This chapter examines the existence of regional agglomerations of manufacturing, service and creative industries, and the relationship between these industries and the wealth of regions. Through an analysis of 250 European regions, three important conclusions can be inferred from the results obtained in this chapter. The first is that creative industries play an important role in the wealth of the regions. The second is that the most creative regions are characterized by having more high-tech manufacturing industries than the rest of the regions, although the number of low-tech manufacturing firms is similar. Lastly, in the richest regions, a greater share of high-tech manufacturing industries coexists with a greater share of creative industries. The importance of this chapter resides in the fact that up until now no analysis has demonstrated that creative industries are the most important industries in regional wealth.
Top1. Introduction
The localization of creative industries, as highlighted by the work of Stam et al. (2008), Cooke (2008), Lazzeretti et al. (2008), Capone (2008) and Power and Nielsen (2010), is an area of increasing importance in the literature on geographic agglomerations. These industries are in fact groupings of specific sectors of low-technology manufacturing and knowledge-intensive services, which is why their importance is related to the ever-increasing dependence of manufacturing sectors on service industries (Peneder et al., 2003; Pilat and Wölfl, 2005; Drejer and Vinding, 2005; Wood, 2006; Aslesen and Isaksen, 2007b), and on what we could call the Knowledge and Service Economy (Windrum and Tomlinson, 1999; Bishop, 2008; Aslesen and Isaksen, 2007a; Aslesen and Isaksen 2007b; Strambach 2008).
Existing studies show which activities can be included in creative industries (UNCTAD, 2010), and why these industries form agglomerations (Lazzeretti et al., 2009; Lorenzen and Frederiksen, 2008). However, although the existence of relationships between manufacturing and services agglomerations in their different definitions of high and low (Leydesdorff et al., 2006; Leydesdorff and Fritsch, 2006; Vence-Deza and González-López, 2008; Heidenreich, 2009) have been the aim of some analyses, the above mentioned analyses have not been carried out to examine the possible relationship between manufacturing agglomerations – including both high and low-tech industries – and creative industries. This paper attempts to fill this gap. In addition, the core importance of the paper is based on the fact that it points out empirically how important creative industries are in developing economies and bringing prosperity to European regions. The results obtained have implications for academia as well as for policymakers.
The empirical study is based on a sample of 250 regions in 24 European countries. The data was taken from Eurostat’s Structural Business Statistics and Regional Economic Accounts databases. The data was used to evaluate manufacturing, service and creative industry agglomerations based on the Location Quotient (LQ) of firms by regions. Following this, the relationship between these agglomerations and GDP was verified.
Three important conclusions are pointed out. The first is that creative industries play an important role in the wealth of a region. The second is that the most creative regions are characterized by having more high-tech manufacturing sectors than others, although they have a similar share of low-tech manufacturing firms. The third is that in the richest regions a greater share of high-tech manufacturing industries coexists with a greater share of creative industries, thus showing the importance of creativity.
The outline that we have used in this paper is as follows: in Sections 2 we briefly summarize the recent basic theory on the study of maps of manufacturing, service and creative industry agglomerations to determine the localization patterns and the relationships they have on the aforementioned studies. In Section 3, we include the empirical study where we set out the variables used, the sources the data was extracted from and the methodology used for their study as well as the results obtained. Conclusions can be found in Section 4.