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Bridging the short-term and long-term dynamics of economic structural change

Author

Listed:
  • James McNerney
  • Yang Li
  • Andres Gomez-Lievano
  • Frank Neffke

Abstract

Economic transformation -- change in what an economy produces -- is foundational to development and rising standards of living. Our understanding of this process has been propelled recently by two branches of work in the field of economic complexity, one studying how economies diversify, the other how the complexity of an economy is expressed in the makeup of its output. However, the connection between these branches is not well understood, nor how they relate to a classic understanding of structural transformation. Here, we present a simple dynamical modeling framework that unifies these areas of work, based on the widespread observation that economies diversify preferentially into activities that are related to ones they do already. We show how stylized facts of long-run structural change, as well as complexity metrics, can both emerge naturally from this one observation. However, complexity metrics take on new meanings, as descriptions of the long-term changes an economy experiences rather than measures of complexity per se. This suggests relatedness and complexity metrics are connected, in a hitherto overlooked way: Both describe structural change, on different time scales. Whereas relatedness probes transformation on short time scales, complexity metrics capture long-term change.

Suggested Citation

  • James McNerney & Yang Li & Andres Gomez-Lievano & Frank Neffke, 2021. "Bridging the short-term and long-term dynamics of economic structural change," Papers 2110.09673, arXiv.org, revised Mar 2023.
  • Handle: RePEc:arx:papers:2110.09673
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    References listed on IDEAS

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    8. Andres Gomez-Lievano & Oscar Patterson-Lomba, 2018. "Estimating the drivers of urban economic complexity and their connection to economic performance," Papers 1812.02842, arXiv.org, revised Sep 2021.
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    Cited by:

    1. Koen Frenken & Frank Neffke, 2024. "Economic Geography and Complexity Theory," Papers in Evolutionary Economic Geography (PEEG) 2431, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2024.
    2. Li, Yang & Neffke, Frank M.H., 2024. "Evaluating the principle of relatedness: Estimation, drivers and implications for policy," Research Policy, Elsevier, vol. 53(3).
    3. Tacchella, Andrea & Zaccaria, Andrea & Miccheli, Marco & Pietronero, Luciano, 2023. "Relatedness in the era of machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    4. Bernardo Caldarola & Dario Mazzilli & Aurelio Patelli & Angelica Sbardella, 2024. "Structural Change, Employment, and Inequality in Europe: an Economic Complexity Approach," Papers 2410.07906, arXiv.org.
    5. Hidalgo, César A., 2023. "The policy implications of economic complexity," Research Policy, Elsevier, vol. 52(9).
    6. Carlo Bottai & Jacopo Di Iorio & Martina Iori, 2024. "Reinterpreting Economic Complexity: A co-clustering approach," Papers 2406.16199, arXiv.org.

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