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Global Income Distributions and Inequality, 1993 and 2000: Incorporating Country-Level Inequality Modeled with Beta Distributions

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Listed:
  • Duangkamon Chotikapanich

    (Monash University)

  • William E. Griffiths

    (University of Melbourne)

  • D. S. Prasada Rao

    (University of Queensland)

  • Vicar Valencia

    (Rochester Institute of Technology)

Abstract

Using a method-of-moments estimator, flexible three-parameter beta distributions are fitted to aggregate country-level income data to overcome an untenable assumption of previous studies that persons within each income group receive the same income. Regional and global income distributions are derived as weighted mixtures of country-specific distributions. Analytical expressions for Gini and Theil's measures of inequality at country, regional, and global levels are derived in terms of the parameters of the beta distributions. Application to data for 91 countries in 1993 and 2000 reveals a high degree of global inequality, with evidence of declining inequality, largely attributable to growth in China. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Duangkamon Chotikapanich & William E. Griffiths & D. S. Prasada Rao & Vicar Valencia, 2012. "Global Income Distributions and Inequality, 1993 and 2000: Incorporating Country-Level Inequality Modeled with Beta Distributions," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 52-73, February.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:1:p:52-73
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    Cited by:

    1. 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.
    2. Hajargasht, Gholamreza & Griffiths, William E., 2013. "Pareto–lognormal distributions: Inequality, poverty, and estimation from grouped income data," Economic Modelling, Elsevier, vol. 33(C), pages 593-604.
    3. Griffiths, William & Hajargasht, Gholamreza, 2015. "On GMM estimation of distributions from grouped data," Economics Letters, Elsevier, vol. 126(C), pages 122-126.
    4. Andreas Chai & Wolfhard Kaus, 2013. "Signalling to whom? Conspicuous spending and the local density of the social group income distribution," Papers on Economics and Evolution 2012-18, Philipps University Marburg, Department of Geography.
    5. Jordá, Vanesa & Alonso, José M., 2017. "New Estimates on Educational Attainment Using a Continuous Approach (1970–2010)," World Development, Elsevier, vol. 90(C), pages 281-293.
    6. William E. Griffiths and Gholamreza Hajargasht, 2012. "GMM Estimation of Mixtures from Grouped Data:," Department of Economics - Working Papers Series 1148, The University of Melbourne.
    7. 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.
    8. Niklas Potrafke, 2015. "The Evidence on Globalisation," The World Economy, Wiley Blackwell, vol. 38(3), pages 509-552, March.

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