We estimate a structural dynamic programming model of schooling decisions with unobserved heterogeneity in school ability and market ability on a sample taken from the National Longitudinal Survey of Youth (NLSY). Both the instantaneous utility of attending school and the wage regression...

We estimate a dynamic programming model of schooling decisions in which the degree of risk aversion can be inferred from schooling decisions. In our model, individuals are heterogeneous with respect to school and market abilities but homogeneous with respect to the degree of risk aversion. We...

We estimate a finite mixture dynamic programming model of schooling decisions in which the log wage regression function is set in a random coefficient framework. The model allows for absolute and comparative advantages in the labor market and assumes that the population is composed of 8 unknown...

We estimate a structural dynamic programming model of schooling decisions and obtain individual specific estimates of the local (and average) returns to schooling as well as the returns to experience. Homogeneity of the returns to human capital is strongly rejected in favor of a discrete...

This paper formulates a structural dynamic programming model of preschool investment choices of altruistic parents and then empirically estimates the structural parameters of the model using the NLSY79 data. The paper finds that preschool investment significantly boosts cognitive and...

In this paper, we formulate and estimate a structural model of post-schooling training that explicitly allows for possible complementarity between initial schooling levels and returns to training. Precisely, the wage outcome equation depends on accumulated schooling and on the incidence of...

This paper contains a survey of the recent literature devoted to the returns to schooling within a dynamic structural framework. I present a historical perspective on the evolution of the literature, from early static models set in a selectivity framework (Willis and Rosen, 1979) to the recent...