Statistical Mechanics of Household Income and Wealth: Derivation from Firm Dynamics via Maximum Entropy and Mixture Aggregation
Robert T. Nachtrieb

TL;DR
This paper derives the two-class income and wealth distribution in developed economies from firm dynamics using maximum entropy and mixture aggregation, providing a mechanistic understanding and empirical validation.
Contribution
It introduces a first-principles derivation of income and wealth distributions from firm-level processes, linking firm growth, wages, and returns to wealth tail behavior.
Findings
Reproduces the Pareto wealth tail exponent without tuned parameters.
Provides a quantitative estimate of returns-per-employee size exponent.
Predicts firm exit rates consistent with empirical data.
Abstract
The distribution of income and wealth in developed economies exhibits a robust two-class structure: an exponential (Boltzmann--Gibbs) bulk covering of the population, and a power-law (Pareto) tail in the upper . We derive this structure from first principles via an explicit mechanistic chain: Gibrat's law for firm growth implies a Zipf firm-size distribution; maximum entropy applied to within-firm wages, combined with mixture aggregation across firms, yields a Boltzmann--Gibbs income distribution with temperature for employees; additive-noise wealth dynamics with a reflecting wall at zero produce a Boltzmann--Gibbs employee wealth distribution with temperature . For firm owners, multiplicative capital returns produce a Pareto wealth tail with exponent , where encodes how total returns scale with firm size. The empirical…
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