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Statistical Inference for Inequality and Poverty Measurement with Dependent Data

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  • Christian Schluter

    (University of Bristol, U.K.)

  • Mark Trede

    (Universit”t zu K–ln, Germany)

Abstract

This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporaneously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well. Copyright Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association

Suggested Citation

  • Christian Schluter & Mark Trede, 2002. "Statistical Inference for Inequality and Poverty Measurement with Dependent Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 43(2), pages 493-508, May.
    • Handle: RePEc:ier:iecrev:v:43:y:2002:i:2:p:493-508
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    Citations

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    Cited by:

    1. Callealta Barroso, Francisco Javier & Fedriani Martel, Eugenio M. & Martín Caraballo, Ana M. & Sánchez Sánchez, Ana María, 2012. "Análisis de la evolución temporal de las desigualdades con datos irregulares || Analyzing the Income Inequalities with Irregular Time Series," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 13(1), pages 73-96, June.
    2. Heshmati, Almas, 2004. "Data Issues and Databases Used in Analysis of Growth, Poverty and Economic Inequality," IZA Discussion Papers 1263, Institute of Labor Economics (IZA).
    3. Stephen Jenkins, 2005. "Estimation of inequality indices from survey data, allowing for design effects," United Kingdom Stata Users' Group Meetings 2005 07, Stata Users Group.
    4. Philippe Kerm, 2002. "Inference on inequality measures: A Monte Carlo experiment," Journal of Economics, Springer, vol. 77(1), pages 283-306, December.
    5. Judith Clarke & Nilanjana Roy, 2012. "On statistical inference for inequality measures calculated from complex survey data," Empirical Economics, Springer, vol. 43(2), pages 499-524, October.
    6. Juan Ramón García, "undated". "La desigualdad salarial en España. Efectos de un diseño muestral complejo," Working Papers 2003-26, FEDEA.
    7. P. Jenkins, Stephen & Biewen, Martin, 2003. "Estimation of Generalized Entropy and Atkinson inequality indices from survey data," ISER Working Paper Series 2003-11, Institute for Social and Economic Research.
    8. Martin Biewen & Stephen P. Jenkins, 2003. "Estimation of Generalized Entropy and Atkinson Inequality Indices from Complex Survey Data," Discussion Papers of DIW Berlin 345, DIW Berlin, German Institute for Economic Research.

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