This paper uses a microfinance field experiment in two Lima shantytowns to measure the relative importance of social networks and prices for borrowing. Our design randomizes the interest rate on loans provided by a micro-finance agency, as a function of the social distance between the borrower and the cosigner. This design effectively varies the relative price (interest rate differential) of having a direct friend versus an indirect friend as a cosigner. After loans are processed, a second randomization relieves some cosigners from their responsibility. These experiments yield three main results. (1) As emphasized by sociologists, connections are highly valuable: having a friend cosigner is equivalent to 18 per cent of the face value of a 6 month loan. (2) While networks are important, agents do respond to price incentives and switch to a non-friend cosigner when the interest differential is large. (3) Relieving responsibility of the cosigner reduces repayment for direct friends but has no effect otherwise, suggesting that different social mechanisms operate between friends and strangers: Non-friends cosign known high types, while friends also accept low types because of social collateral or altruism.
This paper builds a theory of trust based on informal contract enforcement in social networks. In our model, network connections between individuals can be used as social collateral to secure informal borrowing. We define networkbased trust as the largest amount one agent can borrow from another agent and derive a reduced-form expression for this quantity, which we then use in three applications. (1) We predict that dense networks generate bonding social capital that allows transacting valuable assets, whereas loose networks create bridging social capital that improves access to cheap favors such as information. (2) For job recommendation networks, we show that strong ties between employers and trusted recommenders reduce asymmetric information about the quality of job candidates. (3) Using data from Peru, we show empirically that network-based trust predicts informal borrowing, and we structurally estimate and test our model.