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Returns to Schooling in Europe: Evidence From Quantile Regression on EU-SILC Data

Author

Listed:
  • Marco Biagetti

    (Ministry for Economic Development, Department for the Development and the Economic Cohesion)

  • Sergio Scicchitano

    (Ministry for Economic Development, Department for the Development and the Economic Cohesion)

Abstract

In this paper the effects of schooling on wage inequality across a group of European countries are examined. Using the latest available EU-SILC dataset, a positive relation between these two variables is found across the whole distribution by means of a quantile regression (QR) approach. Such effect is stronger effect at the highest quantiles than at the lowest. This result corroborates the possibility of an increase in the wage dispersion when higher educational attainments are achieved and it is found to be rather robust as showed by linear hypothesis tests. Finally, the country-specific results highlight that the OLS technique misleads relevant information over cross-countries heterogeneity in the impact of education on within group inequality at different points of the wage distribution compared to the QR approach. Journal: Journal of Knowledge Management, Economics and Information Technology

Suggested Citation

  • Marco Biagetti & Sergio Scicchitano, 2012. "Returns to Schooling in Europe: Evidence From Quantile Regression on EU-SILC Data," Journal of Knowledge Management, Economics and Information Technology, ScientificPapers.org, vol. 2(5), pages 1-9, October.
  • Handle: RePEc:spp:jkmeit:1318
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    References listed on IDEAS

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    1. Hartog, Joop, 2000. "Over-education and earnings: where are we, where should we go?," Economics of Education Review, Elsevier, vol. 19(2), pages 131-147, April.
    2. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
    3. Budría, Santiago & Moro-Egido, Ana I., 2008. "Education, educational mismatch, and wage inequality: Evidence for Spain," Economics of Education Review, Elsevier, vol. 27(3), pages 332-341, June.
    4. Joop Hartog & Pedro Pereira & Jose Vieira, 2001. "Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quantile regression estimators," Applied Economics, Taylor & Francis Journals, vol. 33(8), pages 1021-1037.
    5. 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.
    6. 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.
    7. Piero Esposito & Robert Stehrer, 2009. "The sector bias of skill-biased technical change and the rising skill premium in transition economies," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 36(3), pages 351-364, August.
    8. 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.
    9. Amanda Gosling & Stephen Machin & Costas Meghir, 2000. "The Changing Distribution of Male Wages in the U.K," Review of Economic Studies, Oxford University Press, vol. 67(4), pages 635-666.
    10. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    11. Daron Acemoglu, 2002. "Technical Change, Inequality, and the Labor Market," Journal of Economic Literature, American Economic Association, vol. 40(1), pages 7-72, March.
    12. Adrian Wood, 1995. "How Trade Hurt Unskilled Workers," Journal of Economic Perspectives, American Economic Association, vol. 9(3), pages 57-80, Summer.
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    More about this item

    JEL classification:

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

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