Social Identity in Online Microfinance and Public Goods Provision
Social Identity in Online Microfinance and Public Goods Provision
批准号:
1111019
负责人:
Yan Chen
金额:
$50.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-09-15 至 2015-08-31
中文摘要
本项目是一项跨学科的研究工作,旨在调查社会身份对在线小额信贷社区(kiva.org)中贷款人行为的影响以及社会身份的选择。经济学中的社会认同研究预测,显著的群体认同和群体竞争会增加个人对公共产品的贡献。研究人员将运用社会认同和社会偏好理论分析个人和团队层面的Kiva贷款数据。分析的结果将对社会身份研究中最重要但尚未解决的问题之一产生见解,即身份的选择和社会群体内规范的形成。这项工作代表了将文本和网络数据挖掘技术与社会身份理论联系起来的第一次尝试。将开发新的文本挖掘和机器学习模型,以推进在线社区挖掘的最新技术。全球有超过10亿人生活在绝对贫困中,其中大多数人也被排除在正规银行部门之外。为了减轻贫困,世界上许多地方出现了小额信贷项目,向穷人提供小额贷款。目前约有1000万户家庭受益于小额信贷项目。本研究调查了增加Kiva上贷款人参与和贷款活动的激励措施。初步分析表明,贷款团队计划在增加贷款活动方面是有效的。分析结果将有利于Kiva和其他小额信贷项目。增加参与和贷款活动将有助于实现世界银行为1亿贫困家庭提供服务的目标。
英文摘要
This project represents an interdisciplinary research effort to investigate the effects of social identity on lender behavior in an online microfinance community (kiva.org) and the selection of social identities. Social identity research in economics predicts that a salient group identity and group competition increases individual contribution to public goods.The researchers will apply social identity and social preference theory to analyze the Kiva lending data at the individual and team level. Results from the analysis will yield insights to one of the most important yet unresolved problems in social identity research, the selection of identity and the formation of norms within a social group. This work represents the first attempt to bridge text and network data mining techniques with social identity theory. Novel text mining and machine learning models will be developed that advance the state-of-the-art of mining of online communities.More than one billion people globally live in absolute poverty, most also excluded from the formal banking sector. To alleviate poverty, microfinance programs emerge in many parts of the world to provide small loans to the poor. Currently about 10 million households are served by microfinance programs. This research investigates incentives to increase lender participation and lending activities on Kiva. Preliminary analysis indicates that the lending teams program is effective in increasing loan activities. Results from the analysis will benefit Kiva and other microfinance programs. Increased participation and lending activities will help realize the World Bank's goal of serving 100 million poor households.
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