68 CHAPTER 4
between health and schooling in the human capital production function. For example, if health capital is an input in the schooling production function, enabling children to attend classes every day, whether health capital aug- ments the productivity of schooling investments or substitutes for schooling inputs, it affects the optimal level of schooling investments.3 In the former case, I predict a cumulative process of widening inequality among siblings, given differences in nutritional status and health capital in early childhood, since healthy children tend to have better schooling outcomes. If parents are averse to sibling inequality in future earnings, however, they will make greater schooling investments in unhealthy children.
Second, parents learn about potential returns to schooling investments from the outcomes of early-stage investments (in nutritional status and health, in our context) and make decisions regarding optimal investments at later stages. In these decisions, parents’ preferences concerning sibling inequality in human capital and future income matter. If parents are averse to inequality among their children, they may increase investments in the schooling of their less well-endowed children to equalize the children’s future incomes (Quisumbing, Estudillo, and Otsuka 2003). In the context of human capital production, since the outcomes of early-childhood investments signal the expected outcomes of investment at a later stage, parents can react to those signals by changing late-stage investments to maximize their objectives.
Third, health capital, as well as schooling investments, generates positive economic returns, especially in the developing-country context (Strauss 1986; Haddad and Bouis 1991; Thomas and Strauss 1997). Therefore, health capital may increase opportunity costs for schooling investments (that is, higher wages), and it may affect intertemporal decisionmaking, creating heteroge- neity in the effect of health capital on schooling investments. In empirically assessing this issue, we encounter challenging problems even with longitudinal data for children. The first problem arises from the potential endogeneity of nutritional status; fixed household-specific unobserved factors may affect both child health capital and schooling decisions, creating a positive correlation between them. To eliminate this problem, our approach requires household fixed effects, which base inference on (often small) sib- ling variations.4
3 Health capital is a part of human capital, measuring physical development and conditions in children (height, weight, and health status) but not including such endowments as inborn differ-
ences in intelligence. 4 The literature offers a few qualified empirical studies, which solve these problems. Employ- ing longitudinal data for children from Zimbabwe, Alderman, Hoddinott, and Kinsey (2006) use civil war and drought periods that affected growth in children below the age of 3 to identify the effect of early-childhood malnutrition on schooling in a maternal fixed-effect model. This iden-
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