IDEAS home Printed from https://ideas.repec.org/a/cml/moneta/vxxviiiy2005i4p339-364.html
   My bibliography  Save this article

Cambios en la estructura salarial: una historia desde la regresión cuanfílica

Author

Listed:
  • Héctor Manuel Zárate S.

    (Banco de la República Colombia)

Abstract

El objetivo principal de éste artículo es analizar el cambio en los retornos de la educación y la experiencia en diferentes puntos de la distribución salarial a través de una aplicación empírica en el mercado laboral colombiano. También, se analiza la evolución de la desigualdad salarial y sus características distribucionales para el período de 1991 a 2000. El artículo se basa en la ecuación de Mincer y utiliza la técnica semi-paramétrica de regresión cuantílica. Los datos se obtienen de las Encuestas Nacionales de Hogares. Aunque los retornos tienen patrones de comportamiento similares, las magnitudes y la variabilidad difieren entre los cuantiles analizados. La desigualdad salarial se incrementó en el final del periodo de estudio de acuerdo a las habilidades de cada grupo.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Héctor Manuel Zárate S., 2005. "Cambios en la estructura salarial: una historia desde la regresión cuanfílica," Monetaria, CEMLA, vol. 0(4), pages 339-364, octubre-d.
  • Handle: RePEc:cml:moneta:v:xxviii:y:2005:i:4:p:339-364
    as

    Download full text from publisher

    File URL: http://www.cemla.org/PDF/monetaria/PUB_MON_XXVIII-04.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Belman, Dale & Heywood, John S, 1991. "Sheepskin Effects in the Returns to Education: An Examination on Women and Minorities," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 720-724, November.
    2. Andrew Weiss, 1995. "Human Capital vs. Signalling Explanations of Wages," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 133-154, Fall.
    3. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    4. Buchinsky, Moshe, 1995. "Quantile regression, Box-Cox transformation model, and the U.S. wage structure, 1963-1987," Journal of Econometrics, Elsevier, vol. 65(1), pages 109-154, January.
    5. Deaton, Angus S & Ruiz-Castillo, Javier & Thomas, Duncan, 1989. "The Influence of Household Composition on Household Expenditure Patterns: Theory and Spanish Evidence," Journal of Political Economy, University of Chicago Press, vol. 97(1), pages 179-200, February.
    6. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    Full references (including those not matched with items on IDEAS)

    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. Héctor Manuel Zárate S., 2003. "Cambios en la Estructura Salarial:Una Historia desde la Regresión Cuantílica," Borradores de Economia 245, Banco de la Republica de Colombia.
    2. Melly, Blaise, 2005. "Decomposition of differences in distribution using quantile regression," Labour Economics, Elsevier, vol. 12(4), pages 577-590, August.
    3. Chen, Songnian, 2010. "An integrated maximum score estimator for a generalized censored quantile regression model," Journal of Econometrics, Elsevier, vol. 155(1), pages 90-98, March.
    4. Ordu, Beyza Mina & Oran, Adil & Soytas, Ugur, 2018. "Is food financialized? Yes, but only when liquidity is abundant," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 82-96.
    5. Tae-Hwan Kim & Christophe Muller, 2004. "Two-stage quantile regression when the first stage is based on quantile regression," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 218-231, June.
    6. Xavier D’Haultfoeuille & Pauline Givord, 2014. "La régression quantile en pratique," Économie et Statistique, Programme National Persée, vol. 471(1), pages 85-111.
    7. Jorge David Quinteo Otero & William Orlando Prieto Bustos & Fernando Barrios Aguirre & Laura Elena Leviller Guardo, 2008. "Determinantes de la eficiencia técnica en las empresas colombianas, 2001-2004," Revista Semestre Económico, Universidad de Medellín, November.
    8. Il-Horn Hann & Jeffrey A. Roberts & Sandra A. Slaughter, 2013. "All Are Not Equal: An Examination of the Economic Returns to Different Forms of Participation in Open Source Software Communities," Information Systems Research, INFORMS, vol. 24(3), pages 520-538, September.
    9. Jiang, Rong & Qian, Weimin & Zhou, Zhangong, 2012. "Variable selection and coefficient estimation via composite quantile regression with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 308-317.
    10. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    11. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    12. Lei Chen & Rangan Gupta & Zinnia Mukherjee & Peter Wanke, 2016. "Technical efficiency of Connecticut Long Island Sound lobster fishery: a nonparametric approach to aggregate frontier analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1533-1548, April.
    13. You, Wanhai & Guo, Yawei & Zhu, Huiming & Tang, Yong, 2017. "Oil price shocks, economic policy uncertainty and industry stock returns in China: Asymmetric effects with quantile regression," Energy Economics, Elsevier, vol. 68(C), pages 1-18.
    14. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    15. Marcelo Cajias & Philipp Freudenreich & Anna Freudenreich, 2020. "Exploring the determinants of real estate liquidity from an alternative perspective: censored quantile regression in real estate research," Journal of Business Economics, Springer, vol. 90(7), pages 1057-1086, August.
    16. Jooyong Shim & Changha Hwang & Kyungha Seok, 2014. "Composite support vector quantile regression estimation," Computational Statistics, Springer, vol. 29(6), pages 1651-1665, December.
    17. 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.
    18. Schmidt, Christoph M. & Tauchmann, Harald, 2011. "Heterogeneity in the intergenerational transmission of alcohol consumption: A quantile regression approach," Journal of Health Economics, Elsevier, vol. 30(1), pages 33-42, January.
    19. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    20. Crespo, Anna Risi Vianna & Reis, Mauricio Cortez, 2009. "Sheepskin Effects and the Relationship between Earnings and Education: Analyzing the Evolution over Time in Brazil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 63(3), August.

    More about this item

    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:cml:moneta:v:xxviii:y:2005:i:4:p:339-364. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Matias Ossandon Busch (email available below). General contact details of provider: https://edirc.repec.org/data/cemlamx.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.