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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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 |
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2021-07-12T15:06:03Z |
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2021 |
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eng |
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eng |
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Derechos Reservados - Universidad EIA, 2021 |
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https://creativecommons.org/licenses/by-nc-nd/4.0/ |
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Universidad EIA |
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Envigado (Antioquia, Colombia) |
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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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