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Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model

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dc.contributor.authorChowdhury, Reajul
dc.contributor.authorCeballos-Sierra, Federico
dc.contributor.authorSulaiman, Munshi
dc.date.accessioned2021-07-12T15:06:03Z
dc.date.available2021-07-12T15:06:03Z
dc.date.issued2021
dc.description29 páginasspa
dc.description.abstractABSTRACT: Anti-poverty interventions often face a trade-off between immediate reduction in poverty, measured by consumption, and building assets for longer-term gains. An “Ultra-poor Graduation” model, found effective on both dimensions in several rigorous studies, generally leans towards asset building. By using data from a large-scale RCT in Bangladesh, we find significant variation in impact on assets where the top quintile gainers experience asset growth of 344% while asset growth is only 192% for the bottom quintile. Heterogeneity in impact on household expenditures is found to be present but of lower magnitude than that of assets. Importantly, the machine learning techniques we apply reveal contrasts in characteristics of beneficiaries who made the most in assets vs. consumption. The results identify beneficiary characteristics that can be used in targeting households either to maximize impact on the desired dimension and/or to customize interventions for balancing the asset and consumption trade-offeng
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://repository.eia.edu.co/handle/11190/3382
dc.language.isoengspa
dc.publisherUniversidad EIAspa
dc.publisher.placeEnvigado (Antioquia, Colombia)spa
dc.rightsDerechos Reservados - Universidad EIA, 2021spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.creativecommonsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.proposalUltra-pooreng
dc.subject.proposalImpact heterogeneityeng
dc.subject.proposalMachine Learningeng
dc.subject.proposalBangladesheng
dc.titleGrow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Modeleng
dc.typeDocumento de trabajospa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/workingPaperspa
dc.type.redcolhttps://purl.org/redcol/resource_type/WPspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dspace.entity.typePublication
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