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.

Mazza A., Battisti M., Ingrassia S., Punzo A. (2019). Modeling return to education in heterogeneous populations: An application to Italy. In F. Greselin, L. Deldossi, L. Bagnato, M. Vichi (a cura di), Statistical learning of complex data (pp. 121-131). Springer [10.1007/978-3-030-21140-0_13].

Modeling return to education in heterogeneous populations: An application to Italy

Battisti M.;
2019

Abstract

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.
Settore SECS-P/01 - Economia Politica
Mazza A., Battisti M., Ingrassia S., Punzo A. (2019). Modeling return to education in heterogeneous populations: An application to Italy. In F. Greselin, L. Deldossi, L. Bagnato, M. Vichi (a cura di), Statistical learning of complex data (pp. 121-131). Springer [10.1007/978-3-030-21140-0_13].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/478980
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