CLUSTERING OF COUNTRIES OF THE WORLD AND ASSESSMENT OF THE INFORMATIVENESS OF THE HUMAN DEVELOPMENT INDEX

Abstract

Introduction. Assessing the real quality of life beyond purely economic indicators is an important task, as it enables the evaluation of the effectiveness of social policies and serves as a basis for monitoring access to healthcare, education, and environmental safety. One of the most important directions in assessing the quality of life in any country is determining the level of human development. The most widely used integral indicator for such analysis is the Human Development Index (HDI). Despite its widespread use, it is important to analyze not only the final value of the index but also the structure and interrelationships between the indicators that determine it. The application of cluster analysis methods makes it possible to identify typological groups of countries with similar characteristics of social development and to analyze structural differences between them. Comparing the obtained clusters with HDI values allows identifying groups of countries with similar socio-demographic characteristics, even if their overall HDI values differ. At the same time, such a comparison makes it possible to reveal cases where countries with identical HDI values belong to different clusters, which indicates differences in the structure of human development factors.

Purpose. The purpose of the study is to apply cluster analysis to group countries based on key indicators of human development and to compare the obtained clusters with Human Development Index values. The results of such a comparison make it possible to assess the degree of correspondence between the aggregated HDI indicator and the actual socio-economic development of countries.

Materials and Methods. The statistical basis of the study is a dataset covering 74 countries in 2023, including the following indicators: life expectancy, expected years of schooling, mean years of schooling, and the Human Development Index. The study is based on the application of multivariate statistical methods, in particular hierarchical agglomerative methods and the k-means method. The calculations were performed using the Statistica software package.

Results. The study substantiates the division of the dataset into five clusters. For each cluster, the number and composition of elements are provided, and key characteristics are identified based on the average values of the selected indicators. The clusters are ordered according to the level of human development in descending order. The following groups of countries are identified: countries with the highest level of human development (11 countries), countries with a high level of development (20 countries), countries with a medium and above-average level of human development (12 countries), countries with a moderate and heterogeneous level of human development (11 countries), and countries with a moderately lower level of human development (20 countries).

The results show that the cluster structure generally corresponds to the ranking of countries by HDI. At the same time, certain discrepancies between the clustering results and the HDI ranking have been identified, which can be explained by differences in the structure of development factors. An important finding of the cluster analysis is the observed pattern that countries with identical HDI values may belong to different clusters. This indicates that the HDI, as an integral indicator, does not always fully reflect structural differences between countries. The use of cluster analysis complements the HDI by revealing hidden differences between countries and increasing the analytical informativeness of human development assessment.

Discussion. Further research may involve conducting cluster analysis in dynamics, which would allow assessing changes in the structure of human development over time. It is also advisable to refine the cluster structure by increasing the number of clusters and expanding the system of indicators. A separate direction of research is the inclusion of data on Ukraine, which would make it possible to determine its position among countries of the world in terms of the structural characteristics of human development.