Robust mean-squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw
The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate smal...
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Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2017-10, Vol.180 (4), p.1137-1161 |
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description | The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness. |
doi_str_mv | 10.1111/rssa.12311 |
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However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness.</description><identifier>ISSN: 0964-1998</identifier><identifier>EISSN: 1467-985X</identifier><identifier>DOI: 10.1111/rssa.12311</identifier><language>eng</language><publisher>Oxford: John Wiley & Sons Ltd</publisher><subject>Computer simulation ; Developing countries ; Estimates ; LDCs ; Mapping ; Model misspecification ; Poverty ; Poverty mapping ; Robustness ; Simulation ; Small area estimation ; Statistical analysis ; Variability ; World Bank method</subject><ispartof>Journal of the Royal Statistical Society. 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Series A, Statistics in society</title><description>The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness.</description><subject>Computer simulation</subject><subject>Developing countries</subject><subject>Estimates</subject><subject>LDCs</subject><subject>Mapping</subject><subject>Model misspecification</subject><subject>Poverty</subject><subject>Poverty mapping</subject><subject>Robustness</subject><subject>Simulation</subject><subject>Small area estimation</subject><subject>Statistical analysis</subject><subject>Variability</subject><subject>World Bank method</subject><issn>0964-1998</issn><issn>1467-985X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKsX70LAm7g1s0mzm2OR-gEFoVXwtmSzE9rSbtpk19J_b-paj84lmeR55-Ml5BrYAGI8-BD0AFIOcEJ6IGSWqHz4eUp6TEmRgFL5ObkIYckOkWU9sp66sg0NXaOuk7BttceKovfOUwzNYq2bhaupjenGfaFv9sdnDLTUIcLxu5ljLNDMXcwsHa9K9OGeTnS9dO2O6ro63i_JmdWrgFe_Z598PI3fH1-Sydvz6-NokhiuUkiGCtOcMS4F40oaMMYaw6UCERfIEHPLMyN5BZW1oLDUDJnI0IoSANGmvE9uu7ob77ZtnLiI3X0dWxagJHDORaoidddRxrsQPNpi4-Nqfl8AKw52Fgc7ix87IwwdvFuscP8PWUxns9FRc9NplqFx_k8jhMxTKTP-DeNAgsA</recordid><startdate>20171001</startdate><enddate>20171001</enddate><creator>Das, Sumonkanti</creator><creator>Chambers, Ray</creator><general>John Wiley & Sons Ltd</general><general>Oxford University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20171001</creationdate><title>Robust mean-squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw</title><author>Das, Sumonkanti ; Chambers, Ray</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3921-59e28003640396c1ccfcc369149987ee8f37c63d1dff19eba0e047ef4b11eef23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer simulation</topic><topic>Developing countries</topic><topic>Estimates</topic><topic>LDCs</topic><topic>Mapping</topic><topic>Model misspecification</topic><topic>Poverty</topic><topic>Poverty mapping</topic><topic>Robustness</topic><topic>Simulation</topic><topic>Small area estimation</topic><topic>Statistical analysis</topic><topic>Variability</topic><topic>World Bank method</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Das, Sumonkanti</creatorcontrib><creatorcontrib>Chambers, Ray</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Journal of the Royal Statistical Society. 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source | Wiley Online Library Journals Frontfile Complete; Business Source Complete; Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current) |
subjects | Computer simulation Developing countries Estimates LDCs Mapping Model misspecification Poverty Poverty mapping Robustness Simulation Small area estimation Statistical analysis Variability World Bank method |
title | Robust mean-squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw |
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