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Varieties of Regional Innovation Systems around the World and Catch-up by Latecomers

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  • Jinhee Kim
  • Keun Lee

Abstract

This study identifies the characteristics and types of the regional innovation systems (RIS) of regions and cities in emerging economies in comparison to those in advanced economies. It uses the citation data of the US patents filed by 30 regions. Some RIS variables are newly developed, and they include intra-regional, inter-regional, and inter-national sourcing of knowledge and local ownership of innovation. The cluster analysis of these variables enables us to identify four major types of RIS around the world and link them to regional economic performance. The four types are, in the descending order of their per capita income levels, as follows: large, mature RIS characterized by a combination of long cycle technology specialization and high local ownership (Group 1), mixed RIS characterized by a long cycle and low local ownership (Group 2), “strong catch-up†characterized by short cycle and high local ownership (Group 3), and “weak catch-up†characterized by short cycle and low local ownership (Group 4). Groups 3 and 4 include only the regions in emerging world. They similarly specialize in the same short cycle time of technologies (CTT)-based sectors but show different records of economic performance. The key differentiating variable is the degree of local ownership of knowledge, which can be a basis for increasing domestic sourcing of knowledge and sustained catching up. Another important variable is decentralization, of which the level is lower in the strong catch-up group than in the weak catch-up group. In this Group 3, catching up is led by big businesses. Several cities experiencing upgrading, like Moscow, Beijing, and Shanghai, also show an increasing trend of local ownership and centralization.

Suggested Citation

  • Jinhee Kim & Keun Lee, 2022. "Varieties of Regional Innovation Systems around the World and Catch-up by Latecomers," Papers in Evolutionary Economic Geography (PEEG) 2219, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2022.
    • Handle: RePEc:egu:wpaper:2219
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    File URL: http://econ.geo.uu.nl/peeg/peeg2219.pdf
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    References listed on IDEAS

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    1. Leogrande, Angelo & Drago, Carlo & Mallardi, Giulio & Costantiello, Alberto & Magaletti, Nicola, 2024. "Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering," MPRA Paper 123081, University Library of Munich, Germany.
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O50 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - General
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

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