A Comparison of Small Area Estimation Methods for Poverty Mapping

María Guadarrama · Isabel Molina · Jon N. K. Rao

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demographic modeling and climate adaptation Income, Poverty, and Inequality Survey Sampling and Estimation Techniques

Abstract

Abstract We review main small area estimation methods for the estimation of general non-linear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, the Fay-Herriot area level model (Fay and Herriot, 1979), the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies.

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References

1984A Class of Decomposable Poverty Measures 2003Micro-Level Estimation of Poverty and Inequality 2014Small area estimation of general parameters with application to poverty indicators: A hierarchical Bayes approach

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