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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 2023.  Literature review updated July 2019.
Head Start is a federal program that funds early childhood education, social services, and health services for low-income children ages 0-5 to support child development and learning. Studies in this analysis focus on preschool Head Start programs for children ages 3-5 years old. Head Start offers half- and full-day programs that typically last during the school year.
 
ALL
BENEFIT-COST
META-ANALYSIS
CITATIONS
For an overview of WSIPP's Benefit-Cost Model, please see this guide. The estimates shown are present value, life cycle benefits and costs. All dollars are expressed in the base year chosen for this analysis (2023).  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 $10,294 Benefits minus costs $17,262
Participants $12,400 Benefit to cost ratio $2.66
Others $7,749 Chance the program will produce
Indirect ($2,799) benefits greater than the costs 71%
Total benefits $27,643
Net program cost ($10,381)
Benefits minus cost $17,262

^WSIPP’s benefit-cost model does not monetize this outcome.

^^WSIPP does not include this outcome when conducting benefit-cost analysis for this program.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the program impacts measured in the research literature (for example, impacts on 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. See Estimating Program Effects Using Effect Sizes for additional information on how we estimate effect sizes.

The effect size may be adjusted from the unadjusted effect size estimated in the meta-analysis. Historically, WSIPP adjusted effect sizes to some programs based on the methodological characteristics of the study. For programs reviewed in 2024 or later, we do not make additional adjustments, and we use the unadjusted effect size whenever we run a benefit-cost analysis.

Research shows the magnitude of effects may change 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. 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 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
Standardized, validated tests of academic achievement.
4 Primary 7 6046 0.129 0.029 5 0.040 0.032 17 0.129 0.001
Crime
Any criminal conviction according to court records, sometimes measured through charges, arrests, incarceration, or self-report.
 4 Primary 3 988 -0.144 0.137 19 -0.144 0.137 27 -0.144 0.295
K-12 grade repetition
Repeating a grade. This is sometimes called "grade retention."
 4 Primary 6 2848 -0.122 0.063 13 -0.122 0.063 13 -0.122 0.051
High school graduation
On-time completion of high school with a diploma (excluding GED attainment).
 4 Primary 4 1485 0.126 0.069 18 0.126 0.069 19 0.126 0.069
K-12 special education
Placement into special education services.
4 Primary 4 1734 -0.112 0.101 14 -0.112 0.101 14 -0.112 0.268
Major depressive disorder
Clinical diagnosis of major depression or symptoms measured on a validated scale.
 4 Primary 1 526 -0.190 0.062 15 0.000 0.310 19 -0.190 0.002
Externalizing behavior symptoms
Symptoms of externalizing behavior (e.g., aggressive, hostile, or disruptive behavior) measured on a validated scale.
 4 Primary 7 6203 -0.030 0.026 8 -0.016 0.017 11 -0.030 0.258
Internalizing symptoms
Symptoms of internalizing behavior (e.g., sadness, anxiety, or withdrawal) measured on a validated scale.
 4 Primary 2 1905 0.013 0.048 8 0.013 0.048 11 0.013 0.784
Obesity
Obese based on clinical guidelines for adults (body mass index of 30 or higher) or children (body mass index at or above the 95th percentile for children of the same age and sex), as appropriate.
4 Primary 2 1419 0.124 0.157 6 0.000 0.101 9 0.124 0.430
Teen births under age 18
Becoming a parent before age 18.
4 Primary 2 824 -0.126 0.253 17 -0.126 0.253 17 -0.126 0.619
Employment
Any employment, including part-time work.
29 Secondary 2 1775 0.079 0.094 31 0.000 0.000 32 0.079 0.401
Smoking before end of middle school^^
Any smoking of tobacco by the end of middle school, typically by age 13.
 4 Primary 1 634 -0.131 0.072 12 -0.131 0.072 23 -0.131 0.070
Alcohol use before end of middle school^^
Any use of alcohol by the end of middle school, typically by age 13.
4 Primary 1 634 -0.211 0.069 12 -0.211 0.069 23 -0.211 0.002
Employment^^
Any employment, including part-time work.
4 Primary 1 461 -0.157 0.099 20 0.000 0.000 0 -0.157 0.114
Illicit drug use before end of middle school^^
Any use of illicit drugs by the end of middle school, typically by age 13. When possible, we exclude cannabis/marijuana use disorder from this outcome.
 4 Primary 1 634 0.116 0.091 12 0.116 0.091 23 0.116 0.201
Grade point average^
Non-standardized measure of student performance calculated across subjects.
4 Primary 1 255 0.012 0.071 13 0.000 0.000 0 0.012 0.868
School attendance^
Number or percentage of school days present in a given enrollment period.
 4 Primary 1 214 0.080 0.075 13 0.000 0.000 0 0.080 0.288
Suspensions/expulsions^
In-school suspensions, out-of-school suspensions, or expulsions from school
 4 Primary 1 263 0.064 0.093 13 0.000 0.000 0 0.064 0.490
Enroll in any college^
Enroll in either a 2-year or 4-year higher education institution.
 4 Primary 4 1658 -0.071 0.051 25 0.000 0.000 0 -0.071 0.163
Social and emotional development^
A broad range of skills relevant to self, emotion, and relationships. These skills are typically measured through validated assessments that measure self-awareness, social competence, and self-control.
4 Primary 4 4158 0.012 0.039 7 0.000 0.000 0 0.012 0.749
GED attainment^
Successful attainment of a General Educational Development (GED) credential.
 29 Secondary 2 1775 0.062 0.043 31 0.000 0.000 0 0.062 0.148
Graduate with any degree^
Graduate with a degree from either a 2-year or 4-year higher education institution.
 29 Secondary 2 1775 0.088 0.089 31 0.000 0.000 0 0.088 0.321
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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
Affected outcome: Resulting benefits:1 Benefits accrue to:
Taxpayers Participants Others2 Indirect3 Total
Crime Criminal justice system $1,435 $0 $3,215 $718 $5,367
High school graduation Labor market earnings associated with high school graduation $3,657 $8,614 $4,674 $0 $16,945
Costs of higher education ($710) ($603) ($198) ($355) ($1,866)
K-12 grade repetition K-12 grade repetition $188 $0 $0 $94 $282
K-12 special education K-12 special education $3,811 $0 $0 $1,905 $5,716
Major depressive disorder Mortality associated with depression $0 $0 $0 $1 $1
Externalizing behavior symptoms Health care associated with externalizing behavior symptoms $64 $18 $67 $32 $181
Internalizing symptoms Health care associated with internalizing symptoms ($8) ($2) ($8) ($4) ($23)
Obesity Labor market earnings associated with obesity $0 $0 $0 $0 $0
Mortality associated with obesity $0 $0 $0 $0 $0
Subtotals $8,437 $8,027 $7,749 $2,391 $26,605
From secondary participant
Employment Labor market earnings $1,856 $4,373 $0 $0 $6,229
Subtotals $1,856 $4,373 $0 $0 $6,229
Program cost Adjustment for deadweight cost of program $0 $0 $0 ($5,190) ($5,190)
Totals $10,294 $12,400 $7,749 ($2,799) $27,643
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Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $13,550 2018 Present value of net program costs (in 2023 dollars) ($10,381)
Comparison costs $4,750 2018 Cost range (+ or -) 20%
The costs of Head Start participation are calculated by dividing the total federal funding by the total enrollment in Washington in 2019. The costs of Head Start participation were provided by T. Saenz-Thompson (personal communication, Office of Head Start Region 10, October 24, 2019). The comparison group consists of children receiving state-funded pre-kindergarten services, state-funded childcare subsidies, or children receiving no state-funded care. Costs for these children are estimated from Washington’s Early Childhood Education Assistance Program (ECEAP) for low-income preschoolers (2018-19 ECEAP Caseload Forecast Report December 2018 https://www.dcyf.wa.gov/sites/default/files/pdf/eceap/ECEAP_Caseload_Forecast.pdf) and Washington’s childcare subsidy reimbursement rates as of February 2019 (https://www.dcyf.wa.gov/node/1640). Comparison group costs are a weighted average of the costs in pre-kindergarten, state subsidized childcare, and no state-funded care.
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.
Benefits Minus Costs
Benefits by Perspective
Taxpayer Benefits by Source of Value
Benefits Minus Costs Over Time (Cumulative 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 discounted dollars. 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.

Citations Used in the Meta-Analysis

Aughinbaugh, A. (2001). Does Head Start yield long-term benefits? The Journal of Human Resources, 36(4), 641-665.

Carneiro, P., & Ginja, R. (2014). Long-term impacts of compensatory preschool on health and behavior: Evidence from Head Start. American Economic Journal: Economic Policy, 6(4), 135-173.

Coley, R.L, Votruba-Drzal, E., Collins, M., & Cook, K.D. (2016). Comparing public, private, and informal preschool programs in a national sample of low-income children. Early Childhood Research Quarterly, 36(3), 91-105.

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.

Frisvold, D. (2006). Head Start Participation and childhood obesity (Vanderbilt University Economics Working Paper No. 06-WG01). Nashville, TN: Vanderbilt University.

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

Gormley, W.T., Phillips, D., & Gayer, T. (2008). Preschool programs can boost school readiness. Science, 320(5884), 1723-4.

Lee, R., Zhai, F., Han, W., Brooks-Gunn, J., & Waldfogel, J. (2013). Head Start and children's nutrition, weight, and health care receipt. Early Childhood Research Quarterly, 28(4), 723-733.

Lee, R., Zhai, F., Brooks-Gunn, J., & Han, W. (2014). Head Start participation and school readiness: Evidence from the early childhood longitudinal study-birth cohort. Journal of Developmental Psychology, 50(1), 202-215.

Pages, R.J.C., Lukes, D.J., Bailey, D.H., & Duncan, G.J. (2019). Elusive longer-run impacts of Head Start: Replications within and across cohorts (EdWorkingPaper No. 19-27). Providence, RI: Annenberg Institute.

Phillips, D., Gormley, W., & Anderson, S. (2016). The effects of Tulsa's CAP Head Start program on middle-school academic outcomes and progress. Journal of Developmental Psychology, 52(8), 1247-1261.

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).

Sabol, T.J., & Chase-Lansdale, P.L. (2015). The influence of low-income children's participation in Head Start on their parents' education and employment. Journal of Policy Analysis and management, 34(1), 136-161.

U.S. Department of Health and Human Services. (2010). Head Start impact study: Final report. Washington, DC.

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.