Stability and change in the Hungarian city network

Abstract

A considerable amount of literature has been published in Hungarian on the settlements’ innovation capability in the past twenty years. However, among these studies we can barely find a complex examination that would embrace the entire Hungarian city network. We cannot set aside its importance, since the situation exposed and the results revealed by such an examination could serve as a basis for evolving different strategies defined by the peculiar features of the city network.

The basis for our study is an analysis of innovation potential carried out in 2005. Sticking to its methodological structure, we made an attempt to reveal the dispersion of Hungarian city networks by organizing the unique variables into five dimensions. These five dimensions are as follows: economy, education and management, social activity, human resources, innovation.

This method made it possible to explore the differences within the city networks, as well as to shed light on the degree of innovational capabilities of the methodologically well-defined groups of city networks. The dimensions were formed based on the data from 327 cities. The sample does not include the indicators of Budapest and other 18 cities which gained urban status in July 2013.

We used cluster analysis based on the principal component scores to determine homogeneous groups of cities. Twenty-five cities with the best complex indicators were separated and a hierarchical cluster analysis was done on them. Groups of the remaining cities were determined by the k-means cluster analysis. Human resources and innovation indicators played a great role in the stratification of the twenty-five “elite” cities. Six groups were identified among them as follows: regional centers with complex structure; regional centers with economic emphasis; cities with research and development dominance; suburban economic centers; cities with significant knowledge base and higher education; sub-regional centers with balanced structure. The remaining 302 cities were classified into four clusters. Here we can observe a “second tier” in the city network, consisting of 37 cities. Availability of human resources and innovation is also relevant in this category, but clusters below this level – containing mostly small towns and “quasi-towns” with deficient central functions – are determined along the primary economic and social factors.

The results also prove that regional inequalities are constantly increasing. Differences between cities decreased slightly, however, the gap between the dimensions became even wider. The economic and innovation potential do not necessarily meet in the case of well-performing cities.