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Sustainable development goal-1 of the United Nations is to end poverty in all its forms everywhere. The estimates of poverty related parameters obtained from large scale sample survey are often available at large domain level (e.g. state level). But, poverty rates are not uniformly distributed across the regions. The regional variations are masked in such large domain level estimates. However, for monitoring the progress of poverty alleviation programmes aimed at reduction of poverty often require micro or disaggregate level estimates. The traditional survey estimation approaches are not suitable for generating the reliable estimates at this level because of sample size problem. It is the main endeavor of Small Area Estimation (SAE) approach to produce micro level statistics with acceptable precision without incurring any extra cost and utilizing existing survey data. In this study, the Hierarchical Bayes approach of SAE has been applied to generate reliable and representative district level poverty incidence for the State of Odisha in India using the Household Consumer Expenditure Survey 2011–2012 data of National Sample Survey Office and linked with Population Census 2011. The results show the precise performance of model based estimates generated by SAE method to a greater extent than the direct survey estimates. A poverty map has also been produced to observe the spatial inequality in poverty distribution.

This paper uses small area estimation techniques to update Vietnam's province and district-level poverty map to 2009. It finds that poverty rates continue to be highest in the northern and central mountainous regions, where ethnic minorities make up a large fraction of the population. Poverty has fallen in most provinces and districts over this decade, but the pace of poverty reduction has been least pronounced in those localities with high initial poverty or inequality levels. As a result, poverty rates have become more spatially concentrated over time, which is consistent with widely observed growth processes linked to agglomeration. The authors hypothesize that this makes geographic targeting of the poor more relevant as a means to re-balance growing welfare disparities between geographic areas. Simulations indicate that in both 1999 and 2009, geographic targeting for poverty alleviation improves upon a uniform lump-sum transfer and this becomes more evident the more spatially disaggregated the target populations. The analysis further indicates that the gains from geographic targeting have become more pronounced over time in Vietnam. Although poverty reduction in Vietnam has been impressive, further progress may thus warrant increased attention to geographic targeting.