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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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    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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