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The Small-Sample Bias of the Gini Coefficient: Results and Implications for Empirical Research

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  • George Deltas

    (University of Illinois, Urbana-Champaign.)

Abstract

The Gini coefficient is a downward-biased measure of inequality in small populations when income is generated by one of three common distributions. The paper discusses the sources of bias and argues that this property is far more general. This has implications for (i) the comparison of inequality among subsamples, some of which may be small, and (ii) the use of the Gini in measuring firm size inequality in markets with a small number of firms. The small-sample bias has often led to misperceptions about trends in industry concentration. A small-sample adjustment results in a reduced bias, which can no longer be signed. This remaining bias rises with the dispersion and falls with increasing skewness of the distribution. Finally, an empirical example illustrates the importance of using the adjusted Gini. In this example it is shown that, controlling for market characteristics, larger shipping cartels include a set of firms that is stochastically identical (in terms of relative size) to those of smaller shipping cartels. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Bibliographic Info

Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 85 (2003)
Issue (Month): 1 (February)
Pages: 226-234

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Handle: RePEc:tpr:restat:v:85:y:2003:i:1:p:226-234

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Web page: http://mitpress.mit.edu/journals/

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Cited by:
  1. Russell Davidson, 2009. "Reliable inference for the GINI Index," Working Papers, HAL halshs-00443553, HAL.
  2. Heshmati, Almas, 2004. "Data Issues and Databases Used in Analysis of Growth, Poverty and Economic Inequality," IZA Discussion Papers 1263, Institute for the Study of Labor (IZA).
  3. James Alm & Yongzheng Liu, 2014. "China's Tax-for-Fee Reform and Village Inequality," Oxford Development Studies, Taylor & Francis Journals, Taylor & Francis Journals, vol. 42(1), pages 38-64, March.
  4. Wodon, Quentin & Yitzhaki, Shlomo, 2003. "The effect of using grouped data on the estimation of the Gini income elasticity," Economics Letters, Elsevier, Elsevier, vol. 78(2), pages 153-159, February.
  5. Kaja Bonesmo Fredriksen, 2012. "Income Inequality in the European Union," OECD Economics Department Working Papers, OECD Publishing 952, OECD Publishing.
  6. Billings, Stephen B. & Johnson, Erik B., 2012. "The location quotient as an estimator of industrial concentration," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 42(4), pages 642-647.
  7. Andrew J. Cassey & Ben O. Smith, 2012. "Simulating Confidence for the Ellison-Glaeser Index," Working Papers, School of Economic Sciences, Washington State University 2012-8, School of Economic Sciences, Washington State University.
  8. César Martinelli & Rich Sicotte, 2004. "Voting in Cartels: Theory and Evidence from the Shipping Industry," Levine's Bibliography 122247000000000598, UCLA Department of Economics.
  9. Amlan Majumder & Takayoshi Kusago, 2013. "A discreet approach to study the distribution-free downward biases of Gini coefficient and the methods of correction in cases of small observations," Working Papers, ECINEQ, Society for the Study of Economic Inequality 298, ECINEQ, Society for the Study of Economic Inequality.
  10. Thomas Demuynck, 2012. "An (almost) unbiased estimator for the S-Gini index," Journal of Economic Inequality, Springer, Springer, vol. 10(1), pages 109-126, March.
  11. Tom Van Ourti & Philip Clarke, 2008. "The Bias of the Gini Coefficient due to Grouping," Tinbergen Institute Discussion Papers, Tinbergen Institute 08-095/3, Tinbergen Institute.
  12. David E. Giles, 2005. "The Bias of Inequality Measures in Very Small Samples: Some Analytic Results," Econometrics Working Papers, Department of Economics, University of Victoria 0514, Department of Economics, University of Victoria.

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