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

Maria da Conceição Figueiredo and Elsa Fontainha

No 2015/01, Working Papers Department of Economics from ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa

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.

Keywords: wage function; quantile regression; Linked Employer-Employee Data; Labour Force Survey; male-female wage differences (search for similar items in EconPapers)
JEL-codes: C21 J31 (search for similar items in EconPapers)
Date: 2015-01
New Economics Papers: this item is included in nep-lab
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More papers in Working Papers Department of Economics from ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa Department of Economics, ISEG - Lisbon School of Economics and Management, Universidade de Lisboa, Rua do Quelhas 6, 1200-781 LISBON, PORTUGAL.
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