POVERTY MEASUREMENT WITH CONTAMINATED DATA - A ROBUST APPROACH

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