ABSTRACT

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.

摘要

当社会计划有助于同时促进金字塔底层的基层金融协会的可持续性时，有效利用发展资源。这项研究将机器学习应用于全球基层协会的数据库，以便确定哪些社会计划是群体财务收益的良好预测指标。结果表明，教育，创收活动和保健方案是发展机构提供的最频繁的方案。商业培训并不是应用于基层协会的最频繁的干预措施，但实际上，它是鼓励财务可持续性的最重要的社会计划，特别是在开发机构停止与团体合作并离开社区之后。