R12 - Size and Spatial Distributions of Regional Economic ActivityReturn

First investment incentives in the Czech Republic were granted in 1998. From 1998 to 2021 there were 1,319 granted investment incentives in total with 1,444 locations of implementation of the investment projects. The total amount of the supported investment projects reached nearly 983 billion CZK, and the applicants promised to create more than 203,000 new jobs. This paper evaluated the spatial distribution of the investment projects with granted investment incentive in the Czech Republic in the period 1998-2021. The spatial distribution was examined on the basis of three indicators calculated for the district level – the amount of investment per capita, the number of newly created jobs per 1,000 inhabitants and the state aid ceiling per capita. As the spatial distribution was investigated, spatial autocorrelation of each indicator was tested using the Moran test. In terms of calculated indicators, granted investment incentives in the Czech Republic were distributed unequally and tended to be clustered especially in the districts of the Ústí Region, the Central Bohemian Region and the Moravian-Silesian Region. The Moran test confirmed the presence of statistically significant spatial autocorrelation across all indicators related to granted investment incentives on the district level in the period 1998-2021.

Endogenous concepts of regional development are currently being enforced in the regional policy of the state. This fact is reflected in the specific attention given to small and medium-sized. Therefore, we have assumed differences in the qualitative characteristics of small and medium-sized enterprises in underdeveloped and developed regions, as well as the presence of a relationship between the level of a region's development and the qualitative characteristics of small and medium-sized enterprises. To identify the qualitative characteristics of small and medium-sized enterprises in underdeveloped and developed regions, we applied a two-step cluster analysis. An analysis of the results showed a smaller percentage representation of innovative elements in the qualitative characteristics of small and medium-sized enterprises in underdeveloped regions. By binary regression analysis, we have determined significance of qualitative characteristics of small and medium-sized enterprises in the underdeveloped and developed regions.

The present article is focused on the analysis of selected indicators that characterize the economic sector of information and communication technology at regional levels in the Czech Republic between years 1995 and 2015. The aim of this paper is to evaluate, by a specific index that is based on a combination of point method and economical-geographic methods, regional divergences in the area of the economic branch of information and communication technology and defines development and regional differences in the ICT branch at NUTS3 level of the regions of the Czech Republic. The quest is to find regions where information and communication technology is developing and to define an order (ranking) of regions that succeeded in developing the ICT sector. The analysis of the information and communication sector which is defined by NACE Rev.2., in section J, is based on empirical data from the Czech Statistical Office at NUTS3 regional level.

There are many tools to measure and compare the innovation potential and the innovation performance. Each of these instruments uses more or less similar indicators. This article identifies the indicators related to the research, development and innovation, which are available at regional level NUTS3 (14 regions) in the Czech Republic. These indicators are analysed with respect to their development in the period 2001-2014. Via a coefficient calculated they are subsequently combined into one aggregate indicator characterizing the innovation potential of each region. This merger was made both for the beginning and the end of the reporting period to be able to compare the development of innovation potential of Czech regions. Absolutely the best results were achieved in Prague followed by the South Moravian Region and the worst are found in the Karlovy Vary Region in the long term. However, through a calculated coefficient it was found that there is stabilization in the development of regional differences of innovation potential of Czech regions. In the last part, the article outlines options for further follow-up research.

This article is focusing on analysis of sector of information and communication technology in the Czech Republic between years 1995 and 2012. Branch of information and communication technology is one of the most important economic activities because of potential for further growth of output performance and productivity. In this short study development of information and communication sector during the selected 18 years in aggregate economic levels is described through performance measured by gross value added, sectoral employment and also sectoral productivity. Emphasis is placed on developing information and communication technologies in selected 14 regions of the Czech Republic. The main aim is to find the regions which are concentrated economic activities related to information and communication technology in and to evaluate their success in interregional comparisons. It was found out that the overwhelming majority of economic activities associated with information and communication technology is concentrated in four - five regions of the Czech Republic and the regional disparities in the area of this economic branch have tendency to deepen.

The assessment of knowledge infrastructure, implemented on the level of administrative districts of authorized municipalities - 206 units in total, is aimed at the institutional support for knowledge base development, the fundamental component of which is the infrastructure for education. The main attention is paid to the complexity of appropriate supply. The main objective of the article is dividing of districts into five qualitative groups and their synthesis by regions. The biggest weight is assigned to the location of public universities in regions, lower weight to private universities, colleges and secondary schools. The obtained data about the educational infrastructure has been complemented by an analysis of location of science and research institutes, science and technology parks and business incubators. The most significant centres found in the best classification group are Prague and Brno. The second group contains all the remaining regional capitals except Karlovy Vary, together with Opava - a traditional university centre. The strongest centre within this group is Ostrava, the weakest is Jihlava. Based on this dividing, possibilities to influence the characteristics of knowledge infrastructure factors in the future were suggested.

Human capital belongs to main factors when discussing causes of regional disparities on one hand and the ways how to stimulate regional development on the other. In this regard, the process of human capital building is traditionally connected with increasing productivity. It is noteworthy that the process of human capital building is based on education and learning. Moreover, human capital is perceived as an important factor of regional differentiation. Then, it is not surprising that human capital was firmly positioned in the fundamental conceptual framework of European cohesion policy with its goal to reduce spatial disparities. This article is focused on spatial analysis of projects supported from the Operational Programme Education for Competiveness. Just this Programme may be regarded as the most relevant Czech operational programme related to the themes human capital and education. The main findings of our analysis are a bit ambivalent when considering the convergence goal of cohesion policy. Thus, the projects were allocated in all Czech districts. However, an important influence of settlement hierarchy may be identified. In addition, the absorption capacity of tertiary education institutions plays a substantial role in regional differentiation.