Lending to the Unbanked: Relational Contracting with Loan Sharks
We study roughly 11,000 loans from unlicensed moneylenders to over 1,000 borrowers in Singapore and provide basic information about this understudied market. Borrowers frequently expect to repay late. While lenders do rely on additional punishments to enforce loans, the primary cost of not repaying on time is compounding of a very high interest rate. We develop a very simple model of the relational contract between loan sharks and borrowers and use it to predict the effect of a crackdown on illegal moneylending. Consistent with our model, the crackdown raised the interest rate and lowered the size of loans.
The data were collected by Leong who first obtained approval to use this dataset to study the unlicensed moneylending market by writing to the appropriate Singaporean authorities. He then submitted an application to the Nanyang Technological University (NTU) IRB to obtain ethics approval. Waiver of the signature acknowledging informed consent was requested because almost all borrowers Leong spoke to prior to conducting the study explained that borrowers would typically not sign any documents because they fear being identified. Thus, they were not willing to have any paper trace of their name. Instead, verbal consent was obtained before the commencement of the interviews. The data collection was self-funded. Leong received a Singapore Ministry of Education Tier 1 grant for this research. Lang's contribution was funded in part by NSF grant SES 1851636. We thank Costas Cavounidis, Marina Halac, Bart Lipman, Dilip Mookherjee, Linh T. To and participants in the Boston University empirical microeconomics workshop for their helpful comments. The usual caveat applies. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.