Tetiana Yakovenko

Several specific features and circumstances can characterize Ukraine’s policy of external public debt management, and the results are not always unambiguous. The study aims to assess the effect of external public debt on Ukraine’s economy from 2014 to 2022, a period that includes the Crimea annexation, the onset of the COVID-19 pandemic, and the beginning of the open Russian military aggression. To analyze the contemporary state of public debt and assess the degree of external debt burden’s impact on the country’s economy, a factor analysis technique known as the principal components method was used. Via the STATISTICA.12 software, it was substantiated that the debt situation worsens with the growth of debt burden and solvency indicators as their values approach the thresholds. The application of the Kaiser criterion allowed the selection of the most influential indicators (principal components) for assessing the external debt burden. The eigenvalue of the first component (inflation rate) is 4.48, and it explains 50% of the variance; the second component (production of export-oriented goods) has an eigenvalue of 2.43, explaining 27% of the variance; the third component (government spending on military purposes) has an eigenvalue of 1.24, and it explains 14% of the variance.

Type of the article: Research Article

Abstract
Socio-economic development in the EU countries is a complex process encompassing both social and economic progress. It involves enhancements in living standards, quality of life, and overall well-being, alongside economic growth and structural changes. Thus, the paper aims to identify and assess the key parameters for clustering EU countries by the components of their socio-economic development. The study utilized fifteen indicators from the Social Progress Index and the Human Development Index, reflecting different components of countries’ social development. Using the k-means method, the EU population is divided into three clusters (13, 5, and 9 countries, respectively) based on their similarity in social development. Then, using the decision tree method, the above indicators were assessed, including the following: “Nutrition and Medical Care,” “Health,” “Environmental Quality,” “Rights and Voice,” “Freedom and Choice,” and “Advanced Education.” These indicators are used as the key parameters for clustering countries by components of socio-economic development; therefore, their change largely determines the positions of countries as a whole and, accordingly, their convergence at the EU level. The study found significant differences between EU countries in their socio-economic aspects, particularly between the “old” and “new” members. The results obtained can be used to justify the priorities of EU socio-economic policy to ensure overall progress.

Acknowledgment
This article was published as an output of the project VEGA 1/0392/23: Changes in the approach to the development of distribution management concepts of companies influenced by the impact of social and economic crisis caused by the global pandemic and increased security risks. Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09103-03-V01-00042.

Type of the article: Research Article

Abstract
The study aims to empirically group European countries based on competitiveness determinants and adult education demand to form a generalized cluster representation of their socio-economic characteristics. The sample covers 36 European countries from 2015 to 2024. The information base was formed using a set of indicators derived from the Global Competitiveness Index (GCI), together with an indicator reflecting adult education demand. The methodology includes standardization of indicators, selection of relevant variables using principal component analysis, and cluster analysis. The first two principal components explain 76.3% of the total variance, allowing a substantial reduction in the dimensionality of the dataset while preserving most of the information contained in the initial indicators. Clustering was conducted using Ward’s hierarchical method and the k-means algorithm, with verification of differences between clusters by analysis of variance (p < 0.05). To examine structural changes over time, clustering was performed for three benchmark years: 2015, 2020, and 2024. The results reveal five clusters of countries differing in institutional development, innovation potential, business environment characteristics, and adult education participation. A relatively stable core of highly competitive economies was identified, including Austria, Belgium, Germany, France, Ireland, Luxembourg, Denmark, the Netherlands, Norway, Sweden, Finland, and Switzerland. Other clusters show greater variability in composition. Across the benchmark years selected within the 2015–2024 observation period, Ukraine remained within the cluster characterized by the lowest values of competitiveness determinants and adult education demand, reflecting persistent structural constraints in the development of human capital and lifelong learning systems.