IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1103.1249.html
   My bibliography  Save this paper

Randomizing world trade. II. A weighted network analysis

Author

Listed:
  • Tiziano Squartini
  • Giorgio Fagiolo
  • Diego Garlaschelli

Abstract

Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of the binary projections of the ITN by considering its weighted representations. We show that, unlike the binary case, all possible weighted representations of the ITN (directed/undirected, aggregated/disaggregated) cannot be traced back to local country-specific properties, which are therefore of limited informativeness. Our two papers show that traditional macroeconomic approaches systematically fail to capture the key properties of the ITN. In the binary case, they do not focus on the degree sequence and hence cannot characterize or replicate higher-order properties. In the weighted case, they generally focus on the strength sequence, but the knowledge of the latter is not enough in order to understand or reproduce indirect effects.

Suggested Citation

  • Tiziano Squartini & Giorgio Fagiolo & Diego Garlaschelli, 2011. "Randomizing world trade. II. A weighted network analysis," Papers 1103.1249, arXiv.org, revised Nov 2011.
  • Handle: RePEc:arx:papers:1103.1249
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1103.1249
    File Function: Latest version
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Theodore Tsekeris, 2017. "Network analysis of inter-sectoral relationships and key sectors in the Greek economy," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(2), pages 413-435, July.
    2. 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.
    3. Giuditta Prato & Daniel Nepelski, 2014. "Global technological collaboration network: network analysis of international co-inventions," The Journal of Technology Transfer, Springer, vol. 39(3), pages 358-375, June.
    4. Matthieu Cristelli & Andrea Gabrielli & Andrea Tacchella & Guido Caldarelli & Luciano Pietronero, 2013. "Measuring the Intangibles: A Metrics for the Economic Complexity of Countries and Products," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-20, August.
    5. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    6. 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.
    7. Michael Lebacher & Paul W. Thurner & Göran Kauermann, 2021. "A dynamic separable network model with actor heterogeneity: An application to global weapons transfers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 201-226, January.
    8. Marco Dueñas & Giorgio Fagiolo, 2014. "Global Trade Imbalances: A Network Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(03n04), pages 1-29.
    9. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    10. Giuditta De Prato & Daniel Nepelski, 2012. "Global R&D network. A network analysis of international R&D centres," JRC Research Reports JRC79478, Joint Research Centre (Seville site), revised Nov 2012.
    11. 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.
    12. Luu, Duc Thi & Lux, Thomas & Yanovski, Boyan, 2017. "Structural correlations in the Italian overnight money market: An analysis based on network configuration models," Economics Working Papers 2017-02, Christian-Albrechts-University of Kiel, Department of Economics.
    13. Monica Billio & Roberto Casarin & Matteo Iacopini & Sylvia Kaufmann, 2023. "Bayesian Dynamic Tensor Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(2), pages 429-439, April.
    14. Leonardo Ermann & Dima L. Shepelyansky, 2015. "Google matrix analysis of the multiproduct world trade network," Papers 1501.03371, arXiv.org.
    15. Di Vece, Marzio & Garlaschelli, Diego & Squartini, Tiziano, 2023. "Reconciling econometrics with continuous maximum-entropy network models," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    16. Leticia Blázquez & Carmen Díaz-Mora & Belén González-Díaz, 2020. "The role of services content for manufacturing competitiveness: A network analysis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-22, January.
    17. Nicole Palan & Nadia Simoes & Nuno Crespo, 2021. "Measuring fifty years of trade globalisation," The World Economy, Wiley Blackwell, vol. 44(6), pages 1859-1884, June.
    18. Hoppe, K. & Rodgers, G.J., 2015. "A microscopic study of the fitness-dependent topology of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 64-74.
    19. L. Blázquez & C. Díaz-Mora & B. González-Díaz, 2023. "Slowbalisation or a “New” type of GVC participation? The role of digital services," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 50(1), pages 121-147, March.
    20. Vincenza Carchiolo & Marco Grassia & Michele Malgeri & Giuseppe Mangioni, 2022. "Co-Authorship Networks Analysis to Discover Collaboration Patterns among Italian Researchers," Future Internet, MDPI, vol. 14(6), pages 1-15, June.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1103.1249. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.