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The distributive Effects of Education: An Unconditional Quantile Regression Approach

Author

Listed:
  • 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)

Abstract

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

    as
    1. 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.
    2. 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.
    3. 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.
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    5. 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.
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    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. repec:ran:wpaper:824 is not listed on IDEAS
    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. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Corrado Andini, 2022. "Tertiary education for all and wage inequality: policy insights from quantile regression," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(6), pages 1281-1296, November.
    2. Darío Judzik & Lucía Trujillo & Soledad Villafañe, 2017. "A tale of two decades: Income inequality and public policy in Argentina (1996-2014)," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 36(72), October.
    3. 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.
    4. repec:zbw:rwirep:0455 is not listed on IDEAS
    5. Dai Binh Tran & Sasiwimon Warunsiri Paweenawat, 2023. "The returns to education and wage penalty from overeducation: New evidence from Vietnam," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1267-1290, October.
    6. Schreurs, Eloi & Peeters, Ludo & Van Passel, Steven, 2014. "Analyzing the impacts of soil contamination and urban development pressure on farmland values: Unconditional quantile regression estimation," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182741, European Association of Agricultural Economists.
    7. Ebru Çaglayan Akay & Fulden Komuryakan, 2021. "What Do Conditional and Unconditional Quantile Regression Models Tell Us Something Different About Wage Inequality in Turkey?," Journal of Economy Culture and Society, Istanbul University, Faculty of Economics, vol. 64(64), pages 257-277, December.
    8. 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.
    9. 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.
    10. Stefan Schneck, 2020. "Self-employment as a source of income inequality," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 10(1), pages 45-64, March.
    11. Schneck, Stefan, 2018. "The effect of self-employment on income inequality," Working Papers 05/18, Institut für Mittelstandsforschung (IfM) Bonn.
    12. Petra Sauer & Philippe Van Kerm & Daniele Checchi, 2023. "Higher Education Expansion & Labour Income Inequality in High-income Countries: A Gender-specific Perspective," LIS Working papers 837, LIS Cross-National Data Center in Luxembourg.
    13. Roxana Maurizio & Luis Beccaria & Ana Monsalvo, 2022. "Labour Formalization and Inequality: The Distributive Impact of Labour Formalization in Latin America since 2000," Development and Change, International Institute of Social Studies, vol. 53(1), pages 117-165, January.

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    More about this item

    Keywords

    unconditional quantile regression; income inequality; education; Argentina;
    All these keywords.

    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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