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Modeling Supply-Chain Networks with Firm-to-Firm Wire Transfers

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  • Thiago C. Silva
  • Diego R. Amancio
  • Benjamin M. Tabak

Abstract

We study a novel economic network (supply chain) comprised of wire transfers (electronic payment transactions) among the universe of firms in Brazil (6.2 million firms). We construct a directed and weighted network in which vertices represent cities and edges connote pairwise economic dependence between cities. Cities (vertices) represent the collection of all firms in that location and links denote intercity wire transfers. We find a high degree of economic integration among cities in the trade network, which is consistent with the high degree of specialization found across Brazilian cities. We are able to identify which cities have a dominant role in the entire supply chain process using centrality network measures. We find that the trade network has a disassortative mixing pattern, which is consistent with the power-law shape of the firm size distribution in Brazil. After the Brazilian recession in 2014, we find that the disassortativity becomes even stronger as a result of the death of many small firms and the consequent concentration of economic flows on large firms. Our results suggest that recessions have a large impact on the trade network with meaningful and heterogeneous economic consequences across municipalities. We run econometric exercises and find that courts efficiency plays a dual role. From the customer perspective, it plays an important role in reducing contractual frictions as it increases economic transactions between different cities. From the supplier perspective, cities that are central suppliers to the supply chain seem to use courts inefficiency as a lawsuit barrier from their customers.

Suggested Citation

  • Thiago C. Silva & Diego R. Amancio & Benjamin M. Tabak, 2020. "Modeling Supply-Chain Networks with Firm-to-Firm Wire Transfers," Papers 2001.06889, arXiv.org, revised Aug 2020.
  • Handle: RePEc:arx:papers:2001.06889
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    References listed on IDEAS

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    1. Restrepo, Natalia & Uribe, Jorge M. & Manotas, Diego, 2018. "Financial risk network architecture of energy firms," Applied Energy, Elsevier, vol. 215(C), pages 630-642.
    2. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    3. Wang, Lei & Li, Shouwei & Wu, Chaoqun, 2019. "Risk contagion in inter-firm credit guarantee network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    4. Tsai, Li-Chuan & Zhang, Ruhui & Zhao, Cuifang, 2019. "Political connections, network centrality and firm innovation," Finance Research Letters, Elsevier, vol. 28(C), pages 180-184.
    5. Silva, Thiago Christiano & Guerra, Solange Maria & Tabak, Benjamin Miranda & de Castro Miranda, Rodrigo Cesar, 2016. "Financial networks, bank efficiency and risk-taking," Journal of Financial Stability, Elsevier, vol. 25(C), pages 247-257.
    6. Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk & Tabak, Benjamin M., 2017. "Not all emerging markets are the same: A classification approach with correlation based networks," Journal of Financial Stability, Elsevier, vol. 33(C), pages 163-186.
    7. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
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    Cited by:

    1. Mohammed Adil Saleem & Faraz Zaidi & Céline Rozenblat, 2023. "World City Networks and Multinational Firms: An Analysis of Economic Ties Over a Decade," Networks and Spatial Economics, Springer, vol. 23(3), pages 559-580, September.

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