Suchergebnisse

The aim of the research is an analysis of the influence of the economic indicators on the socio-economic development of EU countries. The synthetic measure of socio-economic development is calculated by means of the following determinants: 'Economy and Finance', 'Science and Technology', 'Health', 'Education' and 'Living Conditions'. This index of the socio-economic development of residents of the European Union countries has been created as an arithmetic mean of indicators, counted for particular determinants. The index, which has been created, is treated as a modified Human Development Index due to the fact that it is completed with the added information. The data has been collected from the Eurostat for the years 2006–2016. In the second part of the research there have been developed the models for the synthetic measure of socio-economic development in terms of particular economic indicator, used in the analysis, as well as the analysis of the relationship between the synthetic measure of socio-economic development in EU countries, and the selected economic measures: unemployment rate, GDP per capita, indicator of real expenditure per capita, and the percentage of people at risk of poverty. The results are obtained, using the Statistica 12 program.

Aus polnischer Sicht

During the conference entitled Spatial Econometrics and Regional Economic Analyses, which took place in Lodz in 2014, there was a proposition to introduce the spatial coherence property into the Ward method, which is applied to group administrative units [1]. At each stage of agglomeration in the modified Ward method, there are included only those aggregates which are adjacent to each other. This work is an extension of this concept based upon other methods of hierarchical clustering, in particular the single and complete linkage method. The study highlighted the benefits of cluster­ing methods with the coherence property, also emphasizing the limitations of these procedures. First of all, the introduction of the restricting condition during the procedure of the hierarchical clustering reduces the homogeneity of isolated clusters. Spatial constraints may also lead to a situation where the distance between the clusters linked at a later stage is smaller than at an earlier stage (graphically, we can talk about the dendrogram "backflow"). The method of complete linkage is free of these aberration where the distance between the clusters is defined as the maximum distance between their elements. The modified clustering algorithm was implemented as an extension of STATISTICA software. Examples of an application of the hierarchical clustering method with the coherence concern sector changes in the European regional space. The aim of the analysis was to isolate spatially coherent areas that demonstrate a similar direction and intensity of structural change in selected areas of the labour market.

The aim of the research is an analysis of the influence of the economic indicators on the socio-economic development of EU countries. The synthetic measure of socio-economic development is calculated by means of the following determinants: 'Economy and Finance', 'Science and Technology', 'Health', 'Education' and 'Living Conditions'. This index of the socio-economic development of residents of the European Union countries has been created as an arithmetic mean of indicators, counted for particular determinants. The index, which has been created, is treated as a modified Human Development Index due to the fact that it is completed with the added information. The data has been collected from the Eurostat for the years 2006–2016. In the second part of the research there have been developed the models for the synthetic measure of socio-economic development in terms of particular economic indicator, used in the analysis, as well as the analysis of the relationship between the synthetic measure of socio-economic development in EU countries, and the selected economic measures: unemployment rate, GDP per capita, indicator of real expenditure per capita, and the percentage of people at risk of poverty. The results are obtained, using the Statistica 12 program.

W roku 2000 w tzw. Strategii Lizbońskiej Unia Europejska próbowała "zadekretować" innowacyjność, która miała być głównym panaceum na kryzysy gospodarcze i społeczne XXI wieku. Jednakże w nowej strategii "Europa 2020" zrezygnowano z nacisku na "innowacyjność dla innowacyjności", próbując powiązać unowocześnianie gospodarki z efektami społecznymi. W tym kontekście zasadne wydaje się zbadanie zależności wybranych miar poziomu życia mieszkańców od poziomu innowacyjności gospodarki w obrębie państw Unii Europejskiej. Stanowi to główny cel poniższego artykułu. Analizą objęto dane dotyczące wskaźników innowacyjności gospodarek państw unijnych w 2015 roku oraz wybrane cechy odzwierciedlające pewne aspekty poziomu życia mieszkańców w tym samym okresie. W obliczeniach uwzględniono dodatkowo podział względem sytuacji geopolitycznej przed 1989 rokiem, bowiem mimo upływu ponad 25 lat, poziom rozwoju państw postkomunistycznych jest ciągle niższy niż pozostałych państw europejskich. Podstawową metodą statystyczną była analiza korelacji rang Spearmana. Z uzyskanych wyników można wysnuć wnioski o co najwyżej przeciętnej korelacji wskaźników innowacyjności z poziomem życia mieszkańców państw unijnych. Analizowane zależności były wyraźnie słabsze w grupie państw postkomunistycznych.