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Interpreting Economic Complexity

Author

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  • Penny Mealy
  • J. Doyne Farmer
  • Alexander Teytelboym

Abstract

Two network measures known as the Economic Complexity Index (ECI) and Product Complexity Index (PCI) have provided important insights into patterns of economic development. We show that the ECI and PCI are equivalent to a spectral clustering algorithm that partitions a similarity graph into two parts. The measures are also related to various dimensionality reduction methods and can be interpreted as vectors that determine distances between nodes based on their similarity. Our results shed a new light on the ECI's empirical success in explaining cross-country differences in GDP/capita and economic growth, which is often linked to the diversity of country export baskets. In fact, countries with high (low) ECI tend to specialize in high (low) PCI products. We also find that the ECI and PCI uncover economically informative specialization patterns across US states and UK regions.

Suggested Citation

  • Penny Mealy & J. Doyne Farmer & Alexander Teytelboym, 2017. "Interpreting Economic Complexity," Papers 1711.08245, arXiv.org, revised Sep 2018.
    • Handle: RePEc:arx:papers:1711.08245
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    References listed on IDEAS

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    Cited by:

    1. Benjamin Davies & David C. Maré, 2021. "Relatedness, complexity and local growth," Regional Studies, Taylor & Francis Journals, vol. 55(3), pages 479-494, March.
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    3. Sebastian Bustos & Muhammed A. Yildirim, 2019. "Production Ability and Economic Growth," CID Working Papers 110a, Center for International Development at Harvard University.

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