Factors Affecting Debt Default on Poverty Alleviation Loan of Nayoby Bank: The Case Study of Pha Udom District Service Unit of the Nayoby Bank, Luang Namtha Branch - Phounnaly, phayvanh; MANYVANG, Kang; BOUNPHONG , Xay (Souphanouvong University Journal Multidisciplinary Research and Development, 2026)
The purposes of study were to study: 1) the debt default situation of the poverty alleviation loan, 2) the causes contributing to debt default in poverty alleviation loans, and 3) factors affecting debt default on poverty alleviation loan of Nayoby bank. The target population for this research was 156 debtors from the Pha Oudom district service unit of the Nayoby bank, Luang Namtha branch. Data analysis was conducted by using statistical methods including frequency, percentage, mean, standard deviation, and an ordered probit model to estimate coefficients. The research found that: 1) the debt default situation of the poverty alleviation loan of Pha Oudom district service unit of the Nayoby bank, Luang Namtha branch, most had outstanding balances of between 11 and 20 million kip. The majority of these debts was no more than three months past their due date. In these cases, debtors contacted the bank to request a repayment extension under the original loan conditions. The solution to the default problem would be implemented at the district committee level, with bank officers directly following up with debtors to collect the debts. 2) the causes contributing to debt default in poverty alleviation loans of Pha Oudom district service unit of the Nayoby bank, Luang Namtha branch stem from three main factors: debtor factors, internal factors related to the Policy Bank, and external factors. 3) factors affecting debt default on poverty alleviation loan of Pha Oudom district service unit of the Nayoby bank, Luang Namtha branch, consist of five factors such as: loan amount, family problems, changes in loan interest rates, inconvenient payment channels, premiums, and technological changes at a statistical significance level of 0.1. The ordered probit regression model achieved a prediction accuracy of 91.7%.