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Social Capital as Soft Information in Predicting Microfinance Loan Repayment
Vlaswinkel, Jesse Evert (2025) Social Capital as Soft Information in Predicting Microfinance Loan Repayment.
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| Abstract: | Loan repayment success is a vital part of the continuation of microfinance institutions (MFIs), making the ability to predict loan repayment success an important aspect of loan decision-making. Using a large dataset from an MFI in the Netherlands, this research investigates whether borrowers’ social capital can predict loan repayment success, utilizing a keyword-based method. Social capital is operationalized through bonding, bridging, and linking. Bonding (strong ties such as family and close friends) shows the strongest and most consistent predictive power; however, overall results are weak, indicating social capital alone is not a strong predictor of successful loan repayment. This contradicts prior literature that understates the economic role of bonding social capital. Furthermore, it indicates that not all types of soft information are strong predictors of loan repayment success. Limitations of this research stem from the social capital extraction method (keyword search) and the procedures used by loan officers to record soft information. This research encourages MFIs to explore new ways to document soft information. Nevertheless, this research highlights the contextual challenges and predictive potential of soft information in microfinance. |
| Item Type: | Essay (Bachelor) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 83 economics, 85 business administration, organizational science |
| Programme: | International Business Administration BSc (50952) |
| Link to this item: | https://purl.utwente.nl/essays/106884 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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