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Modeling income distribution: An econophysics approach.
Hossein Jabbari Khamnei1, Sajad Nikannia2, Masood Fathi3,4
1Department of Statistics, Faculty of Mathematics; Statistics and computer Science, University of Tabriz, Tabriz, Iran.
Mathematical Biosciences and Engineering : MBE
|July 28, 2023
Summary
This study reveals that Iran
Area of Science:
- Econophysics
- Income Distribution Modeling
- Statistical Analysis
Background:
- Understanding income distribution is crucial for economic policy.
- Traditional models like Pareto and Lognormal may not fully capture complex income dynamics.
- Econophysics offers novel approaches to analyze economic phenomena.
Purpose of the Study:
- To develop and validate appropriate models for income distribution in Iran.
- To assess the applicability of Pareto, Lognormal, and Gibbs-Boltzmann distributions to Iranian income data.
- To identify the best-fitting statistical model for explaining income distribution patterns.
Main Methods:
- Utilized an econophysics approach to model income distribution.
- Analyzed household income data from Iran (2006-2018).
- Compared the fitting accuracy of Pareto, Lognormal, and generalized Gibbs-Boltzmann distributions.
Main Results:
- Income distribution in Iran did not consistently follow Pareto or Lognormal distributions.
- The generalized Gibbs-Boltzmann distribution accurately modeled Iranian income distribution across all study years.
- The generalized Gibbs-Boltzmann distribution demonstrated a superior fit compared to Pareto and Lognormal distributions.
Conclusions:
- The generalized Gibbs-Boltzmann distribution is a robust model for income distribution in Iran.
- This finding has implications for economic policy and social welfare analysis.
- Econophysics provides valuable tools for understanding national income patterns.
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