We examine the sensitivity of poverty indices to data contamination using the concept of the influence function, and demonstrate that an important commonly used subclass of poverty measures will be robust under data contamination. This is illustrated using simulations. In this respect poverty and inequality indices have fundamentally different robustness properties. We investigate both the case where the poverty line is exogenously fixed and where it must be estimated from the data.

Frank A. Cowell, Maria-Pia Victoria-Feser (1996). Poverty measurement with contaminated data: A robust approach. EUROPEAN ECONOMIC REVIEW, 40(9), 1761-1771 [10.1016/0014-2921(95)00048-8].

Poverty measurement with contaminated data: A robust approach

Maria-Pia Victoria-Feser
1996

Abstract

We examine the sensitivity of poverty indices to data contamination using the concept of the influence function, and demonstrate that an important commonly used subclass of poverty measures will be robust under data contamination. This is illustrated using simulations. In this respect poverty and inequality indices have fundamentally different robustness properties. We investigate both the case where the poverty line is exogenously fixed and where it must be estimated from the data.
1996
Frank A. Cowell, Maria-Pia Victoria-Feser (1996). Poverty measurement with contaminated data: A robust approach. EUROPEAN ECONOMIC REVIEW, 40(9), 1761-1771 [10.1016/0014-2921(95)00048-8].
Frank A. Cowell; Maria-Pia Victoria-Feser
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/952920
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