Which social program supports sustainable grass-root finance? Machine-learning evidence
Datos Bibliográficos
| ID | 19500331 |
|---|---|
| Autores | Rebecca G Martinez (0000-0001-5401-780X, Handelshøyskolen, University of Agder, Kristiansand, Norway, autor de correspondencia) |
| Año | 2020 |
| Volumen | 27 |
| Número | 5 |
| Páginas | 389-395 |
| Fecha de publicación | 2020-07-03 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | International Journal of Sustainable Development & World Ecology (JOURNAL) |
| Identificadores de la revista | ISSN: 1350-4509 • E-ISSN: 1745-2627 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/13504509.2019.1706059 |
| OpenAlex | W2996496451 |
| Idioma | EN |
| Citas recibidas | 5 |
| Referencias citadas | 23 |
Resources for development are used efficiently when social programs help to promote at the same time the sustainability of grass-root financial associations at the bottom of the pyramid. This study applies machine-learning to a worldwide database of grass-root associations in order to identify which social programs are good predictors of financial returns in the groups. The results indicate that education, income-generating activities and health programs are the most frequent programs provided by development agencies. Business training is not the most frequent intervention applied to grass-root associations, but it is in fact the most important social program to encourage financial sustainability, particularly after a development agency stops working with a group and leaves the community. Theoretical and practical implications of the findings are discussed
Business · Economic growth · Economics · Political science · Social science · Sociology · Sustainability · Sustainable development · Agricultural Innovations and Practices · Innovation and Socioeconomic Development · Microfinance and Financial Inclusion · Psychology · Finance
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| Obras citantes distintas | 5 |
|---|---|
| Citas por año | 1,25 |
| Intervalo de citas | 2022 - 2026 (5) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 5 |