A k-generalized statistical mechanics approach to income analysis

TL;DR

This paper introduces a novel k-generalized statistical mechanics model for income distribution that accurately captures the entire income spectrum, from low to high incomes, and provides analytical tools for measuring inequality.

Contribution

It develops a new income distribution function based on k-generalized statistics, enabling comprehensive analysis of income inequality with closed-form econometric tools.

Findings

Model fits US income data remarkably well

Provides analytical expressions for income distribution and inequality measures

Offers a new framework for analyzing income inequality

Abstract

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of k-generalized statistics, is derived that is particularly suitable to describe the whole spectrum of incomes, from the low-middle income region up to the high-income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters reveals very powerful.