IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp2828.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://ftp.iza.org/dp2828.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Corrado Andini, 2007. "Returns to education and wage equations: a dynamic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 14(8), pages 577-579.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. 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.
    7. Pedro Telhado Pereira & Pedro Silva Martins, 2004. "Returns to education and wage equations," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 525-531.
    8. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    9. Joshua D. Angrist & Alan B. Keueger, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, Oxford University Press, vol. 106(4), pages 979-1014.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    6. 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.
    7. Arkhipova, Marina & Egorov, Alexey & Sirotin, Viacheslav, 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.
    8. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Corrado Andini, 2010. "A dynamic Mincer equation with an application to Portuguese data," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2091-2098.
    3. Andersson, Roland & Nabavi Larijani, Pardis & Wilhelmsson, Mats, 2013. "The impact of vocational education and training on income in Sweden," Working Paper Series 13/4, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    4. Andini, Corrado, 2009. "How Fast Do Wages Adjust to Human-Capital Productivity? Dynamic Panel-Data Evidence from Belgium, Denmark and Finland," IZA Discussion Papers 4583, Institute of Labor Economics (IZA).
    5. Andini, Corrado & Pereira, Pedro T., 2007. "Full-time Schooling, Part-time Schooling, and Wages: Returns and Risks in Portugal," IZA Discussion Papers 2651, Institute of Labor Economics (IZA).
    6. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute of Labor Economics (IZA).
    7. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    8. Balestra, Simone & Backes-Gellner, Uschi, 2017. "Heterogeneous returns to education over the wage distribution: Who profits the most?," Labour Economics, Elsevier, vol. 44(C), pages 89-105.
    9. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    10. Cinthya G. Caamal Olvera, 2017. "Decreasing returns to schooling in Mexico," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 32(1), pages 27-63.
    11. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Working Papers halshs-02272874, HAL.
    12. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    13. Christophe Muller & Christophe J. Nordman, 2008. "Intra-Firm Human Capital Externalities in Tunisia," THEMA Working Papers 2008-38, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    14. Christophe Muller & Christophe Nordman, 2004. "Which Human Capital Matters For Rich And Poor'S Wages: Evidence From Matched Worker-Firm Data From Tunisia," Working Papers. Serie AD 2004-28, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    15. Tushar Agrawal, 2011. "Returns to education in India: Some recent evidence," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2011-017, Indira Gandhi Institute of Development Research, Mumbai, India.
    16. Arkhipova, Marina & Egorov, Alexey & Sirotin, Viacheslav, 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.
    17. Andrew Chesher, 2005. "Nonparametric Identification under Discrete Variation," Econometrica, Econometric Society, vol. 73(5), pages 1525-1550, September.
    18. Bartelsman, Eric & Dobbelaere, Sabien & Peters, Bettina, 2013. "Allocation of Human Capital and Innovation at the Frontier: Firm-Level Evidence on Germany and the Netherlands," IZA Discussion Papers 7540, Institute of Labor Economics (IZA).
    19. Massimiliano Agovino Author-Email: agovino.massimo@gmail.com & Antonio Garofalo Author-Email: antonio.garofalo@uniparthenope.it, 2016. "The Impact of Education on Wage Determination between Workers in Southern and Central-Northern Italy," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 63(1), pages 25-43, March.
    20. Pedro Pires & João Pedro Pereira & Luís Filipe Martins, 2015. "The Empirical Determinants of Credit Default Swap Spreads: a Quantile Regression Approach," European Financial Management, European Financial Management Association, vol. 21(3), pages 556-589, June.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp2828. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Holger Hinte). General contact details of provider: http://www.iza.org .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.