Robust methods for the analysis of income distribution, inequality and poverty

Income distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribut ion to decrease inequality, or to implement social policies to reduce pover ty. The available data come mostly from surveys (and not censuses as it is often believed) and are often subject to long debates about their reliabili ty because the sources of errors are numerous. Moreover the forms in which the data are available is not always as one would expect, i.e. complete and continuous (micro data) but one also can only have data in a grouped form (in income classes) and/or truncated data where a portion of the original d ata has been omitted from the sample or simply not recorded. Because of these data features, it is important to complement classical sta tistical procedures with robust ones, In this paper such methods are presen ted, especially for model selection, model fitting with several types of da ta, inequality and poverty analysis and ordering tools. The approach is bas ed on the Influence Function (IF) developed by Hampel (1974) and further de veloped by Hampel, Ronchetti, Rousseeuw & Stahel (1986), It is also shown t hrough the analysis of real UK and Tunisian data, that robust techniques ca n give another picture of income distribution, inequality or poverty when c ompared to classical ones.