Micro–Level Estimation of Poverty and Inequality

Recent theoretical advances have brought income and wealth distributions back into a prominent position in growth and development theories, and as determinants of specific socio-economic outcomes, such as health or levels of violence. Empirical investi- gation of the importance of these relationships, however, has been held back by the lack of sufficiently detailed high quality data on distributions. Household surveys that include reasonable measures of income or consumption can be used to calculate distributional measures, but at low levels of aggregation these samples are rarely representative or of sufficient size to yield statistically reliable estimates. At the same time, census (or other large sample) data of sufficient size to allow disaggregation either have no information about income or consumption, or measure these variables poorly. This note outlines a statistical procedure to combine these types of data to take advantage of the detail in household sample surveys and the comprehensive coverage of a census. It extends the literature on small area statistics (Ghosh and Rao (1994), Rao (1999)) by developing esti- mators of population parameters that are nonlinear functions of the underlying variable of interest (here unit level consumption), and by deriving them from the full unit level distribution of that variable. In examples using Ecuadorian data, our estimates have levels of precision compa- rable to those of commonly used survey based welfare estimates—but for populations as small as 15,000 households, a 'town.' This is an enormous improvement over survey based estimates, which are typically only consistent for areas encompassing hundreds of thousands, even millions, of households. Experience using the method in South Africa, Brazil, Panama, Madagascar, and Nicaragua suggest that Ecuador is not an unusual case (Alderman et al. (2002), and Elbers et al. (2002)).