A Comparison of Small Area Estimation Methods for Poverty Mapping

• Authors: Guadarrama María , Molina Isabel , Rao J. N. K.
• Languages of publication: EN

Abstracts

EN

We review main small area estimation methods for the estimation of general nonlinear 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

EN

area level model; non-linear parameters; empirical best estimator; hierarchical Bayes; poverty mapping; unit level models

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2016
• Volume: 17
• Issue: 1
• Pages: 41-66

Contributors

author

Guadarrama María

• 1Department of Statistics, Universidad Carlos III de Madrid. Address: C/Madrid 126, 28903 Getafe (Madrid), Spain, Tf: [phone]

author

Molina Isabel

• 2Department of Statistics, Universidad Carlos III de Madrid. Address: C/Madrid 126, 28903 Getafe (Madrid), Spain, Tf: [phone]

author

Rao J. N. K.

• 3School of Mathematics and Statistics, Carleton University

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