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Averaging Income Distributions

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Author Info

  • 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.

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Bibliographic Info

Paper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 798.

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Length: 26 pages
Date of creation: 2001
Date of revision:
Handle: RePEc:mlb:wpaper:798

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Related research

Keywords: Bayesian model averaging; Gini coefficient; grouped data;

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References

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  1. Griffiths, William E & Chotikapanich, Duangkamon, 1997. "Bayesian Methodology for Imposing Inequality Constraints on a Linear Expenditure System with Demographic Factors," Australian Economic Papers, Wiley Blackwell, vol. 36(69), pages 321-41, December.
  2. Danilov, D.L. & Magnus, J.R., 2001. "On the Harm that Pretesting Does," Discussion Paper 2001-37, Tilburg University, Center for Economic Research.
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Cited by:
  1. 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.

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