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Growth in regions, knowledge bases and relatedness: some insights from the Italian case

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  • Niccolò Innocenti
  • Luciana Lazzeretti

Abstract

The present work uses an evolutionary economic geography framework to contribute to the literature on the combinatorial dimension of differentiated knowledge bases (DKB). The aim is to determine if there is a pattern of knowledge creation that does not rely on one specific knowledge base, and if the three knowledge bases require the presence of other related sectors to exploit their innovative capacity leading to the growth of the region. We apply Hidalgo, Klinger, Barabási, and Hausmann’s [2007. The product space conditions the development of nations. Science, 317(5837), 482–487.] methodology of a revealed relatedness measure between sectors, thus measuring the relatedness between the three KB and the relatedness of each KB with all other sectors (outside). The results show how, at the local level, the sectors characterized by synthetic and symbolic knowledge bases in the presence of other sectors with a high degree of relatedness are able to increase the employment growth of the area.

Suggested Citation

  • Niccolò Innocenti & Luciana Lazzeretti, 2019. "Growth in regions, knowledge bases and relatedness: some insights from the Italian case," European Planning Studies, Taylor & Francis Journals, vol. 27(10), pages 2034-2048, October.
    • Handle: RePEc:taf:eurpls:v:27:y:2019:i:10:p:2034-2048
    DOI: 10.1080/09654313.2019.1588862
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    Cited by:

    1. Qin Ye, 2021. "The impact of knowledge depth and breadth on the geography of analytical industry technological networks: Evidence from China’s biotechnology industry," Growth and Change, Wiley Blackwell, vol. 52(4), pages 2238-2255, December.
    2. Hidalgo, César A., 2023. "The policy implications of economic complexity," Research Policy, Elsevier, vol. 52(9).
    3. C'esar A. Hidalgo, 2022. "The Policy Implications of Economic Complexity," Papers 2205.02164, arXiv.org, revised Aug 2023.

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