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Washington State Institute for Public Policy
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Head Start

Pre-K to 12 Education
Benefit-cost methods last updated December 2017.  Literature review updated December 2013.
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Head Start is a federal program that funds early childhood education, social services and health services for children ages 0-5. Studies in this analysis focus on center-based Head Start programs for 3- and 4- year olds.
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 $9,371 Benefits minus costs $19,760
Participants $14,367 Benefit to cost ratio $3.21
Others $8,267 Chance the program will produce
Indirect ($3,300) benefits greater than the costs 81 %
Total benefits $28,706
Net program cost ($8,946)
Benefits minus cost $19,760
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 $1,428 $0 $3,195 $712 $5,335
Labor market earnings associated with high school graduation $7,064 $15,555 $7,132 $0 $29,751
K-12 grade repetition $72 $0 $0 $36 $109
Public assistance $7 ($3) $0 $3 $7
Health care associated with educational attainment $1,698 ($462) ($1,841) $844 $238
Costs of higher education ($937) ($792) ($259) ($466) ($2,454)
Subtotals $9,332 $14,297 $8,226 $1,130 $32,986
From secondary participant
Labor market earnings associated with high school graduation $35 $76 $35 $0 $145
K-12 grade repetition $1 $0 $0 $0 $1
Health care associated with educational attainment $8 ($2) $7 $4 $18
Costs of higher education ($4) ($4) ($1) ($2) ($12)
Subtotals $39 $70 $41 $2 $153
Adjustment for deadweight cost of program $0 $0 $0 ($4,432) ($4,432)
Totals $9,371 $14,367 $8,267 ($3,300) $28,706
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $9,469 2012 Present value of net program costs (in 2016 dollars) ($8,946)
Comparison costs $903 2012 Cost range (+ or -) 10 %
Per-child costs calculated using a weighted average of Head Start, American Indian Alaska Native Head Start and Migrant and Seasonal Head Start costs, including administrative costs per slot (http://www.del.wa.gov/publications/partnerships/docs/ECEAP_HS_Profile_2012.pdf). Comparison group costs reflect the range of other options that low-income children in Washington might receive, including state-subsidized child care and Washington’s Early Childhood Education and Assistance Program (ECEAP). Comparison group costs were calculated by dividing the cost of ECEAP ($55,867,278) by the number of children who are eligible but not served by HS (32,291). The number of eligible students includes all ECEAP students; http://www.del.wa.gov/publications/partnerships/docs/ECEAP_HS_Profile_2012.pdf.
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 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 2 517 -0.183 0.270 21 -0.183 0.270 31 -0.183 0.497
High school graduation 4 Primary 2 517 0.181 0.077 18 0.181 0.077 18 0.181 0.018
K-12 grade repetition 4 Primary 5 1738 -0.075 0.133 12 -0.075 0.133 12 -0.075 0.572
Teen births under age 18 4 Primary 1 327 -0.466 0.292 19 -0.466 0.292 19 -0.466 0.111
Test scores 4 Primary 7 4750 0.172 0.027 4 0.036 0.006 17 0.172 0.001
Teen births (second generation) 1 Secondary 1 327 -0.466 0.292 19 -0.466 0.292 19 -0.466 0.111

Citations Used in the Meta-Analysis

Abbott-Shim, M., Lambert, R. & McCarty, F. (2003). A comparison of school readiness outcomes for children randomly assigned to a Head Start program and the program's wait list. Journal of Education for Students Placed at Risk, 8(2), 191- 214.

Aughinbaugh, A. (2001). Does Head Start yield long-term benefits? The Journal of Human Resources, 36(4), 641-665. Currie J., & Thomas, D. (1995). Does Head Start make a difference? The American Economic Review, 85(3), 341-364. Currie, J., & Thomas, D. (1999). Does Head Start help Hispanic children? Journal of Public Economics, 74(2), 235-262.

Deming, D. (2009). Early childhood intervention and life-cycle skill development: Evidence from Head Start. American Economic Journal: Applied Economics, 1(3), 111-134.

Garces, E., Thomas, D., & Currie, J. (2002). Longer-term effects of Head Start. The American Economic Review, 92(4), 999-1012.

Lee, V.E., Brooks-Gunn, J., Schnur, E. (1988). Does Head Start work?: A 1-year follow-up comparison of disadvantaged children attending Head Start, no preschool, and other preschool programs. Developmental Psychology, 24(2), 210-222.

Lee, V.E., Brooks-Gunn, J., Schnur, E., & Liaw, F.R. (1990). Are Head Start effects sustained? A longitudinal follow-up comparison of disadvantaged children attending Head Start, no preschool, and other preschool programs. Child Development, 61(2), 495-507.

Puma, M., Bell, S., Cook, R., Heid, C., Shapiro, G., Broene, P., . . . Spier, E. (2010). Head Start impact study: Final report. Washington, DC: U.S. Department of Health and Human Services.

Roy, A. (2003). Evaluation of the Head Start Program: Additional evidence from the NLSCM79 data (Doctoral dissertation, University at Albany, State University of New York).

Zhai, F., Brooks-Gunn, J., & Waldfogel, J. (2011). Head start and urban children's school readiness: A birth cohort study in 18 cities. Developmental Psychology, 47(1), 134-152.