Identifying Credit-Worthy Entrepreneurs

While the average impacts of microfinance have been the topic of much recent policy debate, recent evidence suggests that the impacts of increased access to credit may be quite large for a subset of businesses with high growth potential. Identifying these high-growth businesses is a key challenge for lenders. First, in a new environment lenders might not know who is interested; second, without a history of lending, it can be difficult to screen the population for credit worthiness. We propose to use lessons from network theory to restructure the recruitment protocols to two lenders to increase (i) the volume and (ii) the quality of applications. Our idea builds on the fact that the quality of a word-of-mouth referral process, used by many financial institutions, can depend on the location of initial seeds in a social network. As more central individuals are both better connected and have more information about others’ creditworthiness and entrepreneurial capacities, targeting central individuals in a referral process can potentially generate gains both in terms of volume and yield. This presents a major challenge, as MFIs will not have network data; our proposed protocol identifies a method to use referrals to target central individuals without having the MFI collect any new data than it already does. We will also explore how social constraints may hinder the value of referrals. Namely, our cross-cutting design allows us to quantify how much the quality of the referral falls when the act of making referrals is observable to others in the community.