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The ARDL Test of Gender Kuznets Curve for G7 Countries

Author

Listed:
  • Dilara Kýlýnç

    (Department of Economics, Izmir University of Economics)

  • Esra Onater

    (Department of Economics, Izmir University of Economics)

  • Ý. Hakan Yetkiner

    (Department of Economics, Izmir University of Economics)

Abstract

The Gender Kuznets Curve (GKC) hypothesis argues that economic development has a non-linear effect on the female share of workers. There is, however, growing debate on the exact shape of this non-linear relationship. The aim of this paper is to test the GKC hypothesis in order to determine whether data supports a quadratic or a cubic GKC for each G7 countries in the long run. The ARDL bounds testing approach of cointegration yields evidence for the following: Canada, United Kingdom and United States have an inverted U-shaped GKC; Japan has an S-shaped GKC and France has an inverted-S shaped GKC; and finally that Italy and Germany have no long run GKC relationship in the respective periods of countries considered. We conclude that gender equality is not a direct result of development, and therefore policy makers having a gender equalization policy need to subsidize the employment of female workers in periods of fall.

Suggested Citation

  • Dilara Kýlýnç & Esra Onater & Ý. Hakan Yetkiner, 2013. "The ARDL Test of Gender Kuznets Curve for G7 Countries," Working Papers 1305, Izmir University of Economics.
  • Handle: RePEc:izm:wpaper:1305
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    References listed on IDEAS

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    More about this item

    Keywords

    Gender Kuznets Curve; Economic Development; ARDL;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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