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Determinants of Female Labour Force Participation: Panel Data Analysis

Author

Listed:
  • Ivana Marjanović
  • Žarko Popović
  • Sandra Milanović

Abstract

The growing participation of women in the labour market has been undoubtedly one of the main features of the evolution of the economies of the European Union (EU) during the last few decades. Nevertheless, maintaining this increase remains an important policy goal due to the existing population ageing and gender employment gap. This paper aims to analyse the main determinants of female labour force participation (FLFP) in the EU countries using panel data regression analysis covering the period from 2000 to 2021. By employing the fixed-effects regression analysis, an influence of dependent variables such as GDP per capita, presence of anti-discrimination laws, equal opportunities laws and affirmative action, women's participation in parliament work, total fertility rate, paid parental leave, retirement age with full pension benefits, women's education and annual net earnings are assessed on the female labour participation rate in the European Union. Moreover, the robust standard error procedure is performed for model estimation. The empirical results indicate that GDP per capita, total fertility rate, equal age of retirement for men and women, annual net earnings, female upper secondary and post-secondary non-tertiary education and tertiary education enrolment represent significant determinants of FLFP, where all the stated variables have a positive influence on the FLFP rates. Therefore, improvement in education levels, annual net earnings and gender equality policies influence women to participate more in labour markets. These findings demonstrate the significance of improving education and earnings levels and the necessity of the development of adequate policies that will provide that. Implications for Central European audience: The results of this study provide insightful findings concerning the main drivers of female labour force participation in the European Union by taking into account the COVID-19 pandemic period labour market indicators.

Suggested Citation

  • Ivana Marjanović & Žarko Popović & Sandra Milanović, 2024. "Determinants of Female Labour Force Participation: Panel Data Analysis," Central European Business Review, Prague University of Economics and Business, vol. 2024(2), pages 69-88.
    • Handle: RePEc:prg:jnlcbr:v:2024:y:2024:i:2:id:348:p:69-88
    DOI: 10.18267/j.cebr.348
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    References listed on IDEAS

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    1. Ratna Sahay & Martin Cihak, 2018. "Women in Finance: A Case for Closing Gaps," IMF Staff Discussion Notes 18/05, International Monetary Fund.
    2. Anne Gauthier, 2007. "The impact of family policies on fertility in industrialized countries: a review of the literature," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(3), pages 323-346, June.
    3. Besamusca, Janna & Tijdens, Kea & Keune, Maarten & Steinmetz, Stephanie, 2015. "Working Women Worldwide. Age Effects in Female Labor Force Participation in 117 Countries," World Development, Elsevier, vol. 74(C), pages 123-141.
    4. Ms. Ratna Sahay & Mr. Martin Cihak, 2018. "Women in Finance: A Case for Closing Gaps," IMF Staff Discussion Notes 2018/005, International Monetary Fund.
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    More about this item

    Keywords

    female labour force participation; panel data analysis; gender equality policies; EU countries;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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