AI Article Synopsis
- This study uses the econophysics approach to model income distribution in Iran from 2006 to 2018, focusing on three key distributions: Pareto, Lognormal, and Gibbs-Boltzmann.
- The findings reveal that the income distribution in Iran largely deviates from the Pareto and Lognormal models, consistently aligning with the generalized Gibbs-Boltzmann distribution across all years analyzed.
- The generalized Gibbs-Boltzmann distribution not only fits the income data more accurately than both the Pareto and Lognormal distributions but also provides a clearer explanation of income distribution dynamics in Iran.
Article Abstract
This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006-2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions.
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| http://dx.doi.org/10.3934/mbe.2023587 | DOI Listing |
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