Publication:
Nowcasting Global Poverty

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creativeworkseries.issn1564-698X
dc.contributor.authorMahler, Daniel Gerszon
dc.contributor.authorAguilar, R. Andrés Castañeda
dc.contributor.authorNewhouse, David
dc.date.accessioned2024-02-29T19:38:40Z
dc.date.available2024-02-29T19:38:40Z
dc.date.issued2022-10-06
dc.description.abstractThis paper evaluates different methods for nowcasting country-level poverty rates, including methods that apply statistical learning to large-scale country-level data obtained from the World Development Indicators and Google Earth Engine. The methods are evaluated by withholding measured poverty rates and determining how accurately the methods predict the held-out data. A simple approach that scales the last observed welfare distribution by a fraction of real GDP per capita growth performs nearly as well as models using statistical learning on 1,000 plus variables. This GDP-based approach outperforms all models that predict poverty rates directly, even when the last survey is up to five years old. The results indicate that in this context, the additional complexity introduced by applying statistical learning techniques to a large set of variables yields only marginal improvements in accuracy.en
dc.identifierhttp://documents.worldbank.org/curated/en/099658312112338950/IDU0519867a504b6f04e4c0bf3d0e3b9b7b70d3b
dc.identifier.citationThe World Bank Economic Review
dc.identifier.doi10.1596/41135
dc.identifier.issn0258-6770 (print)
dc.identifier.issn1564-698X (online)
dc.identifier.urihttps://hdl.handle.net/10986/41135
dc.languageEnglish
dc.language.isoen_US
dc.publisherPublished by Oxford University Press on behalf of the World Bank
dc.relation.ispartofseriesWorld Bank Economic Review
dc.rightsCC BY-NC-ND 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/igo
dc.subjectPOVERTY
dc.subjectNOWCASTING
dc.subjectMACHINE LEARNING
dc.subjectMEASUREMENT
dc.titleNowcasting Global Povertyen
dc.typeJournal Article
dspace.entity.typePublication
okr.crossref.titleNowcasting Global Poverty
okr.date.disclosure2023-12-11
okr.date.lastmodified2023-12-11T00:00:00Zen
okr.doctypeJournal Article
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099658312112338950/IDU0519867a504b6f04e4c0bf3d0e3b9b7b70d3b
okr.guid099658312112338950
okr.identifier.docmidIDU-519867a5-4b6f-4e4c-bf3d-e3b9b7b70d3b
okr.identifier.doi10.1093/wber/lhac017
okr.identifier.doihttps://doi.org/10.1596/41135
okr.identifier.externaldocumentum34212174
okr.identifier.internaldocumentum34212174
okr.identifier.report186346
okr.import.id3311
okr.importedtrueen
okr.language.supporteden
okr.pagenumber835–856
okr.pdfurlhttp://documents.worldbank.org/curated/en/099658312112338950/pdf/IDU0519867a504b6f04e4c0bf3d0e3b9b7b70d3b.pdfen
okr.peerreviewAcademic Peer Review
okr.region.geographicalWorld
okr.topicSocial Protections and Labor::Social Protections & Assistance
okr.topicMacroeconomics and Economic Growth::Economic Forecasting
okr.unitDevelopment Indicators and Data (DECID)
okr.volume36(4)
relation.isJournalIssueOfPublication5a577d27-aa9f-4a35-ac12-1d6e8900a090
relation.isJournalIssueOfPublication.latestForDiscovery5a577d27-aa9f-4a35-ac12-1d6e8900a090
relation.isJournalOfPublicationc41eae2f-cf94-449d-86b7-f062aebe893f
relation.isJournalVolumeOfPublicationdd213de9-52ab-40e9-bb44-86bf0edc81b4
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