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Distributionally-Sensitive Inequality Indices And The Gb2 Income Distribution

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  • Stephen P. Jenkins

Abstract

The Generalized Beta of the Second Kind (GB2) income distribution provides an excellent description of income distributions. However the degree of inequality implied by GB2 parameter estimates is typically summarized using the Gini coefficient only. This paper provides formulae for the Generalized Entropy class of inequality indices for GB2 distributions, thereby providing a full range of top-sensitive and bottom-sensitive measures. The usefulness of having a portfolio of distributionally-sensitive indices is demonstrated using GB2-based estimates of British income inequality in 1994/95 and 2004/05. Copyright 2009 The Author. Journal compilation International Association for Research in Income and Wealth 2009.

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Bibliographic Info

Article provided by International Association for Research in Income and Wealth in its journal Review of Income and Wealth.

Volume (Year): 55 (2009)
Issue (Month): 2 (06)
Pages: 392-398

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Handle: RePEc:bla:revinw:v:55:y:2009:i:2:p:392-398

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Cited by:
  1. Kenneth Harttgen & Stephan Klasen, 2010. "A Household-Based Human Development Index," Human Development Research Papers (2009 to present) HDRP-2010-22, Human Development Report Office (HDRO), United Nations Development Programme (UNDP).
  2. Burkhauser, Richard V. & Feng, Shuaizhang & Jenkins, Stephen P. & Larrimore, Jeff, 2008. "Estimating trends in US income inequality using the Current Population Survey: the importance of controlling for censoring," ISER Working Paper Series 2008-25, Institute for Social and Economic Research.
  3. Jose Maria Sarabia & Francisco Azpitarte, 2012. "On the relationship between objective and subjective inequality indices and the natural rate of subjective inequality," Working Papers 248, ECINEQ, Society for the Study of Economic Inequality.
  4. Cecilia García-Peñalosa & Elsa Orgiazzi, 2013. "Factor Components of Inequality: A Cross-Country Study," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(4), pages 689-727, December.
  5. Richard V. Burkhauser & Shuaizhang Feng & Stephen P. Jenkins & Jeff Larrimore, 2009. "Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data," NBER Working Papers 15320, National Bureau of Economic Research, Inc.
  6. Markus Schneider, 2013. "Illustrating the Implications of How Inequality is Measured: Decomposing Earnings Inequality by Race and Gender," Journal of Labor Research, Springer, vol. 34(4), pages 476-514, December.
  7. Michał Brzeziński, 2013. "Parametric Modelling of Income Distribution in Central and Eastern Europe," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(3), pages 207-230, September.
  8. Michał Brzeziński, 2014. "Empirical modeling of the impact factor distribution," Working Papers 2014-01, Faculty of Economic Sciences, University of Warsaw.
  9. Fiorio, Carlo V & Saget, Catherine, 2010. "Reducing or aggravating inequality? : Preliminary findings from the 2008 financial crisis," ILO Working Papers 456487, International Labour Organization.
  10. Fabio Clementi & Mauro Gallegati & Giorgio Kaniadakis, 2012. "A new model of income distribution: the κ-generalized distribution," Journal of Economics, Springer, vol. 105(1), pages 63-91, January.
  11. David Warner & Prasada Rao & William E. Griffiths & Duangkamon Chotikapanich, 2011. "Global Inequality: Levels and Trends, 1993-2005," Discussion Papers Series 436, School of Economics, University of Queensland, Australia.
  12. 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.

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