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Comparative analysis of technological fitness and coherence at different geographical scales

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  • Matteo Straccamore
  • Matteo Bruno
  • Andrea Tacchella

Abstract

Debates over the trade-offs between specialization and diversification have long intrigued scholars and policymakers. Specialization can amplify an economy by concentrating on core strengths, while diversification reduces vulnerability by distributing investments across multiple sectors. In this paper, we use patent data and the framework of Economic Complexity to investigate how the degree of technological specialization and diversification affects economic development at different scales: metropolitan areas, regions and countries. We examine two Economic Complexity indicators. Technological Fitness assesses an economic player’s ability to diversify and generate sophisticated technologies, while Technological Coherence quantifies the degree of specialization by measuring the similarity among technologies within an economic player’s portfolio. Our results indicate that a high degree of Technological Coherence is associated with increased economic growth only at the metropolitan area level, while its impact turns negative at larger scales. In contrast, Technological Fitness shows a U-shaped relationship with a positive effect in metropolitan areas, a negative influence at the regional level, and again a positive effect at the national level. These findings underscore the complex interplay between technological specialization and diversification across geographical scales. Understanding these distinctions can inform policymakers and stakeholders in developing tailored strategies for technological advancement and economic growth.

Suggested Citation

  • Matteo Straccamore & Matteo Bruno & Andrea Tacchella, 2025. "Comparative analysis of technological fitness and coherence at different geographical scales," PLOS ONE, Public Library of Science, vol. 20(8), pages 1-19, August.
    • Handle: RePEc:plo:pone00:0329746
    DOI: 10.1371/journal.pone.0329746
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    References listed on IDEAS

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    1. Giambattista Albora & Matteo Straccamore & Andrea Zaccaria, 2024. "Machine learning-based similarity measure to forecast M&A from patent data," Papers 2404.07179, arXiv.org.
    2. Dieter F. Kogler & David L. Rigby & Isaac Tucker, 2013. "Mapping Knowledge Space and Technological Relatedness in US Cities," European Planning Studies, Taylor & Francis Journals, vol. 21(9), pages 1374-1391, September.
    3. Hausmann, Ricardo & Hidalgo, Cesar, 2014. "The Atlas of Economic Complexity: Mapping Paths to Prosperity," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262525429, December.
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