The Gender Polarization of Education and Employment in the European Union Countries (in 2005-2019): Practical Implications

Abstract

Purpose: The main purpose of this article is to define the level of education of the European Union citizens and to determine the gaps in this scope between men and women. Design/Approach/Methodology: The analyzed indicators are percentage of the population with tertiary education (X1), percentage of early school leavers (X2), the participation rate in preschool education (X3), and adult participation in learning (X4). What was also analyzed were such indicators as the percentage of employed graduates (Y1) and general employment level (Y2). The source of empirical data was the information collected by the European Statistical Office (Eurostat) about 28 member states of in the years 2005-2019. Findings: In recent years, the EU's education (28) member states citizens have been growing steadily. However, according to ISCED, more women than men improve their knowledge and gain an education at the education level of 5-8, and the gap in this scope is getting wider, to the detriment of men. This diversification can be observed particularly in such countries as Estonia, Lithuania, and Latvia. Practical Implications: In recent years, one could observe that the EU member states that recent graduates' employment rate remained stable at a high level and that the total employment rate increased steadily. This applies both to men and women. Originality/Value: For women, education and qualifications raising on the labor market should be important as the research indicated significant correlations between the indicators that characterize the differences in the level of education of women in the EU (28) countries and the differences in their employment, which was not observed in case of men.

Gender differences in weight status and early school leaving in Italy

“…Being male increases the probability of being an early leaver in all macro-areas. This result is in line with the literature (Michalcewicz-Kaniowska et al, 2021;OECD, 2015), but the coefficient is higher in southern regions than in northern regions (Table 5, Models 4, 5 and 6). This could be due to the higher rate of unemployment in the southern regions, which reduces the incentive to continue studies and/or the opportunity to find a job in the shadow economy with low education.…”