Measuring the returns to education nonparametrically

This article uses a nonparametric model of earnings to measure the returns to education. Under very general smoothness conditions, a nonparametric estimator reveals the true shape of the earnings profiles up to random sampling error. Thus, the nonparametric model should provide better predictions than its parametric counterpart. We find that the nonparametric model predicts very different estimated returns than standard Mincer formulations. Depending on the experience and education level, returns measured in log earnings estimated from nonparametric model can be nearly twice those obtained from the Mincer model. Finally, this article examines what structural features parametric models should include.