Complementing clusters: a competitiveness rationale for infrastructure investments
Journal article, Peer reviewed
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- Scientific articles 
Original versionCompetitiveness Review, 25(2015)3: 242-257 10.1108/CR-12-2014-0040
Purpose – The purpose of this paper is to present a novel application of cluster theory and cluster methodology to evaluate large infrastructure investments. The complementing clusters approach, which builds on the notion of infrastructure as connecting isolated “economic islands”, is able to assess the potential for value creation effects of new infrastructure investment. Design/methodology/approach – The author uses simulation analysis based on a unique data set encompassing all employees and employers, as well as cluster mapping, for every pair of “economic islands” being connected by the examined infrastructure investments. The empirical setting is of large fjord crossings in Western Norway, the so-called E39 project. Findings – The empirical findings show that productivity gains are higher when an integrated labor market hosting complementary clusters is formed. Limitations remain regarding the economic integration path. Research limitations/implications – The authors provide an ex-ante analysis using information over the past 10 years. Following the expected infrastructure investments, future research should examine the extent to which productivity gains materialized and the reasons underlying the achieved materialization levels. Practical implications – Current evaluation of large infrastructure investments focuses on transportation economics effects, technical feasibility and environmental consequences. The authors complement this current practice by advancing a theoretically grounded value creation perspective that can affect future evaluation practices. Originality/value – Cluster complementarity-based evaluation is a novel methodology that is applicable to investment decisions which are central for economic development. Cluster analysis of infrastructure investments provides new and valuable data for making such investments decisions.
This is the authors' accepted and refereed manuscript to the article