The Mincer human capital earnings function is a regression model that relates individual’s earnings to schooling and experience. It has been used to explain individual behavior with respect to educational choices and to indicate productivity on a large number of countries and across many different demographic groups. However, recent empirical studies have shown that often the population of interest embed latent homogeneous subpopulations, with different returns to education across subpopulations, rendering a single Mincer’s regression inadequate. Moreover, whatever (concomitant) information is available about the nature of such a heterogeneity, it should be incorporated in an appropriate manner. We propose a mixture of Mincer’s models with concomitant variables: it provides a flexible generalization of the Mincer model, a breakdown of the population into several homogeneous subpopulations, and an explanation of the unobserved heterogeneity. The proposal is motivated and illustrated via an application to data provided by the Bank of Italy’s Survey of Household Income and Wealth in 2012.
|Titolo della pubblicazione ospite||Statistical learning of complex data|
|Numero di pagine||11|
|Stato di pubblicazione||Published - 2019|
|Nome||STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION|