Applications of statistical physics distributions to several types of income

TL;DR

This paper applies statistical physics distributions, specifically Fermi-Dirac and polynomial models, to analyze various income types including gross income, pensions, and wages in France and the UK, revealing their robustness and applicability.

Contribution

It introduces the use of Fermi-Dirac and polynomial distributions to model different income types, including pensions, which are less studied in this context.

Findings

Both distributions effectively describe income variations.

Fermi-Dirac distribution fits pension income well.

Distributions are robust across different income categories.

Abstract

This paper explores several types of income which have not been explored so far by authors who tackled income and wealth distribution using Statistical Physics. The main types of income we plan to analyze are income before redistribution (or gross income), income of retired people (or pensions), and income of active people (mostly wages). The distributions used to analyze income distributions are Fermi-Dirac distribution and polynomial distribution (as this is present in describing the behavior of dynamic systems in certain aspects). The data we utilize for our analysis are from France and the UK. We find that both distributions are robust in describing these varieties of income. The main finding we consider to be the applicability of these distributions to pensions, which are not regulated entirely by market mechanisms.