based evaluation of five state pre-kindergarten programs. Journal of Policy Analysis and Management, 27(1), 122-154.