Publication:
Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments

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dc.contributor.authorZheng, Zhuo
dc.contributor.authorWu, Timothy
dc.contributor.authorLee, Richard
dc.contributor.authorNewhouse, David
dc.contributor.authorKilic, Talip
dc.contributor.authorBurke, Marshall
dc.contributor.authorErmon, Stefano
dc.contributor.authorLobell, David B.
dc.date.accessioned2025-02-06T19:05:11Z
dc.date.available2025-02-06T19:05:11Z
dc.date.issued2025-02-06
dc.description.abstractAccurate and comprehensive measurement of household livelihoods is critical for monitoring progress toward poverty alleviation and targeting social assistance programs for those who most need it. However, the high cost of traditional data collection has historically made comprehensive measurement a difficult task. This paper evaluates alternative satellite-based deep learning approaches using detailed household census extracts from four African countries to accelerate progress toward comprehensive, fine-scale, and dynamic measurement of asset wealth at scale. The results indicate that transformer architectures solve multiple open measurement problems, by providing the most accurate measurement of local-level variation in household asset wealth across countries and cities, as well as changes in household asset wealth over time. Experiments that artificially restrict data availability show the model’s ability to achieve high performance with limited data. The proposed approach demonstrates the promise of combining satellite imagery, publicly available geo-features, and new deep learning architectures for hyperlocal and dynamic measurement of wealth in data-scarce environments.en
dc.identifierhttp://documents.worldbank.org/curated/en/099459402062534040/IDU1712697de11d0114fdb180921fde649dcb32a
dc.identifier.doi10.1596/1813-9450-11058
dc.identifier.urihttps://hdl.handle.net/10986/42772
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 11058
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectECONOMIC WELL-BEING
dc.subjectHIGH-RESOLUTION
dc.subjectPOVERTY MAPPING
dc.subjectSATELLITE IMAGE
dc.subjectDEEP LEARNING
dc.titleDynamic, High-Resolution Wealth Measurement in Data-Scarce Environmentsen
dc.typeWorking Paper
dspace.entity.typePublication
okr.date.disclosure2025-02-06
okr.date.doiregistration2025-04-14T11:53:17.525723Z
okr.date.lastmodified2025-02-06T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099459402062534040/IDU1712697de11d0114fdb180921fde649dcb32a
okr.guid099459402062534040
okr.identifier.docmidIDU-712697de-1d01-4fdb-8092-fde649dcb32a
okr.identifier.doi10.1596/1813-9450-11058
okr.identifier.externaldocumentum34453694
okr.identifier.internaldocumentum34453694
okr.identifier.reportWPS11058
okr.import.id6513
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099459402062534040/pdf/IDU1712697de11d0114fdb180921fde649dcb32a.pdfen
okr.region.geographicalWorld
okr.topicMacroeconomics and Economic Growth::Econometrics
okr.topicPoverty Reduction::Poverty Assessment
okr.unitLiving Standards Measurement (DECLS)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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