Publication:
What Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?

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dc.contributor.authorLucchetti, Leonardo
dc.date.accessioned2018-08-15T19:29:29Z
dc.date.available2018-08-15T19:29:29Z
dc.date.issued2018-08
dc.description.abstractThis paper implements a machine learning approach to estimate intra-generational economic mobility using cross-sectional data. A Least Absolute Shrinkage and Selection Operator (Lasso) procedure is applied to explore poverty dynamics and household-level welfare growth in the absence of panel data sets that follow individuals over time. The method is validated by sampling repeated cross-sections of actual panel data from Peru. In general, the approach performs well at estimating intra-generational poverty transitions; most of the mobility estimates fall within the 95 percent confidence intervals of poverty mobility from the actual panel data. The validation also confirms that the Lasso regularization procedure performs well at estimating household-level welfare growth between two years. Overall, the results are sufficiently encouraging to estimate economic mobility in settings where panel data are not available or, if they are, to improve panel data when they suffer from serious non-random attrition problems.en
dc.identifierhttp://documents.worldbank.org/curated/en/949841533741579213/What-can-we-machine-learn-about-welfare-dynamics-from-cross-sectional-data
dc.identifier.doi10.1596/1813-9450-8545
dc.identifier.urihttps://hdl.handle.net/10986/30235
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 8545
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectPOVERTY
dc.subjectPOVERTY TRANSITIONS
dc.subjectLASSO
dc.subjectMACHINE LEARNING
dc.subjectWELFARE DYNAMICS
dc.subjectSYNTHETIC PANELS
dc.subjectCLASS MOBILITY
dc.titleWhat Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?en
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.crossref.titleWhat Can We (Machine) Learn about Welfare Dynamics from Cross-Sectional Data?
okr.date.disclosure2018-08-08
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.docurlhttp://documents.worldbank.org/curated/en/949841533741579213/What-can-we-machine-learn-about-welfare-dynamics-from-cross-sectional-data
okr.guid949841533741579213
okr.identifier.doi10.1596/1813-9450-8545
okr.identifier.externaldocumentum090224b0880e951c_2_0
okr.identifier.internaldocumentum30335457
okr.identifier.reportWPS8545
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/949841533741579213/pdf/WPS8545.pdfen
okr.region.administrativeLatin America & Caribbean
okr.region.countryPeru
okr.statistics.combined2023
okr.statistics.dr949841533741579213
okr.statistics.drstats1697
okr.topicPoverty Reduction::Development Patterns and Poverty
okr.topicPoverty Reduction::Inequality
okr.topicPoverty Reduction::Poverty Assessment
okr.topicPoverty Reduction::Poverty Lines
okr.topicPoverty Reduction::Poverty Monitoring & Analysis
okr.unitPoverty and Equity Global Practice
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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