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Gender Wage Gap : A Semi-parametric Approach with Sample Selection Correction

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  • Matteo PICCHIO

    () (Tilburg University, Department of Economics, CentER, ReflecT and IZA, Germany)

  • Chiara MUSSIDA

    () (Department of Economics and Social Sciences, Università Cattolica, Milan and Prometeia spa, Bologna)

Abstract

Sizeable gender differences in employment rates are observed in many countries. Sample selection into the workforce might therefore be a relevant issue when estimating gender wage gaps. This paper proposes a new semi-parametric estimator of densities in the presence of covariates which incorporates sample selection. We describe a simulation algorithm to implement counterfactual comparisons of densities. The proposed methodology is used to investigate the gender wage gap in Italy. It is found that when sample selection is taken into account gender wage gap widens, especially at the bottom of the wage distribution. Explanations are offered for this empirical finding.

Suggested Citation

  • Matteo PICCHIO & Chiara MUSSIDA, 2010. "Gender Wage Gap : A Semi-parametric Approach with Sample Selection Correction," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2010005, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
  • Handle: RePEc:ctl:louvir:2010005
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    Cited by:

    1. Christl, Michael & Köppl-Turyna, Monika, 2017. "Gender wage gap and the role of skills: evidence from PIAAC dataset," Working Papers 05, Agenda Austria.
    2. Chi, Wei & Li, Bo, 2014. "Trends in China’s gender employment and pay gap: Estimating gender pay gaps with employment selection," Journal of Comparative Economics, Elsevier, vol. 42(3), pages 708-725.
    3. Grandner, Thomas & Gstach, Dieter, 2012. "Decomposing wage discrimination in Germany and Austria with counterfactual densities," Department of Economics Working Paper Series 3614, WU Vienna University of Economics and Business.
    4. Chiara Mussida & Matteo Picchio, 2014. "The gender wage gap by education in Italy," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 12(1), pages 117-147, March.
    5. Thomas Grandner & Dieter Gstach, 2015. "Decomposing wage discrimination in Germany and Austria with counterfactual densities," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 49-76, February.
    6. Manuel Arellano & Stéphane Bonhomme, 2015. "Quantile selection models: with an application to understanding changes in wage inequality," CeMMAP working papers CWP75/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Lawrence M. Kahn, 2012. "Labor market policy: A comparative view on the costs and benefits of labor market flexibility," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 31(1), pages 94-110, December.
    8. Marco Biagetti & Sergio Scicchitano, 2016. "Are women in supervisory positions more discriminated against? A multinomial approach," Working Papers 2, Department of the Treasury, Ministry of the Economy and of Finance.
    9. Chiara Mussida & Matteo Picchio, 2014. "The trend over time of the gender wage gap in Italy," Empirical Economics, Springer, vol. 46(3), pages 1081-1110, May.
    10. Ekaterina Selezneva & Philippe Van Kerm, 2016. "A distribution-sensitive examination of the gender wage gap in Germany," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(1), pages 21-40, March.
    11. Karolina Goraus & Joanna Tyrowicz, 2013. "The Goodwill Effect? Female Access to the Labor Market Over Transition: A Multicountry Analysis," Working Papers 2013-19, Faculty of Economic Sciences, University of Warsaw.
    12. Oliveira, Pedro Rodrigues de & Belluzzo, Walter & Pazello, Elaine Toldo, 2013. "The public–private test score gap in Brazil," Economics of Education Review, Elsevier, vol. 35(C), pages 120-133.
    13. Nordman, Christophe J. & Robilliard, Anne-Sophie & Roubaud, François, 2011. "Gender and ethnic earnings gaps in seven West African cities," Labour Economics, Elsevier, vol. 18(S1), pages 132-145.
    14. Magdalena Smyk & Joanna Tyrowicz & Barbara Liberda, 2014. "Age-productivity patterns in talent occupations for men and women: a decomposition," Post-Communist Economies, Taylor & Francis Journals, vol. 26(3), pages 401-414, September.
    15. Salma Ahmed & Pushkar Maitra, 2015. "A Distributional Analysis of the Gender Wage Gap in Bangladesh," Journal of Development Studies, Taylor & Francis Journals, vol. 51(11), pages 1444-1458, November.
    16. Thomas Grandner & Dieter Gstach, 2012. "Decomposing wage discrimination in Germany and Austria with counterfactual densities," Department of Economics Working Papers wuwp145, Vienna University of Economics and Business, Department of Economics.
    17. Chiara Mussida, 2015. "L’impatto dell’istruzione sui salari per genere in Italia / Appendice Statistica," ECONOMIA E SOCIETÀ REGIONALE, FrancoAngeli Editore, vol. 2015(2), pages 94-113.
    18. Alvarado, Rafael, 2012. "Wages differentials in Ecuador: A regional approach with sample selection of Heckman and Oaxaca-Blinder decomposition," MPRA Paper 37470, University Library of Munich, Germany.
    19. Joanna Tyrowicz & Lucas van der Velde & Irene van Staveren, 2015. "Differences in the Estimates of Gender Wage Gap Over The Life Cycle," Working Papers 2015-29, Faculty of Economic Sciences, University of Warsaw.
    20. repec:bla:revinw:v:63:y:2017:i:1:p:118-146 is not listed on IDEAS
    21. Joanna Tyrowicz & Lucas van der Velde, 2017. "When the opportunity knocks: large structural shocks and gender wage gaps," GRAPE Working Papers 2, GRAPE Group for Research in Applied Economics.

    More about this item

    Keywords

    gender wage gap; hazard function; sample selection; glass ceiling; sticky floor;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • 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

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