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School Nutrition Programs and the Incidence of Childhood Obesity

School Nutrition Programs and the Incidence of Childhood Obesity. Rusty Tchernis Indiana University Daniel Millimet Southern Methodist University Muna Hussain Southern Methodist University. Motivation. If policy is to be effective it needs to target children before habits are formed

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School Nutrition Programs and the Incidence of Childhood Obesity

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

  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. Prevalence of Childhood Obesity Source: CDC

  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. 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 beliefswere that the effects of SBP are positive

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

  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. SBP Results (OLS) ‡ p<0.10, † p<0.05, * p<0.01

  9. Summary Statistics

  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

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