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2. Literature Review and Conceptual Framework 2.1. Literature Review
The ability of borrowers in repaying their microcredit loans is an important issue that needs high attention. Borrowers can either make the payment of their loan or decide to default their loan. Borrowers who fail to repay their loan may be voluntary and involuntary (Brehanu and Futa, 2008). Involuntary defaults on borrowed funds may be caused by unexpected situations. For instance, natural disasters and borrowers’ sickness which may lead to the capability of the borrower’s repayment. On the contrary, voluntary defaults, borrowers have the capability to repay the borrowed funds but choose to default because the institution has poor level enforcement mechanisms.
Most previous research that investigated the determinants of loan default in the microfinance area apply the logistic regression model to identify significant variables. For instance, Mokhtar, Nartea, and Gan (2012) investigate the determinants of microcredit loans repayment problem in Malaysia. They find that borrowers' characteristics (age, gender, type of business) and microcredit loan’s features (mode of repayment, repayment amount) are the influential factors contributing to the problem of loan repayment. Nanayakkara and Stewart (2014) conduct a study on gender and other repayment determinants of microfinancing in Indonesia and Sri Lanka. They point out that age has a positive correlation coefficient with the dependent variable. Interest rate, agriculture loan and gender (female) also have positive correlation coefficients, but they are not statistically significant. Unexpectedly, the sign of coefficient of training is positive and is significant statistically. Ellis Kofi and Portia (2015) conduct research on the determinants of business loan default in Ghana. They find a positive relationship between extra income, diversion of loan purpose, and multiple borrowing on loan default. On the other side, location or distance between borrower and lending institution are statistically insignificant. Yibrie and Ramakrishna (2017) conduct a study on the determinants of loan repayment performance in ACSI. They find out that sex, age, education level, loan size, interest rate, loan tenure and training are statistically significant affecting loan default. Jote (2018) study on the determinants of loan repayment in Gedeo Zone. He points out that education level, nearness of borrowers to institution and training are the significant variables associated
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positively with loan repayment whereas family size has a negative relation with loan repayment.
Loan size, loan diversion rate and celebrating and participating in social festivals are not statistically significant though.
Authors use different statistical methods to find out the influential determinants on repayment performance in the microfinance sector such as Mota, Moreira, and Brandao (2018), they study on determinants of microcredit repayment in Portugal by using an ordered logistic regression (OLR). They conclude that the relationship between educational level, marital status (being married), employment status has a positive sign. Conversely, marital status (being single) has a negative sign on loan repayment performance but it is not statistically significant. Berhanu and Fufa (2008) examine the repayment rate of loans on semi-formal financial institutions among small-scale farmers in Ethiopia. The authors employ a two-limit Tobit model in their study, they conclude that, total land holding size (for agriculture activities), income from off-farm activities affect the loan repayment rate of off-farmers statistically significant and positively.
On the contrary, age, education level, gender and expenditure on festivals variables are statistically insignificant. Siaw, Ntiamoah, Oteng, and Opoku study on loan default rate of microfinance institutions in Ghana 2014, by using correlation and regression to obtain the empirical results, they strongly confirm that schedule of repayment has a positive correlation coefficient, meaning that this factor influences loan repayment significantly. Mwaka (2014) investigated the factors influencing repayment among microfinance loan consumers in Makueni county. He employs inferential statistics, the findings indicate that customers’ literacy, off-farm income and utilization of loan have a positive sign associated with loan repayment. However, a researcher also finds that gender, marital status, repayment duration is statistically insignificant.
Muthoni (2016) conducts a study on assessing borrower’s and business’ factors causing microcredit default in Kenya. He uses a multiple regression model and Pearson correlation to establish relationships among the variables, the study reveals that loan diversion, family size, gender(male) have a positive relationship with repayment performance. on the other side, the association between age (older group), marital status (married) and business experience are statistically significant. Muthoni, Mutuku, and Kamau (2017) conduct the study on a comparative analysis of microfinance institutions and financial intermediaries. They apply a
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multiple regression model and Pearson correlation to find out the association among the variables. The results of the study indicate that loan amount, and loan payment duration have a positive relationship with loan default significantly statistically. Moreover, Adurayemi, Sunday, and Abiola (2019) study Microfinance banks’ loan size and default in Lagos state, Nigeria. The linear regression is the statistical tool of their study to find out the relationship among the variables. The findings show that loan size contributes to loan default significantly. Selvaraj, Karim, Rahman, and Chamhuri (2019) study the determinants of microcredit quality in Malaysia. They apply a static panel technique to examine the relationship among the variables, the result shows that females have a negative relationship but household’s income has a positive relationship on microcredit quality. Overall, the loan repayment performance factors can be categorized into three categories, namely borrower characteristics, loan characteristics and business characteristics.
Alternatively, some researchers use different statistical methods to find out the factors that influence loan repayment problems. For example, Korankye (2014) studies cause and control of loan default/delinquency in microfinance institutions in Ghana. His research instrument includes questionnaire and interview guide, he finds that unfavorable payment terms, inadequate loan sizes and lack of training for the clients before and after disbursement are the causes of load default enumerated by the clients. Boateng, Amoah, and Anaglo (2015) also examine the influence of demographic factors, products and service characteristics of microfinance institutions on microfinance in eastern region, Ghana by using Chi square statistics to assess the relationship between variables. The result reveals that the default rate is higher among males (78%) in comparison with females (51%), in terms of age (ages were categorized into 14-40, 41-60 and 61+), the authors also find that defaulters are younger than non-defaulters on average. The default rate is higher for clients with no formal educational background compared with clients who received formal education, and also the default rate is lower if clients revived training compared to those clients who have not received any training.
Murthy & Mariadas (2017) investigate an exploratory study on the factors contributing loan repayment default among the loan borrowers in microfinance institutions in Shah Alam, Selangor. They use a 5-point Likert scale to find out the relationship among the variables, they find that the unit increase in age of borrowers influence the loan repayment default positively
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and as the increase of a unit repayment schedule also result to an increase in loan repayment default; however, both variables (age and repayment schedule) are not statically significant.
Table 1: Literature Review (outside of Laos) Author/
Total agricultural land holding size, income from off-farm activities affect the loan repayment rate of business involved) and microcredit loan’s characteristics (mode of repayment, repayment amount) are factors contribute to microcredit loan repayment problem of loan have a positive sign associated with loan repayment. Conversely, gender, marital status, repayment duration is statistically insignificant approves and disburses the loan, loan cycle, gender and age, a group/ individual loan, interest repayment
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frequency and gender variables influence loan payment statically Unrealistic term and schedule of repayment variables have positive correlation coefficient with terms, inadequate loan sizes and lack of training comparison with females (51%). Default rate is default include Extra income, diversion of loan purpose, and multiple borrowing positive relationship with repayment performance positive relationship with loan default significantly statistically loan size, interest rate, loan tenure and training factors
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Malaysia The unit increase in age of borrowers influences the loan repayment default positively and as the increase of a unit repayment schedule also results an increase in loan repayment default;
however, both variables (age and repayment schedule) are not statically significant.
Jote (2018) Determinants of loan repayment
Education level, nearness of borrowers to institution and training are the significant variables associated positively with loan repayment whereas family size has a negative relation with loan repayment. Loan size, loan diversion rate and
celebrating and
participating in social festivals are not statistically
The relationship between educational level, marital status (being married), employment status has a positive sign. Conversely, marital status (being single) has a negative sign on loan repayment performance but it is not statistically significant
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positive relationship on microcredit qualityIn Laos, recently, there are many authors study determinants of loan default in the area of microfinance in different countries by using both quantitative and qualitative methods to obtain the relationship among the variables. In recent years, there are also some empirical studies about microfinance in Laos (Table 2). For instance, Sayvaya (2012) conducts a study on does microfinance reduce poverty in Laos. Phonesavanh (2015) studies on the impact of microfinance on poverty reduction in Oudomxay, Northern province, Laos. Chansathith et al.
(2011) discuss Should Microcredit B a Right for the Poor? Credit Demand of Poor Households in Laos. Chaleunsinh et al. (2015) conduct a study on an Analysis on Borrowing Behavior of Rural Households in Vientiane Municipality.
We can conclude that most of the studies about microfinance in Laos focus on the impact of microfinance on defaults. There is no empirical research on the determinants of loan defaults.
Hence, we conduct this study to investigate the determinants of loan defaults in the microfinance institutions in Laos.
Table 2: Literature Review (in Laos) Author/ have correlation with credit demand. The poor who are non-farm self-employed tend to borrow and ones who work in non-farm wage/salary paid are less likely to have a demand for loan.
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Village Development Funds program does not have significant impact on
outcome as household income, expenditure and saving are totally large over the years lenders do for coping with emergencies. Savings groups nearly 40% of the loans use for production purposes specially in agriculture.
2.2. Conceptual Framework
This section presents a conceptual framework of determinants affecting loan payment performance. It assesses the research variables derived from literature to test whether there are significant associations diagrammatically between dependent variable and independent variable.
This study adopts the conceptual framework Jote (2018) to demonstrate the relationship between loan dependent variable (loan repayment) and the independent variables which demonstrates in Figure 2.
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Figure 2: Conceptual Framework of the Study
Loan Repayment Borrower’s characteristics: age, gender,
education, marital status, and family member who earn the living.
Borrower's financial conditions: no agriculture land holding, agriculture land value, employment status, livestock, land size, and additional income (remittance).
Loan features: agriculture loan, construction loan, loan size, and loan period
Culture: expenses on festivals less than 1million LAK, and loan diversion rate.
Others: commute time to financial
institution office, loan from moneylender, and training.
(Dependent Variable)
(Independent Variables)
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