The Role of Rating and Loan Characteristics in Online Microfunding Behaviors

Gaurav Paruthi, Enrique Frias-Martinez, Vanessa Frias-Martinez

We propose an in-depth study of lending behaviors in Kiva using a mix of quantitative and large-scale data mining techniques. Kiva is a non-profit organization that offers an online platform to connect lenders with borrowers. Their site, kiva.org, allows citizens to microlend small amounts of money to entrepreneurs (borrowers) from different countries. The borrowers are always affiliated with a Field Partner (FP) which can be a microfinance institution (MFI) or other type of local organization that has partnered with Kiva. Field partners give loans to selected businesses based on their local knowledge regarding the country, the business sector including agriculture, health or manufacture among others, and the borrower.Our objective is to understand the relationship between lending activity and various features offered by the online platform. Specifically, we focus on two research questions: (i) the role that MFI ratings play in driving lending activity and (ii) the role that various loan features have in the lending behavior. The first question analyzes whether there exists a relationship between the MFI ratings - that lenders can explore online - and their lending volumes. The second research question attempts to understand if certain loan features - available online at Kiva - such as the type of small business, the gender of the borrower, or the loan's country information might affect the way lenders lend.

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