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A k-generalized statistical mechanics approach to income analysis

Listed author(s):
  • F. Clementi
  • M. Gallegati
  • G. Kaniadakis

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.

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Paper provided by in its series Papers with number 0902.0075.

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Date of creation: Jan 2009
Date of revision: Feb 2009
Publication status: Published in Journal of Statistical Mechanics: Theory and Experiment, 16 February 2009, start page: P02037
Handle: RePEc:arx:papers:0902.0075
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