The article analyzes inclusive growth in European Union countries using clustering based on key socio-economic indicators. The indicators were standardized to ensure comparability and a reliable typology, and multiple complementary clustering algorithms were applied. Specifically, standardized indicators were analyzed using k-means, k-medoids (PAM), and Ward’s hierarchical method, with the optimal number of clusters determined. The countries were grouped into two clusters (21 and 5 countries, respectively), and the classification proved robust, as alternative algorithms yielded similar allocations. Interpreting the cluster profiles showed that countries in the second cluster lag markedly on several inclusive-development metrics, including higher poverty and social inequality and larger shares of youth not in employment, education, or training (NEET). At the same time, some paradoxical results emerged: for example, population health indicators (such as life expectancy) in the lagging countries were not lower than in the first cluster. The findings underscore that high employment rates (including women’s labor-force participation), human capital development (education), and effective reductions in poverty and inequality are key to securing inclusive growth and its resilience to financial, energy, and other crises.
Saher et al. (2025) studied this question.