1. 1 School Nutrition Programs and the Incidence of Childhood Obesity Rusty Tchernis Indiana University Daniel Millimet Southern Methodist University Muna Hussain Southern Methodist University

2. 2 Motivation If policy is to be effective it needs to target children before habits are formed Here we look at the role of school nutrition programs: School Breakfast Program (SBP) and National School Lunch Program (NSLP) We use panel data to measure the “long run” effect of SBP and NSLP

3. 3 Prevalence of Childhood Obesity Source: CDC

4. 4 Program Description NSLP started in 1946 and covers 95% of schools, 29M children, with 17.5M receiving reduced price or free lunch. SBP started in 1966, covers 82,000 schools (or 83% of schools on NSLP) and 9.6M children, with 7.7M receiving reduced price or free breakfast. For 100 students on NSLP roughly 45 are on SBP

5. 5 Previous Studies Fertig, Glomm, and Tchernis (2006) show that consuming fewer meals (skipping breakfast) increases weight Bhattacharya, Currie, and Haider (2006) show that school breakfast improves the nutritional quality of the diet von Hippel, Powell, Downey, and Rowland (2007) show that most of the weight gain takes place during summer Hence, our prior beliefs were that the effects of SBP are positive

6. 6 Economics of food transfers Consumption A C B ■ ■ Food

7. 7 Data Set Early Childhood Longitudinal Study- Kindergarten Class of 1998-99 (ECLS-K) The sample includes over 13,500 children from kindergarten to third grade Treatment – SBP in kindergarten (20%) Outcome – multiple weight measures in spring third grade

8. 8 SBP Results (OLS) Dep. Var. Sample BMIBMI growth rate BMI percentile Pr(Obese) Full 0.281*0.010*2.176*0.055 Normal 0.319*0.014*3.148*0.185* Overweight -0.159-0.010-1.524-0.104 Obese 0.580 ‡ 0.017 ‡ 0.4990.129 ‡ p<0.10, † p<0.05, * p<0.01

9. 9 SBPNo SBP NSLP Participation (1 = Yes)0.8930.42 Urban Fringe & Large Town (1 = Yes)0.2590.419 South (1 = Yes)0.5080.286 Household Income (dollars)34,54457,415 Number of Children's Books in Household48.64681.193 Summary Statistics

10. 10 Future Work Better control for observable differences between treated and untreated populations (propensity scores) Gauge the effect of selection on unobserved variables Use Instrumental Variables to deal with selection into treatment