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Financially Constrained Fluctuations in an Evolving Network Economy

Author

Listed:
  • Domenico Delli Gatti
  • Mauro Gallegati
  • Bruce C. Greenwald
  • Alberto Russo
  • Joseph E. Stiglitz

Abstract

We explore the properties of a credit network characterized by inside credit - i.e. credit relationships connecting downstream (D) and upstream (U) firms - and outside credit - i.e. credit relationships connecting firms and banks. The structure of the network changes over time due to the preferred-partner choice rule: each agent chooses the partner who charges the lowest price. The net worth of D firms turns out to be the driver of fluctuations. U production, in fact, is determined by demand of intermediate inputs on the part of D firms and production of the latter is financially constrained, i.e. determined by the availability of internal finance proxied by net worth. The output of simulations shows that at the macroeconomic level a business cycle can develop as a consequence of the complex interaction of the agents' financial conditions. We can also reproduce the main stylized facts of firms' demography, i.e. the power law distribution of firms' size and the Laplace distribution of firms' growth rates.

Suggested Citation

  • Domenico Delli Gatti & Mauro Gallegati & Bruce C. Greenwald & Alberto Russo & Joseph E. Stiglitz, 2008. "Financially Constrained Fluctuations in an Evolving Network Economy," NBER Working Papers 14112, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14112
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    Cited by:

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    2. De Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 95-117.
    3. Dosi, Giovanni & Fagiolo, Giorgio & Napoletano, Mauro & Roventini, Andrea & Treibich, Tania, 2015. "Fiscal and monetary policies in complex evolving economies," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 166-189.
    4. P. Sieczka & D. Sornette & J. Holyst, 2011. "The Lehman Brothers effect and bankruptcy cascades," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 82(3), pages 257-269, August.
    5. Leonardo Bargigli & Gabriele Tedeschi, 2013. "Major trends in agent-based economics," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(2), pages 211-217, October.
    6. Russo, Alberto, 2010. "Elementi di novità, meccanismi noti e cause di fondo della recente crisi [Elements of novelty, known mechanisms, and fundamental causes of the recent crisis]," MPRA Paper 21648, University Library of Munich, Germany.
    7. Hou, Yunzhang & Wang, Xiaoling & Wu, Yenchun Jim & He, Peixu, 2018. "How does the trust affect the topology of supply chain network and its resilience? An agent-based approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 229-241.
    8. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046, September.

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    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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