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Frontiers in Small Area Estimation Research: Application to Welfare Indicators

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dc.contributor.authorMolina, Isabel
dc.date.accessioned2024-06-28T21:20:59Z
dc.date.available2024-06-28T21:20:59Z
dc.date.issued2024-06-28
dc.description.abstractThis paper reviews the main methods for small area estimation of welfare indicators. It begins by discussing the importance of small area estimation methods for producing reliable disaggregated estimates. It mentions the baseline papers and describes the contents of the different sections. Basic direct estimators obtained from area-specific survey data are described first, followed by simple indirect methods, which include synthetic procedures that do not account for the area effects and composite estimators obtained as a composition (or weighted average) of a synthetic and a direct estimator. The previous estimators are design-based, meaning that their properties are assessed under the sampling replication mechanism, without assuming any model to be true. The paper then turns to proper model-based estimators that assume an explicit model. These models allow obtaining optimal small area estimators when the assumed model holds. The first type of models, referred to as area-level models, use only aggregated data at the area level to fit the model. However, unit-level survey data were previously used to calculate the direct estimators, which act as response variables in the most common area-level models. The paper then switches to unit-level models, describing first the usual estimators for area means, and then moving to general area indicators. Semi-parametric, non-parametric, and machine learning procedures are described in a separate section, although many of the procedures are applicable only to area means. Based on the previous material, the paper identifies gaps or potential limitations in existing procedures from a practitioner’s perspective, which could potentially be addressed through research over the next three to five years.en
dc.identifierhttp://documents.worldbank.org/curated/en/099035506262422943/IDU199b2f16a1839c148df1bec41178c0d80c122
dc.identifier.doi10.1596/41801
dc.identifier.urihttps://hdl.handle.net/10986/41801
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 10828
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectEMPIRICAL BEST LINEAR UNBIASED PREDICTOR (EBLUP)
dc.subjectELL
dc.subjectEMPIRICAL BEST
dc.subjectPOVERTY MAPPING
dc.subjectPOVERTY MAP
dc.subjectREVIEW
dc.subjectSMALL AREA ESTIMATION
dc.subjectWELFARE ESTIMATION
dc.subjectNO POVERTY
dc.subjectSDG 1
dc.titleFrontiers in Small Area Estimation Researchen
dc.title.subtitleApplication to Welfare Indicatorsen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleFrontiers in Small Area Estimation Research: Application to Welfare Indicators
okr.date.disclosure2024-06-28
okr.date.lastmodified2024-06-26T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099035506262422943/IDU199b2f16a1839c148df1bec41178c0d80c122
okr.guid099035506262422943
okr.identifier.docmidIDU-99b2f16a-839c-48df-bec4-178c0d80c122
okr.identifier.doihttps://doi.org/10.1596/1813-9450-10828
okr.identifier.doihttps://doi.org/10.1596/41801
okr.identifier.externaldocumentum34350766
okr.identifier.internaldocumentum34350766
okr.identifier.reportWPS10828
okr.import.id4646
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099035506262422943/pdf/IDU199b2f16a1839c148df1bec41178c0d80c122.pdfen
okr.region.geographicalWorld
okr.sectorCentral Government (Central Agencies)
okr.themeInclusive Growth,Mitigation,Gender,Human Development and Gender,Data Development and Capacity Building,Economic Policy,Rural Development,Social Development and Protection,Economic Growth and Planning,Environment and Natural Resource Management,Disease Control,Pandemic Response,Fragility, Conflict and Violence,Public Sector Management,Climate change,Urban and Rural Development,Adaptation,Geospatial Services,Data production, accessibility and use
okr.topicMacroeconomics and Economic Growth::Econometrics
okr.topicPoverty Reduction::Small Area Estimation Poverty Mapping
okr.topicSocial Protections and Labor::Social Protections & Assistance
okr.unitEFI-Poverty and Equity-GE (EPVGE)
okr.unitEFI-AFR2-POV-Poverty and Equity (EAWPV)
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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