Abstract:

In this paper, the economic similarities and differences among the EU’s member states in the last years are evaluated by using an array of variables, each of which representing an aspect of the knowledge economy. In the first section of the work, a brief overview of economic growth theory is provided. In the second section, some indicators as proxies of economic growth determinants (mainly human capital and technology) are introduced and the performances of the above mentioned countries are investigated. Finally, a SOM (Self-Organizing Map) artificial neural network has been chosen to better represent economic clusters and gaps. Results confirm the goodness of the underlying theoretical paradigm, according to which growth and development dynamics may be explained by making reference to some peculiarities of the knowledge economy.
The analysis of socio-economic development in the European Union (EU) has to be necessarily revised for a couple of reasons, at least:

1. The process of EU enlargement, which reached its culmination in 2007 with the entry of Bulgaria and Romania, following the EU border extension to the East and the Mediterranean Sea in 2004 with the accession of ten new member states ;
2. Trends and developments as reflected in recent literature about the economic growth and its determinants.

The widening of the European Union was indeed aimed to creating a broader and more integrated common market, able to offer economic advantages to old and new member states, by spurring economic growth. Actually, economists have always paid attention to the topic of growth and its main determinants. However, owing to the contrasting conclusions reached by scholars, there remains some disagreement over the identification of growth-determining factors.
In the last few years, scholars have been increasingly focusing on the implications of new knowledge as a product of R&D activities, which would be able of improving factor productivity and – as a consequence – raising per capita output levels. At the same time, a number of contributions has also been made in studying the role that human capital, the degree of openness to international trade and the new technologies of information and communication (NTIC) may play in knowledge diffusion, generally recognized as the primary engine of growth.
Following the dominant literature, we will argue that the amount of knowledge and the way it is used are the key determinants for productivity, whose increase exerts a positive impact on growth and development of the economic system as a whole.
The main goal of the paper is to test and demonstrate the robustness of the underlying paradigm, through the analysis of the economic structure of the EU’s member states in the last years. In this regard, a brief overview of the relevant literature on economic growth, with a review of empirical evidence on its possible determinants, will be provided. In the second section, by means of a descriptive analysis of the European economies under investigation, some indicators measuring specific aspects of the knowledge economy will be introduced. Subsequently, a SOM (Self-Organizing Map) neural network methodology, chosen as a tool for research, will be set out; to this effect, an analysis will be made in order to spot patterns of multidimensional similarity among the EU’s member states. Finally, conclusive remarks will be made on the main results achieved by the analysis.

Keywords:

Economic growth, knowledge economy, human capital, technology, neural networks.