Not AvailableSustainable 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 2011β2012 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.Not Availabl