Статья

Functional Structure in Production Networks

C. Mattsson, F. Takes, E. Heemskerk, C. Diks, G. Buiten, A. Faber, P. Sloot,
2021

Production networks are integral to economic dynamics, yet dis-aggregated network data on inter-firm trade is rarely collected and often proprietary. Here we situate company-level production networks within a wider space of networks that are different in nature, but similar in local connectivity structure. Through this lens, we study a regional and a national network of inferred trade relationships reconstructed from Dutch national economic statistics and re-interpret prior empirical findings. We find that company-level production networks have so-called functional structure, as previously identified in protein-protein interaction (PPI) networks. Functional networks are distinctive in their over-representation of closed squares, which we quantify using an existing measure called spectral bipartivity. Shared local connectivity structure lets us ferry insights between domains. PPI networks are shaped by complementarity, rather than homophily, and we use multi-layer directed configuration models to show that this principle explains the emergence of functional structure in production networks. Companies are especially similar to their close competitors, not to their trading partners. Our findings have practical implications for the analysis of production networks and give us precise terms for the local structural features that may be key to understanding their routine function, failure, and growth.

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Версии

  • 1. Version of Record от 2021-05-21

Метаданные

Об авторах
  • C. Mattsson
    Computational Network Science Lab, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands, Network Science Institute, Boston, MA, United States
  • F. Takes
    Computational Network Science Lab, Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands, CORPNET, University of Amsterdam, Amsterdam, Netherlands
  • E. Heemskerk
    CORPNET, University of Amsterdam, Amsterdam, Netherlands, Department of Political Science, University of Amsterdam, Amsterdam, Netherlands
  • C. Diks
    Faculty Economics and Business, University of Amsterdam, Amsterdam, Netherlands, Tinbergen Institute, Amsterdam, Netherlands
  • G. Buiten
    Statistics Netherlands, The Hague, Netherlands
  • A. Faber
    Ministry of Economic Affairs & Climate, The Hague, Netherlands
  • P. Sloot
    Computational Science Lab, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands, Institute for Advanced Study, University of Amsterdam, Amsterdam, Netherlands, Complexity Institute, Nanyang Technological University, Singapore, Singapore, Complexity Science Hub Vienna, Vienna, Austria, National Center for Cognitive Research, ITMO University, Saint Petersburg, Russia
Название журнала
  • Frontiers in Big Data
Том
  • 4
Страницы
  • 666712
Издатель
  • Frontiers
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • dimensions