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Generalized Pareto-Type Distribution and Income Inequality: An Extension of Gibrat’s Law
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: Received: 19 February 2024 / Approved: 19 February 2024 / Online: 19 February 2024 (15:12:34 CET)
A peer-reviewed article of this Preprint also exists.
Tao, Y. (2024). Generalized Pareto distribution and income inequality: an extension of Gibrat’s law. AIMS Mathematics, 9(6), 15060-15075. Tao, Y. (2024). Generalized Pareto distribution and income inequality: an extension of Gibrat’s law. AIMS Mathematics, 9(6), 15060-15075.
Abstract
Motivated by empirical observations, we propose a possible extension of Gibrat's law. By applying it into the random growth theory of income distribution (Gabaix, 2009), we find that the income distribution is described by a generalized Pareto-type distribution (GPD) with three parameters. We observe that there is a parameter /elta in the GPD that plays a key role in determining the shape of income distribution. By using the Kolmogorov-Smirnov test, we empirically show that, for typical market-economy countries, /elta is close to 0 significantly, such that the income distribution is characterized by a two-class pattern in which the bottom 90% of the population is approximated by an exponential distribution and the richest 1%~3% is approximated by an asymptotic power law. However, we empirically find that, for China both in planned economy period and in early stages of market reformation (from 1978 to 1990), /elta is significantly deviated from 0, such that the bottom of the population no longer conforms to an exponential distribution.
Keywords
Random growth theory; Kolmogorov forward equation; Income distribution; Generalized Pareto distribution; Kolmogorov-Smirnov test
Subject
Business, Economics and Management, Economics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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