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EN
The purpose of this paper is to determine how changes in the export competitiveness of the EU economy (measured by exports and net exports) depend on changes in the competitiveness of processing industries, on the basis of manufacturing data from 19 EU countries over years 1995-2009 and using a spatial panel data model. The determinants of export competitiveness are selected in the light of predictions from international trade theory, growth theory and the theory of innovation. In particular, the paper explores how the size of foreign demand, the value of domestic demand, the level of ULC in the sector, the degree of openness of the sector to foreign markets, labour productivity and intermediate consumption in a sector affect the export competitiveness of the European economies selected. The results from spatial data models lead to a conclusion about the statistical significance of spatial dependencies in export competitiveness modelling. The analysis indicates the different determinants of export competitiveness, both if it is measured by export value and if it measured by net exports. The authors hope that the results will be a voice in the discussion on enhancing the competitiveness of European industrial sectors
EN
Research background: A strong industrial base is essential for achieving long-term sustainable economic growth and export competitiveness. In that sense, manufacturing remains a significant contributor to exports in the CEE countries. How-ever, its role and its influence vary between CEE economies and change over time. Purpose of the article: The main objective of this paper is to compare the determinants of the international competitiveness, measured by the net exports of the manufacturing sectors in the Czech and Polish economies, by using the database of 13 manufacturing sub-sectors in 1995-2011. The authors research the question of how much foreign and domestic demand, the level of labour costs, the level of sector innovation intensity, the level of sector openness to foreign markets as well as sectoral labour productivity influence the changes in trade balance. Methods: Our approach is based on employing an error correction model and SUR model to disaggregated sectoral manufacturing data. Findings & Value added: The results of the analysis conducted show substantial differences in the roles particular variables play in explaining the net exports in individual sectors. For the majority of Polish and Czech manufacturing sub-sectors, generation of positive trade balance is determined by relative demand growth. An increasing labour productivity influences heavily a positive trade balance of Polish goods in majority of sub-sectors, however, a key factor in Czech sub-sectors is decreasing unit labour costs. The results of the analysis indicate mostly a greater impact of the researched factors on net exports in long rather than short term and the better capacity of the Czech economy to correct deviations from the equilibrium.
EN
Research background: High servitisation of manufacturing makes it impossible to separate services from manufactured goods properly, which implies difficulties in the assessment of the position of the country on the smile curve, i.e. in the proper assignment of products or services to one of the industrial process steps: pre-production, pure fabrication or post-production services. Therefore, we propose to use the business functions of industries identified with the aid of labour market data rather than the industrial classification of products in order to create a more appropriate measure of the position of countries in GVCs. Purpose of the article: We aim to identify and analyse the patterns of functional specialisation for eight Central and Eastern European Countries (CEECs) - the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia - both at the country and industry level. In addition, we analyse functional specialisation patterns for Germany, which serves as a reference country. Methods: To assess functional specialisation patterns, we employ the methodology proposed by Timmer et al. (2019a). It allows us to obtain functional specialisation indices for four different business functions - management, R&D, marketing, and fabrication. To compute them, we combine two sources of data - domestic value added from decomposed sectoral input-output tables (the World Input Output  Database) and the Occupations Database built up by Timmer et al. (2019a). Findings & value added: Our research shows a very heterogeneous pattern in CEEC countries' position in GVCs by taking into account their functional specialisation at the countries and industries levels. Poland and Slovakia focus primarily on low value-added fabrication processes, the Baltic countries and Slovenia specialise in management services, Hungary and Latvia gain in marketing services, and the Czech Republic and Slovenia win in R&D activities. We indicate that some CEE countries (Poland, Slovakia) could be stuck in a functional trap, and our approach could be a valuable tool for assessing the process of coming out of it.
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