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

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Author Info

  • 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.

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File URL: http://hdl.handle.net/10174/8447
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Bibliographic Info

Paper provided by University of Évora, Department of Economics (Portugal) in its series Economics Working Papers with number 13_2006.

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Length: 17 pages
Date of creation: 2006
Date of revision:
Handle: RePEc:evo:wpecon:13_2006

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Keywords: wage differentials; discrimination; sample selection; semi-parametric estimation;

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Cited by:
  1. Patrick Saart & Jiti Gao, 2012. "Semiparametric Methods in Nonlinear Time Series Analysis: A Selective Review," Monash Econometrics and Business Statistics Working Papers 21/12, Monash University, Department of Econometrics and Business Statistics.

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