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Variance estimation for generalized entropy and Atkinson inequality indices: The complex survey data case

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  • Martin Biewen

    ()
    (University of Frankfurt)

Abstract

We derive the sampling variances of generalized entropy and Atkinson indices when estimated from complex survey data, and we show how they can be calculated straightforwardly by using widely available software. We also show that, when the same approach is used to derive variance formulae for the i.i.d. case, it leads to estimators that are simpler than those proposed before. Both cases are illustrated with a comparison of income inequality in Britain and Germany.

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File URL: http://fmwww.bc.edu/repec/dsug2006/biewen.ppt
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Bibliographic Info

Paper provided by Stata Users Group in its series German Stata Users' Group Meetings 2006 with number 04.

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Date of creation: 24 May 2006
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Handle: RePEc:boc:dsug06:04

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Web page: http://www.stata.com/meeting/4german
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Cited by:
  1. Judith A. Clarke & Nilanjana Roy, 2009. "On Statistical Inference for Inequality Measures Calculated from Complex Survey Data," Econometrics Working Papers 0904, Department of Economics, University of Victoria.
  2. repec:ese:iserwp:2007-11 is not listed on IDEAS
  3. Judith A. Clarke & Ahmed A. Hoque, 2014. "On Variance Estimation for a Gini Coefficient Estimator Obtained from Complex Survey Data," Econometrics Working Papers 1401, Department of Economics, University of Victoria.
  4. Stephen P. Jenkins & John Micklewright, 2007. "New Directions in the Analysis of Inequality and Poverty," Discussion Papers of DIW Berlin 700, DIW Berlin, German Institute for Economic Research.
  5. Francesco Caracciolo & Fabio Gaetano Santeramo, 2013. "Price Trends and Income Inequalities: Will Sub-Saharan Africa Reduce the Gap?," African Development Review, African Development Bank, vol. 25(1), pages 42-54, 03.
  6. Goedemé, Tim & Van den Bosch, Karel & Salanauskaite, Lina & Verbist, Gerlinde, 2013. "Testing the Statistical Significance of Microsimulation Results: Often Easier than You Think. A Technical Note," EUROMOD Working Papers em18/13, EUROMOD at the Institute for Social and Economic Research.
  7. Jenkins, Stephen P. & Burkhauser, Richard V. & Feng, Shuaizhang & Larrimore, Jeff, 2009. "Measuring Inequality Using Censored Data: A Multiple Imputation Approach," IZA Discussion Papers 4011, Institute for the Study of Labor (IZA).
  8. Ligon, Ethan, 2010. "Measuring risk by looking at changes in inequality : vulnerability in Ecuador," CUDARE Working Paper Series 1095, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  9. Tim Goedemé, 2013. "How much Confidence can we have in EU-SILC? Complex Sample Designs and the Standard Error of the Europe 2020 Poverty Indicators," Social Indicators Research, Springer, vol. 110(1), pages 89-110, January.
  10. Stéphane Mussard & Pi Alperin María Noel, 2006. "Measuring Significance of Inequalities with Heterogeneous Groups and Income Sources," Cahiers de recherche 06-13, Departement d'Economique de la Faculte d'administration à l'Universite de Sherbrooke.

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