Physics-inspired analysis of the two-class income distribution in the USA in 1983-2018

TL;DR

This paper reviews physics-inspired models of economic inequality and analyzes US income data from 1983 to 2018, revealing a two-class structure with exponential lower class and power-law upper class, and linking inequality growth to digitization.

Contribution

It combines a survey of physics-based approaches with an empirical analysis of income distribution, highlighting the two-class structure and its evolution over time.

Findings

Lower class follows exponential distribution

Upper class follows power law distribution

Inequality growth driven by upper-class income share increase

Abstract

The first part of this paper is a brief survey of the approaches to economic inequality based on ideas from statistical physics and kinetic theory. These include the Boltzmann kinetic equation, the time-reversal symmetry, the ergodicity hypothesis, entropy maximization, and the Fokker-Planck equation. The origins of the exponential Boltzmann-Gibbs distribution and the Pareto power law are discussed in relation to additive and multiplicative stochastic processes. The second part of the paper analyzes income distribution data in the USA for the time period 1983-2018 using a two-class decomposition. We present overwhelming evidence that the lower class (more than 90% of the population) is described by the exponential distribution, whereas the upper class (about 4% of the population in 2018) by the power law. We show that the significant growth of inequality during this time period is due to the sharp increase in the upper-class income share, whereas relative inequality within the lower class remains constant. We speculate that the expansion of the upper-class population and income shares may be due to increasing digitization and non-locality of the economy in the last 40 years.