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Small Area Estimation of Poverty and Wealth Using Geospatial Data: What Have We Learned So Far ?

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dc.contributor.authorNewhouse, David
dc.date.accessioned2023-07-18T16:36:08Z
dc.date.available2023-07-18T16:36:08Z
dc.date.issued2023-07-18
dc.description.abstractThis paper offers a nontechnical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predicts poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant, models based on interpretable features appear to predict at least as well as estimates derived from Convolutional Neural Networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models. Small area estimates using geospatial data can improve the design of social assistance programs, particularly when the existing targeting system is poorly designed.en
dc.identifierhttp://documents.worldbank.org/curated/en/099335306282315995/IDU0ef5eaec903663043e60812b09f97c83f5551
dc.identifier.doi10.1596/1813-9450-10512
dc.identifier.urihttps://openknowledge.worldbank.org/handle/10986/40028
dc.languageEnglish
dc.language.isoen
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Papers; 10512
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectCONVOLUTIONAL NEURAL NETWORKS
dc.subjectGEOSPACIAL DATA
dc.subjectSATELLITE DATA
dc.subjectPOVERTY MAPPING
dc.subjectSMALL AREA ESTIMATION
dc.subjectPOVERTY AND WEALTH DATA PREDICTION
dc.subjectCELL PHONE DATA
dc.titleSmall Area Estimation of Poverty and Wealth Using Geospatial Dataen
dc.title.subtitleWhat Have We Learned So Far ?en
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleSmall Area Estimation of Poverty and Wealth Using Geospatial Data: What Have We Learned So Far ?
okr.date.disclosure2023-06-28
okr.date.lastmodified2023-06-28T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099335306282315995/IDU0ef5eaec903663043e60812b09f97c83f5551
okr.guid099335306282315995
okr.identifier.docmidIDU-ef5eaec9-3663-43e6-812b-9f97c83f5551
okr.identifier.doi10.1596/1813-9450-10512
okr.identifier.doihttp://dx.doi.org/10.1596/1813-9450-10512
okr.identifier.doihttps://doi.org/10.1596/1813-9450-10512
okr.identifier.externaldocumentum34103940
okr.identifier.internaldocumentum34103940
okr.identifier.reportWPS10512
okr.import.id1139
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099335306282315995/pdf/IDU0ef5eaec903663043e60812b09f97c83f5551.pdfen
okr.topicPoverty Reduction::Development Patterns and Poverty
okr.topicPoverty Reduction::Living Standards
okr.topicPoverty Reduction::Poverty Diagnostics
okr.unitData Analytics and Tools (DECAT)
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