Pre-School (Targeted Programs)
Introduction
Cascio (2020) studies the impact of public preschool education programs on test scores using data from 16 US states. The author compares state-funded programs that are universal (serving all 4-year-olds that meet age eligibility requirements) with those that target enrollment based on family income or other risk factors. The author exploits age-eligibility rules to identify the causal effect of these programs on test scores measured by the 2001 Birth Cohort of the Early Childhood Longitudinal Study (ECLS-B). The author finds that universal pre-K programs raise test scores substantially, especially for children from low-income families. Gains for poor children are significantly smaller in targeted programs.
The MVPFs of these pre-school programs are computed separately for targeted and universal programs. This page discusses the calculation for targeted programs. See this page for the calculation for universal programs.
Provides No Benefit on Average
Net Cost
In order to compute the program’s costs, the author adds up three components: i) marginal government outlays in the preschool program, ii) the present value of income taxes collected on extra earnings caused by program participation, and iii) the fiscal savings from substitutions across public programs.
Cascio (2020) estimates government outlays on preschool by adopting two potential strategies. In the first strategy they assume that per-pupil program outlays are equal to per-pupil K-12 outlays (upper bound). In the second strategy they assume that outlays are equal to per-pupil outlays in Head Start.
In order to estimate the impact of pre-school on government tax revenue, the author takes the impact of pre-school on test scores and translates those gains into increased lifetime earnings and, subsequently, increased tax revenue. The paper follows a forecasting approach from Kline and Walters (2016) who use Chetty et al. (2011)‘s result that a one standard deviation increase in test scores yields a 10 percent increase in earnings. To get to the dollar value of increased lifetime earnings, the author multiplies this percentage impact by the total discounted lifetime earnings at age 12 of $522,000 in 2010 dollars from Chetty et al. (2011). To transform the value into lifetime earnings at age 4 the author assumes a 3 percent discount rate. Cascio (2020) then multiplies those earnings gains an average tax rate of 20% to arrive at a figure for increased tax collection.
In order to estimate the impact of the program on spending across other government programs, the author assumes that pupils would attend Head Start if they were not enrolled in a public pre-K program.
Willingness to Pay
To compute the WTP for public pre-K programs, the authors consider two components: i) the net-of-tax increase in lifetime earnings caused by program participation, and ii) the reduction in private expenditures on pre-K. To compute i), the author uses the calculation above for the net impact on lifetime income along with a 20% tax rate on earnings. To compute ii) the author assumes that in the absence of the public pre-K programs, pupils would attend private centers, paying a per-pupil annual price of care equal to $5,000.
MVPF Summary
The net cost calculations together with the WTP values lead to an MVPF of -0.24 for targeted programs, with a standard error of 0.99, which implies a 95% confidence interval of roughly -2.18 to 1.70.
If one assumes the costs of the program are equal to the cost of Head Start instead of the average K12 spending per pupil, this changes the MVPF to -0.39.
References
Cascio, Elizabeth U. (2020). “Does Universal Preschool Hit the Target? Program Access and Preschool Impacts”. Journal of Human Resources. http://jhr.uwpress.org/content/early/2021/01/04/jhr.58.3.0220-10728R1.abstract
Chetty, Raj et al. “How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project STAR.” The Quarterly Journal of Economics 126.4 (2011): 1593-1660. DOI: https://academic.oup.com/qje/article-pdf/126/4/1593/17089543/qjr041.pdf
Kline, Patrick and Christopher R. Walters (2016). “Evaluating Public Programs with Close Substitutes: The Case of Head Start.” The Quarterly Journal of Economics, 131(4), 1795-1848. DOI: https://doi.org/10.1093/qje/qjw027
Policy Features
- Category
- Education
- Sub-Category
- Child Education
- Beneficiary Type(s)
- Children and Youth, Children under five, Students
- Average Age
- 4
- Country of Implementation
- United States
- Year of Implementation
- 2005
- Empirical Method
- Difference in Differences
- Research Type
- Primary
- Peer Reviewed
- Yes
- MVPF Publication Link
- jhr.uwpress.org/content/early/2021/01/04/jhr.58.3.0220-10728R1.abstract