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Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment

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dc.contributor.authorDang, Hai-Anh
dc.contributor.authorKilic, Talip
dc.contributor.authorHlasny, Vladimir
dc.contributor.authorAbanokova, Kseniya
dc.contributor.authorCarletto, Calogero
dc.contributor.authorAbanokova, Ksenia
dc.date.accessioned2024-03-27T13:59:09Z
dc.date.available2024-03-27T13:59:09Z
dc.date.issued2024-03-26
dc.description.abstractSurvey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à-vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.en
dc.identifierhttp://documents.worldbank.org/curated/en/099851203262433827/IDU1dce7aeb018793142f91aae715d889956cee9
dc.identifier.doi10.1596/1813-9450-10738
dc.identifier.urihttps://hdl.handle.net/10986/41291
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 10738
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectCONSUMPTION
dc.subjectPOVERTY
dc.subjectSURVEY-TO-SURVEY IMPUTATION
dc.subjectHOUSEHOLD SURVEYS
dc.subjectTANZANIA
dc.subjectNO POVERTY
dc.subjectSDG 1
dc.titleUsing Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Costen
dc.title.subtitleEvidence from a Randomized Survey Experimenten
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleUsing Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment
okr.date.disclosure2024-03-26
okr.date.doiregistration2025-04-30T04:31:43.373687Z
okr.date.lastmodified2024-03-26T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099851203262433827/IDU1dce7aeb018793142f91aae715d889956cee9
okr.guid099851203262433827
okr.identifier.docmidIDU-dce7aeb0-8793-42f9-aae7-5d889956cee9
okr.identifier.doi10.1596/1813-9450-10738
okr.identifier.doihttps://doi.org/10.1596/1813-9450-10738
okr.identifier.externaldocumentum34288672
okr.identifier.internaldocumentum34288672
okr.identifier.reportWPS10738
okr.import.id3624
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099851203262433827/pdf/IDU1dce7aeb018793142f91aae715d889956cee9.pdfen
okr.region.administrativeAfrica Eastern and Southern (AFE)
okr.region.countryTanzania
okr.topicMacroeconomics and Economic Growth::Economic Modeling and Statistics
okr.topicPoverty Reduction::Poverty Diagnostics
okr.topicInformation and Communication Technologies::Rural Information & Communications Technologies
okr.unitLiving Standards Measurement (DECLS)
okr.unitDevelopment Data Group (DECDG)
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relation.isAuthorOfPublication.latestForDiscoveryd40874d9-de2a-4bac-8cfa-3ddcb7b30070
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
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