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Statistical Inference for Inequality and Poverty Measurement with Dependent Data

  • Christian Schluter

    (University of Bristol, U.K.)

  • Mark Trede

    (Universit”t zu K–ln, Germany)

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

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Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 43 (2002)
Issue (Month): 2 (May)
Pages: 493-508

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Handle: RePEc:ier:iecrev:v:43:y:2002:i:2:p:493-508
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