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The make-up of a regression coefficient: gender gaps in the European labor market

Author

Listed:
  • M. Grazia Pittau

    (Sapienza Universita' di Roma)

  • Shlomo Yitzhaki

    (The Hebrew University of Jerusalem and Central Bureau of Statistics)

  • Roberto Zelli

    (Sapienza Universita' di Roma)

Abstract
We provide a comprehensive picture of the relationship between labor market outcomes and age by gender in all the 28 European countries covered by the European Statistics on Income and Living Conditions (EU-SILC). The analysis is based on a somewhat unconventional approach that refers to concentration curves in the context of Gini regression framework. It allows to identify ranges in the explanatory variables where local slopes change sign and/or size, i.e. the components that \make up" a regression coecient. The European countries are clustered into five groups according to their employment, hours of work and earnings age-profiles by gender, as identified by the concentration curves. The most relevant differences in age pro les concern working-hours-patterns: some countries are characterized by an almost specular behavior in men and women; other countries instead show similar patterns. Generally, earnings increase with age for both men and women. However, local regression coefficients are not monotonic over the entire age range and can even be locally negative in some countries.

Suggested Citation

  • M. Grazia Pittau & Shlomo Yitzhaki & Roberto Zelli, 2013. "The make-up of a regression coefficient: gender gaps in the European labor market," DSS Empirical Economics and Econometrics Working Papers Series 2013/4, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    • Handle: RePEc:sas:wpaper:20134
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    References listed on IDEAS

    as
    1. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
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    11. Shlomo Yitzhaki, 2003. "Gini’s Mean difference: a superior measure of variability for non-normal distributions," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 285-316.
    12. Yitzhaki, Shlomo & Schechtman, Edna, 2012. "Identifying monotonic and non-monotonic relationships," Economics Letters, Elsevier, vol. 116(1), pages 23-25.
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    Cited by:

    1. Ramskogler, Paul & Riedl, Aleksandra & Schoiswohl, Florian, 2020. "Swinging female labor demand – How the public sector influences gender wage gaps in Europe," Department of Economics Working Paper Series 302, WU Vienna University of Economics and Business.
    2. Shlomo Yitzhaki, 2015. "Gini’s mean difference offers a response to Leamer’s critique," METRON, Springer;Sapienza Università di Roma, vol. 73(1), pages 31-43, April.
    3. M. Costa, 2019. "The evaluation of gender income inequality by means of the Gini index decomposition," Working Papers wp1130, Dipartimento Scienze Economiche, Universita' di Bologna.

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    More about this item

    Keywords

    Gini; OLS; Concentration curves; Regression decomposition; European labor market.;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure

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