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.
Poverty measurement with contaminated data: A robust approach / Frank A. Cowell; Maria-Pia Victoria-Feser. - In: EUROPEAN ECONOMIC REVIEW. - ISSN 0014-2921. - STAMPA. - 40:9(1996), pp. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.