R12 - Size and Spatial Distributions of Regional Economic ActivityReturn

Analysing papers that reveal by decomposing territorial inequalities in the EU that the share of disparities attributed to the NUTS 3 level has increased over the last 20 years, this paper aims to examine to what extent the financial support in 2000-2006 from ERDF and CF, which are the main regional policy tools but mainly are directed to address the issues arising at the NUTS 2 level, contributed to supporting convergence at the NUTS 3 level. Our re-search strategy relies on combining a conditional β-convergence model and a difference-in-differences (DiD) estimator. Estimations are generated for four alternative post-policy periods, two ways to measure policy intervention and for different funds as well as different expenditure categories. Our research results bring to light the question of potential negative outcomes of the EU's existing regional policy since the policy that is focused on the NUTS 2 level is enlarging imbalances within these regions, i.e., among NUTS 3 regions.

The article deals with the strategically important problems of metropolisation. In this context, it presents a theoretically based method of assessment of metropolises, the explanatory power of which was verified on the example of the Central Europe. This method is based on three components: population size (initial assumption), economic profile (ties to economic performance) and general attractiveness (the perception of development potential). The results of the evaluation of the 27 identified metropolises were generalized using a typology of their inclusion within the framework of the listed components: most metropolises were classified as type B - an established metropolis, followed by type C - an elementary metropolis, and type A - a dominant metropolis. A practically targeted conceptualization is then demonstrated on the example of the Czech Republic. The main attention was focused on the intensity of the economic links of Prague (and two further Czech centres) with other Central European metropolises.

How is knowledge distributed over space and how are different types of knowledge related? These questions have so far received little attention. In this paper we measure knowledge relatedness based on the relationship between individual patent categories by using coclassification information obtained from EPO patents. We also follow specialization of countries and its evolution over the past three decades. We focus on the EU, the United States and China. The objective of this paper is to identify the knowledge relatedness between technological fields and to map knowledge produced in selected countries. For visualization of knowledge relatedness network analysis has been used.

In this paper we analysed to which extent the changes in market potentials in Poland have been a cause for increasing income disparities, which were observed across Polish regions from 1995 to 2008. To do so we derived and estimated a New Economy Geography Model, which relates per capita GDP growth rates to changes in market potential. The results of the crosssection estimations of the model for the period 1995-2008 and for its different subsamples point to a positive and significant effect of changing market potentials in per capita GDP growth rates. Due to the fact that core-economic regions have mostly benefited in terms of market potential growth during 1995-2008, these results confirm the important role played by the relative changing in market potentials across Polish regions in widening the gap between poor and rich regions in the country in the period of analysis.