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The objective of the hereby paper is to present dynamic analysis and assessment of workforce structure in the European Union countries based on structural and geographical shift-share analysis. Workforce structure in economic sectors, distinguished based on R&D work intensity in the European Union countries in the period of 2008-2010, was the subject of diversification and transformations assessment. Shift-share analysis enabled the decomposition of occurring changes into regional, structural and global effects as well as the identification of the, so called, allocation effect resulting in the classification of the studied countries with regard to combinations of local specialization and competitive advantages. The performed research also allowed for the identification different kinds of workforce structure characterized by smart specialization (significant share of workforce in high-tech manufacturing sector or high-tech services sector) and the assessment of generated structural and competitive effects. ; Celem referatu jest dynamiczna analiza i ocena struktury pracujących w krajach Unii Europejskiej w oparciu o strukturalno-geograficzną metodę przesunięć udziałów (Shift-Share Analysis). Ocenie zróżnicowania i przemian poddano strukturę pracujących w sektorach ekonomicznych wyodrębnionych wg intensywności działalności badawczo- rozwojowej w krajach Unii Europejskiej w latach 2008-2010. Analiza przesunięć udziałów umożliwiła dekompozycję zachodzących zmian na efekty regionalne, strukturalne i globalne, jak również identyfikację tzw. efektu alokacji prowadzącego do klasyfikacji badanych krajów ze względu na występujące kombinacje specjalizacji lokalnej i korzyści konkurencyjności. Przeprowadzone badania pozwoliły na identyfikację różnych rodzajów struktur pracujących cechujących się inteligentną specjalizacją (znaczący udział pracujących w sektorze przemysłu wysokiej techniki lub usług opartych na wiedzy) oraz ocenić generowane przez nie efekty strukturalne i konkurencyjne

The objective of the hereby paper is to present dynamic analysis and assessment of workforce structure in the European Union countries based on structural and geographical shift-share analysis. Workforce structure in economic sectors, distinguished based on R&D work intensity in the European Union countries in the period of 2008-2010, was the subject of diversification and transformations assessment. Shift-share analysis enabled the decomposition of occurring changes into regional, structural and global effects as well as the identification of the, so called, allocation effect resulting in the classification of the studied countries with regard to combinations of local specialization and competitive advantages. The performed research also allowed for the identification different kinds of workforce structure characterized by smart specialization (significant share of workforce in high-tech manufacturing sector or high-tech services sector) and the assessment of generated structural and competitive effects. ; Celem referatu jest dynamiczna analiza i ocena struktury pracujących w krajach Unii Europejskiej w oparciu o strukturalno-geograficzną metodę przesunięć udziałów (Shift-Share Analysis). Ocenie zróżnicowania i przemian poddano strukturę pracujących w sektorach ekonomicznych wyodrębnionych wg intensywności działalności badawczo- rozwojowej w krajach Unii Europejskiej w latach 2008-2010. Analiza przesunięć udziałów umożliwiła dekompozycję zachodzących zmian na efekty regionalne, strukturalne i globalne, jak również identyfikację tzw. efektu alokacji prowadzącego do klasyfikacji badanych krajów ze względu na występujące kombinacje specjalizacji lokalnej i korzyści konkurencyjności. Przeprowadzone badania pozwoliły na identyfikację różnych rodzajów struktur pracujących cechujących się inteligentną specjalizacją (znaczący udział pracujących w sektorze przemysłu wysokiej techniki lub usług opartych na wiedzy) oraz ocenić generowane przez nie efekty strukturalne i ...

The objective of the hereby paper is to present dynamic analysis and assessment of workforce structure in the European Union countries based on structural and geographical shift-share analysis. Workforce structure in economic sectors, distinguished based on R&D work intensity in the European Union countries in the period of 2008-2010, was the subject of diversification and transformations assessment. Shift-share analysis enabled the decomposition of occurring changes into regional, structural and global effects as well as the identification of the, so called, allocation effect resulting in the classification of the studied countries with regard to combinations of local specialization and competitive advantages. The performed research also allowed for the identification different kinds of workforce structure characterized by smart specialization (significant share of workforce in high-tech manufacturing sector or high-tech services sector) and the assessment of generated structural and competitive effects.

The objective of the hereby study is the analysis and assessment of interrelations occurring between the selected attributes of regions' competitiveness, as well as the level and dynamics of economic growth. Spatial scope of the research is based on regional division of the European Union countries, for which NUTS II units, defined according to the Nomenclature of Territorial Units for Statistics, have become the points of reference. Time span of the research covers one year – 2005. The level and dynamics of Gross Domestic Product and attributes illustrating region's competitiveness like: production factors, social and economic climate, related and supporting sectors, constitute basic characteristics used for the analysis. The study is of empirical nature

The main purpose of the study is to analyse and define the position of the Baltic States, i.e. Lithuania, Latvia and Estonia regarding the implementation level of the concept of sustainable development in 2010-2016, measured by the realisation of Sustainable Development Goals (SDG). Moreover, the problems constituting barriers in achieving these goals were identified. The study was based on the indicators monitoring SDG in a global perspective. Eurostat and its Database was the source of data, taking into account their availability and completeness. The synthetic measure of development (SMD) was applied to assess the implementation of the sustainable development concept. The socio-economic situation of the Baltic States was addressed in the introduction. The core part of the study presents the research results showing the position of the Baltic States relative to the implementation level of SDG. The value of SMD in individual years was determined for each of the analysed countries. As a general conclusion, it can be stated that the situation of all countries showed improvement over the analysed period. Nonetheless, the current situation cannot be described as favourable in any of the countries, Estonia was the closest to achieving this status.

The study is focused on the relationship between educational potential and labor market. Educational potential is defined as the resource of knowledge and skills in the region expressed by the level of formal education, the scientific potential, and the tendency to continue to improve qualifications. The labour market is represented by the young people, who enter the labour market after obtaining formal education. The purpose of the study is to assess the spatial autocorrelation of educational potential and the situation of young people in the cross - section of the NUTS - 2 European Union regions in 2016. The analytical tools were spatial statistics (local and global I Moran). The obtained results indicate strong tendency for cluster development . This was very well visible in case of the long life learning indicator and employment rate of young people neither in education nor training. The lowest tendency for clustering was observed in case of early leavers form education and training. Furthermore the results suggest the significance of education for the development of labour market for young people

Within the framework of the Europe 2020 strategy smart growth is listed as one of the leading policy objectives aimed at improving the situation in education, digital society and research and innovation. The objective of this article is to evaluate the relationships between smart growth and economic and social cohesion factors. Aggregate measures were used to describe smart growth pillars. Here, social cohesion is described by the level of employment rate as one of the conditions essential to the well-being and prosperity of individuals. Economic cohesion is defined by the level of GDP per capita in PPS. Observation of these three phenomena forms the basis for the construction of panel data models and undertaking the assessment of the relationships between smart growth and economic and social cohesion factors. The study was performed on the group of 27 European Union countries in the period of 2002-2011.

The study addresses the issue of eco-innovation and innovation in the European Union countries, which is important from the perspective of the sustainable development paradigm. Innovation constitutes a significant factor related to socio-economic development, and it is crucial in constructing the competitive advantage of enterprises, regions, and countries. Nowadays, an increasing importance is attached to eco-innovations, which takes into account the ecological perspective ensuring the reduction of environmental burdens. The purpose of the conducted research was to assess the diversity among the European Union countries regarding the situation related to eco-innovation and innovation, which is focused on the typology of the EU Member States taking a holistic approach to innovation, i.e., considering not only economic but also environmental and social performance. The methods of multivariate statistical analysis, with a particular emphasis on classification methods, were used in the research. A holistic overview of innovation results from the combination of findings based on the research was carried out within the framework of the Eco-Innovation Observatory and the European Innovation Scoreboard. The study covered 28 European Union countries in the period 2013–2019. As a result of the conducted analyses, four classes of the EU Member States were identified (Leaders of Eco-Innovation and Innovation, Moderate Eco-Innovators and Catching-Up Leaders of Innovation, Poor Innovators, The Poorest Eco-Innovators and Innovators).

This presented study discusses problems related to the implementation of the Sustainable Development Goal 1: No Poverty, aimed at eliminating poverty, based on the example of the Visegrad Group (V4) countries. The introduction addresses the general characteristics of the V4 and attempts to define the concept of sustainable development, with particular emphasis on its complex nature and importance for future generations. The purpose of the research was to assess the diversity within the Visegrad Group countries in the years 2005–2018 in terms of poverty and sustainable development level in the No Poverty area and also to identify the impact of the socioeconomic development level in the studied countries on sustainable development in the No Poverty area. Taking into account the analysis of poverty indicators in the Visegrad Group countries, the best results were recorded for Czechia. The second part of the conducted analyzed allowed us to conclude that Czechia definitely presents the highest level of sustainable development, followed by Slovakia. The highest average dynamics of changes occur in Poland and Hungary, which result in the gradual elimination of the existing disproportions. Among other research results, it is worth highlighting that the V4 countries show significant, however, decreasing differences regarding the indicators describing poverty in relation to sustainable developmen ; Program doskonałości naukowo - badawczej INTEREKON . Kierownik projektu w UEW: Ewa Stańczyk-Hugiet, ewa.stanczyk-hugiet@ue.wroc.pl , data rozpoczęcia 01-01-2019, data zakończenia 31-12-2022

Abstract

Research background: Strengthening endogenous potentials, enhancing competitive advantage based on research and innovation remains an important component of regional development policy. Over the years this task has been carried out through e.g. the identification and support focused on smart specialisations.

Purpose: The purpose of the assessment was to identify smart specialisations in the Dolnośląskie voivodeship and evaluate their competitiveness against the background of other voivodeships.

Research methodology: The set of diagnostic indicators as well as the dynamics and location measures calculated on their basis, and also linear ordering methods using a weights system (Synthetic Measure of Smart Specialisations – SMSS) were used to identify regional smart specialisations. A statistical analysis was conducted on the basis of data at the level of PKD divisions (Polish Statistical Classification of Economic Activities; NACE is the EU equivalent). The identification was carried out taking into account the period 2012–2017 and focused primarily on 2017.

Results: As a result, 4 RSSs were identified, of which the first two are the mining of non-ferrous metal ores and the production of motor vehicles.

Novelty: The study proposes, based on the example of the Dolnośląskie voivodeship, the possibility of using linear ordering methods in determining the region's smart specialisations (RSS), i.e. unique regional qualities and assets, which may constitute its competitive advantage, supported by appropriate research and development facilities and essential for the development of modern and innovative sectors of the economy.

Research background: Strengthening endogenous potentials, enhancing competitive advantage based on research and innovation remains an important component of regional development policy. Over the years this task has been carried out through e.g. the identification and support focused on smart specialisations. Purpose: The purpose of the assessment was to identify smart specialisations in the Dolnośląskie voivodeship and evaluate their competitiveness against the background of other voivodeships. Research methodology: The set of diagnostic indicators as well as the dynamics and location measures calculated on their basis, and also linear ordering methods using a weights system (Synthetic Measure of Smart Specialisations – SMSS) were used to identify regional smart specialisations. A statistical analysis was conducted on the basis of data at the level of PKD divisions (Polish Statistical Classification of EconomicActivities; NACE is the EU equivalent). The identification was carried out taking into account the period 2012–2017 and focused primarily on 2017. Results: As a result, 4 RSSs were identified, of which the first two are the mining of non-ferrous metal ores and the production of motor vehicles. Novelty: The study proposes, based on the example of the Dolnośląskie voivodeship, the possibility of using linear ordering methods in determining the region's smart specialisations (RSS), i.e. unique regional qualities and assets, which may constitute its competitive advantage, supported by appropriate research and development facilities and essential for the development of modern and innovative sectors of the economy.