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.
