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International competitiveness is influenced by globalization processes in the world economy. This process changes the comparative advantages of each country and thus the shares of individual countries in world trade. BRICS countries have quickly strengthened their influence in international trade, and thus the European Union must face new pressure in competitiveness from their side. The aim of this paper is to define key factors of foreign trade competitiveness by an application of factor analysis and identify countries with similar characteristics of competitiveness factors by an application of cluster analysis. Factor and cluster analysis contain indicators of foreign trade which describe the driving forces of competitiveness, also in terms of long-term potentiality, and those which are direct or indirect outcomes of a competitive society and economy. Based on the results of the factor analysis, it is possible to classify the evaluated territories according to the level of foreign trade advancement by cluster analysis.

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Based on a micro-level approach and using data from the European Working Conditions Survey, covering 27 countries, we analyse the determinants of job quality. With cluster analysis applied to 11 dimensional indices, we form three homogeneous country groups and identify, by estimating twice-censored Tobit models, the main determinant factors affecting the individual level of job quality in each group. We verify the relevance of variables related to worker characteristics, firm characteristics, and the country in which the individual works. Among worker characteristics, education and employment status are the factors with the highest impact on job quality, while the economic sector is the most important firm characteristic. The results suggest the existence of important differences among groups regarding the magnitude of the impact of some factors. The highest dissimilarities are found between the group with better jobs (Nordic countries plus Belgium) and the group with lower quality jobs (Central and Eastern European countries plus Portugal and Greece). Variables related to age, education, dimension of the firm, and economic sector are those in which more heterogeneity is found among the groups.

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, P. J. ( 1990 ): Finding Groups in Data: An Introduction to Cluster Analysis . New York: John&Wiley . Kruskal , J. B. ( 1964 ): Nonmetric Multidimensional Scaling: A

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16 Artis, M. - Zhang, W. (2002): Membership of EMU: A Fuzzy Clustering Analysis of Alternative Criteria. Journal of Economic Integration, 17: 54

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This paper examines the impact of EU enlargement and the global economic crisis on the relative development of the EU countries. This effect is assessed by applying multivariate analysis to the whole set of 28 European countries at three representative points in time. The cluster analysis for the years 2002, 2007, and 2012 grouped the countries according to the range of economic development indicators showing within-EU cohesion before the EU enlargement, after the enlargement wave, and after the crisis. The findings show that a decrease in the development differences after the enlargement was replaced with an increase in these differences after the crisis, thus contributing to the existing debate about the success of cohesion and future of European integration. These results are somewhat worrying for the new member states of the EU as well as for EU membership candidates and their prospective development within the integration.

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. – Rousseeuw , P. J. ( 2005 ): Finding Groups in Data: An Introduction to Cluster Analysis . New Jersey, NJ : John Wiley & Sons . Kelemen , Z. – Nagy , P. – Kemény , I. ( 2016

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Acta Oeconomica
Authors: Jorge de Andrés-Sánchez, Ángel Belzunegui-Eraso, and Francesc Valls-Fonayet

and Malta made the type of social policies carried out in the EU more heterogeneous. This encourages us to conduct a cluster analysis to establish patterns within the EU-28 states regarding the effort made in social spending and its effectiveness in

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Europe: A Cluster Analysis . Economic Modelling , 33 : 689 – 694 . Niebuhr , A. – Schlitte , F. ( 2009 ): EU Enlargement and Convergence: Does Market Access Matter? Eastern

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Society and Economy
Authors: Raul Manuel Da Silva Laureano, Maria João Cardoso Vieira Machado, and Luís Miguel Da Silva Laureano

The purpose of this paper is to characterize the level of maturity of management accounting in Portuguese industrial SMEs. Specifically, the study classifies firms using Kaplan’s Four-Stage model; and introduces a new model to classify them better. The research design is exploratory. The data were collected through interviews with those responsible for management accounting in 58 Portuguese industrial SMEs. The analysis used descriptive and inferential statistics and a cluster analysis was performed to classify firms according to their management accounting characteristics. The results showed that all the SMEs belong to stage 2 of Kaplan’s model and that it is possible to classify them in one of the four stages of the proposed new model. Moreover, the type of firm and the source of capital have no influence on the level of maturity, although larger firms tend to have greater maturity. The study offers evidence that there is a clear difference between management accounting knowledge and practices, which should motivate top management to focus on the continuous training of firm employees on the latest developments in management accounting methods.

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The paper studies the relationship between key factors influencing senior entrepreneurship and the level of inclusiveness of seniors in entrepreneurial activity in Europe. The objective is to cluster countries with similar patterns in senior entrepreneurial inclusivity and to identify the factors leading to inclusive entrepreneurship of seniors and their social cohesion. The focus is on European countries which participated in Global Entrepreneurship Monitor (GEM) between 2001 and 2012, using GEM data as the main source for the analyses. Initially, the authors identify the key factors influencing entrepreneurial activity of seniors within Europe based upon data contained within the literature review. At the same time, utilizing the senior entrepreneurship inclusivity index, the authors measure the level of inclusiveness in each European country. Using the results of these analyses the authors subsequently implement a cluster analysis method to create clusters among European countries based upon the similarities in the relationship between the levels of senior entrepreneurship and entrepreneurial activity of the general population. This helps them identify countries with above average levels of senior entrepreneurship inclusivity. The results allow the authors to assess key similarities in clustered economies in terms of entrepreneurial culture and policies which have a major influence on senior entrepreneurship.

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