published in: Journal of Econometrics, 2007, 140 (2), 333-948
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 types. Overall, labor market skills (as opposed to taste for
schooling) appear to be the prime factor explaining schooling attainments. The estimates
indicate a higher cross-sectional variance in the returns to experience than in the returns to
schooling. From various simulations, we find that the sub-population mostly affected by a
counterfactual change in the utility of attending school is composed of individuals who have
any combination of some of the following attributes: absolute advantages in the labor market,
high returns to experience, low utility of attending school and relatively low returns to
schooling. Unlike what is often postulated in the average treatment effect literature, the weak
correlation (unconditional) between the returns to schooling and the individual reactions to
treatment is not sufficient to reconcile the discrepancy between OLS and IV estimates of the
returns to schooling often found in the literature.
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