1. Young Lives Early Nutrition and Cognition in Peru: A Within-Sibling Investigation Washington, DC, October 2009

2. Overview of proposal  Investigate outcomes of 4-5 year olds by comparing with younger siblings at a similar age ~3yrs later  Nutrition (height-for-age) and Cognitive Development (PPVT-TVIP)  First strategy: reduced form for both: Role of SES (lagged), changes in community & hh shocks (contemporaneous)  Second: structural, nutrition CD

3. Young Lives Team  GRADE (Lima) – Santiago Cueto, Javier Escobal  IIN (Lima) – Mary Penny & fieldwork team  University of Oxford (UK) – Stefan Dercon, Ingo Outes-Leon, Catherine Porter, Alan Sanchez

4. Young Lives Data- overview  12,000 children in 4 countries  Long-term cohort study core-funded by UK-DFID since 2002 (and until 2017)  2000 children in each country born 2001 (Ninos del Milenio)  1000 aged 7-8 years older than them  Random sample in Peru

5. YL index children  Born 2001-2, surveyed in 2002, 2006 and currently in field for 3 rd Round  Will be followed 2 more rounds  In R1 and R2 we have information on  Assets, Consumption, Economic shocks  Caregiver characteristics  Time use and schooling of adults/children  Anthropometrics and cognitive development of Index Child  Breastfeeding & Early health

7. 2009-10 Siblings Data  In R3 IADB funding allows us to collect anthropometric data and cognitive development data (PPVT) of next-sibling-down (same mother, usually same father)  Anthropometrics from age 3  Only do PPVT if sibling>3yrs  Making big effort to find sibling (so far around a third of sample)  NB: IADB Funding has stimulated funding for other 3 countries to study siblings

8. Dates and ages of children in survey rounds RoundOneTwoThree Year200220062009 Age of index child6-18mths4-5 yrs7-8 years Age of sibling-0-3 years3-6 years

9. ECD proxy variables  PPVT- TVIP (Spanish language version) has been used in R2- continue in R3  Used by other studies in LAC (Paxson & Schady)  Designed for spanish speakers  Height-for-age z-scores proxy long-term nutrition

10. Descriptive statistics *Paired-sibling sample based on households with younger siblings aged between 1 and 4 years in Round 2. Some of the children aged 1 year might not be eligible.

11. Situating our paper in literature  Schady (2006) review notes paucity of info on ECD outcomes in LAC (esp causal links)  Inspired by Paxson/Schady (2007) but we can go further given that we have sibling data  Causal links literature- Glewwe et.al (2001), Alderman et al (2006)

12. Part One: Reduced form  What is the effect of socioeconomic status, household and community characteristics on child nutritional and cognitive development?  Policy relevant- especially in context of crisis, food price hikes  Not attempting structural model, but exploring correlates, whilst controlling for household fixed effects.

13. Empirical issues  Todd and Wolpin (03,07) review empirical estimation strategies – and their implicit assumptions  Cross-section estimation will be biased if we can’t control for unobservables  We have lagged data on SES/caregiver/community characteristics  We have data on siblings in R3  Sibling outcomes R3 - Index child outcomes R2  On SES/HH chars R2 – SES/HH chars R1

14. Econometric specification  Cognitive achievement of child ‘k’ from household ‘h’: where: : cognitive achievement (PPVT std. score) or : height-for-age : child and household and community observable characteristics : child unobservable characteristics : household unobservable characteristics : random error, iid (1)

15. Sibling differences (reduced form) ECD outcome of child k from household h where siblings k=i, j Includes the iid error and innate ability of child k. represent time-varying household investments, where t is specified at the time when child k is aged 6-18 months

16. Estimation issues  We are therefore assuming:  Omitted inputs are not correlated with the error  Inputs associated with each child do not respond to own or sibling’s endowment  In practice we have only household level investments for the siblings, hence we specify inputs at time t-1 (critical period investments)  Shocks in time t can proxy for investments age 3-5

17. Explanatory variables  Time varying characteristics of the household  Change in poverty status (income, consumption and assets)  Water and sanitation, adult nutrition, food security  Parenting attitudes not collected for the sibling (e.g. we do not have information on attitudes or practice of breastfeeding for the sibling, nor their immunizations)  But we will have information on the psycho-social status of the mother (including maternal depression, social capital, self- efficacy and self-esteem).  Spending on various goods that are inputs to child development such as food or healthcare may also have a significant impact on ECD outcomes.  Time varying inputs at the community level include prices and the availability of certain services (pre-school, school, sanitation, social protection programs eg Juntos).

18. Second part  Linking early childhood nutrition to cognitive development

19. Differences in R2 PPVT scores between R1 stunted and non-stunted children

20. Differences in Round 2 PPVT scores between stunted and non-stunted children

21. Linking Nutrition to Cognitive Development Extensive literature - linkages between nutritional deficiencies at an early stage of child development and reduced cognitive ability, educational attainment and ultimately lower market wages at a later age. (Behrman and Lavy 1994), both a child’s health and her cognitive achievement can be understood as the outcomes of a utility-maximization process whereby parents choose to invest in a child’s human capital subject to initial conditions – genetic innate abilities –, parental taste for child’s quality and budget constraints. Parental taste for child quality and a child’s genetic ability are unobserved, OLS estimations of nutrition link to cognitive development likely to be biased.

22. Solving endogeneity problem  Experiments (e.g. Guatamala, Pollit et al(93) Maluccio et al (09))  Sibling differences + IV  Glewwe et al (01) birthweight  Alderman et al (06) drought shock  We follow Alderman et al 2006 and Glewwe et al 2001 in combining household fixed effects and instrumental variable techniques to deal with the endogeneity of nutrition.  We also propose to include observable differences between siblings as well as time-variant household and community characteristics as controls in the estimation.

23. Econometric specification  Link nutrition and cognitive achievement of child ‘k’ from household ‘h’: where: : cognitive achievement (PPVT std. score) : height-for-age : child and household observable characteristics : child unobservable characteristics : household unobservable characteristics : random error, iid (1)

24. Empirical strategy: part I  Using specification (1) and taking the siblings-difference between children ‘i’ and ‘j’ from hh. ‘h’’: (2)  Specification (2)-(2a) is useful because all household unobservable characteristics that are common across siblings are removed.  It also controls for observable differences across siblings that might lead to differential investments within the household (differences in age, gender, relative birth order included in vector ΔX).  Due to data constraints, in practice we estimate: (2a)

25. Empirical strategy: part II (C1) Parental nutritional investments could be adjusted as a child’s innate cognitive abilities are revealed. (C2) The health status and the cognitive ability of a child can be correlated through a common unobserved genetic endowment.  In specification (2a), endogeneity of child’s nutrition still remains a problem due to child-specific unobservable characteristics:  To deal with these possibilities, we further instrument the within-siblings nutrition.

26. Empirical strategy: part II 1.Within-siblings birth weight  Deals with (C1) but not with (C2). 2.Child-specific shocks  Deals with (C1) and (C2).  The 2000-1 shocks affected only the index children, at 0-2 years. Younger siblings were not yet born.  The 2005-6 shocks affected the younger siblings when they were 0-3 years.  Preliminary analysis identifies the following shocks as affecting a substantial number of children in the paired-siblings sample: in 2005-6, food shortage events; in 2000-1, job loss of the head of the hh and severe illness of family members.  Two types of instrumental variables available:

27. Other empirical issues: time-varying characteristics  While not explicitly included in specification (1)-(2a), differences in cognitive and nutrition outcomes between siblings could also be driven by time-varying household and community characteristics that benefit the development of one sibling over the other. To take this into account we add as controls: 1.Changes in household consumption levels (excluding food) between Rounds –to control for life-cycle patterns-. 2.Changes in community characteristics –as reported in community questionnaires from Round 2 and 3-. This includes changes in local prices, community infrastructure, etc.

28. Other empirical issues: preschool enrolment  Final concern:  whether differences in cognitive results might be driven by differences in age of preschool enrolment  itself driven by nutritional differences between siblings. 1.Auxiliary regressions: In the full sample, we can make estimations of the determinants of age of preschool enrolment. Testing the nutrition effect in this estimation will indicate the problem we are faced with. Preliminary results show that nutrition does not predict preschool enrolment. However, this is not entirely the endogeneity we need to address. To deal with this, a number of strategies are at hand:

29. 2. Sub-samples: (a) For those siblings aged between 5 and 7 years, age of pre- school enrolment can be directly controlled for. This sub-sample has an estimated size of 300-350 paired-siblings. (b) In sites with no preschool facilities, small or zero bias resulting from not including age of preschool enrolment. Other empirical issues: preschool enrolment

30. 3. Sources of exogenous variation in preschool enrolment The minimum age of preschool enrolment in Peru is 3 years, whereas enrolment is compulsory at 5 years. Using month of birth as a source of exogenous variation in age of preschool enrolment could partially capture differences in cognitive achievement driven by differences in preschool enrolment. Other empirical issues: preschool enrolment

31. Summary  Looking at socio-economic determinants of ECD outcomes in Peru  Plus more structural look at nutrition- cognitive development nexus  Currently in the field  PDA for 50% of households  PPVT on paper for all