The recentered influence function and unidimensional poverty measurement

This chapter discusss the applicability of the recentered influence function (RIF) to the analysis of poverty differentials between distributions (regression-based decomposition into composition and income structure effects). It is shown that the predominant approach in the empirical literature estimates the relationship between individual poverty functions of additive measures, particularly the head-count ratio, and household attributes. Given that the recentered influence function of these measures is also their poverty function, this approach is simply a specific case of the one-stage recentered influence function decomposition, using non-linear probability models. However, the use of recentered influence function provides a more general approach that better accounts for individual contributions to poverty for non-additive poverty measures (such as that of Sen and its extensions) as well. At the same time, the use of reweighting in a first stage allows to avoid imposing any functional form on the relationship between poverty and characteristics at the aggregate level.