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Evidence on Gender Wage Discrimination in Portugal: parametric and semi-parametric approaches

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  • Aurora Galego

    (Department of Economics, University of Évora)

  • João Pereira

    (Department of Economics, University of Évora)

Abstract

In this paper we use two alternative approaches to study the extent of gender wage discrimination in Portugal. Both methods involve the estimation of wage equations for males and females and the Blinder [1973] and Oaxaca [1973] decomposition. However, to take into account possible sample selection bias, we consider both parametric and semi-parametric methods. First, we consider a parametric approach that relies on distributional assumptions about the distribution of the error terms in the model (Vella (1992, 1998) and Wooldridge (1998)). Within this approach, if the distributional assumption is not satisfied, the parameters? estimates may be inconsistent. Secondly, we apply Li and Wooldridge [2002] semi-parametric estimator, which does not assume any known distribution on the joint distribution of the errors of the wage equation and of the sample selection equation; the distribution has an unknown form and is estimated through non-parametric kernel techniques.We employ micro data for Portugal from the European Community Household Panel (ECHP). The results from both approaches provide evidence in favour of the existence of gender wage discrimination in Portugal. However, the extent of labour market discrimination decreases when sample selection bias corrections are taken into account.

Suggested Citation

  • Aurora Galego & João Pereira, 2006. "Evidence on Gender Wage Discrimination in Portugal: parametric and semi-parametric approaches," Economics Working Papers 13_2006, University of Évora, Department of Economics (Portugal).
  • Handle: RePEc:evo:wpecon:13_2006
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    File URL: http://hdl.handle.net/10174/8447
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    References listed on IDEAS

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    4. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    5. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
    6. Christofides, Louis N, et al, 2003. "Recent Two-Stage Sample Selection Procedures with an Application to the Gender Wage Gap," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 396-405, July.
    7. José Vieira & Ana Cardoso & Miguel Portela, 2005. "Gender segregation and the wage gap in Portugal: an analysis at the establishment level," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 3(2), pages 145-168, August.
    8. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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    Cited by:

    1. Patrick Saart & Jiti Gao & Nam Hyun Kim, 2014. "Semiparametric methods in nonlinear time series analysis: a selective review," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(1), pages 141-169, March.

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    More about this item

    Keywords

    wage differentials; discrimination; sample selection; semi-parametric estimation;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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