Putting Robust Statistical Methods into Practice: Poverty Analysis in Tunisia

Poverty analysis often results in the computation of poverty indexes based on so-called poverty lines which can be region specific poverty lines. The poverty lines are made of two components, namely the amount of income to satisfy the food and the non food needs. For both components, one needs to estimate quantities such as local prices or the consumers’ average basket, and this is often done through a parametric model. The resulting estimates depend on the data at hand and on the type of estimators that are used. Classical estimators (and testing procedures) such as the maximum likelihood estimator (MLE) or the least squares (LS) estimator are extremely sensitive to model deviations such as contamination in the data and hence are said not robust. The resulting analysis can therefore give a picture which is far from reality. Therefore a robust statistical approach to the estimation of the poverty lines is very important especially because these lines will serve to compute poverty indices. The main purpose of this paper is therefore to show how robust statistical procedure can be used in poverty analysis and the different picture on poverty comparisons they can give to the Tunisian case.