that individuals and their parental families make investments in each life-cycle stage within a dynamic, reduced-form demand context, given initial conditions (including parental family background F0, initial community prices and policies C0, genetic and other endowments E0, and individual characteristics I0 such as sex and age) and changes that occurred over time such as those in markets, policies and other conditions ΔC (conditional on the individual’s birth year and subsequent age):

- (2) Ka = K(F0, C0, E0, I0, ΔC, V), where Δ refers to observed changes or shocks from the initial conditions to the end of the early-adolescent life-cycle stage, and V refers to unobserved idiosyncratic influences.

This expression encapsulates the results of many decisions that parents, and then increasingly the children themselves, make over the children’s life cycle, given initial conditions and time-varying factors outside of the control of the family. The elements in relation (2) are in general vectors of opportunities and constraints to which the family responds. One example is the genetic endowments (E0), a vector that includes innate “ability” endowments related to learning and “physical” endowments related to physical growth.<sup>4</sup> Consideration of relations (1) and (2) illustrates the three important aspects of the problems of estimates in the literature of relation (1) that are mentioned in the introduction. <u>First, all of the</u> human capital stocks at the end of the pre-adolescent stage are determined in part by genetic and other endowments (E0) that also are posited to have direct effects on the outcomes of interest in relation (1). This means that to obtain consistent estimates of the impacts of schooling (or of other human capital stocks) on age of first union formation and age of first parenting and other outcomes of interest, some combination of data and estimation method must be used to avoid the bias that otherwise would result from correlations between the capital stocks and the expanded compound error term in relation (1) that includes unobserved genetic and other endowments (E0) in addition to the idiosyncratic error (U). This problem can be addressed in principle by random assignment of human capital stocks, by direct control for genetic and other endowments (E0) or by instrumental variable (IV) estimates in which each component of the capital stocks in relation (1) is replaced by its predicted value from relation (2), which is not correlated with the unobserved E0 and therefore not correlated with the compound disturbance in relation (1). In the present (and almost all) data sets, only the third of these is an option.<sup>5</sup> However in the literature on the impact of schooling on formation of first union and initiating parenting in developing countries, there are to our knowledge no satisfactory efforts to deal with the fact that schooling is determined by behavioral choices. We do so in this study using IV estimators. Second, all of the human capital stocks are determined in part by the same initial conditions (F0, C0, E0, I0) and some common observed community changes ΔC and unobserved influences V. This means that the capital stocks are likely to be correlated, with

> 4 These various endowments may be significantly correlated but they need not be positively correlated.  A recent study for the United States, for example, finds that the endowments related to schooling and earnings on one hand and those related to physical health on the other, are negatively correlated (Behrman and Rosenzweig 2004).  If that were the case in this study, the failure to control for genetic endowments could result in an overestimate of the effect of schooling on the quality of the union partner as measured, for example by the partner’s adult cognitive skills, but an underestimate of the effect of an indicator of physical development (such as height-for-age) on those same skills.

> 5 Within-sibling estimates could be used to control for the average family genetic endowments, but previous studies suggest that the individual-specific deviations from those family averages have important impact on human capital investments (Behrman, Rosenzweig and Taubman 1994, 1996).  If there are gender differences in the coefficients, moreover, within-brother and within-sibling estimates would be required to control for family endowments, and our data set does not have sufficient numbers of brother and sister pairs to permit such estimates.

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