Selection into and across Credit Contracts: Theory and Field Research

Various theories make predictions about the relative advantages of individual loans versus joint liability loans. If we imagine that lenders facing moral hazard make relative performance comparisons in determining stringency in repayment, then individual loans should vary positively with covariance of output across funded projects. Relatively new work also highlights inequality and heterogeneity in preferences, establishing that wealth of the agents relative to the bank, and wealth dispersion among potential joint liability partners, are important factors determining the likelihood of the joint liability regime. An alternative imperfect information model also addresses the question of which agents will accept a group contract and borrow and which will pursue outside options. We attempt to test these various models using relatively rich data gathered in field research in Thailand, measuring not only the presence of joint liability versus individual loans, but also measuring various of the key variables suggested by these theories. As predicted by one of the theories, the prevalence of joint liability contracts relative to individual contracts exhibits a U-shaped relationship with the wealth of the borrowing pair and increases with the wealth dispersion. (We control for wealth that can be used as collateral.) Contrary to one theory, we find no evidence joint liability borrowing becomes less likely as covariance of output increases. We do find, consistent with our modified version of the model with adverse selection, that higher correlation makes joint liability borrowing more likely relative to all outside options. We also find direct evidence consistent with adverse selection in the credit market, in that the likelihood of joint-liability borrowing increases the lower is the probability of project success. We are able to distinguish this result from an alternative moral hazard explanation. Strikingly, most of the results disappear if we do not condition the sample according to the dictates of the models.