Dynamics of Money and Income Distributions

TL;DR

This paper analyzes a model of wealth exchange among agents, deriving equations for wealth distribution, showing the emergence of Pareto-like tails with an exponent of 1, and comparing theoretical predictions with numerical simulations.

Contribution

It provides a mean field approximation for wealth distribution in agent-based models and demonstrates the limitations of the model in replicating empirical wealth distributions.

Findings

Wealth distribution exhibits Pareto tail with exponent exactly 1.

Narrowing savings distribution shifts wealth distribution from Pareto to exponential.

Model cannot reproduce empirical wealth distribution exponents of 1.6-1.7.

Abstract

We study the model of interacting agents proposed by Chatterjee et al that allows agents to both save and exchange wealth. Closed equations for the wealth distribution are developed using a mean field approximation. We show that when all agents have the same fixed savings propensity, subject to certain well defined approximations defined in the text, these equations yield the conjecture proposed by Chatterjee for the form of the stationary agent wealth distribution. If the savings propensity for the equations is chosen according to some random distribution we show further that the wealth distribution for large values of wealth displays a Pareto like power law tail, ie P(w)\sim w^{1+a}. However the value of $a$ for the model is exactly 1. Exact numerical simulations for the model illustrate how, as the savings distribution function narrows to zero, the wealth distribution changes from a Pareto form to to an exponential function. Intermediate regions of wealth may be approximately described by a power law with $a>1$. However the value never reaches values of \~ 1.6-1.7 that characterise empirical wealth data. This conclusion is not changed if three body agent exchange processes are allowed. We conclude that other mechanisms are required if the model is to agree with empirical wealth data.