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Poverty mapping that displays spatial distribution of various poverty indicesis most useful to policymakers and researchers when they are disaggregated intosmall geographic units, such as cities, municipalities or other administrativepartitions of a country. Typically, national household surveys that containwelfare variables such as income and expenditures provide limited or no datafor small areas. It is well-known that while direct survey-weighted estimatesare quite reliable for national or large geographical areas they are unreliablefor small geographic areas. If the objective is to find areas with extremepoverty, these direct estimates will often select small areas due to the highvariabilities in the estimates. Empirical best prediction and Bayesian methodshave been proposed to improve on the direct point estimates. However, theseestimates are not appropriate for different inferential purposes. For example,for identifying areas with extreme poverty, these estimates would often selectareas with large sample sizes. In this paper, using databases used by theChilean Ministry for their Small Area Estimation production, we illustrate howappropriate Bayesian methodology can be developed to address differentinferential problems.

A General Bayesian Approach to Meet Different Inferential Goals in Poverty Research for Small Areas