Using Experimental Data to Validate a Dynamic Behavioral Model of Human Capital Investments in Children: Evaluating the Impact of Mexico's PROGRESA Program

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The project provides estimates of a behavioral model of parental decision-making about child schooling and fertility in order to evaluate the impact of a controlled social experiment in rural Mexico designed to augment completed schooling levels of children. The program provides subsidies to parents conditional on the school attendance of their children. Social experiments are often limited in their ability to evaluate variations in the characteristics of the experimental program as well as longer run effects that extend beyond the life of the experiment, and they cannot be used at all to evaluate radically different programs. In contrast, to the extent that the estimated behavioral model is valid it can be used to evaluate the impact of counterfactual policies. The following analyses are performed. First, the impact of the program at subsidy levels not explicitly part of the program is assessed. The program itself did not experimentally vary the amount of the subsidy. Second, the impact of extending the program to some or all ineligible families is assessed. Third, because the program has been in effect for only two years, it is not possible by simply comparing the treated to the untreated households to evaluate the longer run impact of the program. For example, even if the program is viewed by the experimental group as permanent, the impact of the program on the existing families is conditioned on their current circumstances, e.g., the number of children they have. However, the longer-run impact of the program may be to affect those circumstances, e.g., the number of children new households will have. Fourth, we compare the impact of the subsidy program on schooling to the radically different policy of placing legal restrictions on the use of child labor. The project also makes a methodological contribution. Structural estimation of behavioral models requires auxiliary assumptions about functional forms, i.e., of preferences, technology and other constraints, and the distributions of unobservable random elements. Assessing the validity of such models by relying on tests of model fit to sample elements of the data used in estimation provides useful, but far from compelling, evidence on the validity of the model. Such models are often subject to a form of "pre-test" estimation in that the final formulation of the model is based on the fit of prior formulations to certain aspects of the data. In this research, we use a different approach to model validation. The validity of the model will be ascertained according to how well structural estimates of the model based on data from the randomized-out control group predict the experimental impacts of the program.