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Calculating Poverty Measures from the Generalised Beta Income Distribution

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  • Duangkamon Chotikapanich
  • William Griffiths
  • Wasana Karunarathne
  • D.S. Prasada Rao

Abstract

Data for measuring poverty and income inequality are frequently available in a summary form that describes the proportion of income or expenditure for each of a number of population proportions. While various discrete measures can be applied directly to data in this limited form, these discrete measures typically ignore inequality within each group. This problem can be overcome by fitting a parametric income distribution to the grouped data and computing required quantities from the estimated parameters of this distribution. In this paper we show how to calculate several poverty measures from parameters of the generalized beta distribution of the second kind, and its popular special cases. An analysis of poverty changes in ten countries from South and Southeast Asia is used to illustrate the methodology.
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  • Duangkamon Chotikapanich & William Griffiths & Wasana Karunarathne & D.S. Prasada Rao, 2013. "Calculating Poverty Measures from the Generalised Beta Income Distribution," The Economic Record, The Economic Society of Australia, vol. 89, pages 48-66, June.
    • Handle: RePEc:bla:ecorec:v:89:y:2013:i::p:48-66
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    File URL: http://hdl.handle.net/10.1111/1475-4932.12031
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    Cited by:

    1. Duangkamon Chotikapanich & William E. Griffiths & Gholamreza Hajargasht & D. S. Prasada Rao & Charley Xia, 2018. "Inequality and Poverty in Africa: Comparing Panels of Income Distributions from Different Data Sources," Department of Economics - Working Papers Series 2042, The University of Melbourne.
    2. Brian A'Hearn & Nicola Amendola & Giovanni Vecchi, 2016. "On Historical Household Budgets," Rivista di storia economica, Società editrice il Mulino, issue 2, pages 137-176.
    3. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 5(3), pages 207-230, September.
    4. Callealta Barroso, Francisco Javier & García-Pérez, Carmelo & Prieto-Alaiz, Mercedes, 2020. "Modelling income distribution using the log Student’s t distribution: New evidence for European Union countries," Economic Modelling, Elsevier, vol. 89(C), pages 512-522.
    5. Chotikapanich, Duangkamon & Griffiths, William E. & Rao, D.S. Prasada & Karunarathne, Wasana, 2014. "Income Distributions, Inequality, and Poverty in Asia, 1992–2010," ADBI Working Papers 468, Asian Development Bank Institute.
    6. 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.
    7. 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.

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    More about this item

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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