Abstract
Solomon and Golo (Account Econ Law 3(3):167–260, 2013) have recently proposed an autocatalytic (self-reinforcing) feedback model which couples a macroscopic system parameter (the interest rate), a microscopic parameter that measures the distribution of the states of the individual agents (the number of firms in financial difficulty) and a peer-to-peer network effect (contagion across supply chain financing). In this model, each financial agent is characterized by its resilience to the interest rate. Above a certain rate the interest due on the firm’s financial costs exceeds its earnings and the firm becomes susceptible to failure (ponzi). For the interest rate levels under a certain threshold level, the firm loans are smaller then its earnings and the firm becomes ‘hedge.’ In this paper, we fit the historical data (2002–2009) on interest rate data into our model, in order to predict the number of the ponzi firms. We compare the prediction with the data taken from a large panel of Italian firms over a period of 9 years. We then use trade credit linkages to discuss the connection between the ponzi density and the network percolation. We find that the ‘top-down’–‘bottom-up’ positive feedback loop accounts for most of the Minsky crisis accelerator dynamics. The peer-to-peer ponzi companies contagion becomes significant only in the last stage of the crisis when the ponzi density is above a critical value. Moreover the ponzi contagion is limited only to the companies that were not dynamic enough to substitute their distressed clients with new ones. In this respect the data support a view in which the success of the economy depends on substituting the static ‘supply-network’ picture with an interacting dynamic agents one.
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Notes
We present a discrete iterative process where at each iteration either the quantity of loans or the interest rate adjust for loan supply costs. Following this, lowering the interest rate will decrease demand, while raising the interest rate will lower demand. This is similar to Walras groping process. The quantity of loans are accommodative and initially nominal interest rates are sticky but will adjust in accordance with the cost of lending. This is similar Marshallian process, see for instance Humphrey (1992). Our combined Marshall–Walras process is described in detail in Solomon and Golo (2013). The mathematics of such iterative processes is studied in detail in the monograph (Galor 2007). For a continuous time treatment see Alessandro (2011).
Without an immediate cash flow from the “next generation”, the suppliers could not afford to continue in business.
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Acknowledgments
We thank the Institute for New Economic Thinking (INET), as this work has been performed with their support under Grant ID INO1100017. We thank The Annual Workshop on Economic Science with Heterogeneous Interacting Agents (WEHIA) 2012 and 2013 organizers for comments on our preliminary results and for allowing us to present them here.
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Golo, N., Brée, D.S., Kelman, G. et al. Too dynamic to fail: empirical support for an autocatalytic model of Minsky’s financial instability hypothesis. J Econ Interact Coord 11, 247–271 (2016). https://doi.org/10.1007/s11403-015-0163-7
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DOI: https://doi.org/10.1007/s11403-015-0163-7