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Kiva is an online non-profit crowdsouring microfinance platform that raisesfunds for the poor in the third world. The borrowers on Kiva are small businessowners and individuals in urgent need of money. To raise funds as fast aspossible, they have the option to form groups and post loan requests in thename of their groups. While it is generally believed that group loans pose lessrisk for investors than individual loans do, we study whether this is the casein a philanthropic online marketplace. In particular, we measure the effect ofgroup loans on funding time while controlling for the loan sizes and otherfactors. Because loan descriptions (in the form of texts) play an importantrole in lenders' decision process on Kiva, we make use of this informationthrough deep learning in natural language processing. In this aspect, this isthe first paper that uses one of the most advanced deep learning techniques todeal with unstructured data in a way that can take advantage of its superiorprediction power to answer causal questions. We find that on average, forminggroup loans speeds up the funding time by about 3.3 days.

A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform