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

29 páginas

Autores:
Chowdhury, Reajul
Ceballos-Sierra, Federico
Sulaiman, Munshi
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad EIA .
Repositorio:
Repositorio EIA .
Idioma:
eng
OAI Identifier:
oai:repository.eia.edu.co:11190/3382
Acceso en línea:
https://repository.eia.edu.co/handle/11190/3382
Palabra clave:
Ultra-poor
Impact heterogeneity
Machine Learning
Bangladesh
Rights
openAccess
License
Derechos Reservados - Universidad EIA, 2021
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dc.title.eng.fl_str_mv Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
title Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
spellingShingle Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
Ultra-poor
Impact heterogeneity
Machine Learning
Bangladesh
title_short Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
title_full Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
title_fullStr Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
title_full_unstemmed Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
title_sort Grow the pie or have it? Using machine learning for impact heterogeneity in the Ultra-poor Graduation Model
dc.creator.fl_str_mv Chowdhury, Reajul
Ceballos-Sierra, Federico
Sulaiman, Munshi
dc.contributor.author.none.fl_str_mv Chowdhury, Reajul
Ceballos-Sierra, Federico
Sulaiman, Munshi
dc.subject.proposal.eng.fl_str_mv Ultra-poor
Impact heterogeneity
Machine Learning
Bangladesh
topic Ultra-poor
Impact heterogeneity
Machine Learning
Bangladesh
description 29 páginas
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-07-12T15:06:03Z
dc.date.available.none.fl_str_mv 2021-07-12T15:06:03Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Documento de trabajo
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url https://repository.eia.edu.co/handle/11190/3382
dc.language.iso.spa.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad EIA, 2021
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.spa.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad EIA
dc.publisher.place.spa.fl_str_mv Envigado (Antioquia, Colombia)
institution Universidad EIA .
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spelling Chowdhury, Reajulf2380e6430d6e5c415e4758b115666a8Ceballos-Sierra, Federicoe98a4684096f95fd698ea306b276844eSulaiman, Munshi0cc494579965fc3f065c2b75e1e975856002021-07-12T15:06:03Z2021-07-12T15:06:03Z2021https://repository.eia.edu.co/handle/11190/338229 páginasABSTRACT: 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-offapplication/pdfengUniversidad EIAEnvigado (Antioquia, Colombia)Derechos Reservados - Universidad EIA, 2021https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Grow the pie or have it? 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