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Head Start

Pre-K to 12 Education
Benefit-cost methods last updated December 2019.  Literature review updated July 2019.
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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.
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 (2018). 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 $7,588 Benefits minus costs $12,481
Participants $9,914 Benefit to cost ratio $2.42
Others $6,611 Chance the program will produce
Indirect ($2,831) benefits greater than the costs 70 %
Total benefits $21,281
Net program cost ($8,800)
Benefits minus cost $12,481
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 $1,218 $0 $2,731 $609 $4,558
Labor market earnings associated with high school graduation $3,131 $7,353 $4,011 $0 $14,495
K-12 grade repetition $138 $0 $0 $69 $208
K-12 special education $2,385 $0 $0 $1,193 $3,578
Health care associated with externalizing behavior symptoms $57 $16 $59 $29 $161
Health care associated with internalizing symptoms ($7) ($2) ($7) ($4) ($20)
Labor market earnings associated with obesity $0 $0 $0 $0 $0
Costs of higher education ($656) ($557) ($183) ($328) ($1,723)
Mortality associated with depression $0 $0 $0 $1 $1
Mortality associated with obesity $0 $0 $0 $0 $0
Subtotals $6,267 $6,811 $6,611 $1,569 $21,258
From secondary participant
Labor market earnings $1,321 $3,103 $0 $0 $4,424
Subtotals $1,321 $3,103 $0 $0 $4,424
Adjustment for deadweight cost of program $0 $0 $0 ($4,400) ($4,400)
Totals $7,588 $9,914 $6,611 ($2,831) $21,281
Detailed Annual Cost Estimates Per Participant
Annual cost Year dollars Summary
Program costs $13,550 2018 Present value of net program costs (in 2018 dollars) ($8,800)
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.
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.

^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 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
Alcohol use before end of middle school^^ 4 Primary 1 634 -0.211 0.069 12 -0.211 0.069 23 -0.211 0.002
Smoking before end of middle school^^ 4 Primary 1 634 -0.131 0.072 12 -0.131 0.072 23 -0.131 0.070
Illicit drug use before end of middle school^^ 4 Primary 1 634 0.116 0.091 12 0.116 0.091 23 0.116 0.201
Youth binge drinking^^ 4 Primary 1 584 -0.096 0.078 16 -0.096 0.078 27 -0.096 0.218
Employment^^ 4 Primary 1 461 -0.157 0.099 20 0.000 0.000 0 -0.157 0.114
Suspensions/expulsions^ 4 Primary 1 263 0.064 0.093 13 0.000 0.000 0 0.064 0.490
Social and emotional development^ 4 Primary 4 4158 0.012 0.039 7 0.000 0.000 0 0.012 0.749
School attendance^ 4 Primary 1 214 0.080 0.075 13 0.000 0.000 0 0.080 0.288
Grade point average^ 4 Primary 1 255 0.012 0.071 13 0.000 0.000 0 0.012 0.868
Enroll in any college^ 4 Primary 4 1658 -0.071 0.051 25 0.000 0.000 0 -0.071 0.163
GED attainment^ 29 Secondary 2 1775 0.062 0.043 31 0.000 0.000 0 0.062 0.148
Graduate with any degree^ 29 Secondary 2 1775 0.088 0.089 31 0.000 0.000 0 0.088 0.321
Crime 4 Primary 3 988 -0.144 0.137 19 -0.144 0.137 27 -0.144 0.295
Externalizing behavior symptoms 4 Primary 7 6203 -0.030 0.026 8 -0.016 0.017 11 -0.030 0.258
High school graduation 4 Primary 4 1485 0.126 0.069 18 0.126 0.069 19 0.126 0.069
Internalizing symptoms 4 Primary 2 1905 0.013 0.048 8 0.013 0.048 11 0.013 0.784
K-12 grade repetition 4 Primary 6 2848 -0.122 0.063 13 -0.122 0.063 13 -0.122 0.051
K-12 special education 4 Primary 4 1734 -0.112 0.101 14 -0.112 0.101 14 -0.112 0.268
Major depressive disorder 4 Primary 1 526 -0.190 0.062 15 0.000 0.310 19 -0.190 0.002
Obesity 4 Primary 2 1419 0.124 0.157 6 0.000 0.101 9 0.124 0.430
Teen births under age 18 4 Primary 2 824 -0.126 0.253 17 -0.126 0.253 17 -0.126 0.619
Test scores 4 Primary 7 6046 0.129 0.029 5 0.040 0.032 17 0.129 0.001
Employment 29 Secondary 2 1775 0.079 0.094 31 0.000 0.000 32 0.079 0.401

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.