Determinants of Female Labour Force Participation: Panel Data Analysis

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.