The relative importance of supply and demand factors in determining preschool attendance

1. The relative importance of supply and demand factors in determining preschool attendance Rob Wassmer, Chairperson and Professor, Public Policy and Administration, Sacramento State

2. Summary • Introduction • Literature Review • Regression Model • Data Description • Regression Results • Policy Implications • Conclusion

3. Introduction • … But today, fewer than 3 in 10 four year-olds are enrolled in a high-quality preschool program.  Most middle-class parents can’t afford a few hundred bucks a week for a private preschool.  And for poor kids who need help the most, this lack of access to preschool education can shadow them for the rest of their lives.  So tonight, I propose working with states to make high-quality preschool available to every single child in America… • (President Obama’s State of the Union Address, 2/12/2013)

4. Introduction http://greatergreatereducation.org/post/18902/daycare-is-education-part-2-early-childhood-care-matters

5. Introduction For 2009; http://earlyed.newamerica.net/blogposts/2010/2009_snapshot_of_preschool_programs_state_by_state_shows_losses_and_gains-31358

6. Introduction • All programs analyzed were high quality • Proposed is universal, analyzed programs were targeted to low-income and disadvantaged Lynch, R. G. (2004). Exceptional Returns: Economic, Fiscal, and Social Benefits of Investment in Early Childhood Development. Washington, DC: Economic Policy Institute

7. Introduction • From CHIS survey data used here • Mimics what found elsewhere • Latino Difference • Income Difference Figure 1: Preschool Attendance for 10 or more Hours a Week for Californians Aged 3-5 in 2011-12 (From California Health Interview Survey; 95 Percent Confidence Interval in Parentheses)

8. Introduction • Strong Start for Children Act • November 2013, multi-year $100B federal subsidies for high quality state preschool programs • Serve three- to five-year-olds, teachers hold Bachelor’s degrees, maximum class size, specific staff to student ratio, full-day, curriculum aligned with state standards, pay in line with K-12, regular state monitoring, comprehensive services offered, teacher professional development, meet Head Start standards, and health/safety standards. • California State Assembly commissioned this study

9. Literature Review • Economic model of parent(s)’ demand for child’s preschool education and available supply determining observed attendance • Table A1 offers summary of previous regression studies • Demand factors • Income • Clearly a normal good • Substitute availability • Greater use if grandparent not available, mother/father/partner not present • Preferences • Race/ethnicity, English proficiency, employment, maternal education • Supply of providers (price) • None did an adequate job of accounting for this • Greater use if lower average tuition price in county, preschool statewide funding, urban residence

10. Regression Model • Preschool Hoursi = f(Incomei, Substitute Availabilityi, Preferencesi, Supply of Providersi, Controlsi) • Where i = 1, 2, 3, …1,928 households with children aged three to five in CHIS survey • Preschool Hoursi = f(Incomei, Familyi, Parentsi, Race/Ethnicityi, Childi, Neighborhoodi, Child Care Facilitiesi)

11. Data Description • June 2011 to Jan 2013 telephone survey of 7,344 CA households containing newborns to age 18 • Data from 1,928 respondents with child age 3-5 • Questions used • Regular childcare of more than 10 hours a week in private preschool/nursery school or public Head Start/state preschool (yes for 499) • Number of hours in an average week last year • Survey weights used

12. Data Description

13. Data Description

14. Data Description

15. Regression Results • Use STATA Two-Part Model (TPM) • Logistic regression • One for 499 respondents, zero for 1,429 respondents • OLS • Typical weekly hours in preschool for 499 respondents (mean=28.5) • Robust standard errors (heteroskedasticity and survey weights) • Also calculate conditional marginal effects at mean values for all statistically significant influences on overall preschool hours (comparable to Tobit) • Table 2 • Four sets of Logit/OLS results with (1) no provider measure, (2) providers within 5 miles, (3) providers within 10 miles, (4) providers within 20 miles • Only show (4)

16. Table 2: Logit (Preschool Attend or Not) and OLS (Preschool Hours Attended) Regression Results for Various Measures of Provider Availability (estimated with STATA Two-Part Model) 1Excluded category is More than Three Times Poverty Line; 2Excluded category is Less than High School; 3 Excluded category is Primary Caregiver Less than Age 30; 4Excluded category is White; 5Excluded category is Child Age Six; 6Excluded category is Urban.

17. Table 3: Provider Interaction Results (Other Explanatory Variables Included but Results not Recorded)

18. Table 4: Conditional Marginal Effects Calculated at Mean Values for all Statistically Significant Influences on Overall Value Preschool Hours (from a mean of 7.387) ~Derived using STATA’s margins, dy/dx atmean command. These indicate the expected change in hours of preschool attended for a one-unit change in the respective variable, holding all other explanatory variables constant.

19. Policy Implications • Different demand and supply influences found important to parent(s)’ decision to (1) send child to preschool and (2) hours to attend • Demand factors and attendance • 50% greater: Household income > three times federal poverty level • 70% smaller: MKA does not speak English • 80% smaller: MKA believes neighborhood people not helpful • 150 to 500% greater: MKA holds Bachelor’s to doctorate degree • 200 and 400% greater: African and Native American compared to White • Latino affect disappears • Above findings could help guide outreach/subsidy efforts

20. Policy Implications • Demand factors and hours (mean = 28.5) attended • 4.0 hours less: Asian • 4.5 hours more: Own home • 5 hours less: Married (as compared to unmarried) parents • 5.5 hours less: Age 30 to 39 (as compared to less than age 30) • Above findings could help guide outreach/subsidy efforts • Note cumulative affect of being Asian, renting, married and age 30 to 39 is 19 hours less

21. Policy Implications • Supply factors and attendance • 40% smaller: Suburban (as compared to urban) residence • Supply of preschool slots per 3-5 year olds within • 10 mile radius is 0.165 • 20 mile radius is 0.177 • Raise supply of preschool slots one standard deviation (0.06) • Within 10 mile radius, increase likelihood of attendance by 75% • Within 20 miles radius, increases likelihood of attendance by 300% • Supply of preschool slots influence nearly as great as education influences noted earlier

22. Policy Implications • Supply factors and hours (mean = 28.5) attended • Only detected when preschool slots per 3-5 year olds interacted with type of residence • Consider findings for provider measure within 20 miles • Suburban (as compared to urban) attends13.8 less hours • Town/Rural (as compared to urban) attends18.3 less hours • These hours increase based upon preschool slots per 3-5 year old in Town/Rural area • If minimum of 0.017 then 0.63 more • If mean of 0.177 then 6.58 more • If maximum of 0.319 then 11.86 more • But never enough to overcome Town/Rural deficit

23. Conclusion • Public policy interventions designed to increase preschool attendance should not only target outreach/subsidies at the groups identified here, but also consider ways to increase providers in areas with low preschools slots per 3-5 year olds • More public and Head Start providers • Public subsidies to private providers • Reducing zoning codes previously found to negatively influence presence of providers (Queralt and Witte, 1998)