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Male and Female Wage Functions: A Quantile Regression Analysis using LEED and LFS Portuguese Databases

Author

Listed:
  • Maria da Conceição Figueiredo
  • Elsa Fontainha

Abstract

The research aims to study the distribution of hourly wages for men and women in Portugal, adopting a quantile regression (QR) approach. Two databases are used for the estimation of the wage functions: the Quadros de Pessoal, Linked Employer-Employee Data (QP-LEED) and the Inquérito ao Emprego, Portuguese Labour Force Survey (IE-LFS). Three basic models are considered to explain the hourly wages for men and women: the first model, using each database separately, is estimated adopting education, tenure, potential experience, activity sector, and job as independent variables; the second, using data from QP-LEED, includes additional determinants related to firm (firm size and foreign social capital); and the third, using data from the IE-LFS, includes additional independent variables related to the worker's family (marital status and children). The results indicate that: (i) Regardless of the database used, the quantile regression (QR) shows superiority over OLS approach; (ii) In general, the same model specification estimated using each database - one administrative (QP-LEED), and the other based on a survey (IE-LFS) - present convergent results; (iii) Independently of the database used, the equations for men and for women reveal that the levels of education have a higher impact on wage determination; (iv) In general, the variables related to the firm contribute to the explanation of wages of men and women while those related to family only contribute to the explanation of men's wages; and (v) the clear different returns for the same characteristics found between men and women, and the pattern of differences which increase across quantiles strongly indicates that the present study should continue in the future, with the analysis of the explanation of the gender wage gap.

Suggested Citation

  • Maria da Conceição Figueiredo & Elsa Fontainha, 2015. "Male and Female Wage Functions: A Quantile Regression Analysis using LEED and LFS Portuguese Databases," Working Papers Department of Economics 2015/01, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    • Handle: RePEc:ise:isegwp:wp012015
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    References listed on IDEAS

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    More about this item

    Keywords

    wage function; quantile regression; Linked Employer-Employee Data; Labour Force Survey; male-female wage differences;
    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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