Disaggregate-level disparity in the incidence of poverty in Chhattisgarh, India

Abstract

Poverty is widespread in India, especially in far-flung rural areas, and disparities exist among and between states and social groups. To design programmes that alleviate poverty, policymakers need disaggregated data. The small area estimation (SAE) technique using the hierarchical Bayes model generates representative, micro-level estimates of poverty incidence. The results of a study in Chhattisgarh show that the SAE-based estimates are precise, and can help the government formulate micro-level antipoverty strategies.