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Wage inequality and returns to schooling in Europe: a semi-parametric approach using EU-SILC data

Author

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  • Biagetti, Marco
  • Scicchitano, Sergio
Abstract
In this paper we apply a semi-parametric approach (quantile regression - QR) to the last 2007 wave of the EU-SILC data set, in order to explore the connection between education and wage inequality in 8 European countries. We find that wages increase with education and this holds true across the whole distribution. Furthermore, this effect is generally more important at the highest quantiles of the distribution than at the lowest, implying that schooling increases wage dispersion. This evidence is found to be rather robust as showed through tests of linear hypothesis. We also corroborate the idea that, although OLS coefficients estimates are substantially in line with the QR’s, the former technique really misleads relevant information about cross-countries heterogeneity in the impact of education on within group inequality at different points of the wage distribution. Hence this paper confirms that a semi-parametric QR approach is more interesting, as well as more appropriate, because it measures the wage effect of education at different quantiles, thus describing relevant cross-countries changes or bounces not only in the location, but also in the shape of the distribution.

Suggested Citation

  • Biagetti, Marco & Scicchitano, Sergio, 2009. "Wage inequality and returns to schooling in Europe: a semi-parametric approach using EU-SILC data," MPRA Paper 19060, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:19060
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    References listed on IDEAS

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    3. Santiago Budría & Pedro Telhado-Pereira, 2011. "Educational Qualifications And Wage Inequality: Evidence For Europe," Revista de Economia Aplicada, Universidad de Zaragoza, Departamento de Estructura Economica y Economia Publica, vol. 19(2), pages 5-34, Autumn.
    4. Martins, Pedro S. & Pereira, Pedro T., 2004. "Does education reduce wage inequality? Quantile regression evidence from 16 countries," Labour Economics, Elsevier, vol. 11(3), pages 355-371, June.
    5. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    6. Seamus McGuinness & Frances McGinnity & Philip J. O'Connell, 2009. "Changing Returns to Education During a Boom? The Case of Ireland," LABOUR, CEIS, vol. 23(s1), pages 197-221, March.
    7. Corrado Andini, 2010. "Within-groups wage inequality and schooling: further evidence for Portugal," Applied Economics, Taylor & Francis Journals, vol. 42(28), pages 3685-3691.
    8. José A. F. Machado & José Mata, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465, May.
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    Cited by:

    1. Concetta Mendolicchio & Thomas Rhein, 2014. "The gender gap of returns on education across West European countries," International Journal of Manpower, Emerald Group Publishing Limited, vol. 35(3), pages 219-249, May.

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    More about this item

    Keywords

    Returns to education; Wage inequality; Quantile regression; Europe;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

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