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Minskyan model with credit rationing in a network economy

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Abstract

The global financial crisis of 2007/2008 has shown the importance of modeling economic agents not in isolation but as interconnected and interactive components of dynamically evolving systems. Within this framework, the field of complex systems for the study of economic dynamics has been the object of renewed interest. This paper is based on Minsky’s Financial Instability Hypothesis and on the literature of Agent-Based Models to analyze a bank credit market where heterogeneous firms and banks interact following game theory rules. The objective is twofold: (1) to evaluate the influence of bank behavior on the formation of the credit network and the spread of financial difficulties in an agent-based model; and, (2) to analyze the properties of the emerging credit network and its influence on macroeconomic performance. Our simulations suggest that aggregate economic instability may arise as a result of the liquidity preference behavior of banks that restrict credit to the productive sector when they have pessimistic expectations.

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Data Availability Statement

Codes for replication of the simulations are available from the authors upon request.

Notes

  1. See, e.g. Chiarella and Di Guilmi (2011) for a dynamic model distinguishing between two groups of firms, speculative and hedge firms.

  2. See, e.g., Delli Gatti et al. (2005); Delli et al. (2008); Russo et al. (2007); Delli Gatti et al. (2007, 2010); Riccetti et al. (2013, 2016); Tedeschi et al. (2019).

  3. See, e.g., Allen and Gale (2000); Iori et al. (2006); Gai and Kapadia (2010); Wagner (2011); Battiston et al. (2012); Gai and Kapadia (2019); Eboli (2019); Mazzarisi et al. (2020); Noguera and Montes-Rojas (2022).

  4. For a detailed discussion of the use of this function see Delli Gatti et al.( 2010,1630:1631).

  5. This way of modeling a specific exogenous shock for each productive unit is common in the literature, see, for example, Delli Gatti et al. (2010); Riccetti et al. (2013, 2016).

  6. \({\mathcal {Z}}\) indicates the set of banks and subscripts i and t indicate the firm that observes them and the period, respectively.

  7. This is consistent with what happens in Argentina. According to the statistics presented by DErasmo et al. (2020) more than 90% of the firms have credit relationships with 1 or 2 banks.

  8. Given that the analysis focuses on the banks behavior and their interaction with productive firms in the credit market, it is possible to assume without loss of generality that banks can obtain the necessary amounts of deposits.

  9. For more on this topic, see, e.g. Kukacka and Barunik (2017); Anufriev et al. (2019); Assenza et al. (2019); Dosi et al. (2020); Schmitt and Westerhoff (2021); Reissl (2021) and Gusella (2022).

  10. Although, according to Basel II and III, credits are weighted according to risk, in the model there is no risk differentiation between assets, so (for simplicity) we establish \(CS=A/L\).

  11. As happened with foreign banks that started operating in some Latin American countries in the 1990 s, particularly in Argentina, see, e.g., Martinez Peria and Mody (2004)

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Funding

The research was supported by project PICT-2019–3517 “Modelos y contrastes econométricos para redes” Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina, and UBACYT 20020190100078BA "Modelos y contrastes econométricos para redes: Teoría y aplicaciones empíricas" Universidad de Buenos Aires, Argentina.

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Authors and Affiliations

  1. Universidad Nacional de La Plata, La Plata, Argentina

    Deborah Noguera

  2. Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires, Argentina

    Gabriel Montes-Rojas

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  1. Deborah Noguera
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Gabriel Montes-Rojas and Deborah Noguera participated equally in the research and writing of the paper.

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Correspondence to Gabriel Montes-Rojas.

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Noguera, D., Montes-Rojas, G. Minskyan model with credit rationing in a network economy. SN Bus Econ 3, 75 (2023). https://doi.org/10.1007/s43546-023-00446-z

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