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Within-Groups Wage Inequality and Schooling: Further Evidence for Portugal

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  • Andini, Corrado

    (University of Madeira)

Abstract

This paper provides further evidence on the positive impact of schooling on within-groups wage dispersion in Portugal, using data on male workers from the 2001 wave of the European Community Household Panel. The issue of schooling endogeneity is taken into account by using the newest available instrumental-variable technique for quantile regression, i.e. the control-function estimator due to Lee (forthcoming, 2007). The findings are compared with earlier results based on different techniques, i.e. the instrumental-variable estimator due to Arias, Hallock and Sosa-Escudero (2001) and the standard exogeneity-based estimator due to Koenker and Bassett (1978).

Suggested Citation

  • Andini, Corrado, 2007. "Within-Groups Wage Inequality and Schooling: Further Evidence for Portugal," IZA Discussion Papers 2828, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp2828
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    References listed on IDEAS

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    1. José A. F. Machado & José Mata, 2001. "Earning functions in Portugal 1982-1994: Evidence from quantile regressions," Empirical Economics, Springer, vol. 26(1), pages 115-134.
    2. Maria Alberta Oliveira & Carlos Santos, 2005. "Assessing school efficiency in Portugal using FDH and bootstrapping," Applied Economics, Taylor & Francis Journals, vol. 37(8), pages 957-968.
    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. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    5. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
    6. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(8), pages 577-579.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. Insik Min & Inchul Kim, 2004. "A Monte Carlo comparison of parametric and nonparametric quantile regressions," Applied Economics Letters, Taylor & Francis Journals, vol. 11(2), pages 71-74.
    9. Pedro Telhado Pereira & Pedro Silva Martins, 2004. "Returns to education and wage equations," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 525-531.
    10. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 106(4), pages 979-1014.
    11. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    12. Corrado Andini, 2007. "The total impact of schooling on within-groups wage inequality in Portugal," Applied Economics Letters, Taylor & Francis Journals, vol. 15(2), pages 85-90.
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    Cited by:

    1. Aysit Tansel & Fatma Bircan Bodur, 2012. "Wage Inequality and Returns to Education in Turkey: A Quantile Regression Analysis," Review of Development Economics, Wiley Blackwell, vol. 16(1), pages 107-121, February.
    2. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute of Labor Economics (IZA).
    3. Joao Pereira & Aurora Galego, 2013. "Intra-Regional Regional Wage Inequality In Portugal: A Quantile Based Decomposition Analisys," ERSA conference papers ersa13p158, European Regional Science Association.
    4. Rafal Kierzenkowski & Isabell Koske, 2012. "Less Income Inequality and More Growth – Are they Compatible? Part 8. The Drivers of Labour Income Inequality – A Literature Review," OECD Economics Department Working Papers 931, OECD Publishing.
    5. Kasey S. Buckles & Daniel M. Hungerman, 2013. "Season of Birth and Later Outcomes: Old Questions, New Answers," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 711-724, July.
    6. Marina Arkhipova & Alexey Egorov & Viacheslav Sirotin, 2017. "Returns to schooling in Russia and Ukraine: Comparative analysis," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 47, pages 100-122.
    7. Aurora A. C. Teixeira & Ana Sofia Loureiro, 2019. "FDI, income inequality and poverty: a time series analysis of Portugal, 1973–2016," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 18(3), pages 203-249, October.
    8. Andreas Behr & Ulrich Pötter, 2010. "What determines wage differentials across the EU?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 8(1), pages 101-120, March.
    9. Daniel Reiter & Mario Thomas Palz & Margareta Kreimer, 2020. "Intergenerational transmission of economic success in Austria with a focus on migration and gender," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-20, December.
    10. Rafal Kierzenkowski & Isabell Koske, 2013. "The Drivers Of Labor Income Inequality — A Literature Review," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-32.
    11. 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.
    12. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.

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    More about this item

    Keywords

    schooling; wage inequality; quantile regression; endogeneity;
    All these keywords.

    JEL classification:

    • 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
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other

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