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Reconstructing the world trade multiplex: the role of intensive and extensive biases

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  • Rossana Mastrandrea

    (CPT - E5 Physique statistique et systèmes complexes - CPT - Centre de Physique Théorique - UMR 7332 - AMU - Aix Marseille Université - UTLN - Université de Toulon - CNRS - Centre National de la Recherche Scientifique, CPT - Centre de Physique Théorique - UMR 7332 - AMU - Aix Marseille Université - UTLN - Université de Toulon - CNRS - Centre National de la Recherche Scientifique, Institute of Economics of Sant'Anna [Pisa] - SSSUP - Scuola Universitaria Superiore Sant'Anna = Sant'Anna School of Advanced Studies [Pisa])

  • Squartini Tiziano

    (ISC - Istituto Sistemi Complessi [ROME] - CNR - National Research Council of Italy | Consiglio Nazionale delle Ricerche, INSTITUUT LORENTZ - Instituut Lorentz - Universiteit Leiden = Leiden University)

  • Giorgio Fagiolo

    (Institute of Economics of Sant'Anna [Pisa] - SSSUP - Scuola Universitaria Superiore Sant'Anna = Sant'Anna School of Advanced Studies [Pisa])

  • Diego Garlaschelli

    (INSTITUUT LORENTZ - Instituut Lorentz - Universiteit Leiden = Leiden University)

Abstract

In economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting deviations of an empirical network from economically meaningful benchmarks or reconstruct the most likely structure of an economic network when the latter is unknown. However, several studies have proved that real economic networks and multiplexes topologically differ from configurations inferred only from node strengths. Here we provide a detailed analysis of the world trade multiplex by comparing it to an enhanced null model that simultaneously reproduces the strength and the degree of each node. We study several temporal snapshots and almost 100 layers (commodity classes) of the multiplex and find that the observed properties are systematically well reproduced by our model. Our formalism allows us to introduce the (static) concept of extensive and intensive bias, defined as a measurable tendency of the network to prefer either the formation of extra links or the reinforcement of link weights, with respect to a reference case where only strengths are enforced. Our findings complement the existing economic literature on (dynamic) intensive and extensive trade margins. More generally, they show that real-world multiplexes can be strongly shaped by layer-specific local constraints.

Suggested Citation

  • Rossana Mastrandrea & Squartini Tiziano & Giorgio Fagiolo & Diego Garlaschelli, 2014. "Reconstructing the world trade multiplex: the role of intensive and extensive biases," Post-Print hal-01113938, HAL.
  • Handle: RePEc:hal:journl:hal-01113938
    DOI: 10.1103/PhysRevE.90.062804
    Note: View the original document on HAL open archive server: https://hal.science/hal-01113938
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    References listed on IDEAS

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    1. David Hummels & Peter J. Klenow, 2002. "The Variety and Quality of a Nation's Trade," NBER Working Papers 8712, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348, arXiv.org, revised Feb 2019.
    2. Luu, Duc Thi & Lux, Thomas, 2018. "Multilayer overlaps and correlations in the bank-firm credit network of Spain," Economics Working Papers 2018-04, Christian-Albrechts-University of Kiel, Department of Economics.
    3. Jeroen van Lidth de Jeude & Riccardo Di Clemente & Guido Caldarelli & Fabio Saracco & Tiziano Squartini, 2019. "Reconstructing Mesoscale Network Structures," Complexity, Hindawi, vol. 2019, pages 1-13, January.
    4. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2022. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Papers 2206.06309, arXiv.org, revised Dec 2022.
    5. Ren, Zhuo-Ming & Zhao, Li & Du, Wen-Li & Weng, Tong-Feng & Liu, Chuang & Kong, Yi-Xiu & Zhang, Yi-Cheng, 2024. "Tunable resource allocation dynamics for interpreting economic complexity," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    6. Luiz G. A. Alves & Giuseppe Mangioni & Isabella Cingolani & Francisco A. Rodrigues & Pietro Panzarasa & Yamir Moreno, 2018. "The nested structural organization of the worldwide trade multi-layer network," Papers 1803.02872, arXiv.org, revised Sep 2019.
    7. Zhang, Xiaohang & Cui, Huiyuan & Zhu, Ji & Du, Yu & Wang, Qi & Shi, Wenhua, 2017. "Measuring the dissimilarity of multiplex networks: An empirical study of international trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 380-394.
    8. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    9. Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna, 2023. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    10. Sun, Qingru & Gao, Xiangyun & Zhong, Weiqiong & Liu, Nairong, 2017. "The stability of the international oil trade network from short-term and long-term perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 345-356.
    11. Zhuo-Ming Ren & An Zeng & Yi-Cheng Zhang, 2020. "Bridging nestedness and economic complexity in multilayer world trade networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.

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