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

Abstract

The distribution of income and wealth in developed economies exhibits a robust two-class structure: an exponential (Boltzmann--Gibbs) bulk covering \!97\% of the population, and a power-law (Pareto) tail in the upper \!3\%. 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 Ty for employees; additive-noise wealth dynamics with a reflecting wall at zero produce a Boltzmann--Gibbs employee wealth distribution with temperature Tw. For firm owners, multiplicative capital returns produce a Pareto wealth tail with exponent αw = 1/θ, where θ encodes how total returns scale with firm size. The empirical value αw ≈ 1.30 Yakovenko2009 is reproduced with no tuned parameters from the observed firm value scaling V = V0(s/s0)0.77~Axtell2001, and simultaneously yields the first quantitative estimate of the returns-per-employee size exponent: ζ = θ - 1 ≈ -0.23. For empirical values ≈ 0.3, c ≈ 0.81, k ≈ 0.15 (BEA long-run savings rate ≈ 5\%), the model gives Tw/Ty ≈ 1.7\,yr, i.e.\ lower-class households hold roughly 1--2 years of income as wealth, with the precise ratio depending on savings and tax rates and testable cross-country. As a parameter-free empirical test, firms near zero profit have a cash martingale whose first-passage time gives establishment exit rate t-1/2; convolving with the Zipf firm-size distribution yields firm-level exit rate t-1/2\! t, with apparent exponent b = 0.295 0.03, confirmed against BDS firm-age data with no free parameters.

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