Joint liability aspires to improve micro-loan performance through the support of, and pressure from, the group borrowers. This paper examines how the group composition, in terms of the mixture of kinship (Family) ties and social (Friends and Neighbours) ties among the borrowers, affects the default rates. Using binary logistics regression and three machine learning models, responses from 507 group micro-loan borrowers from four major Moroccan cities were analysed. The results show that the stronger the family and kinship ties are within a loan group, the higher is the default rate. On the contrary, the stronger the social ties are among the group, the lower is the default rate. Other key findings include that the diversion of fund usage from investment to consumption is not found to significantly cause default. Also, loan default can be a consequence of borrowers' strategic choice rather than financial distress. As compared to the female members, male borrowers are found to be causing higher default rates. Interestingly, the gender-related default rates are lower when more of the male borrowers are only socially related. Finally, group size is found to be positively associated with default. Our findings can help micro-finance institutions refine their lending policies and guidance on groups' composition and size to reduce default rates.
|Number of pages||16|
|Journal||International Journal of Finance and Economics|
|Publication status||Published - 14 Feb 2023|
- machine learning
- social links
ASJC Scopus subject areas
- Economics and Econometrics