A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

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ISSN: 1234-7655
eISSN: 2450-0291

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VOLUME 17 , ISSUE 1 (March 2016) > List of articles

A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

María Guadarrama * / Isabel Molina * / J. N. K. Rao *

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

Citation Information : Statistics in Transition New Series. Volume 17, Issue 1, Pages 41-66, DOI: https://doi.org/10.21307/stattrans-2016-005

License : (CC BY 4.0)

Published Online: 06-July-2017

ARTICLE

ABSTRACT

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.

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