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

  • Biagetti, Marco
  • Scicchitano, Sergio

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.

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File URL: http://mpra.ub.uni-muenchen.de/19060/1/MPRA_paper_19060.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 19060.

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Date of creation: 28 Nov 2009
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Handle: RePEc:pra:mprapa:19060
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  1. Budria, Santiago & Telhado-Pereira, Pedro, 2005. "Educational Qualifications and Wage Inequality: Evidence for Europe," MPRA Paper 91, University Library of Munich, Germany.
  2. 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.
  3. 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.
  4. Martins, Pedro S., 2004. "Industry wage premia: evidence from the wage distribution," Economics Letters, Elsevier, vol. 83(2), pages 157-163, May.
  5. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(8), pages 577-579.
  6. Corrado Andini, 2010. "Within-groups wage inequality and schooling: further evidence for Portugal," Applied Economics, Taylor & Francis Journals, vol. 42(28), pages 3685-3691.
  7. 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, 03.
  8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  9. José Mata & José A. F. Machado, 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.
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