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Returns to schooling in Russia and Ukraine: Comparative analysis

Author

Listed:
  • Arkhipova, Marina

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

  • Egorov, Alexey

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

  • Sirotin, Viacheslav

    (National Research University Higher School of Economics, Moscow, Russian Federation;)

Abstract

The paper is focused on the measuring of return to education. This measure reflects the yield of investments in human capital. The analysis is based on estimation of the modified Mincer’s type equations by means of quantile regressions for panel data. Along with quantile regressions estimation we consider the models of the joint distribution of wage and education duration based on copulas. The methodological approach used in the study allows comparing the significance of the factors included in the model for the wage formation. Also it allows investigating the structure of the relationship between wage and education and answering the question devoted to the following dilemma – in which country return to education was higher after the decade of independent development.

Suggested Citation

  • 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.
    • Handle: RePEc:ris:apltrx:0326
    as

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    References listed on IDEAS

    as
    1. Griliches, Zvi & Mason, William M, 1972. "Education, Income, and Ability," Journal of Political Economy, University of Chicago Press, vol. 80(3), pages 74-103, Part II, .
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    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.
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    5. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    6. Brodsky, Boris & Penikas, Henry & Safaryan, Irina, 2009. "Detection of Structural Breaks in Copula Models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 16(4), pages 3-15.
    7. Barro, Robert J. & Lee, Jong Wha, 2013. "A new data set of educational attainment in the world, 1950–2010," Journal of Development Economics, Elsevier, vol. 104(C), pages 184-198.
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    11. Corrado Andini, 2010. "Within-groups wage inequality and schooling: further evidence for Portugal," Applied Economics, Taylor & Francis Journals, vol. 42(28), pages 3685-3691.
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    14. Chamberlain, Gary & Griliches, Zvi, 1975. "Unobservables with a Variance-Components Structure: Ability, Schooling, and the Economic Success of Brothers," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(2), pages 422-449, June.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    capitalization of professional knowledge; quantile regression; wage; longitudinal data;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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