| Abstract [eng] |
53 pages, 7 figures, 17 tables, 76 references. The main objective of this Master’s thesis is to compare the dynamics of productivity indices and indicators of the European Union (EU) countries and to quantitatively identify the impact of indicators on the development of the indices by applying comparative analysis, clustering, and dynamic modelling methodologies. This thesis seeks to reveal how innovation ecosystems, human capital, and demographic changes determine the grouping of countries into different productivity levels and how these factors affect productivity dynamics in the long term. Thesis is divided into four main parts: a theoretical analysis of the concept of productivity, a methodological justification of the research, an interpretation of empirical results, and general conclusions. The theoretical part provides a systematised review of the scientific literature on productivity measurement methods, the assumptions of endogenous growth theory, and an overview of the convergence process of the EU countries. The empirical part covers 2000 – 2024 period, in which a hybrid Ward.D2 clustering method is used to identify the evolution of productivity levels across four economically significant periods: the pre-2008 financial crisis period, the crisis period, the post-crisis recovery phase, and the COVID-19 pandemic period. In order to simplify the set of macroeconomic indicators and to avoid the problem of multicollinearity, Principal Component Analysis (PCA) and Factor Analysis (FA) were conducted. These analyses identified three latent dimensions: human capital and public resources, innovation and technological progress, and demographic pressure. To determine dynamic causality, a panel vector autoregression (PVAR) model was constructed, estimated using the GMM method, and impulse response functions (IRFs) were generated. The results of the study reveal that the EU productivity structure has become increasingly fragmented: from a bipolar “core–periphery” system to the formation of six distinct productivity regimes during the pandemic period. A “productivity paradox” was identified, whereby the structural potential of countries does not directly correlate with their actual productivity levels but is critically important for their growth rates. The PVAR model confirmed that investments in human capital generate the most stable positive effect on productivity, while population ageing acts as a direct structural constraint. It was also found that the impact of innovation is characterised by an adaptation lag of 1-2 years. The conclusions and recommendations provide insights into the necessity of prioritising EU structural support towards improving the quality of human capital as a counterbalance to negative demographic trends. |
