159 research outputs found
The Impact of Educational Activities on Regional Development
A region's pattern of growth depends on its power to attract economic activities and the right blend of people to run them. This power depends on economic and social factors that may be combined into a variable which is referred to as the Image of a region and has been presented in some earlier works (Angelis 1980,1990) The role of a region's location is crucial for its development. Hence it is difficult for remote and isolated regions to attract economic activities involving production and transportation of tangible goods. An alternative way of development for such regions is to attract activities involving production of intangible goods. The provision of tertiary education is such an activity. Universities are traditionally are thought to affect both the economic and the social dimension of a region. Their key economic impacts on a given region, as identified by literature, are the increase of local disposable income and employment opportunities. Their key social impacts, on the other hand, include upgrading the human capital stock and raising the cultural level of the local community. The Image of a region has so far been expressed as a function of two Indicators, Economic and Social; furthermore each one of those is expressed as a function of a number of Multipliers related to economic and social aspects of a region. The goal of this paper is to use the concept of a region's Image in order to measure the effect of university's operation on the region of its location. Toward this end we: - Define the Educational Multiplier of a region which expresses the impact of tertiary education aspects on the region's development. - Redefine the region's Basic Image function so as to include the Educational Multiplier. - Estimate a region's Basic Image value twice, using the initial and the redefined Basic Image equation respectively, focus on the difference between the two values and suggest ways for maximizing the positive effect of Educational Multiplier on the region's well being. The model developed is applied to selected regions and the results obtained are presented and discussed
Identifying Clusters of European Regions Based on Their Economic and Social Characteristics.
Nowadays, globalization, technological innovation, migration and population ageing, make it increasingly difficult to predict the future of regions. Identifying the key problems that regions face and considering how these findings could be effectively used as a basis for planning region's improvement, are essential in order to improve the conditions in the European Union regions. Measuring the development of a region means going beyond a purely economic description of human activities and integrate economic, social and environmental concerns. Working in this context, we have so far defined a variable which is called the Image of a region and expresses its power to attract both economic activities and the right blend of people to run them. The regions' Image is a function of a multitude of factors physical, economic, social and environmental, some common for all potential movers and some specific for particular groups of them. In some earlier works in this area, we have classified the 27 EU countries according to their economic, social and environmental characteristics in 3, 2 and 3 clusters respectively. By estimating the Image of every cluster member we have defined the country which could be characterized as the leader of the cluster and serve as a benchmark for the others. As the "region" is in the center of interest of the seventh European Framework Program (FP7), in this study we are going one step further and we focus on the regions of the 27 EU countries. More specifically, our objective is to estimate the Basic Image values for selected EU regions and then group them into clusters on the basis of the same characteristics used in country level, namely their Economic and Social Indicators. Preliminary results show that the regions of a given country may be allocated to different clusters. The final results will be presented and critically discussed
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