Beyond ‘periphery’: a detailed and nuanced taxonomy of the Norwegian regions

ABSTRACT

Increasing attention is being paid towards the influence of regional contexts on innovation activities within regional development studies. Some of the literature in economic geography tends to consider the various peripheral areas as being homogenous and partly characterized by their remote location, weak innovation inputs and lack of knowledge exchange. This paper questions this approach by examining the role of innovation activities in peripheral regions. We offer a detailed and multifaceted taxonomy of the Norwegian economic regions. From an empirical viewpoint, the adoption of cluster analysis and a broad set of innovation, economic and territorial indicators allowed us to provide a nuanced picture of the current fabric of Norwegian innovation and economic-production. With the benefit of insights from relevant strands of literature (e.g. regional development, innovation systems and multi-scalar innovation networks), the case of Norway presented in our paper contributes to the scholarly debate on the role of structural preconditions for the innovation of firms in diverse peripheral areas.

KEYWORDS:

• Economic geography
• regional innovation systems
• regional development
• core–periphery
• innovation in the periphery
• cluster analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 There are 89 economic regions in Norway. We excluded Holmestrand from our final dataset, as this is a very small economic region, which would represent a clear outlier forming an uninformative cluster.

2 In the economic geography literature, a ‘cluster’ is generally defined as an agglomeration of geographically concentrated organisations (e.g. firms and research institutions) specialised in one or a few interrelated sectors (e.g. Porter 1990). To avoid misunderstandings, it may be helpful to clarify that, in this paper, the term ‘cluster’ exclusively refers to a given group of Norwegian economic regions that are more similar to each other than to those making up the other identified groups through K-means clustering. Such differentiation between clusters was based on the set of socioeconomic variables illustrated in Table 1 (see Section 2).