Kinship, gender and social links impact on micro group lending defaults

Adil EL Fakir, Richard Fairchild, Youssef Alaoui, Dora Chan, Mohamed Tkiouat, Zaid Amer

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)2527-2542
Number of pages16
JournalInternational Journal of Finance and Economics
Issue number2
Early online date14 Feb 2023
Publication statusPublished - 7 Apr 2024

Bibliographical note

The authors are indebted to the anonymous referees and the editor for their extensive and valuable comments. The authors would like to thank Sheffield Business School at Sheffield Hallam University, SIST-Associate College of Cardiff Metropolitan University, EMI School of Engineering, LERMA and IFE-lab, University of Bath and IDB for their support in delivering this work. Special thanks go to Robert Marshall (who has now left the university), Denzil Watson, Firoz Bhaiyat, Rob Wilson, Mark Thompson, Damion Taylor, Jayne Revill, Lucian Tipi, Sean Kemp and everyone in the FABS team at Sheffield Hallam University for all their support and extensive efforts in helping to deliver this work.

Data Availability Statement

Data available in article supplementary material: The data that supports the findings of this study are available in the supplementary material of this article.


  • default
  • gender
  • kinship
  • machine learning
  • microfinance
  • social links

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics


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