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Modeling Return to Education in Heterogeneous Populations: An Application to Italy

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Statistical Learning of Complex Data (CLADAG 2017)

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.

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Notes

  1. 1.

    See [12, 13] for a discussion on the use of polynomial terms for experience.

  2. 2.

    Hourly wages are defined as: yearly net earnings/(months worked × weekly hours worked × 4).

  3. 3.

    Standard and not actual year of formal schooling are recorded. Since students who fail to reach a standard have to repeat the year, the actual number of years is likely to be underestimated.

  4. 4.

    We exclude self-employed because of the low reliability of their declared earnings.

  5. 5.

    Monthly or annual wages would in addition capture the effect of individual’s decisions on working hours. Given the only weak positive correlation between working time and educational attainment, it is reasonable to assume that the choice of hours worked reflects individual preferences rather than educational levels.

  6. 6.

    Notice that hourly measure of earnings can be affected by measurement errors due to the fact that we calculate hourly wages as total earnings divided by hours of work; for instance, there might be part-time workers that do 2 weeks a month committing the whole day.

  7. 7.

    Standard, not actual, years of formal schooling are recorded. Since students who fail to reach a standard have to repeat the year, the actual number of years is likely to be underestimated.

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Authors and Affiliations

  1. Dipartimento di Economia e Impresa, University of Catania, Catania, Italy

    Angelo Mazza, Salvatore Ingrassia & Antonio Punzo

  2. Dipartimento di Scienze Giuridiche, della Società e dello Sport, University of Palermo, Palermo, Italy

    Michele Battisti

Authors
  1. Angelo Mazza
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  2. Michele Battisti
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  3. Salvatore Ingrassia
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  4. Antonio Punzo
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Correspondence to Antonio Punzo .

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Editors and Affiliations

  1. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy

    Francesca Greselin

  2. Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy

    Laura Deldossi

  3. Department of Economic and Social Sciences, Università Cattolica del Sacro Cuore, Piacenza, Italy

    Luca Bagnato

  4. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maurizio Vichi

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Mazza, A., Battisti, M., Ingrassia, S., Punzo, A. (2019). Modeling Return to Education in Heterogeneous Populations: An Application to Italy. In: Greselin, F., Deldossi, L., Bagnato, L., Vichi, M. (eds) Statistical Learning of Complex Data. CLADAG 2017. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-21140-0_13

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