A k-generalized statistical mechanics approach to income analysis

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.

LaTeX2e; 15 pages with 1 figure; corrected typos

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• $\kappa$-generalized models of income and wealth distributions: A survey
• Composition law of $\kappa$-entropy for statistically independent systems
• Finite-Size Effects on Return Interval Distributions for Weakest-Link-Scaling Systems
• Economic inequality and mobility in kinetic models for social sciences
• Relativistic Roots of $\kappa$-Entropy
• Relativistic kinetics and power-law tailed distributions
• Nonlinear Kinetics on Lattices based on the Kinetic Interaction Principle