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Averaging Income Distributions

Author

Listed:
  • Chotikapanich, D.
  • Griffiths, W.E.
  • Rao, D.S.P.

Abstract

Various inequality and social welfare measures often depend heavily on the choice of a distribution of income. Picking a distribution that best fits the data in some sense involves throwing away information and does not allow for the fact that, by chance, a wrong choice can be made. It also does not allow for the statistical inference implications of making the wrong choice. Instead, Bayesian model averaging utilises a weighted average of the results from a number of income distributions, with each weight given by the probability that a distribution is 'correct'. In this study prior densities are placed on mean income, the mode of income and the Gini coefficient for Australian income units with one parent (1997-98). Then, using grouped sample data on incomes, posterior densities for the mean and mode of income, and the Gini coefficient are derived for a variety of income distributions. The model-averaged results from these income distributions are obtained.

Suggested Citation

  • Chotikapanich, D. & Griffiths, W.E. & Rao, D.S.P., 2001. "Averaging Income Distributions," Department of Economics - Working Papers Series 798, The University of Melbourne.
    • Handle: RePEc:mlb:wpaper:798
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    References listed on IDEAS

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    Cited by:

    1. Hikaru Hasegawa & Kazuhiro Ueda, 2016. "Multidimensional inequality for current status of Japanese private companies’ employees," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 357-373, December.
    2. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print halshs-04135764, HAL.
    3. Omar Abdul Rahman Kittaneh, 2019. "Estimating the Income Distribution of Some Muslim Countries Based on Entropy Measures تقدير توزيع الدخل لبعض الدول الإسلامية بناء على مقاييس عشوائية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 32(1), pages 159-169, January.
    4. Michel Lubrano & Zhou Xun, 2021. "The Bayesian approach to poverty measurement," AMSE Working Papers 2133, Aix-Marseille School of Economics, France.
    5. Duangkamon Chotikapanich & William E. Griffiths, 2006. "Bayesian Assessment of Lorenz and Stochastic Dominance in Income Distributions," Department of Economics - Working Papers Series 960, The University of Melbourne.
    6. Michel Lubrano & Zhou Xun, 2023. "The Bayesian approach to poverty measurement," Post-Print hal-04347292, HAL.

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    More about this item

    Keywords

    Bayesian model averaging; Gini coefficient; grouped data;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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