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Something New, Something Old: Parametric Models for the Size of Distribution of Income

Author

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  • Robert F. Bordley
  • James B. McDonald
  • Anand Mantrala

Abstract

Two new probability density functions (generalized beta and quadratic elasticity) are considered as models for the size distribution of income. The generalized beta distribution nests the generalized beta of the first and second kind, generalized gamma, lognormal and Pareto as well as introducing a number of new distributions to the literature. The quadratic elasticity distribution provides another generalization of the gamma distribution. The beta, gamma and lognormal distributions have been widely used in the income distribution literature. These distributions and many of their special cases are fit into five sets of US family income data for 1970, 1975, 1980, 1985, and 1990. This permits a comparison of the relative fit of several distributions over time.

Suggested Citation

  • Robert F. Bordley & James B. McDonald & Anand Mantrala, 1997. "Something New, Something Old: Parametric Models for the Size of Distribution of Income," Journal of Income Distribution, Ad libros publications inc., vol. 6(1), pages 5-5, June.
    • Handle: RePEc:jid:journl:y:1997:v:06:i:1:p:5-5
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    Cited by:

    1. Ellis Scharfenaker, Markus P.A. Schneider, 2019. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Paper Series, Department of Economics, University of Utah 2019_08, University of Utah, Department of Economics.
    2. Andrew M. Jones & James Lomas & Nigel Rice, 2014. "Applying Beta‐Type Size Distributions To Healthcare Cost Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 649-670, June.
    3. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D.S. Prasada Rao, 2018. "Using the GB2 Income Distribution: A Review," Department of Economics - Working Papers Series 2036, The University of Melbourne.
    4. Frank A. Cowell & Emmanuel Flachaire, 2014. "Statistical Methods for Distributional Analysis," Working Papers halshs-01115996, HAL.
    5. Kazuhiko Kakamu, 2022. "Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data," Papers 2210.05115, arXiv.org, revised Sep 2023.
    6. Li Tan, 2021. "Imputing Top‐Coded Income Data in Longitudinal Surveys," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 66-87, February.
    7. Kazuhiko Kakamu & Haruhisa Nishino, 2019. "Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 625-645, August.
    8. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
    9. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & Wasana Karunarathne & D. S. Prasada Rao, 2018. "Using the GB2 Income Distribution," Econometrics, MDPI, vol. 6(2), pages 1-24, April.
    10. Nartikoev, Alan & Peresetsky, Anatoly, 2019. "Modeling the dynamics of income distribution in Russia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 54, pages 105-125.
    11. Ellis Scharfenaker & Markus P. A. Schneider, 2023. "Labor Market Segmentation and the Distribution of Income: New Evidence from Internal Census Bureau Data," Working Papers 23-41, Center for Economic Studies, U.S. Census Bureau.

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