A Comparison of Small Area Estimation Methods for Poverty Mapping

Guadarrama María (), Molina Isabel () and Rao J. N. K. ()
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Guadarrama María: Department of Statistics, Universidad Carlos III de Madrid. Address: C/Madrid 126, 28903 Getafe (Madrid), Madrid, ; Spain
Molina Isabel: Department of Statistics, Universidad Carlos III de Madrid. Madrid, ; Address: C/Madrid 126, 28903 Getafe (Madrid), Spain
Rao J. N. K.: School of Mathematics and Statistics, Carleton University, ; Carleton, ; Canada

Statistics in Transition New Series, 2016, vol. 17, issue 1, 41-66

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.

Keywords: area level model; non-linear parameters; empirical best estimator; hierarchical Bayes; poverty mapping; unit level models (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:17:y:2016:i:1:p:41-66:n:3

DOI: 10.21307/stattrans-2016-005

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