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The “Make-up” of a Regression Coefficient: Gender Gaps in the European Labor Market

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Listed:
  • M. Grazia Pittau
  • Shlomo Yitzhaki
  • Roberto Zelli

Abstract

type="main"> We provide a comprehensive picture of the relationship between labor market outcomes and age by gender in the 28 European countries covered by the European Statistics on Income and Living Conditions. The analysis is based on a somewhat unconventional approach that refers to concentration curves in the Gini regression framework. It allows identification of ranges in the explanatory variables where local slopes change sign and/or size, i.e. the components that “make up” a regression coefficient. Gender is a crucial factor differentiating participation among workers, although employment–age profiles do not substantially differ. Relevant differences in age profiles 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, 2015. "The “Make-up” of a Regression Coefficient: Gender Gaps in the European Labor Market," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(3), pages 401-421, September.
    • Handle: RePEc:bla:revinw:v:61:y:2015:i:3:p:401-421
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    File URL: http://hdl.handle.net/10.1111/roiw.12094
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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

    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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