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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.