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The distributive Effects of Education: An Unconditional Quantile Regression Approach


  • Javier Alejo

    (Universidad Nacional de La Plata and CONICET)

  • María Florencia Gabrielli

    (Universidad Nacional de Cuyo and CONICET)

  • Walter Sosa Escudero

    (Universidad de San Andres and CONICET)


En este trabajo utilizamos los más recientes métodos de regresión no condicionada por cuantiles (RNCQ) para estudiar los efectos distributivos de la educación en Argentina. Los métodos estándar se centran, por lo general, en efectos promedio o estudian los efectos distributivos ya sea haciendo uso de suposiciones estrictas al modelar y/o a través de descomposiciones contrafácticas que requieren varias observaciones temporales. La aplicación empírica en este trabajo muestra la flexibilidad y utilidad de los métodos de RNCQ. Nuestra aplicación para el caso de Argentina sugiere que la educación ha contribuido positivamente al aumento de la desigualdad, debido principalmente a efectos fuertemente heterogéneos de la educación sobre los ingresos.

Suggested Citation

  • Javier Alejo & María Florencia Gabrielli & Walter Sosa Escudero, 2011. "The distributive Effects of Education: An Unconditional Quantile Regression Approach," CEDLAS, Working Papers 0125, CEDLAS, Universidad Nacional de La Plata.
  • Handle: RePEc:dls:wpaper:0125

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    References listed on IDEAS

    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    2. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    3. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    4. 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.
    5. Javier Alejo, 2006. "Desigualdad Salarial en el Gran Buenos Aires: Una Aplicación de Regresión por Cuantiles en Microdescomposiciones," CEDLAS, Working Papers 0036, CEDLAS, Universidad Nacional de La Plata.
    6. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    7. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    8. 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.
    9. Walter Sosa Escudero & Sergio Petralia, 2010. "“I Can Hear the Grass Grow”: The Anatomy of Distributive Changes in Argentina," CEDLAS, Working Papers 0106, CEDLAS, Universidad Nacional de La Plata.
    10. repec:ran:wpaper:824 is not listed on IDEAS
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    Cited by:

    1. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute for the Study of Labor (IZA).
    2. repec:zbw:rwirep:0455 is not listed on IDEAS
    3. Deborah A. Cobb-Clark & Sonja C. Kassenboehmer & Mathias G. Sinning, 2013. "Locus of Control and Savings," Ruhr Economic Papers 0455, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    4. Cobb-Clark, Deborah A. & Kassenboehmer, Sonja C. & Sinning, Mathias G., 2016. "Locus of control and savings," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 113-130.
    5. repec:col:000093:015844 is not listed on IDEAS
    6. Tiiu Paas & Maryna Tverdostup, 2016. "Assessment of labour market returns in the case of gender unique human capital," ERSA conference papers ersa16p157, European Regional Science Association.

    More about this item


    unconditional quantile regression; income inequality; education; Argentina;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • D3 - Microeconomics - - Distribution


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