Computational Analysis of the Properties of Post-Keynesian Endogenous Money Systems

摘要

The debate about whether or not a growth imperative exists in debt-based, interest-bearing monetary systems has not yet been settled. It is the goal of this paper to introduce a new perspective in this discussion. For that purpose, an SFC computational model is constructed that simulates a post-Keynesian endogenous money system without including economic parameters such as production, wages, consumption and savings. The case is made that isolating the monetary system allows for better analysis of the inherent properties of such a system. Loan demands, which are assumed to happen, are the driving force of the model. Simulations can be run in two modes, each based on a different assumption. Either the growth rate of the money stock is assumed to be constant or the loan ratio, expressed as a percentage of the money stock, is assumed to be constant. Simulations with varying parameters were run in order to determine the conditions under which the model converges to stability, which is defined as converging to a bounded debt ratio. The analysis showed that the stability of the model is dependent on net bank profit ratios, expressed relative to their debt assets, remaining below the growth rate of the money stock. Based on these findings, it is argued that the question about the existence of a growth imperative in debt-based, interest-bearing monetary systems needs to be reframed. The question becomes whether a steady-state economy can realistically support such a system without destabilising it. In order to answer this question, the real-world behaviour of economic actors must be included in the model. It was concluded that there are indications that it might not be feasible for a steady-state economy to support a stable debt-based, interest-bearing monetary system without strong interventions. However, more research is necessary for a definite answer. Real-world observable data should be analysed through the lens of the presented model to bring more clarity.