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Export dynamics as an optimal growth problem in the network of global economy

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  • Michele Caraglio
  • Fulvio Baldovin
  • Attilio L. Stella

Abstract

We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.

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  • Michele Caraglio & Fulvio Baldovin & Attilio L. Stella, 2016. "Export dynamics as an optimal growth problem in the network of global economy," Papers 1609.04956, arXiv.org.
    • Handle: RePEc:arx:papers:1609.04956
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    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, September.
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    Cited by:

    1. Gianluca Teza & Michele Caraglio & Attilio L. Stella, 2021. "Entropic measure unveils country competitiveness and product specialization in the World trade web," Papers 2106.01936, arXiv.org.
    2. Charles D. Brummitt & Andres Gomez-Lievano & Ricardo Hausmann & Matthew H. Bonds, 2018. "Machine-learned patterns suggest that diversification drives economic development," Papers 1812.03534, arXiv.org.

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