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Geography of Science: Competitiveness and Inequality

Author

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  • Aurelio Patelli
  • Lorenzo Napolitano
  • Giulio Cimini
  • Andrea Gabrielli

Abstract

Using ideas and tools of complexity science we design a holistic measure of \textit{Scientific Fitness}, encompassing the scientific knowledge, capabilities and competitiveness of a research system. We characterize the temporal dynamics of Scientific Fitness and R\&D expenditures at the geographical scale of nations, highlighting patterns of similar research systems, and showing how developing nations (China in particular) are quickly catching up the developed ones. Down-scaling the aggregation level of the analysis, we find that even developed nations show a considerable level of inequality in the Scientific Fitness of their internal regions. Further, we assess comparatively how the competitiveness of each geographic region is distributed over the spectrum of research sectors. Overall, the Scientific Fitness represents the first high quality estimation of the scientific strength of nations and regions, opening new policy-making applications for better allocating resources, filling inequality gaps and ultimately promoting innovation.

Suggested Citation

  • Aurelio Patelli & Lorenzo Napolitano & Giulio Cimini & Andrea Gabrielli, 2021. "Geography of Science: Competitiveness and Inequality," Papers 2110.01615, arXiv.org.
    • Handle: RePEc:arx:papers:2110.01615
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    References listed on IDEAS

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    1. Cesar A. Hidalgo & Ricardo Hausmann, 2009. "The Building Blocks of Economic Complexity," Papers 0909.3890, arXiv.org.
    2. Emanuele Pugliese & Giulio Cimini & Aurelio Patelli & Andrea Zaccaria & Luciano Pietronero & Andrea Gabrielli, 2017. "Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production," Papers 1707.05146, arXiv.org, revised Dec 2017.
    3. Tacchella, A. & Cristelli, M. & Caldarelli, G. & Gabrielli, A. & Pietronero, L., 2013. "Economic complexity: Conceptual grounding of a new metrics for global competitiveness," Journal of Economic Dynamics and Control, Elsevier, vol. 37(8), pages 1683-1691.
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    Cited by:

    1. Dario Mazzilli & Manuel Sebastian Mariani & Flaviano Morone & Aurelio Patelli, 2022. "Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms," Papers 2212.12356, arXiv.org, revised Mar 2024.

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