期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：52
页码：14944-14948
DOI：10.1073/pnas.1606085113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceWith three billion people subsisting on the equivalent of $2.50 per day, alleviating poverty is one of the most urgent challenges facing the world today. One solution to this problem has been to encourage the growth of small enterprises through microlending. A successful innovation is represented by Kiva.org, which matches citizen lenders with low-income entrepreneurs in developing countries. To increase prosocial lending, we use a large-scale field experiment and machine-learning methods to recommend lending teams to lenders. We find that lenders who join a team contribute significantly more compared with those who do not. Our results suggest team recommendation can be an effective and low-cost behavioral mechanism to increase charitable contributions. This paper reports the results of a large-scale field experiment designed to test the hypothesis that group membership can increase participation and prosocial lending for an online crowdlending community, Kiva. The experiment uses variations on a simple email manipulation to encourage Kiva members to join a lending team, testing which types of team recommendation emails are most likely to get members to join teams as well as the subsequent impact on lending. We find that emails do increase the likelihood that a lender joins a team, and that joining a team increases lending in a short window (1 wk) following our intervention. The impact on lending is large relative to median lender lifetime loans. We also find that lenders are more likely to join teams recommended based on location similarity rather than team status. Our results suggest team recommendation can be an effective behavioral mechanism to increase prosocial lending.
关键词：social identity ; charitable giving ; microfinance ; field experiment ; recommender systems
