Sergej Vasić

This study examines the interconnections between transformations in the education sphere, migrations waves, and labor market in 2000–2021 based on a panel data set for 14 Central and Eastern European countries (7 – former members of the Council for Mutual Economic Assistance; 5 – former republics of the USSR, and 2 – former republics of Yugoslavia). Statistical data were collected from the World Bank, the Organisation for Economic Cooperation and Development, and the International Labour Organization databases. To describe this interconnection, a pool of parameters was formed. Those that cause the greatest variability were selected using exploratory factor analysis: for education – the number of teachers and students in higher education and public spending on education; for migration – the net migration flow, personal remittances sent and received; for labor market –unemployment rate and the share of highly educated people among the employed. Confirmatory factor analysis identified the most influential determinants: for education – the number of students in higher education; for migration – paid personal remittances; for labor market – unemployment rate. The covariance analysis demonstrated a robust direct correlation between education and migration (positive shifts in the education sector serve as a catalyst for pursuing superior employment opportunities or continuing education abroad). A relatively weak direct correlation was between education and the labor market (a more highly educated workforce has only a limited impact on the structure and dynamics of the labor market). Finally, a moderate inverse correlation was between migration and the labor market (deteriorating labor market conditions give rise to migration waves).

Acknowledgment
This study is funded in terms of the projects “Business-Education-Science” Coopetition: Institutional and Economic Models of Innovation Transfer for National Security and Sustainable Development (№ 0122U000772) and “Modelling educational transformations in wartime to preserve the intellectual capital and innovative potential of Ukraine” (№0123U100114).

The structure of the higher education market in 2012–2021 in 38 European countries was analyzed using concentration levels and Herfindahl-Hirschman indices based on the number of higher education institutions and their share in the QS World University Rankings, and the number of students. This market in 2021 has a low concentration: the 3 countries with the largest number of higher education institutions (Germany, Ukraine, France) covered about 36% of the market in total; the 3 countries with the largest number of universities in the QS (United Kingdom, Germany, Italy) – 5%; the 3 countries with the largest number of students (Germany, France, United Kingdom) – 37%; and the 3 countries with the largest number of foreign students (United Kingdom, Germany, France) – 5%. Using parametric and non-parametric comparison tests, it was found that although the number of higher education institutions and students does not generally depend on the population’s income level, the number of universities ranked in the QS and foreign students does. The correlation analysis revealed that GDP and GNI, population, and separately the employment and unemployment rates (for ranked universities and foreign students) are important factors that determine the uneven structure of the higher education market. The identified factors formed the basis for clustering countries using Ward’s hierarchical method, which revealed the clear existence of 3 clusters: the smallest of them accumulates the 4 largest European economies with the most ranked universities; the largest (24 countries) is quite diverse, which indicates relatively equal opportunities in the market and its unification.

Acknowledgment
Tetiana Vasylieva and Artem Artyukhov thank project 0122U000772, and Nadiia Artyukhova thanks project 0124U000545 for carrying out their part of this research.