Wealth distribution in modern societies: collected data and a master equation approach

TL;DR

This paper introduces a mean-field stochastic model with growth and reset to describe wealth distribution, deriving an analytical density function that fits observed data across all wealth categories, including the Pareto tail.

Contribution

It presents a novel LGGR model with an analytical solution for wealth distribution, aligning well with empirical data and previous theoretical approaches.

Findings

The model accurately describes wealth distribution across categories.

The stationary density exhibits the Tsallis-Pareto tail.

Results agree with earlier mean-field exchange models.

Abstract

A mean-field like stochastic evolution equation with growth and reset terms (LGGR model) is used to model wealth distribution in modern societies. The stationary solution of the model leads to an analytical form for the density function that is successful in describing the observed data for all wealth categories. In the limit of high wealth values the proposed density function has the accepted Tsallis-Pareto shape. Our results are in agreement with the predictions of an earlier approach based on a mean-field like wealth exchange process.