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A decomposition method to evaluate the "paradox of progress", with evidence for Argentina

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  • Walter Sosa Escudero
  • Javier Alejo
  • Leonardo Gasparini
  • Gabriel Montes Rojas

Abstract

The `paradox of progress' is an empirical regularity that associates more education with larger income inequality. Two driving and competing factors behind this phenomenon are the convexity of the `Mincer equation' (that links wages and education) and the heterogeneity in its returns, as captured by quantile regressions. We propose a joint least-squares and quantile regression statistical framework to derive a decomposition in order to evaluate the relative contribution of each explanation. The estimators are based on the `functional derivative' approach. We apply the proposed decomposition strategy to the case of Argentina 1992 to 2015.
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Suggested Citation

  • Walter Sosa Escudero & Javier Alejo & Leonardo Gasparini & Gabriel Montes Rojas, 2021. "A decomposition method to evaluate the "paradox of progress", with evidence for Argentina," Asociación Argentina de Economía Política: Working Papers 4523, Asociación Argentina de Economía Política.
    • Handle: RePEc:aep:anales:4523
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    1. Sebastián Galiani & Rocío Titiunik, 2005. "Changes in the Panamanian Wage Structure: A Quantile Regression Analysis," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0(1-2), pages 3-27, January-D.
    2. 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.
    3. Staneva, Anita & Arabsheibani, Reza & Murphy, Philip D., 2010. "Returns to Education in Four Transition Countries: Quantile Regression Approach," IZA Discussion Papers 5210, Institute of Labor Economics (IZA).
    4. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2005. "Rising Wage Inequality: The Role of Composition and Prices," NBER Working Papers 11628, National Bureau of Economic Research, Inc.
    5. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    6. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    7. Sattinger, Michael, 1993. "Assignment Models of the Distribution of Earnings," Journal of Economic Literature, American Economic Association, vol. 31(2), pages 831-880, June.
    8. 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.
    9. Jan Tinbergen, 1972. "The Impact Of Education On Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 18(3), pages 255-265, September.
    10. 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.
    11. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    12. Victor Chernozhukov & Christian Hansen, 2005. "An IV Model of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 73(1), pages 245-261, January.
    13. repec:bla:revinw:v:18:y:1972:i:3:p:255-65 is not listed on IDEAS
    14. Luis Beccaria & Roxana Maurizio & Gustavo V�zquez, 2015. "Recent decline in wage inequality and formalization of the labour market in Argentina," International Review of Applied Economics, Taylor & Francis Journals, vol. 29(5), pages 677-700, September.
    15. Diego Battistón & Carolina García-Domench & Leonardo Gasparini, 2014. "Could an Increase in Education Raise Income Inequality? Evidence for Latin America," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 51(1), pages 1-39, May.
    16. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
    17. Ariel Fiszbein & Paula Inés Giovagnoli & Harry Anthony Patrinos, 2007. "Estimating the Returns to Education in Argentina Using Quantile Regression Analysis: 1992-2002," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 0(1-2), pages 53-72, January-D.
    18. Sergio P. Firpo & Nicole M. Fortin & Thomas Lemieux, 2018. "Decomposing Wage Distributions Using Recentered Influence Function Regressions," Econometrics, MDPI, vol. 6(2), pages 1-40, May.
    19. Xulia GonzÂlez & Daniel Miles, 2001. "Wage inequality in a developing country: decrease in minimum wage or increase in education returns," Empirical Economics, Springer, vol. 26(1), pages 135-148.
    20. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    21. Card, David, 2001. "Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems," Econometrica, Econometric Society, vol. 69(5), pages 1127-1160, September.
    22. José A. F. Machado & José Mata, 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, May.
    23. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1.
    24. Moshe Buchinsky, 2001. "Quantile regression with sample selection: Estimating women's return to education in the U.S," Empirical Economics, Springer, vol. 26(1), pages 87-113.
    25. Evangelos M. Falaris, 2008. "A Quantile Regression Analysis of Wages in Panama," Review of Development Economics, Wiley Blackwell, vol. 12(3), pages 498-514, August.
    26. John Ariza & Gabriel Montes-Rojas, 2019. "Decomposition methods for analyzing inequality changes in Latin America 2002–2014," Empirical Economics, Springer, vol. 57(6), pages 2043-2078, December.
    27. Francois Bourguignon & Francisco H.G. Ferreira & Nora Lustig, 2005. "The Microeconomics of Income Distribution Dynamics in East Asia and Latin America," World Bank Publications - Books, The World Bank Group, number 14844.
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    More about this item

    Keywords

    inequality; quantile regression; education;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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